Ground Truths

“Eventually, my dream would be to simulate a virtual cell.”—Demis Hassabis

The aspiration to build the virtual cell is considered to be equivalent to a moonshot for digital biology. Recently, 42 leading life scientists published a paper in Cell on why this is so vital, and how it may ultimately be accomplished. This conversation is with 2 of the authors, Charlotte Bunne, now at EPFL and Steve Quake, a Professor at Stanford University, who heads up science at the Chan-Zuckerberg Initiative

The audio (above) is available on iTunes and Spotify. The full video is linked here, at the top, and also can be found on YouTube.

TRANSCRIPT WITH LINKS TO AUDIO

Eric Topol (00:06):

Hello, it's Eric Topol with Ground Truths and we've got a really hot topic today, the virtual cell. And what I think is extraordinarily important futuristic paper that recently appeared in the journal Cell and the first author, Charlotte Bunne from EPFL, previously at Stanford’s Computer Science. And Steve Quake, a young friend of mine for many years who heads up the Chan Zuckerberg Initiative (CZI) as well as a professor at Stanford. So welcome, Charlotte and Steve.

Steve Quake (00:42):

Thanks, Eric. It's great to be here.

Charlotte Bunne:

Thanks for having me.

Eric Topol (00:45):

Yeah. So you wrote this article that Charlotte, the first author, and Steve, one of the senior authors, appeared in Cell in December and it just grabbed me, “How to build the virtual cell with artificial intelligence: Priorities and opportunities.” It's the holy grail of biology. We're in this era of digital biology and as you point out in the paper, it's a convergence of what's happening in AI, which is just moving at a velocity that's just so extraordinary and what's happening in biology. So maybe we can start off by, you had some 42 authors that I assume they congregated for a conference or something or how did you get 42 people to agree to the words in this paper?

Steve Quake (01:33):

We did. We had a meeting at CZI to bring community members together from many different parts of the community, from computer science to bioinformatics, AI experts, biologists who don't trust any of this. We wanted to have some real contrarians in the mix as well and have them have a conversation together about is there an opportunity here? What's the shape of it? What's realistic to expect? And that was sort of the genesis of the article.

Eric Topol (02:02):

And Charlotte, how did you get to be drafting the paper?

Charlotte Bunne (02:09):

So I did my postdoc with Aviv Regev at Genentech and Jure Leskovec at CZI and Jure was part of the residency program of CZI. And so, this is how we got involved and you had also prior work with Steve on the universal cell embedding. So this is how everything got started.

Eric Topol (02:29):

And it's actually amazing because it's a who's who of people who work in life science, AI and digital biology and omics. I mean it's pretty darn impressive. So I thought I'd start off with a quote in the article because it kind of tells a story of where this could go. So the quote was in the paper, “AIVC (artificial intelligence virtual cell) has the potential to revolutionize the scientific process, leading to future breakthroughs in biomedical research, personalized medicine, drug discovery, cell engineering, and programmable biology.” That's a pretty big statement. So maybe we can just kind of toss that around a bit and maybe give it a little more thoughts and color as to what you were positing there.

Steve Quake (03:19):

Yeah, Charlotte, you want me to take the first shot at that? Okay. So Eric, it is a bold claim and we have a really bold ambition here. We view that over the course of a decade, AI is going to provide the ability to make a transformative computational tool for biology. Right now, cell biology is 90% experimental and 10% computational, roughly speaking. And you've got to do just all kinds of tedious, expensive, challenging lab work to get to the answer. And I don't think AI is going to replace that, but it can invert the ratio. So within 10 years I think we can get to biology being 90% computational and 10% experimental. And the goal of the virtual cell is to build a tool that'll do that.

Eric Topol (04:09):

And I think a lot of people may not understand why it is considered the holy grail because it is the fundamental unit of life and it's incredibly complex. It's not just all the things happening in the cell with atoms and molecules and organelles and everything inside, but then there's also the interactions the cell to other cells in the outside tissue and world. So I mean it's really quite extraordinary challenge that you've taken on here. And I guess there's some debate, do we have the right foundation? We're going to get into foundation models in a second. A good friend of mine and part of this whole I think process that you got together, Eran Segal from Israel, he said, “We're at this tipping point…All the stars are aligned, and we have all the different components: the data, the compute, the modeling.” And in the paper you describe how we have over the last couple of decades have so many different data sets that are rich that are global initiatives. But then there's also questions. Do we really have the data? I think Bo Wang especially asked about that. Maybe Charlotte, what are your thoughts about data deficiency? There's a lot of data, but do you really have what we need before we bring them all together for this kind of single model that will get us some to the virtual cell?

Charlotte Bunne (05:41):

So I think, I mean one core idea of building this AIVC is that we basically can leverage all experimental data that is overall collected. So this also goes back to the point Steve just made. So meaning that we basically can integrate across many different studies data because we have AI algorithms or the architectures that power such an AIVC are able to integrate basically data sets on many different scales. So we are going a bit away from this dogma. I'm designing one algorithm from one dataset to this idea of I have an architecture that can take in multiple dataset on multiple scales. So this will help us a bit in being somewhat efficient with the type of experiments that we need to make and the type of experiments we need to conduct. And again, what Steve just said, ultimately, we can very much steer which data sets we need to collect.

Charlotte Bunne (06:34):

Currently, of course we don't have all the data that is sufficient. I mean in particular, I think most of the tissues we have, they are healthy tissues. We don't have all the disease phenotypes that we would like to measure, having patient data is always a very tricky case. We have mostly non-interventional data, meaning we have very limited understanding of somehow the effect of different perturbations. Perturbations that happen on many different scales in many different environments. So we need to collect a lot here. I think the overall journey that we are going with is that we take the data that we have, we make clever decisions on the data that we will collect in the future, and we have this also self-improving entity that is aware of what it doesn't know. So we need to be able to understand how well can I predict something on this somewhat regime. If I cannot, then we should focus our data collection effort into this. So I think that's not a present state, but this will basically also guide the future collection.

Eric Topol (07:41):

Speaking of data, one of the things I think that's fascinating is we saw how AlphaFold2 really revolutionized predicting proteins. But remember that was based on this extraordinary resource that had been built, the Protein Data Bank that enabled that. And for the virtual cell there's no such thing as a protein data bank. It's so much more as you emphasize Charlotte, it's so much dynamic and these perturbations that are just all across the board as you emphasize. Now the human cell atlas, which currently some tens of millions, but going into a billion cells, we learned that it used to be 200 cell types. Now I guess it's well over 5,000 and that we have 37 trillion cells approximately in the average person adult's body is a formidable map that's being made now. And I guess the idea that you're advancing is that we used to, and this goes back to a statement you made earlier, Steve, everything we did in science was hypothesis driven. But if we could get computational model of the virtual cell, then we can have AI exploration of the whole field. Is that really the nuts of this?

Steve Quake (09:06):

Yes. A couple thoughts on that, maybe Theo Karaletsos, our lead AI person at CZI says machine learning is the formalism through which we understand high dimensional data and I think that's a very deep statement. And biological systems are intrinsically very high dimensional. You've got 20,000 genes in the human genome in these cell atlases. You're measuring all of them at the same time in each single cell. And there's a lot of structure in the relationships of their gene expression there that is just not evident to the human eye. And for example, CELL by GENE, our database that collects all the aggregates, all of the single cell transcriptomic data is now over a hundred million cells. And as you mentioned, we're seeing ways to increase that by an order of magnitude in the near future. The project that Jure Leskovec and I worked on together that Charlotte referenced earlier was like a first attempt to build a foundational model on that data to discover some of the correlations and structure that was there.

Steve Quake (10:14):

And so, with a subset, I think it was the 20 or 30 million cells, we built a large language model and began asking it, what do you understand about the structure of this data? And it kind of discovered lineage relationships without us teaching it. We trained on a matrix of numbers, no biological information there, and it learned a lot about the relationships between cell type and lineage. And that emerged from that high dimensional structure, which was super pleasing to us and really, I mean for me personally gave me the confidence to say this stuff is going to work out. There is a future for the virtual cell. It's not some made up thing. There is real substance there and this is worth investing an enormous amount of CZIs resources in going forward and trying to rally the community around as a project.

Eric Topol (11:04):

Well yeah, the premise here is that there is a language of life, and you just made a good case that there is if you can predict, if you can query, if you can generate like that. It is reminiscent of the famous Go game of Lee Sedol, that world champion and how the machine came up with a move (Move 37) many, many years ago that no human would've anticipated and I think that's what you're getting at. And the ability for inference and reason now to add to this. So Charlotte, one of the things of course is about, well there's two terms in here that are unfamiliar to many of the listeners or viewers of this podcast, universal representations (UR) and virtual instrument (VIs) that you make a pretty significant part of how you are going about this virtual cell model. So could you describe that and also the embeddings as part of the universal representation (UR) because I think embeddings, or these meaningful relationships are key to what Steve was just talking about.

Charlotte Bunne (12:25):

Yes. So in order to somewhat leverage very different modalities in order to leverage basically modalities that will take measurements across different scales, like the idea is that we have large, may it be transformer models that might be very different. If I have imaging data, I have a vision transformer, if I have a text data, I have large language models that are designed of course for DNA then they have a very wide context and so on and so forth. But the idea is somewhat that we have models that are connected through the scales of biology because those scales we know. We know which components are somewhat involved or in measurements that are happening upstream. So we have the somewhat interconnection or very large model that will be trained on many different data and we have this internal model representation that somewhat capture everything they've seen. And so, this is what we call those universal representation (UR) that will exist across the scales of biology.

Charlotte Bunne (13:22):

And what is great about AI, and so I think this is a bit like a history of AI in short is the ability to predict the last years, the ability to generate, we can generate new hypothesis, we can generate modalities that we are missing. We can potentially generate certain cellular state, molecular state have a certain property, but I think what's really coming is this ability to reason. So we see this in those very large language models, the ability to reason about a hypothesis, how we can test it. So this is what those instruments ultimately need to do. So we need to be able to simulate the change of a perturbation on a cellular phenotype. So on the internal representation, the universal representation of a cell state, we need to simulate the fact the mutation has downstream and how this would propagate in our representations upstream. And we need to build many different type of virtual instruments that allow us to basically design and build all those capabilities that ultimately the AI virtual cell needs to possess that will then allow us to reason, to generate hypothesis, to basically predict the next experiment to conduct to predict the outcome of a perturbation experiment to in silico design, cellular states, molecular states, things like that. And this is why we make the separation between internal representation as well as those instruments that operate on those representations.

Eric Topol (14:47):

Yeah, that's what I really liked is that you basically described the architecture, how you're going to do this. By putting these URs into the VIs, having a decoder and a manipulator and you basically got the idea if you can bring all these different integrations about which of course is pending. Now there are obviously many naysayers here that this is impossible. One of them is this guy, Philip Ball. I don't know if you read the language, How Life Works. Now he's a science journalist and he's a prolific writer. He says, “Comparing life to a machine, a robot, a computer, sells it short. Life is a cascade of processes, each with a distinct integrity and autonomy, the logic of which has no parallel outside the living world.” Is he right? There's no way to model this. It's silly, it's too complex.

Steve Quake (15:50):

We don't know, alright. And it's great that there's naysayers. If everyone agreed this was doable, would it be worth doing? I mean the whole point is to take risks and get out and do something really challenging in the frontier where you don't know the answer. If we knew that it was doable, I wouldn't be interested in doing it. So I personally am happy that there's not a consensus.

Eric Topol (16:16):

Well, I mean to capture people's imagination here, if you're successful and you marshal a global effort, I don't know who's going to pay for it because it's a lot of work coming here going forward. But if you can do it, the question here is right today we talk about, oh let's make an organoid so we can figure out how to treat this person's cancer or understand this person's rare disease or whatever. And instead of having to wait weeks for this culture and all the expense and whatnot, you could just do it in a computer and in silico and you have this virtual twin of a person's cells and their tissue and whatnot. So the opportunity here is, I don't know if people get, this is just extraordinary and quick and cheap if you can get there. And it's such a bold initiative idea, who will pay for this do you think?

Steve Quake (17:08):

Well, CZI is putting an enormous amount of resources into it and it's a major project for us. We have been laying the groundwork for it. We recently put together what I think is if not the largest, one of the largest GPU supercomputer clusters for nonprofit basic science research that came online at the end of last year. And in fact in December we put out an RFA for the scientific community to propose using it to build models. And so we're sharing that resource within the scientific community as I think you appreciate, one of the real challenges in the field has been access to compute resources and industry has it academia at a much lower level. We are able to be somewhere in between, not quite at the level of a private company but the tech company but at a level beyond what most universities are being able to do and we're trying to use that to drive the field forward. We're also planning on launching RFAs we this year to help drive this project forward and funding people globally on that. And we are building a substantial internal effort within CZI to help drive this project forward.

Eric Topol (18:17):

I think it has the looks of the human genome project, which at time as you know when it was originally launched that people thought, oh, this is impossible. And then look what happened. It got done. And now the sequence of genome is just a commodity, very relatively, very inexpensive compared to what it used to be.

Steve Quake (18:36):

I think a lot about those parallels. And I will say one thing, Philip Ball, I will concede him the point, the cells are very complicated. The genome project, I mean the sort of genius there was to turn it from a biology problem to a chemistry problem, there is a test tube with a chemical and it work out the structure of that chemical. And if you can do that, the problem is solved. I think what it means to have the virtual cell is much more complex and ambiguous in terms of defining what it's going to do and when you're done. And so, we have our work cut out for us there to try to do that. And that's why a little bit, I established our North Star and CZI for the next decade as understanding the mysteries of the cell and that word mystery is very important to me. I think the molecules, as you pointed out earlier are understood, genome sequenced, protein structure solved or predicted, we know a lot about the molecules. Those are if not solved problems, pretty close to being solved. And the real mystery is how do they work together to create life in the cell? And that's what we're trying to answer with this virtual cell project.

Eric Topol (19:43):

Yeah, I think another thing that of course is happening concurrently to add the likelihood that you'll be successful is we've never seen the foundation models coming out in life science as they have in recent weeks and months. Never. I mean, I have a paper in Science tomorrow coming out summarizing the progress about not just RNA, DNA, ligands. I mean the whole idea, AlphaFold3, but now Boltz and so many others. It's just amazing how fast the torrent of new foundation models. So Charlotte, what do you think accounts for this? This is unprecedented in life science to see foundation models coming out at this clip on evolution on, I mean you name it, design of every different molecule of life or of course in cells included in that. What do you think is going on here?

Charlotte Bunne (20:47):

So on the one hand, of course we benefit profits and inherit from all the tremendous efforts that have been made in the last decades on assembling those data sets that are very, very standardized. CELLxGENE is very somehow AI friendly, as you can say, it is somewhat a platform that is easy to feed into algorithms, but at the same time we actually also see really new building mechanisms, design principles of AI algorithms in itself. So I think we have understood that in order to really make progress, build those systems that work well, we need to build AI tools that are designed for biological data. So to give you an easy example, if I use a large language model on text, it's not going to work out of the box for DNA because we have different reading directions, different context lens and many, many, many, many more.

Charlotte Bunne (21:40):

And if I look at standard computer vision where we can say AI really excels and I'm applying standard computer vision, vision transformers on multiplex images, they're not going to work because normal computer vision architectures, they always expect the same three inputs, RGB, right? In multiplex images, I'm measuring up to 150 proteins potentially in a single experiment, but every study will measure different proteins. So I deal with many different scales like larger scales and I used to attention mechanisms that we have in usual computer vision. Transformers are not going to work anymore, they're not going to scale. And at the same time, I need to be completely flexible in whatever input combination of channel I'm just going to face in this experiment. So this is what we right now did for example, in our very first work, inheriting the design principle that we laid out in the paper AI virtual cell and then come up with new AI architectures that are dealing with these very special requirements that biological data have.

Charlotte Bunne (22:46):

So we have now a lot of computer scientists that work very, very closely have a very good understanding of biologists. Biologists that are getting much and much more into the computer science. So people who are fluent in both languages somewhat, that are able to now build models that are adopted and designed for biological data. And we don't just take basically computer vision architectures that work well on street scenes and try to apply them on biological data. So it's just a very different way of thinking about it, starting constructing basically specialized architectures, besides of course the tremendous data efforts that have happened in the past.

Eric Topol (23:24):

Yeah, and we're not even talking about just sequence because we've also got imaging which has gone through a revolution, be able to image subcellular without having to use any types of stains that would disrupt cells. That's another part of the deep learning era that came along. One thing I thought was fascinating in the paper in Cell you wrote, “For instance, the Short Read Archive of biological sequence data holds over 14 petabytes of information, which is 1,000 times larger than the dataset used to train ChatGPT.” I mean that's a lot of tokens, that's a lot of stuff, compute resources. It's almost like you're going to need a DeepSeek type of way to get this. I mean not that DeepSeek as its claim to be so much more economical, but there's a data challenge here in terms of working with that massive amount that is different than the human language. That is our language, wouldn't you say?

Steve Quake (24:35):

So Eric, that brings to mind one of my favorite quotes from Sydney Brenner who is such a wit. And in 2000 at the sort of early first flush of success in genomics, he said, biology is drowning in a sea of data and starving for knowledge. A very deep statement, right? And that's a little bit what the motivation was for putting the Short Read Archive statistic into the paper there. And again, for me, part of the value of this endeavor of creating a virtual cell is it's a tool to help us translate data into knowledge.

Eric Topol (25:14):

Yeah, well there's two, I think phenomenal figures in your Cell paper. The first one that kicks across the capabilities of the virtual cell and the second that compares the virtual cell to the real or the physical cell. And we'll link that with this in the transcript. And the other thing we'll link is there's a nice Atlantic article, “A Virtual Cell Is a ‘Holy Grail’ of Science. It's Getting Closer.” That might not be quite close as next week or year, but it's getting close and that's good for people who are not well grounded in this because it's much more taken out of the technical realm. This is really exciting. I mean what you're onto here and what's interesting, Steve, since I've known you for so many years earlier in your career you really worked on omics that is being DNA and RNA and in recent times you've made this switch to cells. Is that just because you're trying to anticipate the field or tell us a little bit about your migration.

Steve Quake (26:23):

Yeah, so a big part of my career has been trying to develop new measurement technologies that'll provide insight into biology. And decades ago that was understanding molecules. Now it's understanding more complex biological things like cells and it was like a natural progression. I mean we built the sequencers, sequenced the genomes, done. And it was clear that people were just going to do that at scale then and create lots of data. Hopefully knowledge would get out of that. But for me as an academic, I never thought I'd be in the position I'm in now was put it that way. I just wanted to keep running a small research group. So I realized I would have to get out of the genome thing and find the next frontier and it became this intersection of microfluidics and genomics, which as you know, I spent a lot of time developing microfluidic tools to analyze cells and try to do single cell biology to understand their heterogeneity. And that through a winding path led me to all these cell atlases and to where we are now.

Eric Topol (27:26):

Well, we're fortunate for that and also with your work with CZI to help propel that forward and I think it sounds like we're going to need a lot of help to get this thing done. Now Charlotte, as a computer scientist now at EPFL, what are you going to do to keep working on this and what's your career advice for people in computer science who have an interest in digital biology?

Charlotte Bunne (27:51):

So I work in particular on the prospect of using this to build diagnostic tools and to make diagnostics in the clinic easier because ultimately we have somewhat limited capabilities in the hospital to run deep omics, but the idea of being able to somewhat map with a cheaper and lighter modality or somewhat diagnostic test into something much richer because a model has been seeing all those different data and can basically contextualize it. It's very interesting. We've seen all those pathology foundation models. If I can always run an H&E, but then decide when to run deeper diagnostics to have a better or more accurate prediction, that is very powerful and it's ultimately reducing the costs, but the precision that we have in hospitals. So my faculty position right now is co-located between the School of Life Sciences, School of Computer Science. So I have a dual affiliation and I'm affiliated to the hospitals to actually make this possible and as a career advice, I think don't be shy and stick to your discipline.

Charlotte Bunne (28:56):

I have a bachelor's in biology, but I never only did biology. I have a PhD in computer science, which you would think a bachelor in biology not necessarily qualifies you through. So I think this interdisciplinarity also requires you to be very fluent, very comfortable in reading many different styles of papers and publications because a publication in a computer science venue will be very, very different from the way we write in biology. So don't stick to your study program, but just be free in selecting whatever course gets you closer to the knowledge you need in order to do the research or whatever task you are building and working on.

Eric Topol (29:39):

Well, Charlotte, the way you're set up there with this coalescence of life science and computer science is so ideal and so unusual here in the US, so that's fantastic. That's what we need and that's really the underpinning of how you're going to get to the virtual cells, getting these two communities together. And Steve, likewise, you were an engineer and somehow you became one of the pioneers of digital biology way back before it had that term, this interdisciplinary, transdisciplinary. We need so much of that in order for you all to be successful, right?

Steve Quake (30:20):

Absolutely. I mean there's so much great discovery to be done on the boundary between fields. I trained as a physicist and kind of made my career this boundary between physics and biology and technology development and it's just sort of been a gift that keeps on giving. You've got a new way to measure something, you discover something new scientifically and it just all suggests new things to measure. It's very self-reinforcing.

Eric Topol (30:50):

Now, a couple of people who you know well have made some pretty big statements about this whole era of digital biology and I think the virtual cell is perhaps the biggest initiative of all the digital biology ongoing efforts, but Jensen Huang wrote, “for the first time in human history, biology has the opportunity to be engineering, not science.” And Demis Hassabis wrote or said, ‘we're seeing engineering science, you have to build the artifact of interest first, and then once you have it, you can use the scientific method to reduce it down and understand its components.’ Well here there's a lot to do to understand its components and if we can do that, for example, right now as both of AI drug discoveries and high gear and there's umpteen numbers of companies working on it, but it doesn't account for the cell. I mean it basically is protein, protein ligand interactions. What if we had drug discovery that was cell based? Could you comment about that? Because that doesn't even exist right now.

Steve Quake (32:02):

Yeah, I mean I can say something first, Charlotte, if you've got thoughts, I'm curious to hear them. So I do think AI approaches are going to be very useful designing molecules. And so, from the perspective of designing new therapeutics, whether they're small molecules or antibodies, yeah, I mean there's a ton of investment in that area that is a near term fruit, perfect thing for venture people to invest in and there's opportunity there. There's been enough proof of principle. However, I do agree with you that if you want to really understand what happens when you drug a target, you're going to want to have some model of the cell and maybe not just the cell, but all the different cell types of the body to understand where toxicity will come from if you have on-target toxicity and whether you get efficacy on the thing you're trying to do.

Steve Quake (32:55):

And so, we really hope that people will use the virtual cell models we're going to build as part of the drug discovery development process, I agree with you in a little of a blind spot and we think if we make something useful, people will be using it. The other thing I'll say on that point is I'm very enthusiastic about the future of cellular therapies and one of our big bets at CZI has been starting the New York Biohub, which is aimed at really being very ambitious about establishing the engineering and scientific foundations of how to engineer completely, radically more powerful cellular therapies. And the virtual cell is going to help them do that, right? It's going to be essential for them to achieve that mission.

Eric Topol (33:39):

I think you're pointing out one of the most important things going on in medicine today is how we didn't anticipate that live cell therapy, engineered cells and ideally off the shelf or in vivo, not just having to take them out and work on them outside the body, is a revolution ongoing, and it's not just in cancer, it's in autoimmune diseases and many others. So it's part of the virtual cell need. We need this. One of the things that's a misnomer, I want you both to comment on, we keep talking about single cell, single cell. And there's a paper spatial multi-omics this week, five different single cell scales all integrated. It's great, but we don't get to single cell. We're basically looking at 50 cells, 100 cells. We're not doing single cell because we're not going deep enough. Is that just a matter of time when we actually are doing, and of course the more we do get down to the single or a few cells, the more insights we're going to get. Would you comment about that? Because we have all this literature on single cell comes out every day, but we're not really there yet.

Steve Quake (34:53):

Charlotte, do you want to take a first pass at that and then I can say something?

Charlotte Bunne (34:56):

Yes. So it depends. So I think if we look at certain spatial proteomics, we still have subcellular resolutions. So of course, we always measure many different cells, but we are able to somewhat get down to resolution where we can look at certain colocalization of proteins. This also goes back to the point just made before having this very good environment to study drugs. If I want to build a new drug, if I want to build a new protein, the idea of building this multiscale model allows us to actually simulate different, somehow binding changes and binding because we simulate the effect of a drug. Ultimately, the redouts we have they are subcellular. So of course, we often in the spatial biology, we often have a bit like methods that are rather coarse they have a spot that averages over certain some cells like hundreds of cells or few cells.

Charlotte Bunne (35:50):

But I think we also have more and more technologies that are zooming in that are subcellular where we can actually tag or have those probe-based methods that allow us to zoom in. There's microscopy of individual cells to really capture them in 3D. They are of course not very high throughput yet, but it gives us also an idea of the morphology and how ultimately morphology determine certain somehow cellular properties or cellular phenotype. So I think there's lots of progress also on the experimental and that ultimately will back feed into the AI virtual cell, those models that will be fed by those data. Similarly, looking at dynamics, right, looking at live imaging of individual cells of their morphological changes. Also, this ultimately is data that we'll need to get a better understanding of disease mechanisms, cellular phenotypes functions, perturbation responses.

Eric Topol (36:47):

Right. Yes, Steve, you can comment on that and the amazing progress that we have made with space and time, spatial temporal resolution, spatial omics over these years, but that we still could go deeper in terms of getting to individual cells, right?

Steve Quake (37:06):

So, what can we do with a single cell? I'd say we are very mature in our ability to amplify and sequence the genome of a single cell, amplify and sequence the transcriptome of a single cell. You can ask is one cell enough to make a biological conclusion? And maybe I think what you're referring to is people want to see replicates and so you can ask how many cells do you need to see to have confidence in any given biological conclusion, which is a reasonable thing. It's a statistical question in good science. I think I've been very impressed with how the mass spec people have been doing recently. I think they've finally cracked the ability to look at proteins from single cells and they can look at a couple thousand proteins. That was I think one of these Nature method of the year things at the end of last year and deep visual proteomics.

Eric Topol (37:59):

Deep visual proteomics, yes.

Steve Quake (38:00):

Yeah, they are over the hump. Yeah, they are over the hump with single cell measurements. Part of what's missing right now I think is the ability to reliably do all of that on the same cell. So this is what Charlotte was referring to be able to do sort of multi-modal measurements on single cells. That's kind of in its infancy and there's a few examples, but there's a lot more work to be done on that. And I think also the fact that these measurements are all destructive right now, and so you're losing the ability to look how the cells evolve over time. You've got to say this time point, I'm going to dissect this thing and look at a state and I don't get to see what happens further down the road. So that's another future I think measurement challenge to be addressed.

Eric Topol (38:42):

And I think I'm just trying to identify some of the multitude of challenges in this extraordinarily bold initiative because there are no shortage and that's good about it. It is given people lots of work to do to overcome, override some of these challenges. Now before we wrap up, besides the fact that you point out that all the work has to be done and be validated in real experiments, not just live in a virtual AI world, but you also comment about the safety and ethics of this work and assuming you're going to gradually get there and be successful. So could either or both of you comment about that because it's very thoughtful that you're thinking already about that.

Steve Quake (41:10):

As scientists and members of the larger community, we want to be careful and ensure that we're interacting with people who said policy in a way that ensures that these tools are being used to advance the cause of science and not do things that are detrimental to human health and are used in a way that respects patient privacy. And so, the ethics around how you use all this with respect to individuals is going to be important to be thoughtful about from the beginning. And I also think there's an ethical question around what it means to be publishing papers and you don't want people to be forging papers using data from the virtual cell without being clear about where that came from and pretending that it was a real experiment. So there's issues around those sorts of ethics as well that need to be considered.

Eric Topol (42:07):

And of those 40 some authors, do you around the world, do you have the sense that you all work together to achieve this goal? Is there kind of a global bonding here that's going to collaborate?

Steve Quake (42:23):

I think this effort is going to go way beyond those 40 authors. It's going to include a much larger set of people and I'm really excited to see that evolve with time.

Eric Topol (42:31):

Yeah, no, it's really quite extraordinary how you kick this thing off and the paper is the blueprint for something that we are all going to anticipate that could change a lot of science and medicine. I mean we saw, as you mentioned, Steve, how that deep visual proteomics (DVP) saved lives. It was what I wrote a spatial medicine, no longer spatial biology. And so, the way that this can change the future of medicine, I think a lot of people just have to have a little bit of imagination that once we get there with this AIVC, that there's a lot in store that's really quite exciting. Well, I think this has been an invigorating review of that paper and some of the issues surrounding it. I couldn't be more enthusiastic for your success and ultimately where this could take us. Did I miss anything during the discussion that we should touch on before we wrap up?

Steve Quake (43:31):

Not from my perspective. It was a pleasure as always Eric, and a fun discussion.

Charlotte Bunne (43:38):

Thanks so much.

Eric Topol (43:39):

Well thank you both and all the co-authors of this paper. We're going to be following this with the great interest, and I think for most people listening, they may not know that this is in store for the future. Someday we will get there. I think one of the things to point out right now is the models we have today that large language models based on transformer architecture, they're going to continue to evolve. We're already seeing so much in inference and ability for reasoning to be exploited and not asking for prompts with immediate answers, but waiting for days to get back. A lot more work from a lot more computing resources. But we're going to get models in the future to fold this together. I think that's one of the things that you've touched on the paper so that whatever we have today in concert with what you've laid out, AI is just going to keep getting better.

Eric Topol (44:39):

The biology that these foundation models are going to get broader and more compelling as to their use cases. So that's why I believe in this. I don't see this as a static situation right now. I just think that you're anticipating the future, and we will have better models to be able to integrate this massive amount of what some people would consider disparate data sources. So thank you both and all your colleagues for writing this paper. I don't know how you got the 42 authors to agree to it all, which is great, and it's just a beginning of something that's a new frontier. So thanks very much.

Steve Quake (45:19):

Thank you, Eric.

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Funding for the NIH and US biomedical research is imperiled at a momentous time of progress. Exemplifying this is the work of Dr. Anna Greka, a leading physician-scientist at the Broad Institute who is devoted to unlocking the mysteries of rare diseases— that cumulatively affect 30 million Americans— and finding cures, science supported by the NIH.

A clip from our conversation

The audio is available on iTunes and Spotify. The full video is linked here, at the top, and also can be found on YouTube.

Transcript with audio and external links

Eric Topol (00:06):

Well, hello. This is Eric Topol from Ground Truths, and I am really delighted to welcome today, Anna Greka. Anna is the president of the American Society for Clinical Investigation (ASCI) this year, a very prestigious organization, but she's also at Mass General Brigham, a nephrologist, a cell biologist, a physician-scientist, a Core Institute Member of the Broad Institute of MIT and Harvard, and serves as a member of the institute’s Executive Leadership Team. So we got a lot to talk about of all these different things you do. You must be pretty darn unique, Anna, because I don't know any cell biologists, nephrologists, physician-scientist like you.

Anna Greka (00:48):

Oh, thank you. It's a great honor to be here and glad to chat with you, Eric.

Eric Topol (00:54):

Yeah. Well, I had the real pleasure to hear you speak at a November conference, the AI for Science Forum, which we'll link to your panel. Where I was in a different panel, but you spoke about your extraordinary work and it became clear that we need to get you on Ground Truths, so you can tell your story to everybody. So I thought rather than kind of going back from the past where you were in Greece and somehow migrated to Boston and all that. We're going to get to that, but you gave an amazing TED Talk and it really encapsulated one of the many phenomenal stories of your work as a molecular sleuth. So maybe if you could give us a synopsis, and of course we'll link to that so people could watch the whole talk. But I think that Mucin-1 or MUC1, as you call it, discovery is really important to kind of ground our discussion.

A Mysterious Kidney Disease Unraveled

Anna Greka (01:59):

Oh, absolutely. Yeah, it's an interesting story. In some ways, in my TED Talk, I highlight one of the important families of this story, a family from Utah, but there's also other important families that are also part of the story. And this is also what I spoke about in London when we were together, and this is really sort of a medical mystery that initially started on the Mediterranean island of Cyprus, where it was found that there were many families in which in every generation, several members suffered and ultimately died from what at the time was a mysterious kidney disease. This was more than 30 years ago, and it was clear that there was something genetic going on, but it was impossible to identify the gene. And then even with the advent of Next-Gen sequencing, this is what's so interesting about this story, it was still hard to find the gene, which is a little surprising.

Anna Greka (02:51):

After we were able to sequence families and identify monogenic mutations pretty readily, this was still very resistant. And then it actually took the firepower of the Broad Institute, and it's actually from a scientific perspective, an interesting story because they had to dust off the old-fashioned Sanger sequencing in order to get this done. But they were ultimately able to identify this mutation in a VNTR region of the MUC1 gene. The Mucin-1 gene, which I call a dark corner of the human genome, it was really, it's highly repetitive, very GC-rich. So it becomes very difficult to sequence through there with Next-Gen sequencing. And so, ultimately the mutation of course was found and it's a single cytosine insertion in a stretch of cytosines that sort of causes this frameshift mutation and an early stop codon that essentially results in a neoprotein like a toxic, what I call a mangled protein that sort of accumulates inside the kidney cells.

Anna Greka (03:55):

And that's where my sort of adventure began. It was Eric Lander’s group, who is the founding director of the Broad who discovered the mutation. And then through a conversation we had here in Boston, we sort of discovered that there was an opportunity to collaborate and so that’s how I came to the Broad, and that's the beginnings of this story. I think what's fascinating about this story though, that starts in a remote Mediterranean island and then turns out to be a disease that you can find in every continent all over the world. There are probably millions of patients with kidney disease in whom we haven't recognized the existence of this mutation. What's really interesting about it though is that what we discovered is that the mangled protein that's a result of this misspelling of this mutation is ultimately captured by a family of cargo receptors, they’re called the TMED cargo receptors and they end up sort of grabbing these misfolded proteins and holding onto them so tight that it's impossible for the cell to get rid of them.

Anna Greka (04:55):

And they become this growing heap of molecular trash, if you will, that becomes really hard to manage, and the cells ultimately die. So in the process of doing this molecular sleuthing, as I call it, we actually also identified a small molecule that actually disrupts these cargo receptors. And as I described in my TED Talk, it's a little bit like having these cargo trucks that ultimately need to go into the lysosome, the cells recycling facility. And this is exactly what this small molecule can do. And so, it was just like a remarkable story of discovery. And then I think the most exciting of all is that these cargo receptors turn out to be not only relevant to this one mangled misshapen protein, but they actually handle a completely different misshapen protein caused by a different genetic mutation in the eye, causing retinitis pigmentosa, a form of blindness, familial blindness. We're now studying familial Alzheimer's disease that's also involving these cargo receptors, and there are other mangled misshapen proteins in the liver, in the lung that we're now studying. So this becomes what I call a node, like a nodal mechanism that can be targeted for the benefit of many more patients than we had previously thought possible, which has been I think, the most satisfying part about this story of molecular sleuthing.

Eric Topol (06:20):

Yeah, and it's pretty extraordinary. We'll put the figure from your classic Cell paper in 2019, where you have a small molecule that targets the cargo receptor called TMED9.

Anna Greka (06:34):

Correct.

Expanding the Mission

Eric Topol (06:34):

And what's amazing about this, of course, is the potential to reverse this toxic protein disease. And as you say, it may have applicability well beyond this MUC1 kidney story, but rather eye disease with retinitis pigmentosa and the familial Alzheimer's and who knows what else. And what's also fascinating about this is how, as you said, there were these limited number of families with the kidney disease and then you found another one, uromodulin. So there's now, as you say, thousands of families, and that gets me to part of your sleuth work is not just hardcore science. You started an entity called the Ladders to Cures (L2C) Scientific Accelerator.

Eric Topol (07:27):

Maybe you can tell us about that because this is really pulling together all the forces, which includes the patient advocacy groups, and how are we going to move forward like this?

Anna Greka (07:39):

Absolutely. I think the goal of the Ladders to Cures Accelerator, which is a new initiative that we started at the Broad, but it really encompasses many colleagues across Boston. And now increasingly it's becoming sort of a national, we even have some international collaborations, and it's only two years that it's been in existence, so we're certainly in a growth mode. But the inspiration was really some of this molecular sleuthing work where I basically thought, well, for starters, it cannot be that there's only one molecular node, these TMED cargo receptors that we discovered there's got to be more, right? And so, there's a need to systematically go and find more nodes because obviously as anyone who works in rare genetic diseases will tell you, the problem for all of us is that we do what I call hand to hand combat. We start with the disease with one mutation, and we try to uncover the mechanism and then try to develop therapies, and that's wonderful.

Anna Greka (08:33):

But of course, it's slow, right? And if we consider the fact that there are 30 million patients in the United States in every state, everywhere in the country who suffer from a rare genetic disease, most of them, more than half of them are children, then we can appreciate the magnitude of the problem. Out of more than 8,000 genes that are involved in rare genetic diseases, we barely have something that looks like a therapy for maybe 500 of them. So there's a huge mismatch in the unmet need and magnitude of the problem. So the Ladders to Cures Accelerator is here to address this and to do this with the most modern tools available. And to your point, Eric, to bring patients along, not just as the recipients of whatever we discover, but also as partners in the research enterprise because it's really important to bring their perspectives and of course their partnerships in things like developing appropriate biomarkers, for example, for what we do down the road.

Anna Greka (09:35):

But from a fundamental scientific perspective, this is basically a project that aims to identify every opportunity for nodes, underlying all rare genetic diseases as quickly as possible. And this was one of the reasons I was there at the AI for Science Forum, because of course when one undertakes a project in which you're basically, this is what we're trying to do in the Ladders to Cures Accelerator, introduce dozens of thousands of missense and nonsense human mutations that cause genetic diseases, simultaneously introduce them into multiple human cells and then use modern scalable technology tools. Things like CRISPR screens, massively parallel CRISPR screens to try to interrogate all of these diseases in parallel, identify the nodes, and then develop of course therapeutic programs based on the discovery of these nodes. This is a massive data generation project that is much needed and in addition to the fact that it will help hopefully accelerate our approach to all rare diseases, genetic diseases. It is also a highly controlled cell perturbation dataset that will require the most modern tools in AI, not only to extract the data and understand the data of this dataset, but also because this, again, an extremely controlled, well controlled cell perturbation dataset can be used to train models, train AI models, so that in the future, and I hope this doesn't sound too futuristic, but I think that we're all aiming for that cell biologists for sure dream of this moment, I think when we can actually have in silico the opportunity to make predictions about what cell behaviors are going to look like based on a new perturbation that was not in the training set. So an experiment that hasn't yet been done on a cell, a perturbation that has not been made on a human cell, what if like a new drug, for example, or a new kind of perturbation, a new chemical perturbation, how would it affect the behavior of the cell? Can we make a predictive model for that? This doesn't exist today, but I think this is something, the cell prediction model is a big question for biology for the future. And so, I'm very energized by the opportunity to both address this problem of rare monogenic diseases that remains an unmet need and help as many patients as possible while at the same time advancing biology as much as we possibly can. So it's kind of like a win-win lifting all boats type of enterprise, hopefully.

Eric Topol (12:11):

Yeah. Well, there's many things to get to unpack what you've just been reviewing. So one thing for sure is that of these 8,000 monogenic diseases, they have relevance to the polygenic common diseases, of course. And then also the fact that the patient family advocates, they are great at scouring the world internet, finding more people, bringing together communities for each of these, as you point out aptly, these rare diseases cumulatively are high, very high proportion, 10% of Americans or more. So they're not so rare when you think about the overall.

Anna Greka (12:52):

Collectively.

Help From the Virtual Cell?

Eric Topol (12:53):

Yeah. Now, and of course is this toxic proteinopathies, there's at least 50 of these and the point that people have been thinking until now that, oh, we found a mangled protein, but what you've zeroed in on is that, hey, you know what, it's not just a mangled protein, it's how it gets stuck in the cell and that it can't get to the lysosome to get rid of it, there's no waste system. And so, this is such fundamental work. Now that gets me to the virtual cell story, kind of what you're getting into. I just had a conversation with Charlotte Bunne and Steve Quake who published a paper in December on the virtual cell, and of course that's many years off, but of course it's a big, bold, ambitious project to be able to say, as you just summarized, if you had cells in silico and you could do perturbations in silico, and of course they were validated by actual experiments or bidirectionally the experiments, the real ones helped to validate the virtual cell, but then you could get a true acceleration of your understanding of cell biology, your field of course.

Anna Greka (14:09):

Exactly.

Eric Topol (14:12):

So what you described, is it the same as a virtual cell? Is it kind of a precursor to it? How do you conceive this because this is such a complex, I mean it’s a fundamental unit of life, but it's also so much more complex than a protein or an RNA because not only all the things inside the cell, inside all these organelles and nucleus, but then there's all the outside interactions. So this is a bold challenge, right?

Anna Greka (14:41):

Oh my god, it's absolutely from a biologist perspective, it's the challenge of a generation for sure. We think taking humans to Mars, I mean that's an aspirational sort of big ambitious goal. I think this is the, if you will, the Mars shot for biology, being able to, whether the terminology, whether you call it a virtual cell. I like the idea of saying that to state it as a problem, the way that people who think about it from a mathematics perspective for example, would think about it. I think stating it as the cell prediction problem appeals to me because it actually forces us biologists to think about setting up the way that we would do these cell perturbation data sets, the way we would generate them to set them up to serve predictions. So for example, the way that I would think about this would be can I in the future have so much information about how cell perturbations work that I can train a model so that it can predict when I show it a picture of another cell under different conditions that it hasn't seen before, that it can still tell me, ah, this is a neuron in which you perturbed the mitochondria, for example, and now this is sort of the outcome that you would expect to see.

Anna Greka (16:08):

And so, to be able to have this ability to have a model that can have the ability to predict in silico what cells would look like after perturbation, I think that's sort of the way that I think about this problem. It is very far away from anything that exists today. But I think that the beginning starts, and this is one of the unique things about my institute, if I can say, we have a place where cell biologists, geneticists, mathematicians, machine learning experts, we all come together in the same place to really think and grapple with these problems. And of course we're very outward facing, interacting with scientists all across the world as well. But there's this sort of idea of bringing people into one institute where we can just think creatively about these big aspirational problems that we want to solve. I think this is one of the unique things about the ecosystem at the Broad Institute, which I'm proud to be a part of, and it is this kind of out of the box thinking that will hopefully get us to generate the kinds of data sets that will serve the needs of building these kinds of models with predictive capabilities down the road.

Anna Greka (17:19):

But as you astutely said, AlphaFold of course was based on the protein database existing, right? And that was a wealth of available information in which one could train models that would ultimately be predictive, as we have seen this miracle that Demi Hassabis and John Jumper have given to humanity, if you will.

Anna Greka (17:42):

But as Demis and John would also say, I believe is as I have discussed with them, in fact, the cell prediction problem is really a bigger problem because we do not have a protein data bank to go to right now, but we need to create it to generate these data. And so, my Ladders to Cures Accelerator is here to basically provide some part of the answer to that problem, create this kind of well-controlled database that we need for cell perturbations, while at the same time maximizing our learnings about these fully penetrant coding mutations and what their downstream sequelae would be in many different human cells. And so, in this way, I think we can both advance our knowledge about these monogenic diseases, build models, hopefully with predictive capabilities. And to your point, a lot of what we will learn about this biology, if we think that it involves 8,000 or more out of the 20,000 genes in our genome, it will of course serve our understanding of polygenic diseases ultimately as well as we go deeper into this biology and we look at the combinatorial aspects of what different mutations do to human cells. And so, it's a huge aspirational problem for a whole generation, but it's a good one to work on, I would say.

Learning the Language of Life with A.I.

Eric Topol (19:01):

Oh, absolutely. Now I think you already mentioned something that's quite, well, two things from what you just touched on. One of course, how vital it is to have this inner or transdisciplinary capability because you do need expertise across these vital areas. But the convergence, I mean, I love your term nodal biology and the fact that there's all these diseases like you were talking about, they do converge and nodal is a good term to highlight that, but it's not. Of course, as you mentioned, we have genome editing which allows to look at lots of different genome perturbations, like the single letter change that you found in MUC1 pathogenic critical mutation. There's also the AI world which is blossoming like I've never seen. In fact, I had in Science this week about learning the language of life with AI and how there's been like 15 new foundation models, DNA, proteins, RNA, ligands, all their interactions and the beginning of the cell story too with the human cell.

Eric Topol (20:14):

So this is exploding. As you said, the expertise in computer science and then this whole idea that you could take these powerful tools and do as you said, which is the need to accelerate, we just can't sit around here when there's so much discovery work to be done with the scalability, even though it might take years to get to this artificial intelligence virtual cell, which I have to agree, everyone in biology would say that's the holy grail. And as you remember at our conference in London, Demi Hassabis said that's what we'd like to do now. So it has the attention of leaders in AI around the world, obviously in the science and the biomedical community like you and many others. So it is an extraordinary time where we just can't sit still with these tools that we have, right?

Anna Greka (21:15):

Absolutely. And I think this is going to be, you mentioned the ASCI presidency in the beginning of our call. This is going to be the president gets to give an address at the annual meeting in Chicago. This is going to be one of the points I make, no matter what field in biomedicine we're in, we live in, I believe, a golden era and we have so many tools available to us that we can really accelerate our ability to help more patients. And of course, this is our mandate, the most important stakeholders for everything that we do as physician-scientists are our patients ultimately. So I feel very hopeful for the future and our ability to use these tools and to really make good on the promise of research is a public good. And I really hope that we can advance our knowledge for the benefit of all. And this is really an exciting time, I think, to be in this field and hopefully for the younger colleagues a time to really get excited about getting in there and getting involved and asking the big questions.

Career Reflections

Eric Topol (22:21):

Well, you are the prototype for this and an inspiration to everyone really, I'm sure to your lab group, which you highlighted in the TED Talk and many other things that you do. Now I want to spend a little bit of time about your career. I think it's fascinating that you grew up in Greece and your father's a nephrologist and your mother's a pathologist. So you had two physicians to model, but I guess you decided to go after nephrology, which is an area in medicine that I kind of liken it to Rodney Dangerfield, he doesn't get any respect. You don't see many people that go into nephrology. But before we get to your decision to do that somehow or other you came from Greece to Harvard for your undergrad. How did you make that connect to start your college education? And then subsequently you of course you stayed in Boston, you've never left Boston, I think.

Anna Greka (23:24):

I never left. Yeah, this is coming into 31 years now in Boston.

Anna Greka (23:29):

Yeah, I started as a Harvard undergraduate and I'm now a full professor. It's kind of a long, but wonderful road. Well, actually I would credit my parents. You mentioned that my father, they're both physician-scientists. My father is now both retired, but my father is a nephrologist, and my mother is a pathologist, actually, they were both academics. And so, when we were very young, we lived in England when my parents were doing postdoctoral work. That was actually a wonderful gift that they gave me because I became bilingual. It was a very young age, and so that allowed me to have this advantage of being fluent in English. And then when we moved back to Greece where I grew up, I went to an American school. And from that time, this is actually an interesting story in itself. I'm very proud of this school.

Anna Greka (24:22):

It's called Anatolia, and it was founded by American missionaries from Williams College a long time ago, 150 and more years ago. But it is in Thessaloniki, Greece, which is my hometown, and it's a wonderful institution, which gave me a lot of gifts as well, preparing me for coming to college in the United States. And of course, I was a good student in high school, but what really was catalytic was that I was lucky enough to get a scholarship to go to Harvard. And that was really, you could say the catalyst that propelled me from a teenager who was dreaming about a career as a physician-scientist because I certainly was for as far back as I remember in fact. But then to make that a reality, I found myself on the Harvard campus initially for college, and then I was in the combined Harvard-MIT program for my MD PhD. And then I trained in Boston at Mass General in Brigham, and then sort of started my academic career. And that sort of brings us to today, but it is an unlikely story and one that I feel still very lucky and blessed to have had these opportunities. So for sure, it's been wonderful.

Eric Topol (25:35):

We're the ones lucky that you came here and set up shop and you did your productivity and discovery work and sleuthing has been incredible. But I do think it's interesting too, because when you did your PhD, it was in neuroscience.

Anna Greka (25:52):

Ah, yes. That's another.

Eric Topol (25:54):

And then you switch gears. So tell us about that?

Anna Greka (25:57):

This is interesting, and actually I encourage more colleagues to think about it this way. So I have always been driven by the science, and I think that it seems a little backward to some people, but I did my PhD in neuroscience because I was interested in understanding something about these ion channels that were newly discovered at the time, and they were most highly expressed in the brain. So here I was doing work in the brain in the neuroscience program at Harvard, but then once I completed my PhD and I was in the middle of my residency training actually at Mass General, I distinctly remember that there was a paper that came out that implicated the same family of ion channels that I had spent my time understanding in the brain. It turned out to be a channelopathy that causes kidney disease.

Anna Greka (26:43):

So that was the light bulb, and it made me realize that maybe what I really wanted to do is just follow this thread. And my scientific curiosity basically led me into studying the kidney and then it seemed practical therefore to get done with my clinical training as efficiently as possible. So I finished residency, I did nephrology training, and then there I was in the lab trying to understand the biology around this channelopathy. And that sort of led us into the early projects in my young lab. And in fact, it's interesting we didn't talk about that work, but that work in itself actually has made it all the way to phase II trials in patients. This was a paper we published in Science in 2017 and follow onto that work, there was an opportunity to build this into a real drug targeting one of these ion channels that has made it into phase II trials. And we'll see what happens next. But it's this idea of following your scientific curiosity, which I also talked about in my TED Talk, because you don't know to what wonderful places it will lead you. And quite interestingly now my lab is back into studying familial Alzheimer's and retinitis pigmentosa in the eye in brain. So I tell people, do not limit yourself to whatever someone says your field is or should be. Just follow your scientific curiosity and usually that takes you to a lot more interesting places. And so, that's certainly been a theme from my career, I would say.

Eric Topol (28:14):

No, I think that's perfect. Curiosity driven science is not the term. You often hear hypothesis driven or now with AI you hear more AI exploratory science. But no, that's great. Now I want to get a little back to the AI story because it’s so fascinating. You use lots of different types of AI such as cellular imaging would be fusion models and drug discovery. I mean, you've had drug discovery for different pathways. You mentioned of course the ion channel and then also as we touched on with your Cell paper, the whole idea of targeting the cargo receptor with a small molecule and then things in between. You discussed this of course at the London panel, but maybe you just give us the skinny on the different ways that you incorporate AI in the state-of-the-art science that you're doing?

Anna Greka (29:17):

Sure, yeah, thank you. I think there are many ways in which even for quite a long time before AI became such a well-known kind of household term, if you will, the concept of machine learning in terms of image processing is something that has been around for some time. And so, this is actually a form of AI that we use in order to process millions of images. My lab has by produced probably more than 20 million images over the last few years, maybe five to six years. And so, if you can imagine it's impossible for any human to process this many images and make sense of them. So of course, we've been using machine learning that is becoming increasingly more and more sophisticated and advanced in terms of being able to do analysis of images, which is a lot of what we cell biologists do, of course.

Anna Greka (30:06):

And so, there's multiple different kinds of perturbations that we do to cells, whether we're using CRISPR or base editing to make, for example, genome wide or genome scale perturbations or small molecules as we have done as well in the past. These are all ways in which we are then using machine learning to read out the effects in images of cells that we're looking at. So that's one way in which machine learning is used in our daily work, of course, because we study misshape and mangled proteins and how they are recognized by these cargo receptors. We also use AlphaFold pretty much every day in my lab. And this has been catalytic for us as a tool because we really are able to accelerate our discoveries in ways that were even just three or four years ago, completely impossible. So it's been incredible to see how the young people in my lab are just so excited to use these tools and they're becoming extremely savvy in using these tools.

Anna Greka (31:06):

Of course, this is a new generation of scientists, and so we use AlphaFold all the time. And this also has a lot of implications of course for some of the interventions that we might think about. So where in this cargo receptor complex that we study for example, might we be able to fit a drug that would disrupt the complex and lead the cargo tracks into the lysosome for degradation, for example. So there's many ways in which AI can be used for all of these functions. So I would say that if we were to organize our thinking around it, one way to think about the use of machine learning AI is around what I would call understanding biology in cells and what in sort of more kind of drug discovery terms you would call target identification, trying to understand the things that we might want to intervene on in order to have a benefit for disease.

Anna Greka (31:59):

So target ID is one area in which I think machine learning and AI will have a catalytic effect as they already are. The other of course, is in the actual development of the appropriate drugs in a rational way. So rational drug design is incredibly enabled by AlphaFold and all these advances in terms of understanding protein structures and how to fit drugs into them of all different modalities and kinds. And I think an area that we are not yet harnessing in my group, but I think the Ladders to Cures Accelerator hopes to build on is really patient data. I think that there's a lot of opportunity for AI to be used to make sense of medical records for example and how we extract information that would tell us that this cohort of patients is a better cohort to enroll in your trial versus another. There are many ways in which we can make use of these tools. Not all of them are there yet, but I think it's an exciting time for being involved in this kind of work.

Eric Topol (32:58):

Oh, no question. Now it must be tough when you know the mechanism of these families disease and you even have a drug candidate, but that it takes so long to go from that to helping these families. And what are your thoughts about that, I mean, are you thinking also about genome editing for some of these diseases or are you thinking to go through the route of here's a small molecule, here's the tox data in animal models and here's phase I and on and on. Where do you think because when you know so much and then these people are suffering, how do you bridge that gap?

Anna Greka (33:39):

Yeah, I think that's an excellent question. Of course, having patients as our partners in our research is incredible as a way for us to understand the disease, to build biomarkers, but it is also exactly creating this kind of emotional conflict, if you will, because of course, to me, honesty is the best policy, if you will. And so, I'm always very honest with patients and their families. I welcome them to the lab so they can see just how long it takes to get some of these things done. Even today with all the tools that we have, of course there are certain things that are still quite slow to do. And even if you have a perfect drug that looks like it fits into the right pocket, there may still be some toxicity, there may be other setbacks. And so, I try to be very honest with patients about the road that we're on. The small molecule path for the toxic proteinopathies is on its way now.

Anna Greka (34:34):

It's partnered with a pharmaceutical company, so it's on its way hopefully to patients. Of course, again, this is an unpredictable road. Things can happen as you very well know, but I'm at least glad that it's sort of making its way there. But to your point, and I'm in an institute where CRISPR was discovered, and base editing and prime editing were discovered by my colleagues here. So we are in fact looking at every other modality that could help with these diseases. We have several hurdles to overcome because in contrast to the liver and the brain, the kidney for example, is not an organ in which you can easily deliver nucleic acid therapies, but we're making progress. I have a whole subgroup within the bigger group who's focusing on this. It's actually organized in a way where they're running kind of independently from the cell biology group that I run.

Anna Greka (35:31):

And it's headed by a person who came from industry so that she has the opportunity to really drive the project the way that it would be run milestone driven, if you will, in a way that it would be run as a therapeutics program. And we're really trying to go after all kinds of different nucleic acid therapies that would target the mutations themselves rather than the cargo receptors. And so, there's ASO and siRNA technologies and then also actual gene editing technologies that we are investigating. But I would say that some of them are closer than others. And again, to your question about patients, I tell them honestly when a project looks to be more promising, and I also tell them when a project looks to have hurdles and that it will take long and that sometimes I just don't know how long it will take before we can get there. The only thing that I can promise patients in any of our projects, whether it's Alzheimer's, blindness, kidney disease, all I can promise is that we're working the hardest we possibly can on the problem.

Anna Greka (36:34):

And I think that is often reassuring I have found to patients, and it's best to be honest about the fact that these things take a long time, but I do think that they find it reassuring that someone is on it essentially, and that there will be some progress as we move forward. And we've made progress in the very first discovery that came out of my lab. As I mentioned to you, we've made it all the way to phase II trials. So I have seen the trajectory be realized, and I'm eager to make it happen again and again as many times as I can within my career to help as many people as possible.

The Paucity of Physician-Scientists

Eric Topol (37:13):

I have no doubts that you'll be doing this many times in your career. No, there's no question about it. It's extraordinary actually. There's a couple of things there I want to pick up on. Physician-scientists, as you know, are a rarefied species. And you have actually so nicely told the story about when you have a physician-scientist, you're caring for the patients that you're researching, which is, most of the time we have scientists. Nothing wrong with them of course, but you have this hinge point, which is really important because you’re really hearing the stories and experiencing the patients and as you say, communicating about the likelihood of being able to come up with a treatment or the progress. What are we going to do to get more physician-scientists? Because this is a huge problem, it has been for decades, but the numbers just keep going lower and lower.

Anna Greka (38:15):

I think you're absolutely right. And this is again, something that in my leadership of the ASCI I have made sort of a cornerstone of our efforts. I think that it has been well-documented as a problem. I think that the pressures of modern clinical care are really antithetical to the needs of research, protected time to really be able to think and be creative and even have the funding available to be able to pursue one's program. I think those pressures are becoming so heavy for investigators that many of them kind of choose one or the other route most often the clinical route because that tends to be, of course where they can support their families better. And so, this has been kind of the conundrum in some ways that we take our best and brightest medical students who are interested in investigation, we train them and invest in them in becoming physician-scientists, but then we sort of drop them at the most vulnerable time, which is usually after one completes their clinical and scientific training.

Anna Greka (39:24):

And they're embarking on early phases of one's careers. It has been found to be a very vulnerable point when a lot of people are now in their mid-thirties or even late thirties perhaps with some family to take care of other burdens of adulthood, if you will. And I think what it becomes very difficult to sustain a career where one salary is very limited due to the research component. And so, I think we have to invest in our youngest people, and it is a real issue that there's no good mechanism to do that at the present time. So I was actually really hoping that there would be an opportunity with leadership at the NIH to really think about this. It's also been discussed at the level of the National Academy of Medicine where I had some role in discussing the recent report that they put out on the biomedical enterprise in the United States. And it's kind of interesting to see that there is a note made there about this issue and the fact that there needs to be, I think, more generous investment in the careers of a few select physician-scientists that we can support. So if you look at the numbers, currently out of the entire physician workforce, a physician-scientist comprised of less than 1%.

Anna Greka (40:45):

It’s probably closer to 0.8% at this point.

Eric Topol (40:46):

No, it's incredible.

Anna Greka (40:48):

So that's really not enough, I think, to maintain the enterprise and if you will, this incredible innovation economy that the United States has had this miracle engine, if you will, in biomedicine that has been fueled in large part by physician investigators. Of course, our colleagues who are non-physician investigators are equally important partners in this journey. But we do need a few of the physician-scientists investigators I think as well, if you really think about the fact that I think 70% of people who run R&D programs in all the big pharmaceutical companies are physician-scientists. And so, we need people like us to be able to work on these big problems. And so, more investment, I think that the government, the NIH has a role to play there of course. And this is important from both an economic perspective, a competition perspective with other nations around the world who are actually heavily investing in the physician-scientist workforce.

Anna Greka (41:51):

And I think it's also important to do so through our smaller scale efforts at the ASCI. So one of the things that I have been involved in as a council member and now as president is the creation of an awards program for those early career investigators. So we call them the Emerging-Generation Awards, and we also have the Young Physician-Scientist Awards. And these are really to recognize people who are making that transition from being kind of a trainee and a postdoc and have finished their clinical training into becoming an independent assistant professor. And so, those are small awards, but they're kind of a symbolic tap on the shoulder, if you will, that the ASCI sees you, you're talented, stay the course. We want you to become a future member. Don't give up and please keep on fighting. I think that can take us only so far.

Anna Greka (42:45):

I mean, unless there's a real investment, of course still it will be hard to maintain people in the pipeline. But this is just one way in which we have tried to, these programs that the ASCI offers have been very successful over the last few years. We create a cohort of investigators who are clearly recognized by members of the ASCI is being promising young colleagues. And we give them longitudinal training as part of a cohort where they learn about how to write a grant, how to write a paper, leadership skills, how to run a lab. And they're sort of like a buddy system as well. So they know that they're in it together rather than feeling isolated and struggling to get their careers going. And so, we've seen a lot of success. One way that we measure that is conversion into an ASCI membership. And so, we're encouraged by that, and we hope that the program can continue. And of course, as president, I'm going to be fundraising for that as well, it's part of the role. But it is a really worthy cause because to your point, we have to somehow make sure that our younger colleagues stay the course that we can at least maintain, if not bolster our numbers within the scientific workforce.

Eric Topol (43:57):

Well, you outlined some really nice strategies and plans. It's a formidable challenge, of course. And we'd like to see billions of dollars to support this. And maybe someday we will because as you say, if we could relieve the financial concerns of people who have curiosity driven ideas.

Anna Greka (44:18):

Exactly.

Eric Topol (44:19):

We could do a lot to replenish and build a big physician-scientist workforce. Now, the last thing I want to get to, is you have great communication skills. Obviously, anybody who is listening or watching this.

Eric Topol (44:36):

Which is another really important part of being a scientist, no less a physician or the hybrid of the two. But I wanted to just go to the backstory because your TED Talk, which has been watched by hundreds of thousands of people, and I'm sure there's hundreds of thousands more that will watch it, but the TED organization is famous for making people come to the place a week ahead. This is Vancouver used to be in LA or Los Angeles area and making them rehearse the talk, rehearse, rehearse, rehearse, which seems crazy. You could train the people there, how to give a talk. Did you have to go through that?

Anna Greka (45:21):

Not really. I did rehearse once on stage before I actually delivered the talk live. And I was very encouraged by the fact that the TED folks who are of course very well calibrated, said just like that. It's great, just like that.

Eric Topol (45:37):

That says a lot because a lot of people that do these talks, they have to do it 10 times. So that kind of was another metric. But what I don't like about that is it just because these people almost have to memorize their talks from giving it so much and all this coaching, it comes across kind of stilted and unnatural, and you're just a natural great communicator added to all your other things.

Anna Greka (46:03):

I think it’s interesting. Actually, I would say, if I may, that I credit, of course, I actually think that it's important, for us physician-scientists, again, science and research is a public good, and being able to communicate to the public what it is that we do, I think is kind of an obligation for the fact that we are funded by the public to do this kind of work. And so, I think that's important. And I always wanted to cultivate those communication skills for the benefit of communicating simply and clearly what it is that we do in our labs. But also, I would say as part of my story, I mentioned that I had the opportunity to attend a special school growing up in Greece, Anatolia, which was an American school. One of the interesting things about that is that there was an oratory competition.

Anna Greka (46:50):

I got very early exposure entering that competition. And if you won the first prize, it was in the kind of ancient Rome way, first among equals, right? And so, that was the prize. And I was lucky to have this early exposure. This is when I was 14, 15, 16 years old, that I was training to give these oratory speeches in front of an audience and sort of compete with other kids who were doing the same. I think these are just wonderful gifts that a school can give a student that have stayed with me for life. And I think that that's a wonderful, yeah, I credit that experience for a lot of my subsequent capabilities in this area.

Eric Topol (47:40):

Oh, that's fantastic. Well, this has been such an enjoyable conversation, Anna. Did I miss anything that we need to bring up, or do you think we have it covered?

Anna Greka (47:50):

Not at all. No, this was wonderful, and I thoroughly enjoyed it as well. I'm very honored seeing how many other incredible colleagues you've had on the show. It's just a great honor to be a part of this. So thank you for having me.

Eric Topol (48:05):

Well, you really are such a great inspiration to all of us in the biomedical community, and we'll be cheering for your continued success and thanks so much for joining today, and I look forward to the next time we get a chance to visit.

Anna Greka (48:20):

Absolutely. Thank you, Eric.

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Transcript with links to Audio and External Links

Eric Topol (00:07):

Well, hello. It's Eric Topol with Ground Truths, and I am just thrilled today to welcome Carl Zimmer, who is one of the great science journalists of our times. He's written 14 books. He writes for the New York Times and many other venues of great science, journalism, and he has a new book, which I absolutely love called Air-Borne. And you can see I have all these rabbit pages tagged and there's lots to talk about here because this book is the book of air. I mean, we're talking about everything that you ever wanted to know about air and where we need to go, how we missed the boat, and Covid and everything else. So welcome, Carl.

Carl Zimmer (00:51):

Thanks so much. Great to be here.

A Book Inspired by the Pandemic

Eric Topol (00:54):

Well, the book starts off with the Skagit Valley Chorale that you and your wife Grace attended a few years later, I guess, in Washington, which is really interesting. And I guess my first question is, it had the look that this whole book was inspired by the pandemic, is that right?

Carl Zimmer (01:18):

Certainly, the seed was planted in the pandemic. I was working as a journalist at the New York Times with a bunch of other reporters at the Times. There were lots of other science writers also just trying to make sense of this totally new disease. And we were talking with scientists who were also trying to make sense of the disease. And so, there was a lot of uncertainty, ambiguity, and things started to come into focus. And I was really puzzled by how hard it was for consensus to emerge about how Covid spread. And I did some reporting along with other people on this conflict about was this something that was spreading on surfaces or was it the word people were using was airborne? And the World Health Organization said, no, it's not airborne, it's not airborne until they said it was airborne. And that just seemed like not quantum physics, you know what I'm saying? In the sense that it seemed like that would be the kind of thing that would get sorted out pretty quickly. And I think that actually more spoke to my own unfamiliarity with the depth of this field. And so, I would talk to experts like say, Donald Milton at the University of Maryland. I'd be like, so help me understand this. How did this happen? And he would say, well, you need to get to know some people like William Wells. And I said, who?

Eric Topol (02:50):

Yeah, yeah, that's what I thought.

Carl Zimmer (02:53):

Yeah, there were just a whole bunch of people from a century ago or more that have been forgotten. They've been lost in history, and yet they were real visionaries, but they were also incredibly embattled. And the question of how we messed up understanding why Covid was airborne turned out to have an answer that took me back thousands of years and really plunged me into this whole science that's known as aerobiology.

Eric Topol (03:26):

Yeah, no, it's striking. And we're going to get, of course, into the Covid story and how it got completely botched as to how it was being transmitted. But of course, as you go through history, you see a lot of the same themes of confusion and naysayers and just extraordinary denialism. But as you said, this goes back thousands of years and perhaps the miasma, the moral stain in the air that was start, this is of course long before there was thing called germ theory. Is that really where the air thing got going?

A Long History of Looking Into Bad Air

Carl Zimmer (04:12):

Well, certainly some of the earliest evidence we have that people were looking at the air and thinking about the air and thinking there's something about the air that matters to us. Aristotle thought, well, there's clearly something important about the air. Life just seems to be revolve around breathing and he didn't know why. And Hippocrates felt that there could be this stain on the air, this corruption of the air, and this could explain why a lot of people in a particular area, young and old, might suddenly all get sick at the same time. And so, he put forward this miasma theory, and there were also people who were looking at farm fields and asking, well, why are all my crops dead suddenly? What happened? And there were explanations that God sends something down to punish us because we've been bad, or even that the air itself had a kind of miasma that affected plants as well as animals. So these ideas were certainly there, well over 2,000 years ago.

Eric Topol (05:22):

Now, as we go fast forward, we're going to get to, of course into the critical work of William and Mildred Wells, who I'd never heard of before until I read your book, I have to say, talk about seven, eight decades filed into oblivion. But before we get to them, because their work was seminal, you really get into the contributions of Louis Pasteur. Maybe you could give us a skinny on what his contributions were because I was unaware of his work and the glaciers, Mer de Glace and figuring out what was going on in the air. So what did he really do to help this field?

Carl Zimmer (06:05):

Yeah, and this is another example of how we can kind of twist and deform history. Louis Pasteur is a household name. People know who Louis Pasteur is. People know about pasteurization of milk. Pasteur is associated with vaccines. Pasteur did other things as well. And he was also perhaps the first aerobiologist because he got interested in the fact that say, in a factory where beet juice was being fermented to make alcohol, sometimes it would spoil. And he was able to determine that there were some, what we know now are bacteria that were getting into the beet juice. And so, it was interrupting the usual fermentation from the yeast. That in itself was a huge discovery. But he was saying, well, wait, so why are there these, what we call bacteria in the spoiled juice? And he thought, well, maybe they just float in the air.

Carl Zimmer (07:08):

And this was really a controversial idea in say, 1860, because even then, there were many people who were persuaded that when you found microorganisms in something, they were the result of spontaneous generation. In other words, the beet juice spontaneously produced this life. This was standard view of how life worked and Pasteur was like, I'm not sure I buy this. And this basically led to him into an incredible series of studies around Paris. He would have a flask, and he'd have a long neck on it, and the flask was full of sterile broth, and he would just take it places and he would just hold it there for a while, and eventually bacteria would fall down that long neck and they would settle in the broth, and they would multiply in there. It would turn cloudy so he could prove that there was life in the air.

Carl Zimmer (08:13):

And they went to different places. He went to farm fields, he went to mountains. And the most amazing trip he took, it was actually to the top of a glacier, which was very difficult, especially for someone like Pasteur, who you get the impression he just hated leaving the lab. This was not a rugged outdoorsman at all. But there he is, climbing around on the ice with this flask raising it over his head, and he caught bacteria there as well. And that actually was pivotal to destroying spontaneous generation as a theory. So aerobiology among many, many other things, destroyed this idea that life could spontaneously burst into existence.

Eric Topol (08:53):

Yeah, no. He says ‘these gentlemen, are the germs of microscopic beings’ shown in the existence of microorganisms in the air. So yeah, amazing contribution. And of course, I wasn't familiar with his work in the air like this, and it was extensive. Another notable figure in the world of germ theory that you bring up in the book with another surprise for me was the great Robert Koch of the Koch postulates. So is it true he never did the third postulate about he never fulfilled his own three postulates?

Carl Zimmer (09:26):

Not quite. Yeah, so he had these ideas about what it would take to actually show that some particular pathogen, a germ, actually caused a disease, and that involved isolating it from patients, culturing it outside of them. And then actually experimentally infecting an animal and showing the symptoms again. And he did that with things like anthrax and tuberculosis. He nailed that. But then when it came to cholera, there was this huge outbreak in Egypt, and people were still battling over what caused cholera. Was it miasma? Was it corruption in the air, or was it as Koch and others believe some type of bacteria? And he found a particular kind of bacteria in the stool of people who were dying or dead of cholera, and he could culture it, and he consistently found it. And when he injected animals with it, it just didn't quite work.

Eric Topol (10:31):

Okay. Yeah, so at least for cholera, the Koch’s third postulate of injecting in animals, reproducing the disease, maybe not was fulfilled. Okay, that's good.

Eric Topol (10:42):

Now, there's a lot of other players here. I mean, with Fred Meier and Charles Lindbergh getting samples in the air from the planes and Carl Flügge. And before we get to the Wells, I just want to mention these naysayers like Charles Chapin, Alex Langmuir, the fact that they said, well, people that were sensitive to pollen, it was just neurosis. It wasn't the pollen. I mean, just amazing stuff. But anyway, the principles of what I got from the book was the Wells, the husband and wife, very interesting characters who eventually even split up, I guess. But can you tell us about their contributions? Because they're really notable when we look back.

William and Mildred Wells

Carl Zimmer (11:26):

Yeah, they really are. And although by the time they had died around 1960, they were pretty much forgotten already. And yet in the 1930s, the two of them, first at Harvard and then at University of Pennsylvania did some incredible work to actually challenge this idea that airborne infection was not anything real, or at least nothing really to worry about. Because once the miasmas have been cleared away, people who embrace the germ theory of disease said, look, we've got cholera in water. We've got yellow fever in mosquitoes. We've got syphilis in sex. We have all these ways that germs can get from one person to the next. We don't need to worry about the air anymore. Relax. And William Wells thought, I don't know if that's true. And we actually invented a new device for actually sampling the air, a very clever kind of centrifuge. And he started to discover, actually, there's a lot of stuff floating around in the air.

