The Peter Attia Drive - #197 - The science of obesity & how to improve nutritional epidemiology ｜ David Allison, Ph.D.

00:00:00.000 Hey, everyone. Welcome to The Drive Podcast. I'm your host, Peter Atiyah. This podcast,

00:00:15.500 my website, and my weekly newsletter all focus on the goal of translating the science of

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00:00:41.980 head over to peteratiyahmd.com forward slash subscribe. Now, without further delay, here's

00:00:48.100 today's episode. My guest this week is David Allison. David received his PhD from Hofstra

00:00:54.840 University in 1990, and then he went on to complete his postdoctoral fellowship at the

00:00:58.620 Johns Hopkins University School of Medicine and a second postdoctoral fellowship at the New York

00:01:02.760 Obesity Research Center at St. Luke's Roosevelt Hospital Center. He's currently the Dean and

00:01:07.820 Provost Professor at the Indiana University Bloomington School of Public Health. And prior

00:01:12.480 to that, he was an endowed professor and a director of an NIH-funded nutrition organization research

00:01:19.840 center at the University of Alabama in Birmingham. He's authored over 500 scientific publications and

00:01:25.960 received many awards, including the following the 2002 Lilly Scientific Achievement Award from the

00:01:31.080 Obesity Society, the 2002 Andre Mayer Award from the International Association for the Study of

00:01:37.640 Obesity, and the National Science Foundation administered 2006 Presidential Award for Excellence in

00:01:43.800 Science, Mathematics, and Engineering Mentoring. In 2012, he was elected to the National Academy of

00:01:50.100 Medicine and the National Academies. He serves on or has served on many of the editorial boards and

00:01:57.180 currently serves on as an associate editor or statistical editor for Obesity, the International

00:02:02.780 Journal of Obesity, Nutrition Today, Obesity Reviews, Public Library of Science, PLOS, Genetics,

00:02:09.380 Surgery for Obesity and Related Diseases, and the American Journal of Clinical Nutrition.

00:02:15.500 He is also the founding field chief editor of Frontiers in Genetics. David's research interests

00:02:21.280 include obesity and nutrition, quantitative genetics, clinical trials, statistical and research

00:02:25.960 methodology, and research rigor and integrity. I've known David Allison for probably about seven or

00:02:31.580 eight years now. I have always found him to be one of the most insightful and thoughtful people

00:02:35.540 on virtually any topic, and that means fly fishing and genetics and you pick it. In this episode,

00:02:43.040 of course, we don't talk about fly fishing. What we do talk about is obesity, and we spend a lot of

00:02:48.320 time getting into what is known and what is not known about obesity, and also the science of obesity,

00:02:55.320 in particular, the science of nutrition and nutritional epidemiology. We talk about all these pitfalls,

00:03:00.880 which for many of my listeners will be familiar, but what I really appreciate about David is the

00:03:07.200 lens to which he brings a very unemotional and a very insightful and a very logical and thoughtful

00:03:14.080 approach to how he thinks about the pitfalls within this space. There are parts of this discussion,

00:03:19.720 actually, that are quite frustrating in the sense that David acknowledges, for example, that most of the

00:03:24.940 measures that we have in place from a public health perspective are probably not founded in any

00:03:29.540 scientific basis. We then talk a little bit about the reproducibility of science, and we close with

00:03:35.560 a discussion that, for me personally, was the most interesting, which was a very clear elucidation

00:03:41.800 of the challenges that maybe science seems to be facing or maybe is not facing today as this kind of

00:03:47.700 existential threat where science and scientists are often confounded, science and advocacy is often

00:03:54.080 confounded, and there seems to be a bit of a crisis around this. David offers his thoughts on that,

00:03:59.480 whether or not there is a crisis or not. We close our discussion with, I think, one of the most

00:04:04.600 lucid explanations of something that I've been trying to wrap my head around for some time

00:04:10.700 without much success, and that is the seeming lack of credibility that science has today.

00:04:20.480 David is quick to point out that it's probably not science that is that which is being doubted,

00:04:25.480 certainly the scientific method as a means by which knowledge can be reliably and reproducibly

00:04:31.600 gained, but rather it might be the confounding of science and advocacy. I won't try to reproduce

00:04:38.220 what David said here because, heck, he does a much better job than me, but I would encourage you to

00:04:42.860 listen all the way through, even if at the outset you find the topic of obesity not particularly to

00:04:47.460 your interest. So, without further delay, please enjoy my conversation with David Allison.

00:04:56.960 Hey David, great to see you today.

00:04:59.600 My pleasure as always, Peter. Good to see you.

00:05:02.200 It's been a while since we've seen each other in person. I feel like it's probably been,

00:05:05.660 I don't know, three or four years, right?

00:05:09.040 Easily, easily. We've spent many an hour on Zoom together in the last couple of years, but

00:05:14.120 it's been a while since we've been in the same state, in the same building, in the same room at

00:05:18.180 the same time. Now, you're no stranger to this podcast in the sense that I feel like

00:05:22.840 you probably listen to podcasts before anybody. In fact, I think you and Nir Barzalai must hold the

00:05:28.840 record for being the first people to listen to a podcast because they come out on Mondays and it's

00:05:34.860 barely the early part of the morning and usually you and Nir are the first two to send me an email

00:05:41.200 with your thoughts. So, I know you're a fan and it's great to have you on. I've been wanting to

00:05:45.560 do this for some time. Well, thank you. I really admire the podcast and I admire Nir Barzalai. So,

00:05:51.300 to know I'm in league with him is pretty cool. So, you're one of the most interesting people I know

00:05:55.660 and I always love just spending time with you and talking with you about subjects. And I think one of

00:06:01.420 the things I've always admired about you is you're known for being in a field such as obesity which

00:06:06.820 tends to attract a lot of dogmatic thinking and yet you tend to approach this field with a distant

00:06:14.460 lack of emotion. You just come at it very intellectually and I realize as I'm saying that

00:06:20.280 it speaks disparagingly of people in the field of obesity which is not really what I'm trying to say

00:06:24.520 but I think for many people the field of obesity is a loaded field scientifically even let alone

00:06:29.720 politically and all the other things that go with it. So, I've always assumed that that's because

00:06:34.200 your background is slightly different than maybe some of the people who came to obesity through

00:06:41.480 physiology. So, tell us a little bit about your background. What did you study? What did you do

00:06:45.480 your PhD in? That sort of thing. Sometimes we'll have people say, you know, why are you like this?

00:06:50.860 Or why did you do this? Why did you choose that? And the truth is even as a scientist often say

00:06:55.440 following my friend Don Rubin who I admire a lot, we may be able to figure out what the causal effect

00:07:01.420 of X is but we may not be able to assess whether X caused Y. So, I don't really know why I turned out

00:07:06.620 the way I turned out. But even as a kid, I was always the kid asking questions. Sometimes that would

00:07:11.280 interest people. Sometimes that would annoy people and that's true today. But I was always one saying,

00:07:16.380 are you sure? How do you know? Where did that come from? What makes you think that's true? Of course,

00:07:21.340 my kids fed it back to me as they were growing up and heard me say it. And when I would say to them,

00:07:25.380 don't do that, you'll get hurt. They'd say, what's your evidence for that, dad? I went to college and

00:07:29.740 I started out knowing I wanted to be a psychologist. But what that meant to me at the time was Hitchcock

00:07:36.080 films. I was going to interpret people's dreams and I would figure out what the meaning of the clock

00:07:40.900 in the dream was or something like that. And then the person would be cured and it would be a great

00:07:44.960 movie. And then as I got to college, I started to think about, well, what's the evidence for

00:07:50.500 these things? And that sort of opens up this whole can of worms about asking about evidence.

00:07:56.460 Became more and more cognitive, behaviorally oriented. Still thought I wanted to be a

00:08:01.260 clinician. Got to graduate school. And I asked a professor at one point as we're being trained in

00:08:06.300 graduate school, PhD program, to administer IQ tests. And we're learning these different theories

00:08:12.900 of intelligence. Some people think there's one factor. Some people think three. One person thought

00:08:18.320 144. And I said, well, who's right? And the professor says, well, they bring their different

00:08:23.380 evidence to bear and they argue. I said, well, what's the evidence? He said, these factor analyses.

00:08:28.920 I don't really know what that is. What's a factor analysis? He says, well, you have to go study

00:08:32.340 multivariate statistics if you want to understand that. So I don't like to take anybody's word for

00:08:36.800 anything. So I went and studied multivariate statistics and became more and more involved with

00:08:42.140 statistical analysis to the point where eventually later in my career, people started thinking I was a

00:08:46.920 statistician. And I stopped fighting it. I said, okay. The American Statistical Association thinks

00:08:51.640 so. And the university thinks so. And the NIH and NSF think so. I think so too. And so that's great.

00:08:57.280 So I sort of became by evolution a statistician having been trained as a psychologist. But I think

00:09:02.600 all of that comes down to evidence and all of it thinking that people are no different than anything

00:09:08.280 else. Body fat is no different than any other variable. And that still has to obey the same laws of

00:09:13.780 probability and physics and mathematics and so on that we apply to anything else. And I think we

00:09:19.680 often forget that. People talk. And so folks who study people think about the words that people say.

00:09:27.400 Whereas if you're studying atoms and molecules, you don't think about the words they say. You just

00:09:31.960 apply scientific methods. The same person who would never question a diabetologist on what the beta cell

00:09:38.960 of a pancreas is doing, unless they themselves are a diabetologist studying the beta cells of the

00:09:43.400 pancreas, will say to an obesity researcher, well, this is how obesity works. And it's this aspect of

00:09:49.240 food or that aspect of culture or that aspect of child rearing based only on the fact that they were

00:09:54.880 children or had children and had some experience. And so to me, it's just taking a step back and just

00:09:59.800 treating, as my old boss in biostatistics used to say, whether it's X's and Y's, they're just variables

00:10:06.060 and applying the same laws of thinking to all those systems.

00:10:10.440 When was it either post PhD or during PhD that you were drawn to obesity in particular as a

00:10:18.040 clinical field? I actually started as an undergraduate. I think it was a sophomore. And I took a class at

00:10:23.500 Vassar College. And I should say that all my remarks today just reflect my own opinions. I'm not speaking

00:10:28.820 for Indiana University or Vassar College or anybody else, just myself. And I took a class at Vassar College

00:10:34.900 on human emotion and motivation. And we studied the theories of Stanley Schachter, who at the time

00:10:41.680 was a professor of psychology at Columbia University. He's since deceased. An amazing,

00:10:47.500 brilliant man who wrote a book called Emotion, Obesity, and Crime. And he talked about the

00:10:53.540 connections among these and how the distinction between our cognitive state and our physiologic

00:11:00.020 arousal state might lead us to certain kinds of behaviors, including in the case of some people

00:11:06.980 eating more calories than they might otherwise eat. His experiments were so creative. I just loved it.

00:11:12.940 And he would put like a clock on the wall and he would have the clock move a little faster,

00:11:17.300 but not perceptibly faster. And then he would bring some students in and give them some work to do

00:11:22.160 under some ruse. And then the clock would say for some of the students at 11 a.m. that it was noon.

