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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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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Note on Exodus from X/twitter:

Many of you have abandoned the X platform for reasons that I understand. While I intend to continue to post there because of its reach to the biomedical community, I will post anything material here in the Notes section of Ground Truths on a daily basis and cover important topics in the newsletter/analyses.

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.

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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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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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