Carl Zimmer (12:37):

And then with a medical student of his, Richard Riley started to develop a physical model. How does this happen? Well, you and I are talking, as we are talking we are expelling tiny droplets, and those droplets can potentially contain pathogens. We can sneeze out big droplets or cough them too. Really big droplets might fall to the floor, but lots of other droplets will float. They might be pushed along by our breath like in a cloud, or they just may be so light, they just resist gravity. And so, this was the basic idea that he put forward. And then he made real headlines by saying, well, maybe there's something that we can do to these germs while they're still in the air to protect our own health. In the same way you'd protect water so that you don't get cholera. And he stumbled on ultraviolet light. So basically, you could totally knock out influenza and a bunch of other pathogens just by hitting these droplets in the air with light. And so, the Wells, they were very difficult to work with. They got thrown out of Harvard. Fortunately, they got hired at Penn, and they lasted there just long enough that they could run an experiment in some schools around Philadelphia. And they put up ultraviolet lamps in the classrooms. And those kids did not get hit by huge measles outbreak that swept through Philadelphia not long afterwards.

Eric Topol (14:05):

Yeah, it's pretty amazing. I had never heard of them. And here they were prescient. They did the experiments. They had this infection machine where they could put the animal in and blow in the air, and it was basically like the Koch's third postulate here of inducing the illness. He wrote a book, William and he’s a pretty confident fellow quoted, ‘the book is not for here and now. It is from now on.’ So he wasn't a really kind of a soft character. He was pretty strong, I guess. Do you think his kind of personality and all the difficulties that he and his wife had contributed to why their legacy was forgotten by most?

Carl Zimmer (14:52):

Yes. They were incredibly difficult to work with, and there's no biography of the Wellses. So I had to go into archives and find letters and unpublished documents and memos, and people will just say like, oh my goodness, these people are so unbearable. They just were fighting all the time. They were fighting with each other. They were peculiar, particularly William was terrible with language and just people couldn't deal with them. So because they were in these constant fights, they had very few friends. And when you have a big consensus against you and you don't have very many friends to not even to help you keep a job, it's not going to turn out well, unfortunately. They did themselves no favors, but it is still really remarkable and sad just how much they figured out, which was then dismissed and forgotten.

Eric Topol (15:53):

Yeah, I mean, I'm just amazed by it because it's telling about your legacy in science. You want to have friends, you want to be, I think, received well by your colleagues in your community. And when you're not, you could get buried, your work could get buried. And it kind of was until, for me, at least, your book Air-Borne. Now we go from that time, which is 60, 70 years ago, to fast forward H1N1 with Linsey Marr from Virginia Tech, who in 2009 was already looking back at the Wells work and saying, wait a minute there's something here that this doesn't compute, kind of thing. Can you give us the summary about Linsey? Of course, we're going to go to 2018 again all before the pandemic with Lydia, but let's first talk about Linsey.

Linsey Marr

See my previous Ground Truths podcast with Prof Marr here

Carl Zimmer (16:52):

Sure. So Linsey Marr belongs to this new generation of scientists in the 21st century who start to individually rediscover the Welles. And then in Lind\sey Marr's case, she was studying air pollution. She's an atmospheric scientist and she's at Virginia Tech. And she and her husband are trying to juggle their jobs and raising a little kid, and their son is constantly coming home from daycare because he's constantly getting sick, or there's a bunch of kids who are sick there and so on. And that got Linsey Marr actually really curious like what's going on because they were being careful about washing objects and so on, and doing their best to keep the kids healthy. And she started looking into ideas about transmission of diseases. And she got very interested in the flu because in 2009, there was a new pandemic, in other words that you had this new strain of influenza surging throughout the world. And so, she said, well, let me look at what people are saying. And as soon as she started looking at it, she just said, well, people are saying things that as a physicist I know make no sense. They're saying that droplets bigger than five microns just plummet to the ground.

Carl Zimmer (18:21):

And in a way that was part of a sort of a general rejection of airborne transmission. And she said, look, I teach this every year. I just go to the blackboard and derive a formula to show that particles much bigger than this can stay airborne. So there's something really wrong here. And she started spending more and more time studying airborne disease, and she kept seeing the Welles as being cited. And she was like, who are these? Didn't know who they were. And she had to dig back because finding his book is not easy, I will tell you that. You can't buy it on Amazon. It's like it was a total flop.

Eric Topol (18:59):

Wow.

Carl Zimmer (19:00):

And eventually she started reading his papers and getting deeper in it, and she was like, huh. He was pretty smart. And he didn't say any of the things that people today are claiming he said. There's a big disconnect here. And that led her into join a very small group of people who really were taking the idea of airborne infection seriously, in the early 2000s.

Lydia Bourouiba

Eric Topol (19:24):

Yeah, I mean, it's pretty incredible because had we listened to her early on in the pandemic and many others that we're going to get into, this wouldn't have gone years of neglect of airborne transmission of Covid. Now, in 2018, there was, I guess, a really important TEDMED talk by Lydia. I don't know how you pronounce her last name, Bourouiba or something. Oh, yeah. And she basically presented graphically. Of course, all this stuff is more strained for people to believe because of the invisibility story, but she, I guess, gave demos that were highly convincing to her audience if only more people were in her audience. Right?

Carl Zimmer (20:09):

That's right. That's right. Yeah. So Lydia was, again, not an infectious disease expert at first. She was actually trained as a physicist. She studied turbulence like what you get in spinning galaxies or spinning water in a bathtub as it goes down the drain. But she was very taken aback by the SARS outbreak in 2003, which did hit Canada where she was a student.

Carl Zimmer (20:40):

And it really got her getting interested in infectious diseases, emerging diseases, and asking herself, what tools can I bring from physics to this? And she's looked into a lot of different things, and she came to MIT and MIT is where Harold Edgerton built those magnificent stroboscope cameras. And we've all seen these stroboscope images of the droplets of milk frozen in space, or a bullet going through a card or things like that that he made in the 1930s and 1940s and so on. Well, one of the really famous images that was used by those cameras was a sneeze actually, around 1940. That was the first time many Americans would see these droplets frozen in space. Of course, they forgot them.

Carl Zimmer (21:34):

So she comes there and there's a whole center set up for this kind of high-speed visualization, and she starts playing with these cameras, and she starts doing experiments with things like breathing and sneezes and so on. But now she's using digital video, and she discovers that she goes and looks at William Wells and stuff. She's like, that's pretty good, but it's pretty simple. It's pretty crude. I mean, of course it is. It was in the 1930s. So she brings a whole new sophistication of physics to studying these things, which she finds that, especially with a sneeze, it sort of creates a new kind of physics. So you actually have a cloud that just shoots forward, and it even carries the bigger droplets with it. And it doesn't just go three feet and drop. In her studies looking at her video, it could go 10 feet, 20 feet, it could just keep going.

Eric Topol (22:24):

27 feet, I think I saw. Yeah, right.

Carl Zimmer (22:26):

Yeah. It just keeps on going. And so, in 2018, she gets up and at one of these TEDMED talks and gives this very impressive talk with lots of pictures. And I would say the world didn't really listen.

Eric Topol (22:48):

Geez and amazing. Now, the case that you, I think centered on to show how stupid we were, not everyone, not this group of 36, we're going to talk about not everyone, but the rest of the world, like the WHO and the CDC and others was this choir, the Skagit Valley Chorale in Washington state. Now, this was in March 2020 early on in the pandemic, there were 61 people exposed to one symptomatic person, and 52 were hit with Covid. 52 out of 61, only 8 didn't get Covid. 87% attack rate eventually was written up by an MMWR report that we'll link to. This is extraordinary because it defied the idea of that it could only be liquid droplets. So why couldn't this early event, which was so extraordinary, opened up people's mind that there's not this six-foot rule and it’s all these liquid droplets and the rest of the whole story that was wrong.

Carl Zimmer (24:10):

I think there's a whole world of psychological research to be done on why people accept or don't accept scientific research and I'm not just talking about the public. This is a question about how science itself works, because there were lots of scientists who looked at the claims that Linsey Marr and others made about the Skagit Valley Chorale outbreak and said, I don't know, I'm not convinced. You didn't culture viable virus from the air. How do you really know? Really, people have said that in print. So it does raise the question of a deep question, I think about how does science judge what the right standard of proof is to interpret things like how diseases spread and also how to set public health policy. But you're certainly right that and March 10th, there was this outbreak, and by the end of March, it had started to make news and because the public health workers were figuring out all the people who were sick and so on, and people like Linsey Marr were like, this kind of looks like airborne to me, but they wanted to do a closer study of it. But still at that same time, places like the World Health Organization (WHO) were really insisting Covid is not airborne.

“This is so mind-boggling to me. It just made it obvious that they [WHO] were full of s**t.”—Jose-Luis Jimenez

Getting It Wrong, Terribly Wrong

Eric Topol (25:56):

It's amazing. I mean, one of the quotes that there was, another one grabbed me in the book, in that group of the people that did air research understanding this whole field, the leaders, there's a fellow Jose-Luis Jimenez from University of Colorado Boulder, he said, ‘this is so mind-boggling to me. It just made it obvious that they were full of s**t.’ Now, that's basically what he's saying about these people that are holding onto this liquid droplet crap and that there's no airborne. But we know, for example, when you can't see cigarette smoke, you can't see the perfume odor, but you can smell it that there's stuff in the air, it's airborne, and it's not necessarily three or six feet away. There's something here that doesn't compute in people's minds. And by the way, even by March and April, there were videos like the one that Lydia showed in 2018 that we're circling around to show, hey, this stuff is all over the place. It's not just the mouth going to the other person. So then this group of 36 got together, which included the people we were talking about, other people who I know, like Joe Allen and many really great contributors, and they lobbied the CDC and the WHO to get with it, but it seemed like it took two years.

Carl Zimmer (27:32):

It was a slow process, yes. Yes. Because well, I mean, the reason that they got together and sort of formed this band is because early on, even at the end of January, beginning of February 2020, people like Joe Allen, people like Linsey Marr, people like Lidia Morawska in Australia, they were trying to raise the alarm. And so, they would say like, oh, I will write up my concerns and I will get it published somewhere. And journals would reject them and reject them and reject them. They'd say, well, we know this isn't true. Or they'd say like, oh, they're already looking into it. Don't worry about it. This is not a reason for concern. All of them independently kept getting rejected. And then at the same time, the World Health Organization was going out of their way to insist that Covid is not airborne. And so, Lidia Morawska just said like, we have to do something. And she, from her home in Australia, marshaled first this group of 36 people, and they tried to get the World Health Organization to listen to them, and they really felt very rebuffed it didn't really work out. So then they went public with a very strong open letter. And the New York Times and other publications covered that and that really started to get things moving. But still, these guidelines and so on were incredibly slow to be updated, let alone what people might actually do to sort of safeguard us from an airborne disease.

Eric Topol (29:15):

Well, yeah, I mean, we went from March 2020 when it was Captain Obvious with the choir to the end of 2021 with Omicron before this got recognized, which is amazing to me when you look back, right? That here you've got millions of people dying and getting infected, getting Long Covid, all this stuff, and we have this denial of what is the real way of transmission. Now, this was not just a science conflict, this is that we had people saying, you don't need to wear a mask. People like Jerome Adams, the Surgeon General, people like Tony Fauci before there was an adjustment later, oh, you don't need masks. You just stay more than six feet away. And meanwhile, the other parts of the world, as you pointed out in Japan with the three Cs, they're already into, hey, this is airborne and don't go into rooms indoors with a lot of people and clusters and whatnot. How could we be this far off where the leading public health, and this includes the CDC, are giving such bad guidance that basically was promoting Covid spread.

Carl Zimmer (30:30):

I think there are a number of different reasons, and I've tried to figure that out, and I've talked to people like Anthony Fauci to try to better understand what was going on. And there was a lot of ambiguity at the time and a lot of mixed signals. I think that also in the United States in particular, we were dealing with a really bad history of preparing for pandemics in the sense that the United States actually had said, we might need a lot of masks for a pandemic, which implicitly means that we acknowledge that the next pandemic might to some extent be airborne. At least our healthcare folks are going to need masks, good masks, and they stockpiled them, and then they started using them, and then they didn't really replace them very well, and supplies ran out, or they got old. So you had someone like Rick Bright who was a public health official in the administration in January 2020, trying to tell everybody, hey, we need masks.

The Mess with Masks

Carl Zimmer (31:56):

And people are like, don't worry about it, don't worry about it. Look, if we have a problem with masks, he said this, and he recounted this later. Look, if the health workers run out of masks, we just tell the public just to not use masks and then we'll have enough for the health workers. And Bright was like, that makes no sense. That makes no sense. And lo and behold, there was a shortage among American health workers, and China was having its own health surge, so they were going to be helping us out, and it was chaos. And so, a lot of those messages about telling the public don't wear a mask was don't wear a mask, the healthcare workers need them, and we need to make sure they have enough. And if you think about that, there's a problem there.

Carl Zimmer (32:51):

Yeah, fine. Why don't the healthcare workers have their own independent supply of masks? And then we can sort of address the question, do masks work in the general community? Which is a legitimate scientific question. I know there are people who are say, oh, masks don't work. There's plenty of studies that show that they can reduce risk. But unfortunately, you actually had people like Fauci himself who were saying like, oh, you might see people wearing masks in other countries. I wouldn't do it. And then just a few weeks later when it was really clear just how bad things were getting, he turns around and says, people should wear masks. But Jerome Adams, who you mentioned, Surgeon General, he gets on TV and he's trying to wrap a cloth around his face and saying, look, you can make your own mask. And it was not ideal, shall we say?

Eric Topol (33:55):

Oh, no. It just led to mass confusion and the anti-science people were having just a field day for them to say that these are nincompoops. And it just really, when you look back, it's sad. Now, I didn't realize the history of the N95 speaking of healthcare workers and fitted masks, and that was back with the fashion from the bra. I mean, can you tell us about that? That's pretty interesting.

Carl Zimmer (34:24):

Yeah. Yeah, it's a fascinating story. So there was a woman who was working for 3M. She was consulting with them on just making new products, and she really liked the technology they used for making these sort of gift ribbons and sort of blown-fiber. And she's like, wow, you should think about other stuff. How about a bra? And so, they actually went forward with this sort of sprayed polyester fiber bra, which was getting much nicer than the kind of medieval stuff that women had to put up with before then. And then she's at the same time spending a lot of time in hospitals because a lot of her family was sick with various ailments, and she was looking at these doctors and nurses who were wearing masks, which just weren't fitting them very well. And she thought, wait a minute, you could take a bra cup and just basically fit it on people's faces.

Carl Zimmer (35:29):

She goes to 3M and is like, hey, what about this? And they're like, hmm, interesting. And at first it didn't seem actually like it worked well against viruses and other pathogens, but it was good on dust. So it started showing up in hardware stores in the 70s, and then there were further experiments that basically figured showed you could essentially kind of amazingly give the material a little static charge. And that was good enough that then if you put it on, it traps droplets that contain viruses and doesn't let them through. So N95s are a really good way to keep viruses from coming into your mouth or going out.

Eric Topol (36:14):

Yeah. Well, I mean it's striking too, because in the beginning, as you said, when there finally was some consensus that masks could help, there wasn't differentiation between cotton masks, surgical masks, KN95s. And so, all this added to the mix of ambiguity and confusion. So we get to the point finally that we understand the transmission. It took way too long. And that kind of tells the Covid story. And towards the end of the book, you're back at the Skagit Valley Chorale. It's a full circle, just amazing story. Now, it also brings up all lessons that we've learned and where we're headed with this whole knowledge of the aerobiome, which is fascinating. I didn't know that we breathe 2000 to 3000 gallons a day of air, each of us.

Every Breath We Take

Eric Topol (37:11):

Wow, I didn't know. Well, of course, air is a vector for disease. And of course, going back to the Wells, the famous Wells that have been, you've brought them back to light about how we're aerial oysters. So these things in the air, which we're going to get to the California fires, for example, they travel a long ways. Right? We're not talking about six feet here. We're talking about, can you tell us a bit about that?

Carl Zimmer (37:42):

Well, yeah. So we are releasing living things into the air with every breath, but we're not the only ones. So I'm looking at you and I see beyond you the ocean and the Pacific Ocean. Every time those waves crash down on the surf, it's spewing up vast numbers of tiny droplets, kind of like the ocean's own lungs, spraying up droplets, some of which have bacteria and viruses and other living things. And those go up in the air. The wind catches them, and they blow around. Some of them go very, very high, many, many miles. Some of them go into the clouds and they do blow all over the place. And so, science is really starting to come into its own of studying the planetary wide pattern of the flow of life, not just for oceans, but from the ground, things come out of the ground all of the time. The soil is rich with microbes, and those are rising up. Of course, there’s plants, we are familiar with plants having pollen, but plants themselves are also slathered in fungi and other organisms. They shed those into the air as well. And so, you just have this tremendous swirl of life that how high it can go, nobody's quite sure. They can certainly go up maybe 12 miles, some expeditions, rocket emissions have claimed to find them 40 miles in the air.

Carl Zimmer (39:31):

It's not clear, but we're talking 10, 20, 30 miles up is where all this life gets. So people call this the aerobiome, and we're living in it. It's like we're in an ocean and we're breathing in that ocean. And so, you are breathing in some of those organisms literally with every breath.

Eric Topol (39:50):

Yeah, no, it's extraordinary. I mean, it really widens, the book takes us so much more broad than the narrow world of Covid and how that got all off track and gives us the big picture. One of the things that happened more recently post Covid was finally in the US there was the commitment to make buildings safer. That is adopting the principles of ventilation filtration. And I wonder if you could comment at that. And also, do you use your CO2 monitor that you mentioned early in the book? Because a lot of people haven't gotten onto the CO2 monitor.

Carl Zimmer (40:33):

So yes, I do have a CO2 monitor. It's in the other room. And I take it with me partly to protect my own health, but also partly out of curiosity because carbon dioxide (CO2) in the room is actually a pretty good way of figuring out how much ventilation there is in the room and what your potential risk is of getting sick if someone is breathing out Covid or some other airborne disease. They're not that expensive and they're not that big. And taking them on planes is particularly illuminating. It's just incredible just how high the carbon dioxide rate goes up when you're sitting on the plane, they've closed the doors, you haven't taken off yet, shoots way up. Once again, the air and the filter system starts up, it starts going down, which is good, but then you land and back up again. But in terms of when we're not flying, we're spending a lot of our time indoors. Yeah, so you used the word commitment to describe quality standards.

Eric Topol (41:38):

What's missing is the money and the action, right?

Carl Zimmer (41:42):

I think, yeah. I think commitment is putting it a little strongly.

Eric Topol (41:45):

Yeah. Sorry.

Carl Zimmer (41:45):

Biden administration is setting targets. They're encouraging that that people meet certain targets. And those people you mentioned like Joe Allen at Harvard have actually been putting together standards like saying, okay, let's say that when you build a new school or a new building, let's say that you make sure that you don't get carbon dioxide readings above this rate. Let's try to get 14 liters per second per person of ventilated fresh air. And they're actually going further. They've actually said, now we think this should be law. We think these should be government mandates. We have government mandates for clean water. We have government mandates for clean food. We don't just say, it'd be nice if your bottled water didn't have cholera on it in it. We'll make a little prize. Who's got the least cholera in their water? We don't do that. We don't expect that. We expect more. We expect when you get the water or if you get anything, you expect it to be clean and you expect people to be following the law. So what Joseph Allen, Lidia Morawska, Linsey Marr and others are saying is like, okay, let's have a law.

Eric Topol (43:13):

Yeah. No, and I think that distinction, I've interviewed Joe Allen and Linsey Marr on Ground Truths, and they've made these points. And we need the commitment, I should say, we need the law because otherwise it's a good idea that doesn't get actualized. And we know how much keeping ventilation would make schools safer.

Carl Zimmer (43:35):

Just to jump in for a second, just to circle back to William and Mildred Wells, none of what I just said is new. William and Mildred Wells were saying over and over again in speeches they gave, in letters they wrote to friends they were like, we've had this incredible revolution in the early 1900s of getting clean water and clean food. Why don't we have clean air yet? We deserve clean air. Everyone deserves clean air. And so, really all that people like Linsey Marr and Joseph Allen and others are doing is trying to finally deliver on that call almost a century later.

Eric Topol (44:17):

Yeah, totally. That's amazing how it's taken all this time and how much disease and morbidity even death could have been prevented. Before I ask about planning for the future, I do want to get your comments about the dirty air with the particulate matter less than 2.5 particles and what we're seeing now with wildfires, of course in Los Angeles, but obviously they're just part of what we're seeing in many parts of the world and what that does, what carries so the dirty air, but also what we're now seeing with the crisis of climate change.

Carl Zimmer (45:01):

So if you inhale smoke from a wildfire, it's not going to start growing inside of you, but those particles are going to cause a lot of damage. They're going to cause a lot of inflammation. They can cause not just lung damage, but they can potentially cause a bunch of other medical issues. And unfortunately, climate change plus the increasing urbanization of these kinds of environments, like in Southern California where fires, it's a fire ecology already. That is going to be a recipe for more smoke in the air. We will be, unfortunately, seeing more fire. Here in the Northeast, we were dealing with really awful smoke coming all the way from Canada. So this is not a problem that respects borders. And even if there were no wildfires, we still have a huge global, terrible problem with particulate matter coming from cars and coal fire power plants and so on. Several million people, their lives are cut short every year, just day in, day out. And you can see pictures in places like Delhi and India and so on. But there are lots of avoidable deaths in the United States as well, because we're starting to realize that even what we thought were nice low levels of air pollution probably are still killing more people than we realized.

Eric Topol (46:53):

Yeah, I mean, just this week in Nature is a feature on how this dirty air pollution, the urbanization that’s leading to brain damage, Alzheimer’s, but also as you pointed out, it increases everything, all-cause mortality, cardiovascular, various cancers. I mean, it's just bad news.

Carl Zimmer (47:15):

And one way in which the aerobiome intersects with what we're talking about is that those little particles floating around, things can live on them and certain species can ride along on these little particles of pollution and then we inhale them. And there's some studies that seem to suggest that maybe pathogens are really benefiting from riding around on these. And also, the wildfire smoke is not just lofting, just bits of dead plant matter into the air. It's lofting vast numbers of bacteria and fungal spores into the air as well. And then those blow very, very far away. It's possible that long distance winds can deliver fungal spores and other microorganisms that can actually cause certain diseases, this Kawasaki disease or Valley fever and so on. Yeah, so everything we're doing is influencing the aerobiome. We're changing the world in so many ways. We're also changing the aerobiome.

Eric Topol (48:30):

Yeah. And to your point, there were several reports during the pandemic that air pollution potentiated SARS-CoV-2 infections because of that point that you're making that is as a carrier.

Carl Zimmer (48:46):

Well, I've seen some of those studies and it wasn't clear to me. I'm not sure that SARS-CoV-2 can really survive like long distances outdoors. But it may be that, it kind of weakens people and also sets up their lungs for a serious disease. I'm not as familiar with that research as I'd like to be.

Eric Topol (49:11):

Yeah, no, it could just be that because they have more inflammation of their lungs that they're just more sensitive to when they get the infection. But there seems like you said, to be some interactions between pathogens and polluted air. I don't know that we want to get into germ warfare because that's whole another topic, but you cover that well, it's very scary stuff.

Carl Zimmer (49:37):

It’s the dark side of aerobiology.

Eric Topol (49:39):

Oh my gosh, yes. And then the last thing I wanted just to get into is, if we took this all seriously and learned, which we don't seem to do that well in some respects, wouldn’t we change the way, for example, the way our cities, the way we increase our world of plants and vegetation, rather than just basically take it all down. What can we do in the future to make our ecosystem with air a healthier one?

Carl Zimmer (50:17):

I think that's a really important question. And it sounds odd, but that's only because it's unfamiliar. And even after all this time and after the rediscovery of a lot of scientists who had been long forgotten, there's still a lot we don't know. So there is suggestive research that when we breathe in air that's blowing over vegetation, forest and so on. That's actually in some ways good for our health. We do have a relationship with the air, and we've had it ever since our ancestors came out the water and started breathing with their lungs. And so, our immune systems may be tuned to not breathing in sterile air, but we don't understand the relationship. And so, I can't say like, oh, well, here's the prescription. We need to be doing this. We don't know.

Eric Topol (51:21):

Yeah. No, it's fascinating.

Carl Zimmer (51:23):

We should find out. And there are a few studies going on, but not many I would have to say. And the thing goes for how do we protect indoor spaces and so on? Well, we kind of have an idea of how airborne Covid is. Influenza, we're not that sure and there are lots of other diseases that we just don't know. And you certainly, if a disease is not traveling through the air at all, you don't want to take these measures. But we need to understand they're spread more and it's still very difficult to study these things.

Eric Topol (52:00):

Yeah, such a great point. Now before we wrap up, is there anything that you want to highlight that I haven't touched on in this amazing book?

Carl Zimmer (52:14):

I hope that when people read it, they sort of see that science is a messy process and there aren't that many clear villains and good guys in the sense that there can be people who are totally, almost insanely wrong in hindsight about some things and are brilliant visionaries in other ways. And one figure that I learned about was Max von Pettenkofer, who really did the research behind those carbon dioxide meters. He figured out in the mid-1800s that you could figure out the ventilation in a room by looking at the carbon dioxide. We call it the Pettenkofer number, how much CO2 is in the room. Visionary guy also totally refused to believe in the germ theory of disease. He shot it tooth in the nail even. He tried to convince people that cholera was airborne, and he did it. He took a vial. He was an old man. He took a vial full of cholera. The bacteria that caused cholera drank it down to prove his point. He didn't feel well afterwards, but he survived. And he said, that's proof. So this history of science is not the simple story that we imagine it to be.

Eric Topol (53:32):

Yeah. Well, congratulations. This was a tour de force. You had to put in a lot of work to pull this all together, and you're enlightening us about air like never before. So thanks so much for joining, Carl.

Carl Zimmer (53:46):

It was a real pleasure. Thanks for having me.

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—how are gut microbiome drives sugar cravings

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—the surprise finding about doctors and A.I.

In this podcast with Dr. Emily Silverman, an internist and founder of The Nocturnists, an award winning podcast and live show, we discuss what inspired her in medicine, what led to her disillusionment, the essentiality of storytelling, of recognizing uncertainty, the limits of A.I., and promoting humanity in medicine. The audio is available on iTunes and Spotify. The full video is linked here, at the top, and also can be found on YouTube.

“Storytelling is medicine's currency. Storytelling is not just an act of self-healing; it may actually create better physicians.”—Emily Silverman

Transcript with links to audio and relevant publications, websites

Eric Topol (00:07):

Well, hello. This is Eric Topol with Ground Truths, and with me, I am delighted to welcome Dr. Emily Silverman, who is Assistant Volunteer Professor of Medicine at UCSF, an old training grounds for me. And we're going to talk about some of the experience she's had there and she is the Founder of the remarkably recognized podcast, The Nocturnists. It's more than a podcast folks. We'll talk about that too. So Emily, welcome.

Emily Silverman (00:40):

Thank you for having me.

Inspiration by Kate McKinnon

Eric Topol (00:42):

Yeah. Well, I thought I would go back to perhaps when we first synapsed, and it goes back to a piece you wrote in JAMA about going to the Saturday Night Live (SNL) with Kate McKinnon. And it was one of my favorite columns, of course, it brought us together kind of simpatico because you were telling a story that was very personal, and a surprise factor added to it. We'll link to it. But it said, ‘Sometime in 2016, I fell in love with SNL comedian Kate McKinnon.’ You wrote, ‘It was something about her slow-mo swagger; her unilateral dimple, flickering in and out of existence; the way she drinks up her characters and sweats them from her pores.’ I mean, you're an incredible writer, no less podcast interviewer, organizer, doctor. And you talked about my sterile clinical life, which was kind of maybe a warning of things to come and about the fact that there's two very different career paths, comedy and medicine. One could argue they are in essence the same. So maybe you could tell us about that experience and about Kate McKinnon who, I mean, she's amazing.

Emily Silverman (02:09):

You're making me blush. Thank you for the kind words about the piece and about the writing, and I'm happy to give you a bit of background on that piece and where it came from. So I was in my internal medicine residency at UCSF and about halfway through residency really found myself hitting a wall. And that is actually what gave birth to The Nocturnists, which is the medical storytelling program that I run. But I think another symptom of my hitting that wall, so to speak, and we can talk more about what exactly that is and what that means, was me really looking outside of medicine and also outside of my typical day-to-day routine to try to find things that were a part of me that I had lost or I had lost touch with those aspects of myself. And one aspect of myself that I felt like I had lost touch to was my humorous side, my sense of humor, my silly side even you could say.

Emily Silverman (03:17):

And throughout my life I have this pattern where when I'm trying to get back in touch with a side of myself, I usually find somebody who represents that and sort of study it, I guess you could say. So in this case, for whatever reason that landed on Kate McKinnon, I just loved the surrealism of her comedy. I loved how absurd she is and loved her personality and so many things. Everything that you just read and really found her and her comedy as an escape, as a way to escape the seriousness of what I was doing on a day-to-day basis in the hospital and reconnect with those humorous sides of myself. So that's the understory. And then the story of the article is, I happened to be traveling to New York for a different reason and found myself standing in line outside of 30 Rock, hoping to get into Saturday Night Live. And there was basically a zero chance that we were going to get in. And part of the reason why is the musical guest that week was a K-pop band called BTS, which is one of the most famous bands in the world. And there were BTS fans like camped out in three circles around 30 Rock. So that week in particular, it was especially difficult to get in. There was just too many people in line. And we were at the very end of the line.

Eric Topol (04:43):

And it was in the pouring rain, too.

Emily Silverman (04:45):

And it was pouring rain. And my husband, God bless him, was there with me and he was like, what are we doing? And I was like, I don't know. I just have a feeling that we should stay in line, just go with it. So we did stay in line and then in the morning we got a number, and the way it works is you get your number and then that evening you show up with your number and our number was some crazy number that we weren't going to get in. But then that evening when we went back with our number to wait in line again to get in, what ended up happening is a young woman in the NBC gift shop, she passed out in the middle of the gift shop and I was right there. And so, I went over to her and was asking her questions and trying to help her out.

Emily Silverman (05:27):

And fortunately, she was fine. I think she just was dehydrated or something, and the security guards were so appreciative. And the next thing I knew, they were sweeping me backstage and up a staircase and in an elevator and they said, thank you so much for your service, welcome to Saturday Night Live. So it became this interesting moment where the very thing that I had been escaping from like medicine and serving and helping people ended up being the thing that gave me access, back to that side of myself, the humorous side. So it was just felt kind of cosmic, one of those moments, like those butterfly wing flapping moments that I decided to write about it and JAMA was kindly willing to publish it.

Eric Topol (06:15):

Well, it drew me to you and recognize you as quite an extraordinary talent. I don't know if you get recognized enough for the writing because it's quite extraordinary, as we'll talk about in some of your other pieces in the New York Times and in other JAMA journals and on and on. But one thing I just would note is that I resort to comedy a lot to deal with hard times, like the dark times we're in right now, so instead of watching the news, I watch Jimmy Kimmel's monologue or Colbert's monologue or the Comedy Show, anything to relieve some of the darkness that we're dealing with right at the moment. And we're going to get back to comedy because now I want to go back, that was in 2019 when you wrote that, but it was in 2016 when you formed The Nocturnists. Now, before you get to that critical path in your career of this new podcast and how it blossomed, how it grew is just beyond belief. But maybe you could tell us about your residency, what was going on while you were a medical resident at UCSF, because I can identify with that. Well, like any medical residency, it's pretty grueling experience and what that was like for you.

Medical Residency

Emily Silverman (07:45):

There were so many wonderful positive aspects of residency and there were so many challenges and difficult aspects of residency. It's all mixed up into this sticky, complicated web of what residency was. On the positive side, some of the most amazing clinicians I've ever met are at UCSF and whether that was seasoned attendings or chief residents who they just seemed to have so many skills, the clinical, the research, the teaching, just amazing, amazing high caliber people to learn from. And of course, the patient population. And at UCSF, we rotate at three different hospitals, the UCSF hospital, the SF General Hospital, which is the public county hospital and the VA hospital. So having the opportunity to see these different patient populations was just such a rich clinical and storytelling opportunity. So there was a lot there that was good, but I really struggled with a few things.

Emily Silverman (08:48):

So one was the fact that I spent so much of my sitting in front of a computer, and that was not something that I expected when I went into medicine when I was young. And I started to learn more about that and how that happened and when that changed. And then it wasn't just the computer, it was the computer and other types of paperwork or bureaucratic hurdles or administrative creep and just all the different ways that the day-to-day work of physicians was being overtaken by nonclinical work. And that doesn't just mean thinking about our patients, but that also means going to the bedside, sitting with our patients, getting to know them, getting to know their families. And so, I started to think a lot about clinical medicine and what it really means to practice and how that's different from how it was 10, 20, 40 years ago.

Emily Silverman (09:43):

And then the other part of it that I was really struggling with was aspects of medical culture. The fact that we were working 80 hour weeks, I was working 28 hour shifts every fourth night, every other month. And the toll that took on my body, and I developed some health issues as a result of that and just felt in a way, here I am a doctor in the business of protecting and preserving health and my own health is kind of being run into the ground. And that didn't make sense to me. And so, I started asking questions about that. So there was a lot there. And at first I thought, maybe this is a me thing or maybe this is a California thing. And eventually I realized this was a national thing and I started to notice headlines, op-eds, articles, even pre-Covid about the epidemic of clinician burnout in this country.

Emily Silverman (10:40):

And there are so many different facets to that. There's the moral injury aspect of it, there's the working conditions and understaffing aspect of it. I learned about how physicians were starting to think about unionizing, which was something that had not really been in the physician, I think consciousness 20, 40 years ago. So just started learning a lot about how medicine had evolved and was continuing to evolve and felt myself wanting to create a space where people could come together and tell stories about what that was like and what their experience was. And that was the birth of The Nocturnists. But I guess that wasn't really your question. Your question was about residency.

Birth of The Nocturnists

Eric Topol (11:20):

That's a good answer actually. It kind of gives the background, lays the foundation of how you took a fork in the road here, which we're going to get into now. We're going to link to The Nocturnists website of course, but you have an intro there about, ‘shatter the myth of the “physician God” reveal the truth: that healthcare workers are human, just like everyone else, and that our humanity is our strength, not our weakness.’ And that's a very deep and important point that you make to get people interested in The Nocturnists. But now you finished your residency, you're now on the faculty, assistant professor at UCSF, and then you have this gathering that you hadn't already named it the Nocturnists yet had you?

Emily Silverman (12:15):

I named it in residency.

Eric Topol (12:17):

Oh, okay in residency. So this was even before you had finished, you started the podcast before you finished?

Emily Silverman (12:25):

Correct. Before we were a podcast, we were a live show. So the very first live show was in 2016, so I consider that the birth year of the program. And then I graduated residency in 2017, so I started it about halfway through residency.

Eric Topol (12:39):

Got it. So tell us about that first live show. I mean, that's pretty amazing. Yeah.

Emily Silverman (12:46):

Yeah. I went to a live taping of The Moth in San Francisco, which some of your listeners may know. The Moth is a live storytelling show in the US, it's often on the radio on NPR. You may have heard it. It's a very ancient way of telling stories. It's more like monologues, people standing up on stage and just spontaneously telling a story the way you would around a campfire or something like that. It's not hyper scripted or anything like that. So I came out of that event feeling really inspired, and I had always loved live performance and live theater. I grew up going to the theater and ended up deciding that I would try that with my community, with the clinicians in my community. So the very first show that we did was in 2016, it was about 40 people in this living room of this Victorian mansion in San Francisco.

Emily Silverman (13:42):

It was a co-op where different people lived. In the living space, they occasionally rented out for meetings and presentations and gatherings, and it was like $90. So I rented that out and people came and residents, physician residents told stories, but a couple of faculty came and told stories as well. And I think that was a really nice way to set the stage that this wasn't just a med student thing or a resident thing, this was for everybody. And there was definitely an electricity in the air at the show. I think a lot of people were experiencing the same thing I was experiencing, which was having questions about the medical system, having questions about medical culture, trying to figure out how they fit into all of that, and in my case, missing my creative side, missing my humorous side. And so, I think that's the reason people came and showed up was that it wasn't just a night out of entertainment and coming was really more out of a hunger to reconnect with some aspect of ourselves that maybe gets lost as we go through our training. So that was the first show, and people kept asking, when are you going to do another one? When are you going to do another one? The rest is history. We have done many shows since then. So that was the beginning.

Eric Topol (14:58):

Well, you've been to many cities for live shows, you sold out hundreds and hundreds of seats, and it's a big thing now. I mean, it's been widely recognized by all sorts of awards, and the podcast and the shows. It's quite incredible. So a derivative of The Moth to medicine, is it always medical people telling stories? Does it also include patients and non-medical people?

Emily Silverman (15:28):

So we're nine years in, and for the first several years, this question came up a lot. What about the patient voice? What about the patient perspective? And the way that I would respond to that question was two ways. First, I would say the line between doctor and patient isn't as bright as you would think. Doctors are also patients. We also have bodies. We also have our own medical and psychiatric conditions and our own doctors and providers who take care of us. So we're all human, we're all patients. That said, I recognize that the doctor, the clinician has its own unique place in society and its own unique perspective. And that's really what I was trying to focus on. I think when you're making art or when you're making a community, people ask a lot about audience. And for me, for those first several years, I was thinking of The Nocturnists as a love letter by healthcare to healthcare. It was something that I was making for and with my community. And in recent months and years, I have been wondering about, okay, what would a new project look like that pulls in the patient voice a bit more? Because we did the clinician thing for several years, and I think there's been a lot of wonderful stories and material that's come out of that. But I'm always itching for the next thing. And it was actually an interview on the podcast I just did with this wonderful person, Susannah Fox.

Eric Topol (17:04):

Oh yeah, I know Susannah. Sure.

Emily Silverman (17:04):

Yeah. She was the chief technology officer at the Department of Health and Human Services from 2015 to 2017, I want to say. And she wrote a book called Rebel Health, which is all about patients who weren't getting what they needed from doctors and researchers and scientists. And so, they ended up building things on their own, whether it was building medical devices on their own, on the fringes or building disease registries and communities, online disease communities on their own. And it was a fabulous book and it was a fabulous interview. And ever since then I've been thinking about what might a project look like through The Nocturnists storytelling ethos that centers and focuses on the patient voice, but that's a new thought. For the first several years, it was much more focused on frontline clinicians as our audience.

Why is Storytelling in Medicine so Important?

Eric Topol (17:55):

And then I mean the storytelling people that come to the shows or listen to the podcast, many of them are not physicians, they're patients, all sorts of people that are not part of the initial focus of who's telling stories. Now, I want to get into storytelling. This is, as you point out in another JAMA piece that kind of was introducing The Nocturnists to the medical community. We’ll link to that, but a few classic lines, ‘Storytelling is medicine's currency. Storytelling is not just an act of self-healing; it may actually create better physicians.’ And then also toward the end of the piece, “Some people also believe that it is unprofessional for physicians to be emotionally vulnerable in front of colleagues. The greater risk, however, is for the healthcare professional to appear superhuman by pretending to not feel grief, suffer from moral distress, laugh at work, or need rest.” And finally, ‘storytelling may actually help to humanize the physician.’ So tell us about storytelling because obviously it's one of the most important, if not the most important form of communication between humans. You nailed it, how important it is in medicine, so how do you conceive it? What makes it storytelling for you?

Emily Silverman (19:25):

It's so surreal to hear you read those words because I haven't read them myself in several years, and I was like, oh, what piece is he talking about? But I remember now. Look, you on your program have had a lot of guests on to talk about the massive changes in medicine that have occurred, including the consolidation of it, the corporatization of it, the ways in which the individual community practice is becoming more and more endangered. And instead what's happening is practices are getting gobbled up and consolidated into these mega corporations and so on and so forth. And I just had on the podcast, the writer Dhruv Khullar, who wrote a piece in the New Yorker recently called the Gilded Age of Medicine is here. And he talks a lot about this and about how there are some benefits to this. For example, if you group practices together, you can have economies of scale and efficiencies that you can't when you have all these scattered individual self-owned practices.

Emily Silverman (20:26):

But I do think there are risks associated with the corporatization of healthcare. The more that healthcare starts to feel like a conveyor belt or a factory or fast food like the McDonald's of healthcare, MinuteClinic, 15 minutes in and out, the more that we risk losing the heart and soul of medicine and what it is; which is it's not as simple as bringing in your car and getting an oil change. I mean, sometimes it is. Sometimes you just need a strep swab and some antibiotics and call it a day. But I think medicine at its best is more grounded in relationships. And so, what is the modern era of medicine doing to those relationships? Those longitudinal relationships, those deeper relationships where you're not just intimately familiar with a patient's creatinine trend or their kidney biopsy results, but you know your patient and their family, and you know their life story a little bit.

Emily Silverman (21:26):

And you can understand how the context of their renal disease, for example, fits into the larger story of their life. I think that context is so important. And so, medicine in a way is, it is a science, but it's also an art. And in some ways it's actually kind of an applied science where you're taking science and applying it to the messy, chaotic truth of human beings and their families and their communities. So I think storytelling is a really important way to think of medicine. And then a step beyond that, not just with the doctor patient interaction, but just with the medical community and medical culture at large. I think helping to make the culture healthier and get people out of this clamped down place where they feel like they have to be a superhuman robot. Let's crack that open a little bit and remind ourselves that just like our patients are human beings, so are we. And so, if we can leverage that, and this is also part of the AI conversation that we're having is like, is AI ever going to fully substitute for a physician? Like, well, what does a physician have that AI doesn't? What does a human being have that a machine doesn’t? And I think these are really deep questions. And so, I think storytelling is definitely related to that. And so, there's just a lot of rich conversation there in those spaces, and I think storytelling is a great way into those conversations.

Eric Topol (22:57):

Yeah. We'll talk about AI too, because that's a fascinating future challenge to this. But while you're talking about it, it reminds me that I'm in clinic every week. My fellow and I have really worked on him to talk to the patients about their social history. They seem to omit that and often times to crack the case of what's really going on and what gets the patient excited or what their concerns are really indexed to is learning about what do they do and what makes them tick and all that sort of thing. So it goes every which way in medicine. And the one that you've really brought out is the one where clinicians are telling their stories to others. Now you've had hundreds and hundreds of these physician related stories. What are some of the ones that you think are most memorable? Either for vulnerability or comedy or something that grabbed you because you’ve seen so many, and heard so many now.

A Memorable Story

Emily Silverman (24:02):

It's true. There have been hundreds of physician stories that have come through the podcast and some non-physician. I mean, we are, because I'm a doctor, I find that the work tends to be more focused around doctors. But we have brought in nurses and other types of clinicians to tell their stories as well, particularly around Covid. We had a lot of diversity of healthcare professionals who contributed their stories. One that stands out is dialogue that we featured in our live show. So most of our live shows up until that point had featured monologues. So people would stand on stage, tell their story one by one, but for this story, we had two people standing on stage and they alternated telling their story. There was a little bit more scripting and massaging involved. There was still some level of improvisation and spontaneity, but it added a really interesting texture to the story.

Emily Silverman (24:58):

And basically, it was a story of these two physicians who during Covid, one of them came out of retirement and the other one I think switched fields and was going to be doing different work during Covid as so many of us did. And they were called to New York as volunteers and ended up meeting in the JFK airport in 2020 and it was like an empty airport. And they meet there and they start talking and they realize that they have all these strange things in common, and they sit next to each other on the plane and they're kind of bonding and connecting about what they're about to do, which is go volunteer at the peak of Covid in New York City, and they end up staying in hotels in New York and doing the work. A lot of it really, really just harrowing work. And they stay connected and they bond and they call each other up in the evenings, how was your day? How was your day? And they stay friends. And so, instead of framing it in my mind as a Covid story, I frame it more as a friendship story. And that one just was really special, I think because of the seriousness of the themes, because of the heartwarming aspect of the friendship and then also because of the format, it was just really unusual to have a dialogue over a monologue. So that was one that stood out. And I believe the title of it is Serendipity in Shutdown. So you can check that out.

Eric Topol (26:23):

That's great. Love it. And I should point out that a lot of these clinical audio diaries are in the US Library of Congress, so it isn't like these are just out there, they're actually archived and it's pretty impressive. While I have you on some of these themes, I mean you're now getting into some bigger topics. You mentioned the pandemic. Another one is Black Voices in Healthcare, and you also got deep into Shame in Medicine. And now I see that you've got a new one coming on Uncertainty in Medicine. Can you give us the skinny on what the Uncertainty in Medicine's going to be all about?

Uncertainty in Medicine

Emily Silverman (27:14):

Yes. So the American Board of Internal Medicine put out a call for grant proposals related to the topic of uncertainty in medicine. And the reason they did that is they identified uncertainty as an area of growth, an area where maybe we don't talk about it enough or we're not really sure how to tolerate it or handle it or teach about it or work with it, work through it in our practice. And they saw that as an area of need. So they put out this call for grants and we put together a grant proposal to do a podcast series on uncertainty in medicine. And we're fortunate enough to be one of the three awardees of that grant. And we've been working on that for the last year. And it's been really interesting, really interesting because the place my mind went first with uncertainty is diagnostic uncertainty.

Emily Silverman (28:07):

And so, we cover that. We cover diagnostic odyssey and how we cope with the fact that we don't know and things like that. But then there's also so many other domains where uncertainty comes up. There's uncertainties around treatment. What do we do when we don't know if the treatment's working or how to assess whether it's working or it's not working and we don't know why. Or managing complex scenarios where it's not clear the best way to proceed, and how do we hold that uncertainty? Prognostic uncertainty is another area. And then all of the uncertainty that pops up related to the systems issues in healthcare. So for example, we spoke to somebody who was diagnosed with colon cancer, metastatic to the liver, ended up having a bunch of radiation of the mets in the liver and then got all this liver scarring and then got liver failure and then needed a liver transplant and saw this decorated transplant surgeon who recommended the transplant was already to have that done.

Emily Silverman (29:06):

And then the insurance denied the liver transplant. And so, dealing with the uncertainty of, I know that I need this organ transplant, but the coverage isn't going to happen, and the spoiler alert is that he ended up appealing several times and moving forward and getting his transplant. So that one has a happy ending, but some people don't. And so, thinking about uncertainty coming up in those ways as well for patients. So for the last year we've been trying to gather these stories and organize them by theme and figure out what are the most salient points. The other exciting thing we've done with the uncertainty series is we've looked to people outside of medicine who navigate high uncertainty environments to see if they have any wisdom or advice to share with the medical community. So for example, we recently interviewed an admiral in the Navy. And this person who was an admiral in the Navy for many years and had to navigate wartime scenarios and also had to navigate humanitarian relief scenarios and how does he think about being in command and dealing with people and resources and it is life or death and holding uncertainty and managing it.

Emily Silverman (30:18):

And he had a lot of interesting things to say about that. Similarly, we spoke to an improvisational dancer who his whole job is to get on stage and he doesn't know what's going to happen. And to me, that sounds terrifying. So it's like how do you deal with that and who would choose that? And so, that's been really fun too, to again, go outside the walls of medicine and see what we can glean and learn from people operating in these different contexts and how we might be able to apply some of those.

Eric Topol (30:51):

Yeah, I mean this is such a big topic because had the medical community been better in communicating uncertainties in medicine, the public trust during the pandemic could have been much higher. And this has led to some of the real challenges that we're seeing there. So I'm looking forward to that series of new additions in The Nocturnists. Now, when you get this group together to have the live show, I take it that they're not rehearsed. You don't really know much about what they're going to do. I mean, it's kind of like the opposite, the un-TED show. TED Talk, whereby those people, they have to practice in Vancouver wherever for a whole week. It's ridiculous. But here, do you just kind of let them go and tell their story or what?

Emily Silverman (31:44):

In the beginning it was more open mic, it was more let them go. And then as the years went on, we moved more toward a TED model where we would pair storytellers with a story coach, and they would work together pretty intensively in the six to eight weeks leading up to the event to craft the story. That said, it was very important to us that people not recite an essay that they memorized word for word, which surprise, surprise physicians really love that idea. We're like, we're so good at memorization and we love certainty. We love knowing word for word what's going to come. And so, it's really more of this hybrid approach where we would help people get in touch with, all right, what are the five main beats of your story? Where are we opening? Where are we closing? How do we get there?

Emily Silverman (32:34):

And so, we'd have a loose outline so that people knew roughly what was going to, but then it wasn't until the night of that we'd fill in the blanks and just kind of see what happens. And that was really exciting because a lot of unexpected things happened. Certain stories that we thought would be really comedic ended up landing with a much more serious and thoughtful tone and vice versa. Some of the stories that we thought were really heavy would unexpectedly get laughs in places that we didn't expect. So I think the magic of live audience is, I guess you could say uncertainty of not quite knowing what's going to happen, and sort of a one time night.

Eric Topol (33:17):

I’d like to have a storytelling coach. That'd be cool. I mean, we could always be better. I mean, it takes me back to the first story you told with the Saturday Night Live and Kate McKinnon, you told the story, it was so great. But to make telling your story, so it's even more interesting, captivating and expressing more emotion and vulnerability and what makes the human side. I mean, that's what I think we all could do, you never could do it perfectly. I mean, that's kind of interesting how you organize that. Alright, well now I want to go back to your career for a moment because you got into The Nocturnists and these shows and you were gradually, I guess here we are in the middle and still a global burnout, depression, suicide among clinicians, especially physicians, but across the board. And you're weaning your time as a faculty member at UCSF. So what was going through your mind in your life at that time? I guess that takes us to now, too.

A Career Move

Emily Silverman (34:36):

Yeah, when I was a little kid, I always wanted to doctor and fully intended when I went to med school and residency to find my way as a physician and didn't really think I would be doing much else. I mean, I'd always love reading and writing and the arts, but I never quite thought that that would become as big of a piece of my career as it has become. But what ended up happening is I finished residency. I took a job in the division of hospital medicine at SF General and worked as a hospitalist for about four years and was doing that and balancing with my medical storytelling nonprofit and eventually realized that it wasn't quite working, it wasn't the right fit. And ended up taking a step back and taking a little break from medicine for a while to try to figure out how am I going to balance this?

Emily Silverman (35:26):

Am I going to shift and go full medicine and retire The Nocturnists? Am I going to go full art, creative journalism, writing and leave clinical medicine behind? Or am I going to continue to proceed in this more hybrid way where I do a little bit of practicing, and I do a little bit of creative on the side? And thus far, I have continued to pursue that middle road. So I ended up starting a new outpatient job, a part-time job that's actually outside of UCSF. I'm still on faculty at UCSF, but my practice now is in private practice. And so, I do that two days a week and it feeds me in a lot of ways and I'm really glad that I've continued to keep that part of myself alive. And then the rest of the days of the week I work from home and some of that is charting and doing clinical work and some of that time is podcasting and working on these other creative projects. So that's where I've landed right now. And I don't know what it will look like in 5, 10, 20 years, but for now it seems to be working.

Taking On Epic

Eric Topol (36:31):

Yeah. Well, I think it's great that you've found the right kind of balance and also the channel for getting your exceptional talent, your niche if you will, in medicine to get it out there because people I think are really deriving a lot of benefit from that. Now, another piece you wrote in the New York Times, I just want to touch on because it is tied to the burnout story. This was a great op-ed, Our Hospital's New Software Frets About My ‘Deficiencies’ and I want to just warn the listeners or readers or watchers that Epic, this company that you wrote about has non-disparaging agreements with hospitals, censors hospitals and doctors to say anything bad about Epic. So when anybody ever writes something, particularly if it's published in a widely read place, the Epic company doesn't like that and they squash it and whatnot. So what was in your mind when you were writing this op-ed about Epic?

Emily Silverman (37:39):

So this came out of personal experience that I had where, and maybe this is some of the reason why the hospital medicine work wore me down so much is the frequent messages and alerts and popups just having a lot of fatigue with that. But also what the popups were saying, the language that they used. So you'd open up your electronic chart and a message would pop up and it would say, you are deficient, or it would say you are a delinquent. And it was this scary red box with an upside down exclamation point or something. And it really started to get to me, and this was definitely in that phase of my life and career where I was peak burnout and just kind of raging into the machine a little bit, you could say, I think right now I'm somewhat past that. I think part of the reason why is, I've been able to get myself out into a more sustainable situation, but ended up, it actually came out of me, this piece poured out of me one night.

Emily Silverman (38:37):

It was like two, three in the morning and my laptop was open and I was laying in bed and my husband was like, go to sleep, go to sleep. And I said, no, this wants to come out, these moments where things just, you just want to give birth, I guess, to something that wants to come out. So I wrote this long piece about Epic and how tone deaf these messages are and how clinicians are, they're working really hard in a really difficult system and just the lack of sensitivity of that language and ended up pitching that to the New York Times. And I think there was something in there that they appreciated about that. There was some humor in there actually. Maybe my Kate McKinnon side came out a little bit. So yes, that piece came out and I think I did get a message or two from a couple folks who worked at Epic who weren't thrilled.

Eric Topol (39:33):

They didn't threaten to sue you or anything though, right?

Emily Silverman (39:35):

They didn't. No

Eric Topol (39:37):

Good.

Emily Silverman (39:37):

Fortunately, yeah.

Medicine and A.I.

Eric Topol (39:38):

Yeah. Wow. Yeah, it was great. And we'll link to that, too. Now, as they say in comedy, we're going to have a callback. We're going to go to AI, which we talked about and touched on. And of course, one of the things AI is thought that it could help reduce the burden of data clerk work that you've talked about and certainly affected you and affects every person in working in medicine. But I wanted to get to this. For me, it was like a ChatGPT moment of November 2022. Recently, I don’t know if you've ever delved into NotebookLM.

Emily Silverman (40:18):

I have.

Eric Topol (40:19):

Okay, so you'll recognize this. You put in a PDF and then you hit audio and it generates a podcast of two agents, a man and a woman who are lively, who accurately take, it could be the most complex science, it could be a book, and you can put 50 of these things in and they have a really engaging conversation that even gets away from some of the direct subject matter and it's humanoid. What do you think about that?

Emily Silverman (40:57):

Well, a lot of what I know about AI, I learned from your book, Eric. And from the subsequent conversation that we had when you came on my podcast to talk about your book. So I'm not sure what I could teach you about this topic that you don't already know, but I think it's a deeply existential question about what it means to be human and how machine intelligence augments that, replaces that, threatens that. I don't really know how to put it. I had Jamie Metzl on the podcast. He's this great historian and science policy expert, and he was saying, I don't like the phrase artificial intelligence because I don't think that's what we're making. I think we're making machine intelligence and that's different from human intelligence. And one of the differences is human beings have physical bodies. So being a human is an embodied experience.

Emily Silverman (41:57):

A machine can’t enjoy, I was going to say a cheeseburger and I was like, wait, I'm talking to a cardiologist. So a machine intelligence being can't enjoy a cucumber salad, a machine intelligence can't feel the endorphins of exercise or have sex or just have all of these other experiences that human beings have because they have bodies. Now, does empathy and emotion and human connection and relationships also fall into that category? I don't know. What is the substrate of empathy? What is the substrate of human connection and relationships and experience? Can it be reduced to zeros and ones or whatever, quantum computing, half zeros and half ones existing simultaneously on a vibrating plane, or is there something uniquely human about that? And I actually don't know the answer or where the edges are. And I think in 5, 10, 20 years, we'll know a lot more about what that is and what that means.

Emily Silverman (42:55):

What does that mean for medicine? I don't know about the human piece of it, but I think just practically speaking, I believe it will transform the way that we do medicine on so many levels. And this is what your book is about. Some of it is image analysis and EKG analysis, X-ray analysis and MRI analysis. And some of it is cognition, like diagnostic reasoning, clinical reasoning, things like that. I already use OpenEvidence all the time. I don't know if you use it. It's this basically a search engine kind of GPT like search engine that's trained on high quality medical evidence. I'm always going to OpenEvidence with questions. And I actually saw a headline recently, oh gosh, I'll have to fish it out and email it to you and you can link it in the show notes. But it's a little bit about how medical education and also medical certification and testing is going to have to quickly bring itself up to speed on this.

Emily Silverman (43:56):

The USMLE Step 1 exam, which all physicians in the US have to pass in order to practice medicine. When I took it anyway, which was back in I think 2012, 2013, was very recall based. It was very much based on memorization and regurgitation. Not all, some of it was inference and analysis and problem solving, but a lot of it was memorization. And as you said, I think Eric on our interview on my podcast, that the era of the brainiac memorizing Doogie Howser physician is over. It's not about that anymore. We can outsource that to machines. That's actually one of the things that we can outsource. So I'm excited to see how it evolves. I hope that medical schools and hospitals and institutions find ways safely, of course, to embrace and use this technology because I think it can do a lot of good, which is also what your book is about, the optimistic lens of your book.

Eric Topol (44:55):

Well, what I like though is that what you're trying to do in your work that you're passionate about is bringing back and amplifying humanity. Enriching the humanity in medicine. Whether that's physicians understanding themselves better and realizing that they are not just to be expected to be superhuman or non-human or whatever, to how we communicate, how we feel, experience the care of patients, the privilege of care of patients. So that's what I love about your efforts to do that. And I also think that people keep talking about artificial general intelligence (AGI), but that's not what we are talking about here today. We're talking about human emotions. Machines don't cry, they don't laugh. They don't really bond with humans, although they try to. I don't know that you could ever, so this fixation on AGI is different than what we're talking about in medicine. And I know you’re destined to be a leader in that you already are. But I hope you'll write a book about medical storytelling and the humanity and medicine, because a natural for this and you're writing it is just great. Have you thought about doing that?

Emily Silverman (46:24):

It's very kind of you to say. I have thought about if I were to embark on a book project, what would that look like? And I have a few different ideas and I'm not sure. I'm not sure. Maybe I'll consult with you offline about that.

Eric Topol (46:42):

Alright, well I'd like to encourage you because having read your pieces that some of them cited here you have it. You really are a communicator extraordinaire. So anyway, Emily, thank you for joining today. I really enjoyed our conversation and your mission not just to be a physician, which is obviously important, but also to try to enhance the humanity in medicine, in the medical community particularly. So thank you.

Emily Silverman (47:14):

Thank you. Thank you for having me.

***************************************

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The Chief Scientific Advisor at Novo Nordisk, Lotte Bjerre Knudsen, was the key force who pushed hard to develop GLP-1 drugs for treating obesity and subsequently for Alzheimer’s. She was recently recognized by the 2024 Lasker Medical Research Award, and the 2024 AAAS Bhaumik Breakthrough of the Year Award. That recognition is richly deserved, since it is unclear if the GLP-1 drug path to obesity treatment, and all of the associated benefits, would have been seen at this time without her influence. That’s especially true given the mystery for why people with Type 2 diabetes (for which these drugs were used for many years) did not exhibit much in the way of weight loss. We discussed that and the future of these drugs, including their potential to prevent neurodegenerative diseases. And about dressing up in pink!

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Our entire conversation can also be seen by video at YouTube along with all of the Ground Truths podcasts. If you like the video format, please subscribe to this channel. Even if you prefer video, please take a look at the transcript with graphics and useful links to citations.

A Video Clip below on the barriers of a woman scientist to push Novo Nordisk to develop GLP-1 for obesity.

“I was always just been a nerdy little scientist who kind of found home here in this company for 35 years.”—Lotte Bjerre Knudsen, 60 Minutes

Transcript with Links to audio and external references

Eric Topol (00:06):

Well, hello, it's Eric Topol with Ground Truths, and I have with me a special guest. She's the Chief Science Officer of Novo Nordisk and it's Lotte Bjerre Knudsen, and we're delighted to have her. She's a recent recipient of the Lasker Award, which I think is considered like the pre-Nobel Award here in the United States. And I was involved with her in terms of researching who was the principal person who brought the GLP-1 drugs to the forefront for obesity, and it turned out to be Lotte. So welcome, Lotte.

Lotte Bjerre Knudsen (00:48):

Thank you very much. And also very, very happy to be here. I'm not the Chief Science Officer for Novo Nordisk, I'm the Chief Scientific Advisor of working for the Chief Science Officer of Novo Nordisk, but maybe too many people, not so different, right?

From Laundry Detergents to GLP-1 Drugs

Eric Topol (01:06):

Yes. Thank you, I actually meant to say advisor, but yes, I'm glad you cleared that up. I know from speaking to some of your colleagues, I actually spoke to Robin yesterday that you are looked to very highly, the most highly regarded person in science there, so not surprisingly. What I want to do is first talk about the glucagon-like peptide-1 (GLP-1) that got its legs back in, I guess 1984. So we're going way back. And what's also interesting is that you go way back at Novo Nordisk to 35 years in 1989. And so, there had been this work with this extraordinary hormone and neurotransmitter with a very short half-life that you knew about. But when you first started in Novo Nordisk, you weren't working on this. As I understand it, you're working on laundry detergent enzymes. How did you make this pivot from the laundry enzymes to getting into the GLP-1 world?

Lotte Bjerre Knudsen (02:16):

Yeah, thank you for that question. I'm from the technical University of Denmark, so I'm trained in biotechnology, and we're a small country, so not that many companies to work for. And I always had my mind set on, I wanted to work for Novo as it was called back then, and it just happened to be in the industrial enzyme part that I got my foot in first. And then I had a very interesting boss at the time. Unfortunately, he's not alive anymore, but he was both a medical doctor as well as a chemist. So he was actually put in charge of actually, let's see if we can do something new in diabetes. And then since he hired me and I had not been there that long, I simply tagged along as the youngest scientist on the team, and then suddenly I became a diabetes researcher. Around the same time, I think you remember that all of pharma was interested in obesity in the early 90s, everyone wanted to do diabetes as well as obesity, but they were separate teams and they all wanted to do small molecules, but it just happens to be so that the best idea we could find at that time was actually GLP-1, because we actually had clinical data relatively early that GLP-1 was a really good candidate as a treatment for diabetes because of the glucose sensitivity of the actions.

(03:43):

So you'd have efficient lowering of glucose through a dual mechanism with increasing insulin, lowering glucagon, and then it was safe because there wasn't this hypoglycemia you get from insulin. But then I had other colleagues who were working on obesity, and I was just kind of listening, right, what's going on there? And then also a colleague that I had, we had, I don’t know if you remember the old Hagedorn Research Institute, but Novo actually had kind of like an academic research institute that was affiliated with us. And there was this group that were working on this glucagon tumor model that produced high levels of glucagon, GLP-1 and PYY. And these rats, they starved themselves to death. And I knew about that from 1994. So that actually inspired my thinking. So when Stephen Bloom's paper came out in January of 1996, and he was the first one to call GLP-1 a neurotransmitter, I think, but I was already way into actually screening these kind of molecules that later then became liraglutide.

No One Else Thought About This [Obesity]

(04:54):

And then I thought, why on earth should we not actually do both things at the same time? If we have an idea that can both work in diabetes in a much safer way than in insulin, and then also at the same time work in obesity. But the reality is that no one else thought about this, or if they thought about it, they didn't really think that it would a good idea. But I think I had the luxury of being in a biotech company, so everyone was working with peptides and proteins. So I don't think I got the same challenge that the other people in the other pharma’s got when they all wanted small molecules.

Eric Topol (05:36):

Well, also just to set the foundation here, which you alluded to, there had been so many attempts to come up with a drug that would work, not just of course in diabetes where there are many classes of drugs, but moreover, to treat the condition of obesity. Actually, I was involved with one of them, Rimonabant and did the large trial, which as you know, led to having to stop the drug, discontinue it because it was associated with suicidal ideation and actual some suicide. So there had been such a long history of checkered inability to come up with a drug. But what was striking is the challenge, and this is one of the first important questions about, when you had the extended half-life of the first GLP-1 drug, that instead of having to take multiple times a day, you could actually, with liraglutide get to a point where you were starting to get to an extended half-life. This is now going back to 1997 with approval in 2010, still 14 years ago. But when you came up with this drug, because this was certainly one of your great contributions, this drug was just a step along the way in this kind of iterative process, wouldn't you say? It wasn't the long half-life and the potency that eventually got us to where we are today. Is that true?

Lotte Bjerre Knudsen (07:15):

Yeah, it was a stepwise process. And what's super interesting about this class of medicines is that they're actually so different. If you talk about a class of medicine where small molecules, they can be different, but they're usually more alike than they're different. And when it comes to this class with these medium-sized peptides, people tried a whole bunch of different things. So they're actually really, really different. Some are simple peptides. So the idea that I came up with was to use this fatty acid isolation principle, and that's then a subclass in the class. And then the first, once weekly, for example, was an antibody-based molecule liraglutide. So they're much, much, much larger molecule compared to the small peptides. So they're very different. And neither the simple peptides nor the really big antibody derived molecules, they don't give a lot of weight loss. So we actually get more weight loss with these kinds of molecules, which is also why you can now see that it has actually kind of inspired a whole industry to kind of try and go and make similar kinds of molecules.

Eric Topol (08:27):

Well, inspired a whole industry is an understatement. It’s become the most extraordinary class of drugs, I think in medical history, having been a student of various, I mean obviously statins have been a major contribution, but this seems to have transcended that already. We're going to talk about more about where things are headed, but this fatty acid acetylation was a major step forward in extending the half-life of the drug, whereby today you can give semaglutide once a week. And this, I think, of course, there are many ways that you might've been able to extend the half-life, but you were starting with a hormone, a natural hormone neurotransmitter that had such an exquisitely short half-life of basically second or minutes rather than that you could give for a week. So I know there were many different ways you could have protected or extended the half-life one way or another, but this seemed to be a breakthrough of many along the chain of breakthroughs. But the question I have is when you were giving this to the diabetics, which was the precedent, that was really what these drugs were first intended, they didn't lose that much weight, and they never, still today when it's looked at for obese non-diabetics versus diabetics, there's a gap in weight loss. Why is that at the exact same dose, with the exact same peptide that the weight loss differs for people with type 2 diabetes as compared to those who have pure obesity?

The Mystery of Why People With Type 2 Diabetes Don’t Lose Weight Like Those With Obesity

Lotte Bjerre Knudsen (10:09):

Yeah, I can't give you a molecular answer to that, right. But I think the notion, I think it's the same for example with metformin, even though it gives less weight loss because that has also been tried in both people with diabetes and people without diabetes. So I think it's just for somehow people with diabetes are more resistant to weight loss. I think it's a really good question that I'm hoping maybe we could get through, for example, with proteomics and actually comparing people with diabetes and people without diabetes and looking at people who have the similar kind of weight loss. That could be really interesting. But I really don't have a good molecular answer for you, but it's just a really, really strong fact. But it also leads me to wanting to say it’s interesting, because if that had been our motivation to actually say, oh, there's weight loss in diabetes, let's pursue it in people with obesity, I don't think we would've done that because the weight loss in people with diabetes wasn't that impressive. So it was very important for our chain of thought and decision early on that we actually knew that GLP-1 had these separate effects and that they could work in the brain and have a separate effect on well-known pathways in the brain. And that was more our motivation to actually continue to invest in obesity.

Eric Topol (11:42):

Yeah, no, I think this is when we did the research on the committee for the American Association for Advancement of Science (AAAS) award, the Mani L. Bhaumik Award, that you were recognized for the breakthrough of the year, this year. We tried to scour all the work and we actually had to hit Danish translations and all sorts of other papers they reviewed. And we learned through that process working on this committee that you were the one to be the champion of pushing this towards obesity, and it would've easily been missed because as we've been discussing, the weight loss in people with diabetes was small, but you push for it. And this was an extraordinarily important push because what it has resulted in, of course, has been spectacular. And obviously as we're going to get into much more than just obesity and obesity related conditions. But before we get to those other conditions, and as you've been known in the medical community as “the mother of GLP-1”, you were dubbed that term. The GLP-1 receptor is expressed in many parts of the body. Maybe you could just tell us about the distribution because this, I think is tied into these central nervous system effects that are not just related to the gut hormone type of axis.

GLP-1 Receptors and the Brain

Lotte Bjerre Knudsen (13:17):

So I spent a lot of time on that together with my amazing colleague, Charles Pyke, who's an histology expert because it turned out to be so very important. In general, when you're trying to make new medicines, understanding the mechanism, sometimes people say, yeah, who cares? But actually, it should matter, I think because where it becomes really important can be an understanding what they do not do. We've had to do a lot of proving the negatives for GLP-1. We went through these issues with thyroid cancer, pancreatitis, pancreas cancer. In all of that work, it was actually really important that we could show where the GLP-1 receptor was not expressed. So in the pancreas, we know that it's primarily on the insulin producing cells, and then we also have them in the intestine where they're probably involved in regulating inflammation and really creating a much healthier gut.

(14:15):

And then we have a lot of receptors in the brain. They're typically expressed on neurons, but they're also on astrocytes, they’re also on smooth muscle cells. We have them on the heart and the sinus node. That's why there's a small increase in heart rate. We have them in the kidney, on again some smooth muscle cells that are renin positive. So there we can start thinking blood pressure and other things. So it turns out that you can go around the body and there are all of these specific GLP-1 receptor population, that you can see how they tie into the pharmacology. But obviously in physiology, they're not as important as they have turned out to be in pharmacology when we suddenly come with 24 hours a day exposure for a day or a week or for as long as the administration interval is. So, but specifically for obesity, I think it's in the vein, it's hard to, you should always be careful.

(15:18):

That's something I've learned to never say never. Of course, there could be a contribution from the peripheral nervous system as well to the effects in obesity. But I do think there are so many important and well described neuronal populations that have the GLP-1 receptor and which are accessible from the periphery. So just to mention, maybe one of the most, well-known is a POMC/CART neuron in the hypothalamus. They have the GLP-1 receptor, they're activated, but there also is an inhibitory tone on the AgRP and NPY neurons, and it fits very well with that. We know that people report that they feel more sated, they feel less hungry. But then there are also effects in the hindbrain and in some of the reward centers also have GLP-1 receptors. And we know that also now, we have really good actually clinical studies that show that there is a change in food choice and people can control their food intake better. So I think that fits very well with effects on the reward system. So it's a whole myriad, or maybe you could say that GLP-1 orchestrates a number of different neuronal populations to have these overall effects that reduce energy intake.

Eric Topol (16:42):

Yeah, it's pretty striking. It's almost like we're all walking around with GLP-1 deficiency, that if we had this present at higher levels around the clock, and of course eventually we'll see things that are well beyond obesity, how well this has an impact. Now, there was an extraordinary review in Cell Metabolism on the brain and GLP-1, and not just the brain, but the essential nervous system, the neurovascular, it's called the “GLP-1 programs and neurovascular landscape.”

(17:20):

And in this review, it got into the brain effects that were well beyond, I think what are generally appreciated. Not only the protection of the integrity of the blood-brain barrier, this whole neuroglial vascular unit, the myelin sheath protection, reducing inflammation within the brain, improving the glymphatic flow, which is of course critical for clearing waste and promoting cerebral vascular remodeling and more, so the brain effects here is what it seems to be. You mentioned the reward circuit, of course, but the brain effects here seem to be diverse, quite a bit of breath and extraordinary. And as we've seen in the clinic now with the work that's been done, we're seeing things about addiction, even gambling, alcohol, drugs, I mean neuropsychiatric impact, it's pretty profound. Maybe you could comment about that.