00:11:28.100 And he'd say, by the way, I have a big tray of roast beef sandwiches out here. So whenever you're

00:11:34.000 hungry for lunch, just let me know and you can have some sandwiches. And for some people, the clock

00:11:38.800 wouldn't say it was noon until 1 p.m. And for some people would say it was noon at noon. And he'd say,

00:11:43.420 who would be following the clock and who would be following the actual time? And obese or overweight

00:11:50.040 persons were more likely to follow the clock. And this became the external theory. And then it

00:11:55.640 didn't always work out. Then you'd have another experiment and say, well, it only holds up under

00:11:58.860 this circumstance. And it was incredibly creative how we'd keep looping through. And so I just got

00:12:04.360 hooked on it. And then we had to write lots and lots of papers at Vassar College. I got a great

00:12:09.300 education in writing and thinking there. Not so many tests, lots of papers. So when I would take

00:12:14.500 physiologic psychology, I would write about the physiologic psychology of obesity. And when I took

00:12:19.220 behavioral psychology, I would write behavioral and obesity. I went to developmental. I'd write

00:12:22.880 about developmental aspects of obesity. And I love the fact that you not only could, but in my opinion,

00:12:27.960 had to look at obesity from so many of those different angles that no one thing was going to

00:12:33.180 do it. Everybody's got their pet thing. This nutrient is toxic. It's this food marketing practice.

00:12:38.380 It's this cultural practice. It's exercise. It's not exercise. I think there are many factors

00:12:43.180 involved and I enjoyed studying it and still do from many angles.

00:12:46.240 Now, this is happening in the late 80s, correct? When you're in school, finishing your PhD?

00:12:52.460 Mid to late 80s, right.

00:12:53.920 At the time, what was the both political and medical view of obesity? I don't think it was

00:13:00.300 the same issue it was today, was it? Obesity rates weren't particularly high in the 80s or were

00:13:04.780 they? I don't really recall. They weren't as high as they are now. There had been steady increases.

00:13:10.420 If you go back and look at data from at least the 1700s forward, there have been relatively steady

00:13:17.080 increases in obesity rates in westernized, industrialized countries since then. But there

00:13:23.760 seemed to be this wake-up moment in the early 90s with the NHANES-3. So at that point, we didn't have

00:13:30.560 the NHANES, which is the National Health and Nutrition Examination Survey, which annually does today

00:13:35.940 a survey of a representative sample of Americans with measured heights and weights. That's how we

00:13:41.220 track obesity levels. At that point, it was only done every few years. And so there was this big gap

00:13:46.900 in years between NHANES-2 and NHANES-3. So NHANES-2 had happened, I think, in the early 80s

00:13:53.900 or mid-80s. And obesity rates were climbing, but not dramatically. And so obesity was an issue,

00:14:00.520 but it wasn't on top of everybody's mind, especially for kids. Pharmaceuticals were still

00:14:06.540 seen as the realm of charlatans at that point. If you were a doc and you proposed the use of

00:14:12.100 obesity medications, you were called a diet doc, and that was like a very bad stigma. There weren't

00:14:17.680 many good medications available. Surgery was still looked at as a very peculiar thing for very extreme

00:14:24.400 cases. And even many medical professionals disdained it. Then something happened in the very early 1990s,

00:14:33.620 which is NHANES-3 data came out. And what you saw was this big jump in obesity levels. And that got

00:14:40.580 everybody's attention. And words like epidemic started to be used, especially around childhood.

00:14:46.740 And the public health policy and environmental perspective came in. And things started to shift

00:14:54.320 in the mid-90s. And that was when you had people like Kelly Brownell, probably one of the most

00:14:59.420 forceful voices at the time, who was a sort of old school, University of Pennsylvania, colleague of Tom

00:15:08.200 Wadden, devotee, a student of Mickey Stunkard, of the cognitive behavioral individual clinical treatment

00:15:16.200 approach to obesity, suddenly starting to say, maybe we've got it wrong. Maybe it's this environment.

00:15:23.920 I don't know if Kelly coined the term toxic environment, but he certainly, he either coined

00:15:28.280 it or popularized it. And people started to think about prevention and children and the overall

00:15:36.740 environment, the political, economic, social, food marketing environment we lived in. It all started

00:15:43.900 to take off. And ideas from the cigarette world, the same public health people who had been battling

00:15:49.920 cigarette companies and tobacco for decades, many of they and their tools came in and said,

00:15:56.320 we know how to deal with things that are environmental, social, political problems.

00:16:00.500 And it began to be seen as an environmental, social, political problem. And in some way, a lot was gained

00:16:05.860 because we got a lot more funding for it. We had a lot more attention, a lot more efforts,

00:16:10.460 a lot more research, but in some ways it was lost. In the early nineties, when I kind of grew up in

00:16:15.840 the field, I grew up in the first federally funded obesity research center, which is the New York

00:16:21.200 Obesity Research Center, for a long time, the only one. And by gosh, if as a young postdoc, I were to

00:16:27.080 say something that didn't quite jibe with physiology or genetics or anatomy or clinical medicine, there was

00:16:34.260 a physiologist and a geneticist and an anatomist and a medical doctor who would say, uh-uh. And they

00:16:40.340 knew all, they knew each other. They knew the arguments. They knew the literature for the last

00:16:44.700 20 years. Then the public health people came in fresh without kind of knowing so much. So on the

00:16:49.720 one hand, it launched things up a lot, which was great. But on the other hand, back to what you were

00:16:55.320 saying earlier about the dilution of the rigor of the field intellectually, it became a lot more of

00:17:01.260 opinion, a lot more of advocacy in the absence of rigorous evidence showing what worked. You're

00:17:08.160 just saying, this seems like it ought to work. So David, prior to NHANES 3 and prior to Kelly and

00:17:15.840 others saying, hey, we should look for something else, what was believed to be the environmental or

00:17:23.080 otherwise trigger for obesity? In other words, what was viewed as the cause? I don't know that there was

00:17:29.260 a single cause. I mean, clearly what some people today call the energy balance model, which we can

00:17:35.060 go back to and ask whether that really is even a model in some realistic sense, I think was then and

00:17:41.100 still is today by most people in the field accepted as valid, but maybe not as a model, maybe as a

00:17:48.740 description of what occurs. Yeah. I was going to say that's sort of a tautology and doesn't really

00:17:54.020 tell us anything. It's implied and obvious and not explanatory.

00:17:59.820 Right. I think not explanatory is maybe the best way of describing it in some sense. The analogy I

00:18:07.000 would use to that is I think the energy balance statement, I won't say model, I'll say the energy

00:18:12.900 balance statement is as long as you accept the laws of thermodynamics unequivocally correct.

00:18:18.240 In the same sense as if you accept Euclidean geometry, the statements about the relationship

00:18:23.640 between the legs and the hypotenuse of a triangle are unequivocally correct. But if I say to you,

00:18:29.140 I have a triangle of this size with legs of length A and B, and I proportionately imagine another

00:18:36.260 triangle that has legs greater than length A and B, it doesn't cause the hypotenuse to be bigger.

00:18:43.400 It means the hypotenuse is bigger. And if I say, I imagine one with a bigger hypotenuse,

00:18:49.000 it doesn't cause the legs to be bigger. It means the legs are bigger. And any question about whether

00:18:54.200 the legs cause the hypotenuse or the hypotenuse cause the legs is nonsense. They don't cause each

00:18:58.680 other. They're inherent in the definition of triangle, just as inherent in the definition of

00:19:04.900 energy is the law of conservation. And it just says delta energy stores equals delta energy in minus

00:19:12.300 delta energy out. It's just a statement. So was the belief though, that this was behavioral

00:19:17.400 prior to NHANES 3, was the belief that, well, if a person is overweight or obese,

00:19:23.420 they're just eating more than they're expending because of a choice?

00:19:26.900 It's almost hard to answer that because I think the thinking was and still remains so sloppy that

00:19:33.540 people don't even distinguish among things well. I was just invited to a panel literally today that

00:19:40.480 I'm going to be on in a few months where somebody said, we're going to contrast for obesity,

00:19:46.860 the relative influence of biology versus behavior, as though we could behave without biology.

00:19:52.680 I don't know about you, but I'm not a disembodied soul. If you're a disembodied soul, maybe. But for

00:19:57.660 the rest of us who are in the material world, you behave with a body and it's got to obey the same

00:20:03.980 laws of physics and so on that any other body has to behave. So I think there's a lot of sloppy

00:20:09.740 thinking, but I think if you really drill down and you got to smart people who understood,

00:20:14.980 they would say, of course, their genetic component. Any rancher could have told you

00:20:19.440 a hundred or more years ago that there's a genetic component. We can selectively breed animals

00:20:23.940 for being thinner or fatter. That shows you there's a genetic component. There are many other things

00:20:29.180 that show you that, but that's probably the strongest prima facie evidence. You can selectively

00:20:33.320 breed animals to be fatter or leaner. It's prima facie evidence.

00:20:36.860 What, by the way, is the concordance of identical twins separated at birth? I've always found that

00:20:42.580 to be one of the most interesting ways to assess everything from autism to schizophrenia. It's

00:20:48.580 really a beautiful natural experiment, but I don't know what the concordance is with respect to obesity

00:20:53.260 and identical twins separated. Do you?

00:20:55.620 Yeah. There are a lot of different ways to quantify that and we've written a couple of papers on it,

00:21:00.440 but I think the easiest way is just the Pearson product moment correlation coefficient,

00:21:04.640 the ordinary correlation coefficient of BMI twin to twin. And it turns out that for monozygotic or

00:21:13.080 identical twins separated at or nearly after birth, it's nearly the same magnitude as it is for twins

00:21:21.460 reared together, which is about 0.9-ish. It varies from study to study. So it's very similar. Now,

00:21:29.880 of course, you can argue, well, that also takes into account the intrauterine environment,

00:21:33.360 maybe some epigenetic things. But bottom line is whatever those things are,

00:21:38.800 they're not just child rearing of the home. It looks like for many traits, including obesity,

00:21:44.060 the child rearing of the home does have an influence, but it's not a huge influence.

00:21:49.160 That's an incredible degree of concordance.

00:21:51.520 It sure is.

00:21:52.720 When was that realized? When were those studies first done?

00:21:55.420 Well, it's interesting because you can go back to quite some period of time to find the hints of

00:22:03.280 those studies. And often when you look at science, it's often that at some level we kind of knew

00:22:08.440 something, the information was sort of there, but there was this moment where a key figure had to

00:22:14.580 come in and both see it and say it with a degree of crispness that wasn't there before.

00:22:20.780 As I said, so lots of people could have seen the ranching data and the mouse data and family data.

00:22:25.740 In 1923, Davenport, who looked at now probably as a racist from the past, but at the time,

00:22:32.580 Davenport published under the Carnegie Foundation, these studies of concordance of families. And you

00:22:37.940 could have seen the genetic component in his 1923 data, but it was Albert J. Mickey Stunkard, who was

00:22:45.020 someone I had the great privilege of knowing and a wonderful, wonderful man. And Mickey did the first

00:22:52.200 major high quality twin and adoption studies. And he did what he was great at. He got on a plane and

00:23:00.120 went around the world and he met bright, young, interesting people. And he said, you have resources

00:23:04.700 and you have ideas and you have data and you're smart. Can I work with you? Can we work on this

00:23:09.480 together? And he got Torquild Sorensen and other people to start working on twin studies with the

00:23:16.160 great Nordic data, Sweden, Denmark, and so on, and adoption studies. And it was the twin and adoption

00:23:22.560 studies coming out of Sweden and Denmark that I think really nailed it. That finally, New England

00:23:28.380 Journal of Medicine, with Mickey Stunkard behind it, with clear writing, with high quality data,

00:23:33.260 people said, we got it. There's a big genetic component.

00:23:36.980 And this was approximately what year, David?