On to Alzheimer’s and Parkinson’s Diseases

Lotte Bjerre Knudsen (18:23):

Yeah. I haven't read that paper yet, but I just saw it earlier. And I have been following this for about actually more than 10 years because when I was kind of over the big work of actually getting the approval for diabetes and obesity. I thought I had a little bit of capacity to actually look at Alzheimer's and Parkinson's disease because I just thought there's such an insane unmet need and what if GLP-1 could actually make a difference? And the first big paper that talked about this was actually in Nature Medicine in 2003, and it was originally, I think I should credit Nigel Greig. Greig, he's from NIH or from NIA, I can't remember, right. But he was actually the first one, I think to say if GLP-1 has all of these important effects in the pancreas and to protect cells, and there are all these GLP-1 receptors in the brain, maybe it also protects neurons.

(19:25):

So that was the first hypothesis. And the paper on Nature Medicine in 2003 describes how the GLP-1 receptor in the hippocampus is involved in cognition. And then we did a couple of studies in different animal models, and I was, to be honest, really confused. But then there was a new paper in Nature Medicine in 2018 that started to focus in on neuroinflammation. And by that time, I knew much more about inflammation and knew GLP-1 actually lower CRP by about 50% in the different trials. So I was really tuned into the potential importance of that in cardiovascular and kidney disease. But I was like, oh, what if that's also something that is important in the brain? Then it made more sense to me to try and build some evidence for that. So that was how we actually started looking at a hypothesis for Alzheimer's and Parkinson’s.

(20:21):

And we now have a really large phase three study ongoing, but of course, it's a hypothesis, right? And no one has yet, I think, proven that GLP-1 has really important effects on these indications, but we are testing it in 4,000 people with Alzheimer's disease. So our hypothesis is around neuroinflammation, but defined in a way where you could say it's both peripheral inflammation and the effect it has on the vasculature, it's the effect on the blood-brain barrier. It's the astrocytes and the microglia, and there are probably also some T cells that have the GLP-1 receptor that could be important. And then couple that up also with some of the new information from neurons, because there are two papers to think in the last year that has highlighted neurons either in the hindbrain or a little bit further on. Both of them are probably hindbrain populations that actually seem to be really important in regulating both peripheral as well as central information.

(21:27):

So what if neurons are actually also an overlooked mechanism here, and both of these neuronal populations have the GLP-1 receptor and are accessible from the periphery, even though the child super paper in Nature doesn't mention that, but they do have the GLP-1 receptor. So there are all these different mechanisms that GLP-1 can have an impact on the broad definition maybe of neuroinflammation. And maybe the way one should start thinking about it is to say it's not an anti-inflammatory agent, but maybe it induces homeostasis in these systems. I think that could maybe be a good way to think about it, because I think saying that GLP-1 is anti-inflammatory, I think that that's wrong because that's more for agents that have a really strong effect on one particular inflammatory pathway.

Eric Topol (22:22):

That's a very important point you're making because I think we conceive of these drugs as anti-inflammatory agents from these more diverse actions that we've just been reviewing. But I like this restoring homeostasis. It's an interesting way to put it. This brings us, you mentioned about the Parkinson's, and when I reviewed the three randomized Parkinson's trials, they're all small, but it appears to be the first disease modifying drug ever in Parkinson's. Of course, these were done with different drugs that were older drugs. We haven't seen the ones that yet to be with semaglutide or other agents. And I wondered if you pushed, just like you did for obesity within Novo Nordisk, you pushed to go into obesity. Did you also force to push for Alzheimer's?

Lotte Bjerre Knudsen (23:19):

Yes. So that is also me who had to argue for that. I'm happy to do these things. I was born brave. I am happy to do these things.

Eric Topol (23:31):

That's wonderful. Without you, we would be way behind, and it took decades to get to this point. But look where we are now, especially with all the rigorous trials, the large clinical trials. You're into one right now of some 20,000 participants to see whether not just people with prior heart disease, but people without known heart disease to see whether or not this will have an effect. And there's so much data now, of course, already a completed trial with reduction of heart attacks and strokes. But now to extend this to people who are not such high risk, but these large trials, we keep learning more. Like for example, the reduction of inflammatory markers is occurring even before the weight loss that starts to manifest. So we learned a lot from the trials that are just even beyond some of the major primary outcomes. Would you agree about that?

Lotte Bjerre Knudsen (24:34):

So I'm not sure we can say that it comes before the weight loss because the energy intake reduction happens instantly. The glycemic response happens instantly. And all of these improvements will of course also have an effect to dampen inflammation. We do not have data that supports that it comes before because we haven't sampled that much in the beginning.

Eric Topol (25:04):

Okay.

Lotte Bjerre Knudsen (25:05):

I wouldn't be able to say that, and I don't think there are any, well, it's hard to keep up that the entire literature on GLP-1 these days, but I don't think anyone has actually shown that there is a separation because it's super hard to separate when things are occurring at the same time.

Eric Topol (25:24):

Yeah, I'm just citing the heart disease trial where in the New England Journal that point was made. But I think your point also that there was already a change in energy intake immediately is apropos for sure. Now, when we get into this new paper of yours, the proteomics, can you tell us about that because that's really exciting. We're in a high throughput proteomics era right now that we can analyze thousands of plasma proteins in any given individual. What are you learning about proteomics with the GLP-1 drug?

The GLP-1 Drug Impact on Proteomics

Lotte Bjerre Knudsen (26:07):

Yeah, yeah. So I'm also the super excited about omics, right? Because I have worked in a wonderful organization of people who can do these large scale clinical trials, and we used to not collect a lot of samples for future use, but we've done that for some years now. So now we have this amazing collection of samples we can learn from and actually both inform the patients and the physicians, but also inform future research. So we have been doing that in our semaglutide trials, and we've just published the proteomics data from the step one and step two trials. So the phase 3a trials that supported the approval of semaglutide for the treatment of obesity. So one of them in people with obesity and one in people with obesity and diabetes, and those data are now published in Nature Medicine. [3 January 2025]. And we were learning a lot of things because you can compare the proteome effects to what has been done in the decode cohort.

(27:11):

So they have all these disease signature. So that's one thing that you can for sure see, and you can see a lot of things there with hints towards addiction. And then also you can take more predefined signatures also to look into what actually might be driving the cardiovascular risk. So I think there are so many things that you can learn from this, and of course it can also inform when you look at what's actually mediating the effect and probably something around inflammation is important. We have already also shown a more standard mediation analysis that shows that actually the most explainable factor for the effect on MACE [major adverse cardiovascular events] in the select trial is inflammation. It doesn't explain everything, but it actually looks like it's more important than BMI and weight loss. So that's really interesting how much we can learn from there. We're making the data are available at the summary statistic level so people can go and play with them ourselves.

(28:23):

And I think as we have more different kinds of medicines available in obesity, it's also a way to kind of compare how these different medicines work. And as we get more and more better at maybe also characterizing people with obesity, because I think that's a great thing that's going to happen now is there's going to be more funding for obesity research. Because I think that's what the attention that we are seeing right now is also giving. Then we can better start to understand. We always, we've been saying that people probably have different kinds of obesity, but we don't really know. So now we can actually start to understand that much better and maybe also understand how these different classes of medicines will work if we have the proteome data from different trials.

Eric Topol (29:10):

No, I'm absolutely fascinated about the proteomics. I call it a quiet revolution because many people don't know about it. [My recent post on this topic here.]

(29:18):

The ability to assess thousands of proteins in each individual, and it's giving us new insights about cause and effect as you alluded to, the relationship with as you said, MACE (major adverse cardiovascular events) and the actions of this drug class. I mean, there's just so much we can learn here from the proteomics. Another thing that's fascinating about the GLP-1 is its effect on epigenetic clocks. And recently at one of the meetings it was presented, this is Steven Horvath that we had on Ground Truths not long ago. He talked about at this talk that for the first time to see that you could basically slow the epigenetic clock with a GLP-1. Is there any further information about that?

Lotte Bjerre Knudsen (30:16):

Yeah, no. We've never had enough of a sample size to actually be able to look at it, so unfortunately, no. But there is something else, right, because there is this group at the Stanford, Tony Wyss-Coray or something.

Eric Topol (30:33):

Yes, Tony Wyss-Coray.

Lotte Bjerre Knudsen (30:35):

Now he published a paper, is it two years ago? Where he did it using proteomics. He defined an anti-aging signature for various different organs.

Lotte Bjerre Knudsen (30:46):

We are in the process of trying to see if we could take those signatures and apply them on to our data.

Eric Topol (30:55):

Well, what's interesting is we're pretty close friends, and he, not only that paper you mentioned on organ clocks, which is a phenomenal contribution, but he has a paper coming out soon in Nature Medicine, the preprint is up, and what he showed was that the brain and the immune system was the main organ clocks that were associated with longevity. And so, it takes another step further and it's looking at 11,000 plasma proteins. So it's really interesting how this field is evolving because the omics, as you put it, whether it's proteomics, and now we're learning also about the epigenome and what brings us to the potential that this class of drugs would have an impact on health span in all people, not just those who are obese. Would you project that's going to be possible in the years ahead?

Lotte Bjerre Knudsen (32:02):

I don't know about health span, but because certainly there's been so many studies with metformin and there's been a lot of wonderful data showing an effect on the epigenetic clocks, but not really an effect on lifespan because that metformin is so widely used. If that was the case, it would be easy to dig those data out of different registries. But certainly a healthier aging is the most obvious one because when you have one class of medicine that actually has so many different effects. Right now we are looking at them at a one by one case, but we really should be looking at them so you are getting the benefits on the heart and the vasculature on the brain and the kidneys and the diabetes and the knees. You're getting all of that at the same time, and that certainly should lead to much, much healthier lives. And then of course, we just need to get people to eat healthier. Also, maybe we should talk a little bit about the food industry. I heard you did that in some of your podcast, right?

Eric Topol (33:17):

Yes. That is the big food, if you will. It's a big problem, a very big problem, and the ultra-processed foods. And so, lifestyle is not good and trying to compensate for that with a drug intervention strategy is like chasing your tail. So you're absolutely right about that. I mean, I guess what I'm getting into here is that whereas today we keep seeing the effects, whether it's the liver, the kidney, the heart, obesity, and people with diabetes. But for example, in the Alzheimer's trial, do you have to be obese to be enrolled in the Alzheimer's trial, or is it just people who are at risk for developingAlzheimer's?

Lotte Bjerre Knudsen (34:01):

Yeah, no, you do not have to be obese. It's a standard Alzheimer's trial.

GLP-1 Pills

Eric Topol (34:07):

So this will be one of the really important trials to get a readout in people who are not having an obesity background. Now, the future, of course, gets us to oral GLP-1 drugs, which obviously you have there at Novo Nordisk. And it seems to me once that happens, if it can simulate the effects we see with the injectables, that would be another big step forward. What do you think about that?

Lotte Bjerre Knudsen (34:39):

Yeah. Isn't it interesting, what we've learned is that people actually don't mind the injections, right? Also, because I think it's simple, once a week injection and the needles are so small, obviously there are people who really have needle phobia, but take those aside, it's relatively few. I would argue if you close your eyes and somebody else used this needle on you, you would not be able to feel where it was inserted, right? They're so small. So it becomes maybe a personal preference. Would you like to have once a day or maybe twice a day tablets, or are you fine with once a week injection? And I think there probably will be quite a few once they've tried it. And now so many have tried it and they actually, maybe it gives us a simple lifestyle. You don't have to do it every day, right? You can just have a weekly reminder.

Eric Topol (35:46):

Yeah, no, I think that's really interesting what you're bringing up. I never thought we would evolve to a point where injectables were becoming some common, and I even have some physician colleagues that are taking three different injectable drugs.

Lotte Bjerre Knudsen (36:00):

That's also just mentioned Richard DiMarchi, who I shared the Breakthrough Prize with, and also Svetlana Mojsov, who I was one of the other two recipients for the Lasker prize because they both been at Rockefeller, and they both have worked a lot with peptides, and they both say the same thing. They were told so many times, this is not medicines, these kinds of molecules just they're not medicines. Forget about it. It turns out people were wrong. And peptides can be medicines, and they can even be produced also in a sustainable manner with fermentation, which is not a bad way of producing medicines. And people actually don't mind. Maybe some people actually even like it because it's once a week and then it's done.

Confronting Barriers

Eric Topol (36:58):

Yeah, no, that's a very important point. And the quest for the oral, which have more issues with bioavailability versus the peptides that are having such pronounced impact is really interesting to ponder. Well, before we wrap up, it's very clear the impact you've had has been profound, not just obviously at Novo Nordisk, but for the world of advancing health and medicine. And you've mentioned some of the key other people who have made seminal contributions, but I think you stand out because when we went deep into who took this field forward into obesity and who might also wind up being credited for Alzheimer's, it was you. And as a woman in science, especially in an era that you've been at Novo now for three and a half decades, there weren't many women in science leaders. And for one to be, as you said, you're brave for the good old boys to listen to the woman in science. Tell us about that challenge. Was this ever an issue in your career? Because obviously we want to have this whole landscape change. It is in the midst of change, but it's certainly still a ways to go. So maybe you can give us insight about that.

Lotte Bjerre Knudsen (38:27):

Yeah. Well, it for sure was a thing. It was a very male dominated world, and in a way, it might have prevented other people from doing it. But then, as I said, I was born brave for some reason. I'm not really sure why. It actually motivated me to kind of like, yeah, I'm going to show them. I'm going to show them. So it never really got to me that people, not everyone was nice to say. There was the first 10 years of my career, I think they were quite lonely, but then I was really inspired. I was so happy to be allowed to work on this. I thought it was super fun. And I did find people who wanted to play with me. And I also have to say that the CSO back then, Mads Krogsgaard Thomsen, he always supported me. So maybe I didn't get everything I wanted, but I always got what I needed in order to progress.

(39:29):

So on the women's side, and I think that yes, and there's still a change to be made, and I'm actually a little bit on behalf of my generation, maybe not too proud of the change we made because we didn't do a lot of change. It was all the women coming from the arts and the culture. They were the ones who actually make the big change here like 5 or 10 years ago. So I've also started to be more open about sharing my journey and advocating for women in science. So that's why I show up in pink to some of these award sessions just to be a little bit different and to maybe also just show that you don't have to be a certain type in order to fit into a certain job. But there is still a change to be made where people should be better at listening to what a person say and what ideas they say.

(40:28):

And they should be mindful about not always labeling women as passionate. When people call me passionate, I say like, no, thank you. I'm actually not too happy about the mother of either, because men always are being told. They're being told that they're brave and ambitious and courageous and strategic, whereas we we’re, oh, you're so passionate. No, thank you. I'm also brave and strategic and ambitious and all of that. So we simply put different vocabulary on. I don't think people don't do it on purpose. I think we need to be better at actually giving people at work the same kind of vocabulary for their contributions. And I think that would mean that we get listened to in the same way. And that would be important. And then I also have to say that science, whether it comes from men or women, doesn't really matter.

(41:32):

Successful science is always the work of many. And I hope that some of you will actually listen to my last speech because that's what I speak about, how it's always the work of the many. And also, how if you want to do something novel, then you actually have to do it at a time when no one else is doing it, and you should believe in your ideas. So believe in it, listen to the critique, but believe in it, and then come back with new arguments or give up if you can't come up with any new arguments, right?

Eric Topol (42:05):

Well, we'll definitely put a link to the Lasker Awards speech that you gave. And I just want to say that the parallels here, for example, with Kati Karikó , my friend who had the Nobel Award for mRNA, she spent three decades trying to get people to listen to her and never got a grant from the NIH or other places [our conversation here]. And it was a really tough battle. And as you already touched on Svetlana Mojsov, who did some of the seminal work at Rockefeller to isolate the portion of GLP-1, that really was the key part peptide, and it was overlooked for years. And so, it's a tough fight, but you're paving the way here. And I think the contributions you've made are just so extraordinary. And I hope that over the years we will continue to see this momentum because people like what you've done, deserve this extraordinary recognition. I'm glad to see. And the Lasker Award is really capping off some of that great recognition that is so well deserved. We’ve covered a lot of ground today, and I want to make sure if I missed anything that you wanted to get into before we wrap up.

Lotte Bjerre Knudsen (43:30):

I think we've been around all the exciting biology of GLP-1, both in diabetes, obesity, cardiovascular, kidney, potential in Alzheimer's and addiction. We'll see, we need the clinical data and we've put out a message to inspire people to do new science. There's still a lot of unmet need out there. There's a lot of diseases that don't have good treatments. Even in the diseases we've talked about there’s a lot of money for diabetes. There are no disease modifying therapies for diabetes. It's not really changing the course of the disease. So there's a lot of things that needs great scientists.

Eric Topol (44:17):

And I guess just in finishing the discovery of this class of drugs and what it's led to, tells us something about that, there's so much more to learn that is, this has taken on perhaps the greatest obstacle in medicine, which was could you safely treat obesity and have a marked effect. Which decades, many decades were devoted to that and gotten nowhere. It's like a breakthrough in another way is that here you have an ability to triumph over such a frustrating target, just like we've seen with Alzheimer's, of course, which may actually intersect with Alzheimer's, with a graveyard of failed drugs. And the ones that it were approved so far in certain countries, like the US are so questionable as to the safety and efficacy. But it gives us an inspiration about what is natural that can be built on the basic science that can lead to with people like you who push within the right direction, give the right nudges and get the support you need, who knows what else is out there that we're going to be discovering in the years ahead. It's a broad type of lesson for us.

Lotte Bjerre Knudsen (45:38):

Yeah, there is another hormone that's also in phase three clinical development, right? The amylin hormone. We've had pramlintide on the market for years, but we have this long-acting version that is in phase three clinical development. That could be the same kind of story because there's also additional biology on that one.

Eric Topol (45:58):

Yeah, this is what grabs me Lotte, because these gut hormone, we've known about them, and there's several more out there, of course. And look what they're having. They're not just gut hormones, like you said, they're neurotransmitters and they're body-wide receptors waiting to be activated, so it's wild. It's just wild. And I'm so glad to have had this conversation with you. Now, congratulations on all that you've done, and I know the Nature Medicine paper that just came out is going to be just one of many more to come in your career. So what a joy to have the chance to visit with you, and we'll be following the work that you and your colleagues are doing with great interest.

Lotte Bjerre Knudsen (46:45):

And thank you very much, and thank you for your wonderful podcast. They’re really great to listen to on the go. Very easy listening.

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Piezo touch and pressure-sensing ion channels are showing up everywhere as the explanation for physiologic phenomena, both at the macro and micro levels. Ardem Patapoutian, my friend and colleague at Scripps Research, discovered these receptors back in 2010 and was awarded the Nobel Prize in 2021 for his work. As you’ll see/hear from our conversation, the field has exploded. And you’ll get to know Ardem, who is such a fun, charismatic, and down-to-earth person. He also recently got a unique tattoo (videos below) and I wonder (unlikely) if any other Nobel laureates have one related to their discovery?!

Below is a video clip from our conversation. Full videos of all Ground Truths podcasts can be seen on YouTube here. The current one is here. If you like the YouTube format, please subscribe! The audios are also available on Apple and Spotify.

Transcript with links to audio

Eric Topol (00:07):

Well, hello. It's Eric Topol with Ground Truths, and I've really got a special guest today. The first time for the podcast, I've been able to interview a colleague and faculty at Scripps Research, Ardem Patapoutian, who just by the way happens to be the 2021 Nobel Laureate in Physiology or Medicine. So welcome, Ardem. It's so wonderful to have you.

Ardem Patapoutian (00:30):

Thanks so much, Eric. Looking forward to chatting with you.

Eric Topol (00:34):

Well, this has been interesting because although I've known you for several years, I didn't research you. I mean, I had to learn about more than I even do. And of course, one of the great sources of that is on the Nobel Prize website where you tell your whole story. It is quite a story and not to review all of it, but I wanted to go back just before you made the call to move to Los Angeles from Beirut, Lebanon and with the scare that you went through at that time, it seemed like that was just extraordinary that you had to live through that.

Ardem Patapoutian (01:11):

Yeah, so I am of Armenian origin, but I was born in Lebanon and born in 1967, so I was eight years old when the civil war started. So it's a kind of bizarre childhood in the sense that with all the bombs and fighting in Lebanon. So it was tough childhood to have, but it was never personal. It was bombs and such. And so, the event you're talking about is, I happened to be kidnapped while crossing East to West Beirut. They only held me for four or five hours at first asking me questions to see who I am, but I think they pretty soon figured out that I was not a dangerous guy and they ended up letting me go. But before that, that incident really had a huge impact on me so that by the time I got home, I literally said, I'm out of here. I'm going to find a way to leave the country. And so, that's what, very quickly within a few months I packed and came to United States.

Eric Topol (02:19):

And how did you pick LA to be your destination?

Ardem Patapoutian (02:22):

Being from the Armenian community, there's a lot of Armenians in Los Angeles. My cousins already had moved there. They also grew up in Lebanon. And my brother, who's a few years older than me, got admitted to USC graduate school in engineering. So he was going to be there. So it made a lot of sense.

Eric Topol (02:44):

Oh yeah.

Ardem Patapoutian (02:45):

Unlike him, I came with no school or job prospects because it happened so fast that I kind of just left. One year I was at American University of Beirut for one year, but then just left and came here. So worked for a year in various jobs and then started going back to school to UCLA.

Eric Topol (03:07):

Yeah, I saw how there was about a year where you were delivering pizzas and before you got into UCLA, and that must have been an interesting off year, if you will. Well, the story of course, just to fast forward, you did your baccalaureate at UCLA, your PhD at Caltech, postdoc at UCSF, and then you came to Scripps Research 24 years ago along with Pete Schultz, and it's been quite an amazing run that you've had. Now, before we get into PIEZO receptors, the background, maybe you could help me understand, the precursor work seems to be all related to the transient receptor potential (TRP) series, also ion channels. They were of course related to whether it was heat and temperature or somatosensory. How do these channels compare to the ones that you discovered years later?

Background on these Ion Channels

Ardem Patapoutian (04:09):

Yeah, so the somatosensory neurons that innervate your fingertips and everywhere else in your body, their main job is to sense temperature and pressure. And this is very different than any other neuron or any other cell. So when you touch a hot stove that’s burning hot, you need to know about that immediately within milliseconds or something cold. So the opposite side of it is pressure sensing, and it also comes in light touch, which is pleasant or a hammer hitting your finger, which is unpleasant. But all of these have the same characteristic anyway, that is your body has learned at the molecular level to translate a physical stimulus such as temperature and pressure into an electrical signal that neurons use to communicate with each other. But this idea of how you translate physical stimuli into chemical or electrical signal has been a long open question because as you know, most of our cells communicate by chemicals, whether that's hormones or small molecules, we know how that works, receptor bind to ligand, confirmational change and you get a kinase activation and that's enough. But here, how do you sense pressure? How do you sense temperature? It was just, there wasn't much known about that. And that's why our earlier work on TRP channels, which were temperature sensors came before the pressure. And so, they're very related in that sense.

Eric Topol (05:52):

The structure of these, if you were to look at them, do they look pretty similar? What the TRP as you say, and what you did back in the 2010 Science paper, which we'll link to, of course the classic paper where you describe PIEZO1 and PIEZO2, but if you were to look at this structures, would they look pretty similar?

Ardem Patapoutian (06:14):

No, that's a good question. And they absolutely don't. That's why finding these receptors were so hard. So if you go back to other sensory receptors, vision rhodopsin G-protein coupled receptor (GPCRs), larger G-protein coupled receptor look the same. So for example, when it was identified by chemically, that smell also works through G-protein coupled receptor. Richard Axel and Linda Buck, who also won the Nobel Prize, found those receptors by homology to visual GPCRs. The ion channels other than the fact that they crossed the membrane a few times or more, they have nothing else in common. If you looked at their structure, you can't even immediately tell they’re ion channels. So you couldn't find these by structural homology or sequence homology. So you had to do something else. And usually that means functional screens and et cetera.

Eric Topol (07:09):

Well, yeah, and I'm in touch with the screening. We'll get to that and how you dig these up and find them. But the somatosensory ones are really interesting because I don't think a lot of people realize that when you have wasabi or you have Listerine mouthwash and feel the burn and that these are all mediated through these channels, right?

Ardem Patapoutian (07:35):

Yeah. So there's this whole field of chemesthesis, which means senses in your mouth, for example, that are not explained by taste transduction and olfactory. And these are actually by the same somatosensory neurons that help you sense temperature and pressure. And some of these receptors are the same. Their evolution has taken over and used them for many different things. The prime example of this is the capsaicin receptor that David Julius my co-laureate identified, which is also heat receptors. So all languages describe chili peppers as hot, and that's not a coincidence. It actually activates heat activated channel, and that's why we think of it as hot. And so, the same goes to another one of these TRP channels that you mentioned, which is TRPA1, and this one is also activated, but a lot of spicy foods other than the chili pepper active ingredient includes what's in garlic and onions and everything that has this burning sensation and chemicals of this and wasabi and chemicals of this are used in over the counter products like Listerine that cause that burning sensation.

Eric Topol (08:54):

So when you're chopping onions and it makes you cry, is that all part of it as well?

Ardem Patapoutian (08:59):

That’s all TRPA1, yeah.

The Discovery, A Test of Perseverance

Eric Topol (09:01):

It's wild. Now, this was the groundwork. There were these heat temperature and somatic sensory, and then you were starting to wonder what about touch, what about out pressure and proprioception. And so, you went on a hunt, and it's actually kind of an incredible story about how you were able to find out of these cells that you had, screening hundreds or I guess you got to 72 different small interfering RNA blocking that you finally found the one. Is that right?

Ardem Patapoutian (09:37):

That's right. So in retrospect, looking back at it, I think there's such an interesting scientific message there. And so, many of us were looking for this touch pressure sensors and we were all looking in the DRG sensory neurons that are complicated heterogeneous, they don't divide. It's not easy to do a screen on them. And ultimately after a lot of failures, what worked for us is to take a step back and ask a much more simpler question. And that was, can we find one of these cell lines that you could easily homogeneously grow in a culture dish, if they respond to mechanical force, can we find our channel there? And then go back and look if it's relevant in vivo for what process. So I think the message is ask the simplest question to answer the question you're after. And finding what that is, is actually the challenge lots of times.

Ardem Patapoutian (10:36):

But yeah, that's what Bertrand Coste in my lab did is found a simple cell line that neuroscientists had been using for a hundred years and somehow found that they over overexpressed this channel because you can record from them, you can push them and record the currents from them. And then it became a simpler question of finding it. It still took a whole year. He made a list and one by one knocking them out and looking at it. And finally, as you say, number 72 was the hit. When he knocked that out, the current was gone. And that's where we started believing that we have what we were looking for.

Eric Topol (11:12):

Were you all ever about ready to give up at that point?

Ardem Patapoutian (11:16):

Oh yeah. I mean that's another lesson. These are postdocs doing the work, right? And they're here three, four years and this was coming close to end of two years, and he didn't have anything yet. So we started talking about having a backup project and he started that and we said, okay, we were ordering this oligos 30 at a time because they're expensive. And so, the first 30 nothing, the second 30 nothing. And how many more are we going to do before we potentially give up? And we said, well, let's do at least a third and then decide, thank goodness it was in that last set.

Eric Topol (11:54):

Wow, that is so wild. Now what's happened since this discovery, which I guess when you published it in 2010, so it means 14 years ago, but we're on this exponential growth of learning that these piezo receptors are everywhere. They're doing everything. In fact, I recently put on Bluesky, PIEZO ion channels are to human physiology as GLP-1 drugs are to treating many diseases because it's just blowing up. And you've published on some of these of course, on itch and bladder function and vascular function. We'll get to maybe malaria, I mean, but even the cover of Science recently was about wet dog shakes and how animals shake because of water. These receptors are so fundamental to our function. So maybe you could comment, 15 years ago when you were doing the work and you're making this discovery, did you ever envision it was going to blow up like this?

Ardem Patapoutian (12:57):

Not to this level, but I should have. I think that this idea, again, that most of cell communication is through chemicals is of course a lot of it is true.

Ardem Patapoutian (13:12):

But it would be ridiculous for evolution to ignore all the physical forces, the pressures that cells experience. And once they do, you would think you would put an instructive way of sensing this pressure signal and using it beneficially to the system or the cell. And so, when we used to talk about pressure sensing at the beginning, there were a couple of touch, pain, maybe proprioception, hearing are like the poster children of pressure sensing. But I think what these molecules, as you say is enabling us is finding out the much more wider role that pressure sensing is playing in physiology and in disease that no one had thought seriously about. And this is, I compare sometimes the finding the PIEZO molecules. You're going in a dark room, and you need to find a door to get into there. And PIEZO is kind of that finding the door once you get in, now you use that molecule now to find physiology instead of the opposite way around. So by pursuing PIEZO expression and function, we're finding all these new roles that they play in physiology and in disease that we didn't think about. And because they're so specialized to sense tension, membrane tension, they don't do anything else. So if you see them expressed somewhere or if you see a function for them, you can bet that they are playing a role in sensing pressure. A lot of biology has kind of come from this hypothesis.

Eric Topol (15:00):

Well, I mean it is so striking to see the pervasiveness, and I do want to go back just for a second because when you name them PIEZO, you named it after the Greek word. How did you come to that name?

Ardem Patapoutian (15:13):

So Bertrand and I were actually sitting on Google Translate and we were typing pressure and trying to see what it's like in Greek or in Latin or different languages. His native French and my Armenian and píesi in Greek is pressure. And of course, what's really cool is that the word that more people know about this is piezoelectric device.

Eric Topol (15:41):

Oh, right.

Ardem Patapoutian (15:41):

Actually, translates physical force into electricity and vice versa. And in a way, this is a little molecular machine that does the same thing, and he uses this piezoelectric device to actually push on the cell. That's his assay. So it all came together as a very appropriate name for this gene and protein.

Call from the Nobel Committee

Eric Topol (16:04):

Oh really, it’s perfect. And you get to name it, even that's fun too, right? Now we're going to go to getting the call at 2:00 AM, but it didn't come to you because your phone from the Nobel Committee was on ‘do not disturb’ and your 94-year-old father, Sarkis. How did the Nobel Committee know to get ahold of him? How did they reach him in the middle of the night?

Ardem Patapoutian (16:37):

Yeah, so I mean, since receiving it, I've had conversations with various committee members, and they are very resourceful folks, and they have assistants who throughout the year collect information on all potential people who might win. They're also doing last minute searches. So they looked for other Patapoutian’s in California. So they just called my dad who initially yelled at them for disturbing him at 2:00 AM.

Eric Topol (17:17):

And he could get through to you because he was not on your list of ‘do not disturb’ or something like that.

Ardem Patapoutian (17:22):

I didn't even know this. And I don’t know if the policy has changed, but in some phones the ‘do not disturb’ if it's called by someone who's in your contacts or favorites.

Ardem Patapoutian (17:34):

After I think they called twice and they get through, and that's how.

Getting a Tattoo!

Eric Topol (17:39):

That's amazing. Wow. Well, that's quite a way to find out that you're getting recognized like this. Now recently you got a tattoo, which I thought was really remarkable, but we're going to put that of course in the post. Tell us about your decision to get the PIEZO channel on your arm.

Ardem Patapoutian (18:02):

So as you can tell, I'm obsessed about PIEZO and it's been good to me. And I had the idea a while ago, and my very wise wife, Nancy Hong, said that you might be going through midlife crisis. Why don't you wait a year? If you still believe in it, you should do it. And that's what I did. I waited a year, and I was like, I still want to do it. And I guess I could show it. Here it is.

Eric Topol (18:32):

Oh yeah, there it is. Oh wow.

Ardem Patapoutian (18:33):

What’s cool is that I can pretty much flex to show the activation mechanism because the channel is like bent like this in the plasma membrane. When it's stretched, it opens and it actually flattens like this. So I feel like other than being a tattoo, this is both performance art and instructional device. When I'm giving talks without PowerPoint slides, I could give a demonstration how this ion channel works.[Below is from a presentation that Ardem recently gave, the Harvey Lecture, at Rockefeller University.]

Eric Topol (19:04):

It's wild. Now how did you find a tattoo artist that could, I mean, it's pretty intricate. I mean, that's not your typical tattoo.

Ardem Patapoutian (19:14):

Yeah, I put it up on social media that I was thinking of doing this, and many scientists are into tattoos, so I actually got so many recommendations. And one of them was a local here in San Diego, and she is very popular. I waited six months to get this, I was on a waiting list. The appointment was six months off when we made it. So she's very popular and she's very good.

Eric Topol (19:45):

Was it painful to get that done?

Ardem Patapoutian (19:47):

Well, that's actually really cool, right? Because PIEZO2 is involved in pain sensation, and I felt it while it was being tattooed on my arm. The whole day, I was there like six and a half hours.

New Prospect for Pain Medication

Eric Topol (20:00):

Oh my gosh. Wow. Now that gets me to pain because, I'd like you to talk a bit about the people that don't have mutations or loss of function PIEZO receptors and also what your thoughts are in the future as to maybe we could develop a lot better pain medications.

Ardem Patapoutian (20:22):

Yeah, we're working on it. So you're right. One of the great parts of the science story, and this is mainly the work of Alex Chesler and Carsten Bönnemann at the NIH, where they identified people who came to the clinic for undiagnosed conditions, and they were uncoordinated and had difficulty walking. And when they did whole-exome sequencing, they found that they had mutations in PIEZO2, there were loss of function, as you say. So complete loss on both chromosomes. And when they started testing them, they realized that just like we had described them in animal models, humans without PIEZO2 as well, didn't sense touch, don't have proprioception. This sense of where your limbs are, that's so important for balance and most other daily functions that we take it for granted. So they were completely lacking all of those sensations. They also do not feel their bladder filling.

Ardem Patapoutian (21:26):

And so, they have learned to go on a schedule to make sure they don't have accidents. And many of these projects that we've done in the lab collaboration with Alex Chesler, et cetera, have come from the observations of what else these individuals experience. And so, it's been a great kind of collaboration communication between mechanistic animal model studies and the clinic. And so, one of the things that these individuals don't sense in addition to touch, is something called tactile allodynia, which is simply when touch becomes painful. You and I experienced this after small injury or sunburn where just touching your shoulder becomes painful, but for peripheral neuropathy and other neuropathic pain conditions, this is one of the major complaints that individuals have. And we know from the NIH studies that these individuals don't have this tactile allodynia. So touch becomes painful and doesn't apply to them, which tells us that if we block PIEZO2, we can actually get interesting relief from various aspects relative to neuropathic pain on other pain related neuropathies. But given everything we talked about, Eric, about how this is important for touch and proprioception, you don't want to make a pill that blocks PIEZO2 and you take it because this will have some serious on target side effects. But we are developing new compounds that block PIEZO2 and hope that it might be useful, at least as a topical medication pain and other indications. And we're actively working on this, as I said.

Eric Topol (23:15):

Yeah, I mean the topical one sounds like a winner because of peripheral neuropathy, but also I wonder if you could somehow target it to sick cells rather than if giving it in a systemic targeted way. I mean it has tremendous potential because we are on a serious hunt for much better relief of pain than exists today.

Ardem Patapoutian (23:41):

Absolutely.

Eric Topol (23:42):

Yeah. So that's exciting. I mean, that's another potential outgrowth of all this. Just going back, I mean the one that prompted me in November to write that about the human physiology in PIEZO, it was about intestinal stem cell fate decision and maintenance. I mean, it's just everywhere. But the work you've done certainly now has spurred on so many other groups to go after these different and many unanticipated functions. Were there any ones, of course, you've been pretty systematically addressing these that actually surprised you? You said, oh, are you kidding me when you read this? I never would've guessed this, or pretty much they followed suit as things were moving along.

Ardem Patapoutian (24:33):

So one of them is this role in macrophages that I found fascinating that we found a few years ago. So again, this came from human studies where PIEZO1 gain-of-function mutations. So in relation to loss of function, their gain-of-function where there's more activity given a certain amount of pressure. They have dehydrated red blood cells, which I'm not going to talk about right now. But they also have shown that in these patients, individuals, it's not really that pathological. They also have age-onset iron overload. What does that have to do with pressure sensing? And we brought that information into animal models, and we found that macrophages, their rate of phagocytosis depends on PIEZO, so that if you have too little PIEZO, they don't phagocytosis as much. If you have too much PIEZO, the phagocytosis too much. And this increased rate of phagocytosis in the long term because it's constantly eating red blood cells and the iron is circulating more causes long-term effects in iron overload. And again, as you kind of set that up, who would've thought that mechanical sensation is important for this basic hematology type?

Eric Topol (25:52):

Yeah, I mean, because we've been talking about the macro things, and here it is at the cellular level. I mean, it's just wild.

Ardem Patapoutian (25:59):

If you go back and look at a video of a macrophage eating up red blood cells, then you go, oh, I see how this has to do with pressure sensing because it is like extending little arms, feeling things letting go, going somewhere else. So again, I want to bring it back by this simple cell biological function of a cell type, like macrophage, exploring its environment is not just chemical, but very mechanical as well. And so, in retrospect, it is maybe not that surprising, that pressure sensing is important for its physiology.

Career Changing?

Eric Topol (26:33):

Yeah, that's extraordinary. Well, that gets me to how your life has changed since 2021, because obviously this a big effect, big impact sort of thing. And I know that you're the first Armenian, first person from Lebanon to get this recognition. You recognized by the Lebanese Order of Merit. There's even a stamp of you, your picture characterized in 2022.

Eric Topol (27:04):

So if you were to sum up how it's changed because I see no change in you. You're the same person that has a great sense of humor. Often the tries to humor relaxed, calming. You haven't changed any to me, but how has it affected you?

Ardem Patapoutian (27:26):

Thank you, Eric. That's very kind of you. I try very hard for it not to change me. I do get a little bit more attention, a ton more invites, which unfortunately I have to say no to a lot of them because, and I'm sure you're very familiar with that concept and a lot of things are offered to you that I feel like it's so tempting to say yes because they're wonderful opportunities and an honor to be asked. But the end of the day, I'm trying to be very disciplined and not taking things on that I can do as an opportunity. But things that I really want to do. I think that's so hard to do sometimes is to separate those two. Why am I doing this? Is this really important for the goals that I have? So in one way, the answer for that is that I just want to stay in the lab and do my research with my students and postdoc, which is what I enjoy the most. But on the other hand, as you said, being the first Armenian who's received this, literally after the Nobel, I got this whole elementary school, all Armenian kids write to me multiple letters.

Ardem Patapoutian (28:39):

And they said, you look like me. I didn't think I could do this, but maybe I can. So in a sense, to ignore that and say, no, I just want to do my science, I don't want to be involved in any of that is also wrong. So I'm trying to balance being engaged in science outreach and helping to make science understood by the general public, realize that we're just regular people and at the same time how awesome science is. I love science and I like to project that, but leave plenty of time for me to just be a scientist and be in my lab and interact with my colleagues at Scripps, including you.

Immigrant Scientists

Eric Topol (29:21):

Well, we're so lucky to have that chance. And I do want to mention, because you're prototyping in this regard about great immigrant scientists and other domains of course, but every year the Carnegie Foundation names these great immigrants and one year you were of course recognized. And in recent years, there have been more difficulties in people wanting to come to the US to get into science, and they wind up going to other places. It seems like that's a big loss for us. I mean, what if we weren't able to have had you come and so many hundreds, thousands of others that have contributed to this life science community? Maybe you could comment about that.

Ardem Patapoutian (30:10):

Yeah, I think it is tragic, as you say. I think in some circles, immigrants have this negative image or idea of what they bring, but at every level, immigrants have contributed so much to this country. It's a country of immigrants, of course, to start with. And I think it is important to put up a positive image of immigration and science is the ultimate example of that, right? I mean, I think when you go into any laboratory, you probably find if there's a lab of 16 people, you probably find people from 10 different countries. And we all work together. And the idea of also immigrant and especially about science is that I'm a big believer of changing field, changing things because just like that, immigrants have changed their whole life. So they come to a new culture, they bring with them their own way of thinking and their way of seeing things. And then you come into a new environment, and you see it a little bit differently. So that kind of change, whether it's because of physical immigration or immigrating from one field to another in science is really beneficial for science and society. And I think positive examples of this are an important part of highlighting this.

Eric Topol (31:40):

I couldn't agree with you more really.

Bluesky vs Twitter/X

Eric Topol (31:41):

Now, speaking of migration, there's been recently a big migration out of X, formerly Twitter to Bluesky, which I like the metaphor you liken to the Serengeti. Can you tell us about, now I know you're posting on Bluesky and of course so many others that you and I are mutual contacts, and our different networks are. What do you think about this migration outside of what was the platform where a lot of this, we shared things on X or before Musk took over known as Twitter? Thoughts about Bluesky?

Ardem Patapoutian (32:27):

Yeah, I think I use social media for a few reasons. The number one reason should be is to see new science by colleagues. My main point is that, but also, again, having fun in science is a big part of my draw to this. And as you can see from my posts, it's a bit lighthearted, and that's really me.

Eric Topol (32:52):

Right. Yeah.

Ardem Patapoutian (32:52):

I think on Twitter, things start getting a little bit dark and too many negative comments, and it was just not productive. And I just felt like after the elections, I felt like it was time to migrate. And I find Bluesky a great scientific community, and it's remarkable how quickly people have migrated from Twitter to Bluesky. But the counter argument for this is that you should stay in a place where majority of people are, because being in a bubble surrounding yourself by people like you doesn't help society. And so, I get that perspective as well. It just depends on what you're using the platform for and it's a difficult issue. But yeah, I've taken a break probably long-term break from Twitter. I'm on Bluesky now.

Eric Topol (33:48):

Yeah, no, the point you're bringing up about the echo chamber and is there going to be one for people that are leaning one way and they're thinking, and another with a whole different, often politically charged and even extreme views? It's really unfortunate if it does wind up that way. But right now, it seems like that migration is ongoing and it's substantial. And I guess we'll see how it settles out. I share your concern, and so far, I've been trying to keep a foot in both areas because I think if we all were to leave, then we're just kind of caving into a, it's tricky though. It really is because the noxious toxic type of comments, even when you try to avoid comments, you say, only followers can make a comment, they’ll of course, quote your thing and then try to ding you and whatever. It's just crazy stuff, really.

Ardem Patapoutian (34:53):

I mean, what I think is that, that's why I said depends on why. I mean, your presence on social media is such an important part of science education. And I could almost say you can't afford to do what I do, which is I'm just putting my goofy posts and having fun. So we have different purposes in a way, and yeah, that affects what you use and how you use it.

Eric Topol (35:17):

Yeah, no, it's tricky it really is. We covered a lot of ground. Is there anything I missed that you want to get out there? Any part of this, your story and the PIEZO story, science and everything else that I didn't bring up?

The Essentiality of Basic Science

Ardem Patapoutian (35:42):

I just think that the basic science community is really suffering from decreasing amounts of funding and appreciation of doing basic science. And one of my goals, in addition to this immigrant scientist thing, is to remind people that all medicines start with basic science work. And funding this has mainly been through NIH and it's getting harder and harder for basic scientists to secure funding and I'm really worried about this. And we need to find ways to be okay for people to do basic science. And I'll give you one example. Whenever we make a publication and there's a journalist talking to us or some kind of press coverage, they ask, how is this directly affecting patients? And my work actually is very much related to patients, and I answer that question, but I also say, but it's also important to do science for the science sake because you don't know where the applications are going to come from. And we need to, as a society, encourage and fund and support basic science as the seeds of all these translational work. And I think doing that just kind of highlights that this is important too. We should support it, not just things that right now seem very related to translational that directly helps patients.

Eric Topol (37:16):

Well, I'm so glad you emphasized that because I mean, the PIEZO story is the exemplar. Look what's come of it, what might still come of it. In many respects here you are maybe 15 years into the story and there's still many parts of this that are untold, but if it wasn't for the basic science, we wouldn't have these remarkable and diverse insights. And recently you cited, and I think so many people read about the ‘crown jewel’ NIH, front page New York Times, and how it's under threat because the new NIH director doesn't have a regard for basic science. He's actually, he's confirmed, which is likely, he's an economist, physician economist, never practiced medicine, but he doesn't really have a lot of regard for basic science. But as you point out, almost every drug that we have today came out of NIH basic work. And I mean, not just that, but all the disease insights and treatments and so much.

Eric Topol (38:25):

So this is really unfortunate if we have not just an NIH and other supporting foundations that don't see the priority, the fundamental aspect of basic science to then lead to, as we call translational, and then ultimately the way to promote human health, which is I think what we're all very much focused on ultimately. But you can't do it without getting to first base, and that's what you have done. You served it up and it's a great example. Well, Ardem, it's always a pleasure. This is a first time talking through a podcast. I hope we’ll have many, many visits informally that will complement the ones we've already had, and we will follow the PIEZO work. Obviously, you have had just an exceptional impact, but you're still young and who knows what's next, right? I mean, look what happened to Barry Sharpless. He won here. He won two Nobel prizes, so you never know where things are headed.

Ardem Patapoutian (39:36):

Thank you, Eric, and I really appreciate what you do for the biomedical community. I think it's wonderful through your social media and this podcast, we all appreciate it.

***********************************************************************************

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American healthcare is well known for its extreme cost and worst outcomes among industrialized (such as the 38 OECD member) countries, and beyond that to be remarkably opaque. The high cost of prescription drugs contributes, and little has been done to change that except for the government passing the Affordable Insulin Now Act at the end of 2022, enacted in 2023. But in January 2022 Mark Cuban launched Cost Plus Drugs that has transformed how many Americans can get their prescriptions filled at a fraction of the prevailing prices, bypassing pharmacy benefit managers (PBMs) that control 80% of US prescriptions. That was just the beginning of a path of creative destruction (disruptive innovation, after Schumpeter) of many key components American healthcare that Cuban is leading, with Cost Plus Marketplace, Cost Plus Wellness and much more to come. He certainly qualifies as a master disrupter: “someone who is a leader in innovation and is not afraid to challenge the status quo.”

Below is a video clip from our conversation dealing with insurance companies. Full videos of all Ground Truths podcasts can be seen on YouTube here. The current one is here. If you like the YouTube format, please subscribe! The audios are also available on Apple and Spotify.

Transcript with External links to Audio

(00:07):

Hello, it's Eric Topol with Ground Truths, and I have our special phenomenal guest today, Mark Cuban, who I think you know him from his tech world contributions and Dallas Mavericks, and the last few years he's been shaking up healthcare with Cost Plus Drugs. So Mark, welcome.

Mark Cuban (00:25):

Thanks for having me, Eric.

Eric Topol (00:27):

Yeah, I mean, what you're doing, you’ve become a hero to millions of Americans getting them their medications at a fraction of the cost they're used to. And you are really challenging the PBM industry, which I've delved into more than ever, just in prep for our conversation. It's just amazing what this group of companies, namely the three big three CVS Caremark, Optum of UnitedHealth and Express Scripts of Cigna with a market of almost $600 billion this year, what they're doing, how can they get away with all this stuff?

Inner Workings of Pharmacy Benefit Managers

Mark Cuban (01:03):

I mean, they're just doing business. I really don't blame them. I blame the people who contract with them. All the companies, particularly the bigger companies, the self-insured companies, where the CEO really doesn't have an understanding of their healthcare or pharmacy benefits. And so, the big PBMs paid them rebates, which they think is great if you're a CEO, when in reality it's really just a loan against the money spent by your sickest employees, and they just don't understand that. So a big part of my time these days is going to CEOs and sitting with them and explaining to them that you're getting ripped off on both your pharmacy and your healthcare side.

Eric Topol (01:47):

Yeah, it's amazing to me the many ways that they get away with this. I mean, they make companies sign NDAs. They're addicted to rebates. They have all sorts of ways a channel of funds to themselves. I mean, all the things you could think of whereby they even have these GPOs. Each of these companies has a group purchasing organization (I summarized in the Table below).

Mark Cuban (02:12):

Yeah, which gives them, it's crazy because with those GPOs. The GPO does the deal with the pharmacy manufacturer. Then the GPO also does the deal with the PBM, and then the PBM goes to the self-insured employer in particular and says, hey, we're going to pass through all the rebates. But what they don't say is they've already skimmed off 5%, 10%, 20% or more off the top through their GPO. But that's not even the worst of it. That's just money, right? I mean, that's important, but I mean, even the biggest companies rarely own their own claims data.

Mark Cuban (02:45):

Now think about what that means. It means you can't get smarter about the wellness of your employees and their families. You want to figure out the best way to do GLP-1s and figure out how to reduce diabetes, whatever it may be. You don't have that claims data. And then they don't allow the companies to control their own formularies. So we've seen Humira biosimilars come out and the big PBMs have done their own version of the biosimilar where we have a product called Yusimry, which is only $594 a month, which is cheaper than the cheapest biosimilar that the big three are selling. And so, you would think in a normal relationship, they would want to bring on this new product to help the employer. No, they won't do it. If the employer asks, can I just add Cost Plus Drugs to my network? They'll say no, every single time.

Mark Cuban (03:45):

Their job is not to save the employer money, particularly after they've given a rebate. Because once they give that loan, that rebate to the employer, they need to get that money back. It's not a gift. It's a loan and they need to have the rebates, and we don't do rebates with them at all. And I can go down the list. They don't control the formula. They don't control, you mentioned the NDAs. They can't talk to manufacturers, so they can't go to Novo or to Lilly and say, let's put together a GLP-1 wellness program. All these different things that just are common sense. It's not happening. And so, the good news is when I walk into these companies that self-insured and talk to the CEO or CFO, I'm not asking them to do something that's not in their best interest or not in the best interest of the lives they cover. I'm saying, we can save you money and you can improve the wellness of your employees and their families. Where's the downside?

Eric Topol (04:40):

Oh, yeah. Yeah. And the reason they can't see the claims is because of the privacy issues?

Mark Cuban (04:46):

No, no. That's just a business decision in the contract that the PBMs have made. You can go and ask. I mean, you have every right to your own claims. You don't need to have it personally identified. You want to find out how many people have GLP-1s or what are the trends, or God forbid there's another Purdue Pharma thing going on, and someone prescribing lots of opioids. You want to be able to see those things, but they won't do it. And that's only on the sponsor side. It's almost as bad if not worse on the manufacturer side.

Eric Topol (05:20):

Oh, yeah. Well, some of the work of PBMs that you've been talking about were well chronicled in the New York Times, a couple of major articles by Reed Abelson and Rebecca Robbins: The Opaque Industry Secretly Inflating Prices for Prescription Drugs and The Powerful Companies Driving Local Drugstores Out of Business. We'll link those because I think some people are not aware of all the things that are going on in the background.

Mark Cuban (05:39):

You see in their study and what they reported on the big PBMs, it's crazy the way it works. And literally if there was transparency, like Cost Plus offers, the cost of medications across the country could come down 20%, 30% or more.

Cost Plus Drugs

Eric Topol (05:55):

Oh, I mean, it is amazing, really. And now let's get into Cost Plus. I know that a radiologist, Alex Oshmyansky contacted you with a cold email a little over three years ago, and you formed Cost Plus Drugs on the basis of that, right?

Mark Cuban (06:12):

Yep, that's exactly what happened.

Eric Topol (06:15):

I give you credit for responding to cold emails and coming up with a brilliant idea with this and getting behind it and putting your name behind it. And what you've done, so you started out with something like 110 generics and now you're up well over 1,200 or 2,500 or something like that?

Mark Cuban (06:30):

And adding brands. And so, started with 111. Now we're around 2,500 and trying to grow it every single day. And not only that, just to give people an overview. When you go to www.costplusdrugs.com and you put in the name of your medication, let's just say it's tadalafil, and if it comes up. In this case, it will. It'll show you our actual cost, and then we just mark it up 15%. It's the same markup for everybody, and if you want it, we'll have a pharmacist check it. And so, that's a $5 fee. And then if you want ship to mail order, it's $5 for shipping. And if you want to use our pharmacy network, then we can connect you there and you can just pick it up at a local pharmacy.

Eric Topol (07:10):

Yeah, no, it's transparency. We don't have a lot of that in healthcare in America, right?

Mark Cuban (07:15):

No. And literally, Eric, the smartest thing that we did, and we didn't expect this, it's always the law of unintended consequences. The smartest thing we did was publish our entire price list because that allowed any company, any sponsor, CMS, researchers to compare our prices to what others were already paying. And we've seen studies come out saying, for this X number of urology drugs, CMS would save $3.6 billion a year. For this number of heart drugs at this amount per year, for chemotherapy drugs or MS drugs this amount. And so, it's really brought attention to the fact that for what PBMs call specialty drugs, whether there's nothing special about them, we can save people a lot of money.

Eric Topol (08:01):

It's phenomenal. As a cardiologist, I looked up a couple of the drugs that I'm most frequently prescribed, just like Rosuvastatin what went down from $134 to $5.67 cents or Valsartan it went down from $69 to $7.40 cents. But of course, there's some that are much more dramatic, like as you mentioned, whether it's drugs for multiple sclerosis, the prostate cancer. I mean, some of these are just thousands and thousands of dollars per month that are saved, brought down to levels that you wouldn't think would even be conceivable. And this has been zero marketing, right?

Mark Cuban (08:42):

Yeah, none. It's all been word of mouth and my big mouth, of course. Going out there and doing interviews like this and going to major media, but it's amazing. We get emails and letters and people coming up to us almost single day saying, you saved my grandma's life. You saved my life. We weren't going to be able to afford our imatinib or our MS medication. And it went from being quoted $2,000 a month to $33 a month. It's just insane things like that that are still happening.

Eric Topol (09:11):

Well, this is certainly one of the biggest shakeups to occur in US healthcare in years. And what you've done in three years is just extraordinary. This healthcare in this country is with its over 4 trillion, pushing $5 trillion a year of expenditure.[New CMS report this week pegs the number at $4.867 trillion for 2023]

Mark Cuban (09:30):

It's interesting. I think it's really fixable. This has been the easiest industry to the disrupt I've ever been involved in. And it's not even close because all it took was transparency and not jacking up margins to market. We choose to use a fixed margin markup. Some choose to price to market, the Martin Shkreli approach, if you will. And just by being transparent, we've had an impact. And the other side of it is, it's the same concept on the healthcare side. Transparency helps, but to go a little field of pharmacy if you want. The insane part, and this applies to care and pharmacy, whatever plan we have, whether it's for health or whether it's for pharmaceuticals, there's typically a deductible, typically a copay, and typically a co-insurance.

Insurance Companies

Mark Cuban (10:20):

The crazy part of all that is that people taking the default risk, the credit risk are the providers. It's you, it's the hospital, it's the clinics that you work for. Which makes no sense whatsoever that the decisions that you or I make for our personal insurance or for the companies we run, or if we work for the government, what we do with Medicare or Medicare Advantage, the decisions we all make impacts the viability of providers starting with the biggest hospital systems. And so, as a result, they become subprime lenders without a car or a house to go after if they can't collect. And so, now you see a bunch of people, particularly those under the ACA with the $9,000, the bronze plans or $18,000 out-of-pocket limits go into debt, significant medical debt. And it's unfortunate. We look at the people who are facing these problems and think, well, it must be the insurance companies.

Mark Cuban (11:23):

It's actually not even the insurance companies. It's the overall design of the system. But underneath that, it's still whoever picks the insurance companies and sets plans that allow those deductibles, that's the core of the problem. And until we get to a system where the providers aren't responsible for the credit for defaults and dealing with all that credit risk, it's almost going to be impossible to change. Because when you see stories like we've all seen in news of a big healthcare, a BUCA healthcare (Blue Cross Blue Shield (BCBS), UnitedHealth, Cigna, and Aetna/CVS) plan with all the pre-authorizations and denials, typically they're not even taking the insurance risk. They're acting as the TPA (third party administrator) as the claims processor effectively for whoever hired them. And it goes back again, just like I talked about before. And as long as CMS hires or allows or accepts these BUCAs with these plans for Medicare for the ACA (Affordable care Act), whatever it may be, it's not going to work. As long as self-insured employers and the 50 million lives they cover hire these BUCAs to act as the TPAs, not as insurance companies and give them leeway on what to approve and what to authorize and what not to authorize. The system's going to be a mess, and that's where we are today.

Academic Health System Partnerships

Eric Topol (12:41):

Yeah. Well, you've been talking of course to employers and enlightening them, and you're also enlightening the public, of course. That's why you have millions of people that are saving their cost of medications, but recently you struck a partnership with Penn Medicine. That's amazing. So is that your first academic health system that you approached?

Cost Plus Marketplace

Mark Cuban (13:00):

I don't know if it was the first we approached, but it was certainly one of the biggest that we signed. We've got Cost Plus Marketplace (CPM) where we make everything from injectables to you name it, anything a hospital might buy. But again, at a finite markup, we make eight and a half percent I think when it's all said and done. And that saves hospital systems millions of dollars a year.

Eric Topol (13:24):

Yeah. So that's a big change in the way you're proceeding because what it was just pills that you were buying from the pharma companies, now you're actually going to make injectables and you're going to have a manufacturing capability. Is that already up and going?

Mark Cuban (13:39):

That's all up and going as of March. We're taking sterile injectables that are on the shortage list, generic and manufacturing them in Dallas using a whole robotics manufacturing plant that really Alex created. He's the rocket scientist behind it. And we’re limited in capacity now, we're limited about 2 million vials, but we'll sell those to Cost Plus Marketplace, and we'll also sell those direct. So Cost Plus Marketplace isn't just the things we manufacture. It's a wide variety of products that hospitals buy that we then have a minimal markup, and then for the stuff we manufacture, we'll sell those to direct to like CHS was our first customer.

Eric Topol (14:20):

Yeah, that's a big expansion from going from the pills to this. Wow.

Mark Cuban (14:24):

It's a big, big expansion, but it goes to the heart of being transparent and not being greedy, selling on a markup. And ourselves as a company, being able to remain lean and mean. The only way we can sell at such a low markup. We have 20 employees on the Cost Plus side and 40 employees involved with the factories, and that's it.

Eric Topol (14:46):

Wow. So with respect to, you had this phenomenal article and interview with WIRED Magazine just this past week. I know Lauren Goode interviewed you, and she said, Mark, is this really altruistic and I love your response. You said, “how much f*****g money do I need? I'm not trying to land on Mars.” And then you said, “at this point in my life, it’s just like more money, or f**k up the healthcare industry.” This was the greatest, Mark. I mean, I got to tell you, it was really something.

Mark Cuban (15:18):

Yeah.

Eric Topol (15:19):

Well, in speaking of that, of course, the allusion to a person we know well, Elon. He posted on X/Twitter in recent days , I think just three or four days ago, shouldn't the American people be getting their money's worth? About this high healthcare administration costs where the US is completely away from any other OECD country. And as you and I know, we have the worst outcomes and the most costs of all the rich countries in the world. There's just nothing new here. Maybe it's new to him, but you had a fabulous response on both X and Bluesky where you went over all these things point by point. And of course, the whole efforts that you've been working on now for three years. You also mentioned something that was really interesting that I didn't know about were these ERISA lawsuits[Employee Retirement Income Security Act (ERISA) of 1974.] Can you tell us about that?

ERISA Lawsuits

Mark Cuban (16:13):

Yeah, that's a great question, Eric. So for self-insured companies in particular, we have a fiduciary responsibility on a wellness and on a financial basis to offer the members, your employees and their families the best outcomes at the best price. Now, you can't guarantee best outcomes, but you have to be able to explain the choices you made. You don't have to pick the cheapest, but again, you have to be able to explain why you made the choices that you did. And because a lot of companies have been doing, just like we discussed earlier, doing deals on the pharmacy side with just these big PBMs, without accounting for best practices, best price, best outcomes, a couple companies got sued. Johnson and Johnson and Wells Fargo were the first to get sued. And I think that's just the beginning. That's just the writing on the wall. I think they'll lose because they just dealt with the big pharmacy PBMs. And I think that's one of the reasons why we're so busy at Cost Plus and why I'm so busy because we're having conversation after conversation with companies and plenty of enough lawyers for that matter who want to see a price list and be able to compare what they're paying to what we sell for to see if they're truly living up to that responsibility.

Eric Topol (17:28):

Yeah, no, that's a really important thing that's going on right now that I think a lot of people don't know about. Now, the government of the US think because it's the only government of any rich country in the world, if not any country that doesn't negotiate prices, i.e., CMS or whatever. And only with the recent work of insulin, which is a single one drug, was there reduction of price. And of course, it's years before we'll see other drugs. How could this country not negotiate drugs all these years where every other place in the world they do negotiate with pharma?

Mark Cuban (18:05):

Because as we alluded to earlier, the first line in every single pharmaceutical and healthcare contract says, you can't talk about this contract. It’s like fight club. The number one rule of fight club is you can't talk about fight club, and it's really difficult to negotiate prices when it's opaque and everything's obfuscated where you can't really get into the details. So it's not that we're not capable of it, but it's just when there's no data there, it's really difficult because look, up until we started publishing our prices, how would anybody know?

Mark Cuban (18:39):

I mean, how was anybody going to compare numbers? And so, when the government or whoever started to negotiate, they tried to protect themselves and they tried to get data, but those big PBMs certainly have not been forthcoming. We’ve come along and publish our price list and all that starts to change. Now in terms of the bigger picture, there is a solution there, as I said earlier, but it really comes down to talking to the people who make the decisions to hire the big insurance companies and the big PBMs and telling them, no, you're not acting in your own best interest. Here's anybody watching out there. Ask your PBM if they can audit. If you can audit rather your PBM contract. What they'll tell you is, yeah, you can, but you have to use our people. It's insane. And that's from top to bottom. And so, I'm a big believer that if we can get starting with self-insured employers to act in their own best interest, and instead of working with a big PBM work with a pass-through PBM. A pass-through PBM will allow you to keep your own claims, own all your own data, allow you to control your own formulary.

Mark Cuban (19:54):

You make changes where necessary, no NDA, so you can't talk to manufacturers. All these different abilities that just seem to make perfect sense are available to all self-insured employers. And if the government, same thing. If the government requires pass-through PBMs, the price of medications will drop like a rock.

Eric Topol (20:16):

Is that possible? You think that could happen?

Mark Cuban (20:19):

Yes. Somebody's got to understand it and do it. I'm out there screaming, but we will see what happens with the new administration. There's nothing hard about it. And it's the same thing with Medicare and Medicare Advantage healthcare plans. There's nothing that says you have to use the biggest companies. Now, the insurance companies have to apply and get approved, but again, there's a path there to work with companies that can reduce costs and improve outcomes. The biggest challenge in my mind, and I'm still trying to work through this to fully understand it. I think where we really get turned upside down as a country is we try to avoid fraud from the provider perspective and the patient perspective. We're terrified that patients are going to use too much healthcare, and like everybody's got Munchausen disease.

Mark Cuban (21:11):

And we're terrified that the providers are going to charge too much or turn into Purdue Pharma and over-prescribe or one of these surgery mills that just is having somebody get surgery just so they can make money. So in an effort to avoid those things, we ask the insurance companies and the PBMs to do pre-authorizations, and that's the catch 22. How do we find a better way to deal with fraud at the patient and provider level? Because once we can do that, and maybe it's AI, maybe it's accepting fraud, maybe it's imposing criminal penalties if somebody does those things. But once we can overcome that, then it becomes very transactional. Because the reality is most insurance companies aren't insurance companies. 50 million lives are covered by self-insured employers that use the BUCAs, the big insurance companies, but not as insurance companies.

Eric Topol (22:07):

Yeah, I was going to ask you about that because if you look at these three big PBMs that control about 80% of the market, not the pass-throughs that you just mentioned, but the big ones, they each are owned by an insurance company. And so, when the employer says, okay, we're going to cover your healthcare stuff here, we're going to cover your prescriptions there.

Mark Cuban (22:28):

Yeah, it's all vertically integrated.

Mark Cuban (22:36):

And it gets even worse than that, Eric. So they also own specialty pharmacies, “specialty pharmacies” that will require you to buy from. And as I alluded to earlier, a lot of these medications like Imatinib, they'll list as being a specialty medication, but it's a pill. There's nothing special about it, but it allows them to charge a premium. And that's a big part of how the PBMs make a lot of their money, the GPO stuff we talked about, but also forcing an employer to go through the specialty mail order company that charges an arm and the leg.

Impact on Hospitals and Procedures

Eric Topol (23:09):

Yeah. Well, and the point you made about transparency, we've seen this of course across US healthcare. So for example, as you know, if you were to look at what does it cost to have an operation like let's say a knee replacement at various hospitals, you can find that it could range fivefold. Of course, you actually get the cost, and it could be the hospital cost, and then there's the professional cost. And the same thing occurs for if you're having a scan, if you're having an MRI here or there. So these are also this lack of transparency and it's hard to get to the numbers, of course. There seems to be so many other parallels to the PBM story. Would you go to these other areas you think in the future?

Mark Cuban (23:53):

Yeah, we're doing it now. I'm doing it. So we have this thing called project dog food, and what it is, it's for my companies and what we've done is say, look, let's understand how the money works in healthcare.

Mark Cuban (24:05):

And when you think about it, when you go to get that knee done, what happens? Well, they go to your insurance company to get a pre-authorization. Your doctor says you need a knee replacement. I got both my hips replaced. Let's use that. Doctor says, Mark, you need your hips replaced. Great, right? Let's set up an appointment. Well, first the insurance company has to authorize it, okay, they do or they don't, but the doctor eats their time up trying to deal with the pre-authorization. And if it's denied, the doctor's time is eaten up and an assistance's time is eaten up. Some other administrator's time is eaten up, the employer's time is eaten up. So that's one significant cost. And then from there, there's a deductible. Now I can afford my deductible, but if there is an individual getting that hip replacement who can't afford the deductible, now all of a sudden you're still going to be required to do that hip replacement, most likely.

Mark Cuban (25:00):

Because in most of these contracts that self-insured employers sign, Medicare Advantage has, Medicare has, it says that between the insurance company and the provider, in this case, the hospital, you have to do the operation even if the deductibles not paid. So now the point of all this is you have the hospital in this case potentially accumulating who knows how much bad debt. And it's not just the lost amount of millions and millions and billions across the entire healthcare spectrum that's there. It's all the incremental administrative costs. The lawyers, the benefits for those people, the real estate, the desk, the office space, all that stuff adds up to $10 billion plus just because the hospitals take on that credit default risk. But wait, there's more. So now the surgery happens, you send the bill to the insurance company. The insurance company says, well, we're not going to pay you. Well, we have a contract. This is what it says, hip replacement's $34,000. Well, we don't care first, we're going to wait. So we get the time value of money, and then we're going to short pay you.

Mark Cuban (26:11):

So the hospital gets short paid. So what do they have to do? They have to sue them or send letters or whatever it is to try to get their money. When we talk to the big hospital systems, they say that's 2%. That's 2% of their revenue. So you have all these associated credit loss dollars, you've got the 2% of, in a lot of cases, billions and billions of dollars. And so, when you add all those things up, what happens? Well, what happens is because the providers are losing all that money and having to spend all those incremental dollars for the administration of all that, they have to jack up prices.

Eric Topol (26:51):

Yeah. Right.

Mark Cuban (26:53):

So what we have done, we've said, look for my companies, we're going to pay you cash. We're going to pay you cash day one. When Mark gets that hip replacement, that checks in the bank before the operation starts, if that's the way you want it. Great, they're not going to have pre-authorizations. We're going to trust you until you give us a reason not to trust you. We're not short paying, obviously, because we're paying cash right there then.

Mark Cuban (27:19):

But in a response for all that, because we're cutting out all those ancillary costs and credit risk, I want Medicare pricing. Now the initial response is, well, Medicare prices, that's awful. We can't do it. Well, when you really think about the cost and operating costs of a hospital, it's not the doctors, it's not the facilities, it's all the administration that cost all the money. It's all the credit risks that cost all the money. And so, if you remove that credit risk and all the administration, all those people, all that real estate, all those benefits and overhead associated with them, now all of a sudden selling at a Medicare price for that hip replacement is really profitable.

Eric Topol (28:03):

Now, is that a new entity Cost Plus healthcare?

Mark Cuban (28:07):

Well, it's called Cost Plus Wellness. It's not an entity. What we're going to do, so the part I didn't mention is all the direct contracts that we do that have all these pieces, as part of them that I just mentioned, we're going to publish them.

Eric Topol (28:22):

Ah, okay.

Mark Cuban (28:23):

And you can see exactly what we've done. And if you think about the real role of the big insurances companies for hospitals, it's a sales funnel.

Getting Rid of Insurance Companies

Eric Topol (28:33):

Yeah, yeah. Well, in fact, I really was intrigued because you did a podcast interview with Andrew Beam and the New England Journal of Medicine AI, and in that they talked about getting rid of the insurers, the insurance industry, just getting rid of it and just make it a means test for people. So it's not universal healthcare, it's a different model that you described. Can you go over that? I thought it was fantastic.

Mark Cuban (29:00):

Two pieces there. Let's talk about universal healthcare first. So for my companies, for our project dog food for the Mark Cuban companies, if for any employee or any of the lives we cover, if they work within network, anybody we have the direct contract with its single-payer. They pay their premiums, but they pay nothing else out of pocket. That's the definition of single-payer.

Eric Topol (29:24):

Yeah.

Mark Cuban (29:25):

So if we can get all this done, then the initial single-payers will be self-insured employers because it'll be more cost effective to them to do this approach. We hope, we still have to play it all through. So that's part one. In terms of everybody else, then you can say, why do we need insurance companies if they're not even truly acting as insurance companies? You're not taking full risk because even if it's Medicare Advantage, they're getting a capitated amount per month. And then that's getting risk adjusted because of the population you have, and then there's also an index depending on the location, so there's more or less money that occurs then. So let's just do what we need to do in this particular case, because the government is effectively eliminating the risk for the insurance company for the most part. And if you look at the margins for Medicare Advantage, I was just reading yesterday, it's like $1,700 a year for the average Medicare Advantage plan. So it's not like they're taking a lot of risk. All they're doing is trying to deny as many claims as they can.

Eric Topol (30:35):

Deny, Deny. Yeah.

Mark Cuban (30:37):

So instead, let's just get somebody who's a TPA, somebody who does the transaction, the claims processing, and whoever's in charge. It could be CMS, can set the terms for what's accepted and what's denied, and you can have a procedure for people that get denied that want to challenge it. And that's great, there's one in place now, but you make it a little simpler. But you take out the economics for the insurance company to just deny, deny, deny. There's no capitation. There's no nothing.

Mark Cuban (31:10):

The government just says, okay, we're hiring this TPA to handle the claims processing. It is your job. We're paying you per transaction.

Mark Cuban (31:18):

You don't get paid more if you deny. You don't get paid less if you deny. There's no bonuses if you keep it under a certain amount, there's no penalties If you go above a certain amount. We want you just to make sure that the patient involved is getting the best care, end of story. And if there's fraud involved as the government, because we have access to all that claims data, we're going to introduce AI that reviews that continuously.

Mark Cuban (31:44):

So that we can see things that are outliers or things that we question, and there's going to mean mistakes, but the bet was, if you will, where we save more and get better outcomes that way versus the current system and I think we will. Now, what ends up happening on top of that, once you have all that claims data and all that information and everybody's interest is aligned, best care at the best price, no denials unless it's necessary, reduce and eliminate fraud. Once everybody's in alignment, then as long as that's transparent. If the city of Dallas decides for all the lives they cover the 300,000 lives they cover between pharmacy and healthcare, we can usually in actuarial tables and some statistical analysis, we can say, you know what, even with a 15% tolerance, it's cheaper for us just to pay upfront and do this single-pay program, all our employees in the lives we cover, because we know what it's going to take.