00:23:39.580 This would have been 83-ish, plus or minus a couple.

00:23:44.120 So again, trying to bring this back to kind of the clinical side of things. So if by the early 80s

00:23:50.180 should be patently clear that genetics are playing an important role, it would be at least a decade

00:23:57.120 before people would say, well, pharmacologic therapy might make sense here. Just as for example,

00:24:04.500 if somebody is genetically more likely to have hypothyroidism, a very relatively straightforward

00:24:10.760 endocrine system to understand, we wouldn't really think twice about replacing or upgrading

00:24:17.860 thyroid hormone as necessary. So where was that disconnect where it's the early 80s and we realize

00:24:25.520 there's a very strong genetic component to this? We probably have some medical tools that we could

00:24:30.720 use, but it still seems like that wasn't being done. What was being done? Was it just a case of

00:24:37.400 counseling people to eat less and exercise more?

00:24:40.420 It was mostly developing the tools that would help people reduce energy intake. And I think today,

00:24:47.720 that's still most of what we do. So even surgery and drugs mostly, but not only, help people reduce

00:24:54.280 energy intake. We know that reducing energy intake works. It's just really hard to do. And telling

00:25:00.240 people to do it and saying, I'll try to do it is not all that effective. And so all these things were

00:25:05.440 tools and we use the tools of behavior therapy starting in, I would say really the 60s is probably

00:25:11.180 where the formal behavioral stuff really kicked in with, I think it was Stuart and the behavioral

00:25:17.000 thinking about obesity. And then it continued to get better and better all the way through present and

00:25:21.960 still continues to get better and better. But my sense is that we've been asymptoting for a long

00:25:26.680 time. That is, we're getting incrementally better, not meaningfully better with many of the behavioral

00:25:32.820 cognitive things. What happened then in the early 90s is, and I remember because I was a postdoc at the

00:25:39.600 New York Obesity Research Center at the time, is a few people started to really kick in with

00:25:47.020 pharmaceuticals. And they started to come out of this idea that you're a diet doc and it's a bad

00:25:53.100 thing. And fen-fen, which now we know was dangerous and is gone, at least one of the fens in fen-fen,

00:25:59.680 that came up and that caught people's attention. Tell folks a little bit about fen-fen. Many people 0.96

00:26:05.320 listening probably won't be familiar with what it is, how it worked, and what the unfortunate consequence

00:26:10.180 was in a small but non-trivial subset of people. Fen-fen is a nickname for two drugs used in

00:26:17.380 combination, fentermine and fenfluramine. Fentermine is a drug that was and still is approved by the

00:26:24.400 FDA for the treatment of obesity, though it's never been approved for long-term. So it goes back a long

00:26:30.460 way. It's a catecholaminergic agonist. It's a relatively safe drug. No drug is perfectly safe.

00:26:36.380 And it's modestly effective. Fenfluramine is a selective serotonergic reuptake inhibitor,

00:26:44.720 which I think originally was used for depression. And people realize sometimes those drugs, those

00:26:51.600 SSRIs cause weight loss. Somebody realized you could put those two together and seem to get better

00:26:57.720 results. So that became a big craze. And there were many unscrupulous medical providers who did

00:27:04.720 provide that in ways they shouldn't have. But there were many scrupulous medical providers who

00:27:09.540 used it carefully and thoughtfully. And it did seem to have much better benefit than other people had

00:27:16.140 predicted before that. And so suddenly pharmaceutical treatment was no longer the realm of drive-through

00:27:23.900 pill mills and charlatans and diet docs and rainbow pills and so on. It was starting to be credible.

00:27:30.460 You had people like George Bray, who's still around today and working on obesity,

00:27:35.820 jumping on board and starting to really think through all the cornucopia of pharmaceuticals we

00:27:40.560 knew about and which ones might be useful, others weighing in. Then a very smart young person whose

00:27:46.580 name I no longer recall, but was a pathologist, not an obesity researcher as far as I know, at the Mayo

00:27:52.700 Clinic, started noticing some people coming through as cadavers. And she was being asked to do autopsies

00:28:01.920 and did autopsies and started noticing a peculiar valvulopathy that she didn't see very often. And she

00:28:09.040 started to see a little run of cases and then noticed that all of these cases had been on fen-fen,

00:28:14.060 reported it. And then many, many other people started to investigate it. And very quickly people

00:28:20.760 realized, in fact, this did produce a certain valvulopathy. And further investigation made it

00:28:27.120 clear it was the fenfluramine component, not the fentramine component that did that. So the

00:28:32.000 fenfluramine was withdrawn. Fentramine is still used today. But I think what was terrible, of course,

00:28:39.680 is that one of the many drugs in the history of obesity that hurt people. But what was good about it

00:28:45.780 is that it really started getting people thinking more about obesity as a serious medical disorder

00:28:54.640 that merited serious attention from credible physicians and credible scientists working at the

00:29:02.040 molecular, pharmaceutical, and physiologic level. And how much of fen-fen's mechanism was understood?

00:29:08.640 I mean, obviously, at the superficial level it was. But in terms of its direct action,

00:29:13.740 was the belief that one component was simply increasing energy expenditure while the other

00:29:19.320 was reducing appetite? I don't think it was quite that simple. And I don't think with any of these

00:29:23.800 things we ever fully know the components. This is where you know far more than I. But I mean,

00:29:28.660 even in the case of statins, which we think so highly of and use so well, whether they really have

00:29:33.220 their beneficial effects by reducing LDL cholesterol, which is sort of the initial thought, or through other

00:29:38.120 mechanisms, I think is being debated now. I think we suspected and still would believe that they

00:29:44.060 reduced appetite and therefore help people control their food intake better. That the two things did

00:29:50.400 it through slightly different mechanisms, one being more serotonergic, one being more adrenergic.

00:29:55.780 They may have also had effects on energy expenditure, albeit probably modest ones. And that was

00:30:01.280 probably more the phentermine than the fenflormine. In terms of the mechanism for the valvulopathy,

00:30:06.740 I'll have to leave that to you. That's out of my realm. When was fen-fen told? Was that the mid to 0.94

00:30:11.860 late 90s? I think it was the late 90s, but I don't remember the exact date, probably 97-ish,

00:30:18.360 somewhere around there. What about gastric bypass and other surgical approaches? Now we have sleeves,

00:30:26.100 Roux-en-Y, all sorts of different approaches to it. But when did that sort of go from being

00:30:30.960 presumably incredibly fringe to, quite frankly, where we are today, where this is something that

00:30:37.040 will be covered by an insurance company if the BMI is high enough? I don't know that there was a single

00:30:42.200 moment. So I think there's been a sort of a relatively steady march, but I do think there

00:30:46.140 was one key step that was kind of a step function in the credibility of it. And that was the Swedish

00:30:51.880 Obese Subject Study, as from Lars Jostrom in Sweden. Surgery had been around for a while.

00:30:59.320 Interestingly, even among physicians and scientists, it was very controversial. It still is.

00:31:04.040 Some people think it's barbaric. Some people think it's abhorrent. Again, I think you just have to take

00:31:08.340 the data as it is. You might say, wouldn't it be better if we had a world where no one needed surgery,

00:31:13.160 where our solution to obesity was not let children start to gain weight, and then when they become

00:31:18.760 massively obese adults, give them surgery and rearrange their internal anatomy, you'd say,

00:31:24.720 yeah, probably would be better. But that's not the world we live in. We live in this world. And in

00:31:29.260 this world, that's the most effective, life-saving, life-changing treatment we have, and it's a good

00:31:36.080 one. So what happened is many physicians would battle among each other. I remember John Kroll,

00:31:41.960 who's since passed on, a very good surgeon, one of another physician colleague chastising him over

00:31:47.640 dinner at a conference and saying, when you're done with a patient, Dr. Karl, they will never eat

00:31:53.200 normally again. And he said, no, when I'm done, they will never eat abnormally again. So there were

00:31:59.640 these real vitriolic battles. I think what happened is the surgeries, of course, got better and better

00:32:04.980 and better. As everything else, people learn how to do things better. Mortality rates went way down.

00:32:09.620 efficacy went up. But what Lars Sjostrom did is he said, I want to look at whether this prolongs life.

00:32:17.240 And I remember him telling me many decades ago that his senior mentor, Per Bjornthorp,

00:32:23.440 who was known for fat cell theories and apple versus pear idea, right? Originally, Jean Vogue was 1.00

00:32:29.440 apple versus pear from France in the 1950s. But then in the 80s, really, 70s and 80s, Bjornthorp was one of

00:32:35.960 the big people who picked it up. Bjornthorp laughed at his mentor. He says, Lars, everybody knows this.

00:32:42.200 Everybody knows that surgery will cause weight loss and help people live longer. There's no point in

00:32:46.780 doing this study. And Lars said, you may be right that it does, but we have to do this study because

00:32:53.460 everybody doesn't accept and believe that it hasn't been shown. We know over and over again, you've got

00:32:58.780 to do the experiment. As John Hunter famously said to Edward Jenner, when Jenner says, I think, and he's

00:33:04.420 thinking about the first vaccine. And Hunter comes back and says, well, I think, do the experiment.

00:33:10.980 Got to do the experiment and show it. Sjostrum didn't do a pure experiment. It's not a randomized

00:33:16.200 trial, but it's a controlled trial. And the IRB at the time didn't think it was ethical to randomly

00:33:23.040 assign people to either surgery or not. So if they're willing to get surgery, he would find a closely

00:33:27.600 matched control and give them usual care. And then what he showed well more than a decade later in the

00:33:33.420 New England Journal of Medicine is that the surgery reduced mortality rate. It reduced obesity,

00:33:41.540 very powerful effects, clearly a life-saving and a beneficially life-changing treatment. I think

00:33:48.120 that was probably the big jump. And then since then, many other trials, some randomized, have been done

00:33:53.700 showing positive benefits on many things, including longevity.

00:33:57.880 Do you recall what the risk reduction was in all-cause mortality in that study?

00:34:02.680 I don't recall in that study per se, but when you look across studies, it varies quite a bit from

00:34:08.980 study to study. There's a lot of heterogeneity. It's usually well on the order of a 50% reduction,

00:34:14.600 sometimes a little bit more. Maybe getting too in the weeds on this, but how does that change when

00:34:19.160 you do and do not correct for the presence of type 2 diabetes at the outset? In other words,

00:34:24.040 I would imagine that that's a combination of people who had and did not have type 2 diabetes.

00:34:30.640 And do you have a sense of what the differences are amongst those two cohorts?

00:34:35.480 Your premise is correct that it is among people who do and don't have type 2 diabetes. But no,

00:34:40.560 I don't know off the top of my head. I share your intuition that it's probably stronger

00:34:44.780 among people who do. And we need to also look at many different factors. So for example,

00:34:49.880 in the Swedish Obese Subject Study, and I'm not sure this would hold up at all,

00:34:53.240 one of the things that was very puzzling and surprising, at least to me,

00:34:55.840 was that although diabetes really came down and stayed down very well, even if weight came back

00:35:02.360 up a little, which it did on average, hypertension came down very well, but did not stay down very

00:35:08.800 well. It would come back up. Why that is, is it damage to the endothelial elasticity that's not

00:35:15.060 really repairable? And so you get a short-term effect of negative energy balance that's not

00:35:19.720 sustained. I don't know, but it goes back to that John Hunter thing. You've got to do the experiment.