Mark Cuban (32:45):

If the government decides, well, instead of Medicare Advantage the way it was, we know all the costs. Now we can say for all Medicare patients, we'll do Medicare for all, simply because we have definitive and deterministic pricing. Great. Now, there's still going to be outlier issues like all the therapies that cost a million dollars or whatever. But my attitude there is if CMS goes to Lilly, Novo, whoever for their cure for blindness that's $3.4 million. Well, that's great, but what we'll say is, okay, give us access to your books. We want to know what your breakeven point is. What is that breakeven point annually? We'll write you a check for that.

Eric Topol (33:26):

Yeah.

Mark Cuban (33:27):

If we have fewer patients than need that, okay, you win. If we have more patients than need that, it's like a Netflix subscription with unlimited subscribers, then we will have whatever it is, because then the manufacturer doesn't lose money, so they can't complain about R&D and not being able to make money. And that's for the CMS covered population. You can do a Netflix type subscription for self-insured employers. Hey, it's 25 cents per month per employee or per life covered for the life of the patent, and we'll commit to that. And so, now all of a sudden you get to a point where healthcare starts becoming not only transparent but deterministic.

Eric Topol (34:08):

Yeah. What you outline here in these themes are extraordinary. And one of the other issues that you are really advocating is patient empowerment, but one of the problems we have in the US is that people don't own their data. They don't even have all their data. I expect you'd be a champion of that as well.

Mark Cuban (34:27):

Well, of course. Yeah. I mean, look, I've got into arguments with doctors and public health officials about things like getting your own blood tested. I've been an advocate of getting my own blood tested for 15 years, and it helped me find out that I needed thyroid medication and all of these things. So I'm a big advocate. There's some people that think that too much data gives you a lot of false positives, and people get excited in this day and age to get more care when it should only be done if there are symptoms. I'm not a believer in that at all. I think now, particularly as AI becomes more applicable and available, you'll be able to be smarter about the data you capture. And that was always my final argument. Either you trust doctors, or you don't. Because even if there's an aberrational TSH reading and minus 4.4 and it's a little bit high, well the doctor's going to say, well, let's do another blood test in a month or two. The doctor is still the one that has to write the prescription. There's no downside to trusting your doctor in my mind.

Eric Topol (35:32):

And what you're bringing up is that we're already seeing how AI can pick up things even in the normal range, the trends long before a clinician physician would pick it up. Now, last thing I want to say is you are re-imagining healthcare like no one. I mean, there's what you're doing here. It started with some pills and it's going in a lot of different directions. You are rocking it here. I didn't even know some of the latest things that you're up to. This seems to be the biggest thing you've ever done.

Mark Cuban (36:00):

I hope so.

Mark Cuban (36:01):

I mean, like we said earlier, what could be better than people saying our healthcare system is good. What changed? That Cuban guy.

Eric Topol (36:10):

Well, did you give up Shark Tank so you could put more energy into this?

Mark Cuban (36:16):

Not really. It was more for my kids.

Eric Topol (36:19):

Okay, okay.

Mark Cuban (36:20):

They go hand in hand, obviously. I can do this stuff at home as opposed to sitting on a set wondering if I should invest in Dude Wipes again.

Eric Topol (36:28):

Well, look, we're cheering for you. This is, I've not seen a shakeup in my life in American healthcare like this. You are just rocking. It's fantastic.

Mark Cuban (36:37):

Everybody out there that's watching, check out www.costplusdrugs.com, check out Cost Plus Marketplace, which is business.costplusdrugs.com and just audit everything. What I'm trying to do is say, okay, if it's 1955 and we're starting healthcare all over again, how would we do it? And really just keep it simple. Look to where the risk is and remove the risk where possible. And then it comes down to who do you trust and make sure you trust but verify. Making sure there aren't doctors or systems that are outliers and making sure that there aren't companies that are outliers or patients rather that are outliers. And so, I think there's a path there. It's not nearly as difficult, it's just starting them with corporations, getting those CEOs to get educated and act in their own best interest.

Eric Topol (37:32):

Well, you're showing us the way. No question. So thanks so much for joining, and we'll be following this with really deep interest because you're moving at high velocity, and thank you.

**************************************************

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Footnote

The PBMS (finally) are under fire—2 articles from the past week

A leader for conducting rigorous randomized trials of humans along with animal models for understanding nutrition and metabolism, Dr. Kevin Hall is a Senior Investigator at the National Institutes of Health, and Section Chief of the Integrative Physiology Section, NIDDK. In this podcast, we reviewed his prolific body of research a recent publications. The timing of optimizing our diet and nutrition seems apropos, now that we’re in in the midst of the holiday season!

Below is a video snippet of our conversation on his ultra-processed food randomized trial.

Full videos of all Ground Truths podcasts can be seen on YouTube here. The current one is here. If you like the YouTube format, please subscribe! The audios are also available on Apple and Spotify.

Note: I’ll be doing a Ground Truths Live Chat on December 11th at 12 N EST, 9 AM PST, so please mark your calendar and join!

Transcript with links to publications and audio

Eric Topol (00:05):

Well, hello. This is Eric Topol with Ground Truths, and I'm really delighted to have with me today, Dr. Kevin Hall from the NIH. I think everybody knows that nutrition is so important and Kevin is a leader in doing rigorous randomized trials, which is not like what we usually see with large epidemiologic studies of nutrition that rely on food diaries and the memory of participants. So Kevin, it's really terrific to have you here.

Kevin Hall (00:34):

Thanks so much for the invitation.

Ultra-Processed Foods

Eric Topol (00:36):

Yeah. Well, you've been prolific and certainly one of the leaders in nutrition science who I look to. And what I thought we could do is go through some of your seminal papers. There are many, but I picked a few and I thought we'd first go back to the one that you published in Cell Metabolism. This is ultra-processed diets cause excessive caloric intake and weight gain. (Main results in graph below.) So maybe you can take us through the principle findings from that trial.

Kevin Hall (01:10):

Yeah, sure. So that was a really interesting study because it's the first randomized control trial that's investigated the role of ultra-processed foods in potentially causing obesity. So we've got, as you mentioned, lots and lots of epidemiological data that have made these associations between people who consume diets that are very high in ultra-processed foods as having greater risk for obesity. But those trials are not demonstrating causation. I mean, they suggest a strong link. And in fact, the idea of ultra-processed foods is kind of a new idea. It's really sort of appeared on the nutrition science stage probably most prominently in the past 10 years or so. And I first learned about this idea of ultra-processed foods, which is really kind of antithetical to the way most nutrition scientists think about foods. We often think about foods as nutrient delivery vehicles, and we kind of view foods as being the fraction of carbohydrates versus fats in them or how much sodium or fiber is in the foods.

Kevin Hall (02:17):

And along came this group in Brazil who introduced this new way of classifying foods that completely ignores the nutrient composition and says what we should be doing is classifying foods based on the extent and purpose of processing of foods. And so, they categorize these four different categories. And in the fourth category of this so-called NOVA classification scheme (see graphic below) , they identified something called ultra-processed foods. There's a long formal definition and it's evolved a little bit over the years and continues to evolve. But the basic ideas that these are foods that are manufactured by industries that contain a lot of purified ingredients made from relatively cheap agricultural commodity products that basically undergo a variety of processes and include additives and ingredients that are not typically found in home kitchens, but are typically exclusively in manufactured products to create the wide variety of mostly packaged goods that we see in our supermarkets.

Kevin Hall (03:22):

And so, I was really skeptical that there was much more about the effects of these foods. Other than that they typically have high amounts of sugar and saturated fat and salt, and they're pretty low in fiber. And so, the purpose of this study was to say, okay, well if there's something more about the foods themselves that is causing people to overconsume calories and gain weight and eventually get obesity, then we should do a study that's trying to test for two diets that are matched for these various nutrients of concern. So they should be matched for the macronutrients, they should be matched for the sugar content, the fat, the sodium, the fiber, and people should just be allowed to eat whatever they want and they shouldn't be trying to change their weight in any way. And so, the way that we did this was, as you mentioned, we can't just ask people to report what they're eating.

Kevin Hall (04:19):

So what we did was we admitted these folks to the NIH Clinical Center and to our metabolic ward, and it's a very artificial environment, but it's an environment that we can control very carefully. And so, what we basically did is take control over their food environment and we gave them three meals a day and snacks, and basically for a two-week period, they had access to meals that were more than 80% of calories coming from ultra-processed foods. And then in random order, they either received that diet first and give them simple instructions, eat as much as little as you want. We're going to measure lots of stuff. You shouldn't be trying to change your weight or weight that gave them a diet that had no calories from ultra-processed foods. In fact, 80% from minimally processed foods. But again, both of these two sort of food environments were matched for these nutrients that we typically think of as playing a major role in how many calories people choose to eat.

Kevin Hall (05:13):

And so, the basic idea was, okay, well let's measure what these folks eat. We gave them more than double the calories that they would require to maintain their weight, and what they didn't know was that in the basement of the clinical center where the metabolic kitchen is, we had all of our really talented nutrition staff measuring the leftovers to see what it was that they didn't eat. So we knew exactly what we provided to them and all the foods had to be in our nutrition database and when we compute what they actually ate by difference, so we have a very precise estimate about not only what foods they chose to ate, but also how many calories they chose to eat, as well as the nutrient composition.

And the main upshot of all that was that when these folks were exposed to this highly ultra-processed food environment, they spontaneously chose to eat about 500 calories per day more over the two-week period they were in that environment then when the same folks were in the environment that had no ultra-processed foods, but just minimally processed foods. They not surprisingly gained weight during the ultra-processed food environment and lost weight and lost body fat during the minimally processed food environment. And because those diets were overall matched for these different nutrients, it didn't seem to be that those were the things that were driving this big effect. So I think there's a couple of big take homes here. One is that the food environment really does have a profound effect on just the biology of how our food intake is controlled at least over relatively short periods of time, like the two-week periods that we were looking at. And secondly, that there's something about ultra-processed foods that seem to be driving this excess calorie intake that we now know has been linked with increased risk of obesity, and now we're starting to put some of the causal pieces together that really there might be something in this ultra-processed food environment that's driving the increased rates of obesity that we've seen over the past many decades.

Eric Topol (07:18):

Yeah, I mean I think the epidemiologic studies that make the link between ultra-processed foods and higher risk of cancer, cardiovascular disease, type 2 diabetes, neurodegenerative disease. They're pretty darn strong and they're backed up by this very rigorous study. Now you mentioned it short term, do you have any reason to think that adding 500 calories a day by eating these bad foods, which by the way in the American diet is about 60% or more of the average American diet, do you have any inkling that it would change after a few weeks?

Kevin Hall (07:54):

Well, I don't know about after a few weeks, but I think that one of the things that we do know about body weight regulation and how it changes in body weight impact both metabolism, how many calories were burning as well as our appetite. We would expect some degree of moderation of that effect eventually settling in at a new steady state, that's probably going to take months and years to achieve. And so the question is, I certainly don't believe that it would be a 500 calorie a day difference indefinitely. The question is when would that difference converge and how much weight would've been gained or lost when people eventually reached that new plateau? And so, that's I think a really interesting question. Some folks have suggested that maybe if you extrapolated the lines a little bit, you could predict when those two curves might eventually converge. That's an interesting thought experiment, but I think we do need some longer studies to investigate how persistent are these effects. Can that fully explain the rise in average body weight and obesity rates that have occurred over the past several decades? Those are open questions.

Eric Topol (09:03):

Yeah. Well, I mean, I had the chance to interview Chris van Tulleken who wrote the book, Ultra-Processed People and I think you might remember in the book he talked about how he went on an ultra-processed diet and gained some 20, 30 pounds in a short time in a month. And his brother, his identical twin brother gained 50, 60 pounds, and so it doesn't look good. Do you look at all the labels and avoid all this junk and ultra-processed food now or are you still thinking that maybe it's not as bad as it looks?

Kevin Hall (09:38):

Well, I mean I think that I certainly learned a lot from our studies, and we are continuing to follow this up to try to figure out what are the mechanisms by which this happen. But at the same time, I don't think we can throw out everything else we know about nutrition science. So just because we match these various nutrients in this particular study, I think one of the dangers here is that as you mentioned, there's 60% of the food environment in the US and Great Britain and other places consist of these foods, and so they're unavoidable to some extent, right? Unless you're one of these privileged folks who have your backyard garden and your personal chef who can make all of your foods, I'm certainly not one of those people, but for the vast majority of us, we're going to have to incorporate some degree of ultra-processed foods in our day-to-day diet.

Kevin Hall (10:24):

The way I sort of view it is, we really need to understand the mechanisms and before we understand the mechanisms, we have to make good choices based on what we already know about nutrition science, that we should avoid the foods that have a lot of sugar in them. We should avoid foods that have a lot of saturated fat and sodium. We should try to choose products that contain lots of whole grains and legumes and fruits and vegetables and things like that. And there's some of those, even in the ultra-processed food category. I pretty regularly consume a microwavable ready meal for lunch. It tends to be pretty high in whole grains and legumes and low in saturated fat and sugar and things like that. But to engineer a food that can heat up properly in a microwave in four minutes has some ultra-processing technology involved there. I would be pretty skeptical that that's going to cause me to have really poor health consequences as compared to if I had the means to eat homemade French fries every day in tallow. But that's the kind of comparison that we have to think about.

Eric Topol (11:36):

But I think what you're touching on and maybe inadvertently is in that NOVA class four, the bad ultra-processed foods, there's a long, long list of course, and some of those may be worse than others, and we haven't seen an individual ranking of these constituents. So as you're alluding to what's in that microwave lunch probably could be much less concerning than what's in these packaged snacks that are eaten widely. But I would certainly agree that we don't know everything about this, but your study is one of the most quoted studies ever in the ultra-processed food world. Now, let me move on to another trial that was really important. This was published in Nature Medicine and it's about a plant-based diet, which is of course a very interesting diet, low-fat versus an animal-based ketogenic diet. Also looking at energy intake. Can you take us through that trial?

Plant-Based, Low Fat Diet vs Animal-Based, Low Carbohydrate Ketogenic Diet

Kevin Hall (12:33):

Sure. So it's actually interesting to consider that trial in the context of the trial we just talked about because both of these diets that we tested in this trial were relatively low in ultra-processed foods, and so both of them contained more than a kilogram of non-starchy vegetables as a base for designing these, again, two different food environments. Very similar overall study design where people again were exposed to either diets that were vegan plant-based diet that was really high in starches and was designed to kind of cause big insulin increases in the blood after eating the meals. And the other diet had very, very few carbohydrates of less than 10% in total, and we built on that kind of non-starchy vegetable base, a lot of animal-based products to kind of get a pretty high amount of fat and having very low carbohydrates. Both diets in this case, like I mentioned, were pretty low in ultra-processed foods, but what we were really interested in here was testing this idea that has come to prominence recently, that high carbohydrate diets that lead to really large glucose excursions after meals that cause very high insulin levels after meals are particularly obesogenic and should cause you to be hungrier than compared to a diet that doesn't lead to those large swings in glucose and insulin and the prototypical case being one that's very low in carbohydrate and might increase the level of ketones that are floating around in your blood, which are hypothesized to be an appetite suppressant. Same sort of design, these minimally processed diets that one was very high in carbs and causes large swings in insulin and the other that's very low in carbs and causes increases in ketones.

Kevin Hall (14:22):

We ask people, again, while you're in one food environment or the other, don't be trying to gain weight or lose weight, eat as much or as little as you'd like, and we're going to basically measure a lot of things. They again, don't know what the primary outcome of the study is. We're measuring their leftovers afterwards. And so, the surprise in this particular case was that the diet that caused the big swings in glucose and insulin did not lead to more calorie consumption. In fact, it led to about 700 calories per day less than when the same people were exposed to the ketogenic diet. Interestingly, both food environments caused people to lose weight, so it wasn't that we didn't see the effect of people over consuming calories on either diet, so they were reading fewer calories in general than they were when they came in, right. They're probably eating a pretty ultra-processed food diet when they came in. We put them on these two diets that varied very much in terms of the macronutrients that they were eating, but both were pretty minimally processed. They lost weight. They ended up losing more body fat on the very low-fat high carb diet than the ketogenic diet, but actually more weight on the ketogenic diet than the low-fat diet. So there's a little bit of a dissociation between body fat loss and weight loss in this study, which was kind of interesting.

Eric Topol (15:49):

Interesting. Yeah, I thought that was a fascinating trial because plant-based diet, they both have their kind of camps, you know.

Kevin Hall (15:57):

Right. No, exactly.

Immune System Signatures for Vegan vs Ketogenic Diets

Eric Topol (15:58):

There are people who aren't giving up on ketogenic diet. Of course, there's some risks and some benefits and there's a lot of interest of course with the plant-based diet. So it was really interesting and potentially the additive effects of plant-based with avoidance or lowering of ultra-processed food. Now, the more recent trial that you did also was very interesting, and of course I'm only selecting ones that I think are particularly, there are a lot of trials you've done, but this one is more recent in this year where you looked at vegan versus ketogenic diets for the immune signature, immune response, which is really important. It's underplayed as its effect, and so maybe you can take us through that one.

[Link to a recent Nature feature on this topic, citing Dr. Hall’s work]

Kevin Hall (16:43):

Yeah, so just to be clear, it's actually the same study, the one that we just talked about. This is a secondary sort of analysis from a collaboration we had with some folks at NIAID here at the NIH to try to evaluate immune systems signatures in these same folks who wonder what these two changes in their food environment. One is vegan, high carbohydrate low-fat diet and the other, the animal-based ketogenic diet. And again, it was pretty interesting to me that we were able to see really substantial changes in how the immune system was responding. First of all, both diets again seem to have improved immune function, both adaptive and innate immune function as compared to their baseline measurements when they came into the study. So when they're reading their habitual diet, whatever that is typically high in ultra-processed foods, they switched to both of these diets.

Kevin Hall (17:39):

We saw market changes in their immune system even compared to baseline. But when we then went and compared the two diets, they were actually divergent also, in other words, the vegan diet seemed to stimulate the innate immune system and the ketogenic diet seemed to stimulate the adaptive immune system. So these are the innate immune system can be thought of. Again, I'm not an immunologist. My understanding is that this is the first line defense against pathogens. It happens very quickly and then obviously the adaptive immune system then adapts to a specific pathogen over time. And so, this ability of our diet to change the immune system is intriguing and how much of that has to do with influencing the gut microbiota, which obviously the gut plays a huge role in steering our immune system in one direction versus another. I think those are some really intriguing mechanistic questions that are really good fodder for future research.

Eric Topol (18:42):

Yeah, I think it may have implications for treatment of autoimmune diseases. You may want to comment about that.

Kevin Hall (18:51):

Yeah, it's fascinating to think about that the idea that you could change your diet and manipulate your microbiota and manipulate your gut function in a way to influence your immune system to steer you away from a response that may actually be causing your body damage in your typical diet. It's a fascinating area of science and we're really interested to follow that up. I mean, it kind of supports these more anecdotal reports of people with lupus, for example, who've reported that when they try to clean up their diet for a period of time and eliminate certain foods and eliminate perhaps even ultra-processed food products, that they feel so much better that their symptoms alleviate at least for some period of time. Obviously, it doesn't take the place of the therapeutics that they need to take, but yeah, we're really interested in following this up to see what this interaction might be.

Eric Topol (19:46):

Yeah, it's fascinating. It also gets to the fact that certain people have interesting responses. For example, those with epilepsy can respond very well to a ketogenic diet. There's also been diet proposed for cancer. In fact, I think there's some even ongoing trials for cancer of specific diets. Any comments about that?

Kevin Hall (20:10):

Yeah, again, it's a really fascinating area. I mean, I think we kind of underappreciate and view diet in this lens of weight loss, which is not surprising because that's kind of where it's been popularized. But I think the role of nutrition and how you can manipulate your diet and still you can have a very healthy version of a ketogenic diet. You can have a very healthy version of a low-fat, high carb diet and how they can be used in individual cases to kind of manipulate factors that might be of concern. So for example, if you're concerned about blood glucose levels, clearly a ketogenic diet is moderating those glucose levels over time, reducing insulin levels, and that might have some positive downstream consequences and there's some potential downsides. Your apoB levels might go up. So, you have to kind of tune these things to the problems and the situations that individuals may face. And similarly, if you have issues with blood glucose control, maybe a high carbohydrate diet might not be for you, but if that's not an issue and you want to reduce apoB levels, it seems like that is a relatively effective way to do that, although it does tend to increase fasting triglyceride levels.

Kevin Hall (21:27):

So again, there's all of these things to consider, and then when you open the door beyond traditional metabolic health markers to things like inflammation and autoimmune disease as well as some of these other things like moderating how cancer therapeutics might work inside the body. I think it's a really fascinating and interesting area to pursue.

Eric Topol (21:55):

No question about it. And that also brings in the dimension of the gut microbiome, which obviously your diet has a big influence, and it has an influence on your brain, brain-gut axis, and the immune system. It's all very intricate, a lot of feedback loops and interactions that are not so easy to dissect, right?

Kevin Hall (22:16):

Absolutely. Yeah, especially in humans. That's why we rely on our basic science colleagues to kind of figure out these individual steps in these chains. And of course, we do need human experiments and carefully controlled experiments to see how much of that really translates to humans, so we need this close sort of translational partnership.

On the Pathogenesis of Obesity, Calories In and Calories Out

Eric Topol (22:35):

Yeah. Now, you've also written with colleagues, other experts in the field about understanding the mechanisms of pathogenesis of obesity and papers that we'll link to. We're going to link to everything for what we've been discussing about calories in, calories out, and that's been the longstanding adage about this. Can you enlighten us, what is really driving obesity and calories story?

Kevin Hall (23:05):

Well, I co-organized a meeting for the Royal Society, I guess about a year and a half ago, and we got together all these experts from around the world, and the basic message is that we have lots of competing theories about what is driving obesity. There's a few things that we all agree on. One is that there is a genetic component. That adiposity in a given environment is somewhere between 40% to 70% heritable, so our genes play a huge role. It seems like there's certain genes that can play a major role. Like if you have a mutation in leptin, for example, or the leptin receptor, then this can have a monogenic cause of obesity, but that's very, very rare. What seems to be the case is that it's a highly polygenic disease with individual gene variants contributing a very, very small amount to increased adiposity. But our genes have not changed that much as obesity prevalence has increased over the past 50 years. And so, something in the environment has been driving that, and that's where the real debates sort of starts, right?

Kevin Hall (24:14):

I happen to be in the camp that thinks that the food environment is probably one of the major drivers and our food have changed substantially, and we're trying to better understand, for example, how ultra-processed foods which have risen kind of in parallel with the increased prevalence of obesity. What is it about ultra-processed foods that tend to drive us to overconsume calories? Other folks focus maybe more on what signals from the body have been altered by the foods that we're eating. They might say that the adipose tissue because of excess insulin secretion for example, is basically driven into a storage mode and that sends downstream signals that are eventually sensed by the brain to change our appetite and things like that. There's a lot of debate about that, but again, I think that these are complementary hypotheses that are important to sort out for sure and important to design experiments to try to figure out what is more likely. But there is a lot of agreement on the idea that there's something in our environment has changed.

Kevin Hall (25:17):

I think there's even maybe a little bit less agreement of exactly what that is. I think that there's probably a little bit more emphasis on the food environment as opposed to there are other folks who think increased pollution might be driving some of this, especially endocrine disrupting chemicals that have increased in prevalence. I think that's a viable hypothesis. I think we have to try to rank order what we think are the most likely and largest contributors. They could all be contributing to some extent and maybe more so in some people rather than others, but our goal is to try to, maybe that's a little simple minded, but let's take the what I think is the most important thing and let's figure out the mechanisms of that most important thing and we'll, number one, determine if it is the most important thing. In my case, I think something about ultra-processed foods that are driving much of what we're seeing. If we could better understand that, then we could both advise consumers to avoid certain kinds of foods because of certain mechanisms and still be able to consume some degree of ultra-processed foods. They are convenient and tasty and relatively inexpensive and don't require a lot of skill and equipment to prepare. But then if we focus on the true bad guys in that category because we really understand the mechanisms, then I think that would be a major step forward. But that's just my hypothesis.

Eric Topol (26:43):

Well, I’m with you actually. Everything I've read, everything I've reviewed on ultra-processed food is highly incriminating, and I also get frustrated that nothing is getting done about it, at least in this country. But on the other hand, it doesn't have to be either or, right? It could be both these, the glycemic index story also playing a role. Now, when you think about this and you're trying to sort out calories in and calories out, and let's say it's one of your classic experiments where you have isocaloric proteins and fat and carbohydrate exactly nailed in the different diets you're examining. Is it really about calories or is it really about what is comprising the calorie?

Kevin Hall (27:29):

Yeah, so I think this is the amazing thing, even in our ultra-processed food study, if we asked the question across those people, did the people who ate more calories even in the ultra-processed diet, did they gain more weight? The answer is yes.

Kevin Hall (27:44):

There's a very strong linear correlation between calorie intake and weight change. I tend to think that I started my career in this space focusing more on the metabolism side of the equation, how the body's using the calories and how much does energy expenditure change when you vary the proportion of carbs versus fat, for example. The effect size is there, they might be there, but they're really tiny of the order of a hundred calories per day. What really struck me is that when we just kind of changed people's food environments, the magnitude of the effects are like we mentioned, 500 to 700 calories per day differences. So I think that the real trick is to figure out how is it that the brain is regulating our body weight in some way that we are beginning to understand from a molecular perspective? What I think is less well understood is, how is that food intake control system altered by the food environment that we find ourselves in?

The Brain and GLP-1 Drugs

Kevin Hall (28:42):

There are a few studies now in mice that are beginning to look at how pathways in the brain that have been believed to be related to reward and not necessarily homeostatic control of food intake. They talk to the regions of the brain that are related to homeostatic control of food intake, and it's a reciprocal sort of feedback loop there, and we're beginning to understand that. And I think if we get more details about what it is in our foods that are modulating that system, then we'll have a better understanding of what's really driving obesity and is it different in different people? Are there subcategories of obesity where certain aspects of the food environment are more important than others, and that might be completely flipped in another person. I don't know the answer to that question yet, but it seems like there are certain common factors that might be driving overall changes in obesity prevalence and how they impact this reward versus homeostatic control systems in the brain, I think are really fascinating questions.

Eric Topol (29:43):

And I think we're getting much more insight about this circuit of the reward in the brain with the food intake, things like optogenetics, many ways that we're getting at this. And so, it's fascinating. Now, that gets me to the miracle drug class GLP-1, which obviously has a big interaction with obesity, but of course much more than that. And you've written about this as well regarding this topic of sarcopenic obesity whereby you lose a lot of weight, but do you lose muscle mass or as you referred to earlier, you lose body fat and maybe not so much muscle mass. Can you comment about your views about the GLP-1 family of drugs and also about this concern of muscle mass loss?

Kevin Hall (30:34):

Yeah, so I think it's a really fascinating question, and we've been trying to develop mathematical models about how our body composition changes with weight gain and weight loss for decades now. And this has been a long topic, one of the things that many people may not realize is that people with obesity don't just have elevated adiposity, they also have elevated muscle mass and lean tissue mass overall. So when folks with obesity lose weight, and this was initially a pretty big concern with bariatric surgery, which has been the grandfather of ways that people have lost a lot of weight. The question has been is there a real concern about people losing too much weight and thereby becoming what you call sarcopenic? They have too little muscle mass and then they have difficulties moving around. And of course, there are probably some people like that, but I think what people need to realize is that folks with obesity tend to start with much higher amounts of lean tissue mass as well as adiposity, and they start off with about 50% of your fat-free mass, and the non-fat component of your body is skeletal muscle.

Kevin Hall (31:45):

So you're already starting off with quite a lot. And so, the question then is when you lose a lot of weight with the GLP-1 receptor agonist or with bariatric surgery, how much of that weight loss is coming from fat-free mass and skeletal muscle versus fat mass? And so, we've been trying to simulate that using what we've known about bariatric surgery and what we've known about just intentional weight loss or weight gain over the years. And one of the things that we found was that our sort of expectations for what's expected for the loss of fat-free mass with these different drugs as well as bariatric surgery, for the most part, they match our expectations. In other words, the expected amount of fat loss and fat free mass loss. The one outlier interestingly, was the semaglutide study, and in that case, they lost more fat-free mass than would be expected.

Kevin Hall (32:44):

Now, again, that's just raising a little bit of a flag that for whatever reason, from a body composition perspective, it's about a hundred people underwent these repeated DEXA scans in that study sponsored by Novo Nordisk. So it's not a huge number of people, but it's enough to really get a good estimate about the proportion of weight loss. Whether or not that has functional consequences, I think is the open question. There's not a lot of reports of people losing weight with semaglutide saying, you know what? I'm really having trouble actually physically moving around. I feel like I've lost a lot of strength. In fact, it seems to be the opposite, right, that the quality of the muscle there seems to be improved. They seem to have more physical mobility because they've lost so much more weight, that weight had been inhibiting their physical movement in the past.

Kevin Hall (33:38):

So it's something to keep an eye on. It's an open question whether or not we need additional therapies in certain categories of patients, whether that be pharmacological, there are drugs that are interesting that tend to increase muscle mass. There's also other things that we know increase muscle mass, right? Resistance exercise training, increase this muscle mass. And so, if you're really concerned about this, I certainly, I'm not a physician, but I think it's something to consider that if you go on one of these drugs, you might want to think about increasing your resistance exercise training, maybe increasing the protein content of your diet, which then can support that muscle building. But I think it's a really interesting open question about what the consequences of this might be in certain patient populations, especially over longer periods of time.

Dietary Protein, Resistance Exercise, DEXA Scans

Eric Topol (34:30):

Yeah, you've just emphasized some really key points here. Firstly, that resistance exercise is good for you anyway. And get on one of these drugs, why don't you amp it up or get it going? The second is about the protein diet, which it'd be interesting to get your thoughts on that, but we generally have too low of a protein diet, but then there are some who are advocating very high protein diets like one gram per pound, not just one gram per kilogram. And there have been studies to suggest that that very high protein diet could be harmful, but amping up the protein diet, that would be a countering thing. But the other thing you mentioned is a DEXA scan, which can be obtained very inexpensively, and because there's a variability in this muscle mass loss if it's occurring, I wonder if that's a prudent thing or if you just empirically would just do the things that you mentioned. Do you have any thoughts about that?

Kevin Hall (35:32):

Yeah, that's really a clinical question that I don't deal with on a day-to-day basis. And yeah, I think there's probably better people suited to that. DEXA scans, they're relatively inexpensive, but they're not readily accessible to everyone. I certainly wouldn't want to scare people away from using drugs that are now known to be very effective for weight loss and pretty darn safe as far as we can tell, just because they don't have access to a DEXA scanner or something like that.

Eric Topol (36:00):

Sure. No, that makes a lot of sense. I mean, the only reason I thought it might be useful is if you're concerned about this and you want to track, for example, how much is that resistant training doing?

Kevin Hall (36:13):

But I think for people who have the means to do that, sure. I can't see any harm in it for sure.

Continuous Glucose Sensors?

Eric Topol (36:19):

Yeah. That gets me to another metric that you've written about, which is continuous glucose tracking. As you know, this is getting used, I think much more routinely in type one insulin diabetics and people with type 2 that are taking insulin or difficult to manage. And now in recent months there have been consumer approved that is no prescription needed, just go to the drugstore and pick up your continuous glucose sensor. And you've written about that as well. Can you summarize your thoughts on it?

Kevin Hall (36:57):

Yeah, sure. I mean, yeah, first of all, these tools have been amazing for people with diabetes and who obviously are diagnosed as having a relative inability to regulate their glucose levels. And so, these are critical tools for people in that population. I think the question is are they useful for people who don't have diabetes and is having this one metric and where you target all this energy into this one thing that you can now measure, is that really a viable way to kind of modulate your lifestyle and your diet? And how reliable are these CGM measurements anyway? In other words, do they give the same response to the same meal on repeated occasions? Does one monitor give the same response as another monitor? And those are the kinds of experiments that we've done. Again, secondary analysis, these trials that we talked about before, we have people wearing continuous glucose monitors all the time and we know exactly what they ate.

Kevin Hall (37:59):

And so, in a previous publication several years ago, we basically had two different monitors. One basically is on the arm, which is the manufacturer's recommendation, the other is on the abdomen, which is the manufacturer's recommendation. They're wearing them simultaneously. And we decided just to compare what were the responses to the same meals in simultaneous measurements. And they were correlated with each other thankfully, but they weren't as well predictive as you might expect. In other words, one device might give a very high glucose reading to consuming one meal and the other might barely budge, whereas the reverse might happen for a different meal. And so, we asked the question, if we were to rank the glucose spikes by one meal, so we have all these meals, let's rank them according to the glucose spikes of one device. Let's do the simultaneous measurements with the other device.

Kevin Hall (38:53):

Do we get a different set of rankings? And again, they're related to each other, but they're not overlapping. They're somewhat discordant. And so, then the question becomes, okay, well if I was basically using this one metric to kind of make my food decisions by one device, I actually start making different decisions compared to if I happen to have been wearing a different device. So what does this really mean? And I think this sort of foundational research on how much of a difference you would need to make a meaningful assessment about, yeah, this is actionable from a lifestyle perspective, even if that is the one metric that you're interested in. That sort of foundational research I don't think has really been done yet. More recently, we asked the question, okay, let's ignore the two different devices. Let's stick to the one where we put it on our arm, and let's ask the question.

Kevin Hall (39:43):

We've got repeated meals and we've got them in this very highly regimented and controlled environment, so we know exactly what people ate previously. We know the timing of the meals, we know when they did their exercise, we know how much they were moving around, how well they slept the night before. All of these factors we could kind of control. And the question that we asked in that study was, do people respond similarly to the same meal on repeated occasions? Is that better than when you actually give them very different meals? But they match overall for macronutrient content, for example. And the answer to that was surprisingly no. We had as much variability in the glucose response to the same person consuming the same meal on two occasions as a whole bunch of different meals. Which suggests again, that there's enough variability that it makes it difficult to then recommend on for just two repeats of a meal that this is going to be a meal that's going to cause your blood glucose to be moderate or blood glucose to be very high. You're going to have to potentially do this on many, many different occasions to kind of figure out what's the reliable response of these measurements. And again, that foundational research is typically not done. And I think if we're really going to use this metric as something that is going to change our lifestyles and make us choose some meals other than others, then I think we need that foundational research. And all we know now is that two repeats of the same meal is not going to do it.

Eric Topol (41:21):

Well, were you using the current biosensors of 2024 or were you using ones from years ago on that?

Kevin Hall (41:27):

No, we were using ones from several years ago when these studies were completed. But interestingly, the variability in the venous measurements to meal tests is also very, very different. So it's probably not the devices per se that are highly variable. It's that we don't really know on average how to predict these glucose responses unless there's huge differences in the glycemic load. So glycemic load is a very old concept that when you have very big differences in glycemic load, yeah, you can on average predict that one kind of meal is going to give rise to a much larger glucose excursion than another. But typically these kind of comparisons are now being made within a particular person. And we're comparing meals that might have quite similar glycemic loads with the claim that there's something specific about that person that causes them to have a much bigger glucose spike than another person. And that we can assess that with a couple different meals.

Eric Topol (42:31):

But also, we know that the spikes or the glucose regulation, it's very much affected by so many things like stress, like sleep, like exercise. And so, it wouldn't be at all surprising that if you had the exact same food, but all these other factors were modulated that it might not have the same response. But the other thing, just to get your comment on. Multiple groups, particularly starting in Israel, the Weizmann Institute, Eran Segal and his colleagues, and many subsequent have shown that if you give the exact same amount of that food, the exact same time to a person, they eat the exact same amount. Their glucose response is highly heterogeneous and variable between people. Do you think that that's true? That in fact that our metabolism varies considerably and that the glucose in some will spike with certain food and some won’t.

Kevin Hall (43:29):

Well, of course that's been known for a long time that there's varying degrees of glucose tolerance. Just oral glucose tolerance tests that we've been doing for decades and decades we know is actually diagnostic, that we use variability in that response as diagnostic of type 2 diabetes.

Eric Topol (43:49):

I'm talking about within healthy people.

Kevin Hall (43:53):

But again, it's not too surprising that varying people. I mean, first of all, we have a huge increase in pre-diabetes, right? So there's various degrees of glucose tolerance that are being observed. But yeah, that is important physiology. I think the question then is within a given person, what kind of advice do we give to somebody about their lifestyle that is going to modulate those glucose responses? And if that's the only thing that you look at, then it seems like what ends up happening, even in the trials that use continuous glucose monitors, well big surprise, they end up recommending low carbohydrate diets, right? So that's the precision sort of nutrition advice because if that's the main metric that's being used, then of course we've all known for a very long time that lower carbohydrate diets lead to a moderated glucose response compared to higher carbohydrate diets. I think the real question is when you kind of ask the issue of if you normalize for glycemic load of these different diets, and there are some people that respond very differently to the same glycemic load meal compared to another person, is that consistent number one within that person?

Kevin Hall (45:05):

And our data suggests that you're going to have to repeat that same test multiple times to kind of get a consistent response and be able to make a sensible recommendation about that person should eat that meal in the future or not eat that meal in the future. And then second, what are you missing when that becomes your only metric, right? If you're very narrowly focused on that, then you're going to drive everybody to consume a very low carbohydrate diet. And as we know, that might be great for a huge number of people, but there are those that actually have some deleterious effects of that kind of diet. And if you're not measuring those other things or not considering those other things and put so much emphasis on the glucose side of the equation, I worry that there could be people that are being negatively impacted. Not to mention what if that one occasion, they ate their favorite food and they happen to get this huge glucose spike and they never eat it again, their life is worse. It might've been a complete aberration.

Eric Topol (46:05):

I think your practical impact point, it's excellent. And I think one of the, I don't know if you agree, Kevin, but one of the missing links here is we see these glucose spikes in healthy people, not just pre-diabetic, but people with no evidence of glucose dysregulation. And we don't know, they could be up to 180, 200, they could be prolonged. We don't know if the health significance of that, and I guess someday we'll learn about it. Right?

Kevin Hall (46:36):

Well, I mean that's the one nice thing is that now that we have these devices to measure these things, we can start to make these correlations. We can start to do real science to say, what a lot of people now presume is the case that these spikes can't be good for you. They must lead to increased risk of diabetes. It's certainly a plausible hypothesis, but that's what it is. We actually need good data to actually analyze that. And at least that's now on the table.

Eric Topol (47:04):

I think you're absolutely right on that. Well, Kevin, this has been a fun discussion. You've been just a great leader in nutrition science. I hope you'll keep up your momentum because it's pretty profound and I think we touched on a lot of the uncertainties. Is there anything that I didn't ask you that you wish I did?

Kevin Hall (47:23):

I mean, we could go on for hours, I'm sure, Eric, but this has been a fascinating conversation. I really appreciate your interest. Thank you.

Eric Topol (47:30):

Alright, well keep up the great stuff. We'll be following all your work in the years ahead, and thanks for joining us on Ground Truths today.

**************************************

Footnote, Stay Tuned: Julia Belluz and Kevin Hall have a book coming out next September titled “WHY WE EAT?

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Below is a brief video snippet from our conversation. Full videos of all Ground Truths podcasts can be seen on YouTube here. The current one is here. If you like the YouTube format, please subscribe! This one has embedded one of my favorite TikTok’s from Will. There are several links to others in the transcript. The audios are also available on Apple and Spotify.

Transcript with links to both audio and videos, commencement addresses, NEJM article coverage

Eric Topol (00:06):

Hi, it's Eric Topol from Ground Truths, and I've got an amazing couple with me today. It's Will Flanary and Kristin Flanary, the Glaucomfleckens. I've had the chance to get to know them a bit through Knock Knock, Hi! which is their podcast. And of course, everyone knows Dr. Glaucomflecken from his TikTok world and his other about 4 million followers on Instagram and Twitter and all these other social media, and YouTube. So welcome.

Will Flanary (00:43):

Thanks for having us.

Kristin Flanary (00:44):

Thank you. Happy to be here.

By Way of Background

Eric Topol (00:45):

Yeah. Well, this is going to be fun because I'm going to go a quick background so we can go fast forward because we did an interview back in early 2022.

Kristin Flanary (00:56):

Yes.

Eric Topol (00:57):

And what you've been doing since then is rocking it. You're like a meteoric, right. And it was predictable, like rarefied talent and who couldn't love humor, medical humor, but by way of background, just for those who are not up to speed. I guess you got your start, Will, as a class clown when your mother was a teacher in the sixth grade.

Will Flanary (01:22):

Yep, yep. I misbehaved a little bit. It helped that I still made good grades, but I cut up a bit in class.

Eric Topol (01:32):

And then you were already in the comedy club circuits doing standup in Houston as an 18-year-old.

Will Flanary (01:40):

It was all amateur stuff, nothing, just dabble in it and trying to get better. I was always kind of naturally funny just with my friend group and everything. I loved making people laugh, but doing standups is a whole different ball game. And so, I started doing that around Houston as a high school senior and kept that going through college and a little bit into med school.

Kristin Flanary (02:02):

Houston was a good training ground, right? That where Harris Wittels was also coming up.

Will Flanary (02:07):

Yeah. A lot of famous comedians have come through Houston. Even going back to Bill Hicks back in the, was that the 80s, I think? Or 90s?

Eric Topol (02:17):

Well, and then of course, it was I think in 2020 when you launched Dr. Glaucomflecken, I think. Is that right?

Will Flanary (02:28):

That's when it really started to take off. I was on Twitter telling jokes back in 2016.

Kristin Flanary (02:39):

GomerBlog before that, that's actually where it was born.

Will Flanary (02:41):

I was doing satire writing. I basically do what I'm doing now, but in article form, trying to be The Onion of medicine. And then the pandemic hit, started doing video content and that's really with lockdown. That's when, because everybody was on social media, nobody had anything else to do. So it was right place, right time for me and branching out into video content.

On to Medical School Commencement Addresses

Eric Topol (03:11):

Alright, so that's the background of some incredible foundation for humor. But since we last got together, I'll link the Medicine and the Machine interview we did back then. What has been happening with you two is nothing short of incredible. I saw your graduation speeches, Will. Yale in 2022, I watched the UCSF in 2023 and then the University of Michigan in 2024. Maybe there's other ones I don't even know.

Kristin Flanary (03:45):

There’s a few others.

Will Flanary (03:45):

There's a few. But I feel like you've done, I'm sure your fair share of commencement addresses as well. It's kind of hard to come up with different ways to be inspirational to the next generation. So fortunately, we have together, we have some life experiences and learned a thing or two by doing all of this social media stuff and just the things we've been through that I guess I have enough things to say to entertain an interest.

Eric Topol (04:18):

Well, you're being humble as usual, but having watched those commencement addresses, they were the best medical commencement addresses I've ever seen. And even though you might have told us some of the same jokes, they were so great that it was all right. Yeah, and you know what is great about it is you've got these, not the students, they all love you of course, because they're probably addicted to when's your next video going to get posted.

(04:44):

But even the old professors, all the family members, it's great. But one of the things I wanted to get at. Well, I'll start with the graduation speeches, because you were such an inspiration, not just with humor, but your message. And this gets back to you as a couple and the tragedies you've been through. So you really, I think, got into this co-survivor story and maybe Kristin, since you are the co-survivor of two bouts of Will’s testicular cancer, and then the sudden cardiac death. I mean, people don't talk about this much, so maybe you could help enlighten us.

Tragedies and Being a Co-Survivor

Kristin Flanary (05:26):

Yeah, it’s funny because the experience of being a co-survivor is nothing new. It’s as long as we've had human beings, we've had co-survivors. But the concept around it and giving it a name and a label, a framework to be able to think about it, that is what I think is new and what people haven't talked about before. So co-survivor is just this idea that when a medical trauma happens to a patient, the patient has their experience and if they survive it, they are a survivor and they have a survivor experience. And also, most people are closely attached to at least one other person, if not many. And those people are co-surviving the medical event along with the survivor. That event is happening in their lives as was happening to them too. If someone comes in with a patient to the hospital, that person, you can just assume by default that their lives are pretty intimately or profoundly intertwined or else why would that person be there? And so, thinking of it as there's the patient and then there's also a co-patient, that family members in the past have only been thought of as caregivers if they've been thought of at all. And that is certainly one aspect of the role, but it's important to remember that whatever it is that's happening to the patient is also affecting the family members' lives in a really deep and profound way.

Eric Topol (07:04):

That's really helpful. Now, the fact that you recognize that in your graduation speech, Will, I think is somewhat unique. And of course, some of the other things that you touched on like playing to your creativity and the human factors, I mean, these are so important messages.

Will Flanary (07:23):

Well, in the discussion about co-survivorship and because I talk about that whenever I do my keynotes and when I do the commencement addresses, but all credit goes to Kristin for really being the driving force of this idea for me and for many others because as a physician, we take care of patients. Our focus is always on the patient. And it really wasn't until this happened to me and my family and Kristin in particular that I started to understand exactly what she's talking about and this idea. And so, Kristin gets a lot of credit for just really bringing that term and that idea to the forefront.

Eric Topol (08:09):

Yeah, well, you saved his life. It's just not many have that bond. And then the other thing I just want to mention now, you've been recognized by the American Heart Association and a whole bunch of other organizations awarded because of your advocacy for CPR. And you even mentioned that I think in one of your commencement addresses.

Will Flanary (08:31):

Yeah, I tried to get the crowd to do CPR. Like team up, partner up, and it kind of fell flat. It wasn't quite the right time, I think, to try to do a mass class on CPR. So maybe next time.

Eric Topol (08:47):

Right. Well, so you had this foundation with the Glaucomflecken General Hospital and taking on 37 specialties and all these incredible people that became part of the family, if you will, of spoof on medicine and your alter ego and these videos that you would do. And sometimes you have three or four different alter egos in there playing out, but now you've branched into new things. So one which is an outgrowth of what we were just talking about. You've been on this country tour, Wife & Death.

“Wife and Death,” A Nationwide Tour

Kristin Flanary (09:28):

Yes.

Eric Topol (09:29):

Wife and death. I mean, yeah, I guess we can make the connect of how you named it that, but what is it you've been selling out in cities all over the country, and by the way, I'm really upset you haven't come to San Diego, but tell us about wife and death.

Will Flanary (09:44):

Yeah. Well, we have this amazing story and all these medical challenges we've been through, and then developing the Glaucomflecken brand and universe, and we've done keynotes together for years, and then we thought, let's have more fun with it. Let's do keynotes. They're great. We can get our message out, but sometimes they're just a bit stuffy. It's an academic environment.

Kristin Flanary (10:15):

They're usually at seven in the morning also, so that's the downside.

Will Flanary (10:21):

So we thought, let's just put together our own live show. Let's put together something that we could just creatively, we can do whatever we want with it. I could dress up as characters, Kristin, who has these beautiful writing and monologues that she's put together around her experience and just to create something that people can come into a theater and just experience this wide range of emotions from just laughter to tears of all kinds, and just have them feel the story and enjoy this story. Fortunately, it has a happy ending because I'm still alive and it's been so much fun. The audiences have been incredible. Mostly healthcare, but even some non-healthcare people show up, and we've been blown away by the response. Honestly, we should have done bigger theaters. That's our lesson for the first go round.

Eric Topol (11:21):

I saw you had to do a second show in Pittsburgh.

Will Flanary (11:24):

We did.

Kristin Flanary (11:26):

That one sold out too. Something about Pittsburgh, that was a good crowd, and there was a lot of them.

Will Flanary (11:33):

It was almost like in Pittsburgh, they rarely ever get any internet comedian ophthalmologists that come through. I don't know.

Eric Topol (11:41):

Well, I see you got some still to come in Denver and Chicago. This is amazing. And I wondered who was coming and I mean, it's not at all surprising that there'd be this phenomenal popularity. So that's one thing you've done that's new, which is amazing. And of course, it's a multidimensional story. The one that shocked me, I have to tell you, shocked me, was the New England Journal partnership. The New England Journal is the most stodgy, arrogant, I mean so difficult. And not only that.

Kristin Flanary (12:17):

You said that. Not us.

Partnering with the New England Journal of Medicine!

Eric Topol (12:19):

Yeah, yeah. They'll get this too. They know we don't get along that well, but that's okay. You even made fun of journals. And now you're partnering with the New England Journal, God's greatest medical journal, or whatever. Tell us about that.

Will Flanary (12:39):

Well, so one thing that I really enjoy doing, and I've done it with my US healthcare system content is almost like tricking people into learning things. And so, if you make something funny, then people will actually sit there and listen to what you have to say about deductibles and physician-owned hospitals and all these inner workings. DIR fees and pharmacy, all these things that are really dry topics. But if you can make them funny, all of a sudden people will actually learn and listen to it. And the New England Journal of Medicine, they approached me with an idea. Basically just to take one or two of their trials per month. And I just make a skit out of that trial with the idea being to help disseminate some of the research findings that are out there, because I guess it's getting harder and harder for people to actually read, to sit down and read a journal article.

(13:43):

And so, I have to credit them for having this idea and thinking outside the box of a different way to get medical information and knowledge out to the masses. And you're absolutely right, that I have been critical of journals, and particularly I've been critical of the predatory nature of some of the larger journals out there, like Elsevier. I've specifically named Elsevier, Springer, these journals that have a 40% profit margin. And I certainly thought about that whenever I was looking into this partnership. And the reason I was okay with doing it with the New England Journal is because they're a nonprofit, first of all, so they're run by the Massachusetts Medical Society. That's the publisher for that journal. And so, I feel okay partnering with them because I feel like they're doing it in a much better way than some of the bigger journal corporations out there.

Kristin Flanary (14:54):

Well, and also part of the deal that we negotiated was that those articles that you make skits about those will be available open access.

Will Flanary (15:03):

Oh yeah. That was a prerequisite. Yes. It was like, if I'm going to do this, the articles that I'm talking about need to be free and readily available. That's part of it.

Eric Topol (15:14):

I think you've done about five already, something like that. And I watched them, and I just was blown away. I mean, the one that got me where I was just rolling on the floor, this one, the Belantamab Mafodotin for Multiple Myeloma. And when you were going on about the Bortezomib, Dexamethasone. We'll link to this. I said, oh my God.

Will Flanary (15:40):

Yeah. The joke there is, you don't have any idea how long it took me to say those things that quickly. And so, I was writing this skit and I'm like, wouldn't it be funny if somehow that triggered a code stroke in the hospital because this person is saying all these random words that don't have any meaning to anybody. Man, I tell you, I am learning. Why would I ever need to know any of this information as an ophthalmologist? So it's great. I know all this random stuff about multiple myeloma that I probably would never have learned otherwise.

Kristin Flanary (16:21):

It's the only way, you won't read a journal either.

Eric Topol (16:23):

Well, and if you read the comments on the post. These doctors saying, this is the only way they want to get journal information from now on.

Will Flanary (16:33):

Which is double-edged sword, maybe a little bit. Obviously, in a 90 second skit, there's no way I'm going to cover the ins and outs of a major trial. So it's really, in a lot of ways, it's basically like, I call it a comedy abstract. I’m not going much further than an abstract, but hopefully people that are actually interested in the topic can have their interest piqued and want to read more about it. That's kind of the idea.

Eric Topol (17:06):

Yeah. Well, they're phenomenal. We'll link to them. People will enjoy them. I know, because I sure did. And tenecteplase for stroke and all that you've done. Oh, they're just phenomenal.

Will Flanary (17:20):

Every two weeks we come out with a new one.

Eric Topol (17:24):

And that is basically between the fact that you are now on the commencement circuit of the top medical schools and doing New England Journal videos on their articles. You've crossed a line from just making fun of insurance companies and doctors of specialties.

Kristin Flanary (17:44):

Oh, he has crossed many lines, Dr. Topol.

Eric Topol (17:46):

Yeah. Oh yeah. Now you've done it, really. Back two years ago when we convened, actually it's almost three, but you said, when's it going to be your Netflix special?

Will Flanary (18:02):

Oh, gosh.

Eric Topol (18:02):

Is that in the works now?

Will Flanary (18:04):

Well, I'll tell you what's in the works now.

Kristin Flanary (18:06):

Do you know anyone at Netflix?

Will Flanary (18:09):

A New Animated Series

No. We're working on an animated series.

Eric Topol (18:12):

Oh, wow. Wow.

Will Flanary (18:13):

Yeah. All these characters. It's basically just this fictional hospital and all these characters are very cartoonish, the emergency physician that wears the bike helmet and everything. So it's like, well, what do we have together? What do we, Kristin and I have time for? And it wasn't like moving to LA and trying to make a live action with actors and do all, which is something we probably could have tried to do. So instead, we were like, let's just do an animated series.

Kristin Flanary (18:48):

Let's have someone else do the work and draw us.

Will Flanary (18:51):

So we've worked with a writer for the first time, which was a fun process, and putting together a few scripts and then also an animator. We learned a lot about that process. Kristin and I are doing the voiceovers. And yeah, it's in process.

Kristin Flanary (19:10):

We're the only actors we could afford.

Will Flanary (19:12):

Right, exactly.

Eric Topol (19:13):

I can't wait to see it. Now when will it get out there?

Will Flanary (19:17):

Well, we're hoping to be able to put it out on our YouTube channel sometime early next year. So January, February, somewhere around there. And then we can't fund the whole thing ourselves. So the idea is that we do this, we do this pilot episode, and then we'll see what kind of interest we can generate.

Eric Topol (19:37):

Well, there will be interest. I am absolutely assured of that. Wow.

Will Flanary (19:42):

Let us know if you know anybody at the Cartoon Network.

Kristin and Will Flanary (19:45):

Yeah, we're open to possibilities. Whatever, Discovery channel. I don't know.

Eric Topol (19:51):

You've gotten to a point now where you're ready for bigger things even because you're the funniest physician couple in medicine today.

Kristin Flanary (20:05):

Well, that's a very low bar, but thank you.

Will Flanary (20:08):

There are some funny ones out there, but yeah, I appreciate that.

Eric Topol (20:11):

Well, I'm a really big comedy fan. Every night I watch the night before, since I'm old now, but of Colbert and Jimmy Kimmel, just to hear the monologues. Trevor Noah, too. And I can appreciate humor. I'll go to see Sebastian Maniscalco or Jim Gaffigan. That's one of the things I was going to ask you about, because when you do these videos, you don't have an audience.

Will Flanary (20:39):

Oh yeah.

Eric Topol (20:40):

You're making it as opposed to when you are doing your live shows, commencement addresses and things like that. What's the difference when you're trying to be humorous, and you have no audience there?

Will Flanary (20:55):

Well, whenever I'm filming a skit, it's just all production. In fact, I feel like it's funny. I think it's funny, but it's really not until I see the response to it, or I show Kristin, or what I have is where I really know if it's going to work. It's great to put the content out there and see the responses, but there's nothing like live interaction. And that's why I keep coming back to performing. And Kristin's been a performer too in her life. And I think we both really enjoy just the personal interaction, the close interaction, the response from people to our story.

Kristin Flanary (21:36):

We do most of our work alone in this room. I do a lot of writing. He does a lot of playing.

Will Flanary (21:44):

Dress up.

Kristin Flanary (21:44):

All the people in his head, and we do that very isolated. And so, it's very lovely to be able to actually put names to faces or just see human bodies instead of just comments on YouTube.

Will Flanary (21:59):

Meet people.

Kristin Flanary (21:59):

It's really nice.

Will Flanary (22:01):

We’ve been doing meet and greets at the live shows and seeing people come up wearing their costumes.

Eric Topol (22:07):

Oh, wow.

Will Flanary (22:11):

Some of them talk about how they tell us their own stories about their own healthcare and talk about how the videos help them get through certain parts of the pandemic or a difficult time in their life. And so, it reinforces that this means something to a lot of people.

Kristin Flanary (22:29):

It's been really fun for me, and probably you too, but to get to see the joy that he has brought so many people. That's really fun to see in person especially.

Eric Topol (22:42):

No question. Now, when you're producing it together, do you ever just start breaking into laughter because it's you know how funny this is? Or is it just you're on kind of a mission to get it done?

Will Flanary (22:54):

Well, the skits I do by myself. And sometimes when I'm writing out the skit, when I'm writing the skit itself, I will laugh at myself sometimes. Not often, but sometimes they're like, oh, I know that's really funny. I just wrote a skit that I'm actually going to be debuting. I'm speaking at the American Academy of PM&R, so the big PM&R conference. I'm writing a skit, it's How to Ace your PM&R residency interview.

Will Flanary (23:28):

I was writing up that skit today and kind of chuckling to myself. So sometimes that happens, but whenever we do our podcast together, we definitely have outtakes.

Kristin Flanary (23:38):

Oh yeah, we've got some.

Will Flanary (23:40):

We crack each other up.

Kristin Flanary (23:41):

We do.

Will Flanary (23:42):

Sometimes we're getting a little punchy toward the end of the day.

Eric Topol (23:47):

And how is the Knock Knock, Hi! podcast going?

Will Flanary (23:51):

It's awesome. Yeah.

Kristin Flanary (23:52):

Yeah. It's a really fun project.

Will Flanary (23:54):

We still enjoy. You can work with your spouse and in close proximity and still be happily married. So it's doable everyone.

Kristin Flanary (24:06):

That's right. And we're in that phase of life that's really busy. We've got kids, we've got a gazillion jobs. House, my parents are around, and so it's like the only time all week that we actually get to sit down and talk to each other. So it's actually kind of like a part of our marriage at this point.

Will Flanary (24:28):

We’re happy to involve the public in our conversations, but we couldn't do it because we have all these things going on, all our hands and all these little places. We can't do it without a team.

Kristin Flanary (24:41):

Yeah, absolutely.

Will Flanary (24:41):

And that's the thing that I've learned, because I've always been a very loner type content creator. I just wanted to do it all myself. It's in my head and I have trouble telling others, describing what's in my head. And Kristin and our producers have helped me to be able to give a little bit of control to others who are really good at what they do. And that's really the only way that we've been able to venture out into all these different things we've talked about.

Eric Topol (25:12):

Well, I think it comes down to, besides your ability to get to people in terms of their laughter receptors, you have this incisive observer capability. And that's one of the things I don't, I can't fathom because when you can understand the nuances of each specialty or of each part of healthcare, and you haven't necessarily interacted with these specialists or at least in recent years, but you nail it every time. I don't know how you do it, really that observational, is that a central quality of a comedian, you think?

Will Flanary (25:52):

There's definitely a big part of that. You got to get the content from somewhere. But for the specialties, it's really first about just getting the personalities down. And that doesn't change over time.

Kristin Flanary (26:08):

Or around the world.

Will Flanary (26:09):

Or around the world. We hear from people from all over the world about, oh, it's the same in Guatemala as it is in the US.

Kristin Flanary (26:18):

Surgeons are the same.

Will Flanary (26:19):

Yeah.

Kristin Flanary (26:20):

Emergency is the same.

Will Flanary (26:21):

Which has been really cool to see. But so, I draw on my experience interacting with all these specialties back in my med school and intern days. You're right, as an ophthalmologist, we don't get out very much.

Eric Topol (26:33):

No.

Will Flanary (26:35):

So very few people have ever seen an ophthalmologist. We do exist. But then beyond that, I do have to include some actual medical things. And so, I actually, I do a lot of research. I find myself learning more about other fields sometimes than I do in my own field. So especially the further out I get from med school, I know less and less.

Eric Topol (27:00):

Yeah, that's what I was thinking. But you're always spot on. It's interesting to get that global perspective from both of you. Now you're still doing surgery and practicing ophthalmology. Have you reduced it because this has just been taking off so much more over the recent years or keeping it the same?

Will and Kristin Flanary (27:21):

Nope, I’m still. Do you know how many years I had to come along on all of this medical training? He is not allowed to give this up.

Will Flanary (27:29):

I know there's something called a sunk cost fallacy, but this is no fallacy. There's enough of a sunk cost. I got to stick with it. No, I still enjoy it. That's the thing. It actually, it informs my comedy, it grounds me. All of the social media stuff is built upon this medical foundation that I have. And if I stopped practicing, I guess I could maybe cut back. But I'm not planning on doing that. If I stop practicing medicine, I feel like it would make my content less.

Kristin Flanary (28:07):

Authentic.

Will Flanary (28:08):

Less authentic, yeah. That's a good way to put it.

Eric Topol (28:09):

Yeah, no, that makes a lot of sense. That's great you can get that balance with all the things you're doing.

Will Flanary (28:17):

And if I stop practicing medicine, they're not going to invite me to any more commencement addresses, Dr. Topol. So I got to draw the line somewhere.

Eric Topol (28:28):

One of the statements you made at some point earlier was, it was easier to go to become a doctor than to try to be a comedian. And yeah, I mean you proven that.

Will Flanary (28:38):

A lot of ways. That's true.

Eric Topol (28:40):

Wow. I am pretty awestruck about the rarefied talent that you bring and what you both have done for medicine today. And the thing is, you're so young, you have so much time ahead to have an impact.

Will Flanary (28:57):

You hear that Kristin, we're young. Look at that.

Kristin Flanary (29:00):

That's getting less and less true.

Will Flanary (29:01):

Kristin, she just turned 40. It's right around the corner for me. So I don't know.

Will Flanary (29:11):

We got some years left.

Eric Topol (29:12):

You're like young puppies. Are you kidding? You're just getting started. But no, I think that what you brought to medicine in terms of comedy, there's no other entity, no person or people like you have done. And just the last thing I want to ask you about is, you have a platform for advocacy. You've been doing that. We talked about co-survivor. We talked about nurturing the human qualities in physicians like creativity and also taking on the insurance companies, which are just monstrous. I'll link a couple of those, but the brain MRI one or the Texaco.

Will Flanary (29:54):

Texaco Mike.

Eric Topol (29:55):

Yeah, that one is amazing. But there is so many. I mean, you've just taken them apart and they deserve every bit of it. Do you have any other targets for advocacy or does that just kind of come up as things go?

Will Flanary (30:08):

It kind of comes up as things go. There's things I keep harping on. The prior authorization reform, which I've helped in a couple of different states. There's a lot of good people around the country doing really good work on prior authorization and reforming that whole process. And I've been able to just play a small part in that in a couple of different ways. And it's been really fun to do that. And so, I do plan on continuing that crusade as it were. There's certain things I'd like to see. I've been learning more about what pharmacists are dealing with as well as a physician. Unfortunately, we are very separate in a lot of ways and just how we come up in medicine. And so, I have had my eyes opened a lot to what community pharmacists are dealing with. For all the terrible things that we have to deal with as physicians in the healthcare system. Pharmacists have just as much, if not more of the things that they're doing that are threatening their livelihoods. And so, I had love to see some more reform on the PBM side of things, pharmacy benefit managers, Caremark, Optum, all of them. They're causing lots of problems.

Eric Topol (31:24):

I couldn't agree with you more. In fact, I'm going to have Mark Cuban on in a few weeks and we're going to get into that. But the pharmacists get abused by these chains.

Will Flanary (31:33):

Oh, it's bad. It's really bad.

Eric Topol (31:35):

Horrible, horrible. I feel, and every time I am in a drugstore working with one of them, I just think what a tough life they have to deal with.

Will Flanary (31:45):

I guess from an advocacy standpoint, the good news is that there's never a shortage of terrible injustices that are being foisted upon the public and physicians and healthcare workers.

Kristin Flanary (31:59):

Yes. The US healthcare system is ripe for advocacy.

Will Flanary (32:01):

Yes. And that's a lesson that I tell people too, and especially the med students coming up, is like, there's work to be done and get in touch with your state societies and there's always work to be done.

Eric Topol (32:18):

Now you've stayed clear of politics. Totally clear, right?

Will Flanary (32:24):

For the most part, yeah. Yeah. It depends on what you consider politics. It depends on what you consider politics.

Eric Topol (32:32):

It being election day, you haven't made any endorsements.

Will Flanary (32:36):

I haven't. And I don't know. I can only handle so much. I've got my things that I really care about. Of course I'm voting, but I want to talk on the things that I feel like I have the expertise to talk about. And I think there's nothing wrong with that. Everybody can't have an opinion on everything, and it means something. So I am happy to discuss the things that I have expertise about, and I'm always on the side of the patient and wanting to make life better for our patients. And that's the side I'm on.

Kristin Flanary (33:25):

I think also he never comes out and explicitly touches on certain topics, but it's not hard to tell where he falls.

Will Flanary (33:34):

If you really want read into it all.

Kristin Flanary (33:38):

It's not like it's a big secret.

Eric Topol (33:40):

I thought that too. I'm glad you mentioned it, Kristin. But it doesn't come out wide open. But yeah, it's inferred for sure.

Eric Topol (33:49):

I think the point being there is that because you have a reach, I think there's no reach that it has 4 million plus people by your posts and no less the tours and keynotes and everything else. So you could go anywhere but sticking to where you're well grounded, it makes a lot of sense. And anyway, I am going to be staying tuned. This is our two-year checkup. I'm hoping you're going to come to San Diego on your next tour.

Kristin Flanary (34:21):

We're working on 2025 plans.

Will Flanary (34:23):

Oh, we got more shows coming up. And we'll hit up other parts of the country too.

Eric Topol (34:28):

I feel like I got to meet you in person, give you a hug or something. I just feel like I'm missing out there. But it's just a joy to have had a chance to work with you on your podcast. And thanks for coming back on one of mine. There's lots of podcasts out there, but having you and joining you is such fun. So thank you.

Will Flanary (34:54):

This has been great. Thank you for having us.

Kristin Flanary (34:55):

Yeah, thank you.

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Above is a brief video snippet from our conversation. Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify.

Transcript with links to audio and external citations

Eric Topol (00:06):

Well, hello it's Eric Topol with Ground Truths, and I'm really delighted to welcome Dr. Rachael Bedard, who is a physician geriatrician in New York City, and is actually much more multidimensional, if you will. She's a writer. We're going to go over some of her recent writings. She's actually quite prolific. She writes in the New Yorker, New York Magazine, New York Times, New York Review of Books. If it has New York in front of it, she's probably writing there. She's a teacher. She works on human rights, civil rights, criminal justice in the prison system. She's just done so much that makes her truly unique. That's why I really wanted a chance to meet her and talk with her today. So welcome, Rachael.

Rachael Bedard (00:52):

Thank you, Dr. Topol. It's an honor to be here.

Eric Topol (00:55):

Well, please call me Eric and it's such a joy to have a chance to get acquainted with you as a person who is into so many different things and doing all of them so well. So maybe we'd start off with, because you're the first geriatrician we've had on this podcast.

Practicing Geriatrics and Internal Medicine

Eric Topol (01:16):

And it’s especially apropos now. I wanted maybe to talk about your practice, how you got into geriatrics, and then we'll talk about the piece you had earlier this summer on aging.

Rachael Bedard (01:32):

Sure. I went into medicine to do social justice work and I was always on a funny interdisciplinary track. I got into the Mount Sinai School of Medicine through what was then called the Humanities and Medicine program, which was an early acceptance program for people who were humanities focused undergrads, but wanted to go into medicine. So I always was doing a mix of politics and activist focused work, humanities and writing, that was always interested in being a doctor. And then I did my residency at the Cambridge Health Alliance, which is a social medicine program in Cambridge, Massachusetts, and my chief residency there.

(02:23):

I loved being an internist, but I especially loved taking care of complex illness and I especially loved taking care of complex illness in situations where the decision making, there was no sort of algorithmic decision-making, where you were doing incredibly sort of complex patient-centered shared decision making around how to come up with treatment plans, what the goals of care were. I liked taking care of patients where the whole family system was sort of part of the care team and part of the patient constellation. I loved running family meetings. I was incredibly lucky when I was senior resident and chief resident. I was very close with Andy Billings, who was one of the founders of palliative care and in the field, but also very much started a program at MGH and he had come to work at Cambridge Hospital in his sort of semi-retirement and we got close and he was a very influential figure for me. So all of those things conspired to make me want to go back to New York to go to the Sinai has an integrated geriatrics and palliative care fellowship where you do both fellowships simultaneously. So I came to do that and just really loved that work and loved that medicine so much. There was a second part to your question.

Eric Topol (03:52):

Is that where you practice geriatrics now?

Rachael Bedard (03:55):

No, now I have ever since finishing fellowship had very unusual practice settings for a geriatrician. So right out of fellowship, I went to work on Rikers Island and then New York City jail system, and I was the first jail based geriatrician in the country, which is a sort of uncomfortable distinction because people don't really like to think about there being a substantial geriatric population in jails. But there is, and I was incredibly lucky when I was finishing fellowship, there was a lot of energy around jail healthcare in New York City and I wrote the guy who was then the CMO and said, do you think you have an aging problem? And he said, I'm not sure, but if you want to come find out, we'll make you a job to come find out. And so, that was an incredible opportunity for someone right out of fellowship.

(04:55):

It meant stepping off the sort of academic track. But I went and worked in jail for six years and took care of older folks and people with serious illness in jail and then left Rikers in 2022. And now I work in a safety net clinic in Brooklyn that takes care of homeless people or people who have serious sort of housing instability. And that is attached to Woodhull Hospital, which is one of the public hospitals in New York City. And there I do a mix of regular internal medicine primary care, but I preferentially see the older folks who come through, which is a really interesting, painful, complicated patient population because I see a fair amount of cognitive impairment in folks who are living in the shelter system. And that's a really hard problem to address.

Frailty, The Aged, and Longevity

Eric Topol (05:54):

Well, there's a theme across your medical efforts. It seems to me that you look after the neglected folks, the prisoners, the old folks, the homeless people. I mean that's kind of you. It's pretty impressive. And there's not enough of people like you in the medical field. Now, no less do you do that, but of course you are a very impressive author, writer, and of many topics I want to get into with you, these are some recent essays you've written. The one that piqued my interest to start to understand who you were and kind of discover this body of work was the one that you wrote related to aging and President Biden. And that was in New York Times. And I do want to put in a quote because as you know very well, there's so much interest in longevity now.

Eric Topol (06:51):

Interrupting the aging process, and this one really stuck with me from that op-ed, “Time marches forward, bodies decline, and the growing expectation that we might all live in perfect health until our 100th birthdays reflects a culture that overprizes longevity to the point of delusion.” So maybe if you could tell us, that was a rich piece, you got into frailty, you related it to the issues that were surrounding President Biden who at that time had not withdrawn from the race. But what were you thinking and what are your thoughts about the ability to change the aging process?

Rachael Bedard (07:36):

I am very interested in, I mean, I'm incredibly interested in the science of it. And so, I guess I think that there are a few things.

(07:49):

One thing is that the framework that, the part that gives me pause the most is this framework that anything less than perfect health is not a life worth living. So if you're going to have a long life, life should not just be long and sort of healthy in relative terms to your age cohort, but healthy that when you're 80 you should feel like you have the health of a 45-year-old is my understanding of the culture of longevity science. And while I understand why that's aspirational and everybody worry about my body's decline, I think it's a really problematic thing to say that sick bodies are bodies that have disability or people who have cognitive difference are somehow leading lesser lives or lives that are not meaningful or not worth living. I think it's a very, very slippery slope. It puts you in a place where it sort of comes up against another trend or another emerging cultural trend, which is really thinking a lot about physician-assisted suicide and end of life choices.