00:35:25.860 We can't make our priori assumptions about the effects of treatments. We've got to do the

00:35:30.400 experimental look at the effects of treatments. It's interesting that hypertension would return

00:35:35.100 and yet mortality would still improve, which suggests that perhaps the benefits of improved

00:35:43.460 insulin sensitivity, which do persevere, play a greater role in mortality than body weight,

00:35:50.500 which does not surprise me, but also hypertension, which is somewhat surprising given how causally

00:35:56.320 related hypertension is atherosclerosis. Well, it may not be a greater role as much as an additional

00:36:02.400 role. It could be that if you got the hypertension down, you'd get an even bigger reduction.

00:36:07.400 When did this idea of an obesity paradox, which I know you've written about, when did that start

00:36:16.180 to become observed? That phrase obesity paradox has never been really crisply defined and whether

00:36:22.600 it really is something that's a paradox is not clear. People use it to mean at least two different

00:36:27.940 things. One thing is that there's this so-called U-shaped curve, more accurately concave upward as a

00:36:35.440 bathtub-shaped curve. If you imagine a Cartesian or XY plot in which the Y or ordinate is mortality

00:36:42.800 rate or some function of it, and the X is BMI or relative body weight or something. So one is that

00:36:49.760 it is U-shaped, that it's not monotonic increasing. So people at the very thin end also die earlier

00:36:56.580 than people in the middle, just as people at the very heavy end die earlier. The Duchess of Windsor

00:37:01.640 supposedly said, you can never be too rich or too thin. And she may have been right on the rich part,

00:37:07.420 I don't know, haven't gotten there yet, but apparently not on the too thin part. Now we can

00:37:12.200 argue whether it's causal and there are lots of arguments about whether it's causal. But that's

00:37:16.780 one thing. It's the idea that thinner people than sort of intermediate levels of BMI also die earlier.

00:37:23.280 That's one part. The other part is that even though obesity is associated with increased mortality

00:37:29.260 rate or decreased longevity, that when you look at people who are sick or injured, they often live

00:37:37.200 the longest. So if somebody comes in with kidney failure or someone comes in after a major injury

00:37:42.740 or a major infection, they're in the hospital, it's often the heaviest people that live longer.

00:37:48.420 And that started to be talked about 10, 15 years ago, maybe. It's very difficult to disentangle

00:37:54.460 cause and effect. We can observe lots of associations, but it's just hard to know what

00:37:59.560 to make of all of these associations and what's causal. As the philosophers say, the hypotheses are

00:38:05.840 underdetermined by the data. There are multiple hypotheses that are consistent with the data.

00:38:11.680 That's why the randomized experiment allows us to do things that otherwise we can't do. It sort of

00:38:16.800 eliminates more competing hypotheses. So I don't think we know what's causal yet. We've thrown out a

00:38:22.620 model, a man named Doug Childers and I, who's a postdoc, who's a good mathematician working with me,

00:38:28.460 in which we said, what if obesity makes it more likely that you get a major illness or injury?

00:38:35.580 So it's not good for your health in that sense. Age, of course, also makes it more likely that you get

00:38:41.560 a major illness or injury. But once you get a major illness or injury, being heavier, more obese,

00:38:48.860 may reduce your risk of dying from it. And an analogy I use is this. Suppose you and I are

00:38:55.300 sort of going for a hike on the edge of the Grand Canyon and we go to some outfitter who's setting us

00:38:59.920 up. And he says to you, who's a, you're about 10 years younger than me, I think. And he says to you,

00:39:06.240 you have a choice. I can give you this fat suit you can wear, has lots of padding on it. If you fall

00:39:11.180 off the cliff, it makes it much more likely you'll survive. But it makes you a little clumsy. So it makes

00:39:16.100 you more likely to fall off the cliff. You might say, I've got good balance and good eyesight and

00:39:20.000 I'm young and strong. And no. Comes to me and I say, I'm not quite as young and strong. Maybe.

00:39:24.740 I don't know. Comes to somebody 10 years older than me, that person says, I'm really struggling

00:39:30.660 with my eyesight or balance or strength. I'll take the fat suit. And so whether the fat suit is good

00:39:37.100 for you may depend upon your probability of falling to begin with. And under that mathematical

00:39:43.080 model, we can show in fact, that the point of BMI, the nadir of that bathtub shape, concave upward curve

00:39:50.040 should keep moving to the right as you age, which is exactly what it does. We have a model that's

00:39:56.340 consistent with the data, but the data don't prove our model. There are other models that would be

00:40:02.480 consistent with it. Let's give people an idea of what those numbers look like. So just for someone

00:40:07.940 listening to this, maybe I'm not even watching, it can be hard to imagine what we're talking about,

00:40:12.360 right? But a U shaped curve that at its nadir is giving you the optimal BMI. This would be the lowest

00:40:19.080 mortality. And what you're saying is rather than just plot one of these curves for everybody, let's

00:40:24.520 plot them by decade. So what does the U shaped curve look like for people in their twenties, in their

00:40:29.520 thirties, forties, fifties, sixties, up to their nineties. And what you're saying is if your hypothesis

00:40:34.720 is correct, one place you would expect to see that is that these curves would not only not look

00:40:41.560 the same by decade, but they would move rightward, meaning the nadir is getting to a heavier and

00:40:47.820 heavier BMI. Can you give me a sense of how much movement there is by decade?

00:40:53.420 Yeah. In fact, I'm going to smooth over things. So the story I'm about to tell you is not perfectly

00:41:01.400 true, but it's sort of a roughly true and it conveys the sort of nice element of this.

00:41:05.860 The average American might gain about a pound a year. For an average height American, about six

00:41:13.020 or so pounds might be a BMI unit, right? If you gained about six pounds, you might gain about a BMI

00:41:18.340 unit. And so that might mean that over six years, you'd be about one BMI unit heavier. And that's not

00:41:26.260 too far off from how the nadir moves. It's almost as though your weight is increasing

00:41:32.520 to keep you on at the nadir, which is an interesting speculation. And if you looked at people who are

00:41:40.480 maybe 20 years old, that nadir might not be too far from 20 in some populations. An astute reader of the

00:41:48.720 epidemiologic literature who's listening now might appropriately be saying, hey, wait a minute now,

00:41:55.140 Allison needs to make distinctions by age, race, and sex. He's doing age. He's not doing race and sex

00:42:00.480 and other factors. And they'd be right. We can come back to that if you wish. But putting that aside

00:42:05.800 for the moment, very loosely speaking, you might say that nadir might be far down near 20 when you're

00:42:11.040 20. And by the time you're 80, it's not to 80. But by the time you're 80, it might be well above 30,

00:42:17.860 might be in the low 30s where that nadir is, which is we generally say 30 is the beginning of obesity.

00:42:24.340 And for those who are not used to BMIs, I used to lay it out like this. The supermodel Kate Moss,

00:42:30.080 at least from a few decades ago when she was the top model, had a BMI, I think, in 16 to 17 range.

00:42:36.480 We say that about 18.5 is sort of the beginning of normal weight. We think less than that is

00:42:42.440 underweight. My BMI right now is probably 21-ish. Bill Clinton's, the height of his presidency,

00:42:49.480 since the last weight was probably 27, 28. We say 30 is where obesity begins. And a secretory sumo

00:42:57.760 wrestler, top class sumo wrestler, about 43, 44. So that gives you a sense of what those BMIs are.

00:43:05.260 And so it's really 30, 32, where you see people 70, 80 and above having that lowest mortality rate.

00:43:13.060 Is there a risk that we're looking at confounders here? I don't know if this would be a confounder.

00:43:19.700 It wouldn't seem like it would be a confounder in this year, meaning in this time and place we're in,

00:43:25.480 but would it be a confounder for affluence, for example, as it may have been hundreds of years ago,

00:43:31.900 other things like that. And the other thing about BMI is when you look at other data that look at body

00:43:38.340 composition, do we see the same thing hold up? So you can have a BMI of 30, even though you're

00:43:46.600 obese by BMI, no one would look at you and say you're obese. You might have a body fat of 15%,

00:43:52.800 which is pretty low, and be incredibly muscular, for example. So how do we reconcile, one,

00:43:59.760 the potential confounders of that type of an analysis with this other next layer of granularity

00:44:05.880 in the data that look at adiposity versus lean mass?

00:44:10.560 Let's start with the confounding question. Now let's get to the body composition course.

00:44:14.880 The confounding question is a nightmare. This is the challenge of observational research in general.

00:44:21.660 And there's no simple solutions to it other than, in my opinion, trying to triangulate on the answer

00:44:27.980 with multiple studies. What we really want is the pure randomized experiment in humans,

00:44:34.280 in large samples, with perfect compliance for many years, and we want to randomly assign people to

00:44:39.760 be obese or not obese. Obviously, we can't do that. So what do we do? Well, we look at observational

00:44:45.280 epidemiology. We look at randomized trials where we have people lose weight. We look at non-randomized

00:44:50.160 trials like Sjostrums with surgery. We look at mouse studies. We look at all these different things.

00:44:55.040 We try to just put the puzzle together as best we can. But there's absolutely a lot of possibility

00:45:00.280 for confounding. Cigarette smoking was one of the early ones. Very famous paper by Joanne Manson

00:45:05.780 and colleagues published in JAMA, pointing out that the, quote, right way, according to them,

00:45:11.300 to analyze BMI and mortality data was you must exclude ever smokers. You only look at never smokers.

00:45:18.060 Otherwise, you may have confounding by smoking. Smoking makes you thinner. Smoking kills you earlier.

00:45:22.860 Got to take care of that. You've got to throw out the subjects who die early because subjects who die

00:45:27.420 early may have been sick. Sickness makes you thinner. Sickness makes you die earlier. That

00:45:31.680 is a confounder. We've since proved mathematically that that's not a good thing to do. But for a long

00:45:37.160 time, it was believed. Is that even true, David, on the low end? Because that does strike me as one

00:45:41.860 of the most obvious explanations for the uptick in mortality at very low BMIs is all of the liver disease,

00:45:50.060 the kidney disease, the types of chronic diseases that actually do lead to muscle wasting and things like

00:45:55.860 that. Are you also including them in your analysis? Yes, we are. What we showed is not

00:46:01.820 that that confounding doesn't occur. That confounding absolutely can occur. We agree that

00:46:06.840 it can occur. We speculate. We think it's likely. What we've shown is that throwing out subjects who

00:46:11.500 die early in the analysis doesn't help or doesn't reliably help. It was very interesting back to sort

00:46:18.040 of the discussion of the field when I went to analyze my first BMI mortality data set. I had no

00:46:23.200 training in this. So I get a data set and I'm about to try it. I don't know what I'm doing.

00:46:27.940 So I call up my friends and I say, I read this paper in JAMA and it says you have to eliminate

00:46:33.120 the early deaths. How do I do that? How do I pick how many years to do? Exactly how does this work?

00:46:39.240 And it was interesting because when I would call the epidemiologists and I would say, how does this

00:46:45.580 work? And then I'd say, by the way, is there a reference that explains and proves that this works?

00:46:49.480 They'd say, no, it's obvious that it works. I said, well, it's not obvious to me. And they said,

00:46:55.520 well, maybe you're not smart enough to be an epidemiologist.

00:46:57.760 Those words were actually uttered? 0.88

00:46:59.860 Jokingly. But yeah. Then I would call my friends who are statisticians and I would say,

00:47:04.320 there's this paper and it says you have to eliminate the early deaths. They would laugh at

00:47:07.020 me like, what? That's the most ridiculous thing I've ever heard. We don't just throw out data and 0.99

00:47:11.120 think it makes things better. You've got to have a model that you fit things to. We then got a small

00:47:16.360 grant from the CDC and we looked at mathematical proofs, computer simulations, and meta-analyses.