(09:04):

And that in some ways that conversation can also be very focused on this idea that there's just no way that it's worth living if you're sick. And that's just not true, I think, and that's not been true for many, many, many of my patients, some of whom have lived with enormous disability and incredible burden of illness, people who are chronically seriously ill and are still leading lives that for them and for the people who love them are filled with meaning. So that's my concern about the longevity stuff. I'm interested in the science around the longevity stuff for sure. I'm interested in, I think we're living in this really interesting moment where there's so much happening across so many of the chronic disease fields where the things that I think have been leading to body decay over the last several decades for the majority of the population, we're sort of seeing a lot of breakthroughs in multiple fronts all at once. And that's really exciting. I mean, that's really exciting. And so, certainly if it's possible to make it to 100 in wonderful health, that's what I'd wish for all of us. But to hold it up as the standard that we have to achieve, I think is both unrealistic and a little myopic.

Eric Topol (10:28):

Yeah. Well, I certainly agreed with that and I think that that particular essay resonated so well and you really got into frailty and the idea about how it can be potentially prevented or markedly delayed. And I think before we move on to one of those breakthroughs that you were alluding to, any comments about the inevitability of frailty in people who are older, who at some point start to get the dwindles, if you will, what do you have to say about that?

Rachael Bedard (11:11):

Well, from a clinical standpoint, I guess the caveat versus that not everybody becomes frail and dwindles exactly. Some people are in really strong health up until sort of their final years of life or year of life and then something happens, they dwindle quickly and that's how they die. Or some people die of acute events, but the vast majority of us are going to become more frail in our final decades than we are in our middle decades. And that is the normal sort of pattern of wear and tear on the body. And it is an extraordinary framework, I think frailty because the idea of this sort of syndrome of things where it's really not a disease framework, it is a syndrome framework and it's a framework that says many, many small injuries or stressors add up to create a lot of stress and change in a body and trauma for our body. And once you are sort of past a tipping point of an amount of stress, it's very hard to undo those things because you are not sort of addressing one pathologic process. You're addressing, you're trying to mitigate many processes all at once.

(12:31):

When I wrote that piece, it was inspired by the conversation surrounding President Biden's health. And I was particularly struck by, there was a huge amount of clinical speculation about what was going on with him, right? I'm sure you remember there were people, there was all of this talk about whether he had Parkinson's and what his cognitive status was. And it felt to me like there was an opportunity to do some public education around the idea that you need not have one single sort of smoking gun illness to explain decline. What happens to most of us is that we're going to decline in many small ways sort of simultaneously, and it's going to impact function when it tips over a little bit. And that pattern of decline is not going to be steady day over day worsening. It's going to be up and down. And if you slept better the night before, you might have a better day the next day. And if you slept badly, you might have a worse day. And without knowing anything specific about his clinical situation, it felt like a framework that could explain so much of what we were seeing in public. And it was important also, I think to say that nothing was necessarily being hidden from anybody and that this is the kind of thing that, this has accumulated stress over time that then presents suddenly all at once after having been submerged.

Eric Topol (14:01):

Yeah, you reviewed that so well about the wear and tear and everything related to that. And before I move on to the second topic, I want to just circle back to something you alluded to, which is when Peter Attia wrote about this medicine 3.0 and how you would be compressed and you'd have no comorbidities, you'd have no other illnesses and just fall off the cliff. As a geriatrician, do you think that that is even conceivable?

Rachael Bedard (14:35):

No. Do you think it is?

Eric Topol (14:37):

No, but I just wanted to check the reality. I did challenge on an earlier podcast and he came up with his pat answer. But no, there's no evidence of that, that maybe you can delay if there ever was a way to do that. But I think there's this kind of natural phenomena that you just described, and I'll refer people also to that excellent piece that you get into it more.

Rachael Bedard (15:06):

Peter Attia, I mean, he is certainly the sort of standard bearer in my mind of that movement and that science or that framework of thinking about science. And there's stuff in there that's really valuable. The idea of thinking about lifestyle in your middle decades is having meaningful impact on how you will age, what your final years will look like. That seems intuitively true, I think. And so, thinking about his emphasis on exercise, I mean, his emphasis on exercise is particularly intense and not super achievable for the average person, but the idea that you should sort of be thinking about keeping your body strong because it will decline eventually. And so, you want to do that from a higher peak. That makes a lot of sense to me. The idea that where we sort of draw pathologic disease cutoffs is obviously a little bit arbitrary. And so, wanting to think about optimizing pre-disease states and doing prevention, that's obviously, I think pretty appealing and interesting. It's just really in an evidence free zone.

Ozempic for the Indigent

Eric Topol (16:18):

Yeah, that's what I confronted him with, of course, he had a different perspective, but you summed that up really well. Now let's switch to a piece you had in New York magazine. It was entitled, What If Ozempic Is Just a Good Thing? And the reason, of course, this ties into the first thing we're discussing. There's even talk now, the whole GLP-1 family of drugs with the dual triple receptors, pills to come that we're going to be able to interrupt a path towards Alzheimer's and Parkinson's. Obviously you've already seen impact in heart disease, liver disease, kidney disease way before that, diabetes and obesity. So what are your thoughts? Because you wrote a very interesting, you provided a very interesting perspective when you wrote that one.

Rachael Bedard (17:11):

So that piece I wrote because I have this unbelievably privileged, interesting clinical practice. In New York City, there is public health insurance basically available to anybody here, including folks who are undocumented. And the public hospital system has pharmacies that are outpatient pharmacies that have, and New York Medicaid is very generous and they arranged through some kind of brilliant negotiating. I don't quite know how to make Ozempic to make semaglutide available to people who met criteria which meant diabetes plus obesity, but that we could prescribe it even for our very, very poor patients and that they would be able to get it reliably, that we would have it in stock. And I don't know how many other practices in the country are able to reliably provide GLP-1s to marginalized folks like that. I think it feels like a really rare opportunity and a very distinct perspective.

(18:23):

And it has just been the most amazing thing, I think to have this class of drugs come along that, as you say, addresses so many problems all at once with at least in my prescribing experience, a relatively mild tolerable side effect profile. I have not had patients who have become incredibly sick with it. And for folks where making that kind of impact on their chronic illness is so critical to not just their longevity, but their disease status interacts so much with their social burden. And so, it's a very meaningful intervention I think around poverty actually.

(19:17):

I really feel that almost all of the popular press about it has focused very much on use amongst the wealthy and who's getting it off label and how are they getting it and which celebrities are taking it, and what are the implications for eating and diet culture and for people who have eating disorders. And that's a set of questions that's obviously sort of interesting, but it's really interesting in a very rarefied space. There's an unbelievable diabetes epidemic in this country, and the majority of people who have diabetes are not the people who are getting written about over and over again in those pieces. It's the patients that I take care of, and those people are at risk of ending up on dialysis or getting amputations. And so, having a tool this effective is really miraculous feeling to me.

Eric Topol (20:10):

Well, it really gives me some hope because I don't know any program like that one, which is the people who need it the most. It's getting provided for them. And we have been talking about a drug that costs a thousand dollars a month. It may get down to $500 a month, but that's still a huge cost. And of course, there's not much governmental coverage at this point. There might be some more for Medicare, Medicaid, whatever in the future, but it's really the original criteria of diabetes, and it took almost 20 years to get to where we are right now. So what's so refreshing here is to know that there's at least one program that is helping to bridge the inequities and to not make it as was projected, which was, as you say, for celebrities and wealthy people more exclusively, so that's great. And we still don't know about the diverse breadth of these effects, but as you well know, there's trials in Alzheimer's. I spoke to Steve Horvath recently on the podcast and he talked about how it's reset the epigenetic clock, GLP-1.

Rachael Bedard (21:24):

Does he think so?

Eric Topol (21:26):

Whoa. Yeah, there was evidence that was just presented about that. I said, well, if that does correspond to aging, the thing that we spoke about first, that would be very exciting.

Rachael Bedard (21:37):

It’s so wild. I mean, it's so exciting. It's so exciting to me on so many levels. And one of them is it's just exploding my mental model of disease pathogenesis, and it's making me think, oh my goodness, I have zero idea actually how metabolism and the brain and sort of cardiovascular disease, all of those things are obviously, what is happening in the interplay between all of those different systems. It's really so much more complicated and so much more interdependent than I understood it to be. I am really optimistic about the Alzheimer's trial. I am excited for those results, and I think we're going to keep seeing that it prevents different types of tumors.

Eric Topol (22:33):

Yeah, no, and that's been shown at least certainly in obese people, that there’s cancers that gets way reduced, but we never had a potent anti-inflammatory that works at the brain and systemically like this before anyone loses the weight, you already see evidence.

Long Covid and ME/CFS

(22:50):

It is pretty striking. Now, this goes back to the theme that was introduced earlier about looking after people who are neglected, who aren't respected or generally cared for. And I wanted to now get into Long Covid and the piece you wrote in the New Yorker about listening to patients, called “what would it mean for scientists to listen to patients?” And maybe you can talk about myalgic encephalitis/chronic fatigue (ME/CFS), and of course Long Covid because that's the one that is so pervasive right now as to the fact that these people don't get respect from physicians. They don't want to listen to their ailments. There's no blood tests, so there's no way to objectively make a diagnosis supposedly. And they're basically often dismissed, or their suffering is discounted. Maybe you can tell us again what you wrote about earlier this year and any updated thoughts.

Rachael Bedard (24:01):

Have you had my friend Harlan Krumholz on the show to talk about the LISTEN study?

Eric Topol (24:04):

Not yet. I know Harlan very well. Yes.

Eric Topol (24:11):

I know Akiko Iwasaki very well too. They’re very, very close.

Rachael Bedard (24:14):

So, Akiko Iwasaki and Harlan Krumholz at Yale have been running this research effort called the LISTEN study. And I first learned about it sometime in maybe late 2021. And I had been really interested in the emerging discourse around chronic illness in Long Covid in the 2021. So when we were past the most acute phase of the pandemic, and we were seeing this long tail of sequelae in patients, and the conversation had really shifted to one that was about sort of trying to define this new syndrome, trying to understand it, trying to figure out how you could diagnose it, what were we seeing sort of emerge, how are we going to draw boxes around it? And I was so interested in the way that this syndrome was really patient created. It came out of patients identifying their own symptoms and then banning together much, much faster than any kind of institutional science can ever work, getting into message boards together or whatever, and doing their own survey work and then coming up with their own descriptive techniques about what they were experiencing.

(25:44):

And then beyond that, looking into the literature and thinking about the treatments that they wanted to try for themselves. Patients were sort of at the forefront of every step of recognizing, defining, describing this illness presentation and then thinking about what they wanted to be able to do for themselves to address it. And that was really interesting to me. That was incredibly interesting to me. And it was also really interesting because by, I don't know exactly when 2021 or 2022, it was already a really tense landscape where it felt like there were real factions of folks who were in conflict about what was real and what wasn't real, how things ought to be studied, who ought to be studying them, what would count as evidence in this realm. And all of those questions were just really interesting to me. And the LISTEN study was approaching them in this really thoughtful way, which was Harlan and Akiko sort of partnering really closely with patients who enrolled.

(26:57):

And it's a decentralized study and people could enroll from all over the world. There's a portion of patients who do have their blood work evaluated, but you can also just complete surveys and have that data count towards, and those folks would be from anywhere in the world. Harlan did this amazing, amazing work to figure out how to collect blood samples from all over the country that would be drawn at home for people. So they were doing this decentralized study where people from their homes, from within the sort of circumstances of their lives around their chronic illness could participate, which that was really amazing to me. And then they were partnering really thoughtfully with these patients just to figure out what questions they wanted to ask, how they wanted to ask them, and to try to capture a lot of multimodal data all at once.

(27:47):

Survey data, journaling so people could write about their own experience in a freeform journal. They were collecting blood samples, and they were holding these town halls. And the town halls were on a regular basis, Harlan and Akiko, and anybody who was in the study could come on, could log onto a Zoom or whatever, and Harlan and Akiko and their research staff would talk about how things were going, what they were working on, what questions they had, what the roadblocks were, and then they would answer questions from their participants as the study was ongoing. And I didn't think that I had ever heard of something quite like that before. Have you ever heard of anything?

Eric Topol (28:32):

No. I mean, I think this is important to underscore, this was the first condition that was ever patient led, patient named, and basically the whole path was laid by the patient. So yes, and everything you summarize is so well as to the progress that's been made. Certainly, Harlan and Akiko are some of the people that have really helped lead the way to do this properly as opposed to, unfortunately one and a half billion dollars that have been put to the NIH for the RECOVER efforts that haven't yet led to even a significant clinical trial, no less a validated treatment. But I did think it was great that you spotlighted that just because again, it's thematic. And that gets me to the fourth dimension, which is you're the first prison doctor I've ever spoken to. And you also wrote a piece about that called, “the disillusionment of a Rikers Island Doctor” in the New Yorker, I think it was. And I wonder if you could tell us, firstly, now we're four years into Covid, you were for a good part of that at Rikers Island, I guess.

The Rikers Island Prison Doctor During Covid

Rachael Bedard (30:00):

I was, yeah.

Eric Topol (30:00):

Yeah. And what could be a more worrisome spot to be looking after people with Covid in a prison? So maybe you could just give us some insight about all that.

Rachael Bedard (30:17):

Yeah, it was really, I mean, it was the wildest time, certainly in my career probably that I'll ever have. In the end of February and beginning of March of 2020, it became very apparent to my colleagues and I that it was inevitable that this virus that was in Wuhan and in Italy was coming to the US. And jails are, we sort of jokingly described them as the worst cruise ships in the world. They are closed systems where everybody is eating, sleeping, going to the bathroom, everything on top of each other. There's an incredible amount of excess human contact in jails and prisons because people don't have freedom of movement and they don't get to do things for themselves. So every single, somebody brings you your mail, somebody brings you your meals, somebody brings you your medications. If you're going to move from point A to point B, an officer has to walk you there. So for a virus that was going to spread through what we initially thought was droplets and then found out was not just droplets but airborne, it was an unbelievably high-risk setting. It's also a setting where folks tend to be sicker than average for their age, that people bring in a lot of comorbidity to the setting.

(31:55):

And it's not a setting that does well under stress. I mean, jails and prisons are places that are sort of constitutionally violent, and they're not systems that adapt easily to emergency conditions. And the way that they do adapt tends to be through repressive measures, which tends to be violence producing rather than violence quelling. And so, it was just an incredibly scary situation. And in mid-March, Rikers Island, the island itself had the highest Covid prevalence of anywhere in the country because New York City was the epicenter, and Rikers was really the epicenter within New York. It was a wild, wild time. Our first seriously ill patient who ended up getting hospitalized. That was at that time when people were, we really didn't understand very much about what Covid looked like. And there was this guy sitting on the floor and he said, I don't know. I can't really get up.

(32:59):

I don't feel well. And he had an O2 stat of 75 or something. He was just incredibly hypoxic. It's a very scary setting for that kind of thing, right? It's not a hospital, it's not a place where you can't deliver ICU level care in a place like that. So we were also really worried about the fact that we were going to be transferring all of these patients to the city hospitals, which creates a huge amount of extra burden on them because an incarcerated patient is not just the incarcerated patients, the officers who are with that person, and there are special rules around them. They have to be in special rooms and all of these things. So it was just a huge systems crisis and really painful. And we, early on, our system made a bunch of good guesses, and one of our good guesses was that we should just, or one of our good calls that I entirely credit my bosses with is that they understood that we should advocate really hard to get as many people out as we could get out. Because trying to just manage the population internally by moving people around was not going to be effective enough, that we really need to decant the setting.

(34:18):

And I had done all of this work, this compassionate release work, which is work to get people who are sick out of jail so that they can get treatment and potentially die in a free setting. And so, I was sort of involved in trying to architect getting folks who were sort of low enough security risks out of jail for this period of time because we thought that they would be safer, and 1500 people left Rikers in the matter of about six weeks.

Rachael Bedard (34:50):

Which was a wild, wild thing. And it was just a very crazy time.

Eric Topol (34:56):

Yeah. Well, the word compassion and you go together exceptionally well. I think if we learn about you through your writings, that really shines through and what you've devoted your care for people in these different domains. This is just a sampling of your writings, but I think it gives a good cross section. What makes you write about a particular thing? I mean, obviously the Rikers Island, you had personal experience, but why would you pick Ozempic or why would you pick other things? What stimulates you to go after a topic?

Rachael Bedard (35:42):

Sometimes a lot of what I write about relates to my personal practice experience in some way, either to geriatrics or death and dying or to the criminal justice system. I've written about people in death row. I've written about geriatrics and palliative care in sort of a bunch of different ways. I am interested in topics in medicine where things are not yet settled, and it feels very of the moment. I'm interested in what the discourse is around medicine and healthcare. And I am interested in places where I think the discourse, not just that I'm taking a side in that discourse, but where I think the framework of the discourse is a little bit wrong. And I certainly feel that way about the Ozempic discourse. And I felt that way about the discourse around President Biden, that we're having not just a conversation that I have a strong opinion about, but a conversation that I think is a little bit askew from the way that we ought to be thinking about it.

Eric Topol (36:53):

And what I love about each of these is that you bring all that in. You have many different points of view and objective support and they're balanced. They're not just trying to be persuasive about one thing. So, as far as I know, you're extraordinarily unique. I mean, we are all unique, but you are huge standard deviations, Rachael. You cover bases that are, as I mentioned, that are new to me in terms of certainly this podcast just going on for now a couple of years, that is covering a field of both geriatrics and having been on the corrections board and in prison, particularly at the most scary time ever to be working in prison as a physician. And I guess the other thing about you is this drive, this humanitarian theme. I take it you came from Canada.

Rachael Bedard (37:59):

I did.

Eric Topol (37:59):

You migrated to a country that has no universal health.

Rachael Bedard (38:03):

That's right.

Eric Topol (38:03):

Do you ever think about the fact that this is a pretty pathetic situation here?

Rachael Bedard (38:08):

I do. I do think about it all the time.

Eric Topol (38:10):

In our lifetime, we'll probably never see universal healthcare. And then if you just go a few miles up north, you pretty much have that.

Rachael Bedard (38:18):

Yeah, if you've lived in a place that has universal healthcare and you come here, it's really sort of hard to ever get your mind around. And it has been an absolute possessing obsession of my entire experience in the US. I've now been here for over 20 years and still think it is an unbelievably, especially I think if you work with marginalized patients and how much their lack of access compounds the difficulty of their lives and their inability to sort of stabilize and feel well and take care of themselves, it's really frustrating.

Advice for Bringing Humanities to Medicine in a Career

Eric Topol (39:14):

Yeah, yeah. Well, I guess my last question to you, is you have weaved together a career that brings humanities to medicine, that doesn't happen that often. What's your advice to some of the younger folks in healthcare as to how to pull that off? Because you were able to do it and it's not easy.

Rachael Bedard (39:39):

My main advice when people ask me about this, especially to students and to residents who are often the people who are asking is to write when you can or pursue your humanities interests, your critical interests, whatever it is that you're doing. Do it when you can, but trust that your career is long and that you have a lot of time. Because the thing that I would say is I didn't start publishing until I was in fellowship and before that I was busy because I was learning to become a doctor. And I think it's really important that my concern about being a doctor who's a hybrid, which so many of us are now. A doctor or something else is you really do want to be a good doctor. And becoming a good doctor is really hard. And it's okay if the thing that is preoccupying you for the first 10 years of your training is becoming a great clinician. I think that's a really, really important thing to do. And so, for my first 10 years for med school and residency and chief residency and fellowship, I would write privately on the side a fair amount, but not try to publish it, not polish that work, not be thinking in sort of a careerist way about how I was going to become a doctor writer because I was becoming a doctor. And that was really preoccupying.

(41:08):

And then later on, I both sort of had more time and mental space to work on writing. But also, I had the maturity, I think, of being a person who was comfortable in my clinical identity to have real ideas and insights about medicine that felt different and unique to me as opposed to, I barely understand what's going on around me and I'm trying to pull it together. And that's how I would've been if I had done it more, I think when I was younger. Some people are real prodigies and can do it right out the gate, but I wasn't like that.

Eric Topol (41:42):

No, no, I think that's really sound advice because that's kind of the whole foundation for everything else. Is there a book in the works or will there be one someday?

Rachael Bedard (41:53):

There may be one someday. There is not one now. I think about it all the time. And that same advice applies, which is I believe in being a late bloomer and taking your time and figuring out what it is you really want to do.

Eric Topol (42:10):

Yeah. Well, that's great. Have I missed anything? And obviously we only can get to know you in what, 40 minutes to some extent, but have I not touched on something that you want to bring up?

Rachael Bedard (42:23):

No, I don't think so. Thank you for this conversation. It's been lovely.

Eric Topol (42:28):

No, I really enjoyed it. I'll be following your career. It's extraordinary already and you've got decades ahead to make an impact and obviously thinking of all these patients that you look after and have in the past, it’s just extraordinary. So what a joy to talk with you, Rachael, and I hope we'll have a chance to do that again in the times ahead.

Rachael Bedard (42:51):

Me as well. Thank you so much for inviting me.

**********************************************

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Superimposed on an impressive body of work on the blood-brain-barrier and immune system, Prof Akassoglou and her collaborators just published an elegant study in Nature that centered on the direct binding os the SARS-CoV-2 spike protein to fibrin with marked downstream pro-inflammatory effects. The findings and potential treatments have implications beyond Covid, Long Covid to other neurologic diseases.

Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify.

Transcript with links to audio and to relevant papers, graphics

Eric Topol (00:07):

Well, hello this is Eric Topol with Ground Truths, and with me today is Katerina Akassoglou. She is at the Gladstone Institute and she is a remarkable neuroimmunologist who has been doing extraordinary work for three decades to unravel the interactions between the brain, blood vessels and the role of inflammation. So Katerina, there's a lot to discuss, so welcome.

Katerina Akassoglou (00:40):

Thank you. Thank you so much. It's a great pleasure to join.

By Way of Background

Eric Topol (00:43):

It's really interesting going back in your career. First of all, we're thankful that you immigrated here from Greece, and you have become one of the leading scientists in this discipline of important discipline of neuroimmunology, which is not just about Covid that we're going to talk about, but Alzheimer's and neurodegenerative diseases. This is a really big hot area and you're definitely one of the leaders. And what I was impressed is that all these years that you've been working on the integrity of the blood-brain barrier, the importance of fibrinogen and fibrin, and then comes along the Covid story. So maybe what we can do is start with that, which is you've made your mark in understanding this whole interaction between what can get into the brain, through the blood-brain barrier and incite inflammation. So this has been something that you've really taken to the extreme knowledge base. So maybe we can start with your work there before we get into the important seminal Nature paper that you recently published.

Katerina Akassoglou (01:57):

Yes, of course. So since very early on, I was still a graduate student when we made the first discovery and at the time was like mid-90s, so it was really ahead of its time. That dysregulation of cytokine expression in the brain of mice was sufficient to induce the whole cascade of events, triggering neurodegeneration, demyelination in pathological alterations, very reminiscent of multiple sclerosis pathology. And it was really hard to publish that study at the time because it was not yet accepted that this regulation of the immune system modeling the brain can be linked to neurodegeneration. So that was 1995 when we made that discovery, and I became really interested, what are the pathogenic triggers that actually polarized the immune cells in the brain? So with this, of course, this transgenic animal was expressing TNF, it was an artificially made animal that we made, but naturally what were the triggers that would polarize the innate immune cells? So I looked really early on in this mice and what I found was that the very first event was leaks of blood-brain barrier. It was opening of the blood-brain barrier in this mouse before inflammation, before demyelination, before neuronal loss. And this is really what shaped the question that, is it possible that these blood leaks that happened very early in the pathology, could this be the instigators of pathogenic inflammation in the brain?

Eric Topol (03:34):

Yeah. So in a way, you got at this question because of the chicken-and-egg and what happens first, and you got to the temporal saying, which happened first as you said, the leak before you could see evidence of inflammation and being able to study this of course in the experimental model, which you couldn't really do in people. And what I love about the description of your career, which has been quite extraordinary contributions is connecting the dots between the blood, the inflammatory response and the brain. Perhaps no one has done that like you have. And before we get into the recent paper, a lot of people are not aware that a year ago, a group in the UK known as PHOSP-COVID, they published a really important paper in Nature Medicine of over 1,800 people who were hospitalized with Covid and they found that fibrinogen was the best marker for cognitive deficits at 6 and 12 months (Figure below)

(04:40):

So that's just one of many papers, but it's a particularly well done study that already before you got into this work that recently published had emphasized fibrinogen. And by the way, again, having spent a lot of years in clots in the arteries, for me, we have to just get it down to fibrinogen plus thrombin gets you to fibrin. Okay, so fibrin is a major player here when fibrinogen is cleaved. So here we have the basis that you established, which is the fibrinogen leakage into the brain, activating inflammation, activating microglia, which like the macrophages of the brain and inciting the whole process. And before we close, I want to not just talk about Covid, but Alzheimer's too. But now let's get into the study that you did, [Fibrin drives thromboinflammation and neuropathology in COVID-19] which is striking, I mean really striking. And can you kind of take us through, because you not only demonstrated the importance of fibrin in inciting neuroinflammation in this model, but also how you could reverse it or prevent it. So this, and you looked at it in many different ways, this was a systematic approach. Maybe you can take us through how you were able to make such compelling evidence.

The Multimodal Evidence

Katerina Akassoglou (06:09):

Yes, thank you. First of all, thank you for bringing up the human relevance because this was also our inspiration for the work that we did in the Covid study. So as you mentioned in Covid patients, fibrinogen unbiased mass spec analysis was identified as the predictive biomarker for cognitive impairment in Long Covid patients. And this was in addition to also neuropathology data about the abundance of fibrin deposition in the brain. And these were studies that were done by NIH that have found deposition of fibrin in the brain and the reports for the abnormal and puzzling coagulation in Covid that is not setting other infections and also in many cases not always relating with the severity of symptoms. So even mild cases of Covid also had increased coagulation. I was really intrigued by this human, all this evidence in human data, and I thought that maybe the way that we're thinking about this, that it's systemic inflammation that drives the clotting.

(07:24):

Maybe there's another aspect to this. Maybe there is a direct effect of the virus with the coagulation cascade, and in this way maybe this can be an instigator of inflammation. So this was the original idea to be able to reconcile this data from the clinic about why do we have this prevalence of coagulopathy in Covid. And of course, the second question is, could this also be a driver of the disease? And of course, we're in a unique position because we have been studying this pathway now for over 20 years to have all the toolbox, the genetic toolbox, the pharmacologic toolbox to be able to actually really address these questions with genetic loss of function studies, with a blood innate immunity multiomics pipeline that we have set up in the lab. And of course, with preclinical pharmacology in our ABSL3 facility. So we had the infrastructure in place and the source in place to actually really dissect this question with both genetic tools as well as also technology platforms.

Eric Topol (08:29):

And you had in vivo imaging, you're the director of in vivo imaging for Gladstone and UCSF. So you do have the tools to do this.

Katerina Akassoglou (08:38):

Yes. The imaging that you mentioned is really important because this is, we employed that very early in our studies over now 15 years ago. And the reason was sometimes from snapshots of histopathology, you cannot really understand the sequence of events. So by being able to image these processes, both neuronal activity, microglia activation, infiltration of peripheral cells in the brain, this is how we could see the steps that what happens early on and to be able to answer these chicken-and-egg questions that you mentioned. So these were very, they're very important experiments, especially at the beginning because they were hypothesis driving and we were able to ask the right questions to drive our research program.

Eric Topol (09:26):

Now was the binding of the spike protein to one key site in fibrinogen, was that known before? [See outstanding Figure below from Trends in Immunology]

Katerina Akassoglou (09:36):

No, this was not known. So there was evidence that there are abnormal clots in Covid, but it was not known whether the spike protein would directly bind to protein to the coagulation cascade. So one of the key discoveries in our study was to use peptide array mapping and be able to identify not only the binding, but exactly the domains on fibrin that spike binds too. And what we found was two key domains, one the inflammatory domain and the other the plasmin binding site, which is important for fibrin degradation. So this suggested a potential dual deleterious role for this interaction, both by maybe affecting inflammation, but also delaying fibrinolysis, which is the degradation of this toxic protein from the brain. And indeed, we found that this interaction was responsible for all these two aspects, including decreased degradation, more inflammation, but also at the same time increased, increased coagulation. So it was a really pathogenic interaction.

Eric Topol (10:47):

Yeah, actually it's pretty striking. You have these two sites, the plasmin cleavage site of fibrinogen, which as you say, we knew there was a problem with clots. We knew that, but we didn't know exactly the spike protein how exactly it was implicated, particularly with fibrinogen. And then this other site, the CD11b-C18, now that's fancy for surface receptors of macrophages. And basically, this is critical because it's this microglia activation in the brain, and I know you saw it in the lungs as well through this other site that spike protein activated. So you had a twofer here of things that you discovered that the SARS-CoV-2 spike protein was capable of doing. This was a really big revelation. And then you also looked at mice that were genetically manipulated. So maybe you can, because before we get to your antibody monoclonal, the ways that you proved this were, I mean, one thing after another is really systematic. So maybe you can teach us about that.

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Establishing Causality

Katerina Akassoglou (12:08):

Yeah, sure. So the first was about chemistry experiment. So this of course, we had to get to the next step to see is there any causality for this pathway. So we employed genetic loss of function studies and we had knockout mice, either fibrinogen knockout mice, this mice have all blood proteins except fibrinogen, and they have a delay in coagulation so they don't clot properly. But we also had a mutant mouse, which is a fibrinogen NK mouse. And this was a mutation only within this inflammatory domain that you mentioned, inflammatory domain that binds to C11b-C18. Other names for this is of course complement receptor 3, Mac-1 (αMβ2). It's the same, many names for this receptor, that as you mentioned, is expressed not only in microglial in the brain, but also peripheral immune cells including macrophages as well as also neutrophils which are CD11b expressing.

(13:12):

So we now have genetic models to be able to look at both complete depletion of fibrinogen, but also a very specific mutation and very selective mutation that only blocks the inflammatory properties without affecting the properties of fibrin in hemostasis. And these mice were made many years ago by a very close collaborator, Jay Degen at the University of Cincinnati. So what we found is that when we block either the inflammatory domain or we completely deplete fibrinogen, there was this profound protection after infection in internasal infection with the virus in lung inflammation. And this was both suppression of oxidative stress and this pathogenic inflammation in the lung, but also decreasing fibrosis, which has been associated with also Long Covid. And the surprise came from the transcriptomic data. So when we did transcriptomic analysis in this mice in the lungs, we found perhaps the expected decrease in the immune signatures in macrophages. This was in line with our previous work in, as you mentioned, Alzheimer's models, multiple sclerosis models. But what also was really surprising is there was that genes that are associated with activation of NK cells were upregulated. And of course this was the first time we had infected these mice, previously we had not done an infection before. So I think that maybe because of this region we had not seen before in our data this immunomodulatory role of fibrin that not only surprises the macrophage response, but also increases these NK cells that are important for viral clearance.

Eric Topol (15:00):

So again, the finding another important unique finding is the natural killer (NK) cells and effect there from the activation of this, as you said, the inflammation site or the CD11b-C18 that we've been talking about. So now another layer of this, a dimension of your Nature paper was that you tested an antibody that you already had developed so-called 5B8. A monoclonal that specifically binds to the domain of the one we're talking about this inflammation domain of fibrinogen. So can you tell us about what that showed?

Katerina Akassoglou (15:45):

Yes, so we tested this antibody in different models of Covid, which were both models with neuroinvasion and models without neuroinvasion. So we used both transgenic mice for hACE2, the human ACE2 infected with Delta, but we also use mouse adapted viruses like Beta that is just in the wild type mice with no transgenic being involved that these are without neuroinvasion. And we wanted to see if the antibody had any potential protective effects. And what we found is that the antibody protected from inflammation in the lung. So the data looked so similar with a genetic mutation of this pathway, protection from inflammation, decreased fibrosis, increased viral clearance, so decreased spike and viral proteins in the lungs. But we also found a protection in the brain. So the brains of this mice, including both the models we used with neuroinvasion and without, they both have had microglia activation in the brain. And we also found neuronal loss in the Delta infected mice and the antibody protected from both neuroinflammation but also improved neuronal survival in the mice. Showing that there can be this despite regardless of which model we used, there was this protective effect suggesting that by blocking fibrin, either the periphery or in the brain, this could be protected for these models.

Eric Topol (17:28):

Yeah, so I mean this is fascinating because until now, until this report of yours and your colleagues at Gladstone, there was knowledge that there would be neuroinflammation from Covid, both in patients from various biomarkers and imaging as well as in experimental model. But what this did was take it to the fibrin story, and I guess that's one of the questions you nailed that how important fibrin is, but that doesn't necessarily rule out other triggers of neuroinflammation, right?

Katerina Akassoglou (18:04):

Oh, absolutely not. So I think that this is one of the mechanisms that can be very important, especially in some patients. But we know that there are additional of course mechanisms of neuroinflammation including auto-antibody responses, as well as also endotheliopathy that are persistent endotheliopathy, this can be interacting also with each other. So I think that it's important for future research that we understand how do these mechanisms feed into each other? Are there a positive feedback loops between autoimmune mechanisms and coagulopathy and endothelial dysfunction with inflammation? But I think most importantly, I think that if we're thinking of this in the context of patients, can we identify patients with mechanism that might be more prevalent in specific cases of Long Covid and tailor our potential future clinical trials towards the needs of Long Covid patients?

Towards Treatment

Eric Topol (19:06):

Absolutely. I did interview some months back on Grounds Truths, Michelle Monje at Stanford, who I'm sure and interact with, and she's also works not so much on the fibrin side, but on neuroinflammation and the likeness between this condition in people and chemo brain because of the inflammation that's seen there. So we've talked about the multiple triggers that could contribute to brain inflammation, which I think most people would say in Long Covid this is one of the most, besides obviously the lack of energy, the profound fatigue and disability, but the cognitive function hit, not just brain fog is often profound. And we've just seen some reports about that, and particularly in hospitalized patients, how bad that can be. So that gets us to a potential treatment. Now, one of the things that's out there dangling, there's many things that people have talked about in terms of why can't we have a treatment for Long Covid?

(20:13):

And now of course this fibrin pathway, if you will, lends itself to many possibilities, whether it's anticoagulants or fibrinolytics like a tPA or things like nattokinase, which is a Japanese food enzyme that you could get at the nutrition centers or whatever. What are your thoughts? Because we don't have any good studies. There are all these little, tiny studies and they don't provide much conclusion, and you have an antibody that could potentially be effective. As I understand it, you set up a company some years ago, Therini Bio and used to be called MedaRed. You're the first woman scientist at Gladstone to develop a spin out company, which is another point of congratulations on that. But could the antibody be tested in patients or what do you think about these other possibilities?

Katerina Akassoglou (21:15):

Yes, yes. These are great questions. So first of all, the different approaches that you mentioned have very different mechanism of action. So degrading fibrin, the degradation products of fibrin also can have deleterious effects. The dimer, for example, can be very pro-inflammatory. So at the same time, blocking coagulation can also have a diverse effects because this can lead to excessive hemorrhage. So the approach that we took was to selectively block the inflammatory properties of fibrin without affecting beneficial effects of the molecule in normal hemostasis. So the challenge when I made the antibody was to be able to dissect these two functions of fibrin. It's our most important clotting factor, but at the same time, a molecule with profound pro-inflammatory capacity. So the observation that these two domains, the clotting domain and inflammatory domain were not overlapping, was really the foundation of this invention was that we could maybe create this antibody to be able to target them in a selective way.

Other Neurologic Conditions

(22:31):

So the antibody I developed is neutralizing blood toxicity by blocking the inflammatory domain of fibrin without adverse coagulation effects. And it's now completing phase one trials. So it has already completed the single ascending dose at 40 milligram per kilogram. It's interim data were announced already for this trial, with no safety signals. So if the antibody completes this year, the phase one trials, then it should be possible to be tested in different patient populations. You mentioned before chemo brain, and I think it's important that we think that blood-brain barrier disruption occurs among many neurological conditions, and it's an early event associated with early disease onset and worse prognosis in multiple sclerosis, Alzheimer's disease, traumatic injuries. So I think that it's by developing a strategy, therapeutic strategy to neutralize blood toxicity, this can have applications in a wide range of neurological conditions with vascular dysfunction.

Eric Topol (23:54):

Yeah, no. In your Nature Immunology 2020 piece [Figure below], you started with the 1883 identification of multiple sclerosis (MS) lesions were “engorged with blood”, the first link between blood leaks and brain inflammation. So this has enormous potential. And what I like about this Katerina is that you've dissected the clot component versus the inflammatory trigger of the fibrinogen and fibrin story. And this is so vital because if you keep throwing these things that just going to work on the clot and not deal with the pro-inflammatory consequences, then you're going to get the wrong impression that clots are not that important. And by the way, you did mention, and I want to come back to that too, endothelial inflammation, which is another feature of Long Covid is another kind of interactive part of this because when the lining of the blood vessel is inflamed, it will attract microthrombi and also be a participant in this whole affair. What do you think about Alzheimer's and the prospects of being able to interfere with Alzheimer's? We have 20 years in someone before this process takes hold and meets clinical manifestations. Would an antibody like this ever be useful along the way?

Katerina Akassoglou (25:29):

Yeah, so well, our antibody was tested first in Alzheimer's, this models when it was originally published, and we performed reversal trials in Alzheimer's models. So we dosed mice when they have established amyloid plaques, microglia activation, neuronal loss, and we could reverse this effect so it could increase cholinergic neurons in mice, reduce inflammation in a very selective way, only the neurotoxic part of inflammation and for genetic depletion of this pathway with akin mice in Alzheimer's disease. Also, improves from cognitive impairment, and we now have a new paper in Cell Press that is showing this effects also with really nice and unbiased machine learning models for behavioral segmentation [Figure below].

So I think that there is the data both from genetic studies and the antibody show projection in Alzheimer's disease. And of course, as you might have read the recent Lancet report from the Lancet committee on dementia that identified the vascular risk factors as the key contributors, especially post sporadic cases of Alzheimer's disease that is over 90% of Alzheimer's disease that is not genetically linked.

(26:58):

So I think that there is a real need in Alzheimer's disease to be able to block this vascular induced pathology. And an antibody like the fibrin neutralizing therapy could be positioned to be protective from the vascular induced immune-mediated neurodegeneration in this disease as well. I mean, ultimately, I think that we need to be thinking the terms of efficacy. So we want to have a drug that is efficacious, but we also want it to be selective. And the selectivity is really important because the immune system has so many protective functions. So if we block phagocytosis, we end up with more debris, decrease of neurorepair, anti-myelination. So by blocking a ligand here and not blocking, not eliminating a cell type or blocking a global pathway in this cell, but biologic a single ligand, I think we have been able to achieve this balance between efficacy, but also safety because we only block this neurotoxic populations and not the entire innate immune response that also has been beneficial for metastatic functions in the brain.

Blocking Neuroinflammation

Eric Topol (28:19):

So you're bringing up another critical concept about targeting the inflammation, this kind of goldilocks story of how much you interfere with the immune response and how much you are able to reduce the adverse pro-inflammatory effects. So that gets me to what if we don't know in any given patient how much fibrin is having a role in their Long Covid. Although we know it has to be a prominent feature because we saw it in, not just a hospitalized patient series that I mentioned we reviewed, but other papers as well. But what about if you just try to take on inflammation like through a GLP-1 drug or cGAS–STING or any of these really strong anti-inflammatory pathways. Do you see a difference in a generalized approach versus a specific approach that is really fibrin centered?

Katerina Akassoglou (29:22):

Yeah, so we have a focus actually on both because we wanted to dissect the downstream intracellular pathways of fibrin, and it's interesting that we can find specific inflammatory mediators that potentially can also be targeted as well, to be able to preserve that specificity, which I think is really important because if we don't preserve the specificity, we'll end up with a lot of adverse effects by eliminating major immune responses. But the point that you raised I think is really important because it's not enough to have an efficacious and selective drug if you don't know the patient population that will benefit from this drug. So I think that in addition to the drug discovery studies, it's important to develop also biomarker programs with both fluid biomarkers, but also imaging biomarkers to be able to identify the patient populations that will benefit from such treatment.

(30:25):

So if for example, a patient population has a fibrin deposition, blocking only downstream might not be enough, and it might be really important to neutralize this fibrin toxicity in the brain of patients. And with our target engagement studies, we show that at least in animal models, the antibody can be there. So I'm very encouraged by also programs that are going on now in the scientific community to develop noninvasive ligands to be able to image fibrin in the brain that are already tested in different patient populations like multiple sclerosis. Because I think we're going to learn so much from the biology as we start interrogating and asking these questions now in different patient populations.

Eric Topol (31:14):

I think that's a vital point you're making because the success of a clinical trial here in a clinical syndrome that is mosaic with lots of different types of pathways. If you can nail down the patients that would have the most to stand to benefit from a particular intervention, that the chance of you not missing the benefit that is matching the marker, what image marker or other markers is so vital. Well, we've talked, I think, about some fascinating discoveries that you and your colleagues have made. I mean, it's really extraordinary, and obviously we need this in Long Covid. But you know what, Katerina, it's almost made me think that you were warming up to this for three decades, that somehow or other you were working on all this stuff and then came Covid. Is that how you see it, that somehow or other you didn't know that all the work you were doing was going to wind up in this space?

Katerina Akassoglou (32:18):

Oh, I never thought I would work in a virology project. This collaboration started over Zoom with Warner Greene. We were both sheltering in place. It was the beginning of the pandemic, and the first reports were coming out about this puzzling coagulopathy. And our labs were hardly operational at the time, as you know, we had to close down our labs for a while. And however, this was a very big problem, and we thought that this is our role as scientists. If we feel that we can contribute and we have the tools to contribute, we felt that it's important that we pivot some part of our research, and even we wouldn't be doing this before, but it was important to pivot a part of our research and collaborate. And I think studies like this, this study would have been impossible without a team of collaborators. As you know, there were over 50 scientists involved at Gladstone, UCSF, UCLA, UCSD, Stanford University. Without collaboration, this study wouldn't be possible. So I'm really grateful to everyone who came together to solve this problem because I think that's what scientists should be doing. We should be solving problems as they arise.

Eric Topol (33:41):

Well, and also, I think a lot of people don't realize that, for example, when the Covid vaccines came along, people think, oh, well, it all got done in 10 months since the sequence of the virus, when in fact it took 30 years at least between all the factors that went into having an mRNA and sequencing virus and nanoparticles. And in many ways, your arc of this work is like that because it took three decades to have all the tools and the basic understanding, the antibody that you had developed for different reasons and this fascinating unraveling of what's going on in the model and undoubtedly in some patients at least as well. So before we wrap up, have I missed anything about this just remarkable work you've done?

Katerina Akassoglou (34:33):

Oh, thank you. I just want to thank you for this discussion and thank you for emphasizing the different areas and the different decisions that this pathway can have implications both for our understanding, our basic understanding of the blood brain immune interface, as well as also potential translation. And I think that the curiosity sometimes of how things work, I never thought it would work on Covid, like you mentioned at the beginning, but I think that basic science and curiosity driven science can sometimes lead to discoveries with translational implications that hopefully might benefit patients one day.

Eric Topol (35:21):

Yeah, well, undoubtedly it will. We're indebted to you, Katerina and all the folks that you have teamed up with, connecting the dots at the neurovascular interface. Phenomenal work and will follow the subsequent with great interest and it will likely not just a story about Long Covid, but other areas as well, so thank you.

*********************************

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Note: you can select preferences to receive emails about newsletters, podcasts, or all I don’t want to bother you with an email for content that you’re not interested in.

When I think of digital biology, I think of Patrick Hsu—he’s the prototype, a rarified talent in both life and computer science, who recently led the team that discovered bridge RNAs, what may be considered CRISPR 3.0 for genome editing, and is building new generative A.I. models for life science. You might call them LLLMs-large language of life models. He is Co-Founder and a Core Investigator of the Arc Institute and Assistant Professor of Bioengineering and Deb Faculty Fellow at the University of California, Berkeley.

Above is a brief snippet of our conversation. Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify.

Here’s the transcript with links to the audio and external links to relevant papers and things we discussed.

Eric Topol (00:06):

Well hello, it's Eric Topol with Ground Truths and I'm really delighted to have with me today Patrick Hsu. Patrick is a co-founder and core investigator at the Arc Institute and he is also on the faculty at the University of California Berkeley. And he has been lighting things up in the world of genome editing and AI and we have a lot to talk about. So welcome, Patrick.

Patrick Hsu (00:29):

Thanks so much. I'm looking forward to it. Appreciate you having me on, Eric.

The Arc Institute

Eric Topol (00:33):

Well, the first thing I'd like to get into, because you're into so many important things, but one that stands out of course is this Arc Institute with Patrick Collison who I guess if you can tell us a bit about how you two young guys got to meet and developed something that's really quite unique that I think brings together investigators at Stanford, UCSF, and Berkeley. Is that right? So maybe you can give us the skinny about you and Patrick and how all this got going.

Patrick Hsu (01:05):

Yeah, sure. That sounds great. So we started Arc with Patrick C and with Silvana Konermann, a longtime colleague and chemistry faculty at Stanford about three years ago now, though we've been physically operational just over two years and we're an independent research institute working at the interface of biomedical science and machine learning. And we have a few different aspects of our model, but our overall mission is to understand and treat complex human diseases. And we have three pillars to our model. We have this PI driven side of the house where we centrally fund our investigators so that they don't have to write grants and work on their very best ideas. We have a technical staff side of the house more like you'd see in a frontier AI lab or in biotech industry where we have professional teams of R&D scientists working cross-functionally on higher level organizational wide goals that we call our institute initiatives.

(02:05):

One focused on Alzheimer's disease experimentally and one that we call a virtual cell initiative to simulate human biology with AI foundation models. And our third pillar over time is to have things not just end up as academic papers, but really get things out into the real world as products or as medicines that can actually help patients on the translational side. And so, we thought that some really important scientific programs could be unlocked by enabling new organizational models and we are experimenting at the institutional scale with how we can better organize and incentivize and support scientists to reach these long-term capability breakthroughs.

Patrick, Patrick and Silvana

Eric Topol (02:52):

So the two Patrick’s. How did you, one Patrick I guess is a multi-billionaire from Stripe and then there's you who I suspect maybe not quite as wealthy as the other Patrick, how did you guys come together to do this extraordinary thing?

Patrick Hsu (03:08):

Yeah, no, science is certainly expensive. I met Patrick originally through Silvana actually. They actually met, so funny trivia, all three Arc founders did high school science together. Patrick and Silvana originally met in the European version of the European Young Scientist competition in high school. And Silvana and I met during our PhDs in her case at MIT and I was at Harvard, but we met at the Broad Institute sort of also a collaborative Harvard, MIT and Harvard hospitals Institute based in Kendall Square. And so, we sort of in various pairwise combinations known each other for decades and worked together for decades and have all collectively been really excited about science and technology and its potential to accelerate societal progress. Yet we also felt in our own ways that despite a lot of the tremendous progress, the structures in which we do this work, fund it, incentivize it and roll it out into the real world, seems like it's really possible that we'll undershoot that potential. And if you take 15 years ago, we didn't have the modern transformer that launched the current AI revolution, CRISPR technology, single-cell, mRNA technology or broadly addressable LNPs. That’s a tremendous amount of technologies have developed in the next 15 years. We think there's a real unique opportunity for new institutes in the 2020s to take advantage of all of these breakthroughs and the new ones that are coming to continue to accelerate biological progress but do so in a way that's fast and flexible and really focused.

Eric Topol (04:58):

Yeah, I did want to talk with you a bit. First of all before I get to the next related topic, I get a kick out of you saying you've worked or known each other for decades because I think you're only in your early thirties. Is that right?

Patrick Hsu (05:14):

I was lucky to get an early start. I first started doing research at the local university when I was 14 actually, and I was homeschooled actually until college. And so, one of the funny things that you got to do when you're homeschooled is well, you could do whatever you want. And in my case that was work in the lab. And so, I actually worked basically full time as an intern volunteer, cut my teeth in single cell patch clamp, molecular biology, protein biochemistry, two photon and focal imaging and kind of spiraled from there. I loved the lab, I loved doing bench work. It was much more exciting to me than programming computers, which was what I was doing at the time. And I think these sort of two loves have kind of brought me and us to where we are today.

Eric Topol (06:07):

Before you got to Berkeley and Arc, I know you were at Broad Institute, but did you also pick up formal training in computer science and AI or is that something that was just part of the flow?

Patrick Hsu (06:24):

So I grew up coding. I used to work through problems sets before dinner growing up. And so, it's just something that you kind of learn natively just like learning French or Mandarin.

New Models of Funding Life Science

Eric Topol (06:42):

That's what I figured. Okay. Now this model of Arc Institute came along in a kind of similar timeframe as the Arena BioWorks in Boston, where some of the faculty left to go to Arena like my friend Stuart Schreiber and many others. And then of course Priscilla and Mark formed the Chan Zuckerberg Institute and its biohub and its support. So can you contrast for one, these three different models because they’re both very different than of course the traditional NIH pathway, how Arc is similar or different to the others, and obviously the goal here is accelerating things that are going to really make a difference.

Patrick Hsu (07:26):

Yeah, the first thing I would say is zooming out. There have been lots of efforts to experiment with how we do science, the practice of science itself. And in fact, I've recently been reading this book, the Demon Under the Microscope about the history of infectious disease, and it talks about how in the 1910s through the 1930s, these German industrial dye manufacturing companies like Bayer and BASF actually launched what became essentially an early model for industrial scale science, where they were trying to develop Prontosil, Salvarsan and some of these early anti-infectives that targeted streptococcus. And these were some of the major breakthroughs that led to huge medical advances on tackling infectious disease compared to the more academic university bound model. So these trends of industrial versus academic labs and different structures to optimize breakthroughs and applications has been a through current throughout international science for the last century.

(08:38):

And so, the way that we do research today, and that's some of our core tenets at Arc is basically it hasn't always been this way. It doesn't need to necessarily be this way. And so, I think organizational experiments should really matter. And so, there's CZI, Altos, Arena, Calico, a variety of other organizational experiments and similarly we had MRC and Bell Labs and Xerox PARCS, NIBRT, GNF, Google Research, and so on. And so, I think there are lots of different ways that you can organize folks. I think at a high level you can think about ways that you can play with for-profit versus nonprofit structures. Whether you want to be a completely independent organization or if you want to be partnered with universities. If you want to be doing application driven science or really blue sky curiosity driven work. And I think also thinking through internally the types of expertise that you bring together.

(09:42):

You can think of it like a cancer institute maybe as a very vertically integrated model. You have folks working on all kinds of different areas surrounding oncology or immunotherapy and you might call that the Tower of Babel model. The other way that folks have built institutes, you might call the lily pad model where you have coverage of as many areas of biomedical research as possible. Places like the Whitehead or Salk, it will be very broad. You'll have planned epigenetics, folks looking at RNA structural biology, people studying yeast cell cycle, folks doing in vivo melanoma models. It's very broad and I think what we try to do at Arc is think about a model that you might liken more to overlapping Viking shields where there's sort of five core areas that we're deeply investing in, in genetics and genomics, computation, neuroscience, immunology and chemical biology. Now we really think of these as five areas that are maybe the minimal critical mass that you would need to make a dent on something as complicated as complex human diseases. It's certainly not the only thing that you need, but we needed a critical mass of investigators working at least in these areas.

Eric Topol (11:05):

Well, yeah, and they really converge on where the hottest advances are being made these days. Now can you work at Arc Institute without being one of these three universities or is it really that you maintain your faculty and your part of this other entity?

Patrick Hsu (11:24):

So we have a few elements to even just the academic side of the house. We have our core investigators. I'm one of them, where we have dually appointed faculty who retain their latter rank or tenured appointment in their home department, but their labs are physically cited at the Arc headquarters where we built out a lab in Stanford Research Park in Palo Alto. And so, folks move their labs there. They continue to train graduate students based on whatever graduate programs they're formally affiliated with through their university affiliation. And so, we have nearly 40 PhD students across our labs that are training on site every day.

(12:03):

So in addition to our core investigators, we also have what we call our innovation investigators, which is more of a grant program to faculty at our partner universities. They receive unrestricted funding from us to seed a new project or accelerate an existing area in their group and their labs stay at their home campus and they just get that funding to augment their work. The third way is our technical staff model where folks basically just come work at Arc and many of them also are establishing their own research groups focusing on technology R&D areas. And so, we have five of those technology centers working in molecular engineering, multi-omics, complex cellular models, in vivo models, and in machine learning.

Discovery of Bridge RNAs

Eric Topol (12:54):

Yeah, that's a great structure. In fact, just a few months ago, Patrick Collison, the other Patrick came to Stanford HAI where I'm on the board and you've summarized it really well and it's very different than the other models and other entities, companies included that you mentioned. It's really very impressive. Now speaking of impressive on June 26, this past few months ago, which incidentally is coincident with the draft genome in the year 2000, the human sequence. You and your colleagues, perhaps the most impressive jump in terms of an Arc Institute contribution published two papers back-to-back in Nature about bridge RNA: [Bridge RNAs direct programmable recombination of target and donor DNA] and [Structural mechanism of bridge RNA-guided recombination.] And before I get you to describe this breakthrough in genome editing, some would call it genome editing 3.0 or CRISPR 3.0, whatever. But what we have today in the clinic with the approval of CRISPR 1.0 for sickle cell and thalassemia is actually quite crude. I think most people will know it's just a double stranded DNA cleavage with all sorts of issues about repair and it's not very precise. And so, CRISPR 2.0 is supposed to be represented by David Liu's contributions and his efforts at Broad like prime and base editing and then comes yours. So maybe you can tell us about it and how it is has to be viewed as quite an important advance.

Patrick Hsu (14:39):

The first thing I would say before CRISPR, is that we had RNA interference. And so, even before this modern genome editing revolution with programmable CRISPRs, we had this technology that had a lot of the core selling points as well. Any target will now become druggable to us. We simply need to reprogram a guide RNA and we can get genetic access to things that are intracellular. And I think both the discovery of RNA interference by Craig Mello and Andy Fire or the invention or discovery of programmable CRISPR technologies, both depend on the same fundamental biological mechanism. These non-coding guide RNAs that are essentially a short RNA search string that you can easily reprogram to retarget a desired enzyme function, and natively both RNAi and CRISPR are molecular scissors. Their RNA or DNA nucleases that can be reprogrammed to different regions of the genome or the transcriptome to make a cut.

(15:48):

And as bioengineers, we have come up with all kinds of creative ways to leverage the ability to make site specific cuts to do all kinds of incredible things including genome editing or beyond transcriptional up or down regulation, molecular imaging and so on and so forth. And so, the first thing that we started thinking about in our lab was, why would mother nature have stopped only RNAi and CRISPR? There probably are lots of other non-coding RNAs out there that might be able to be programmable and if they did exist, they probably also do more complicated and interesting things than just guide a molecular scissors. So that was sort of the first core kind of intuition that we had. The second intuition that we had on the technology side, I was just wearing my biology hat, I’ll put on my technology hat, is the thing that we call genome editing today hardly involves the genome.

(16:50):

It's really you're making a cut to change an individual base or an individual gene or locus. So really you're doing small scale single locus editing, so you might call it gene level or locus level cuts. And what you really want to be able to do is do things at the genome scale at 100 kb, a megabase at the chromosome scale. And I think that's where I think the field will inevitably go if you follow the technology curves of longer and longer range gene sequencing, longer and longer range gene synthesis, and then longer and longer range gene editing. And so, what would that look like? And we started thinking, could there be essentially recombination technologies that allow you to do cut and paste in a single step. Now, the reason for that is the way that we do gene editing today involves a cut and then a multi-step process of cellular DNA repair that resolves the cut to make the exertion or the error prone deletion or the modification that ends up happening.

(17:59):

And so, it's very complicated and whether that's nucleases or base or prime editing, you're all generally limited to the small-scale single locus changes. However, there are natural mechanisms that have solved this cut and paste problem, right? There are these viruses or bacterial versions of viruses known as phage that have generally been trying to exert their multi kilobase genomes into bacterial hosts and specialize throughout billions of years. So our core thought was, well, if there are these new non-coding RNAs, what kind of functions would we be excited about? Can we look in these mobile genetic elements, these so-called jumping genes for new mechanisms? They're incredibly widespread. Transposons are thought to be some of the most diverse enzyme mechanisms found in nature. And so, we started computationally by asking ourselves a very simple question. If a mobile element inserts itself into foreign DNA and it's able to somehow be programmable, presumably the inside or something encoded in the inside of the element is predictive of some sequence on the outside of the element.

(19:15):

And so, that was the core insight we took, and we thought let's look across the boundaries of many different mobile genetic elements and we zoomed in on a particular sub family of these MGE known as insertion sequence (IS) elements which are the most autonomous minimal transposons. Normally transposons have all kinds of genes that they use to hitchhike around the genomic galaxy and endow the bacterial host with some fitness advantage like some ability to metabolize some copper and some host or some metal. And these IS elements have only the enzymes that they need to jump around. And if you identify the boundaries of these using modern computational methods, this is actually a really non-trivial problem. But if you solve that problem to figure out with nucleotide resolution where the element boundaries end and then you look for the open reading frame of the transposases enzyme inside of this element, you'll find that it's not just that coding sequence.

(20:19):

There are also these non-coding flanks inside of the element boundaries. And when we looked across the non-coding, the entire IS family tree, there are hundreds of these different types of elements. We found that this particular family IS110, had the longest non-coding ends of all IS elements. And we started doing experiments in the lab to try to figure out how these work. And what we found was that these elements are cut and paste elements, so they excise themselves into a circular form and paste themselves back in into a target site linearly. But the circularization of this element brings together two distal ends together, which brings together a -35 and a -10 box that create and reconstitute a canonical bacterial transcriptional promoter. This essentially is like plugging a plug into an electrical socket in the wall and it jacks up transcription. Now you would think this transcription would turn on the transposase enzyme so it can jump around more but it transcribes a non-coding RNA out of this non-coding end.

(21:30):

We're like, holy crap, are these RNAs actually involved in regulating the transposon? Now the boring answer would be, oh, it regulates the expression. It's like an antisense regulate or something. The exciting answer would be, oh, it's a new type of guide RNA and you found an RNA guided integrase. So we started zooming in bound dramatically on this and we undertook a covariation analysis where we were able to show that this cryptic non-coding RNA has a totally novel guide RNA structure, totally distinct from RNAi or CRISPR guide RNAs. And it had a target site that covaried with the target site of the element. And so we're like, oh wow, this could be a programmable transposase. The second thing that we found was even more surprising, there was a second region of complementarity in that same RNA that recognized the donor sequence, which is the circularized element itself. And so, this was the first example of a bispecific guide RNA, and also the first example of RNA guided self-recognition by a mobile genetic element.

Eric Topol (22:39):

It's pretty extraordinary because basically you did a systematic assessment of jumping genes or transposons and you found that they contain things that previously were not at all recognized. And then you have a way to program these to edit, change the genome without having to do any cuts or nicks, right?

Patrick Hsu (23:05):

Yeah. So what we showed in a test tube is when we took this, so-called bridge RNA, which we named because it bridges the target and donor together along with the recombinase enzyme. So the two component system, those are the only two things that you need. They're able to cut and paste DNA and recombine them in a test tube without any DNA repair, meaning that it's independent of cellular DNA repair and it does strand nicking, exchange, junction resolution and religation all in a single mechanism. So that's when we got super excited about its potential applications as bioengineering tool.

Eric Topol (23:46):

Yeah, it's pretty extraordinary. And have you already gone into in vivo assessment?

Patrick Hsu (23:54):

Yes, in our initial set of papers, what we showed is that these are programmable and functional or recombinases in a test tube and in bacterial cells. And by reprogramming the target and donor the right way, you can use these enzymes not just for insertion, but also for flipping and cutting out DNA. And so, we actually have in a single mechanism the ability to do bridge editing, if you will, for universal DNA recombination, insertion, excision or inversion, similar to what folks have been doing for decades with Cre recombinase, but with fully programmable recognition sequences. The work that we're doing now in the lab as you can imagine is to adapt these into robust tools for mammalian genome editing, including of course, human genomes. We're excited about this, we're making good progress. The CRISPR has had thousands of labs over the last 10, 15 years working on it to make these therapeutic level potency and selectivity. We're going to work and follow that same blueprint for getting bridge systems to get to that level of performance, but we're on the path and we're very optimistic for the future.

Exemplar of Digital Biology

Eric Topol (25:13):

Yeah, I think it's quite extraordinary and it's a whole different look to what we've been seeing in the CRISPR era for over the past decade and how that's been advancing and getting more specific and less need for repair and being able to be more versatile. But this takes it to yet another dimension. Now, this brings me to the field that when I think of this term digital biology, I think of you and now our mutual acquaintance, Jensen Huang, who everybody knows now. Back some months ago, he wrote and said at a conference, “Where do I think the next amazing revolution is going to come? And this is going to be flat out one of the biggest ones ever. There’s no question that digital biology is going to be it. For the first time in human history, biology has the opportunity to be engineering, not science.” So can you critique Jensen? Is he right? And tell us how you conceive the field of digital biology.

Patrick Hsu (26:20):

If you look at gene therapy today, the core concepts are actually remarkably simple. They're elegant. Of course, you're missing a broken gene, you need to put it back. And that can be curative. Very simple, powerful concept. However, for complex diseases where you don't have just a single gene that goes wrong, in many cases we actually have no idea what to do. And in fact, when you're trying to put in DNA, that's over more than a gene scale. We kind of very quickly run out of ideas. Is it a CAR and a cytokine, a CAR and a cytokine and another thing? And then we're kind of out of ideas. And so, we started thinking in the lab, how can we actually design genomes where it's not just let's reduce the genome into individual Lego blocks, iGem style with promoters and different genes that we just sort of shuffle the Lego blocks around, but actually use AI to design genome sequences.

(27:29):

So to do that, we thought we would have to first of all, train a model that can learn and decode the foreign language of biology and use that in order to design sequences. And so, we sort of have been training DNA foundation models and virtual cell models at Arc, sort of a major effort of ours where the first thing that we tried was to take a variance of transformer architecture that's used to train ChatGPT from OpenAI, but instead apply this to study the next DNA token, right? Now, the interesting thing about next token prediction in English is that you can actually learn a surprising amount of information by just predicting the next word. You can learn world knowledge is the capital of Azerbaijan, is it Baku or is it London, right? Or if you're walking around in the kitchen, then the next text is, I then left the kitchen or the bathroom, right?

(28:33):

Now you're learning about spatial reasoning, and so you can also learn translation obviously. And so similarly, I think predicting the next token or the next base and DNA can lead you to learn about molecular biochemistry, is the next amino acid residue, hydrophobic or hydrophilic. And it can teach you about the mechanics of some catalytic binding pocket or something. You can learn about a disease mutation. Is the next base, the sick linked base or the wild type base and so on and so forth. And what we found was that at massive scale, DNA foundation models learn about molecular function, not just at the DNA level, but also at the RNA and the protein. And indeed, we could use these to design molecular systems like CRISPR-Cas systems, where you have a protein and the guide RNA. It could also design new DNA transposons, and we could design sequences that look plausibly like real genomes, where we generate a megabase a million bases of continuous genome sequence. And it really looks and feels like it could be a blurry picture of something that you would actually sequence. This has been a wonderful collaboration with Brian Hie, a PI at Stanford and an Arc investigator, and we're really excited about what we've seen in this work because it promises the better performance with even more scale. And so, simply by scaling up these models, by adding in more compute, more training data or more powerful models, they're going to get sharper and sharper.

New A.I. Models in Life Science

Eric Topol (30:25):

Yeah. Well, this whole use of large language models for the language of life, whether it's the genome proteins and on and on, actually RNA and even cells has really taken root. And of course, this is really one of the foundations of that field of digital biology, which brings together generative AI, AI tools and trying to push forward our understanding in biology. And also, obviously what's been emphasized in drug discovery, perhaps it's been emphasized even too much because we still have a lot to learn about biology, but that gets me to these models. Like today, AlphaProteo was announced by DeepMind, as we all know, AlphaFold 1, 2, now 3. They were kind of precursors of being able to predict proteins from amino acid 3D structure. And that kind of took the field by a little bit like ChatGPT for life science, but now it's a new model all the time. So you've been working on various models and Arc Institute, how do you see this unfolding? Are we just going to have every aspect of the language of life being approached in all the different interactions? And this is going to help us get to a much more deep level of understanding.

Patrick Hsu (31:56):

I'll say two things. The first is a lot of models that you just described are what I would call task specific models. A model for de novo design of a binder, a model for protein structure prediction. And there are other models for protein fitness or for RNA structure prediction, et cetera, et cetera. And I think what we're going to move towards are more unifying models where there's different classes of models at different levels of scale. So we will have these atomic level models for looking at generative chemistry or ligand docking. We have models that can unify genomes and their molecules, and then we have models that can unify cells and tissues. And so, for example, if you took an H&E stain of some liver, there are folks building models where you can then predict what the single cell spatial transcriptome will look like of that model. And that's obviously operating at a very different level of abstraction than a de novo protein binder. But in the long run, all of these are going to get, I think unified. I think the reason why this is possible is that biology, unlike physics, actually has this unifying theory of evolution that runs across all of its length scales from atomic, molecular, cellular, organismal to entire ecosystem. And the promise of these models is no short then to make biology a predictive discipline.

Patrick Hsu (33:37):

In physics, the experimentalists win the big prizes for the theorists when they measure gravitational waves or whatever. But in biology, we're very practical people. You do something three times and do a T-test. And I think my prediction is we can actually gauge the success of these LLMs or whatever in biology by how much we respect theory in this field.

The A.I. Scientist

Eric Topol (34:05):

Yeah. Well, that's a really interesting perspective, an important perspective because the proliferation of models, which we're going to get into not just doing the things that you described, but also being able to be “pseudo” scientists, the so-called AI scientist. Maybe you could comment about that concept because that's been the idea that everything from the question that could be asked to the hypothesis and the experiment design and the analysis of data and then the feedback. So what is the role of the scientists, that seems to have been overplayed? And maybe you can put that in context.

Patrick Hsu (34:48):

So yeah, right now there's a lot of excitement that we can use AI agents not just to do software enterprise workflows, but to be a research assistant. And then over time, itself an autonomous research scientist that can read the literature, come up with an idea, maybe run a bunch of robots in the lab or do a bunch of computational analyses and then potentially even analyze data, conclude what is going on and actually write an entire paper. Now, I think the vision of this is compelling in the long term. I think the question is really about timescale. If you break down the scientific method into its constituent parts, like hypothesis generation, doing an experiment, analyzing experiment and iterating, we're clearly going to use AI of some kind at every single step of this cycle. I think different steps will require different levels of maturity. The way that I would liken this is just wet lab automation, folks have dreamed about having pipetting robots that just do their western blots and do their cell culture for them for generations.

(36:01):

But of course, today they don't actually really feel fundamentally different from the same ones that we had in the 90s, let's say. Right? And so, obviously they're getting better, but it seems to me one of the trends I'm very bullish about is the explosion of humanoid robots and robot foundation models that have a world model and a sense of physics and proportionate space loaded onto them. Within five years, we're going to have home robots that can fold your clothes, that can organize your kitchen and do all of this while you're sleeping, so you wake up to a clean home every day.

Eric Topol (36:40):

It’s not going to be just Roomba anymore. There's going to be a lot more, but it isn't just the hardware, it's also the agents playing in software, right?

Patrick Hsu (36:50):

It's the integrated loop of the hardware and the software where the ability to make the same machine generally intelligent will make it adaptable to a broad array of tasks. Now, what I'm excited about is those generally intelligent humanoid robots coming into the lab, where instead of creating a centrifuge or a new type of pipetter that's optimized for your Beckman or Hamilton device, instead you just have robot arms that you snap onto the edge of the bench and then they just work alongside you. And I do think that's coming, although it'll take a lot of hardware and software and computer vision engineering to make that possible.

A Sense of Humor

Eric Topol (37:32):

Yeah, and I think also going back to originating the question, there still is quite a debate about the creativity and the lack of any simulation of AGI, whatever that means anymore. And so, the human in the loop part of this is obviously I think it's still of critical nature. Now, the other thing I learned about you is you have a great sense of humor, which is really important by the way. And recently, which is great that you're active on X or Twitter because that's one way we get to see what you're thinking on a day-to-day basis. But I think you put out a poll which was really quite provocative , and it was about, here's what it said, “do more people in the world *truly* understand transformers or health insurance?” And interestingly, you got 49% for transformers at 51% for health insurance. Can you tell us what you're thinking when you put that poll together? Because obviously a lot of people don't understand either of these.

Patrick Hsu (38:44):

I think the core question is, there are different ways of looking at the world, some of which are very bottom up and some of which are very top down. And one of the very surprising things about transformers is they're taking something that is in principle, an incredibly simple task, which is if you have a string of text, what is the next letter? And somehow at massive, massive scale, you can unlock something that looks an awful lot like reasoning, and you've got these emergent behaviors. Now the bottoms up theory of just the linear algebra that's going on in these models couldn't possibly really help us predict that we have these emerging capabilities. And I think similarly in healthcare, there's a literal set of parts that are operating in some complex way that at massive scale becomes this incredibly confusing and dynamic system for how we can actually incentivize how we make medicines, how we actually take care of people, and how we actually pay for any of this from an economic point of view. And so, I think it was, in some sense if transformers can actually be an explainable by just linear algebra equations, maybe there will be a way to decompose the seemingly incredibly confusing world of healthcare in order to actually build a better way forward.

Computing Power and the GPU Arms Race

Eric Topol (40:12):

Yeah. Well that's great. Now the other thing I wanted to ask you about, we open source and the arms race of GPUs and this whole kind of idea is you touched on the need for coalescing a lot of these tools to exploit the synergy. But we have an issue because many academic labs like here at Scripps Research and so many others, including as I learned even at Stanford, have limited access to GPUs. So computing power of large language models is a problem. And then the models that exist today that can be adopted like Llama or others, and they're somewhat limited. And then we also have a movement towards trying to make things more open source, like for example, recently OpenCRISPR with Profluent Bio that is basically trying to use AI for CRISPR guides. And so, how do you deal with this arms race, computing power, open source, proprietary models that are not easily accessible without a lot of resources?

Patrick Hsu (41:30):

So the first thing I would say is, we are in the academic science sphere really unprepared for the level of resources that are required for doing this type of cutting edge computational work. There are top Stanford computer science professors or computational researchers who have a single GPU in their office, and that's actually what their whole lab runs off of.

(41:58):

The UC Berkeley campus, the grid runs on something like 12 megawatts of power and how are they going to build an on-premises GPU clusters, like a central question that can scale across the entire needs? And these are two of the top computer science universities in the world. And so, I think one of our kind of core beliefs at Arc is, as science both experimentally and computationally has gotten incredibly complex, not just in terms of conceptually, but also just the actual infrastructure and machines and know-how that you need to do things. We actually need to essentially support this. So we have a private GPU cloud that we use to train our models, and we have access to significantly large clusters for large burst kind of train outs as necessary. And I think infrastructurally for running genomics experiments or doing scalable brain organoid screens, right, we're also building out the infrastructure to support that experimentally.

Eric Topol (43:01):

Yeah, no, I think this is one of the advantages of the new model like the Arc Institute because not many centers have that type of plasticity with access to computing power when needed. So that's where a brilliant mind you and the Arc Institute together makes for a formidable recipe for future advances and of course building on the ones you've already accomplished.