00:47:21.720 And the mathematical proofs showed it not only didn't have to help reduce the confounding,

00:47:25.780 it could make it worse. Simulations showed the same thing in realistic data scenarios.

00:47:30.780 And then the meta-analyses showed that on average, it didn't make much difference at all.

00:47:35.120 So throwing out the subjects who die early basically just reduces your power in most practical

00:47:40.620 situations. Then they would say, you've got to not control for the intermediary variables like the

00:47:45.880 diabetes and hypertension. That's probably true. I think I would agree with that.

00:47:50.500 Later in subsequent paper in 1995 in the Journal of Medicine, that same group said,

00:47:55.440 you've got to throw out the subjects who have more weight variability. All the things they said in 1987

00:48:00.120 weren't enough to flatten out that left end of the U-shaped curve. So they said, throw out the

00:48:06.320 subjects who have a lot of weight fluctuation. And that didn't completely get rid of it, but got rid of

00:48:12.180 most of it. That's the bigger one. But what it means is unclear. Catherine Flegel has written very

00:48:19.760 well on this and talked about these different things and how much you're throwing away and

00:48:24.660 pointing out, yes, these are the patterns we observe, but what they mean causally is unclear.

00:48:29.800 So that's the whole confounding issue. The bottom line is it's likely that there's confounding by

00:48:34.700 cigarette smoking and socioeconomic status and stigma. That's one of the big things, right? How much

00:48:40.140 does obesity kill you because it stigmatizes you and it creates some stress? Could be. And that may

00:48:46.600 be, by the way, why the BMI associated with the lowest mortality has been increasing over calendar

00:48:53.420 time. And it's not just with age of individuals, but if you compare data collected in the 1970s to

00:48:59.260 data collected in the 1990s, obesity doesn't, from an association point of view, look quite as bad

00:49:05.360 in the 1990s as it did in the 1970s. And that's true in Denmark, and that's true in the US, and it's

00:49:11.620 true in meta-analysis, although again, not for every age, race, sex group. Why is that? And maybe it's

00:49:17.000 stigma. Maybe it's that, if you think about, you watch like the Three Stooges, those old TV shows,

00:49:22.820 and there was Curly, and he was often mocked as the fat guy, right? By today's standards, he's not that

00:49:29.480 big. What stigmatized changes over time as different BMIs become normative. It may be that

00:49:36.040 that's partially accounting for it. So there's lots of potential confounding going on there,

00:49:41.040 but there's lots of other possible explanations. So bottom line, we don't know. And the real

00:49:45.740 important question then is, what's the effect of intervention? This goes back to my friend and

00:49:51.320 someone I really idolize in the field, one of the clearest thinkers, Don Rubin, statistician at

00:49:56.400 Harvard, talks about the Rubin-Causal model. And he's always asking, what's the intervention?

00:50:02.000 If you say, does this cause that? What do you mean? Compared to what? It's always got to be

00:50:06.580 compared to what, right? And so if you say weight loss is going to increase longevity, that's the

00:50:13.160 question. Well, how are you going to achieve that weight? What are you going to do to get that?

00:50:16.900 Well, then if it's surgery, then what's the effect of surgery? It's a GLP-1 agonist. What's the effect

00:50:21.460 of a GLP-1 agonist, et cetera? So that, I think, is the key question. And I think what

00:50:26.300 we're starting to see is some of those things do prolong life. Surgery, SGLT2 inhibitors, GLP-1

00:50:32.240 agonist, and so forth. Now, to your other question about body composition, many people like to point

00:50:38.120 out that BMI is a measure of mass divided by stature. It was developed by Adolphe Ketley, who was

00:50:46.260 a Belgian astronomer, epidemiologist, statistician, mathematician back in the 19th century. Brilliant

00:50:52.880 guy. We actually named a professorship after him, and I held that title. I was very grateful for that

00:50:58.100 opportunity back when I was at the University of Alabama at Birmingham before I came to IU.

00:51:02.720 Ketley made it. You would think that BMI, that is, or rather, the mass of a three-dimensional object

00:51:09.820 ought to increase in proportion to the cube of a linear dimension. And if, in fact, we were spheres of

00:51:17.340 uniform density, that would be true. But I don't know about you, I'm not a sphere of uniform density.

00:51:22.800 So it turns out not to work that way exactly. It turns out that empirically, it works closer to

00:51:28.540 the square for adult humans. Ketley realized that, and so he said, take mass or weight over stature

00:51:36.980 squared, the square of stature. Then it was called Ketley's index. Now we call it BMI. It was sort of

00:51:42.480 rediscovered in the 60s or 70s by Ancel Keys and termed BMI, body mass index. And every few years,

00:51:50.080 some smart person likes to come along and says, it should be cubed. We go, yeah, yeah, yeah. We get

00:51:54.480 it. We knew that a couple of hundred years ago, and we worked that out. Then they say, and it doesn't

00:51:58.900 really take into account body composition. We say, we know that. And they say, the NBA center would have

00:52:05.600 a BMI greater than 30, and yet look how strong and fit that NBA center is. We go, right. If the NBA

00:52:12.780 center comes to your clinical practice, don't measure his BMI and diagnose him with obesity on

00:52:18.380 that. And what physician in his or her right mind would do such a thing? So clearly, we get that.

00:52:26.820 BMI is a useful tool for epidemiologic research and some simple physiologic research and some simple

00:52:33.260 clinical trials. It's not a perfect clinical tool. For the average person, it works okay.

00:52:39.940 Now, do we have to be worried about different ethnicities? Obviously, the two that come to mind 1.00

00:52:44.260 would be Asian and East Asians, because I think most people are kind of familiar with this idea of

00:52:49.440 skinny fat. So you have someone of East Asian descent whose BMI is 26, which by all intents sounds just 0.94

00:52:58.780 wonderful, but they have metabolic syndrome. There's nothing about this person that's healthy.

00:53:03.760 Every metric across the board, tons of visceral fat, very little muscle mass, et cetera. And that's

00:53:09.880 a different phenotype. That's a slightly different phenotype than perhaps the model was built on. So

00:53:15.440 how much is the metric that we use for BMI? 20 to 25 being perfect, 25 to 30 being overweight,

00:53:23.180 30 to whatever it is, 40 being obese, and then morbidly obese kicks in at some point, I'm sure.

00:53:29.720 Is that validated on other ethnicities as well?

00:53:33.480 Yes and no. So with a few exceptions, the idea that there is a curve and that it's a generally

00:53:39.160 concave up curve, yes. Although again, there are exceptions. But the shape of that curve is not the

00:53:46.940 same in every age, race, and sex group. It does seem that the right side, the part where you're

00:53:53.680 getting too high in BMI and risk is going up, seems to occur a lot earlier among people of Middle Eastern

00:54:00.020 and East Asian descent. So that's a very simple model. It just sort of says, oh, it's all a curve 0.96

00:54:07.120 for everybody. A slightly more complicated model says, well, we know some of the other factors that

00:54:11.920 come in, like how much of your fat is subcutaneous versus how much is visceral. And it's just that

00:54:17.820 that group has more visceral fat than that group for any given body mass index, and therefore it

00:54:23.620 adjusts the curves. And there's probably some truth to that, but it's probably even more than that.

00:54:28.460 So for example, a lot of these statements we hear, and this goes back to way back when I said,

00:54:33.040 way at Vassar College, I loved the complexity of it, that you had to look at it from obesity,

00:54:39.080 from so many different angles. And that's still true. The narratives we have about obesity,

00:54:44.680 including about ethnicity and obesity, are grossly oversimplified. So we often hear obesity is

00:54:49.840 selectively a disease of the poor and uneducated. That often in this country, it's stated, minority 1.00

00:54:55.620 status leads to less income, less education, which in turn leads to poorer access to healthcare,

00:55:02.800 poorer habits, poorer living environments, which leads to poorer health and reduced longevity.

00:55:07.240 And there's probably some truth to that, but it's not one-to-one. It's not simple. So for example,

00:55:13.720 when we hear that there's this inverse relationship between socioeconomic status

00:55:18.680 and obesity, which we hear over and over again, first shown by Mickey Stunkard, again,

00:55:23.580 in the 1950s in the Midtown Manhattan Project, that's true in white women. It's reliably true in adult 0.99

00:55:30.960 white women. When you go outside the group of adult white women, not always true. If you go to

00:55:37.800 African American women, virtually no association between socioeconomic status and obesity. When you 0.99

00:55:45.360 go to African American men and you look at their obesity levels, they're very similar to the obesity 1.00

00:55:52.500 levels of European American men, white American men. But if you look at African American women's 0.64

00:55:58.100 obesity levels and you compare it to European or white American women, much higher. If you bring 1.00

00:56:04.520 Hispanic Americans in, both men and women have higher levels of obesity than do European American

00:56:11.360 men and women. So now you have an ethnicity difference, but not a gender by ethnicity difference.

00:56:19.020 Whereas in African American, you have a gender by ethnicity difference. When you look at mortality, 0.58

00:56:23.580 the African American curves follow similarly, but not identically to the European American curves.

00:56:30.920 But in our research, we can't find an association between BMI and longevity in Hispanic Americans.

00:56:40.500 Wow. Just say that again. That's pretty interesting. That's across all BMIs?

00:56:44.400 We've been unable to find it. Now, the data sets we have are not perfect. The follow-ups may not be long

00:56:49.980 enough. The sample sizes may not be big enough. You can always question things. I don't say it's

00:56:54.980 causal. I'm just saying this is what we observed a few years ago when we got every data set that

00:57:01.240 was publicly available that we could get our hands on that involved Hispanic Americans and BMI and

00:57:07.180 longevity. And we analyzed them all together with common methods as carefully and thoroughly as we

00:57:12.060 could. We could not find an association between BMI, elevated BMI and mortality.

00:57:17.960 As someone who sits on the opposite end of the spectrum, where I don't at all concern myself

00:57:24.620 with public health, I'm not trying to make grand observations about what's happening in society

00:57:30.260 across ethnicities or anything like that. I really just have the luxury of looking at one person at a

00:57:35.560 time and trying to make a determination about the best course of action. My view of BMI is generally

00:57:40.120 quite negative. It's maybe the least bad tool available to get massive data sets and make broad

00:57:48.500 assessments of what's happening at the population level. But to your point earlier, at the individual

00:57:54.520 level, it offers very little insight relative to other tools. When we have patients do DEXA scans,

00:58:00.780 I tell them that we're going to get four important pieces of information out of this,

00:58:05.200 but one of them's really the least important to me, which is your subcutaneous fat. That's the

00:58:10.100 first thing people want to look at when they have a DEXA scan. They want to see what's their percentage

00:58:12.920 of body fat. And I say, there's three things that are much more important to us. One is your visceral

00:58:18.260 fat. One is your bone mineral density. And one is your appendicular lean mass index. So how much muscle

00:58:24.820 mass do you actually have? And we'll look at your subcutaneous fat. But the reality of it is that

00:58:29.100 doesn't really seem to matter because, A, that seems to be incredibly genetic. Body habitus in that

00:58:35.560 regard, very genetic and relatively uncoupled from metabolic health. So the VAT, the visceral

00:58:43.000 adipose tissue, seems to be much more tightly correlated to what we see when we look at more

00:58:48.560 sophisticated biomarkers of insulin sensitivity and metabolic health. Any evidence of liver fat,

00:58:54.060 all of these other things tend to track much more closely with that. So my view on this is that

00:58:59.780 I'm glad that I get to look at these other metrics. I have the luxury that a statistician or an

00:59:05.560 epidemiologist doesn't have, which is when you do things at the individual level, you have much more

00:59:10.580 data and you can be much more nuanced in your appreciation for things. But I can't help but wonder

00:59:17.660 if at the population level, something better than BMI will come along one day for which there will be

00:59:23.940 enough data to actually do what's out there. And again, this example with the Hispanic subset,

00:59:29.700 that's mind boggling to me. And it really speaks to, I think, the futility of that measurement

00:59:35.240 in other populations. That is one plausible interpretation. And I don't think we're far

00:59:41.640 away from these better tools. We published some work years ago on the idea of using 3D photography.

00:59:48.980 Take a picture of somebody from a couple of angles. This came out of work experience way

00:59:53.780 back at the New York Obesity Research Center where Steve Heimsfield, who was my mentor,

00:59:57.500 would study professional basketball players. They would bring the teams in from New York to him.

01:00:03.060 He was the king of body composition and he would study them. In talking with some of the technicians

01:00:08.380 there who did this every day, who measured people's body composition every day, they would say,

01:00:12.600 I can look at a person and tell you how much fat they have and I'll be very accurate. They were good

01:00:17.600 at it. I said, well, if you can do with your eye, why can't my camera do it? We got an NIH grant with

01:00:22.640 Olivia Fuso and I and Steve Heimsfield to look at this, publish the paper on it. Since then,

01:00:27.760 many others have done it. And I think Amazon may be working on this or already have something out

01:00:32.020 on this. So I think we'll get to the point where we have 3D photography. So that's Archimedes'

01:00:36.660 principle. Eureka, I found it. The crown goes under the water. He knows how much gold is in it

01:00:41.180 because he knows how much water it's displaced. So if you can weigh it and you can measure it,

01:00:45.480 if you can measure something's weight and volume, you know its density. If you know a little about

01:00:49.240 human anatomy, you can figure out from density, body fat. That's Archimedes' principle. That's

01:00:54.620 what we can do with a camera. So we have that. We have DEXA. We have pletheismography. We have

01:00:59.460 isotope dilution techniques. All of these can start to be used more on mass on and on. And so I think

01:01:06.320 these will be coming and we'll do better with that. But I also think there's this idea of fit for

01:01:10.820 purpose. If you were to say to me, is my vehicle that I drive, is this precision enough for the

01:01:17.120 Indy 500? No. I mean, they need better vehicles. But I'm not in the Indy 500. I'm just driving five

01:01:23.980 miles to and from work every day. It's fine. And so I think it's fit for purpose. If you say to me,

01:01:29.420 what proportion of that country has obesity? BMI is probably perfectly reasonable. If you say,

01:01:37.340 I just want to know on average what's the approximate correlation between obesity level and

01:01:41.700 whether you play golf or not, that's probably okay. But if you want to say, I want to help this

01:01:47.200 individual patient, especially at the sort of the kind of artisan level that you go at,

01:01:54.780 then you need the better tools. Well, the other thing that comes back to your friend Ruben is

01:02:00.040 you're still limited in the what to do, the now what question. So if you say, well, we're going to take

01:02:06.000 our tool of BMI extrapolation and we're going to look at the state of Indiana. We can pretty accurately

01:02:12.200 probably plot out the histogram of exactly what the BMI is by age and by demographic and all those

01:02:18.600 things. It's the so what question. Well, what do we do with that information? What's the implication

01:02:23.140 of this? Are people in Indiana more or less healthy than people in Kentucky? And are they more or less

01:02:28.280 healthy than the average American? More importantly, are they more or less healthy than the average person

01:02:31.840 on this planet? And what can we do to improve their health, even if we don't want to compare them to

01:02:36.480 anybody other than an absolute standard and say, could everybody become healthier? Could we extend

01:02:41.960 the life of the average person in the state of Indiana by three years? What would the intervention

01:02:48.240 need to be? And that obviously becomes a much more complicated problem, but it's a more germane

01:02:52.340 question, right? And it's not a fanciful question. It's a question that we literally are asking.

01:02:57.020 So this becomes tough because when I talk to people, even physicians, even highly educated

01:03:05.080 scholars, there's this bifurcation. There's the data and those who look at the data. And if they

01:03:13.780 really know the data and they're really honest, they say, we agree. Surgery works, pharmaceuticals work,

01:03:22.200 individualized or group-based clinical treatment with behavioral cognitive techniques work somewhat

01:03:28.640 for some period of time. Meal replacement formulas work somewhat for some period of time. But all the

01:03:35.140 public health stuff we've tried now, if you really are honest and you really scrape away the obfuscated

01:03:43.180 data, there's virtually no evidence thus far that any of those community, school, public health things

01:03:51.720 for obesity work. Doesn't mean that we won't get them someday, but right now they don't.

01:03:57.640 So when people come and say, well, what do we do about the state of Indiana? And I get this question

01:04:01.880 a lot. What's your goal? Is your goal to expose people to ideas so that there's some really smart

01:04:09.060 kid, the next Marie Curie, the next Einstein, the next George Washington Carver is there in that

01:04:16.160 classroom? And maybe the thing you do to try to reduce obesity has no effect on obesity, but that

01:04:21.780 kid is thinking about it for the next 15 years. And then when they become an adult, they go, I got an

01:04:27.120 idea. And suddenly someone smarter than we are or with new knowledge cracks it. Is that your goal? Then

01:04:33.800 it's consciousness raising. Okay, fine. Is your goal to say to communities, we know you're suffering

01:04:39.800 and we know this is concerning and we want you to know we care and we're trying. We're not really

01:04:45.880 going to necessarily reduce obesity levels. We want you to know we care. And you want farmers markets

01:04:51.700 in the school parking lot. You want vending machines changed. You want running tracks built

01:04:57.900 in your neighborhood. We'll build running tracks and so on. And we'll feel better about we're caring for

01:05:01.880 each other. But it's probably not going to affect obesity given what we know right now.

01:05:05.460 Or do you say, I actually want to have less people suffering with and from obesity. I want

01:05:13.240 obesity levels in some definable, countable number of people to go down and I want their health to

01:05:18.740 improve. It's not going to win you any feel good awards, but surgery, pharmaceuticals, and to some

01:05:26.200 extent individualized treatment, cognitive behavioral group-based treatment, including things like meal

01:05:32.640 replacements and so on. Those are ways to go. You could take if the state of Indiana handed me $10

01:05:38.760 million and said, make a difference. I would not go build farmers markets in schoolyards. I would say

01:05:46.640 it'll only be a small number of people, but let's give bariatric surgery to a subset of people. And

01:05:53.440 those subset of people will likely live longer on average.

01:05:55.860 What's the best explanation for why weight loss is not particularly difficult, but weight loss

01:06:03.060 maintained is incredibly difficult?

01:06:06.640 There's probably no single explanation. And I think that question of why is a tough one. Do we

01:06:14.980 mean evolutionarily why? That is what happened in evolution that got us to be what we are today

01:06:20.640 that leads to that? Or is it biochemically and otherwise why that is what are the mechanisms?

01:06:26.620 From either point of view, I don't know all the answers. From the evolutionary point of view,

01:06:31.100 for a long time, the meme was, it's the thrifty gene hypothesis from James Neal, right? So the idea is

01:06:37.480 that animals in general and including and especially humans throughout evolutionary history were on the

01:06:43.960 brink of starvation. Anything you could do to preserve energy, you did. And anything you did

01:06:50.760 that when given the opportunity to get more energy, eat as much as you can while you can.

01:06:56.360 And then you get into this modern environment where there's, for practical purposes for most of us,

01:07:02.160 unlimited consumable energy, you over consume. I think that's simplistic for many, many reasons.

01:07:09.020 First of all, as the lawyers say, objection that assumes facts, not in evidence. That is,

01:07:15.120 it's not at all clear that humans have been on the brink of starvation throughout history. In fact,

01:07:20.080 Robert Fogel, who won the Nobel Prize for looking at these old data going back to at least the 1700s of

01:07:25.720 British naval recruits and other places, you see BMI sort of, on average, they're going up over the

01:07:31.680 centuries. But there's a little fluctuation as things get better and worse in places. Second thing is,

01:07:38.080 how does this account for pregnancy? Maybe now the latest data from John Speakman and colleagues

01:07:44.480 in science with all the double-labeled water about three months ago, maybe suggest that during

01:07:49.040 pregnancy, women's energy intakes don't need to go up that much, but they still go up. And so if humans 1.00

01:07:56.780 have been reproducing for millennia, where did all that extra energy come from if we were all on the

01:08:02.140 brink of starvation? All the time. And then the last thing I'll say is, anybody who ever goes fishing

01:08:08.220 knows that the idea that every animal is hungry all the time and is going to grab every bite of food

01:08:14.640 you throw in front of it, never been fishing. You can see that beautiful bass sitting in the clear

01:08:20.360 water in front of you and you can dangle your worm or killie or whatever it is you've got and

01:08:26.100 sometimes the fish just looks at you. So it's not at all clear that this is the case. Some animals do

01:08:33.320 get obese when given unlimited food, some don't, both within and across species. There's lots of

01:08:38.380 differences. So that's that. John Speakman came along and he said, I'm not sure I'm buying this whole

01:08:44.920 idea. He said, I think it's freedom from predation. Back in history, we were prey. Then there was a certain

01:08:51.640 point where we learned to use tools and hunt together and we stopped being prey and we started

01:08:56.620 being predators. And when we switched from being prey to predators, then we didn't need to hide in

01:09:02.700 our burrows and eat the least we could because every time you came out of your burrow, you were

01:09:07.460 potentially exposed to a predator. We could sort of walk around and eat kind of ad lib. And in that case,

01:09:15.420 the genes that were being selected for gave us satiety mechanisms that kept our weight down.

01:09:21.360 No longer were being selected for. It wasn't that nature was selecting for genes that made us

01:09:27.460 fatter. It just wasn't selecting for genes that kept us thin. And then what happens is it's called

01:09:32.940 drift. Mutations happen and things just drift. It's less about the thinness. The thinness was

01:09:38.760 really a consequence of what the genes were probably selecting for, which presumably was lower

01:09:44.760 appetite or something like that. Probably. There's not one factor. We see this, for example,

01:09:49.960 in the evolution of sexual reproduction, which is called the queen of questions in biology.

01:09:55.700 Nobody can really figure out why do we have sexual reproduction when asexual reproduction seems so 0.99

01:10:01.000 much more efficient from a genetic fitness point of view. And people have proposed different hypotheses

01:10:06.780 for it and no one seems to work mathematically. And what it may be is that it's only by putting them

01:10:12.260 all together that it mathematically works. And it's just an inelegant solution. You see similar things in

01:10:17.860 physics where it may be, you know, these beautiful, simple mathematical things may not hold up. You

01:10:22.060 may just have to have ugly composite theories. Say a bit more about that. That's interesting.

01:10:26.700 I've never heard the argument. I don't actually know much about asexual reproduction. I don't spend

01:10:31.180 much time thinking about plants or other life forms that do it. But what's the argument for why

01:10:36.320 we would be better off with asexual reproduction? 0.95

01:10:39.500 Well, think about something like daft, which is a species that can produce both sexually and 0.99

01:10:43.980 asexually. There are many species like this all the way up through some vertebrates. And if an 0.98

01:10:49.720 organism reproduces itself asexually, just sort of makes a copy of itself, think about what it's

01:10:54.840 done is it's reproduced all of its genes. And so from the Richard Dawkins point of view,

01:11:00.940 the selfish gene, those genes all got their way. Those genes all won. They got copied and genes that

01:11:08.040 are good at getting copied will get copied again in the future. I'd say you have more of those.