The Primacy of Human Talent

Patrick Hsu (43:35):

I would just say, my main skill, if I have one, is to recruit really, really smart people. And so, everything that you're seeing and hearing about is the work of unbelievable colleagues who are curious, passionate, and incredible scientists.

Eric Topol (43:53):

But it also takes the person who can judge those who are in that category set as a role model. And you're certainly doing that. I guess just in closing, I mean, it's just such a delight to get to meet you here and kind of get your thoughts on what is the hottest thing in life science without question, which brings together the fields of AI and what's going on, not just obviously in genome editing, but this digital biology era that we're still in the early phases of, I mean, I think you could say that it's just going to continue to accelerate the exponential curve. We're still kind of on the bottom of that, I would imagine where we're headed. Any other things that you want to bring up that I haven't touched on that will round out this conversation?

Patrick Hsu (44:50):

I mean, I think it's very early days here at Arc.

Patrick Hsu (44:53):

When we founded Arc, we asked ourselves, how do we measure success? We don't have customers or revenue in the way that a typical startup does. And we felt sort of three things. The first was research institutes live and die by their talent. Can we actually hire incredible people when we make offers to people we want to come, do they come? The second was, when those folks do come to Arc, do they feel like they're able to work on important research programs that they couldn't do sort of at their prior university or company? And then longer term, the third thing was, and there's just no shortcut around this, you need to do important work. And I think we've been really excited that there are early signs that we're able to do all three of these things, and we're still, again, just following the same scaling laws that we're seeing in natural language and vision, but for the domain of biology. And so, we're excited about what's ahead and think if there are folks who are interested in learning more about Arc, just shoot me an email or DM.

Eric Topol (46:07):

Yeah, well I would just say, congratulations on what you've already achieved. I know you're going to keep rocking it because you already have in a short time. And for anybody who doesn't know about Arc Institute and your work and your team, I hope this is going to be putting them on notice actually what can be accomplished outside of the usual NIH funded model, which is kind of a risk-free zone where you basically have to have your results nailed down before you send in your proposal frequently, and it doesn't do great things for young people. Really, I think you actually qualify in that demographic where it's hard for them to break in for getting NIH grants and also for this type of work that you're doing. So we'll look for the next bridge beyond bridge RNAs of your just fantastic efforts. So Patrick, thanks so much for joining us today, and we'll be checking back with you and following all the great work that you'll be doing in the times ahead.

Patrick Hsu (47:14):

Thanks so much, Eric. It was such a pleasure to be here today. Appreciate the opportunity.

*******************

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Arvind Narayanan and Sayash Kapoor are well regarded computer scientists at Princeton University and have just published a book with a provocative title, AI Snake Oil. Here I’ve interviewed Sayash and challenged him on this dismal title, for which he provides solid examples of predictive AI’s failures. Then we get into the promise of generative AI.

Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify.

Transcript with links to audio and external links to key publications

Eric Topol (00:06):

Hello, it's Eric Topol with Ground Truths, and I'm delighted to welcome the co-author of a new book AI SNAKE OIL and it's Sayash Kapoor who has written this book with Arvind Narayanan of Princeton. And so welcome, Sayash. It's wonderful to have you on Ground Truths.

Sayash Kapoor (00:28):

Thank you so much. It's a pleasure to be here.

Eric Topol (00:31):

Well, congratulations on this book. What's interesting is how much you've achieved at such a young age. Here you are named in TIME100 AI’s inaugural edition as one of those eminent contributors to the field. And you're currently a PhD candidate at Princeton, is that right?

Sayash Kapoor (00:54):

That's correct, yes. I work at the Center for Information Technology Policy, which is a joint program between the computer science department and the school of public and international affairs.

Eric Topol (01:05):

So before you started working on your PhD in computer science, you already were doing this stuff, I guess, right?

Sayash Kapoor (01:14):

That's right. So before I started my PhD, I used to work at Facebook as a machine learning engineer.

Eric Topol (01:20):

Yeah, well you're taking it to a more formal level here. Before I get into the book itself, what was the background? I mean you did describe it in the book why you decided to write a book, especially one that was entitled AI Snake Oil: What Artificial Intelligence Can Do, What It Can't, and How to Tell the Difference.

Background to Writing the Book

Sayash Kapoor (01:44):

Yeah, absolutely. So I think for the longest time both Arvind and I had been sort of looking at how AI works and how it doesn't work, what are cases where people are somewhat fooled by the potential for this technology and fail to apply it in meaningful ways in their life. As an engineer at Facebook, I had seen how easy it is to slip up or make mistakes when deploying machine learning and AI tools in the real world. And had also seen that, especially when it comes to research, it's really easy to make mistakes even unknowingly that inflate the accuracy of a machine learning model. So as an example, one of the first research projects I did when I started my PhD was to look at the field of political science in the subfield of civil war prediction. This is a field which tries to predict where the next civil war will happen and in order to better be prepared for civil conflict.

(02:39):

And what we found was that there were a number of papers that claimed almost perfect accuracy at predicting when a civil war will take place. At first this seemed sort of astounding. If AI can really help us predict when a civil war will start like years in advance sometimes, it could be game changing, but when we dug in, it turned out that every single one of these claims where people claim that AI was better than two decades old logistic regression models, every single one of these claims was not reproducible. And so, that sort of set the alarm bells ringing for the both of us and we sort of dug in a little bit deeper and we found that this is pervasive. So this was a pervasive issue across fields that were quickly adopting AI and machine learning. We found, I think over 300 papers and the last time I compiled this list, I think it was over 600 papers that suffer from data leakage. That is when you can sort of train on the sets that you're evaluating your models on. It's sort of like teaching to the test. And so, machine learning model seems like it does much better when you evaluate it on your data compared to how it would really work out in the real world.

Eric Topol (03:48):

Right. You say in the book, “the goal of this book is to identify AI snake oil - and to distinguish it from AI that can work well if used in the right ways.” Now I have to tell you, it's kind of a downer book if you're an AI enthusiast because there's not a whole lot of positive here. We'll get to that in a minute. But you break down the types of AI, which I'm going to challenge a bit into three discrete areas, the predictive AI, which you take a really harsh stance on, say it will never work. Then there's generative AI, obviously the large language models that took the world by storm, although they were incubating for several years when ChatGPT came along and then content moderation AI. So maybe you could tell us about your breakdown to these three different domains of AI.

Three Types of AI: Predictive, Generative, Content Moderation

Sayash Kapoor (04:49):

Absolutely. I think one of our main messages across the book is that when we are talking about AI, often what we are really interested in are deeper questions about society. And so, our breakdown of predictive, generative, and content moderation AI sort of reflects how these tools are being used in the real world today. So for predictive AI, one of the motivations for including this in the book as a separate category was that we found that it often has nothing to do with modern machine learning methods. In some cases it can be as simple as decades old linear regression tools or logistic regression tools. And yet these tools are sold under the package of AI. Advances that are being made in generative AI are sold as if they apply to predictive AI as well. Perhaps as a result, what we are seeing is across dozens of different domains, including insurance, healthcare, education, criminal justice, you name it, companies have been selling predictive AI with the promise that we can use it to replace human decision making.

(05:51):

And I think that last part is where a lot of our issues really come down to because these tools are being sold as far more than they're actually capable of. These tools are being sold as if they can enable better decision making for criminal justice. And at the same time, when people have tried to interrogate these tools, what we found is these tools essentially often work no better than random, especially when it comes to some consequential decisions such as job automation. So basically deciding who gets to be called on the next level of like a job interview or who is rejected, right as soon as they submit the CV. And so, these are very, very consequential decisions and we felt like there is a lot of snake oil in part because people don't distinguish between applications that have worked really well or where we have seen tremendous advances such as generative AI and applications where essentially we've stalled for a number of decades and these tools don't really work as claimed by the developers.

Eric Topol (06:55):

I mean the way you partition that, the snake oil, which is a tough metaphor, and you even show the ad from 1905 of snake oil in the book. You're really getting at predictive AI and how it is using old tools and selling itself as some kind of breakthrough. Before I challenge that, are we going to be able to predict things? By the way, using generative AI, not as you described, but I would like to go through a few examples of how bad this has been and since a lot of our listeners and readers are in the medical world or biomedical world, I'll try to get to those. So one of the first ones you mentioned, which I completely agree, is how prediction of Covid from the chest x-ray and there were thousands of these studies that came throughout the pandemic. Maybe you could comment about that one.

Some Flagrant Examples

Sayash Kapoor (08:04):

Absolutely. Yeah, so this is one of my favorite examples as well. So essentially Michael Roberts and his team at the University of Cambridge a year or so after the pandemic looked back at what had happened. I think at the time there were around 500 studies that they included in the sample. And they looked back to see how many of these would be useful in a clinical setting beyond just the scope of writing a research paper. And they started out by using a simple checklist to see, okay, are these tools well validated? Does the training and the testing data, is it separate? And so on. So they ran through the simple checklist and that excluded all but 60 of these studies from consideration. So apart from 60 studies, none of these other studies even passed a very, very basic criteria for being included in the analysis. Now for these 60, it turns out that if you take a guess about how many were useful, I'm pretty confident most cases would be wrong.

(09:03):

There were exactly zero studies that were useful in a clinically relevant setting. And the reasons for this, I mean in some cases the reasons were as bizarre as training a machine learning model to predict Covid where all of the positive samples of people who had Covid were from adults. But all of the negative samples of people who didn't have Covid were from children. And so, essentially claiming that the resulting classifier can predict who has Covid is bizarre because all the classifier is doing is looking at the checks history and basically predicting which x-ray belongs to a child versus an adult. And so, this is the sort of error in some cases we saw duplicates in the training and test set. So you have the same person that is being used for training the model and that it is also used for evaluating the model. So simply memorizing a given sample of x-rays would be enough to achieve a very high performance. And so, for issues like these, I think all 60 of these studies prove to be not useful in a clinically relevant setting. And I think this is sort of the type of pattern that we've seen over and over again.

Eric Topol (10:14):

Yeah, and I agree with you on that point. I mean that was really a flagrant example and that would fulfill your title of your book, which as I said is a very tough title. But on page 29, and we'll have this in the post. You have a figure, the landscape of AI snake oil, hype, and harm.

And the problem is there is nothing good in this landscape. So on the y-axis you have works, hype, snake oil going up on the y-axis. And on the x-axis, you have benign and harmful. So the only thing you have that works and that's benign is autocomplete. I wouldn't say that works. And then you have works facial recognition for surveillance is harmful. This is a pretty sobering view of AI. Obviously, there's many things that are working that aren't on this landscape. So I just would like to challenge, are you a bit skewed here and only fixating on bad things? Because this diagram is really rough. I mean, there's so much progress in AI and you have in here you mentioned the predicting civil wars, and obviously we have these cheating detection, criminal risk prediction. I mean a lot of problems, video interviews that are deep fakes, but you don't present any good things.

Optimism on Generative AI

Sayash Kapoor (11:51):

So to be clear, I think both Arvind and are somewhat paradoxically optimistic about the future of generative AI. And so, the decision to focus on snake oil was a very intentional one from our end. So in particular, I think at various places in the book we outline why we're optimistic, what types of applications we think we're optimistic about as well. And the reason we don't focus on them is that it basically comes down to the fact that no one wants to read a book that has 300 pages about the virtues of spellcheck or AI for code generation or something like that. But I think I completely agree and acknowledge that there are lots of positive applications that didn't make the cut for the book as well. That was because we wanted people to come to this from a place of skepticism so that they're not fooled by the hype.

(12:43):

Because essentially we see even these positive uses of AI being lost out if people have unrealistic expectations from what an AI tool should do. And so, pointing out snake oil is almost a prerequisite for being able to use AI productively in your work environment. I can give a couple of examples of where or how we've sort of manifested this optimism. One is AI for coding. I think writing code is an application that I do, at least I use AI a lot. I think almost half of the code I write these days is generated, at least the first draft is generated using AI. And yet if I did not know how to program, it would be a completely different question, right? Because for me pointing out that, oh, this syntax looks incorrect or this is not handling the data in the correct way is as simple as looking at a piece of code because I've done this a few times. But if I weren't an expert on programming, it would be completely disastrous because even if the error rate is like 5%, I would have dozens of errors in my code if I'm using AI to generate it.

(13:51):

Another example of how we've been using it in our daily lives is Arvind has two little kids and he's built a number of applications for his kids using AI. So I think he's a big proponent of incorporating AI into children's lives as a force for good rather than having a completely hands-off approach. And I think both of these are just two examples, but I would say a large amount of our work these days occurs with the assistance of AI. So we are very much optimistic. And at the same time, I think one of the biggest hindrances to actually adopting AI in the real world is not understanding its limitations.

Eric Topol (14:31):

Right. Yeah, you say in the book quote, “the two of us are enthusiastic users of generative AI, both in our work and our personal lives.” It just doesn't come through as far as the examples. But before I leave the troubles of predictive AI, I liked to get into a few more examples because that's where your book shines in convincing that we got some trouble here and we need to be completely aware. So one of the most famous, well, there's a couple we're going to get into, but one I'd like to review with you, it's in the book, is the prediction of sepsis in the Epic model. So as you know very well, Epic is the most used IT and health systems electronic health records, and they launched never having published an algorithm that would tell when the patient was hospitalized if they actually had sepsis or risk of sepsis. Maybe you could take us through that, what you do in the book, and it truly was a fiasco.

The Sepsis Debacle

Sayash Kapoor (15:43):

Absolutely. So I think back in 2016/2017, Epic came up with a system that would help healthcare providers predict which patients are most at risk of sepsis. And I think, again, this is a very important problem. I think sepsis is one of the leading causes of death worldwide and even in the US. And so, if we could fix that, I think it would be a game changer. The problem was that there were no external validations of this algorithm for the next four years. So for four years, between 2017 to 2021, the algorithm wasn't used by hundreds of hospitals in the US. And in 2021, a team from University of Michigan did this study in their own hospital to see what the efficacy of the sepsis prediction model is. They found out that Epic had claimed an AUC of between 0.76 and 0.83, and the actual AUC was closer to 0.6, and AUC of 0.5 is making guesses at random.

(16:42):

So this was much, much worse than the company's claims. And I think even after that, it still took a year for sepsis to roll back this algorithm. So at first, Epic's claims were that this model works well and that's why hospitals are adopting it. But then it turned out that Epic was actually incentivizing hospitals to adopt sepsis prediction models. I think they were giving credits of hundreds of thousands of dollars in some cases. If a hospital satisfied a certain set of conditions, one of these conditions was using a sepsis prediction model. And so, we couldn't really take their claims at face value. And finally in October 2022, Epic essentially rolled back this algorithm. So they went from this one size fits all sepsis prediction model to a model that each hospital has to train on its own data, an approach which I think is more likely to work because each hospital's data is different. But it's also more time consuming and expensive for the hospitals because all of a sudden you now need your own data analysts to be able to roll out this model to be able to monitor it.

(17:47):

I think this study also highlights many of the more general issues with predictive AI. These tools are often sold as if they're replacements for an existing system, but then when things go bad, essentially they're replaced with tools that do far less. And companies often go back to the fine print saying that, oh, we should always deploy it with the human in the loop, or oh, it needs to have these extra protections that are not our responsibility, by the way. And I think that gap between what developers claim and how the tool actually works is what is most problematic.

Eric Topol (18:21):

Yeah, no, I mean it's an egregious example, and again, it fulfills like what we discussed with statistics, but even worse because it was marketed and it was incentivized financially and there's no doubt that some patients were completely miscategorized and potentially hurt. The other one, that's a classic example that went south is the Optum UnitedHealth algorithm. Maybe you could take us through that one as well, because that is yet another just horrible case of how people were discriminated against.

The Infamous Optum Algorithm

Sayash Kapoor (18:59):

Absolutely. So Optum, another health tech company created an algorithm to prioritize high risk patients for preemptive care. So I think it was around when Obamacare was being introduced that insurance networks started looking into how they could reduce costs. And one of the main ways they identified to reduce costs is basically preemptively caring for patients who are extremely high risk. So in this case, they decided to keep 3% of the patients in the high risk category and they built a classifier to decide who's the highest risk, because potentially once you have these patients, you can proactively treat them. There might be fewer emergency room visits, there might be fewer hospitalizations and so on. So that's all fine and good. But what happened when they implemented the algorithm was that every machine learning model needs like the target variable, what is being predicted at the end of the day. What they decided to predict was how much patient would pay, how much would they charge, what cost the hospital would incur if they admitted this patient.

(20:07):

And they essentially use that to predict who should be prioritized for healthcare. Now unsurprisingly, it turned out that white patients often pay a lot more or are able to pay a lot more when it comes to hospital visits. Maybe it's because of better insurance or better conditions at work that allow them to take leave and so on. But whatever the mechanism is, what ended up happening with this algorithm was I think black patients with the same level of healthcare prognosis were half as likely or about much less likely compared to white ones of getting enrolled in this high risk program. So they were much less likely to get this proactive care. And this was a fantastic study by Obermeyer, et al. It was published in Science in 2019. Now, what I think is the most disappointing part of this is that Optum did not stop using this algorithm after this study was released. And that was because in some sense the algorithm was working precisely as expected. It was an algorithm that was meant to lower healthcare costs. It wasn't an algorithm that was meant to provide better care for patients who need it most. And so, even after this study was rolled out, I think Optum continued using this algorithm as is. And I think as far as I know, even today this is or some version of this algorithm is still in use across the network of hospitals that Optum serves.

Eric Topol (21:31):

No, it's horrible the fact that it was exposed by Ziad Obermeyer’s paper in Science and that nothing has been done to change it, it's extraordinary. I mean, it's just hard to imagine. Now you do summarize the five reasons predictive AI fails in a nice table, we'll put that up on the post as well. And I think you've kind of reviewed that as these case examples. So now I get to challenge you about predictive AI because I don't know that such a fine line between that and generative AI are large language models. So as you know, the group at DeepMind and now others have done weather forecasting with multimodal large language models and have come up with some of the most accurate weather forecasting we've ever seen. And I've written a piece in Science about medical forecasting. Again, taking all the layers of a person's data and trying to predict if they're high risk for a particular condition, including not just their electronic record, but their genomics, proteomics, their scans and labs and on and on and on exposures, environmental.

Multimodal A.I. in Medicine

(22:44):

So I want to get your sense about that because this is now a coalescence of where you took down predictive AI for good reasons, and then now these much more sophisticated models that are integrating not just large data sets, but truly multimodal. Now, some people think multimodal means only text, audio, speech and video images, but here we're talking about multimodal layers of data as for the weather forecasting model or earthquake prediction or other things. So let's get your views on that because they weren't really presented in the book. I think they're a positive step, but I want to see what you think.

Sayash Kapoor (23:37):

No, absolutely. I think maybe the two questions are sort of slightly separate in my view. So for things like weather forecasting, I think weather forecasting is a problem that's extremely tenable for generative AI or for making predictions about the future. And I think one of the key differences there is that we don't have the problem of feedback loops with humans. We are not making predictions about individual human beings. We are rather making predictions about what happens with geological outcomes. We have good differential equations that we've used to predict them in the past, and those are already pretty good. But I do think deep learning has taken us one step further. So in that sense, I think that's an extremely good example of what doesn't really fit within the context of the chapter because we are thinking about decisions thinking about individual human beings. And you rightly point out that that's not really covered within the chapter.

(24:36):

For the second part about incorporating multimodal data, genomics data, everything about an individual, I think that approach is promising. What I will say though is that so far we haven't seen it used for making individual decisions and especially consequential decisions about human beings because oftentimes what ends up happening is we can make very good predictions. That's not in question at all. But even with these good predictions about what will happen to a person, sometimes intervening on the decision is hard because oftentimes we treat prediction as a problem of correlations, but making decisions is a problem of causal estimation. And that's where those two sort of approaches disentangle a little bit. So one of my examples, favorite examples of this is this model that was used to predict who should be released before screening when someone comes in with symptoms of pneumonia. So let's say a patient comes in with symptoms of pneumonia, should you release them on the day of?

(25:39):

Should you keep them in the hospital or should you transfer them to the ICU? And these ML researchers were basically trying to solve this problem. They found out that the neural network model they developed, this was two decades ago, by the way. The neural network model they developed was extremely accurate at predicting who would basically have a high risk of having complications once they get pneumonia. But it turned out that the model was saying essentially that anyone who comes in who has asthma and who comes in with symptoms of pneumonia is the lowest risk patient. Now, why was this? This was because when in the past training data, when some such patients would come into the hospital, these patients would be transferred directly to the ICU because the healthcare professionals realized that could be a serious condition. And so, it turned out that actually patients who had asthma who came in with symptoms of pneumonia were actually the lowest risk amongst the population because they were taken such good care of.

(26:38):

But now if you use this prediction that a patient comes in with symptoms of pneumonia and they have asthma, and so they're low risk, if you use this to make a decision to send them back home, that could be catastrophic. And I think that's the danger with using predictive models to make decisions about people. Now, again, I think the scope and consequences of decisions also vary. So you could think of using this to surface interesting patterns in the data, especially at a slightly larger statistical level to see how certain subpopulations behave or how certain groups of people are likely to develop symptoms or whatever. But I think when as soon as it comes to making decisions about people, the paradigm of problem solving changes because as long as we are using correlational models, I think it's very hard to say what will happen if we change the conditions, what will happen if the decision making mechanism is very different from one where the data was collected.

Eric Topol (27:37):

Right. No, I mean where we agree on this is that at the individual level, using multimodal AI with all these layers of data that have now recently become available or should be available, that has to be compared ideally in a randomized trial with standard of care today, which doesn't use any of that. And to see whether or not that decision's made, does it change the natural history and is it an advantage, that's yet to be done. And I agree, it's a very promising pathway for the future. Now, I think you have done what is a very comprehensive sweep on the predictive AI failures. You've mentioned here in our discussion, your enthusiasm and in the book about generative AI positive features and hope and excitement perhaps even. You didn't really yet, we haven't discussed much on the content moderation AI that you have discreetly categorized. Maybe you could just give us the skinny on your sense of that.

Content Moderation AI

Sayash Kapoor (28:46):

Absolutely. So content moderation AI is AI that's used to sort of clean up social media feeds. Social media platforms have a number of policies about what's allowed and not allowed on the platforms. Simple things such as spam are obviously not allowed because let's say people start spamming the platform, it becomes useless for everyone. But then there are other things like hate speech or nudity or pornography and things like that, which are also disallowed on most if not all social media platforms today. And I think a lot of the ways in which these policies are enforced today is using AI. So you might have an AI model that runs every single time you upload a photo to Facebook, for instance. And not just one perhaps hundreds of such models to detect if it has nudity or hate speech or any of these other things that might violate the platform's terms of service.

(29:40):

So content moderation AI is AI that's used to make these decisions. And very often in the last few years we've seen that when something gets taken down, for instance, Facebook deletes a post, people often blame the AI for having a poor understanding. Let's say of satire or not understanding what's in the image to basically say that their post was taken down because of bad AI. Now, there have been many claims that content moderation AI will solve social media's problems. In particular, we've heard claims from Mark Zuckerberg who in a senate testimony I think back in 2018, said that AI is going to solve most if not all of their content moderation problems. So our take on content moderation AI is basically this. AI is very, very useful for solving the simple parts of content moderation. What is a simple part? So basically the simple parts of content moderation are, let's say you have a large training data of the same type of policy violation on a platform like Facebook.

(30:44):

If you have large data sets, and if these data sets have a clear line in the sand, for instance, with nudity or pornography, it's very easy to create classifiers that will automate this. On the other hand, the hard part of content moderation is not actually just creating these AI models. The hard part is drawing the line. So when it comes to what is allowed and not allowed on platforms, these platforms are essentially making decisions about speech. And that is a topic that's extremely fraught. It's fraught in the US, it's also fraught globally. And essentially these platforms are trying to solve this really hard problem at scale. So they're trying to come up with rules that apply to every single user of the platform, like over 3 billion users in the case of Facebook. And this inevitably has these trade-offs about what speech is allowed versus disallowed that are hard to say one way or the other.

(31:42):

They're not black and white. And what we think is that AI has no place in this hard part of content moderation, which is essentially human. It's essentially about adjudicating between competing interests. And so, when people claim that AI will solve these many problems of content moderation, I think what they're often missing is that there's this extremely large number of things you need to do to get content moderation right. AI solves one of these dozen or so things, which is detecting and taking down content automatically, but all of the rest of it involves essentially human decisions. And so, this is sort of the brief gist of it. There are also other problems. For example, AI doesn't really work so well for low resource languages. It doesn't really work so well when it comes to nuances and so on that we discussed in the book. But we think some of these challenges are solvable in the medium to long term. But these questions around competing interests of power, I think are beyond the domain of AI even in the medium to long term.

Age 28! and Career Advice

Eric Topol (32:50):

No, I think you nailed that. I think this is an area that you've really aptly characterized and shown the shortcomings of AI and how the human factor is so critically important. So what's extraordinary here is you're just 28 and you are rocking it here with publications all over the place on reproducibility, transparency, evaluating generative AI, AI safety. You have a website on AI snake oil that you're collecting more things, writing more things, and of course you have the experience of having worked in the IT world with Facebook and also I guess also Columbia. So you're kind of off to the races here as one of the really young leaders in the field. And I am struck by that, and maybe you could comment about the inspiration you might provide to other young people. You're the youngest person I've interviewed for Ground Truths, by the way, by a pretty substantial margin, I would say. And this is a field where it attracts so many young people. So maybe you could just talk a bit about your career path and your advice for people. They may be the kids of some of our listeners, but they also may be some of the people listening as well.

Sayash Kapoor (34:16):

Absolutely. First, thank you so much for the kind words. I think a lot of this work is with collaborators without whom of course, I would never be able to do this. I think Arvind is a great co-author and supporter. I think in terms of my career parts, it was sort of like a zigzag, I would say. It wasn't clear to me when I was an undergrad if I wanted to do grad school or go into the industry, and I sort of on a whim went to work at Facebook, and it was because I'd been working on machine learning for a little bit of time, and I just thought, it's worth seeing what the other side has to offer beyond academia. And I think that experience was very, very helpful. One of the things, I talked to a lot of undergrads here at Princeton, and one of the things I've seen people be very concerned about is, what is the grad school they're going to get into right after undergrad?

(35:04):

And I think it's not really a question you need to answer now. I mean, in some cases I would say it's even very helpful to have a few years of industry experience before getting into grad school. That has definitely, at least that has been my experience. Beyond that, I think working in a field like AI, I think it's very easy to be caught up with all of the new things that are happening each day. So I'm not sure if you know, but AI has I think over 500-1,000 new archive papers every single day. And with this rush, I think there's this expectation that you might put on yourself on being successful requires a certain number of publications or a certain threshold of things. And I think more often than not, that is counterproductive. So it has been very helpful for me, for example, to have collaborators who are thinking long term, so this book, for instance, is not something that would be very valued within the CS community, I would say. I think the CS community values peer-reviewed papers a lot more than they do books, and yet we chose to write it because I think the staying power of a book or the longevity of a book is much more than any single paper could do. So the other concrete thing I found very helpful is optimizing for a different metric compared to what the rest of the community seems to be doing, especially when it comes to fast moving fields like AI.

Eric Topol (36:29):

Well, that last piece of advice is important because I think too often people, whether it's computer scientists, life scientists, whoever, they don't realize that their audience is much broader. And that reaching the public with things like a book or op-eds or essays, varied ways that are intended for public consumption, not for, in this case, computer scientists. So that's why I think the book is a nice contribution. I don't like the title because it's so skewed. And also the content is really trying to hammer it at home. I hope you write a sequel book on the positive sides of AI. I did want to ask you, when I read the book, I thought I heard your voice. I thought you had written the book, and Arvind maybe did some editing. You wrote about Arvind this and Arvind that. Did you write the first draft of the book and then he kind of came along?

Sayash Kapoor (37:28):

No, absolutely not. So the way we wrote the book was we basically started writing it in parallel, and I wrote the first draft of half the chapters and he wrote the first draft of the other half, and that was essentially all the way through. So we would sort of write a draft, pass it to the other person, and then keep doing this until we sent it to our publishers.

Eric Topol (37:51):

Okay. So I guess I was thinking of the chapters you wrote where it came through. I'm glad that it was a shared piece of work because that's good, because that’s what co-authoring is all about, right? Well, Sayash, it's really been a joy to meet you and congratulations on this book. I obviously have expressed my objections and my disagreements, but that's okay because this book will feed the skeptics of AI. They'll love this. And I hope that the positive side, which I think is under expressed, will not be lost and that you'll continue to work on this and be a conscience. You may know I've interviewed a few other people in the AI space that are similarly like you, trying to assure its safety, its transparency, the ethical issues. And I think we need folks like you. I mean, this is what helps get it on track, keeping it from getting off the rails or what it shouldn't be doing. So keep up the great work and thanks so much for joining.

Sayash Kapoor (39:09):

Thank you so much. It was a real pleasure.

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Francis Collins is a veritable national treasure. He directed the National Institutes of Health from 2009 to 2021. Prior to that he led the National Human Genetics Research Institute (NHGRI) from 1997-2009, during which the human genome was first sequenced. As a physician-scientist, he has made multiple seminal discoveries on the genetic underpinnings of cystic fibrosis, Huntington’s disease, neurofibromatosis, progeria, and others. This brief summary is barely scratching the surface oh his vast contributions to life science and medicine.

A video clip from our conversation on hepatitis C. Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify.

Transcript with external inks and links to audio

Eric Topol (00:06):

Well, I am really delighted to be able to have our conversation with Francis Collins. This is Eric Topol with Ground Truths and I had the chance to first meet Francis when he was on the faculty at the University of Michigan when I was a junior faculty. And he gave, still today, years later, we're talking about 40 years later, the most dazzling Grand Rounds during his discovery of cystic fibrosis. And Francis, welcome, you inspired me and so many others throughout your career.

Francis Collins (00:40):

Well, Eric, thank you and you've inspired me and a lot of other people as well, so it's nice to have this conversation with you in the Ground Truths format.

Eric Topol (00:49):

Well, thank you. We're at the occasion of an extraordinary book you put together. It's the fifth book, but it stands out quite different from the prior books as far as I can tell. It's called The Road to Wisdom: On Truth, Science, Faith and Trust, these four essential goods that build upon each other. And it's quite a book, Francis, I have to say, because you have these deep insights about these four critical domains and so we'll get into them. But I guess the first thing I thought I'd do is just say, how at some point along the way you said, “the goal of this book is to turn the focus away from hyperpartisan politics and bring it back to the most important sources of wisdom: truth, science, faith and trust, resting upon a foundation of humility, knowledge, morality, and good judgment.” So there's a lot there. Maybe you want to start off with what was in the background when you were putting this together? What were you really aiming at getting across?

Reflections on Covid

Francis Collins (02:06):

I'm glad to, and it's really a pleasure to have a chance to chat with you about this. I guess before Covid came along, I was probably a bit of a naive person when it came to how we make decisions. Yeah, I knew there were kind of wacky things that had gone out there from time to time, but I had a sort of Cartesian attitude that we were mostly rational actors and when presented with evidence that's been well defended and validated that most people will say, okay, I know what to do. Things really ran off the rails in the course of Covid. It was this remarkable paradox where, I don't know what you would say, but I would say the development of the vaccines that were safe and highly effective in 11 months using the mRNA platform was one of the most stunning achievements of science in all of history up until now.

Francis Collins (03:02):

And yet 50 million Americans decided they didn't want any part of it because of information that came to them that suggested this was not safe or there was conspiracies behind it, or maybe the syringes had chips that Bill Gates had put in there or all manner of other things that were being claimed. And good honorable people were distracted by that, lost their trust in other institutions like the CDC, maybe like the government in general like me, because I was out there a lot trying to explain what we knew and what we didn't know about Covid. And as a consequence of that, according to Kaiser Family Foundation, more than 230,000 people died between June of 2021 and April of 2022 because of a decision to reject the opportunity for vaccines that were at that time free and widely available. That is just an incredibly terribly tragic thing to say.

Francis Collins (04:03):

More than four times the number of people who died, Americans who died in the Vietnam War are in graveyards unnecessarily because we lost our anchor to truth, or at least the ability to discern it or we couldn't figure out who to trust while we decided science was maybe not that reliable. And people of faith for reasons that are equally tragic were among those most vulnerable to the misinformation and the least likely therefore, to take advantage of some of these lifesaving opportunities. It just completely stunned me, Eric, that this kind of thing could happen and that what should have been a shared sense of working against the real enemy, which was the SARS-CoV-2 virus became instead a polarized, divisive, vitriolic separation of people into separate camps that were many times driven more by politics than by any other real evidence. It made me begin to despair for where we're headed as a country if we can't figure out how to turn this around.

Francis Collins (05:11):

And I hadn't really considered it until Covid how serious this was and then I couldn't look away. And so, I felt if I have a little bit of credibility after having stepped down after 12 years as the NIH Director and maybe a chance to influence a few people. I just have to try to do something to point out the dangers here and then to offer some suggestions about what individuals can do to try to get us back on track. And that's what this book is all about. And yeah, it's called The Road to Wisdom because that's really how I want to think of all this in terms of truth and science and faith and trust. They all kind of give you the opportunities to acquire wisdom. Wisdom is of course knowledge, but it's not just knowledge, it's also understanding it has a moral character to it. It involves sophisticated judgment about difficult situations where there isn't an obvious answer. We need a lot more of that, it seems we’re at short supply.

Deconvoluting Truth

Eric Topol (06:13):

Well, what I really loved about the book among many things was how you broke things down in just a remarkably thoughtful way. So truth, you have this great diagram like a target with the four different components.

in the middle, necessary truth. And then as you go further out, firmly established facts, then uncertainty and then opinion, and truth is not a dichotomous by any means. And you really got that down and you explained each of these different facets of truth with great examples. And so, this among many other things that you broke down, it wasn't just something that you read somewhere, you really had to think this through and perhaps this experience that we all went through, but especially you. But because you bring so much of the book back to the pandemic at times with each of the four domains, so that and the spider web. The spider web of where your core beliefs

are and then the ones further out on the web and you might be able to work on somebody out further periphery, but it's pretty hard if you're going to get to them in the middle where their main thing is science is untrustworthy or something like that.

Eric Topol (07:36):

So how did you synthesize these because the graphics are quite extraordinary?

Francis Collins (07:44):

Well, I will say the artist for the graphics is a remarkable graphic design student at the University of Michigan who happens to be my granddaughter. So it was nice having that ability to have my scratches turned into something actually looks like artwork. The concepts I got to say, Eric, I was feeling pretty unsure of myself. I never took a course in philosophy. I know there are people who've spent their entire careers going all the way back to Socrates and on up until now about what does truth mean and here's this scientist guy who's trying to say, well, let me tell you what I think about it. I'm glad to hear that you found these circles useful. They have been very useful for me and I hadn't thought about it much until I tried to put it in some sort of framework and a lot of the problems we have right now where somebody says, well, that might be true for you, but it's not true for me, that's fine if you're talking about an opinion, like whether that movie was really good or not.

Francis Collins (08:43):

But it's not fine if it's about an established fact, like the fact that climate change is real and that human activity is the main contributor to the fact that we've warmed up dramatically since 1950. I'm sorry, that's just true. It doesn't care how you feel about it, it's just true. So that zone of established facts is where I think we have to re-anchor ourselves again when something's in that place. I'm sorry, you can't just decide you don't like it, but in our current climate and maybe postmodernism has crept in all kinds of ways we're not aware of, the idea that there is such a thing as objective truth even seems to be questioned in some people's minds. And that is the path towards a terrible future if we can't actually decide that we have, as Jonathan Rauch calls it, a constitution of knowledge that we can depend on, then where are we?

Eric Topol (09:37):

Well, and I never heard of the term old facts until the pandemic began and you really dissect that issue and like you, I never had anticipated there would be, I knew there was an anti-science, anti-vaccine sector out there, but the fact that it would become so strong, organized, supported, funded, and vociferous, it's just looking back just amazing. I do agree with the statement you made earlier as we were talking and in the book, “the development of mRNA vaccines for Covid in record time as one of the greatest medical achievements in human history.” And you mentioned besides the Kaiser Family Foundation, but the Commonwealth Fund, a bipartisan entity saved three million lives in the US, eighteen million hospitalizations. I mean it's pretty extraordinary. So besides Covid, which we may come back to, but you bring in everything, you bring in AI. So for example, you quoted the fellow from Google who lost his job and you have a whole conversation with Blake Lemoine and maybe you can give us obviously, where is AI in the truth and science world? Where do you stand there and what were you thinking when you included his very interesting vignette?

Perspective on A.I.

Francis Collins (11:17):

Well, I guess I was trying to talk about where are we actually at the point of AGI (artificial general intelligence) having been achieved? That is the big question. And here's Blake Lemoine who claimed based on this conversation that I quote in the book between him and the Google AI apparatus called LaMDA. Some pretty interesting comments where LaMDA is talking about having a soul and what its soul looks like and it's a portal to all sorts of other dimensions, and I can sort of see why Blake might've been taken in, but I can also see why a lot of people said, oh, come on, this is of course what an AI operation would say just by scanning the internet and picking out what it should say if it's being asked about a soul. So I was just being a little provocative there. My view of AI, Eric, is that it's applications to science and medicine are phenomenal and we should embrace them and figure out ways to speed them up in every way we can.

Francis Collins (12:17):

I mean here at NIH, we have the BRAIN Initiative that's trying to figure out how your brain works with those 86 billion neurons and all their connections. We're never going to sort that out without having AI tools to help us. It's just too complicated of a problem. And look what AI is doing and things like imaging radiologists are going to be going out of business and the pathologists may not be too far behind because when it comes to image analysis, AI is really good at that, and we should celebrate that. It's going to improve the speed and accuracy of all kinds of medical applications. I think what we have to worry about, and I'm not unique in saying this, is that AI when applied to a lot of things kind of depends on what's known and goes and scrapes through the internet to pull that out. And there's a lot of stuff on the internet that's wrong and a lot of it that's biased and certainly when it comes to things like healthcare, the bias in our healthcare system, health disparities, inadequacies, racial inequities are all in there too, and if we're going to count on AI to fix the system, it's building on a cracked foundation.

Francis Collins (13:18):

So we have to watch out for that kind of outcome. But for the most part, generative AI it’s taking really exciting difficult problems and turning them into solutions, I'm all for it, but let's just be very careful here as we watch how it might be incorporating information that's wrong and we won't realize it and we'll start depending on it more than we should.

Breathtaking Advances

Eric Topol (13:42):

Yeah, no, that's great. And you have some commentary on all the major fronts that we're seeing these days. Another one that is a particularly apropos is way back when you were at Michigan and the years before that when you were warming up to make some seminal gene discoveries and cystic fibrosis being perhaps the first major one. You circle back in the book to CRISPR genome editing and how the success story to talk about some extraordinary science to be able to have a remedy, a cure potentially for cystic fibrosis. So maybe you could just summarize that. I mean that's in your career to see that has to be quite remarkable.

Francis Collins (14:32):

It is breathtaking, Eric. I mean I sort of like to think of three major developments just in the last less than 20 years that I never dreamed would happen in my lifetime. One was the ability to make stem cells from people who are walking around from a skin biopsy or a blood sample that are pluripotent. My whole lab studies diabetes, our main approach is to take induced pluripotent stem cells from people whose phenotypes we know really well and differentiate them into beta cells that make insulin and see how we can figure out how the genetics and other aspects of this determine whether something is going to work properly or not. I mean that's just astounding. The second thing is the ability to do single cell biology.

Francis Collins (15:16):

Which really 15 years ago you just had to have a bunch of cells and studying diabetes, we would take a whole eyelid and grind it up and try to infer what was there, ridiculous. Now we can look at each cell, we even can look at each cell in terms of what's its neighbor, does the beta cell next to an alpha cell behave the same way as a beta cell next to a duct? We can answer those questions, and of course the third thing is CRISPR and gene editing and of course the first version of CRISPR, which is the knockout of a gene was exciting enough, but the ability to go in and edit without doing a double stranded break and actually do a search and replace operation is what I'm truly excited about when it comes to rare genetic diseases including one that we work on progeria, which is this dramatic form of premature aging that is caused almost invariably by a C to T mutation in exon 11 of the LMNA gene and for which we have a viable strategy towards a human clinical trial of in vivo gene editing for kids with this disease in the next two years.

Eric Topol (16:24):

Yeah, it's just the fact that we were looking at potential cures for hundreds and potentially even thousands of diseases where there was never a treatment. I mean that's astounding in itself, no less, the two other examples. The fact that you can in a single cell, you can not only get the sequence of DNA and RNA and methylation and who would've ever thought, and then as you mentioned, taking white cells from someone's blood and making pluripotent stem cells. I mean all these things are happening now at scale and you capture this in the book.

On Humility and Trust

Now the other thing that you do that I think is unique to you, I don't know if it's because of your background in growing up in Staunton, Virginia, a very different type of world, but you have a lot of humility in the book. You go over how you got snickered by Bill Maher, how you had a graduate student who was fabricating images and lots of things, how you might not have communicated about Covid perhaps as well as could. A lot of our colleagues are not able to do that. They don't ever have these sorts of things happening to them. And this humility which comes across especially in the chapter on trust where you break down who do you trust, humility is one of the four blocks as you outlined, competence, integrity, and aligned value

So maybe can you give us a little brief lesson on humility?

Eric Topol (18:06):

Because it's checkered throughout the book and it makes it this personal story that you're willing to tell about yourself, which so few of us are willing to do.

Francis Collins (18:17):

Well, I don't want to sound proud about my humility. That would not be a good thing because I’m not, but thanks for raising it. I do think when we consider one of the reasons we decide to trust somebody, that it does have that humility built into it. Somebody who's willing to say, I don't know. Somebody's willing to say I'm an expert on this issue, but that other issue you just asked me about, I don't know any more than anybody else and you should speak to someone else. We don't do that very well. We tend to plunge right in and try to soak it up. I do feel when it comes to Covid, and I talk about this in the book a bit, that I was one of those trying to communicate to the public about what we think are going to be the ways to deal with this worst pandemic in more than a century.

Francis Collins (19:06):

And I wish Eric, I had said more often what I'm telling you today is the best that the assembled experts can come up with, but the data we have to look at is woefully inadequate. And so, it very well could be that what I'm telling you is wrong, when we get more data, I will come back to you as soon as we have something better and we'll let you know, but don't be surprised if it's different and that will not mean that we are jerking you around or we don't know what we're talking about. It's like this is how science works. You are watching science in real time, even though it's a terrible crisis, it's also an opportunity to see how it works. I didn't say that often enough and neither did a lot of the other folks who were doing the communicating. Of course, the media doesn't like to give you that much time to say those things as you well know, but we could have done a better job of preparing people for uncertainty and maybe there would've been less of a tendency for people to just decide, these jokers don't know what they're talking about.

Francis Collins (20:10):

I'm going to ignore them from now on. And that was part of what contributed to those 230,000 unnecessary deaths, it was just people losing their confidence in the information they were hearing. That's a source of grief from my part.

His Diagnosis And Treatment for Prostate Cancer

Eric Topol (20:24):

Well, it's great and a lesson for all of us. And the other thing that along with that is remarkable transparency about your own health, and there's several things in there, but one that coincides. You mentioned in the book, of course, you wrote an op-ed in the Washington Post back in April 2024 about your diagnosis of prostate cancer. So you touched on it in the book and maybe you could just update us about this because again, you're willing to tell your story and trying to help others by the experiences that you've been through.

Francis Collins (21:00):

Well, I sure didn't want to have that diagnosis happen, but once it did, it certainly felt like an opportunity for some education. We men aren't that good about talking about issues like this, especially when it involves the reproductive system. So going out and being public and saying, yep, I had a five year course of watching to see if something was happening, and then the slow indolent cancer suddenly decided it wasn't slow and indolent anymore. And so, I'm now having my prostate removed and I think I'm a success story, a poster boy for the importance of screening. If I hadn't gone through that process of PSA followed by imaging by MRI followed by targeted biopsies, so you're actually sampling the right place to see if something's going on. I probably would know nothing about it right now, and yet incubating within me would be a Gleason category 9 prostate cancer, which has a very high likelihood if nothing was done to become metastatic.

Francis Collins (22:03):

So I wanted that story to be out there. I wanted men who were squeamish about this whole topic to say, maybe this is something to look into. And I've heard a bunch of follow-ups from individuals, but I don't know how much of it impact it hit. I'm glad to say I'm doing really well. I'm four months out now from the surgery, it is now the case I'm pretty much back to the same level of schedule and energy that I had beforehand, and I'm very happy to say that the post-op value of PSA, which is the best measure to see whether you in fact are now cancer free was zero, which is a really nice number.

Eric Topol (22:45):

Wow. Well, the prostate is the curse of men, and I wish we could all have an automated prostatectomy so we don't have to deal with this. It's just horrible.

Francis Collins (22:58):

It was done by a robot. It wasn't quite automated, I have stab wounds to prove that the robot was actually very actively doing what it needed to do, but they healed quickly.

The Promise of Music As Therapy in Medicine

Eric Topol (23:11):

Right. Well, this gets me to something else that you're well known for throughout your career as a musician, a guitarist, a singer, and recently you hooked up with Renée Fleming, the noted opera singer, and you've been into this music is therapy and maybe you can tell us about that. It wasn't necessarily built up much in the book because it's a little different than the main agenda, but I think it's fascinating because who doesn't like music? I mean, you have to be out there if you don't enjoy music, but can you tell us more about that?

Francis Collins (23:53):

Yeah, I grew up in a family where music was very much what one did after dinner, so I learned to play keyboard and then guitar, and that's always been a source of joy and also a source of comfort sometimes when you were feeling a bit down or going through a painful experience. I think we all know that experience where music can get into your heart and your soul in a way that a lot of other things can't. And the whole field of music therapy is all about that, but it's largely been anecdotal since about World War II when it got started. And music therapists will tell you sometimes you try things that work and sometimes they don't and it's really hard to know ahead of time what's going to succeed. But now we have that BRAIN Initiative, which is pushing us into whole new places as far as the neuroscience of the brain, and it's really clear that music has a special kind of music room in the brain that evolution has put there for an important reason.

Francis Collins (24:47):

If we understood that we could probably make music therapy even more scientifically successful and maybe even get third parties to pay for it. All of this became opportunity for building a lot more visibility because of making friends withRenée Fleming, who I hadn't really known until a famous dinner party in 2015 where we both ended up singing to a trio of Supreme Court justices trying to cheer them up after a bent week. And she has become such an incredible partner in this. She's trained herself pretty significantly in neuroscience, and she's a convener and an articulate spokesperson. So over the course of that, we built a whole program called Sound Health that now has invested an additional $35 million worth NIH research to try to see how we can bring together music therapy, musician performers and neuroscientists to learn from each other, speak each other's language and see what we could learn about this particularly interesting input to the human brain that has such power on us and maybe could be harnessed to do even more good for people with chronic pain or people with PTSD, people with dementia where music seems to bring people back to life who'd otherwise seem to have disappeared into the shadows.

Francis Collins (26:09):

It's phenomenal what is starting to happen here, but we're just scratching the surface.

The Big Miss vs Hepatitis C

Eric Topol (26:14):

Well, I share your enthusiasm for that. I mean, it's something that you could think of that doesn't have a whole lot of side effects, but could have a lot of good. Yeah. Well, now before I get back to the book, I did want to cover one other relatively recent op-ed late last year that you wrote about Hepatitis C. Hepatitis C, one of the most important medical advances in the 21st century that we're squandering. Can you tell us about that? Because I think a lot of people don't realize this is a big deal.

Francis Collins (26:47):

It's a really big deal, and I confess I'm a little obsessed about it. So yes, you may regret bringing it up because I'm really going to want to talk about what the opportunity is here, and I am still the lead for the White House in an initiative to try to find the 4 million Americans who are already infected with this virus and get access to them for treatment. The treatment is fantastic, as you just said, one of the most major achievements of medical research, one pill a day for 12 weeks, 95% cure in the real world, essentially no side effects, and yet the cost is quite high and the people who need it many times do not have great healthcare and maybe also in difficult circumstances because you get hepatitis C from infected blood. And the many ways that happens these days are from shared needles from people who are experimenting with intravenous drugs, but they are family too, and many of them now recovering from that, face the irony of getting over their opioid addiction and then looking down the barrel of a really awful final couple of years dying of liver failure. I watched my brother-in-law die of hepatitis C, and it was just absolutely gruesome and heartbreaking.

Francis Collins (28:04):

So this isn't right. And on top of that, Eric, the cost of all this for all those folks who are going to get into liver failure need a transplant or develop liver cancer, this is the most common cause now of liver cancer it is astronomical in the tens of billions of dollars. So you can make a very compelling case, and this is now in the form of legislation sponsored by Senators Cassidy and Van Hollen that in a five-year program we could find and cure most of those people saving tens of thousands of lives and we would save tens of billions of dollars in just 10 years in terms of healthcare that we will not have to pay for. What's not to love here? There's a lot of things that have to be worked out to make it happen. One thing we've already done is to develop, thanks to NIH and FDA, a point of care viral RNA finger stick test for Hep C. You get an answer in less than an hour.

Francis Collins (29:00):

FDA approved that the end of June. That was a big crash program so you can do test and treat in one visit, which is phenomenally helpful for marginalized populations. The other thing we need to do is to figure out how to pay for this and this subscription model, which was piloted in Louisiana, looks like it ought to work for the whole nation. Basically, you ask the companies Gilead and AbbVie to accept a lump sum, which is more than what they're currently making for Medicaid patients and people who are uninsured and people in the prison system and Native Americans and then make the pills available to those four groups for free. They do fine. The companies come out on this and the cost per patient plummets and it gives you the greatest motivation you can imagine to go and find the next person who's infected because it's not going to cost you another dime for their medicine, it's already paid for. That's the model, and I would say the path we're on right now waiting for the congressional budget office to give the final score, it's looking pretty promising we're going to get this done by the end of this year.

The Pledge

Eric Topol (30:04):

Yeah, that's fantastic. I mean, your work there alone is of monumental importance. Now I want to get back to the book the way you pulled it all together. By the way, if anybody's going to write a book about wisdom, it ought to be you, Francis. You've got a lot of it, but you had to think through how are we going to change because there's a lot of problems as you work through the earlier chapters and then the last chapter you come up with something that was surprising to me and that was a pledge for the Road to Wisdom. A pledge that we could all sign, which is just five paragraphs long and basically get on board about these four critical areas. Can you tell us more about the pledge and how this could be enacted and help the situation?

Francis Collins (31:03):

Well, I hope it can. The initial version of this book, I wrote a long piece about what governments should do and what institutions should do and what universities should do and what K through 12 education should do. And then I thought they're not reading this book and I'm not sure any of those folks are really that motivated to change the status quo. Certainly, politicians are not going to solve our current woes. It seems that politics is mostly performance these days and it's not really about governance. So if there's going to be a chance of recovering from our current malaise, I think it's got to come from the exhausted middle of the country, which is about two thirds of us. We're not out there in the shrill screaming edges of the left and the right we're maybe tempted to just check out because it just seems so discouraging, but we're the solution.

Francis Collins (31:56):

So the last chapter is basically a whole series of things that I think an individual could start to do to turn this around. Beginning with doing a little of their own house cleaning of their worldview to be sure that we are re-anchoring to things like objective truths and to loving your neighbor instead of demonizing your neighbor. But yeah, it does go through a number of those things and then it does suggest as a way of making this not just a nice book to read, but something where you actually decide to make a commitment. Look at this pledge. I've tried the pledge out on various audiences so far and I haven't yet really encountered anybody who said, well, those are ridiculous things to ask of people. They're mostly things that make a lot of sense, but do require a commitment. That you are, for instance, you're not going to pass around information on social media in other ways unless you're sure it's true because an awful lot of what's going on right now is this quick tendency for things that are absolutely wrong and maybe anger inducing or fear inducing to go viral where something that's true almost lands with a thud.

Francis Collins (33:07):

Don't be part of that, that's part of this, but also to make an honest effort to reach out to people who have different views from you. Don't stay in your bubble and try to hear their concerns. Listen, not that you're listening in order to give a snappy response, but listen, so you're really trying to understand. We do far too little of that. So the pledge asks people to think about that, and there is a website now which will be as part of the book up on the Braver Angels website and Braver Angels is a group that has made its mission trying to bring together these divided parties across our country and I'm part of them, and you can then go and sign it there and make a public statement that this is who I am, and it will also give you a whole lot of other resources you could start to explore to get engaged in being part of the solution instead of just shaking your head. I think what we're trying to do is to get people to go beyond the point of saying, this isn't the way it should be to saying, this isn't the way I should be. I'm going to try to change myself as part of fixing our society.

Eric Topol (34:14):

Well, I'm on board for this and I hope it creates a movement. This is as you tell the stories in the book, like the fellow that you wrangled with about the pandemic and how you listened to him and it changed your views and you changed his views and this is the health of different opinions and perspectives and we got to get back there. It used to be that way more at least it wasn't always perfect, and as you said in the book, we all have some entrenched biases. We're never going to get rid of all of them, but your wisdom about the road, the pledge here is I think masterful. So I just want to pass on along and I hope listeners will go to the Brave for Angels website and sign up because if we got millions of people to help you on this, that would say a lot about a commitment to a renewed commitment to the way it should be, not the way it is right now. Well, I've covered a bunch of things, of course, Francis, but did I miss something that you're passionate about or in the book or anything that you want to touch on?

Francis Collins (35:32):

Oh my goodness, yeah. You did cover a lot of ground here, including things that I didn't pay much attention to in the book, but I was glad to talk to you about. No, I think we got a pretty good coverage. The one topic in the book that will maybe appeal particularly to believers is a whole chapter about faith because I am concerned that people of faith have been particularly vulnerable to misinformation and disinformation, and yet they stand on a foundation of principles that ought to be the best antidote to most of the meanness that's going on, and just trying to encourage them to recall that and then build upon the strength that they carry as a result of their faith traditions to try to be part of the solution as well.

Eric Topol (36:12):

I'm so glad you mentioned that. It's an important part of the book, and it is also I think something that you were able to do throughout your long tenure at NIH Director that you were able to connect to people across the aisle. You had senators and the Republicans that were so supportive of your efforts to lead NIH and get the proper funding, and it's a unique thing that you're able to connect with people of such different backgrounds, people of really deep commitment to religion and faith and everything else. And that's one of the other things that we talk about Francis here, and many times I gather is we don't have you at the helm anymore at NIH, and we're worried. We're worried because you're a unique diplomat with all this heavy wisdom and it's pretty hard to simulate your ability to keep the NIH whole and to build on it. Do you worry about it at all?

Francis Collins (37:23):

Well, I was privileged to have those 12 years, but I think it was time to get a new perspective in there, and I appreciate you saying those nice things about my abilities. Monica Bertagnolli is also a person of great skill, and I think on the hill she rapidly acquired a lot of fans by her approach, by some of her background. She's from Wyoming, she's a cancer surgeon. She's got a lot of stories to tell that are really quite inspiring. I think though it's just a very difficult time. She walked in at a point where the partisan attitudes about medical research, which we always hoped would kind of stay out of the conversation and become so prominent, a lot of it politically driven, nasty rhetoric on the heels of Covid, which spills over into lots of other areas of medical research and is truly unfortunate. So she's got a lot to deal with there, but I'm not sure I would be much better than she is in trying to continue stay on message, tell the stories about how medical research is saving lives and alleviating suffering, and we're just getting started, and she does that pretty well.

Francis Collins (38:34):

I just hope the people who need to listen are in a listening mood.

Eric Topol (38:38):

Yeah. Well, that's great to hear your perspective. Well, I can't thank you enough for our conversation and moreover for a friendship that's extended many decades now. We're going to be following not just your progeria research and all the other things that you're up to because juggling a bunch of things still, it isn't like you're slowed down at all. And thanks so much for this book. I think it's a gift. I think it's something that many people will find is a pretty extraordinary, thoughtful and easy read. I mean, it's something that I found that you didn't write it for in technical jargon. You wrote it for the public, you wrote it for non-scientists, non-medical people, and I think hopefully that's what's going to help it get legs in terms of what's needed, which is a sign the darn pledge. Thank you.

Francis Collins (39:42):

Eric, thank you. It has been a privilege being your friend for all these years, and this was a really nice interview and I appreciate that you already had carefully read the book and asked some great questions that were fun to try to answer. So thanks a lot.

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Professor Joseph Allen directs the Healthy Buildings Program at Harvard Chan School of Public Health. His expertise extends far beyond what makes buildings healthy. He has been a leading voice and advocate during the Covid pandemic for air quality and ventilation. He coined the term “Forever Chemicals” and has written extensively on this vital topic, no less other important exposures, which we covered In our wide-ranging conversation. You will see how remarkably articulate and passionate Prof Allen is about these issues, along with his optimism for solutions.

A video snippet of our conversation: buildings as the 1st line of defense vs respiratory pathogens. Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify.

Transcript with External Links and Links to Audio

Eric Topol (00:00:06):

Well, hello. It's Eric Topol from Ground Truths and I am just delighted to have with me, Joseph Allen from the Harvard School of Public Health, where he directs the Healthy Buildings Program that he founded and does a whole lot more that we're going to get into. So welcome, Joe.

Joseph Allen (00:00:24):

Thanks. It's great to be here. I appreciate the invitation.

Joe Allen’s Background As A Detective

Eric Topol (00:00:28):

Well, you have been, as I've learned, rocking it for many years long before the pandemic. There's quite a background about you having been a son of a homicide detective, private eye agency, and then you were going to become an FBI agent. And the quote from that in the article that's the Air Investigator is truly a classic. Yeah, you have in there, “I guarantee I'm the only public health student ever to fail an FBI lie detector polygraph in the morning and start graduate school a few hours later.” That's amazing. That's amazing.

Joseph Allen (00:01:29):

All right. Well, you've done your deep research apparently. That's good. Yeah, my dad was a homicide detective and I was a private investigator. That's no longer my secret. It's out in the world. And I switched careers and it happened to be the day I took the polygraph at the FBI headquarters in Boston, was the same day I started graduate studies in public health.

Sick vs Healthy Buildings (Pre-Covid)

Eric Topol (00:01:53):

Well, you're still a detective and now you're a detective of everything that can hurt us or help us environmentally and my goodness, how grateful we are that you change your career path. I don't know anyone who's had more impact on buildings, on air, and we're going to get into chemicals as well. So if we go back a bit here, you wrote a book before the pandemic, talk about being prescient. It’s called Healthy Buildings: How Indoor Spaces Can Make You Sick - or Keep You Well with John Macomber, your co-author. What was it that gave you the insight to write a book before there was this thing called Covid?

Joseph Allen (00:02:41):

Yeah, well, thanks for making the connection too, my past career to current career. For many years, I thought there wasn't a connection, but I agree. There's actually a lot of similarities and I also am really appreciative. I am lucky I found the field of Public Health, it's clearly where I belong. I feel like I belong here. It's a place to make an impact that I want to make in my career. So yeah, the Healthy Buildings book, we started writing years before the pandemic and was largely motivated by, I think what you and others and other people in my field have known, is that buildings have an outsized impact on our health. Yet it's not something that comes to the forefront when you ask people about what matters for their health. Right, I often start presentations by asking people that, what constitutes healthy living? They'll say, I can't smoke, I have to eat well.

(00:03:30):

I have to exercise. Maybe they'll say, outdoor pollution’s bad for you. Very few people, if any, will say, well, the air I breathe inside my building matters a lot. And over the years I had started my public health career doing forensic investigations of sick buildings. People really can get sick in buildings. It can be anything from headaches and not being able to concentrate all the way to cancer clusters and people dying because of the building. And I've seen this in my career, and it was quite frustrating because I knew, we all knew how to design and operate buildings in a way that can actually keep people healthy. But I was frustrated like many in my field that it wasn't advancing. In other words, the science was there, but the practice wasn't changing. We were still doing things the wrong way around ventilation, materials we put in our building, and I would lecture over and over and give presentations and I decided I want to try something new.

(00:04:22):

I do peer-reviewed science. That's great. I write pieces like you for the public, and I thought we'd try a longer form piece in a book, and it's published by Harvard Press. John Macomber for those who know is a professor at Harvard Business School who's an expert in real estate finance. So he'd been talking about the economic benefits of healthier buildings and some hand waving as he describes around public health. I've been talking about the public health benefits and trying to wave an economic argument. We teamed up to kind of use both of our strengths to, I hope make a compelling case that buildings are good for health and they're also just good business. In other words, try to break down as many barriers as we can to adoption. And then the book was published right as Covid hit.

Indoor Air Quality and Cognition

Eric Topol (00:05:05):

Yeah. I mean, it's amazing. I know that typically you have to have a book almost a year ahead to have it in print. So you were way, way ahead of this virus. Now, I'm going to come back to it later, but there were two things beyond the book that are pretty striking about your work. One is that you did all these studies to show with people wearing sensors to show that when the levels of CO2 were high by sensors that their cognition indoors was suffering. Maybe you could just tell us a little bit about these sensors and why aren't we all wearing sensors so that we don't lose whatever cognitive power that we have?

Joseph Allen (00:05:56):

Well, yeah. First I think we will start having these air quality sensors. As you know, they're starting to become a lot more popular. But yeah, when I first joined the faculty full-time at Harvard, one of the first studies I conducted with my team was to look at how indoor air quality influences cognitive function. And we performed a double-blind study where we took people, office workers and put them in a typical office setting. And unbeknownst to them, we started changing the air they were breathing in really subtle ways during the day, so they didn't know what we were doing. At the end of the day, we administered an hour and a half long cognitive function battery, and like all studies, we control for things like caffeine intake, baseline cognitive performance, all the other factors we want to account for. And after controlling for those factors in a double-blind study, we see that indoor air quality, minor improvements to indoor air quality led to dramatic increases in cognitive function test scores across domains that people recognize as important for everyday life.

(00:06:59):

How do you seek out and utilize information? How do you make strategic decisions? How do you handle yourself during a crisis and importantly recover after that crisis? I don't mean the world's ending crisis. I mean something happens at work that's stressful. How do you handle that and how do you respond? Well, it turns out that amongst all the factors that influence how we respond there, indoor air quality matters a lot. We call that study the COGfx Study for cognitive function. We replicated it across the US, we replicated it across the world with office workers around the world, and again, always showing these links, the subtle impact of indoor air quality on cognitive function performance. Now, that also then starts to be the basis for some of the economic analysis we perform with my colleague at Harvard Business School. We say, well, look, if you perform this much better related to air quality, what would happen if we implemented this at scale in a business?

(00:07:51):

And we estimate that there are just massive economic gains to be had. On a per person basis, we found and published on this, that's about $6,000 to $7,000 per person per year benefit across a company. It could lead to 10% gains to the bottom line performance of the company. And again, I'm a public health professor. My goal is to improve people's health, but we add a lens, mental health, brain health is part of health, and we add the economic lens to say, look, this is good for a worker of productivity and the costs are downright trivial when you compare it against the benefits, even just including the cognitive function benefits, not even including the respiratory health benefit.

Eric Topol (00:08:33):

And I mean, it's so striking that you did these studies in a time before sensors were, and they still are not widely accepted, and it really helped prove, and when we start to fall asleep in a group session indoors, it may not just be because we didn't have enough sleep the night before, right.

Joseph Allen (00:08:56):

It's funny you say that. I talk about that too. It's like, do we actually need the study to tell us to quantify what we've all experienced these bad conference rooms, you get tired, you can't concentrate, you get sleepy while you're driving your car. Yeah, a whole bunch of other factors. Maybe the speaker's boring, but a key factor is clearly indoor air quality and things like good ventilation, the chemical load in the space are all contributing.

Eric Topol (00:09:20):

Yeah. No, it's pretty darn striking. Now we're going to get into the pandemic, and this of course is when your work finally crystallized that you've been working on this for years, and then finally your collaboration with some of the aerosol experts. It was a transdisciplinary synergy that was truly extraordinary. And when you were on 60 Minutes last October, you said, “Think about the public health gains we've made over the past hundred years. We've made improvements to water quality, outdoor air pollution, our food safety, we've made improvements to sanitation: absolute basics of public health. Where has indoor air been in that conversation?” You brought it to us. I mean, you led the Lancet Commission on this. You've done a White House Summit keynote. You had a lot of influence. Why did it take us to finally wake up to this issue that you've been working on for years?

Covid is Airborne, Denial

Joseph Allen (00:10:31):

Yeah. Well, I appreciate that, but I also liked what you started with. I mean, there's been a lot of us pulling on this, and I think one of the magical moments, if you could say that when the pandemic happened was that it forced these collaborations and forced a lot of us in our field to be a bit more vocal. And even that comment about the gains we made in public health, that comes from an article that we co-authored with 40 plus scientists around the world in science, trying to drive home the point that we've ignored one of the key factors that determines our health. We were all frustrated at the beginning of the pandemic. The first piece I wrote was January 2020, talking about healthy buildings as the first line of defense, airborne spread, ventilation, filtration. I could not get it published. I could not get it published.

(00:11:20):

So I moved it to an international paper. I wrote it in the Financial Times in early February, but it wasn't until mid-March that the Times took my piece on this airborne spread buildings ventilation. At the same time, we know people like Linsey Marr, Rich Corsi, many others, Shelly Miller out there publishing, doing the fundamental research, all trying to elevate, and I think we started to find each other and say, hey, someone's trying to hit the medical journals. We're not landing there. I'm trying to hit the Times, and we’re not landing there. We're trying to get the reporters to pay attention. It's not landing there. Let's team up. Let's write these joint pieces. And I think what happened was you saw the benefit of the collective effort and interdisciplinary expertise, right? We could all start to come together, start instead of having these separate voices, a little bit of a unified voice despite important scientific minor disagreements, but start to say, hey, we started elevate each other and said, this is really important. It's the missing component of the messaging in the early days of the pandemic, and to know how to defend yourself.

Eric Topol (00:12:20):

Well, I think a lot of people think the big miss, and I know you agree, was the lack of recognition of aerosol transmission instead of just liquid droplets. But what you brought to this was really your priors on the buildings themselves and the ventilation systems and air quality that was highly, I mean, critical to it isn't just the aerosol, it's obviously how buildings are set up. Now, there's an amazing piece of course that appeared in the summer of 2021 called the Air Investigator, which profiled you, and in it brings up several things that finally are, we're starting to get our act together. I mean, ultimately there was in May 2023 years later, the CDC says, we're going to do something about this. Can you tell us what was this very distinct new path that the CDC was at least saying? And also couple that with whatever action if or not action has been taken.

Joseph Allen (00:13:33):

Yeah. So there really was a monumental shift that took, it was years in development, but we finally won the argument, collectively that airborne spread was the dominant mode of transmission. Okay, we got that. Then the question is, well, what changes? Do we actually get guidance here? And that took a little bit longer. I give Rochelle Walensky a lot of credit when she came into the CDC, we talked with her about this. That's when you start to first see ventilation starts showing up and the guidance, including guidance for schools. So I think that was a big win, but still no one was willing to set an official target or standard around higher ventilation rates. So that's important. Early in the pandemic, some people started to hear a message, yes, ventilation is important. What's the obvious next question, well, how much, what do I need? So in the summer of 2020, actually Shelly Miller and I collaborated on this.

(00:14:23):

We published some guidance on ventilation targets for schools. We said four to six air changes per hour (ACH) and target that. Well, it wasn't until 2023, spring of 2023 that you mentioned that CDC published target ventilation rates, and they went with five air changes per hour, which is right where we were talking about in summer 2020. It's what the Lancet of COVID-19 Commission adopted, but it's momentous in this way. It's the first time in CDCs history they've ever published a ventilation rate target for health. Now, I know this seems slow at the time, and it was, but if we think about some of the permanent gains that will come out of the pandemic. Pandemic changes society and science and policy and practice this, we are never going back. Now buildings will be a first line of defense for respiratory pathogens going forward that can no longer be ignored. And now we have the published target by CDC. That's a big deal because it's not just a recognition, but there's actually something to shoot for out there. It's a target I happen to like, I think there are differences between different scientists, but ultimately we've lifted the floor and said, look, we actually have to raise ventilation rates and we have something to shoot for. The public needed that kind of guidance a lot earlier, of course, but it was a big deal that it happened. It’s just too bad it took until spring 2023.

Eric Topol (00:15:46):

Yeah, I certainly agree that it was momentous, but a year plus later, has there been any change as a result of this major proclamation, if you will?

Joseph Allen (00:15:59):

Well, I actually see a lot of change from a practitioner level, but I want to talk about it in two aspects. I see a lot of schools, universities, major companies that have made this shift. For example, in the 60 Minutes piece, I talk that I advised Amazon and globally they're measuring indoor air quality with real-time sensors in their buildings. I've worked with hundreds of school districts that have made improvements to indoor air quality. I know companies that have shifted their entire approach to how they design and operate their buildings. So it's happening. But what really needs to happen, Eric, if this movement is going to benefit everyone, is that these targets need to be codified. They need to go into building codes. It can't just be, oh, I've heard about this. So I made the decision. I have the resources and the money to make this improvement.

(00:16:44):

To create a healthy building or a healthy school, we need to be sure this gets built into our code. So it just becomes the way it's done. That is not happening. There are some efforts. There are some bills at the national level. Some states are trying to pass bills, and I have to say, this is why I'm optimistic. It feels very slow. I'm as frustrated as anybody. I wanted this done before the pandemic. As soon as the pandemic hit, we saw it. We knew what we needed to get done. It didn't happen. But if we think about the long arc here and the public health gains we're actually, it's remarkable to me that we actually have bills being introduced around indoor air quality that ASHRAE has set a new health focused target for the first time really in their history. CDC, first time. New buildings going up in New York City designed to these public health targets. That's really different. I've been in this field for 20 plus years. I've never seen anything like it. So the pace is still slow, but it really is happening. But it has to reach everybody, and the only way that's going to happen is really this gets into building codes and performance standards.

The Old Efficient Energy Buildings

Eric Topol (00:17:52):

Yeah. Well, I like your optimistic perspective. I do want to go back for a second, back decades ago there was this big impetus to make these energy efficient buildings and to just change the way the buildings were constructed so that there was no leak and it kind of set up this problem or exacerbated, didn't it?

Joseph Allen (00:18:19):

Yeah. I mean, I've written about this a lot. I write in the book our ventilation standards, they've been a colossal mistake. They have cost the public in terms of its health because in the seventies, we started to really tighten up our building envelopes and lower the ventilation rates. The standards were no longer focused on providing people with a healthy indoor space. As I write in the book, they were targeted towards minimally acceptable indoor air quality, bare minimums. By the way that science is unequivocal, is not protective of health, not protective against respiratory pathogens, doesn't promote good cognitive function, not good for allergies. These levels led to more illness in schools, more absences for teachers and students, an absolute disaster from a public health standpoint. We've been in this, what I call the sick building era since then. Buildings that just don't bring in enough clean outdoor air. And now you take this, you have a building stock for 40 years tighter and tighter and tighter bumps up against a novel virus that spread nearly entirely indoors. Is it any wonder we had, the disaster we had with COVID-19, we built these bills. They were designed intentionally with low ventilation and poor filtration.