01:11:11.260 That's how evolution works. Whereas if you reproduce sexually, you get another partner. 0.84

01:11:18.900 And of course, this assumes other, this invites other questions like, well, why are there only

01:11:22.400 two sexes? Why in fact have sexes at all? You could exchange DNA without having sexes. Bacteria do it 0.99

01:11:28.680 through conjugation. But we'll put that aside for the moment. Say there are two sexes, male and female.

01:11:34.260 They come together. They mix up their genetic material. The offspring has roughly 50% of the

01:11:40.520 genetic material of one parent and 50% of the other. And you only copied yourself one half. And so

01:11:47.820 you didn't win as much as if you copied yourself entirely. So why would you ever switch to sexual

01:11:54.460 reproduction? It's very inefficient from a genetic fitness point of view. Now you can hypothesize

01:12:00.920 things. The most compelling hypothesis I've heard to me is the so-called red queen hypothesis,

01:12:07.040 which is from Alice in the Looking Glass, where the red queen is running with Alice. And Alice at one

01:12:12.480 point says, we don't seem to be getting anywhere. And the red queen says, oh, in this world, you have

01:12:18.640 to run as fast as you can just to stay in place. And Alice says, oh, my world, we run and we actually

01:12:24.180 get somewhere. And so the red queen hypothesis is the idea that you keep running as fast as you can

01:12:29.980 just to stay steady. Well, what does that mean is, well, as you're living a long time as a human,

01:12:36.860 there are these microbes in you. And they're evolving much more rapidly because they have a

01:12:42.200 much more shorter generation time. And as they evolve, they start to get good at getting past your

01:12:50.240 defenses and your locks. They start developing keys to your locks. And you want to reset the locks.

01:12:57.120 The way you reset the locks is by getting a partner and mixing up your DNA with them.

01:13:02.680 So the idea that it's a way of keeping up with the Joneses, where the Joneses are the bacteria,

01:13:07.460 the microbes in your body. That's called the red queen hypothesis. 1.00

01:13:10.980 That's super elegant, right? Because if you think about it, if it was all asexual, 0.89

01:13:15.720 we'd have a population of identical people. I mean, we would, for all intents and purposes,

01:13:21.920 it'd be interesting to do the math on what it would look like, but you might only have

01:13:25.720 a few hundred thousand gene pools. You'd have much less diversity, much less diversity.

01:13:32.080 From a species point of view, if you believe in group selection, then you say, oh, right,

01:13:36.860 it's good for the species. But the smart evolutionary biologists come along and say,

01:13:40.060 yeah, but that's group selection. It doesn't make sense. Selection occurs at the individual

01:13:43.660 or gene level. You've got to explain how it makes sense for that individual, how it enhances

01:13:49.240 their fitness or their genes' fitness. So you say, okay, well, if only half the genes get reproduced,

01:13:55.720 then it's got to be double the fitness level to break even. That doesn't seem like it really

01:14:00.620 holds up. So what people have said is, you know what? If you take a handful of things,

01:14:05.000 which I can't explain them all right now, it's Mueller's ratchet and there's this and that,

01:14:08.800 and you take all of these things and you put them together, then maybe the math works.

01:14:14.260 But it's very inelegant. It's not one nice little theory. And it's probably the same thing

01:14:19.440 with people and evolution. So there was Neil with the thrifty gene that is, you need to be selected

01:14:27.320 to get food when you can. There's probably some truth to that. Maybe not everyone is dying of

01:14:31.680 starvation. But if you're not getting enough food, you may not be big enough to win the battle for mates

01:14:37.740 in a polygynous physical combat mating system. That may select for wanting to eat more. 1.00

01:14:43.660 You may not be, think of the Frisch hypothesis. You get too thin as a woman, you stop menstruating. 1.00

01:14:50.520 So it may be that it's not that you die of starvation, but that your reproductive fitness

01:14:56.060 goes down. On the other hand, the predation, the freedom for predation idea that John Speakman

01:15:01.880 puts out is also legitimate. And there are yet others. Gary Beauchamp from Monell Chemical Census

01:15:09.120 Center has talked about the idea of the safety of food. Is it possible that if you're back in time

01:15:16.180 and you're not eating out of a refrigerator in a modern safe food supply like we have, you know,

01:15:20.600 we love to insult our food supply. It's probably the safest food supply in history. Every time you

01:15:25.360 eat something, you're exposing yourself potentially to microbes and toxins, not just predators. If you

01:15:31.980 eat less, you're less exposure. So there's, again, there's an optimization problem. But as you now

01:15:36.980 have a safer food supply, you can relax that constraint a little bit. You can even think

01:15:42.260 about it socially. If I were in a species where I'm just out on my own, all right, if I'm a species

01:15:49.220 that just eats eucalyptus leaves or something that doesn't depend on anything else, then maybe I can

01:15:54.340 eat the last eucalyptus leaf. But if I'm a species that very much depends upon cooperative living,

01:16:00.720 if I am so hungry all the time that I'm willing to try to kill you for the last bite of chicken,

01:16:06.700 and you're willing to try to kill me for the last bite of chicken, that's a bad situation, 0.95

01:16:11.360 especially for me because you're a little bigger than me and did more martial arts.

01:16:14.720 So that's not good for fitness. It might be good to have satiety mechanisms just so we can preserve

01:16:20.880 some social order. So after you and I each eat a little chicken, we can actually work together on

01:16:25.100 building tools and engines and steam engines and airplanes and so on.

01:16:29.700 This is super fascinating. I could go down this path forever. I think we'll come back to this next

01:16:34.960 time we have dinner because having this discussion over a meal would make it even better. Let's march

01:16:39.600 on to the topic of nutritional epidemiology. We've talked a little bit about epidemiology.

01:16:45.260 Obviously, epidemiology is married very closely with statistics. Without statistics, you can't really

01:16:50.820 do epidemiology. But it's a field that I think even the casual listener of this podcast will

01:16:56.100 understand has its limitations. If by no other means, then they've heard me rail on it many times.

01:17:02.300 So I don't think we need to define it. I think people understand the nature of it.

01:17:05.860 But let's talk a little bit about your views of it because I think I wouldn't say you're in either

01:17:11.080 camp. There's a camp of people that would say there is absolutely nothing wrong with epidemiology,

01:17:16.700 nutritional epidemiology. It is a masterful tool that provides exceptional insights

01:17:23.160 without which we would be lost. The other end of the spectrum, I'll acknowledge I'm a little closer

01:17:29.260 to the other end of that spectrum. There are people who say, this is a tool that has probably

01:17:34.560 reached its peak of utility. The epidemiologist should probably focus on other problems outside

01:17:41.520 of nutrition now. You're probably more in the middle of those, but I'd like to hear you talk a

01:17:45.840 little bit about the bookends and how you settle out and why, more importantly.

01:17:49.840 I appreciate the opportunity to comment on this. It's a topic that I feel really does need more

01:17:56.220 courageous addressing. I think that you have characterized it well. There are at one end

01:18:02.340 the people I would call the abolitionists. I agree you're close. John Ioannidis, another mutual friend

01:18:07.960 who said, nutrition epidemiology is a dead science and it's time to bury the corpse. Gary Taubes has been

01:18:14.080 very critical. Nina T. Kaltz, you, many others have pointed out that perhaps this is just a worthless

01:18:21.060 waste of time and misleading. At the other end, there are a number of people. They tend to be

01:18:28.780 concentrated in Boston, it seems. There's a little school out east there. I forget the name of it.

01:18:33.300 It's in Cambridge, is it?

01:18:34.540 Yeah, I think I've heard of it. I think I've heard of it.

01:18:36.600 Yeah.

01:18:36.960 There are other places that believe this, but it's probably strongest in Boston

01:18:40.200 that sort of seem to be the defenders of the status quo that say nutrition epidemiology is

01:18:46.700 imperfect as all tools are, but it is still a very valuable tool. There's nothing seriously wrong

01:18:53.940 with it. Those who criticize it are naive. I look at this, naive and ignorant. And I look at a

01:19:01.040 quotation like that and I think, really, you're telling me that Gary Taubes and Johnny Ioannidis 0.99

01:19:06.760 are naive and ignorant. If you want to tell me you don't agree with them, that's fine. 0.96

01:19:11.980 Naive and ignorant, come now. Not really. We know that there's something wrong there. 0.99

01:19:16.680 When we look at the evidence, it's very clear that many findings from nutrition epidemiology

01:19:22.740 have not held up once we've done randomized controlled trials. Now, some people will say,

01:19:28.480 no, you're wrong. And I've had arguments with people from Boston about this. And they said,

01:19:33.100 you're wrong. If you look at these meta-analyses, it looks like non-randomized studies on average

01:19:40.100 give very similar findings to randomized studies. But often they're citing non-randomized intervention

01:19:46.200 studies, non-randomized observational studies, and they're often not citing nutrition studies.

01:19:51.740 They're citing pharmaceutical or other medical treatment. So those are very different situations.

01:19:57.200 When you look at the nutrition epi, it's not clear to me that these things hold up very well

01:20:02.920 when they are studying. In fact, it seems to be more the norm that they don't. If you look at other

01:20:07.500 things going on, as John Ioannidis, probably better than anybody else has done, but many others have

01:20:12.540 done, and you start to peel the hood open on these, and you really look, you see a lot of things that

01:20:19.680 look like obfuscation, exaggeration, sweeping under the rug of measurement error, and so on.

01:20:26.180 So we've got huge issues with confounding. We've got huge measurement problems. And so I think

01:20:31.240 many people, including me, say, no, the status quo is not okay. It's not just minor fine-tuning. We

01:20:37.780 need reformation. But we also need to use these tools well. This field is not going to go away

01:20:44.080 whether you want it to or not. So my feeling is reformation is essential. The status quo is

01:20:50.680 completely unacceptable, but abolition is neither realistic nor desirable. And I want to cite one

01:20:58.660 paper we did, which I think makes an interesting point. This paper, you know, I often end reading

01:21:05.920 the observational epidemiology nutrition study, in which the author correctly and honestly points out

01:21:13.940 that it's an observational study as association, not necessarily showing causation, and then says,

01:21:19.560 but how could I be wrong? Well, it could be that this measurement error creates this problem. They

01:21:23.120 say, but I used a validated food frequency questionnaire, so it's really okay. Could be

01:21:27.860 confounding due to socioeconomic status, but all of my subjects had the same profession. So it's really

01:21:34.720 okay. As though the nurse who works in a poor school in rural Indiana has the same socioeconomic

01:21:42.320 status as the nurse who is married to a billionaire. They say we control for, we only have to never

01:21:49.960 smokers and so on and so on. So really it was okay. They dismiss the idea that it's not really causation.

01:21:57.400 I got this idea once and I said, you know, what they're saying is if I measured everything well

01:22:02.040 and I controlled for food intake and I controlled for diet composition and housing and socioeconomic

01:22:07.600 status and genetic background, then it would be okay. So I said, you could never do this study in

01:22:12.860 humans, but I realized I've done this in mice. And we had a mouse study where we randomly assigned mice

01:22:17.900 to eat different amounts of calories or food energy, but all in the same diet. So composition was fixed.

01:22:24.240 The mice had no choice. There were no restaurants. There was no Grubhub. There was no DoorDash.