Optimal Ventilation and Filtration

Eric Topol (00:19:41):

Yeah. Well, it's extraordinary because now we've got to get a reset and it's going to take a while to get this done. We'll talk a bit about cost of doing this or the investment, if you will, but let's just get some terms metrics straight because these are really important. You already mentioned ACH, the number of air changers per hour, where funny thing you recommended between four and six and the CDC came out with five. There's also the minimum efficiency reporting value (MERV). A lot of places, buildings have MERV 8, which is insufficient. We need MERV 13. Can you tell us about that?

Joseph Allen (00:20:23):

Yeah, sure. So I think when we think about how much, you have two ways to capture these respiratory particles, right? Or get rid of them. One is you dilute them out of the building or you capture them on filters. You can inactivate them through UV and otherwise. But let's just stay on the ventilation and filtration side of this. So the air changing per hour is talking about how often the air is change inside. It's an easy metric. There are some strengths to it, there's some weaknesses, but it's intuitive and I'll you some numbers so you can make sense of this. We recommended four to six air changes per hour. Typical home in the US has half an air change per hour. Typical school designed to three air changes per hour, but they operate usually at one and a half. So we tried to raise this up to four, five, or six or even higher. On the filtration side, you mentioned MERV, right? That's just a rating system for filters, and you can think about it this way. Most of the filters that are in a building are cheap MERV 8 filters, I tend to think of them as filters that protect the equipment. A MERV 13 filter may capture 80 or 90% of particles. That's a filter designed to protect people. The difference in price between a MERV 8 and a MERV 13 is a couple of bucks.

(00:21:30):

And a lot of the pushback we got early in the pandemic, some people said, well, look, there's a greater resistance from the better filter. My fan can't handle it. My HVAC system can't handle it. That was nonsense. You have low pressure drop MERV 13 filters. In other words, there really wasn't a barrier. It was a couple extra bucks for a filter that went from a MERV 8 might capture 20 or 30% to a filter, MERV 13 that captures 80 or 90% with very little, if any impact on energy or mechanical system performance. Absolute no-brainer. We should have been doing this for decades because it also protects against outdoor air pollution and other particles we generate indoors. So that was a no-brainer. So you combine both those ventilation filtration, some of these targets are out there in terms of air change per hour. You can combine the metric if we want to get technical to talk about it, but basically you're trying to create an overall amount of clean air. Either you bring in fresh outdoor air or you filter that air. It really is pretty straightforward, but we just didn't have some of these targets set and the standards we're calling for these minimum acceptable levels, which we're not protective of health.

Eric Topol (00:22:37):

So another way to get better air quality are these portable air cleaners, and you actually just wrote about that with your colleagues in the Royal Society of Chemistry, not a journal that I typically read, but this was an important article. Can you give us, these are not very expensive ways to augment air quality. Can you tell us about these PACs ?

Joseph Allen (00:23:06):

These portable air cleaners (PACs), so the same logic applies if people say, well, I can't upgrade my system. That's not a problem for very low cost, you could have, these devices are essentially a fan and a filter, and the amount of clean air you get depends on how strong the fan is and how good the filter is. Really pretty simple stuff here, and you can put one of these in a room if it's sized right. My Harvard team has built tools to help people size this. If you're not quite sure how to do it, we have a technical explainer. Really, if you size it right, you can get that four, five or six air changes per hour, very cheap and very quickly. So this was a tool I thought would be very valuable. Rich Corsi and I wrote about this all through the summer of 2020 to talk about, hey, a stop gap measure.

(00:23:50):

Let's throw out some of these portable air cleaners. You increase the air changes or clean air delivery pretty effectively for very low cost, and they work. And now the paper we just published in my team a couple of days ago starts to advance this more. We used a CFD model, so computational fluid dynamics. Essentially, you can look at the tracers and the airflow patterns in the room, and we learn a couple things that matter. Placement matters, so we like it in the center of the room if you can or as close as possible. And also the airflow matters. So the air cleaners are cleaning the air, but they're also moving the air, and that helps disperse these kind of clouds or plumes when an infected person is breathing or speaking. So you want to have good ventilation, good filtration. Also a lot of air movement in the space to help dilute and move around some of these respiratory particles so that they do get ventilated out or captured in a filter.

Eric Topol (00:24:40):

Yeah. So let me ask you, since we know outdoors are a lot safer. If you could do all these things indoors with filtration, air changing the quality, can you simulate the outdoors to get rid of the risk or markedly reduce the risk of respiratory viruses like SARS-CoV-2 and others?

Joseph Allen (00:25:04):

Yeah, you can't drop it to zero. There's no such thing as zero risk in any of these environments. But yeah, I think some of the estimates we've seen in my own team has produced in the 60-70% reduction range. I mean, if you do this right with really good ventilation filtration, you can drop that risk even further. Now, things like distancing matter, whether or not somebody's wearing a mask, these things are all going to play into it. But you can really dramatically drop the risk by handling just the basics of ventilation and filtration. And one way to think about it is this, distance to the infector still matters, right? So if you and I are speaking closely and I breathe on you, it's going to be hard to interrupt that flow. But you can reduce it through good ventilation filtration. But really what it's doing also is preventing super spreading events.

(00:25:55):

In other words, if I'm in the corner of a room and I'm infectious and you're on the other side, well if that room is sealed up pretty good, poor ventilation, no filtration, the respiratory aerosols are going to build up and your risk is going to increase and we're in there for an hour or two, like you would be in a room or office and you're exposed to infectious aerosol. With good ventilation filtration, those respiratory particles don't have a chance to reach you, or by the time they do, they're much further diluted. Linsey Marr I think was really great early in the pandemic by talking about this in terms of cigarette smoke. So a small room with no ventilation filtration, someone smoking in the corner, yeah, it's going to fill up over time with smoke you're breathing in that secondhand smoke. In a place with great ventilation filtration, that's going to be a lot further reduced, right? You're not going to get the buildup of the smoke and smoke particles are going to operate similarly to respiratory particles. So I think it's intuitive and it's logical. And if you follow public health guidance of harm reduction, risk reduction, if you drop exposure, you drop risk.

(00:26:58):

The goal is to reduce exposure. How do we do that? Well, we can modify the building which is going to play a key role in exposure reduction.

Eric Topol (00:27:06):

Now, to add to this, if I wear a sensor or have a sensor in the room for CO2, does that help to know that you're doing the right thing?

Joseph Allen (00:27:17):

Yeah, absolutely. So people who are not familiar with these air quality sensors. They're small portal air quality sensors. One of the things they commonly measure is carbon dioxide. We're the main source of CO2 inside. It's a really good indicator of ventilation rate and occupancy. And the idea is pretty simple. If the CO2 is low, you don't have a buildup of particles from the respiratory tract, right? And CO2 is a gas, but it's a good indicator of overall ventilation rate. This room I'm in right now at the Harvard School of Public Health has air quality sensors. We have this at Harvard Business School. We have it at the Harvard Health Clinics. Many other places are doing it, Boston Public schools have real-time air quality monitors. Here's the trick with CO2. So first I'll say we have some guidance on this at the Harvard Healthy Buildings page, if people want to go look it up, how to choose an air quality sensor, how to interpret CO2 levels.

Carbon Dioxide Levels

(00:28:04):

But here's a way to think about it. We generally would like to see CO2 levels less than 800 parts per million. Historically, people in my field have said under 1,000 is okay. We like to see that low. If your CO2 is low, the risk is low. If your CO2 is high, it doesn't necessarily mean your risk is high because that's where filtration can come in. So let me say that a little bit better. If CO2 is low, you're diluting enough of the respiratory particles. If it's high, that means your ventilation is low, but you might have excellent filtration happening. Either those MERV 13 filters we talked about or the portable air cleaners. Those filters don't capture CO2. So high CO2 just means you better have a good filter game in place or the risk is going to be high. So if you CO2 is low, you're in good shape. If it's high, you don't quite know. But if you have bad filtration, then the risk is going to be much higher.

Eric Topol (00:29:01):

I like that 800 number because that's a little lower than some of the other thresholds. And why don't we do as good as we can? The other question about is a particulate matter. So we are worried about the less than 5 microns, less than 2.5 microns. Can you tell us about that and is there a way that you can monitor that directly?

Joseph Allen (00:29:25):

Sure. A lot of these same sensors that measure CO2 also measure PM 2.5 which stands for particular matter. 2.5 microns is smaller, one of the key components of outdoor air pollution and EPA just set new standards, right? WHO has a standard for 5 microgram per cubic meter. EPA just lowered our national outdoor limit from 12 to 9 microgram per cubic meter. So that's a really good indicator of how well your filters are working. Here again, in a place like this or where you are, you should see particle levels really under 5 microgram per cubic meter without any major source happening. What's really interesting about those like the room I'm in now, when the wildfire smoke came through the East coast last year, levels were extraordinary outside 100, 200, 300 microgram per cubic meter. But because we have upgraded our filters, so we use MERV 15 here at Harvard, the indoor levels of particles stayed very low.

(00:30:16):

So it shows you how the power of these filters can actually, they do a really good job of capturing particles, whether it be from our lungs or from some other source. So you can measure this, but I'll tell you what's something interesting, if you want to tie it into our discussion about standards. So we think about particles. We have a lot of standards for outdoor air pollution. So there's a national ambient air quality standard 9 microgram per cubic meter. We don't have standards for indoor air quality. The only legally enforceable standard for indoor particles is OSHA's standard, and it's 5,000 microgram per cubic meter 5,000.

(00:30:59):

And it's absurd, right? It's an absurdity. Here we are EPAs, should it be 12, should it be 9, or should it be 8? And for indoors, the legally enforceable limit for OSHA 5,000. So it points to the big problem here. We talked about earlier about the need for these standards to codify some of this. Yes, we have awareness from the public. We have sensors to measure this. We have CDC now saying what we were saying with the Lancet COVID-19 Commission and elsewhere.

This is big movement, but the standards then need to come up behind it and get into code and new standards that are health focused and health based. And we have momentum, but we can't lose it right now because it's the first time in my career I felt like we're on the cusp of really getting this and we are so close. But of course it's always in danger of slipping through our fingers.

Regulatory Oversight for Air

Eric Topol (00:31:45):

Well, does this have anything to do with the fact that in the US there's no regulatory oversight over air as opposed to let's say Japan or other places?

Joseph Allen (00:31:57):

Yeah, I mean, we have regulatory oversight of outdoor air. That's EPA. There's a new bill that was introduced to give EPA more resources to deal with indoor air. EPA has got a great indoor air environments division, but it doesn't have the legally enforceable mandate or statute that we have for outdoor. So they'd give great guidance and have for a long time. I really like that group at EPA, but there's no teeth behind this. So what we have is worker health protections at OSHA to its own admission, says its standards are out of date. So we need an overhaul of how we think about the standards. I like the market driven approach. I think that's being effective, and I think we can do it from voluntary standards that can get adopted into code at the municipal level. I think that's a real path. I see it happening. I see the influence of all this work hitting legislators. So that's where I think the most promising path is for real change.

The Risks of Outdoor Air Pollution

Eric Topol (00:33:03):

Yeah, I think sidestepping, governmental teeth, that probably is going to be a lot quicker. Now, before we get to the cost issue, I do want to mention, as you know very well, the issue of air pollution in Science

a dedicated issue just a few weeks ago, it brought up, of course, that outdoor air pollution we've been talking about indoor is extraordinary risk for cancer, dementia, diabetes, I mean everything. Just everything. And there is an interaction between outdoor pollution and what goes on indoor. Can you explain basically reaffirm your concern about particulate matter outdoors, and then what about this interaction with what goes on indoors?

Joseph Allen (00:33:59):

Yeah, so it's a great point. I mean, outdoor pollution has been one of the most studied environmental pollutants we know. And there's all of these links, new links between Alzheimer's, dementia, Parkinson's disease, anxiety, depression, cardiovascular health, you named it, right? I've been talking about this and very vocal. It's in the book and elsewhere I called the dirty secret of outdoor air pollution. The reality is outdoor air pollution penetrates indoors, and the amount depends on the building structure, the type of filters you have. But let's take an infiltration value of say 50%. So you have a lot of outdoor air pollution, maybe half of that penetrates inside, so it's lower, the concentration is lower, but 90% of the breaths you take are indoor. And if you do the math on it, it's really straightforward. The majority of outdoor air pollution you breathe happens inside.

(00:34:52):

And people, I think when they hear that think, wait, that can't be right. But that's the reality that outdoor pollution comes inside and we're taking so many breaths inside. Your total daily dose of outdoor air pollution is greater from the time you spend inside. I talk about this all the time. You see any article about outdoor air pollution, what's the cover picture? It's someone outside, maybe they're wearing a mask you can't really see. It's smoky hazy. But actually one of the biggest threats is what's happening inside. The nice thing here, again, the solutions are pretty simple and cost-effective. So again, upgrade from MERV 8 to MERV 13, a portable air cleaner. We are just capturing particles on a filter basic step that can really reduce the threat of outdoor air pollution inside. But it's ignored all the time. When the wildfire smoke hit New York City. New York City's orange, I called colleagues who are in the news business.

(00:35:48):

We have to be talking about the indoor threat because the guidance was good, but incomplete. Talk about Mayor Adams in New York City. Go inside, okay, that's good advice. And go to a place that has good filtration or they should have been giving out these low cost air cleaners. So just going inside isn't going to protect your lungs unless you're actually filtering a lot more of that air coming in. So trying to drive home the point here that actually we talk about these in silos. Well, wildfire smoke and particles, Covid and respiratory particles, we're all talking about these different environmental issues that harm our health, but they're all happening through or mediated by the building performance. And if we just get the building performance right, some basics around good ventilation, good filtration, you start to address multiple threats simultaneously. Outdoor air pollution, wildfire smoke, allergens, COVID-19, influenza, RSV, better cognitive function performance, anxiety. You start addressing the root cause or one of the contributors and buildings we can then start to leverage as a true public health tool. We have not taken advantage of the power of buildings to be a true public health tool.

Eric Topol (00:36:59):

Oh, you say it so well, and in fact your Table on page 44 in Healthy Buildings , we’ll link it because it shows quantitatively what you just described about outdoor and indoor cross fertilization if you will. Now before leaving air pollution outdoors, indoors, in order for us to affect this transformation that would markedly improve our health at the public health individual level, we're talking about a big investment. Can you put that in, you did already in some respects, but if we did this right in every school, I think in California, they're trying to mandate that in schools, in the White House, they're mandating federal buildings. This is just a little piece of what's needed. This would cost whatever trillions or hundreds of billions of dollars. What would it take to do this? Because obviously the health benefits would be so striking.

What’s It Gonna Cost?

Joseph Allen (00:38:04):

Well, I think one of the issues, so we can talk about the cost. A lot of the things I'm talking about are intentionally low cost, right? You look at the Lancet of COVID-19 Commission, our report we wrote a report on the first four healthy building strategies every building should pursue. Number one commission your building that's giving your building a tune-up. Well, guess what? That not only improves air quality, it saves energy and therefore saves money. It actually becomes cost neutral. If not provides an ROI after a couple of years. So that's simple. Increase the amount of outdoor air ventilation coming in that has an energy cost, we've written about this. Improved filtration, that's a couple bucks, really a couple bucks, this is small dollars or portable air cleaners, not that expensive. I think one of the big, and Lawrence Berkeley National Lab has written this famous paper people like to cite that shows there's $20 billion of benefits to the US economy if we do this.

(00:38:59):

And I think it points to one of the problems. And what I try to address in my book too, is that very often when we're having this conversation about what's it going to cost, we don't talk about the full cost benefit. In other words, we say, well, it's going to cost X amount. We can't do that. But we don't talk about what are the costs of sick buildings? What are the costs of kids being out of school for an entire year? What are the costs of hormonal disruption to an entire group of women in their reproductive years due to the material choices we make in our buildings? What are the costs to outdoor air pollution and cardiovascular disease, mental health? Because we don't have good filters in our buildings that cost a couple dollars. So in our book, we do this cost benefit analysis in the proforma in our book, we lay out what the costs are to a company. We calculate energy costs. We say these are the CapEx costs, capital costs for fixed costs and the OpEx costs for operating expenditures. That's a classic business analysis. But we factor in the public health benefits, productivity, reduced absenteeism. And you do that, and I don't care how you model it, you are going to get the same answer that the benefits far outweigh the cost by orders of magnitude.

Eric Topol (00:40:16):

Yeah, I want to emphasize orders of magnitude. Not ten hundred, whatever thousand X, right?

Joseph Allen (00:40:23):

What would be the benefit if we said we could reduce influenza transmission indoors in schools and offices by even a small percent because we improve ventilation and filtration? Think of the hospitalization costs, illness costs, out of work costs, out of school costs. The problem is we haven't always done that full analysis. So the conversation gets quickly to well, that's too much. We can't afford that. I always say healthy buildings are not expensive. Sick buildings are expensive. Totally leave human health out of that cost benefit equation. And then it warps this discussion until you bring human health benefits back in.

Forever Chemicals

Eric Topol (00:40:58):

Well, I couldn't agree more with you and I wanted to frame this by giving this crazy numbers that people think it's going to cost to the reality. I mean, if there ever was an investment for good, this is the one that you've outlined so well. Alright, now I want to turn to this other topic that you have been working on for years long before it kind of came to the fore, and that is forever chemicals. Now, forever chemicals, I had no idea that back in 2018 you coined this term. You coined the term, which is now a forever on forever chemicals. And basically, this is a per- and polyfluoroalkyl substances (PFAS), but no one will remember that. They will remember forever chemicals. So can you tell us about this? Because this of course recently, as you know well in May in the New Yorker, there was an expose of 3M, perhaps the chief offender of these. They're everywhere, but especially they were in 3M products and continue to be in 3M products. Obviously they've been linked with all kinds of bad things. What's the story on forever chemicals?

Joseph Allen (00:42:14):

Yeah, they are a class of chemicals that have been used for decades since the forties. And as consumers, we like them, right? They're the things that make your raincoat repel rain. It makes your non-stick pan, your scrambled eggs don't stick to the pan. We put them on carpets for stain resistance, but they came with a real dark side. These per- and polyfluoroalkyl substances, as I say, a name only a chemist could love have been linked with things like testicular cancer, kidney cancer, interference with lipid metabolism, other hormonal disruption. And they are now a global pollutant. And one of the reasons I wrote the piece to brand them as forever chemicals was because I'm in the field of environmental health. We had been talking about these for a long time and I just didn't hear the public aware or didn't capture their attention. And part of it, I think is how we talk about some of these things.

(00:43:14):

I think a lot about this. Per- and polyfluoroalkyl substances, no one's going to, so the forever chemicals is actually a play on their defining feature. So these chemicals, these stain repellent chemicals are characterized by long chains of the carbon fluorine bond. And when we string these together that imparts this and you put them on top of a product that imparts the property of stain resistance, grease resistance, water resistance, but the carbon fluorine bond is the strongest in all of organic chemistry. And these chains of the carbon fluorine bond never fully break down in the environment. And when we talk in my field about persistent organic pollutants, we talk about chemicals that break down on the order of decades. Forever chemicals don't break down. They break down the order of millennia. That's why we're finding them everywhere. We know they're toxic at very low levels. So the idea of talking about forever chemicals, I wanted to talk about their foreverness.

(00:44:13):

This is permanent. What we're creating and the F and the C are the play on the carbon-fluorine bond and I wrote an article trying to raise awareness about this because some companies that have produced these have known about their toxicity for decades, and it's just starting the past couple of years, we're just starting to pay attention to the scale of environmental pollution. Tens of millions of Americans have forever chemicals in their drinking water above the safe limit, tens of millions. I worked as an expert in a big lawsuit for the plaintiffs that were drinking forever chemicals in their water that was dumped into the drinking water supply by a manufacturing company. I met young men with testicular cancer from drinking forever chemicals in their water. These really has escaped the public's consciousness, it wasn't really talked about. Now of course, we know every water body, we use these things in firefighting foams or every airport has water pollution.

(00:45:17):

Most airports do. Firefighters are really concerned about this, high rates of cancer in the firefighter population. So this is a major problem, and the cleanup is not straightforward or easy because they're now a global pollutant. They persist forever. They're hard to remediate and we're stuck with them. So that's the downside, I can talk about the positives. I try to remain an optimist or things we're doing to try to solve this problem, but that's ultimately the story. And my motivation was I just to have people have language to be able to talk about this that didn't require a degree in organic chemistry to understand what they were.

Eric Topol (00:45:52):

Yeah, I mean their pervasiveness is pretty scary. And I am pretty worried about the fact that we still don't know a lot of what they're doing in terms of clinical sequela. I mean, you mentioned a couple types of cancer, but I don't even know if there is a safe threshold.

Joseph Allen (00:46:16):

Eric, I'll tell you one that'll be really interesting for you. A colleague of mine did a famous study on forever chemicals many years ago now and found that kids with higher levels of forever chemicals had reduced vaccine effectiveness related to these chemicals. So your point is, right, a lot of times we're using these industrial chemicals. We know a couple endpoints for their affecting our bodies, but we don't know all of them. And what we know is certainly alarming enough that we know enough to know we shouldn't be using them.

Eric Topol (00:46:51):

And you wrote another masterful op-ed in the Washington Post, 6 forever chemical just 10,000 to go. Maybe you could just review what that was about.

Joseph Allen (00:47:02):

Yeah, I've been talking a lot about this issue I call chemical whack-a-mole. So forever chemical is the perfect example of it. So we finally got people's attention on forever chemicals. EPA just regulated 6 of them. Well, guess what? There are 10,000 if not many more than that. Different variants or what we call chemical cousins. Now that's important for this reason. If you think about how we approach these from a regulatory standpoint, each of the 10,000 plus forever chemicals are treated as different. So by the time EPA regulates 6, that's important. It does free up funding for cleanup and things like this. But already the market had shifted away from those 6. So in other words, in the many thousand products that still use forever chemicals, they're no longer using those 6 because scientists have told people these things are toxic years ago. So they switch one little thing in the chemical, it becomes a new chemical from a regulatory perspective.

(00:47:57):

But to our bodies, it's the same thing. This happens over and over. This has happened with pesticides. It happens with chemicals and nail polish. It happens in chemicals in e-cigarettes. It happens with flame retardant chemicals. I wrote a piece in the Post maybe six years ago talking about chemical whack-a-mole, and this problem that we keep addressing, these one-off, we hit one, it changes just slightly. Chemical cousin pops up, we hit that one. Five years later, scientists say, hey, the next one doesn't look good either. We're doing this for decades. It's really silly. It's ineffective, it's broken, and there are better ways to handle this going forward.

Eric Topol (00:48:31):

And you know what gets me, and it's like in the pharma industry that I've seen the people who run these companies like 3M that was involved in a multi-decade coverup, they're never held accountable. I mean, they know what they're doing and they just play these games that you outlined. They're still using 16,000 products, according to the New Yorker, the employee that exposed them, the whistleblower in the New Yorker article.

Joseph Allen (00:48:58):

That was an amazing article by Sharon Lerner talking to the people who had worked there and she uncovered that they knew the toxicity back in the seventies, and yes, they were still making these products. One of the things that I think has gotten attention of some companies is while the regulations have been behind, the lawsuits are piling up.

Joseph Allen (00:49:21):

The lawsuit I was a part of as an expert for that was about an $800 million settlement in favor of the plaintiffs. A couple months later is another one that was $750 million. So right there, $1.5 billion, there's been several billion dollars. This has caught the attention of companies. This has caught the attention of product manufacturers who are using the forever chemicals, starting to realize they need to reformulate. And so, in a good way now, that's not the way we should be dealing with this, but it has started to get companies to wake up that maybe they had been sleeping on it, that this is a major problem and actually the markets have responded to it.

Eric Topol (00:50:02):

Well, that's good.

Joseph Allen (00:50:03):

Because these are major liabilities on the books.

Eric Topol (00:50:05):

Yeah, I mean, I think what I've seen of course with being the tobacco industry and I was involved with Vioxx of course, is the companies just appeal and appeal and it sounds really good that they've had to pay $800 million, but they never wind up paying anything because they basically just use their muscle and their resources to appeal and put it off forever. So I mean, it's one way to deal with it is a litigation, but it seems like that's not going to be enough to really get this overhauled. I don't know. You may be more sanguine.

Joseph Allen (00:50:44):

No, no, I agree with you. It's the wrong way. I mean, we don't want to, the solution here is not to go after companies after people are sick. We need get in front of this and be proactive. I mentioned it only because I know it has made other companies pay attention how many billion does so-and-so sue for. So that's a good signal that other companies are starting to move away from forever chemicals. But I do want to talk about one of the positive approaches we're doing at Harvard, and we have a lot of other partners in the private sector doing this. We're trying to turn off the spigot of forever chemicals entering the market in the first place. As a faculty advisor to what we call the Harvard Healthier Building Materials Academy, we publish new standards. We no longer buy products that have forever chemicals in them for our spaces.

(00:51:31):

So we buy a chair or carpet. We demand no forever chemicals. What's really neat about this is we also say, we treat them as a whole class. We don't say we don't want PFOA. That's one of the regulated chemicals. We say we don't want any of the 10,000. We are not waiting for the studies to show us they act like the other ones. We've kind of been burned by this for decades. So we're actually telling the suppliers we don't want these chemicals and they're delivering products to us without these chemicals in them. We have 50 projects on our campus built with these new design standards without forever chemicals and other toxic chemicals. We've also done studies that a doctoral student done the study. When we do this, we find lower levels of these chemicals in air and dust, of course. So we're showing that it works.

(00:52:19):

Now, the goal is not to say, hey, we just want to make Harvard a healthier campus and the hell with everybody else. The goal is to show it can be done with no impact to cost, schedule or product performance. We get a healthier environment, products look great, they perform great. We've also now partnered with other big companies in the tech industry in particular to try and grow or influence the market by saying, look how many X amount of purchasing dollars each year? And it's a lot, and we're demanding that our carpets don't have this, that our chairs don't have it, and the supply chain is responding. The goal, of course, is to just make it be the case that we just have healthy materials in the supply chain for everybody. So if you or I, or anybody else goes to buy a chair, it just doesn't have toxic chemicals in it.

Eric Topol (00:53:06):

Right, but these days the public awareness still isn't there, nor are the retailers that are selling whether it's going to buy a rug or a chair or new pots and pans. You can't go in and say, does this have any forever chemicals? They don't even know, right?

Joseph Allen (00:53:24):

Impossible. I study this and it's hard for me when I go out to try and find and make better decisions for myself. This is one of the reasons why we're working, of course, trying to help with the regulatory side, but also trying to change the market. Say, look, you can produce the similar product without these chemicals, save yourself for future lawsuits. Also, there's a market for healthy materials, and we want everybody to be a part of that market and just fundamentally change the supply chain. It's not ideal, but it's what we can do to influence the market. And honestly, we're having a lot of impact. I've been to these manufacturing plants where they have phased out these toxic chemicals.

Eric Topol (00:54:03):

That’s great to hear.

Joseph Allen (00:54:06):

And we see it working on our campus and other companies’ campuses.

Eric Topol (00:54:10):

Well, nobody can ever accuse you of not taking on big projects, okay.

Joseph Allen (00:54:15):

You don't get into public health unless you want to tackle the big ones that are really going to influence.

Micro(nano) Plastics

Eric Topol (00:54:20):

Well, that's true, Joe, but I don't know anybody who's spearheading things like you. So it's phenomenal. Now before we wrap up, there's another major environmental problem which has come to the fore, which are plastics, microplastics, nanoplastics. They're everywhere too, and they're incriminated with all the things that we've been talking about as well. What is your view about that?

Joseph Allen (00:54:48):

Well, I think it's one, well, you see the extent of the pollution. It's a global pollutant. These are petrochemicals. So it's building up, and these are fossil fuel derivatives. So you can link this not just to the direct human health impacts, the ecosystem impacts, but also ecosystem and health impacts through climate change. So we've seen our reliance on plastics grow exponentially over the past several decades, and now we're seeing the price we're paying for that, where we're seeing plastics, but also microplastics kind of everywhere, much like the forever chemicals. Everywhere we look, we find them and we're just starting to scratch a surface on what we know about the environmental impacts. I think there's a lot more that can be done here. Try to be optimistic again, at least if you find a problem, you got to try and point to some kind of solution or at least a pathway towards solutions.

(00:55:41):

But I like some of the stuff from others colleagues at Yale in particular on the principles of green chemistry. I write about them in my book a little bit, but it's this designing for non-permanence or biodegradable materials so that if we're using anything that we're not leaving these permanent and lasting impacts on our ecosystem that then build up and they build up in the environment, then they build up in all of us and in our food systems. So it seems to me that should be part of it. So think about forever chemicals. Should we be using chemicals that never break down in the environment that we know are toxic? How do we do that? As Harvard, one of the motivating things here for forever chemicals too, is how are we ignoring our own science? Everyone's producing this science, but how do we ignore even our own and we feel we have responsibility to the communities next to us and the communities around the world. We're taking action on climate change. How are we not taking action on these chemicals? I put plastics right in there in terms of the environmental pollutants that largely come from our built environment, food products and the products we purchase and use in our homes and in our bodies and in all the materials we use.

Eric Topol (00:56:50):

When you see the plastic show up in our arteries with a three, four-fold increase of heart attacks and strokes, when you see it in our testicles and every other organ in the body, you start to wonder, are we ever going to do something about this plastic crisis? Which is somewhat distinct from the forever chemicals. I mean, this is another dimension of the problem. And tying a lot of this together, you mentioned, we are not going to get into it today, but our climate crisis isn't being addressed fast enough and it's making all these things exacerbating.

Joseph Allen (00:57:27):

Yeah, let me touch on that because I think it is important. It gets to something I said earlier about a lot of these problems we treat as silos, but I think a lot of the problems run through our buildings, and that means buildings are part of the solution set. Buildings consume 40% of global energy.

(00:57:42):

Concrete and steel count for huge percentages of our global CO2 emissions. So if we're going to get climate solved, we're going to have to solve it through our buildings too. So when you start putting this all together, Eric, right, and this is why I talk about buildings as healthy buildings could potentially be one of the greatest public health interventions we have of this century. If we get it right, and I don't mean we get the Covid part, right. We get the forever chemicals part, right. Or the microplastics part, right. If you start getting this all right, good ventilation, better filtration, healthy materials across the board, energy efficient systems, so we're not drawing on the energy demand of our buildings that are contributing to the climate crisis. Buildings that also address climate adaptation and resilience. So they protect us from extreme heat, wildfire smoke, flooding that we know is coming and happening right now.

(00:58:37):

You put that all together and it shows the centrality of buildings on our collective health from our time spent indoors, but also their contribution to environmental health, which is ultimately our collective human health as well. And this is why I'm passionate about healthy buildings as a real good lens to put this all under. If we start getting these right, the decisions we make around our buildings, we can really improve the human condition across all of these dimensions we're talking about. And I actually don't think it's all that hard in all of these. I've seen solutions.

Eric Topol (00:59:12):

I'm with you. I mean, there's innovations that are happening to take the place of concrete, right?

Joseph Allen (00:59:20):

Sure. We have low emission concrete right now that's available. We have energy recovery ventilation available right now. We have real time sensors. We can do demand control ventilation right now. We have better filters right now. We have healthy materials right now.

(00:59:33):

We have this, we have it. And it's not expensive if we quantify the health benefits, the many, many multiple benefits. So it's all within our reach, and it's just about finding these different pathways. Some of its market driven, some of it's regulatory, some of it's at the local level, some of it's about raising awareness, giving people the language to talk about these things. So I do think it's the real beginning of the healthy buildings era. I really, truly believe it. I've never seen change like this in my field. I've been chasing sick buildings for a long time.

Joseph Allen (01:00:11):

And clearly there's pathways to do better.

Eric Topol (01:00:13):

You're a phenom. I mean, really, you not only have all the wisdom, but you articulate it so well. I mean, you’re leading the charge on this, and we're really indebted to you. I'm really grateful for you taking an hour of your busy time to enlighten us on this. I think what you're doing is it's going to keep you busy for your whole career.

Joseph Allen (01:00:44):

Well, the goal here is for me to put myself out of business. We shouldn't have a healthy buildings program. It just should be the way it's done. So I'm looking forward to the time out of business, hopefully have a healthy building future, then I can retire, be happy, and we'll be onto the next big problem.

Eric Topol (01:00:57):

We'll all be following your writings, which are many, and fortunately not just for science publications, but also for the public though, they're so important because the awareness level as I can't emphasize enough, it's just not there yet. And I think this episode is going to help bring that to a higher level. So Joe, thank you so much for everything you're doing.

Joseph Allen (01:01:20):

Well, I appreciate it. Thanks for what you're doing too, and thanks for inviting me on. We can't get the word out unless we start sharing it across our different audiences, so I appreciate it. Thanks so much.

Eric Topol (01:01:28):

You bet.

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Steve Horvath made the seminal discovery of the—Horvath Clock— an epigenetic clock based on DNA methylation, which is now being used extensively in medical research and offered commercially for individuals (←we talk about that!). He was on the faculty at UCLA from 2000-2022 as a Professor of Human Genetics and Biostatistics, and now works on anti-aging research at Altos Labs.

A perspective on the importance of epigenetic clocks this week’s Nature”This insight is crucial for deriving reliable biological markers of ageing in tissues or blood. Such a feat has been accomplished through the ingenious identification of epigenetic clocks in our genome. But these insights are even more important for revealing targets that enable intervention in the ageing process.”

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Transcript with links to Audio and External Links

Eric Topol (00:06):

Hello, it's Eric Topol with Ground Truths, and I've got a terrific guest with me today, Steve Horvath. He's a geneticist, a statistician, a mathematician. He's got a lot of background that has led to what is a landmark finding in biomedicine, the Horvath clock. So Steve, welcome.

Steve Horvath (00:30):

Thank you for having me.

Eric Topol (00:33):

Well, it's really fascinating. I followed your work for well over a decade since you introduced the pan-tissue clock in 2013, and it's fascinating to go back a bit on that finding, which initially, I guess was in saliva a couple of years prior, and then you found it everywhere you looked, wherever cells had a nucleus and tissues. And what gave you the sense that these markers of methylation on the DNA would give us some clues about the aging process? How did you even come about to make this discovery?

Serendipity

Steve Horvath (01:17):

It was an accidental discovery because before the methylation clock, I had worked very hard on a gene expression clock, a transcriptomic biomarker. I mean, I was at the height of my energy levels. I worked really on weekends, really eight hour days during the week. But all the weekends I had collected a large set of gene expression data and I dredged the data. And for two years and I couldn't get anywhere, there was nothing I could do. But nowadays, of course, you see various publications where people built transcriptomic clocks. But back in the day when we had these arrays, I just couldn't see a signal. And then at some point I got roped into a study of homosexuality where my collaborator at UCLA wanted to see whether there's an epigenetic correlate of sexual orientation in saliva. And so yeah, being a biostatistician, I said, sure, I analyzed the data and I couldn't find any signal for homosexuality.

(02:48):

But then I just looked for an aging signal in the same, and really within an hour of analyzing the data, I knew that I have to completely drop gene expression. I need to go after methylation. And the signal is so profound, and as you said initially we looked at saliva samples and we thought, isn't it curious? You spit in a cup and you can measure someone's age. And we were of course, hoping that this could become a valuable readout of biologic age, but it took, of course, many years to realize that potential. Nowadays, there's several companies that offer a saliva based methylation clock test. But yeah, many years passed, and it was important to fill in the details and to build the case that methylation clocks are predictive of things we care about time to death or time to various forms of morbidity. So it took many, many years to analyze large cohort studies and to accumulate the evidence that it actually works.

Eric Topol (04:16):

Yeah, I mean, it was pretty amazing back almost a decade ago when I would see, we would take tissue or blood sample and look at your clock and it would say, age of the person is 75 years. And then we look at the actual age of the person who is 75 years to say, wait a minute, how can this be? So I mean, the plausibility of this discovery, if you look back, I mean you say, well, this is just kind of the rust of the pipes, or how do you process that the methylation is such a marker potentially of a person's biologic age? Of course, we're going to get into how it could be a way to intervene to change the aging process. But would it be fair to say that its epigenetic clocks are not the same as biologic aging or how do you put all that together?

Epigenetic Age vs Biologic Age

Steve Horvath (05:21):

Yes, for sure. An epigenetic age estimate is certainly not the same as a biologic age estimate. And the reason why I say it is because biologic age is really determined by so many things and by so many organs. And as I mentioned initially, we had a clock for saliva later for blood and so on. And so, if you only have an epigenetic readout of a certain cell type, it's really too limited to assess the whole organismal state. And arguably you would want to measure also proteomics, readouts and many other data modalities. So I typically avoid the terminology biologic age, because to begin with, we don't have a definition of it. Decades of discussions, nobody really has a precise definition of it.

Second Generation Epigenetic Clocks

Eric Topol (06:35):

Well, from the first generation Horvath clock then became this newer second generation, GrimAge, PhenoAge, the DunedinPACE of aging. How has that helped to advance the field? Because as you touched on, they're measuring different things and what is it meant by kind of a second generation clock?

Steve Horvath (07:03):

Yeah, so a second generation clock truly aims to predict mortality or morbidity risk. As opposed to simply chronologic age or what is known as calendar age. And fortunately, there's no doubt that the second generation clocks can do that. I often finish a talk on GrimAge by telling the audience that I give them a money back guarantee, that it will be predictive of mortality in their cohort study. I'm 100% certain that it works if you analyze a hundred people or so. The question is more whether an individual could benefit from such a test. And there are now many providers of various epigenetic clock tests. These biomarkers have different names, but they're quite pricey. A couple of hundred dollars are needed to get such a measurement. And the question is, is it helpful for the individual should you get such a test? And I would say we are not quite there yet for a variety of reasons. The main reason being we don't have good interventions against accelerated epigenetic age. So because when you think about it, why does a doctor order a test for you? For example, cholesterol levels. Well, because they have a drug against elevated cholesterol levels, the statin. And at the moment, we don't have validated interventions against accelerated epigenetic age. So that's kind of missing.

Eric Topol (09:13):

Yeah, we're going to get to that because obviously a lot of things are in the pipeline there, but are you saying then that these people that are getting these consumer tests, that they're getting a test that really wasn't validated at an individual level, so it predicts their mortality that it may be good at a cohort or population level, but maybe it's not so helpful, accurate, or would you say it is accurate? I mean, GrimAge is a good name because since it says when you're going to die. How do you make the differentiation between the individual level or beyond?

Steve Horvath (09:59):

Yeah, I think it's good to compare to other biomarkers. So take glucose levels, hemoglobin A1C, nobody doubts that these levels predict mortality risk when you study couples a hundred people. But how accurate is such a test for an individual? Clearly there is substantial noise associated with a prediction. Two people could have exactly the same hemoglobin A1C levels, but live very different lifespans. And the same holds for epigenetic clocks. They do predict how long you live. In theory, one could arrive at an estimate of age and death. There's a complicated mathematical formula that allows you to do that, but there would be a substantial error bar associated with it, an order of magnitude plus minus five years. And so, for the individual, such an estimate is not that important because the error bar is substantial. But I want to add that these second generation clocks, they do predict mortality risk. There's no question.

Maximal Lifespan

Eric Topol (11:35):

Well, as you know, the longevity space is now very crowded with all sorts of clubs, and it's like a circus out there. And some of these things are being promoted that really don't have the basis or have a false sense to consumers who want to live forever and be healthy forever. But maybe these markers are not really helping guide them so much. Now, you recently published you and your group a fascinating paper, so getting away from the individual for a second, but now at the species level and in Science Advances, and we'll put this diagram with the podcast, but you looked at 348 mammal species for the maximal lifespan with DNA methylation. And it was amazing to see the display from the desert hamster all the way to the humpback whale with somewhere along the way, the humans. So you could predict maximal lifespan pretty well, right?

Steve Horvath (12:43):

Yes. So I collected this very large dataset over seven years, and one of the reasons was to understand the mystery of maximum lifespan. The bowhead whale can live over 211 years, whereas certain mice only three or four years. And my question was, can methylation teach us something about maximum lifespan? And the answer is a resounding, yes. The methylation profiles very much predict the maximum lifespan of a species. And maybe to use a metaphor to explain the patterns. So one can visualize methylation around the DNA molecule, like a landscape. You want that certain regions exhibit high levels of methylation. These regions must be really shut down and other parts of the DNA as opposed to exhibit very low methylation, for example, a transcriptional start sites. And long lived species have a very hilly landscapes, high hills of methylation and steep valleys of low methylation. Where shorter lived species have flatter landscapes. So that was one of the insights of that study. The other perhaps paradoxical insight was that the locations in our DNA that gain methylation with chronologic age, these regions often differ from regions that determine the maximum lifespan of our species. So that's a bit perhaps paradoxical and counterintuitive, but it just shows that the DNA encodes our species characteristics at different locations from our mortality risk.

The Other Clocks

Eric Topol (15:13):

Right. No, and I mean it's fascinating. I can imagine how it could take seven years to pull all that data together. It's amazing. Now, one of the issues of course, is if you're trying to gauge the biologic age, which we already established is somewhat different than epigenetic age or a clock, there are many different ways to do that. And you mentioned transcriptome clocks, which are not as well perhaps developed. Obviously, none of these others are developed like the Horvath clock and newer generation clocks, but there's immuno aging clocks like iAge, there's proteomic clocks, there's organ clocks with high-throughput proteomics, thousands of proteins. Do you see these as complimentary, like orthogonal where they each add to the story? Or do you really see the methylation as distinct?

Steve Horvath (16:20):

Well, I think ideally you measure all of the above to really get a very granular understanding of different facets of aging. And however, scientists always like to find deep connections between different readouts. For example, it would be wonderful if we could use proteomics instead of methylation, or my group has worked on the opposite. So we can actually estimate protein levels in the plasma based on methylation for about 10% of all plasma proteins, you can estimate their levels based on methylation. So yeah, people who are interested in these deeper programs that ideally link everything, some sort of aging program that underlies these different manifestations of aging, they will want to reduce everything. But until we have a deeper understanding, I think let's air on the side of measuring too much.

Eric Topol (17:45):

Well, what's interesting, as you mentioned, I didn't realize you could basically impute the protein story from the methylation, but one of the issues is if you want to do 11,000 plasma proteins, it could cost a thousand dollars. But if you want to do a bisulfite methylation, you might do that for very inexpensively. So there's a practical part of this too, and the immune characterization is even more expensive and difficult from a practical standpoint. So we go back to that initial work that you did and how you got into an area that is practical, inexpensive compared to some of the alternatives. But as you say, they may have features that are also helpful. Now, this is now the craze, this epigenetic clocks, and I want to mention you probably didn't see it because it's not a journal that you would look at, but just yesterday, July 29th, there were 12 papers published in JAMA Network Open.

Modulating Your Epigenetic Clock

(18:51):

Everything from how loss of loved ones changes your epigenetic clock to PTSD, to vegan diets, to inequities. I mean, just incredible. So it is the rage now. It's taken the biomedical community some years to catch up to where you were. And one of the things of course that we know that from your prior work that is an intervention that helps give a less accelerated epigenetic clock is exercise. And in fact, that was highlighted in our Lancet essay in the first week August issue. But can you comment on that and anything else that we know like plant diets and anything that favorably influence our DNA methylation pattern?

Steve Horvath (19:52):

Yes. So interestingly, vegetable intake really has a strong effect on GrimAge and many other epigenetic clocks. And maybe this is obvious to the listener, everybody knows that vegetable intake is healthy. However, it's very surprising to me as a scientist to contemplate how is it that vegetable intake affects the methylation levels of your blood? How does it affect the hematopoietic stem cells? I just don't understand the mechanism behind it, and however, the effect is very strong. So we studied postmenopausal women in the women's health initiative, and for these women, we had blood measures of carotenoid levels. So this is an objective measure of vegetable intake, and the correlations were substantial. So that's one intervention I'm quite certain about. Other intervention that have a strong effect relate to metabolic syndrome, anything that relates to type 2 diabetes such as obesity, high glucose levels, that part of the biology very much affects our epigenetic clocks. So disturbed metabolism has a strong effect.

Eric Topol (21:37):

Has these findings changed your diet or made you exercise more or anything like that?

Steve Horvath (21:44):

. So I eat a lot of frozen vegetables. My freezer as full frozen vegetables.

Eric Topol (21:56):

That's great. Well, there's a lot of uses today as we touched on in the Lancet piece as we're waiting for more benchmarking and more work on this. But for example, we have a shortage of donor organs, and there are people who might be of calendar age advanced, but their epigenetic clock might put them at a much younger age. Is that ready for use in the transplant world as one application?

Steve Horvath (22:37):

I haven't seen that yet. I've seen several studies that have explored that idea. The idea is rather obvious, but I haven't seen it implemented in practicum.

Eric Topol (22:53):

Another one is that we don't, as you've seen from some of these studies on organ clocks, our organs age at different paces and some people are accelerated heart agers or brain agers. If you had access to tissue to get methylation, would you see the same thing or this is of course of interest because we're trying to understand high risk individuals for age related diseases, whether it's dementia or heart disease or cancer. So is the second generation clocks like PhenoAge just good enough, or would you think that the organ clocks would give you some added insight?

Steve Horvath (23:47):

Yeah, I would say this is literally the frontier of research. Several groups attempt to use blood methylation or saliva or skin or fat adipose as surrogates for various other organs. And I've seen very encouraging results. So I do think this idea makes scientific sense, and which comes back to one of the miracles of methylation that this is even possible because if you had written a grant 10 years ago where you said, I will measure blood methylation to assess cognitive functioning, for example, you wouldn't have received any score, not in no funding, but however, interestingly, blood methylation does relate to cognitive functioning and many other organ functions. And so, the proof of concepts have been established. Blood methylation relates to fatty liver disease, kidney disease, lung disease. It has all been done in epidemiological studies. However, the question is how much could a blood methylation measurement help an individual? Should I measure my blood methylation to learn about my liver? And I would say we are not there yet because arguably there are wonderful plasma biomarkers to assess organ functions. And in certain ways, one needs to provide evidence that a methylation measurement is superior or compliments plasma based biomarker. And that's a hard hurdle to take.

Eric Topol (26:02):

Right. I imagine someday it may become the norm of assessing people's risk, but as you say, we're not there yet because it's a tough bar to meet, for sure. Now, you were a Professor from year 2000 at UCLA in multiple departments in genetics and biostats, and then in more recent times you joined the Cambridge unit of Altos, which is one of the companies that has gotten the most attention for its diverse efforts towards modulating, rejuvenating the aging process. So you and many top scientists around the world were recruited to Altos. I know some here at the San Diego campus. Was this thinking that it could help accelerate the whole idea of modulating aging in a favorably way or where do you see that the biotech world can play a role?

Can We Change the Pace of Aging?

Steve Horvath (27:15):

Yes. I mean, speaking for myself, I was getting tired of writing scientific papers and not affecting clinical care. I felt I needed to help identify or validate rejuvenating interventions because of the great promise, and this is perhaps best done in the setting of a biotech that is focused on translation. And that's why I joined. I'm moving away from biomarker development towards finding interventions that move the needle and ideally rejuvenate multiple organs and cell types at the same time.

Eric Topol (28:09):

Right. Now, there's lots of ideas of how we could do that from senolytics that would get rid of specific senescent cells that are bad actors to epigenetic reprogramming or chemical reprogramming or so many anti-inflammatory, like the recent paper of IL-11 that I'm sure you saw in Nature just a couple of weeks ago and many, many other ways to get there. What are you thinking? Is this going to be possible? Obviously, there's lots of naysayers. Is it going to be possible body wide or only for specific ways? For example, maybe we could bring back the thymus from its involution or we could stop ovarian failure in women so that their loss of advantage is delayed many years. Or do you think we're going to get to body wide anti-aging?

Steve Horvath (29:13):

Yeah, I think of it as divide and conquer. So ultimately I do believe that we can rejuvenate most cell types and tissues. The question is how do you roll out this program? Do you look for this one silver bullet that does it? For example, this idea of interrupted reprogramming based on Yamanaka factor combinations that looks of course very promising and rodent models. But then such silver bullet treatments could be risky for patient keyword malignant transformation, cancer risk, and it could be far safer to focus on one organ system or one tissue. For example, David Sinclair's company Life Biosciences looks at optic nerve regeneration for a reason. It could be safer. And so yeah, I'm very happy that companies explore different strategies. Certain companies focus on one condition, fatty liver disease or NASH. Other companies focus on immune system restoration. But I think many people think of one condition as really a first step to establish safety and efficacy, and then hopefully they could translate it to other body systems and organ systems.

Eric Topol (31:02):

But is it fair to say you're optimistic that we will be able to change the aging pace in people?

Steve Horvath (31:10):

Yes, I think yes. I'm very optimistic and there are several reasons for this optimism. The first is that dramatic results can be achieved in mice and rats. So we and others have published studies that show that you can reduce the epigenetic age by 30% or so and you can extend the lifespan, and you cited this very exciting paper by Stuart Cook on IL-11 inhibition that just came out in Nature. So I keep seeing these kinds of headlines, and then I want to think that one of these will actually work for humans. So the second thing that makes me optimistic is really this combination of artificial intelligence and biomedical research. Then going forward, robotics. So I can see several ways of accelerating biomedical research. So I'm quite optimistic.

The Role of A.I.

Eric Topol (32:24):

Maybe go a little deeper on the AI potential to help here. How does AI come into play?

Steve Horvath (32:33):

So AI can help in so many different ways. The first topic is biomarker development. I of course spent 10 years on a certain statistical model for building biomarkers, which is known as penalized regression. It works well, but AI allows the community to build imaging based biomarkers. So for example, based on MRI images, but also cells growing in a dish, we can say this treatment aged the cells growing in the dish or rejuvenated them. So that's one topic, biomarker discovery. The second is, of course, to design small molecules, keyword, these protein design where it has greatly accelerated drug discovery. And there are several companies working in this space, and again, there's wonderful case studies that look very convincing to me. And the third aspect of AI is another obvious one. AI can read many papers. I mean, you could be a 50-year-old professor who has read papers their entire life, but an AI can really read far better and summarize insights better.

Eric Topol (34:27):

Yeah, the complimentary in terms of the reasoning of that information. So absolutely right now, one of the problems we have here is that aging is not seen as a disease. Of course, we can remember when obesity was not considered a disease and then there was a drug and everything changed. But here we don't have a classification it's a disease. It's considered a natural process that is highly variable in people. But the question is, we can't do studies that are going to wait 20, 30 years to find out if we promoted health span and lifespan. And so, we have to rely on these clocks. So how do you see this playing out? Do you think that we might see a regulatory approval on a surrogate proxy, like an advanced Horvath clock, or do you think that's not going to cut it, that you're going to have to show more to get a anti-aging treatment across the regulatory threshold?

Steve Horvath (35:42):

Yeah, that's a very good question. So I believe that the biomarker community has already assembled enough evidence to offer a battery of tests that could be used as surrogate endpoints of interventional study. And we could discuss the components of this battery. But I would say we already have biomarkers beyond just methylation. One could have the readouts of walking speed or muscle function, many readouts, and they could be aggregated into an index to summarize the biologic age, perhaps, of the individual. So that already exists. At the same time, this field is undergoing explosive growth. You mentioned every day new papers come out in the relatively small field of epigenetic clocks. There's so many papers that it's hard to keep track, but I embrace it. I think it's wonderful because clocks get ever more powerful.

(37:11):

So yeah, I would say there should be different versions. Ideally, a regulatory agency would make an executive decision and say, for the next three years, use the following five biomarkers. Then a few years later, as the science advances, they could come up with an updated version, but even a 90% solution would very much accelerate progress in the whole field of rejuvenating interventions. So I would very much embrace a top down decision on which biomarkers should be made, because the bottom up approach, by the way, simply doesn't work. The minute you put three professors in the room to come up with a decision, which biomarker is best, there will be three different opinions. We need impartial arbiter that makes a decision.

GLP-1 Drugs and Aging

Eric Topol (38:23):

Now, the drug class that's come on the scene, of course it was in incubating for decades for diabetes, but now obesity and so of the obesity related. But now we're seeing the GLP-1 drugs that are showing potential effects in Parkinson's and Alzheimer's and cardiovascular disease, and even in obesity related cancers. And I mean across the board. And you mentioned metabolic derangement as one of the things that accelerate aging. Do you think these class of drugs that has greatly passed our expectations already and it's being tested of course, with even more potent drugs or triple receptors and pills and whatnot, will that be a candidate as one of the anti-aging interventions in the future?

Steve Horvath (39:19):

Yeah, for sure. A couple of months ago, I participated in a conference and one of the speakers showed unpublished results from a study, and they looked good to me. I mean, they registered on epigenetic clocks. This is all unpublished, but it made perfect sense to me because I mentioned the clocks do relate to metabolic health. So I was quite pleased that they registered that intervention.

Eric Topol (39:56):

It's fascinating because we could all be taking GLP-1 drugs someday, not for obesity or not for sleep apnea, but for things that are more far reaching. I didn't know about that unpublished result. That's fascinating.

Steve Horvath (40:15):

Yeah, I have a joke, which is I wish I was chubby because I would be using these drugs, but I'm relatively slender, so I don't have any good reason to take them.

Eric Topol (40:28):

That says a lot. I don't know anybody who knows more about this process than you and is very candid and frank about it. So Steve, this has been terrific to have your insights, the body of work that you should be so proud of that extends over many years and many great years and more contributions to come undoubtedly. So thank you for joining us today, and we will follow this continued evolution of our ability, not just to track the aging process, but also to modulate. So thanks very much.

Steve Horvath (41:06):

Thank you. I really like your podcast Ground Truths, it’s very informative. So thank you for this.

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Pradeep is a brilliant geneticist and Director of Preventive Cardiology, holds the Paul & Phyllis Fireman Endowed Chair in Vascular Medicine at Mass General Hospital and on faculty at Harvard Medical School and the Broad Institute. His prolific research has been illuminating for the field of improving our approach to reduce the risk of heart disease. That’s especially important because heart disease is the global (and US) #1 killer and is on the increase. We didn’t get into lifestyle factors here since there was so much ground to cover on new tests. drugs, and strategies.

A video snippet of our conversation on ApoB. Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify.

Transcript with links to key publications and audio

Eric Topol (00:06):

Well, welcome to Ground Truths. I'm Eric Topol and with me is Pradeep Natarajan from Harvard. He's Director of Preventative Cardiology at the Mass General Brigham Health System and he has been lighting it up on the field of cardiovascular. We're going to get to lots of different parts of that story and so, Pradeep welcome.

Pradeep Natarajan (00:31):

Thanks Eric, really delighted and honored to be with you and have this discussion.

Eric Topol (00:36):

Well, for years I've been admiring your work and it's just accelerating and so there's so many things to get to. I thought maybe what we'd start off with is you recently wrote a New England Journal piece about two trials, two different drugs that could change the landscape of cardiovascular prevention in the future. I mean, that's one of the themes we're going to get to today is all these different markers and drugs that will change cardiology as we know it now. So maybe you could just give us a skinny on that New England Journal piece.

Two New Lipid Targets With RNA Drugs

Pradeep Natarajan (01:16):

Yeah, yeah, so these two agents, the trials were published at the same time. These phase two clinical trials for plozasiran, which is an siRNA against APOC3 and zodasiran, which is an siRNA against ANGPTL3. The reason why we have medicines against those targets are based on human genetics observations, that individuals with loss of function mutations and either of those genes have reduced lipids. For APOC3, it's reduced triglycerides for ANGPTL3 reduced LDL cholesterol and reduced triglycerides and also individuals that have those loss of function mutations also have lower risk for coronary artery disease. Now that's a very similar parallel to PCSK9. We have successful medicines that treat that target because people have found that carriers of loss of function mutations in PCSK9 lead to lower LDL cholesterol and lower coronary artery disease.

(02:11):

Now that suggests that therapeutic manipulation without significant side effects from the agents themselves for APOC3 and ANGPTL3 would be anticipated to also lower coronary artery disease risk potentially in complementary pathways to PCSK9. The interesting thing with those observations is that they all came from rare loss of function mutations that are enriched in populations of individuals. However, at least for PCSK9, has been demonstrated to have efficacy in large groups of individuals across different communities. So the theme of that piece was really just the need to study diverse populations because those insights are not always predictable about which communities are going to have those loss of function mutations and when you find them, they often have profound insights across much larger groups of individuals.

Eric Topol (03:02):

Well, there's a lot there that we can unpack a bit of it. One of them is the use of small interfering RNAs (siRNA) as drugs. We saw in the field of PCSK9, as you mentioned. First there were monoclonal antibodies directed against this target and then more recently, there’s inclisiran which isn't an RNA play if you will, where you only have to take it twice a year and supposedly it's less expensive and I’m still having trouble in my practice getting patients covered on their insurance even though it's cheaper and much more convenient. But nonetheless, now we're seeing these RNA drugs and maybe you could comment about that part and then also the surprise that perhaps is unexplained is the glucose elevation.

Pradeep Natarajan (03:53):

Yeah, so for medicines and targets that have been discovered through human genetics, those I think are attractive for genetic-based therapies and longer interval dosing for the therapies, which is what siRNAs allow you to do because the individuals that have these perturbations, basically the naturally occurring loss of function mutations, they have these lifelong, so basically have had a one-time therapy and have lived, and so far, at least for these targets, have not had untoward side effects or untoward phenotypic consequences and only reduce lipids and reduce coronary artery disease. And so, instead of taking a pill daily, if we have conviction that that long amount of suppression may be beneficial, then longer interval dosing and not worrying about the pill burden is very attractive specifically for those specific therapeutics. And as you know, people continue to innovate on further prolonging as it relates to PCSK9.

(04:57):

Separately, some folks are also developing pills because many people do feel that there's still a market and comfort for daily pills. Now interestingly for the siRNA for zodasiran at the highest dose, actually for both of them at the highest doses, but particularly for zodasiran, there was an increase in insulin resistance parameters actually as it relates to hyperglycemia and less so as it relates to insulin resistance, that is not predicted based on the human genetics. Individuals with loss of function mutations do not have increased risks in hyperglycemia or type 2 diabetes, so that isolates it related to that specific platform or that specific technology. Now inclisiran, as you'd mentioned, Eric is out there. That's an siRNA against PCSK9 that's made by a different manufacturer. So far, the clinical trials have not shown hyperglycemia or type 2 diabetes as it relates inclisiran, so it may be related to the specific siRNAs that are used for those targets. That does merit further consideration. Now, the doses that the manufacturers do plan to use in the phase three clinical trials are at lower doses where there was not an increase in hyperglycemia, but that does merit further investigation to really understand why that's the case. Is that an expected generalized effect for siRNAs? Is it related to siRNAs for this specific target or is it just related to the platform used for these two agents which are made by the same manufacturer?

Eric Topol (06:27):

Right, and I think the fact that it's a mystery is intriguing at the least, and it may not come up at the doses that are used in the trials, but the fact that it did crop up at high doses is unexpected. Now that is part of a much bigger story is that up until now our armamentarium has been statins and ezetimibe to treat lipids, but it's rapidly expanding Lp(a), which for decades as a cardiologist we had nothing to offer. There may even be drugs to be able to lower people who are at high risk with high Lp(a). Maybe you could discuss that.

What About Lp(a)?

Pradeep Natarajan (07:13):

Yeah, I mean, Eric, as you know, Lp(a) has been described as a cardiovascular disease risk factors for quite so many years and there are assays to detect lipoprotein(a) elevation and have been in widespread clinical practice increasing widespread clinical practice, but we don't yet have approved therapies. However, there is an abundance of literature preclinical data that suggests that it likely is a causal factor, meaning that if you lower lipoprotein(a) when elevated, you would reduce the risk related to lipoprotein(a). And a lot of this comes from similar human genetic studies. The major challenge of just relating a biomarker to an outcome is there are many different reasons why a biomarker might be elevated, and so if you detect a signal that correlates a biomarker, a concentration to a clinical outcome, it could be related to that biomarker, but it could be to the other reasons that the biomarker is elevated and sometimes it relates to the outcome itself.

(08:10):

Now human genetics is very attractive because if you find alleles that strongly relate to that exposure, you can test those alleles themselves with the clinical outcome. Now the allele assignment is established at birth. No other factor is going to change that assignment after conception, and so that provides a robust, strong causal test for that potential exposure in clinical outcome. Now, lipoprotein(a) is unique in that it is highly heritable and so there are lots of different alleles that relate to lipoprotein(a) and so in a well powered analysis can actually test the lipoprotein(a) SNPs with the clinical outcomes and similar to how there is a biomarker association with incident myocardial infarction and incident stroke, the SNPs related to lipoprotein(a) show the same. That is among the evidence that strongly supports that this might be causal. Now, fast forward to many years later, we have at least three phase three randomized clinical trials testing agents that have been shown to be very potent at lowering lipoprotein(a) that in the coming years we will know if that hypothesis is true. Importantly, we will have to understand what are the potential side effects of these medicines. There are antisense oligonucleotides and siRNAs that are primarily in investigation. Again, this is an example where there's a strong genetic observation, and so these genetic based longer interval dosing therapies may be attractive, but side effects will be a key thing as well too. Those things hard to anticipate really can anticipate based on the human genetics for off target effects, for example.

(09:52):

It's clearly a risk signal and hopefully in the near future we're going to have specific therapies.

Eric Topol (09:57):

Yeah, you did a great job of explaining Mendelian randomization and the fact the power of genetics, which we're going to get into deeper shortly, but the other point is that do you expect now that there's these multiple drugs that lower Lp(a) efficiently, would that be enough to get approval or will it have to be trials to demonstrate improved cardiovascular outcomes?

Pradeep Natarajan (10:24):

There is a great regulatory path at FDA for approval just for LDL cholesterol lowering and inclisiran is on the market and the phase three outcomes data has not yet been reported because there is a wide appreciation that LDL cholesterol lowering is a pretty good surrogate for cardiovascular disease risk lowering. The label will be restricted to LDL cholesterol lowering and then if demonstrated to have clinical outcomes, the label could be expanded. For other biomarkers including lipoprotein(a), even though we have strong conviction that it is likely a causal factor there hasn't met the bar yet to get approval just based on lipoprotein(a) lowering, and so we would need to see the outcomes effects and then we would also need to understand side effects. There is a body of literature of side effects for other therapies that have targeted using antisense oligonucleotides. We talked about potential side effects from some siRNA platforms and sometimes those effects could overtake potential benefits, so that really needs to be assessed and there is a literature and other examples.

(11:31):

The other thing I do want to note related to lipoprotein(a) is that the human genetics are modeled based on lifelong perturbations, really hard to understand what the effects are, how great of an effect there might be in different contexts, particularly when introduced in middle age. There's a lot of discussion about how high lipoprotein(a) should be to deliver these therapies because the conventional teaching is that one in five individuals has high lipoprotein(a), and that's basically greater than 75 nanomoles per liter. However, some studies some human genetic studies to say if you want to get an effect that is similar to the LDL cholesterol lowering medicines on the market, you need to start with actually higher lipoprotein(a) because you need larger amounts of lipoprotein(a) lowering. Those are studies and approaches that haven't been well validated. We don't know if that's a valid approach because that's modeling based on this sort of lifelong effect. So I'm very curious to see what the overall effect will be because to get approval, I think you need to demonstrate safety and efficacy, but most importantly, these manufacturers and we as clinicians are trying to find viable therapies in the market that it won't be hard for us to get approval because hopefully the clinical trial will have said this is the context where it works. It works really well and it works really well on top of the existing therapies, so there are multiple hurdles to actually getting it directly to our patients.

How Low Do You Go with LDL Cholesterol?

Eric Topol (13:02):

Yeah, no question about that. I'm glad you've emphasized that. Just as you've emphasized the incredible lessons from the genetics of people that have helped guide this renaissance to better drugs to prevent cardiovascular disease. LDL, which is perhaps the most impressive surrogate in medicine, a lab test that you already touched on, one of the biggest questions is how low do you go? That is Eugene Braunwald, who we all know and love. They're in Boston. The last time I got together with him, he was getting his LDL down to close to zero with various tactics that might be extreme. But before we leave these markers, you're running preventive cardiology at man's greatest hospital. Could you tell us what is your recipe for how aggressive do you go with LDL?

Pradeep Natarajan (14:04):

Yeah, so when I talk to patients where we're newly getting lipid lowering therapies on, especially because many people don't have a readout of abnormal LDL cholesterol when we're prescribing these medicines, it's just giving them a sense of what we think an optimal LDL cholesterol might be. And a lot of this is based on just empirical observations. So one, the average LDL cholesterol in the modern human is about 100 to 110 mg/dL. However, if you look at contemporary hunter gatherers and non-human primates, their average LDL is about 40 to 50 and newborn babies have an LDL cholesterol of about 30. And the reason why people keep making LDL cholesterol lowering medicines because as you stack on therapies, cardiovascular disease events continue to reduce including down to these very low LDL cholesterol values. So the population mean for LDL cholesterol is high and everybody likely has hypercholesterolemia, and that's because over the last 10,000 years how we live our lives is so dramatically different and there has not been substantial evolution over that time to change many of these features related to metabolism.

(15:16):

And so, to achieve those really low LDL cholesterol values in today's society is almost impossible without pharmacotherapies. You could say, okay, maybe everybody should be on pharmacotherapies, and I think if you did that, you probably would reduce a lot of events. You'll also be treating a lot of individuals who likely would not get events. Cardiovascular disease is the leading killer, but there are many things that people suffer from and most of the times it still is not cardiovascular disease. So our practice is still rooted in better identifying the individuals who are at risk for cardiovascular disease. And so, far we target our therapies primarily in those who have already developed cardiovascular disease. Maybe we'll talk about better identifying those at risk, but for those individuals it makes lots of sense to get it as low as possible. And the field has continued to move to lower targets.

(16:07):

One, because we've all recognized, at least based on these empirical observations that lower is better. But now increasingly we have a lot of therapies to actually get there, and my hope is that with more and more options and the market forces that influence that the cost perspective will make sense as we continue to develop more. As an aside, related aside is if you look at the last cholesterol guidelines, this is 2018 in the US this is the first time PCSK9 inhibitors were introduced in the guidelines and all throughout that there was discussions of cost. There are a lot of concerns from the field that PCSK9 inhibitors would bankrupt the system because so many people were on statins. And you look at the prior one that was in 2013 and cost was mentioned once it’s just the cost effectiveness of statins. So I think the field has that overall concern.

(17:01):

However, over time we've gotten comfortable with lower targets, there are more medicines and I think some of this competition hopefully will drive down some of the costs, but also the overall appreciation of the science related to LDL. So long-winded way of saying this is kind of the things that we discussed just to give reassurance that we can go to low LDL cholesterol values and that it's safe and then we think also very effective. Nobody knows what the lower limit is, whether zero is appropriate or not. We know that glucose can get too low. We know that blood pressure can be too low. We don't know yet that limit for LDL cholesterol. I mean increasingly with these trials we'll see it going down really low and then we'll better appreciate and understand, so we'll see 40 is probably the right range.

Eric Topol (17:49):

40, you said? Yeah, okay, I'll buy that. Of course, the other thing that we do know is that if you push to the highest dose statins to get there, you might in some people start to see the hyperglycemia issue, which is still not fully understood and whether that is, I mean it's not desirable, but whether or not it is an issue, I guess it's still out there dangling. Now the other thing that since we're on LDL, we covered Lp(a), PCSK9, the siRNA, is ApoB. Do you measure ApoB in all your patients? Should that be the norm?

Measuring ApoB

Pradeep Natarajan (18:32):

Yeah, so ApoB is another blood test. In the standard lipid panel, you get four things. What's measured is cholesterol and triglycerides, they're the lipids insoluble in blood to get to the different tissues that get packaged in lipoprotein molecules which will have the cholesterol, triglycerides and some other lipids and proteins. And so, they all have different names as you know, right? Low density lipoprotein, high density lipoprotein and some others. But also in the lipid panel you get the HDL cholesterol, the amount of cholesterol in an HDL particle, and then most labs will calculate LDL cholesterol and LDL cholesterol has a nice relationship with cardiovascular disease. You lower it with statins and others. Lower risk for cardiovascular disease, turns out a unifying feature of all of these atherogenic lipoproteins, all these lipoproteins that are measured and unmeasured that relate to cardiovascular disease, including lipoprotein(a), they all have an additional protein called ApoB. And ApoB, at least as it relates to LDL is a pretty good surrogate of the number of LDL particles.

(19:37):

Turns out that that is a bit better at the population level at predicting cardiovascular disease beyond LDL cholesterol itself. And where it can be particularly helpful is that there are some patients out there that have an unexpected ratio between ApoB and LDL. In general, the ratio between LDL cholesterol and ApoB is about 1.1 and most people will have that rough ratio. I verify that that is the expected, and then if that is the expected, then really there is no role to follow ApoB. However, primarily the patients that have features related to insulin resistance have obesity. They may often have adequate looking LDL cholesterols, but their ApoB is higher. They have more circulating LDL particles relative to the total amount of LDL cholesterol, so smaller particles themselves. However, the total number of particles may actually be too high for them.

(20:34):

And so, even if the LDL cholesterol is at target, if the ApoB is higher, then you need to reduce. So usually the times that I just kind of verify that I'm at appropriate target is I check the LDL cholesterol, if that looks good, verify with the ApoB because of this ratio, the ApoB target should be about 10% lower. So if we're aiming for about 40, that's like 36, so relatively similar, and if it's there, I'm good. If it's not and it's higher, then obviously increase the LDL cholesterol lowering medicines because lower the ApoB and then follow the ApoB with the lipids going forward. The European Society of Cardiology has more emphasis on measuring ApoB, that is not as strong in the US guidelines, but there are many folks in the field, preventive cardiologists and others that are advocating for the increasing use of ApoB because I think there are many folks that are not getting to the appropriate targets because we are not measuring ApoB.

Why Aren’t We Measuring and Treating Inflammation?

Eric Topol (21:37):

Yeah, I think you reviewed it so well. The problem here is it could be part of the standard lipid panel, it would make this easy, but what you've done is a prudent way of selecting out people who it becomes more important to measure and moderate subsequently. Now this gets us to the fact that we're lipid centric and we don't pay homage to inflammation. So I wrote a recent Substack on the big miss on inflammation, and here you get into things like the monoclonal antibody to interleukin-6, the trial that CANTOS that showed significant reduction in cardiovascular events and fatal cancers by the way. And then you get into these colchicine trials two pretty good size randomized trials, and here the entry was coronary disease with a high C-reactive protein. Now somehow or other we abandon measuring CRP or other inflammatory markers, and both of us have had patients who have low LDLs but have heart attacks or significant coronary disease. So why don't we embrace inflammation? Why don't we measure it? Why don't we have better markers? Why is this just sitting there where we could do so much better? Even agents that are basically cost pennies like colchicine at low doses, not having to use a proprietary version could be helpful. What are your thoughts about us upgrading our prevention with inflammation markers?

Pradeep Natarajan (23:22):

Yeah, I mean, Eric, there is an urgent need to address these other pathways. I say urgent need because heart disease has the dubious distinction of being the leading killer in the US and then over the last 20 years, the leading killer in the world as it takes over non-communicable diseases. And really since the early 1900s, there has been a focus on developing pharmacotherapies and approaches to address the traditional modifiable cardiovascular disease risk factors. That has done tremendous good, but still the curves are largely flattening out. But in the US and in many parts of the world, the deaths attributable to cardiovascular disease are starting to tick up, and that means there are many additional pathways, many of them that we have well recognized including inflammation. More recently, Lp(a) that are likely important for cardiovascular disease, for inflammation, as you have highlighted, has been validated in randomized controlled trials.

(24:18):

Really the key trial that has been more most specific is one on Canakinumab in the CANTOS trial IL-1β monoclonal antibody secondary prevention, so cardiovascular disease plus high C-reactive protein, about a 15% reduction in cardiovascular disease and also improvement in cancer related outcomes. Major issues, a couple of issues. One was increased risk for severe infections, and the other one is almost pragmatic or practical is that that medicine was on the market at a very high price point for rare autoinflammatory conditions. It still is. And so, to have for a broader indication like cardiovascular disease prevention would not make sense at that price point. And the manufacturer tried to go to the FDA and focus on the group that only had C-reactive protein lowering, but that's obviously like a backwards endpoint. How would you know that before you release the medicine? So that never made it to a broader indication.

(25:14):

However, that stuck a flag in the broader validation of that specific pathway in cardiovascular disease. That pathway has direct relevance to C-reactive protein. C-reactive protein is kind of a readout of that pathway that starts from the NLRP3 inflammasome, which then activates IL-1β and IL-6. C-reactive protein we think is just a non causal readout, but is a reliable test of many of these features and that's debatable. There may be other things like measuring IL-6, for example. So given that there is actually substantial ongoing drug development in that pathway, there are a handful of companies with NLRP3 inflammasome inhibitors, but small molecules that you can take as pills. There is a monoclonal antibody against IL-6 that's in development ziltivekimab that's directed at patients with chronic kidney disease who have lots of cardiovascular disease events despite addressing modifiable risk factors where inflammatory markers are through the roof.

(26:16):

But then you would also highlighted one anti-inflammatory that's out there that's pennies on the dollar, that's colchicine. Colchicine is believed to influence cardiovascular disease by inhibiting NLRP3, I say believed to. It does a lot of things. It is an old medicine, but empirically has been shown in at least two randomized controlled trials patients with coronary artery disease, actually they didn't measure C-reactive protein in the inclusion for these, but in those populations we did reduce major adverse cardiovascular disease events. The one thing that does give me pause with colchicine is that there is this odd signal for increased non-cardiovascular death. Nobody understands if that's real, if that's a fluke. The FDA just approved last year low dose colchicine, colchicine at 0.5 milligrams for secondary prevention given the overwhelming efficacy. Hasn’t yet made it into prevention guidelines, but I think that's one part that does give me a little bit pause. I do really think about it particularly for patients who have had recurrent events. The people who market the medicine and do research do remind us that C-reactive protein was not required in the inclusion, but nobody has done that secondary assessment to see if measuring C-reactive protein would be helpful in identifying the beneficial patients. But I think there still could be more work done on better identifying who would benefit from colchicine because it's an available and cheap medicine. But I'm excited that there is a lot of development in this inflammation area.

Eric Topol (27:48):

Yeah, well, the development sounds great. It's probably some years away. Do you use colchicine in your practice?

Pradeep Natarajan (27:56):

I do. Again, for those folks who have had recurrent events, even though C-reactive protein isn't there, it does make me feel like I'm treating inflammation. If C-reactive protein is elevated and then I use it for those patients, if it's not elevated, it's a much harder sell from my standpoint, from the patient standpoint. At the lower dose for colchicine, people generally are okay as far as side effects. The manufacturer has it at 0.5 milligrams, which is technically not pennies on the dollar. That's not generic. The 0.6 milligrams is generic and they claim that there is less side effects at the 0.5 milligrams. So technically 0.6 milligrams is off label. So it is what it is.

CHIP and Defining High Risk People for CV Disease

Eric Topol (28:40):

It's a lot more practical, that's for sure. Now, before I leave that, I just want to mention when I reviewed the IL-1β trial, you mentioned the CANTOS trial and also the colchicine data. The numbers of absolute increases for infection with the antibody or the cancers with the colchicine are really small. So I mean the benefit was overriding, but I certainly agree with your concern that there's some things we don't understand there that need to be probed more. Now, one of the other themes, well before one other marker that before we get to polygenic risk scores, which is center stage here, defining high risk people. We've talked a lot about the conventional things and some of the newer ways, but you've been one of the leaders of study of clonal hematopoiesis of indeterminate potential known as CHIP. CHIP, not the chips set in your computer, but CHIP. And basically this is stem cell mutations that increase in people as we age and become exceptionally common with different mutations that account in these clones. So maybe you can tell us about CHIP and what I don't understand is that it has tremendous correlation association with cardiovascular outcomes adverse as well as other system outcomes, and we don't measure it and we could measure it. So please take us through what the hell is wrong there.

Pradeep Natarajan (30:14):

Yeah, I mean this is really exciting. I mean I'm a little bit biased, but this is observations that have been made only really over the last decade, but accelerating research. And this has been enabled by advances in genomic technologies. So about 10 years or plus ago, really getting into the early days of population-based next generation sequencing, primarily whole exome sequencing. And most of the DNA that we collect to do these population-based analyses come from the blood, red blood cells are anucleate, so they're coming from white blood cells. And so, at that time, primarily interrogating what is the germline genetic basis for coronary artery disease and early onset myocardial infarction. At the same time, colleagues at the Broad Institute were noticing that there are many additional features that you can get from the blood-based DNA that was being processed by the whole exome data. And there were actually three different groups that converged on that all in Boston that converged on the same observation that many well-established cancer causing mutations.

(31:19):

So mutations that are observed in cancers that have been described to drive the cancers themselves were being observed in these large population-based data sets that we were all generating to understand the relationship between loss of function mutations in cardiovascular disease. That's basically the intention of those data sets for being generated for other things. Strong correlation with age, but it was very common among individuals greater than 70; 10% of them would have these mutations and is very common because blood cancer is extremely, it's still pretty rare in the population. So to say 10% of people had cancer causing driver mutations but didn't have cancer, was much higher than anyone would've otherwise expected. In 2014, there were basically three main papers that described that, and they also observed that there is a greater risk of death. You'd say, okay, this is a precancerous lesion, so they're probably dying of cancer.

(32:17):

But as I said, the absolute incidence rate for blood cancer is really low and there's a relative increase for about tenfold, but pretty small as it relates to what could be related to death. And in one of the studies we did some exploratory analysis that suggested maybe it's actually the most common cause of death and that was cardiovascular disease. And so, a few years later we published a study that really in depth really looked at a bunch of different data sets that were ascertained to really understand the relationship between these mutations, these cancer causing mutations in cardiovascular disease, so observed it in enrichment and older individuals that had these mutations, CHIP mutations, younger individuals who had early onset MI as well too, and then also look prospectively and showed that it related to incident coronary artery disease. Now the major challenge for this kind of analysis as it relates to the germline genetic analysis is prevalence changes over time.

(33:15):

There are many things that could influence the presence of clonal hematopoiesis. Age is a key enriching factor and age is the best predictor for cardiovascular disease. So really important. So then we modeled it in mice. It was actually a parallel effort at Boston University (BU) that was doing the same thing really based on the 2014 studies. And so, at the same time we also observed when you modeled this in mice, you basically perturb introduce loss of function mutations in the bone marrow for these mice to recapitulate these driver mutations and those mice also have a greater burden of atherosclerosis. And Eric, you highlighted inflammation because basically the phenotype of these cells are hyper inflamed cells. Interestingly, C-reactive protein is only modestly elevated. So C-reactive protein is not fully capturing this, but many of the cytokines IL-1β, IL-6, they're all upregulated in mice and in humans when measured as well.

(34:11):

Now there've been a few key studies that have been really exciting about using anti-inflammatories in this pathway to address CHIP associated cardiovascular disease. So one that effort that I said in BU because they saw these cytokines increased, we already know that these cytokines have relationship with atherosclerosis. So they gave an NLRP3 inflammasome inhibitor to the mice and they showed that the mice with or without CHIP had a reduction in atherosclerosis, but there was a substantial delta among the mice that are modeled as having CHIP. Now, the investigators in CANTOS, the manufacturers, they actually went back and they survey where they had DNA in the CANTOS trial. They measured CHIP and particularly TET2 CHIP, which is the one that has the strongest signal for atherosclerosis. As I said, overall about 15% reduction in the primary outcome in CANTOS. Among the individuals who had TET2 CHIP, it was a 64% reduction in event.

(35:08):

I mean you don't see those in atherosclerosis related trials. Now this has the caveat of it being secondary post hoc exploratory, the two levels of evidence. And so, then we took a Mendelian randomization approach. Serendipitously, just so happens there is a coding mutation in the IL-6 receptor, a missense mutation that in 2012 was described that if you had this mutation, about 40% of people have it, you have a 5%, but statistically significant reduction in coronary artery disease. So we very simply said, if the pathway of this NLRP3 inflammasome, which includes IL-6, if you have decreased signaling in that pathway, might you have an even greater benefit from having that mutation if you had CHIP versus those who didn't have CHIP. So we looked in the UK Biobank, those who didn't have CHIP 5% reduction, who had that IL-6 receptor mutation, and then those who did have CHIP, if they had that mutation, it was about a 60% reduction in cardiovascular disease.

(36:12):

Again, three different lines of evidence that really show that this pathway has relevance in the general population, but the people who actually might benefit the most are those with CHIP. And I think as we get more and more data sets, we find that not all of the CHIP mutations are the same as it relates to cardiovascular disease risk. It does hone in on these key subsets like TET2 and JAK2, but this is pretty cool as a preventive cardiologist, new potential modifiable risk factor, but now it's almost like an oncologic paradigm that is being applied to coronary artery disease where we have specific driver mutations and then we're tailoring our therapies to those specific biological drivers for coronary artery disease. Hopefully, I did that justice. There's a lot there.

Why Don’t We Measure CHIP?

Eric Topol (36:57):

Well, actually, it's phenomenal how you've explained that, but I do want to review for our listeners or readers that prior to this point in our conversation, we were talking about germline mutations, the ones you're born with. With CHIP, we're talking about acquired somatic mutations, and these are our blood stem cells. And what is befuddling to me is that with all the data that you and others, you especially have been publishing and how easy it would be to measure this. I mean, we've seen that you can get it from sequencing no less other means. Why we don't measure this? I mean, why are we turning a blind eye to CHIP? I just don't get it. And we keep calling it of indeterminate potential, not indeterminate. It's definite potential.

Pradeep Natarajan (37:51):

Yeah, no, I think these are just overly cautious terms from the scientists. Lots of people have CHIP, a lot of people don't have clinical outcomes. And so, I think from the lens of a practicing hematologists that provide some reassurance on the spectrum for acquired mutation all the way over to leukemia, that is where it comes from. I don't love the acronym as well because every subfield in biomedicine has its own CHIP, so there's obviously lots of confusion there. CH or clinical hematopoiesis is often what I go, but I think continuing to be specific on these mutations. Now the question is why measure? Why aren't we measuring it? So there are some clinical assays out there. Now when patients get evaluated for cytopenias [low cell counts], there are next generation sequencing tests that look for these mutations in the process for evaluation. Now, technically by definition, CHIP means the presence of these driver mutations that have expanded because it's detectable by these assays, not a one-off cell because it can only be detected if it's in a number of cells.

(38:55):

So there has been some expansion, but there are no CBC abnormalities. Now, if there's a CBC abnormality and you see a CHIP mutation that's technically considered CCUS or clonal cytopenia of unknown significance, sometimes what is detected is myelodysplastic syndrome. In those scenarios still there is a cardiovascular disease signal, and so many of our patients who are seen in the cancer center who are being evaluated for these CBC abnormalities will be detected to have these mutations. They will have undergone some risk stratification to see what the malignancy potential is. Still pretty low for many of those individuals. And so, the major driver of health outcomes for this finding may be cardiovascular. So those patients then get referred to our program. Dana-Farber also has a similar program, and then my colleague Peter Libby at the Brigham often sees those patients as well. Now for prospective screening, so far, an insurance basically is who's going to pay for it.

(39:51):

So an insurance provider is not deemed that appropriate yet. You do need the prospective clinical trials because the medicines that we're talking about may have side effects as well too. And what is the yield? What is the diagnostic yield? Will there actually be a large effect estimate? But there has been more and more innovation, at least on the assay and the cost part of the assay because these initial studies, we've been using whole exome sequencing, which is continuing to come down, but is not a widely routine clinical test yet. And also because as you highlighted, these are acquired mutations. A single test is not necessarily one and done. This may be something that does require surveillance for particular high risk individuals. And we've described some risk factors for the prevalence of CHIP. So surveillance may be required, but because there are about 10 genes that are primarily implicated in CHIP, that can substantially decrease the cost of it. The cost for DNA extraction is going down, and so there are research tests that are kind of in the $10 to $20 range right now for CHIP. And if flipped over to the clinical side will also be reasonably low cost. And so, for the paradigm for clinical implementation, that cost part is necessary.

Eric Topol (41:10):

I don't know the $10 or $20 ones. Are there any I could order on patients that I'm worried about?

Pradeep Natarajan (41:17):

Not yet clinical. However, there is a company that makes the reagents for at least the cores that are developing this. They are commercializing that test so that many other cores, research cores can develop it. I think it's in short order that clinical labs will adopt it as well too.

Eric Topol (41:36):

That's great.

Pradeep Natarajan (41:37):

I will keep you apprised.

What About Polygenic Risk Scores?

Eric Topol (41:39):

I think that's really good news because like I said, we're so darn lipid centric and we have to start to respect the body of data, the knowledge that you and others have built about CHIP. Now speaking of another one that drives me nuts is polygenic risk score (PRS) for about a decade, I've been saying we have coronary disease for most people is a polygenic trait. It's not just a familial hypercholesterolemia. And we progressively have gotten better and better of the hundreds of single variants that collectively without a parental history will be and independently predict who is at double, triple or whatever risk of getting heart disease, whereby you could then guide your statins at higher aggressive or pick a statin, use one or even go beyond that as we've been talking about. But we don't use that in practice, which is just incredible because it's can be done cheap.

(42:45):

You can get it through whether it's 23andMe or now many other entities. We have an app, MyGeneRank where we can process that Scripss does for free. And only recently, Mass General was the first to implement that in your patient population, and I'm sure you were a driver of that. What is the reluctance about using this as an orthogonal, if you will, separate way to assess a person's risk for heart disease? And we know validated very solidly about being aggressive about lipid lowering when you know this person's in the highest 5% polygenic risk score. Are we just deadheads in this field or what?

Pradeep Natarajan (43:30):

Yeah, I mean Eric, as you know, lots of inertia in medicine, but this one I think has a potential to make a large impact. Like CHIP mutations, I said news is about 10% in individuals greater than 70. The prospect here is to identify the risk much earlier in life because I think there is a very good argument that we're undertreating high risk individuals early on because we don't know how to identify them. As you highlighted, Dr. Braunwald about LDL cholesterol. The other part of that paradigm is LDL cholesterol lowering and the duration. And as we said, everybody would benefit from really low LDL cholesterol, but again, you might overtreat that if you just give that to everybody. But if you can better identify the folks very early in life, there is a low cost, low risk therapy, at least related to statins that you could have a profound benefit from the ones who have a greater conviction will have future risk for cardiovascular disease.

(44:21):

You highlighted the family history, and the family history has given the field of clues that genetics play a role. But as the genome-wide association studies have gotten larger, the polygenic risk scores have gotten better. We know that family history is imperfect. There are many reasons why a family member who is at risk may or may not have developed cardiovascular disease. A polygenic risk score will give a single number that will estimate the contribution of genetics to cardiovascular disease. And the thing that is really fascinating to me, which is I think some of a clinical implementation challenge is that the alleles for an individual are fixed. The genotyping is very cheap. That continues to be extremely cheap to do this test. But the weights and the interpretation of what the effects should be for each of the SNPs are continually being refined over time.

(45:18):

And so, given the exact same SNPs in the population, the ability to better predict cardiovascular diseases getting better. And so, you have things that get reported in the literature, but literally three years later that gets outdated and those hypotheses need to be reassessed. Today, I'll say we have a great relative to other things, but we have a great polygenic risk score was just reported last year that if you compare it to familial hypercholesterolemia, which has a diagnostic yield of about 1 in 300 individuals, but readily detectable by severe hypercholesterolemia that has about threefold risk for cardiovascular disease. By polygenic risk score, you can find 1 in 5 individuals with that same risk. Obviously you go higher than that, it'll be even higher risk related to that. And that is noble information very early in life. And most people develop risk factors later in life. It is happening earlier, but generally not in the 30s, 40s where there's an opportunity to make a substantial impact on the curve related to cardiovascular disease.

(46:25):

But there is a lot of momentum there. Lots of interest from NIH and others. The major challenge is though the US healthcare system is really not well set up to prevention, as you know, we practice healthcare after patient's developed disease and prevent the complications related to progression. The stakeholder incentives beyond the patient themselves are less well aligned. We've talked a lot here today about payers, but we don't have a single payer healthcare system. And patients at different times of their lives will have different insurers. They'll start early in life with their parents, their first employer, they'll move on to the next job and then ultimately Medicare. There's no entity beyond yourself that really cares about your longevity basically from the beginning and your overall wellness. That tension has been a major challenge in just driving the incentives and the push towards polygenic risk scores. But there are some innovative approaches like MassMutual Life Insurance actually did a pilot on polygenic risk scoring.

(47:33):

They're in the business of better understanding longevity. They get that this is important data. Major challenges, there are federal protections against non-discrimination in the workplace, health insurance, not necessarily life insurance. So I think that there are lots of things that have to be worked out. Everybody recognizes that this is important, but we really have to have all the incentives aligned for this to happen at a system-wide level in the US. So there's actually lots of investment in countries that have more nationalized healthcare systems, lots of development in clinical trials in the UK, for example. So it's possible that we in the US will not be the lead in that kind of evidence generation, but maybe we'll get there.

The GLP-1 Drugs

Eric Topol (48:16):

Yeah, it's frustrating though, Pradeep, because this has been incubating for some time and now we have multi ancestry, polygenic risk scores, particularly for heart disease and we're not using it, and it's not in my view, in the patient's best interest just because of these obstacles that you're mentioning, particularly here in the US. Well, the other thing I want to just get at with you today is the drugs that we were using for diabetes now blossoming for lots of other indications, particularly the glucagon-like peptide 1 (GLP-1) drugs. This has come onto the scene in recent years, not just obviously for obesity, but it's anti-inflammatory effects as we're learning, mediated not just through the brain but also T cells and having extraordinary impact in heart disease for people with obesity and also with those who have heart failure, about half of heart failure for preserved ejection fraction. So recently you and your colleagues recently published a paper with this signal of optic neuropathy. It was almost seven eightfold increase in a population. First, I wanted to get your sense about GLP-1. We're also going to get into the SGLT2 for a moment as well, but how do you use GLP-1? What's your prognosis for this drug class going forward?

Pradeep Natarajan (49:55):

As it relates to the paper, I can't claim credit as one of my former students who is now Mass Eye and Ear resident who participated, but we can talk about that. There's obviously some challenges for mining real world data, but this was related to anecdotes that they were observing at Mass Eye and Ear and then studied and observed an enrichment. In general though, I feel like every week I'm reading a new clinical trial about a new clinical outcome benefit as it relates to GLP-1 receptor agonists. This is kind of one thing that stands out that could be interrogated in these other clinical trials. So I would have that caveat before being cautious about ocular complications. But the data has been overwhelmingly beneficial, I think, because at minimum, obesity and inflammation are relayed to myriad of consequences, and I'm really excited that we have therapies that can address obesity that are safe.

(50:52):

There's a legacy of unsafe medicines for obesity, especially related to cardiovascular disease. So the fact that we have medicines that are safe and effective for lowering weight that also have real strong effects on clinical outcomes is tremendous. We in cardiology are increasingly using a range of diabetes medicines, including GLP-1 receptor agonists and SGLT2 inhibitors. I think that is also the secular changes of what influences cardiovascular disease over time. I talked about over the last 10 years or so with this increase in deaths attributable to cardiovascular disease. If you look at the influences of traditional clinical risk factors today, many of them have decreased in importance because when abnormal, we recognize them, in general we modify them when recognized. And so, many of the things that are unaddressed, especially the features related to insulin resistance, obesity, they start rising in importance. And so, there is a dramatic potential for these kinds of therapies in reducing the residual risks that we see related to cardiovascular disease. So I'm enthusiastic and excited. I think a lot more biology that needs to be understood of how much of this is being influenced specifically through this pathway versus a very effective weight loss medicine. But also interesting to see the insights on how the effect centrally on appetite suppression has profound influences on weight loss as well too. And hopefully that will lead to more innovations in weight management.

The SGLT-2 Drugs

Eric Topol (52:25):

And likewise, perhaps not getting near as much play, but when it came on the cardiovascular scene that an anti-diabetic drug SGLT2 was improving survival, that was big, and we still don't know why. I mean, there's some ideas that it might be a senolytic drug unknowingly, but this has become a big part of practice of cardiology in patients with diabetes or with preserved ejection fraction heart failure. Is that a fair summary for that drug?

Pradeep Natarajan (53:00):

Yeah, I totally agree. I mean, as there has been increased recognition for heart failure preserved ejection fraction, it has been almost disheartening over the last several years that we have not had very specific effective therapies to treat that condition. Now, it is a tremendous boon that we do have medicines interestingly focused on metabolism that are very helpful in that condition for heart failure with preserved ejection fraction. But there is still much more to be understood as far as that condition. I mean, the major challenge with heart failure, as you know, especially with heart failure preserved ejection fraction, it likely is a mix of a wide variety of different etiologies. So in parallel with developing effective therapies that get at some aspect is really understanding what are the individual drivers and then targeting those specific individual drivers. That requires a lot of unbiased discovery work and further profiling to be done. So lot more innovation, but relative to heart failure itself, it is not had widespread recognition as heart failure reduced ejection fraction. So much more to innovate on, for sure.

Eric Topol (54:07):

Right, right. Yeah, I am stunned by the recent progress in cardiovascular medicine. You have been center stage with a lot of it, and we've had a chance to review so much. And speaking of genetics, I wanted to just get a little insight because I recently came across the fact that your mother here at the City of Hope in Southern California is another famous researcher. And is that, I don't know what chromosome that is on regarding parental transmission of leading research. Maybe you can tell me about that.

Pradeep Natarajan (54:41):

Yeah, I mean, I guess it is a heritable trait when a parent has one profession that there is a higher likelihood that the offspring will have something similar. So both of my parents are PhDs, nonphysicians. There is a diabetes department at the City of Hope, so she's the chair of that department. So very active. We do overlap in some circles because she does investigate both vascular complications and renal complications. And then sometimes will ask my advice on some visualization. But she herself has just had a science translational medicine paper, for example, just a couple of months ago. So it's fun to talk about these things. To be honest, because my parents are researchers, I was not totally sure that I would be a researcher and kind of wanted to do something different in medicine. But many of my early observations and just how common cardiovascular disease is around me and in my community and wanting to do something useful is what got me specifically into cardiology.

(55:45):

But obviously there are numerous outstanding, important questions. And as I went through my career, really focused on more basic investigations of atherosclerosis and lipids. What got me excited sort of after my clinical training was the ability to ask many of these questions now in human populations with many new biological data sets, at least first centered on genetics. And the capabilities continue to expand, so now I teach first year Harvard medical students in their genetics curriculum. And when I talk to them just about my career arc, I do remind them they're all doing millions of things and they're exploring lots of things, but when they get to my shoes, the capabilities will be tremendously different. And so, I really advise them to take the different experiences, mainly in an exercise for asking questions, thoughtfully addressing questions, connecting it back to important clinical problems. And then once they start to understand that with a few different approaches, then they'll totally take off with what the opportunities are down the road.

Eric Topol (56:51):

No, it's great. I mean, how lucky somebody could be in the first year of med school with you as their teacher and model. Wow. Pradeep, we've really gone deep on this and it's been fun. I mean, if there's one person I'm going to talk to you about cardiovascular risk factors and the things that we've been into today, you would be the one. So thank you for taking the time and running through a lot of material here today, and all your work with great interest.

Pradeep Natarajan (57:24):

Thanks, Eric. I really appreciate it. It's tremendous honor. I'm a big fan, so I would be glad to talk about any of these things and more anytime.

***************

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Shane Crotty: A Landmark Study on Upper Airway Mucosal Immunity

Transcript

This is the first time a Ground Truths podcast is being posted simultaneous with a new publication, this one in Nature, by Professor Shane Crotty and his colleagues at La Jolla Institute for Immunology. Shane is one of the leading immunologists and virologists in the country; he and his group published in 2020 the first detailed analysis for how our immune system responds to SARS-CoV-2. Shane also, among many other notable contributions during COVID, illuminated the role of hybrid immunity vs COVID, the differences between and additivity of vaccination and infection.

Today’s paper in Nature is indeed a landmark contribution doing something that hasn’t been done before—to understand the underpinnings of mucosal immunity of the upper airway. 100 participants had monthly nasal and nasopharyngeal swabs throughout the pandemic. With a median of >100,000 cells per swab recovered, they undertook single-cell sequencing and full characterization of the cells (tissue-resident memory B cells, CD4+ and CD8+ T cells, germinal center follicular helper T cells and B cells, etc.) to determine optimal immune protection of the upper airway, the effect of infections by different variants, breakthrough infections, vaccination, and age.

Here is the transcript of our conversation about the new report with links to the audio:

Eric Topol (00:06):

Hello, it's Eric Topol with Ground Truths, and with me today is Professor Shane Crotty from the La Jolla Institute of Immunology (LJI), not too far away from where I work at Scripps. And Shane has been a go-to immunologist colleague here in the Mesa, and he and his colleagues were the ones that really first published the response to SARS-CoV-2 as far as the immunologic response. And today we're doing something very unique. We're going to go over for the first time in the two year plus history of Ground Truths, going to have a publication with at least simultaneous or near simultaneous podcast. Shane, welcome and congratulations on this really important paper in Nature.

Shane Crotty (00:57):

Thanks, Eric. Thanks for having me. Yeah, somebody asked if I was going to go over to Scripps for the podcast and I was like, yeah, we could.

Eric Topol (01:06):

You could. You could. But no, it's good. And it's nice having the logo of this great institute you work at right in the right corner. And you've done so many contributions with your colleagues at La Jolla Institute. It's really a privilege to have a chance to learn from you and particularly about what we're going to talk about today, which is mucosal immunity to upper airway infections, which is especially germane to COVID. And we're actually in the middle of a significant wave of COVID right now. And I guess it would maybe be fair to say, Shane, that we've never truly understood the underpinnings, the real details of upper airway mucosal immunity. Is that a fair statement?

Shane Crotty (01:53):

Yeah, it is a fair statement.

Eric Topol (01:56):

Okay. So today we're going to crack the case. This paper from you and your colleagues, of course, you're the senior author and first author, Sydney Ramirez did a remarkable study. I mean, just extraordinary. This is why we're doing a special podcast about it. Maybe you could just kind of give us the overview of the design because you were doing things that haven't been done before.

Shane Crotty (02:24):

Sure. And, I would say the genesis even of it goes back to what you were introducing. I mean, during the pandemic, we like a lot of scientists spent a lot of time and energy trying to help understanding immune responses to this virus, and immune memory to this virus, and what was involved in protective immunity. And we're certainly proud of the work that we did. And it was hard work. And after a while we were exhausted and we stopped.

Shane Crotty (02:59):

And then we came back to it after a while and said, well, the virus is still here. And so many people have contributed so much to better understanding the virus and creating vaccines. But there are clearly still things we don't understand. What are those biggest knowledge gaps and where might we be able to contribute? And really to me the biggest one was location, location, location. This is a virus that infects your nose, infects your upper airway—your nose, and throat, and oral cavity. And then obviously if you get severe disease, the severe disease and death are from the lungs. And it's just been a big knowledge gap in terms of understanding what actually occurs in those tissues immunologically and what is associated with protective immunity or what could be associated with protective immunity. And sort of looking forward what might be helpful for mucosal vaccine development from things that we could learn.

Shane Crotty (04:12):

So we started from what we would call the basics, and what does immune memory look like in the upper airways in normal people? And that hasn't been available really even in, and we started this two years ago, even in the biggest atlases published of the human body. There was no upper airway tissue representation at all. And that's because technically it's just tough to access and difficult to reproducibly get at. And so, we recruited people to a group of 20 to 30 people to come to LJI once a month, and just started testing out, published and unpublished sampling techniques to see were there ways where we could reproducibly sample immune cells in the upper airways from people. And once we got things, so the keys for us were you got to have enough cells that you can collect to learn something from. And luckily with modern techniques of flow cytometry and single cell sequencing, you don't need that many cells. And so, we could get a hundred thousand cells on a swab and that's enough to do a lot with. And second, how reproducible was it? So we showed, we had people come in every month for a year and we could reproducibly find the same things in their swab; same cell types in their swabs. And the third thing was that people would come back.

Shane Crotty (06:05):

We found that if you have good nurses doing the techniques, we could find ways that this would be a sampling approach that was tolerable and people would come back for repeat measures, which is really valuable to see what's happening in people over time. So that was what we started from in the study and built from.

Eric Topol (06:27):

And if I am correct, you sampled two places with the swabs, one in the nose and one of the throat. Or, I think one which you have in the paper as the MT for something about the median nasal turbinate and the other adenoid in the back of the throat. Is that right?

Shane Crotty (06:50):

So all the sampling is a swab into your nose. And when we were doing that, we were really excited to see the diversity of immune cells, particularly T cells and B cells, memory T cells and B cells that we isolated. They're like, wow, there's actually a lot of interesting immune memory up in there. And the lab said, oh, by the way, we're seeing T follicular helper cells (TFH). Now that happens to be my favorite cell type.

Eric Topol (07:22):

Why is that, Shane? Of all the cells, why do you say that's your favorite? I know you publish a lot on it.

Shane Crotty (07:31):

Because those are the T cells that are required for basically all neutralizing antibody responses. All high-quality antibody responses depend on—almost all high-quality antibody responses depend on—T cell help. That T cell help comes from T follicular helper cells. Antibody evolution is certainly one of the coolest processes of the immune system. And all of that depends on T follicular helper cells. So the fact that for example, you could get Omicron neutralizing antibodies even after only being vaccinated with ancestral vaccine, that's the immune system making guesses of what variants would look like. And those guesses come about through this antibody evolution that's driven by T follicular helper cells. So, it's really one of the most brilliant things the immune system does, and that's a cell type that's really key, but those processes happen in lymphoid tissue. That's what happens in lymph nodes and spleen. And here we were sampling epithelium, your nasal epithelium, so the cells didn't really belong there.

Shane Crotty (08:37):

And so, that's what turned the study in another direction. And we said, okay, let's figure out why is it that these cells are present in these swabs? And we had a couple of possibilities. One possibility was that the swab was going all the way back to the posterior wall of your nasopharynx, your top of your throat and sampling adenoid tissue. So adenoid tonsils and adenoids are a true lymphoid tissue and they're a mucosal lymphoid tissue. And so, we came up with multiple ways to validate that that's what we were testing. And in fact, it was the Sydney Ramirez, a clinician, and the ENTs involved who said, well, let's just look. And so, they actually did endoscopies with the swab to actually see where the swab went. We've got videos of the swabs going into the adenoid crypt in the back, and then we've got measurements of here are the cells that you find on those swabs.

Shane Crotty (09:58):

And what's cool about it is that, yes, so we did studies with two sets. We then shifted to doing studies with two sets of swabs. One where we essentially went “halfway back” where we were detecting that epithelium of your nasal passages and then one where it was all the way back and detecting the adenoid lymphoid tissue. So here we've got two different sites in your upper airways that are about an inch apart, and we're detecting essentially completely different cells of the immune system at those two places. And we tend to think of the cells present in that epithelial tissue as probably the sentinels, the cells that are sitting there that can potentially immediately react and try and protect you against a viral or bacterial infection. Whereas the lymphoid tissue, the adenoids, is really about generating the immune responses in the first place and priming immune responses. And that's where these germinal centers can occur, which are where the TFH are where you can get antibody evolution. And so, we found in the course of the study that with this non-invasive technique that we can.

Eric Topol (11:14):

By the way, I don't want to be signing up for the one way up there because I mean just a mid-nose enough for me. So wow, I got to give credit to your study participants for coming back every month for a year to have that. Some people call it a brain biopsy.

Video of swab of nasopharyngeal tissue

Shane Crotty (11:33):

Right. So I will tell you, it is a different experience than the COVID nasopharyngeal swab might've gotten through your car window. If you're actually sitting down in a comfortable space and there's a nurse doing it with these particular goals. We really found, we had a hundred people in the study and a total of 300 swabs, and the vast majority of people came back if we asked them to.

Eric Topol (12:06):

That's great.

Shane Crotty (12:07):

And we're certainly very thankful for the volunteers. Obviously they were volunteering in the first place to participate. So I'm a little hesitant about the video because I've told people to not show it to potential volunteers because it definitely doesn't encourage you to volunteer. You're like, wait, that's what's happening? But actually, I've had it done on me.

Video of the swab to the nasopharynx for adenoid (lymphoid tissue) access.

Eric Topol (12:37):

Not that bad.

Shane Crotty (12:39):

It's really pretty compelling. And by doing these repeated samples, we actually now have the capacity to look at ongoing immune responses like after an infection or vaccination in people and see how that results in the immune system changing and what might be the source of the protective immunity that comes up. So we've actually got data in the paper looking at this antibody evolution in real time. So we've got affinity maturation of B cells occurring in just normal healthy adults of mucosal B cells against COVID. And so, that's really helping us learn what's possible, basically to figure out, okay, if you're going to try and make a vaccine, what types of immune cells are even possible to generate in this tissue? And where might you try and generate them? Or if you're trying to study some disease state, what are types of cells that might be problematic?

Eric Topol (13:45):

Yeah, I mean, I think the idea that so many of us have been pushing for a nasal vaccine to induce mucosal immunity because, as you know very well, the current shots are not very good at any durable or substantial protection from upper airway infections of COVID or SARS-CoV-2 and other infections. So I think one of the most important parts of this report is that it lends itself well to helping towards artificially, if you will, make a vaccine to get the protective features that you were able to identify. Maybe you could just [speculate], if you had the ideal nasal airway, what would the cellular profile look like?

Shane Crotty (14:44):

Ah, I see. Yeah, great question. So, first of all, antibodies are great. So most of my career has been dedicated to most licensed vaccines. The correlate of protection is antibodies. Antibodies clearly can be protective, and if you can get them that’s excellent, so certainly I would want, in terms of the non-cellular component, I would want antibodies present, neutralizing antibodies present in it.

Eric Topol (15:26):

Are these IgA or IgG?

Shane Crotty (15:31):

Yeah, in an ideal situation, what would I want? I'd want a mix of both, basically. The IgAs look like they have a little more protective efficacy, but the IgGs, just at a molecular level have a longer half-life, stick around a little. So yeah, I'd want both. And then really the premise for most of what we do is saying, in situations where antibody isn't enough or the antibodies don't stay around long enough, or you've got a variant that now obviates the protective efficacy of that particular antibody, are there other types of protective immunity you can have? And the immune system has other stuff besides antibodies for a reason. Of the lymphocytes in your blood, most of them aren't antibody producing cells. Most of them are other things. And so, well sticking with adjacent to antibodies, those antibodies in the mucosa, I'd want them to be made by cells that were literally right there. So plasma cells living in that site so that you've got basically the highest concentration of antibodies you can get because they're not having to diffuse through the whole body. They're just already at their highest concentration right there. Now antibodies come from B cells, that's what encodes the antibodies.

Shane Crotty (17:03):

And so, the B cells can make neutralizing antibodies if it turns out that you haven't made enough neutralizing antibodies, or if there's a variant that escapes those, maybe there are other B cells that could make, once you get infected, more B cells that could make more antibody rapidly infection, or B cells that recognize this variant that is mismatched to the current antibodies you have. But memory B cells are basically a library of different antibody specificities representing different guesses about what viral variants or structures might look like. And so, I would want memory B cells in that upper airway tissue that could reactivate quickly. There are memory B cells in your blood and we don't know how long it takes. And that's one of the reasons we're hoping we and others build upon this study. But it might take, let's say five days for memory B cells to go from your blood into your upper airway.

Eric Topol (18:06):

Oh, right.

Shane Crotty (18:08):

That's right, you were already quite sick by that point. Instead, if memory B cells are right there, as soon as virus showed up, they got activated. Now maybe after (we’re not sure yet), but maybe after 48 hours those cells are now activated and doing something useful. That would be optimal. So then we can pivot to the T cell side. So there’s a fantastic recognition that T cells being physically present in tissues, tissue resident memory cells, as they’re most often called, can really have fantastic protective capacities. From a lot of mouse model systems where you can see T cells are in the skin or the liver, or whatever [tissue] are already there, they’re more protective than if the cells are in the blood. So if you could also have T cells essentially permanently parked in the epithelium of your nasal passages and in the adenoid, hopefully those could essentially be sentinels for protective immunity, and as soon as you get infected, those T cells would reactivate and start killing off infected cells. ’That’s the mix that I would want to see. And I think there’s at least some reasonable evidence in the context of COVID that people who have T cells in their upper airways maybe manage to control the virus so quickly that it’s a subclinical infection; they never notice when they get infected. And so, building on those types of observations, that’s what I would want.

Eric Topol (19:56):

That sounds good. I like that. I’d like to have that in my nasal airway. Now, just to make sure I’ve got this, what you found, of course, the memory B cells, the T cell memory, CD8+, that is the cell-killing T cells that you mentioned, the resident T cells. One clarification on that, they are not really going to do much until there’s been some cells that have been infected with the virus, right? Then they come alive and kill those cells. So they’re not immediate, but they can work pretty quickly still though, right? If they’re resident T cells?

Shane Crotty (20:45):

Yeah, in theory it might take as little as 12 hours for a virus to infect a cell, and then you get some antigen presentation on that cell that could activate the T cell.

Eric Topol (20:58):

And that’s all happening perhaps within the incubation phase of the virus, right?

Shane Crotty (21:07):

Correct. That’s a tough thing to study, but conceptually that’s the way people tend to sketch it out.

Eric Topol (21:13):

Right. Now the other part of the story is, and you alluded to it earlier, is the lymphoid tissue up there, higher up where there are these germinal centers; is there anything different you want in these germinal centers? Do they contribute to mucosal immunity that you haven’t already mentioned?

Shane Crotty (21:36):

So they really contribute in this forward looking sense or really in the classroom kind of sense. The germinal centers are where you’re basically teaching the B cells in advance of seeing the infection either with your vaccine or with your previous infection, evolving better B cells and better antibodies and hopefully instructing them where to go reside to then be ready for the next infection. If you get really great protection that next time, hopefully then you don’t need to start.

Eric Topol (22:14):

Right. So it’s like the training grounds for this coordinated response, I guess. Now you also noted this, I mean this is a rich paper, which is we’re illuminating something that’s never been done before in human beings. I mean it’s pretty damn important and impressive. But you also found that you had an age relationship. Can you tell us about that?

Shane Crotty (22:39):

Sure. This is one of our favorite parts of the study. I’d say in particular for several of the clinicians who were involved, because the general conversations people have about upper airway lymphoid tissue, like your tonsils and including your adenoids, is that adults don’t really have functional lymphoid tissue in the upper airway that your tonsils atrophy by the time maybe you’re 20 or something. So, immunologically, functionally, what that means is if you have let’s say an intranasal vaccine or you get infected with a new [virus] like SARS-CoV-2, if those would normally be the sites that start your immune response, where does it now happen? And instead what we saw was, we had such a diverse group of people in our studies—we realized we had people from age 18 to 68—and so we could directly ask, in normal healthy individuals across a large age span of adulthood is there functional mucosal lymphoid tissue? And the answer was yes, it was there. But it definitely declines over time, and it's declining on a log scale. Our simplest statement was that 75% of everybody we sampled still had functional tissue, but the younger the people were, the more functional it was, and the more germinal centers actually we saw; again these training grounds.

Eric Topol (24:35):

So this is really important because we know for COVID and obviously for influenza and other respiratory infections that people of advanced age are much more susceptible. And here you are finding something that supports that ,and it's almost like, the thymus, it involutes. After that, what age 20, and our lymphoid tissue [involutes]. We're just set up to fail. Old codgers, like me we're defenseless, I guess, right?

Shane Crotty (25:12):

So what I've liked about that in a positive sense is that it's not that all of these things go to zero. Like for example, naive T cells are definitely less abundant in people over the age of 60 than under, but they're not zero. And the mucosal lymphoid tissue is definitely less abundant in people over the age of 60, but in most people it still wasn't zero. And I always think about these things from a vaccine immunology perspective, and fundamentally the difference between getting vaccinated and infected frequently is that the whole point of the vaccine is you get to generate the immune response on your own time. And so, even if you're starting with five times fewer T cells or five times fewer germinal centers, if you're getting to do all that training ground in advance, you can end up with just as many bispecific T cells as a 20-year-old or just as many memory B cells as a 20-year-old because these things occur on an exponential scale because of the cell divisions. And so, it might take you three extra days, for example, to get to the same level, which again, if you're racing a virus, can be the difference between life and death. But if it's not a race and if you're doing it in the context of a vaccine, it's a much smaller factor. And that's some of what we've been trying to learn.

Eric Topol (26:42):

Now we only have started to scratch the surface of your findings. One of the things that drives me nutty in reading papers, especially from great immunologists like you, is that in each figure there's like 20 different panels. We get to one of the figures, figure three is all the way to panel W. I mean that starts with A. That gives you a little impression of the data. It's rich, another one goes to N or R. I mean we're talking about a lot of data. So I've only started to really deconvolute what you've done here, which is just an amazing study. But what are some other things that we should touch on before wrapping up?

Shane Crotty (27:35):

A lot of the goal in this study was to establish baselines of what is normal in humans in the upper airways. And that's one reason why in this case there actually are a lot of figure panels because we could work out a bunch of individual parts of the immune system that really hadn't been characterized in this way before. And something we really cared about was durability of immune memory. It's often talked about, well, mucosal responses are inherently short-lived. And we're like, well, what does that mean? Does that mean there's just no memory? Is it different kinds of memory? And so, this is the first measurement of memory B cells in this tissue in an antigen specific way. And we were doing it in people who had had recent COVID breakthrough infections. And we saw really the mucosal memory was stable for six months. And so, to me that's quite encouraging that it's not one month and it's gone, at least with an infection, it's at least six months and it looks like it'll project out for substantially longer.

Shane Crotty (28:53):

Amongst those cells, many of them are IgA. IgA is this antibody isotype that's particularly mucosal associated. And only 5% of the memory B cells circulating in blood were IgA. Whereas many of the memory B cells in the local tissue were IgA, which we think is also telling us that there's a lot of immune memory and the immune system in this tissue that we're probably not sampling in the blood. And so, sampling blood's great, right? It's accessible and we can learn a lot from it, but it does look like there is some tissue compartmentalization.

Eric Topol (29:37):

Oh, not a question. And the findings you had of the resident T cell is so indicative of that. And what's really striking, of course Shane, is that as we assess the immune system in people at large, we look at a lymphocyte neutrophil ratio [in the blood], we get almost nothing. And then in the course of the pandemic, you and your colleagues there provided such granular data on B and T cells, CD4 and CD8 T cells, and that you illuminated things that are not done ever clinically. These are research, high tier research labs like yours. The only question I have on before I just wrap up with the nasal vaccine story, interferon wasn't really part of this. As we know SARS-CoV-2 can shut down the interferon response, it's considered a frontline part of the defense. Where does that fit into the mucosal immunity of the upper airway?

Shane Crotty (30:46):

Yeah, it's really important. And that's in this basic divide we do in the immune system, the innate immune system and the adaptive immune system. So everything I was talking about is the B cells, the T cells, and antibodies. That's all the adaptive immune system. That's all virus specific. And then the innate immune system is the generalists, and really sort of the fire alarm, just sensing some danger. And definitely in COVID interferon is very important. I'm quite intrigued to see if using these techniques. I'm curious to see if some of these other aspects of the immune system can compensate somewhat for the fact that this virus. To me, if this virus has one superpower, it's its incredible ability to evade triggering interferon for as long as it does. And that has this massive cascading effect to almost everything about the pandemic essentially. And so, I'm intrigued by whether in people who have immunity are there ways that these other cells of the immune system or even antibodies can do things when a viral infection occurs, that helps trigger the overall immune system to recognize that something's there, even in the absence of type 1 interferons. That's where I think for now it fits in.

Eric Topol (32:14):

Well. I think you've so aptly described, not surprisingly, the superpower of SARS-CoV-2, which I think a lot of people haven't realized that it's so good at shutting down that defense system. Now on the basis of you having really gotten this understanding of the mucosal immunity in the upper airway, does this make you think that the nasal vaccine that we aspire to have is more of a reality? Do you kind of know what the ideal profile might look like to keep people healthy and resist infections? Do you think this is achievable in any durable sense at high level success with a nasal spray vaccine?

Shane Crotty (33:04):

I'm optimistic for several reasons. One is we really saw a lot of different immune memory cell types that were present, that was encouraging and seeing the B cell memory durability for at least six months—pretty flat line for that six months—was encouraging. It looks like the immune system knows how to keep these cells around if it wants to for a significant period of time. We'll have to do more in follow up. But again, it was encouraging. Third, we had some people who were vaccinated only and some people who had breakthrough infections. And really in the vaccinated only, we didn't see T cell memory in the upper airways. And I actually consider that encouraging because it suggests local exposure does give you the memory and exposure in your arm really doesn't. So I think there is something to improve upon. It can be improved upon. And lastly, I get asked all the time, I'm sure you get asked all the time: Why aren't there more intranasal vaccines or inhaled vaccines, more mucosal vaccines in some way?

Shane Crotty (34:25):

And I think there's more than one reason, but I tend to be very practical, and I think one practical reason is there's very little to measure, to guide you in your vaccine development. If you have six ideas or six constructs that you think might work in humans as a nasal vaccine, you basically just have to pick one, try something, and hoping there's not much you can measure it clinical trials for what might be the type of response even. So for example, the FluMist vaccine, it's the only licensed inhaled vaccine, intranasal vaccine. In adults it doesn't have a clear correlate of protection. If you get vaccinated with that, your circulating antibody responses don't increase, but also increases in nasal antibody didn't correlate with protection well. So, what does that mean? That probably means there's other things going on up there that could be indicative of protection but weren't being measured before. So I'm hopeful with these types of approaches. Now, if you're an intranasal vaccine developer, you maybe have 4, 5, 6, 7, 8 ideas or constructs. If you can try those in a few people and make these different measurements and you've got your favorite immune profile that you might, now you have something to, it's more of an engineering problem. It's not a throwing a dart problem. You're like, yeah, this has given me the type of response that I like and I'm going to try and push this into clinical trials. So those are the things that I'm optimistic about moving forward.

Eric Topol (36:04):

Well, I love it because we really need it. And if anybody's optimistic that means a lot; it's yours. What you've done here has been quite extraordinary because you defined for the first time really the underpinnings of the mucosal immune response, the upper airway, you did it by age, you did it by variant, you did it by vaccine and infection. And most importantly, perhaps for longer term is you established what are the desirable features to have, which didn't exist before. It seemed like whatever I read for nasal vaccines, they were measuring some IgA or IgG, and they didn't get down to the memory B cells and the tissue resident T cells, memory cells, and all these other things that you found. You did all this single cell sequencing and flow cytometry. The work is just really fantastic. So Shane, just in closing, I just want to congratulate you.

Eric Topol (37:05):

You made seminal findings along the pandemic. You were the one that really illuminated hybrid immunity, the advantage of if you don't want to have an infection of COVID, but if you did have that and a vaccine, you kind of had some extra synergy, if you will. But here you've done something, you and your team. Unique. Congratulations on that. No surprise that it's in Nature this week. I'm sure a lot of people will share your optimism that we will have something beyond just shots in the future because COVID isn't going away. There's other respiratory pathogens. And finally, somebody did the right study, who knows immunology inside and out. So Shane, thanks very much.

Shane Crotty (37:52):

Thanks Eric. Very much appreciated particularly coming from you.

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Transcript with audio and external links

Eric Topol (00:05):

Hello, it's Eric Topol with Ground Truths, and I am really thrilled to have with me Professor Faisal Mahmood, who is lighting it up in the field of pathology with AI. He is on the faculty at Harvard Medical School, also a pathologist at Mass General Brigham and with the Broad Institute, and he has been publishing at a pace that I just can't believe we're going to review that in chronological order. So welcome, Faisal.

Faisal Mahmood (00:37):

Thanks so much for having me, Eric. I do want to mention I'm not a pathologist. My background is in biomedical imaging and computer science. But yeah, I work very closely with pathologists, both at Mass General and at the Brigham.

Eric Topol (00:51):

Okay. Well, you know so much about pathology. I just assume that you were actually, but you are taking computational biology to new levels and you're in the pathology department at Harvard, I take it, right?

Faisal Mahmood (01:08):

Yeah, I'm at the pathology department at Mass General Brigham. So the two hospitals are now integrated, so I'm at the joint department.

Eric Topol (01:19):

Good. Okay. Well, I'm glad to clarify that because as far as I knew you were hardcore pathologist, so you're changing the field in a way that is quite unique, I should say, because a number of years ago, deep learning was starting to get applied to pathology just like it was and radiology and ophthalmology. And we saw some early studies with deep learning whereby you could find so much more on a slide that otherwise would be not even looked at or considered or even that humans wouldn't be able to see. So maybe you could just take us back first to the deep learning phase before these foundation models that you've been building, just to give us a flavor for what was the warmup in this field?

Faisal Mahmood (02:13):

Yeah, so I think around 2016 and 2017, it was very clear to the computer vision community that deep learning was really the state of the art where you could have abstract feature representations that were rich enough to solve some of these fundamental classification problems in conventional vision. And that's around the time when deep learning started to be applied to everything in medicine, including pathology. So we saw some earlier cities in 2016 and 2017, mostly in machine learning conferences, applying this to very basic patch level pathology dataset. So then in 2018 and 2019, there were some studies in major journals including in Nature Medicine, showing that you could take large amounts of pathology data and classify what's known to us and including predicting what's now commonly referred to as non-human identifiable features where you could take a label and this could come from molecular data, other kinds of data like treatment response and so forth, and use that label to classify these images as responders versus non-responders or having a certain kind of mutation or not.

(03:34):

And what that does is that if there is a morphologic signal within the image, it would pick up on that morphologic signal even though humans may not have picked up on it. So it was a very exciting time of developing all of these supervised, supervised foundation models. And then I started working in this area around 2019, and one of the first studies we did was to try to see if we can make this a little bit more data efficient. And that's the CLAM method that we published in 2021. And then we took that method and applied it to the problem of cancers of unknown primary, that was also in 2021.

Eric Topol (04:17):

So just to review, in the phase of deep learning, which was largely we're talking about supervised with ground truth images, there already was a sign that you could pick up things like the driver mutation, the prognosis of the patient from the slide, you could structural variations, the origin of the tumor, things that would never have been conceived as a pathologist. Now with that, I guess the question is, was all this confined to whole slide imaging or could you somehow take an H&E slide conventional slide and be able to do these things without having to have a whole slide image?

Faisal Mahmood (05:05):

So at the time, most of the work was done on slides that were fully digital. So taking a slide and then digitizing the image and creating a whole slide image. But we did show in 2021 that you could put the slide under a microscope and then just capture it with a camera or just with a cell phone coupled to a camera, and then still make those predictions. So these models were quite robust to that kind of domain adaptation. And still I think that even today the slide digitization rate in the US remains at around 4%, and the standard of care is just looking at a glass light under a microscope. So it's very important to see how we can further democratize these models by just using the microscope, because most microscopes that pathologists use do have a camera attached to them. So can we somehow leverage that camera to just use a model that might be trained on a whole slide image, still work with the slide under a microscope?

Eric Topol (06:12):

Well, what you just said is actually a profound point that is only 4% of the slides are being reviewed digitally, and that means that we're still an old pathology era without the enlightenment of machine eyes. I mean these digital eyes that can be trained even without supervised learning as we'll get to see things that we'll never see. And to make, and I know we'll be recalling back in 2022, you and I wrote a Lancet piece about the work that you had done, which is very exciting with cardiac biopsies to detect whether a heart transplant was a rejection. This is a matter of life or death because you have to give more immunosuppression drugs if it's a rejection. But if you do that and it's not a rejection or you miss it, and there's lots of disagreement among pathologists, cardiac pathologists, regarding whether there's a transplant. So you had done some early work back then, and because much of what we're going to talk about, I think relates more to cancer, but it's across the board in pathology. Can you talk about the inner observer variability of pathologists when they look at regular slides?

Faisal Mahmood (07:36):

Yeah. So when I first started working in this field, my kind of thinking was that the slide digitization rate is very low. So how do we get people to embrace and adapt digital pathology and machine learning models that are trained on digital data if the data is not routinely digitized? So one of my kind of line of thinking was that if we focus on problems that are inherently so difficult that there isn't a good solution for them currently, and machine learning provides, or deep learning provides a tangible solution, people will be kind of forced to use these models. So along those lines, we started focusing on the cancers of unknown primary problem and the myocardial biopsy problem. So we know that the Cohen’s kappa or the intra-observer variability that also takes into account agreement by chance is around 0.22. So it's very, very low for endomyocardial biopsies. So that just means that there are a large number of patients who have a diagnosis that other pathologists might not agree with, and the downstream treatment regimen that's given is entirely based on that diagnosis. The same patient being diagnosed by a different cardiac pathologist could be receiving a very different regimen and could have a very, very different outcome.

(09:14):

So the goal for that study is published in Nature of Medicine in 2022, was to see if we could use deep learning to standardize that and have it act as an assistive tool for cardiac pathologists and whether they give more standardized responses when they're given a machine learning based response. So that's what we showed, and it was a pleasure to write that corresponding piece with you in the Lancet.

Eric Topol (09:43):

Yeah, no, I mean I think that was two years ago and so much has happened since then. So now I want to get into this. You've been on a tear every month publishing major papers and leading journals, and I want to just go back to March and we'll talk about April, May, and June. So back in March, you published two foundation models, UNI and CONCH, I believe, both of these and back-to-back papers in Nature Medicine. And so, maybe first if you could explain the foundation model, the principle, how that's different than the deep learning network in terms of transformers and also what these two different, these were mega models that you built, how they contributed to help advance the field.

Faisal Mahmood (10:37):

So a lot of the early work that we did relied on extracting features from a resonant trained on real world images. So by having these features extracted, we didn't need to train these models end to end and allowed us to train a lot of models and investigate a lot of different aspects. But those features that we used were still based on real world images. What foundation models led us do is they leveraged self supervised learning and large amounts of data that would be essentially unlabeled to extract rich feature representations from pathology images that can then be used for a variety of different downstream tasks. So we basically collected as much data as we could from the Brigham and MGH and some public sources while trying to keep it as diverse as possible. So the goal was to include infectious, inflammatory, neoplastic all everything across the pathology department while still being as diverse as possible, including normal tissue, everything.

(11:52):

And the hypothesis there, and that's been just recently confirmed that the hypothesis was that diversity would matter much more than the quantity of data. So if you have lots and lots of screening biopsies and you use all of them to train the foundation model, there isn't enough diversity there that it would begin to learn those fundamental feature representations that you would want it to learn. So we used all of this data and then trained the UNI model and then together with it was an image text model where it starts with UNI and then reinforces the feature representations using images and texts. And that sort of mimics how humans learn about pathology. So a new resident, new trainee learning pathology has a lot of knowledge of the world, but it's perhaps looking at a pathology image for the first time. But besides looking at the image, they're also being reinforced by all these language cues from, whether it's from text or from audio signals. So the hope there was that text would kind of reinforce that and generate better feature representation. So the two studies were made available together. They were published in Nature Medicine back in March, and with that we made both those models public. So at the time we obviously had no idea that they would generate so much interest in this field, downloaded 350,000 times on Hugging Face and used for all kinds of different applications that I would've never thought of. So that's been very exciting to see.

Eric Topol (13:29):

Can you give some examples of some of the things you wouldn't have thought of? Because it seems like you think of everything.

Faisal Mahmood (13:35):

Yeah, people have used it to when there was a challenge for detecting tuberculosis, I think in a very, very different kind of a dataset. It was from the Nightingale Foundation and they have large data sets. So that was very interesting to see. People have used it to create newer data sets that can then be used for training additional foundation models. It's being used to extract rich feature representations from pathology images, corresponding spatial transcriptomic data, trying to predict spatial transcriptomics directly from histology. And there's a number of other options.

Eric Topol (14:27):

Well, yeah, that was March. Before we get to April, you slipped in the spatial omics thing, which is a big deal that is ability to look at tissue, human tissue over time and space. I mean the spatial temporal, it will tell us so much whether an evolution of a cancer process or so many things. Can you just comment because this is one of the major parts of this new era of applying AI to biology?

Faisal Mahmood (15:05):

So I think there are a number of things we can do if we have spatial data spatially resolved omic data with histology images. So the first thing that comes to my mind as a computer scientist would be that can we train a joint foundation model where we would use the spatially resolved transcriptomics to further enforce the pathology signal as a ground truth in a contrastive manner, similar to what we do with text, and can we use that to extract even richer feature representation? So we're doing that. In fact, we made a data set of about a thousand pathology images with corresponding spatial transcriptomic information, both curated from public resources as well as some internal data publicly available so people could investigate that question further. We're entrusted in other aspects of this because there is some indication including a study from James Zou’s group at Stanford showing that we can predict histology, predict the spatial transcriptomic signal directly from histology. So there's early indications that we might also be able to do that in three dimensions. So yeah, it's definitely very interesting. More and more of that data is becoming available and how machine learning can sort of augment that is very exciting.

Eric Topol (16:37):

Yeah, I mean, most of the spatial omics has been a product of single cell sequencing, whether it's single nuclei and different omics, not just DNA, of course, RNA and even methylation, whatnot. So the fact that you could try to impute that from the histologies is pretty striking. Now, that was March and then in April you published to me an extraordinary paper about demographic bias and how generative AI, we're in the generative AI year now since as we discussed with foundation models, here again that gen AI could actually reduce biases and enhance fairness, which of course is so counterintuitive to everything that's been written to date. So maybe you can take us through how we can get a reduction in bias in pathology.

Faisal Mahmood (17:34):

Yeah, so in the study, the study was about, this had been investigated in other fields, but what we try to show is that a model trained on large, diverse, publicly available data. When that's applied internally and we stratify it based on demographic differences, race and so forth, we see these very clear disparities and biases. And we investigated a lot of different solutions that were out there to equalize the distribution of the data to balance the distribution using or sampling and some of these simple techniques. And none of them worked quite well. And then we observed that using foundation models or just having richer feature representations eliminates some of those biases. In parallel, there was another study from Google where they use generative AI to synthesize additional images from those underrepresented groups and then use those images to enhance the training signal. And then they also showed that you could reduce those biases.

(18:49):

So I think the common denominator there is that richer feature representations contribute to reduced biases. So the biases not because there is some inherent signal tied to these subgroups, but the bias is essentially there because the feature representations are not strong enough. Another general observation is that there's some kind of a confounder often there that leads to the bias. And one example would be that patients with socioeconomic disparities might just be diagnosed late and there might not be enough advanced cases in the training dataset. So quite often when you go in and look at what your training distribution looks like and how it varies from your test distribution and what that dataset shift is, you're able to figure out where the bias inherently comes from. But as a general principle, if you use the richest possible feature representation or focus on making your feature representations richer by using better foundation models and so forth, you are able to reduce a lot of the bias.

Eric Topol (19:58):

Yeah, that's really another key point here is about the richer features and the ability counterintuitively to actually reduce bias. And what is important in interrogating data inputs, as you said before, you wind up with a problem with bias. Now, then it comes May since we're just March and April, in May you published TriPath, which is now bringing in the 3D world of pathology. So maybe you can give us a little skinny on that one.

Faisal Mahmood (20:36):

Yeah. So just looking at the spectrum of where pathology is today, I think that we all agree in the community that pathologists often look at extremely sampled tissue. So human tissue is inherently three-dimensional, and by the time it gets to a pathologist, it's been sampled and cut so many times that it often would lack that signal. And there are a number of studies that have shown that if you subsequently cut sections, you get to a different outcome. If you look at multiple slides for a prostate biopsy, you get to a different Gleason score. There are all of these studies that have shown that 3D pathology is important. And with that, there's been a growing effort to build tools, microscopes, imaging tools that can image tissue in 3D. And there are about 10 startups who've built all these different technologies, open-top light-sheet microscopy, microCT and so forth that can image tissue really well in three dimensions, but none of them have had clinical adoption.

(21:39):

And we think that a key reason is that there isn't a good way for a pathologist to examine such a large volume of tissue. If they spend so much time examining this large volume of tissue, they would never be able to get through all the, so the goal here really was to develop a computational tool that would look through the large volume and highlight key regions that a pathologist can then examine. And the secondary goal was that does using three dimensional tissue actually improve patient stratification and does using, essentially using three 3D deep learning, having 3D convolutions extract richer features from the three dimensions that can then be used to separate patients into distinct risk groups. So that's what we did in this particular case. The study relied on a lot of data from Jonathan Liu's group at University of Washington, and also data that we collected at Harvard from tissue that came from the Brigham and Women's Hospital. So it was very exciting to show that what the value of 3D pathology can be and how it can actually translate into the clinic using some of these computational tools.

Eric Topol (22:58):

Do you think ultimately someday that will be the standard that you'll have a 3D assessment of a biopsy sample?

Faisal Mahmood (23:06):

Yeah, I'm really convinced that ultimately 3D would become the standard because the technology to image these tissue is becoming better and better every year, and it's getting closer to a point where the imaging can be fast enough to get to clinical deployment. And then on the computational end, we're increasingly making a lot of progress.

Eric Topol (23:32):

And it seems, again, it's something that human eyes couldn't do because you'd have to look at hundreds of slides to try to get some loose sense of what's going on in a 3D piece of tissue. Whereas here you're again taking advantage, exploiting the digital eyes. Now this culminates to your June big paper PathChat in Nature, and this was a culmination of a lot of work you've been doing. I don't know if you do any sleep or your team, but then you published a really landmark paper. Can you take us through that?

Faisal Mahmood (24:12):

Yeah, so I think that with the foundation models, we could extract very rich feature representation. So to us, the obvious next step was to take those feature representations and link them with language. So a human would start to communicate with a generative AI model where we could ask questions about what's going on in a pathology image, it would be capable of making a diagnosis, it would be capable of writing a report, all of those things. And the reason we thought that this was really possible is because pathology knowledge is a subset of the world's knowledge. And companies like OpenAI are trying to build singular, multimodal, large language models that would harbor the world's information, the world knowledge and pathology is much, much more finite. And if we have the right kind of training data, we should be able to build a multimodal large language model that given any pathology image, it can interpret what's going on in the image, it can make a diagnosis, it can run through grading, prognosis, everything that's currently done, but also be an assistant for research, analyzing lots of images to see if there's anything common across them, cohorts of responders versus non-responders and so forth.

(25:35):

So we started by collecting a lot of instruction data. So we started with the foundation models. We had strong pathology image foundation models, and then we collected a lot of instruction data where we have images, questions, corresponding answers. And we really leveraged a lot of the data that we had here at Brigham and MGH. We're obviously teaching hospitals. We have questions, we have existing teaching training materials and work closely with pathologists at multiple institutions to collect that data. And then finally trained a multimodal large language model where we could give it a whole slide image, start asking questions, what was in the image, and then it started generating all these entrusting morphologic descriptions. But then the challenge of course is that how do you validate this? So then we created validation data sets, validated on what multiple choice questions on free flowing questions where multiple pathologists, we had a panel of seven pathologists look through every response from our model as well as more generic models like the OpenAI, GPT-4 and BiomedCLIP and other models that are publicly available, and then compare how well this pathology specific model does in comparison to some of those other models.

(26:58):

And we found that it was very good at morphologic description.

Eric Topol (27:05):

It's striking though to think now that you have this large language model where you're basically interacting with the slide, and this is rich, but in another way, just to ask you, we talk about multimodal, but what about if you have electronic health record, the person's genome, gut microbiome, the immune status and social demographic factors, and all these layers of data, environmental exposures, and the pathology. Are we going to get to that point eventually?

Faisal Mahmood (27:45):

Yeah, absolutely. So that's what we're trying to do now. So I think that it's obviously one step at a time. There are some data types that we can very easily integrate, and we're trying to integrate those and really have PathChat as being a binder to all of that data. And pathology is a very good binder because pathology is medicine's ground truth, a lot of the fundamental decisions around diagnosis and prognosis and treatment trajectory is all sort of made in pathology. So having everything else bind around the pathology is a very good idea and indication. So for some of these data types that you just mentioned, like electronic medical records and radiology, we could very easily go that next step and build integrative models, both in terms of building the foundation model and then linking with language and getting it to generate responses and so forth. And for other data types, we might need to do some more specific training data types that we don't have enough data to build foundation models and so forth. So we're trying to expand out to other data types and see how pathology can act as a binder.

Eric Topol (28:57):

Well if anybody's going to build it, I'm betting on you and your team there, Faisal. Now what this gets us to is the point that, was it 96% or 95% of pathologists in this country are basically in an old era, we're not eking out so much information from slides that they could, and here you're kind of in another orbit, you're in another world here whereby you're coming up with information. I mean things I never thought really the prognosis of a patient over extended period of time, the sensitivity of drugs to the tumor from the slide, no less the driver mutations to be able to, so you wouldn't even have to necessarily send for mutations of the cancer because you get it from the slide. There's so much there that isn't being used. It's just to me unfathomable. Can you help me understand why the pathology community, now that I know you're not actually a pathologist, but you're actually trying to bring them along, what is the reason for this resistance? Because there's just so much information here.

Faisal Mahmood (30:16):

So there are a number of different reasons. I mean, if you go into details for why digital pathology is not actively happening. Digitizing an entire department is expensive, retaining large amounts of slides is expensive. And then the value proposition in terms of patient care is definitely there. But the financial incentives, reimbursement around AI is not quite there yet. It's slowly getting there, but it's not quite there yet. In the meantime, I think what we can really focus on, and what my group is thinking a lot about is that how can we democratize these models by using what the pathologists already have and they all have a microscope and most of them have a microscope with a camera attached to it. Can we train these models on whole slide images like we have them and adapt them to just a camera coupled to a microscope? And that's what we have done for PathChat2.

(31:23):

I think one of the demos that we showed after the article came out was that you could use PathChat on your computer with the whole slide image, but you can also use it with a microscope just coupled to a camera and you put a glass light underneath. And in an extreme lower source setting, you can also use it with just a cell phone coupled to a microscope. We're also building a lighter weight version of it that wouldn't require internet, so it would just be completely locally deployed. And then it could be active in lower source settings where sometimes sending a consult can take a really, really long time, and quite often it's not very easy for hospitals in lower source settings to track down a patient again once they've actually left because they might've traveled a long distance to get to the clinic and so forth. So the value of having PathChat deployed in a lower source setting where it can run locally without internet is just huge because it can accelerate the diagnosis so much. In particular for very simple things, which it's very, very good at making a diagnosis for those cases.

Eric Topol (32:33):

Oh, sure. And it can help bridge inequities, I mean, all sorts of things that could be an outgrowth of that. But what I still having a problem with from the work that you've done and some of the other people that well that are working assiduously in this field, if I had a biopsy, I want all the information. I don't want to just have the old, I would assume you feel the same way. We're not helping patients by not providing the information that's there just with a little help from AI. If it's going to take years for this transformation to occur, a lot of patients are going to miss out because their pathologists are not coming along.

Faisal Mahmood (33:28):

I think that one way to of course solve this would be to have it congressionally mandated like we had for electronic medical records. And there are other arguments to be made. It's been the case for a number of different hospitals have been sued for losing slides. So if you digitize all your slides and you're not going to lose them, but I think it will take time. So a lot of hospitals are making these large investments, including here at the Brigham and MGH, but it will take time for all the scanners, all the storage solutions, everything to be in place, and then it will also take time for pathologists to adapt. So a lot of pathologists are very excited about the new technology, but there are also a lot of pathologists who feel that their entire career has been diagnosing cases or using a microscope and slide. So it's too big of a transition for them. So I think there'll obviously be some transition period where both would coexist and that's happening at a lot of different institutions.

Eric Topol (34:44):

Yeah, I get what you're saying, Faisal, but when I wrote Deep Medicine and I was studying what was the pathology uptake then of deep learning, it was about 2% and now it's five years later and it's 4% or 5% or whatever. This is a glacial type evolution. This is not keeping up with how the progress that's been made. Now, the other thing I just want to ask you before finishing up, there are some AI pathology companies like PathAI. I think you have a startup model Modella AI, but what can the companies do when there's just so much reluctance to go into the digital era of pathology?

Faisal Mahmood (35:31):

So I think that this has been a big barrier for most pathology startups because around seven to eight years ago when most of these companies started, the hope was that digital pathology would happen much faster than it actually has. So I think one thing that we're doing at Modella is that we understand that the adoption of digital pathology is slow. So everything that we are building, we're trying to enable it to work with the current solutions that exist. So a pathologist can capture images from a pathology slide right in their office with a camera with a microscope and PathChat, for example, works with that. And then the next series of tools that we're developing around generative AI would also be developed in a manner that it would be possible to use just a camera coupled to a microscope. So I think that I do feel that all of these pathology AI companies would have been doing much, much better if everything was digital, because adopting the tools that they developed would very straightforward. Right now, the barrier is that even if you want to deploy an AI driven solution, if your hospital is not entirely digital, it's not possible to do that. So it requires this huge upfront investment.

Eric Topol (37:06):

Yeah, no, it's extraordinary to me. This is such an exciting time and it's just not getting actualized like it could. Now, if somebody who's listening to our conversation has a relative or even a patient or whatever that has a biopsy and would like to get an enlightened interpretation with all the things that could be found that are not being detected, is there a way to send that to a center that is facile with this? Or if that's a no go right now?

Faisal Mahmood (37:51):

So I think at the moment it's not possible. And the reason is that a lot of the generic AI tools are not ready for this. The models are very, very specific for specific purposes. The generalist models are just getting started, but I think that in the years to come, this would be a competitive edge for institutions who do adopt AI. They would definitely have a competitive edge over those who do not. We do from time to time, receive requests from patients who want us to run their slides on the cancers of unknown primary tool that we built. And it depends on whether we are allowed to do so or not, because it has to go through a regular diagnostic first and how much information can we get from the patient? But it's on a case by case basis.

Eric Topol (38:52):

Well, I hope that's going to change soon because you have been, your team there has just been working so hard to eke out all that we can learn from a path slide, and it's extraordinary. And it made me think about what we knew five years ago, which already was exciting, and you've taken that to the fifth power now or whatever. So anyway, just to congratulate you for your efforts, I just hope that it will get translated Faisal. I'm very frustrated to learn how little this is being adopted here in this country, a rich country, which is ignoring the benefits that it could provide for patients.

Faisal Mahmood (39:40):

Yeah. That's our goal over the next five years. So the hope really is to take everything that we have developed so far and then get it in aligned with where the technology currently is, and then eventually deploy it both at our institution and then across the country. So we're working hard to do that.

Eric Topol (40:03):

Well, maybe patients and consumers can get active about this and demand their medical centers to go digital instead of living in an analog glass slide world, right? Yeah, maybe that's the route. Anyway, thank you so much for reviewing at this pace of your publications. It's pretty much unparalleled, not just in pathology AI, but in many parts of life science. So kudos to you, Richard Chen, and your group and so many others that have been working so hard to enlighten us. So thanks. I'll be checking in with you again on whatever the next model that you build, because I know it will be another really important contribution.

Faisal Mahmood (40:49):

Thank you so much, Eric. Thanks.

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