01:22:28.460 We gave the mice the same food. They all ate the same thing. No self-report. They're all genetically

01:22:35.380 identical in probability because they're inbred isogenic strain, all C57 black six chain mice.

01:22:41.440 They're all in the same housing conditions. There's no smoking. And then we take these mice and we

01:22:46.440 randomly assign them to low calorie, medium calorie, high calorie, effectively, or ad lib,

01:22:51.960 ad libidum. And what we find is the more calories we assign them to be allowed to eat,

01:22:58.080 the shorter they live. No big surprise there. This has been shown a thousand times in the literature

01:23:04.180 by Ray Walford and Rick Weindra and others over the decades. But then we take the ad libidum group.

01:23:11.960 And within that, some choose to eat more than others. And within that group, now we have an

01:23:16.520 observational epidemiologic study. And we correlate amount chosen to be eaten with longevity. And those

01:23:24.200 mice that choose to eat more live longer. So the association in the observational component is

01:23:32.660 exactly opposite to the causal effect in the experimental component. And what people say to me

01:23:40.720 is, well, David, it's confounding. What you're seeing probably, almost certainly, is that the mice

01:23:47.700 that are the strongest and healthiest have the biggest appetites. They eat more. And what you're

01:23:53.280 seeing is confounding by general health. To which I say, right. That's the point. The point is, even in

01:24:00.660 a study, an observational study, that is more pristine than anybody will ever be able to do a human study,

01:24:07.900 which there's no smoking, no restaurants, everybody eats the same thing, there's no measurement error,

01:24:12.060 they're all genetically identical in probability. We can't get the observational study to reproduce

01:24:18.820 causal effect. And that suggests to me why reform is so needed. And we talk about different kinds of

01:24:25.460 observational studies. What I would love to never hear again is the puerile analogy of there's no

01:24:33.580 randomized controlled trials with parachute jumping. You know what I'm talking about. That comes up every

01:24:38.720 now and then. And there's a wonderful book called Randomistas, like, you know, Fashionistas,

01:24:43.440 Randomistas by Andrew Lay. And he says, actually, there are randomized controlled trials of parachute

01:24:47.620 jumping. So first of all, it's just not true. But beyond the fact that there really are randomized.

01:24:52.660 How did the IRB approve that?

01:24:54.640 You got to be in the army, I think. The interesting thing there is that this is used often as an excuse to

01:25:00.460 say, okay, I can't do the pure, perfect, pristine, randomized controlled trial. So therefore,

01:25:06.640 you have to accept any old observational study. And I think the answer to that is no. No, we don't.

01:25:13.140 We may have to accept that we're going to draw some inferences from something other than the pure,

01:25:18.860 classic, pristine, randomized controlled trial. But in between that and any old observational

01:25:24.740 epidemiologic study, there's a lot of space. And what we're saying is we need to get to this space

01:25:30.340 here. So co-twin controls. You hinted at that earlier. How about we take your identical twin?

01:25:36.840 We randomly assign you to one and the twin to the other. Or if we can't randomly assign, say,

01:25:41.820 oh, Peter, you exercise a lot. Your twin brother doesn't exercise. But you have the same genotype.

01:25:47.340 Okay. That's a tight control. How about things in which we intervene, even if it's not randomly

01:25:53.880 intervened, as opposed to just observe? So we say, okay, in this town, we're going to build a restaurant.

01:25:59.520 In that town, we're not going to build a restaurant. It's still an intervention study,

01:26:03.880 even if it's not a randomized study. That's the realm of Brian L. Bell, for example,

01:26:08.020 looks at food deserts and things. And says, gee, built a grocery store where there was a food desert.

01:26:12.440 We're told that food deserts were the problem. And it doesn't look like things got better when we

01:26:16.820 built the grocery store. That's a much stronger design than just asking people, how far away do

01:26:21.520 you live from a grocery store? I think that's very eloquent. And that mouse study is remarkable,

01:26:26.500 by the way. I remember when that came out. That needs to be a Sunday email to my group. So let's

01:26:31.140 remember that. I would highlight one thing that you've already alluded to and one that you haven't,

01:26:35.600 but I would just add it to my side of the ledger on why I struggle so much with the legitimacy of

01:26:42.340 epidemiology. Anything that relies on a food frequency questionnaire, I simply can't take

01:26:48.100 seriously for the fact that at least with every patient I've ever come in contact with, to try

01:26:56.340 to accurately assess what they eat based on a food frequency questionnaire would be a fool's errand.

01:27:03.780 It's simply unrelated to what they eat. Full stop. The second issue I have with nutritional epidemiology

01:27:11.420 comes down to the hazard ratios that very commonly show up and lead to grand statements. When you see

01:27:21.580 hazard ratios like 1.16, and we talk about this, like we have demonstrated the causal relationship

01:27:29.320 between bacon and cancer. I mean, what would the actual fathers of epidemiology be saying in their

01:27:36.240 graves if they were looking at these strength of association? I'm not necessarily saying there

01:27:43.540 should be no epidemiology, but boy, there needs to be a referendum on how the media is taught how to

01:27:49.600 interact with such studies on how we scrutinize some of the methodologies behind these things.

01:27:56.160 Because again, checking a food frequency questionnaire once in an eight-year study, it just doesn't mean

01:28:02.960 anything. It means less than nothing actually. And yet it's amazing that that could be the basis of an

01:28:08.700 observation. So it's hard for me to say something good about epidemiology.

01:28:13.220 On the measurement issue, which is often, I think, incorrectly perceived by some people as the key

01:28:19.400 issue and say, yeah, we admit the measurement of food intake is a big problem. But other than that,

01:28:24.260 everything's fine. Even when the measurement is near perfect, as in my mouse study, it's still not fine.

01:28:29.160 You don't have to go back to the fathers of epidemiology or wait for the fathers of epidemiology.

01:28:34.140 You can go back to at least Confucius, who says, to know what you know and to know what you do not

01:28:42.340 know, that is true knowledge. This idea that we have to be honest with ourselves and each other

01:28:48.100 about what we know and don't know and how we know it and don't know it is clear. And I think that's

01:28:54.680 part of that reform. We need a greater level of honesty. Samin Vizier just had a paper in one of

01:29:01.160 the peer-reviewed journals looking at or writing about epistemic humility and saying, you know,

01:29:07.200 when you get to the discussion section of a paper and you consider that the hypothesis that you made,

01:29:14.500 which now seems to be supported by your data, and you say, but I might be wrong. And then you just

01:29:19.840 systematically go through with the greatest effort and art to show how you're not wrong,

01:29:24.800 how you can dismiss all the competing explanations. He says, that's not an honest epistemic.

01:29:29.940 An honest epistemic humility would be to say, I really might be wrong. And here's all the ways I

01:29:35.340 might be wrong that I and others should test going forward. Michael Strevens, in his book,

01:29:41.360 The Knowledge Machine, does a beautiful composition, decomposition, construction,

01:29:46.520 deconstruction of this, and talks about the idea of communities doing this. And so that we need that

01:29:53.040 constructive battle, but it needs to be an honest battle. The battle shouldn't be ad hominem. The

01:29:58.600 battle shouldn't be undercutting each other by who you are and who you work for and what your beliefs

01:30:03.800 are. It needs to be a battle about the data. He calls that the iron rule of evidence. So if I say,

01:30:08.200 Peter, your theory that X causes Y could be mistaken because your measurement of X is not valid,

01:30:14.540 you need to come back and say, I hear you. What if I measure X this way? That's a legitimate thing. 0.56

01:30:20.900 Let's do it and see if we can rule it out. And then we can go back and forth and, well,

01:30:24.520 maybe the measurement of Y is not right. I think we need to be more honest about that.

01:30:28.240 I don't think the nutrition epidemiology field has been quite honest about the limits of its

01:30:32.480 measurement, but so have the attackers not. I've been careful about saying, I think measurements of

01:30:37.540 energy intake and expenditure from self-report methods are so bad that they shouldn't even be used.

01:30:43.480 That is not enough to say, well, it's not perfect. It's so bad, you can't even be guaranteed to get

01:30:48.880 the directional effect. So don't even use it. Better to not do the study at all. But on the

01:30:53.640 other hand, if you said to me, I want to know if people eat vegetarian or not, or eat kosher or not,

01:30:59.320 or eat after midnight or not. I don't know. Maybe people do report those accurately. Maybe they don't.

01:31:03.980 I honestly don't know. I think we have to ask for what purpose.

01:31:06.760 You mentioned very briefly Catherine Flegel earlier. She also wrote a very famous paper

01:31:12.720 about the obesity wars. Give folks a bit of an idea about that, I thought, very excellent paper.

01:31:18.520 Yeah. Catherine Flegel has been a colleague from whom I've had great respect for many, many years.

01:31:23.420 And she's very, very bright and very capable and very careful. I've learned a lot from her,

01:31:28.920 actually. She published a paper in, I think it was 2005, that really got people's attention.

01:31:37.720 And it was interesting in the way, it was a meta-analysis and combined pooling analysis,

01:31:42.100 showed a few things. What was interesting to me is what people harped on, particularly the media,

01:31:47.620 was that BMIs in the overweight range were actually not strongly associated and consistently

01:31:56.000 associated with increased mortality rate. In some cases, were associated with lower mortality rate.

01:32:01.740 The media went crazy with this as though it were a new finding. Just as every few years they write

01:32:07.180 the new finding that BMI is mass and not fat, the new finding that there's a genetic component to

01:32:12.200 obesity that we knew from decades ago. The media went crazy with this. The people that are the 0.58

01:32:17.440 defenders of you can never be too rich or too thin went crazy and attacked her. And the bizarreness

01:32:23.760 of it wasn't new. I mean, it was a meta-analysis. How could it have been new? We all knew these data.

01:32:29.700 You can go back to Linus Pauling, the double Nobel laureate, has a paper in the 1950s on BMI

01:32:36.140 and life expectancy. And he's got a bathtub-shaped curve there. So this is not new at all. But somehow

01:32:44.440 it was seen as new. It was in JAMA. The newspapers went crazy. The people who were defending it went crazy.

01:32:51.180 And they started attacking her very vociferously. One investigator called it a worthless pile of 0.69

01:32:57.200 rubbish. To me, these are very inappropriate statements. 0.99

01:33:00.600 Who were the most vocal critics?

01:33:02.360 Some people in Boston seem to be among the most vocal critics. So that was kind of a very

01:33:07.420 interesting thing. But as I said, it was all old news. Anybody been reading the literature for

01:33:11.840 decades would have said, but I already knew this. What was actually new and to me far more intriguing,

01:33:17.740 but got much less attention, was that the Nader was moving over time. That was what we talked about

01:33:24.180 earlier. That in 1990, it wasn't the same as 1970. That is, to me, very intriguing. We actually have

01:33:30.980 an active NIH grant built on that, trying to figure out what's going on with it. But anyway,

01:33:35.960 they attacked her. And these statements, you know, there's rubbish, and that she didn't know what

01:33:40.480 she was talking about, that there was nobody with a medical background. And these are all ad hominem

01:33:44.760 things. First of all, it's not clear there was no one with a medical background on the paper.

01:33:48.300 And even if it wasn't clear, who cares? The data are the data, whether you have an MD or you don't

01:33:53.400 have an MD. It's the data that matter. What I frequently say in science, three things matter.

01:33:58.660 The data, the methods used to collect the data, which give them their probative value,

01:34:03.540 and the logic, which connects the data and the methods to conclusions. Everything else is not science.

01:34:09.580 Now, if you want to look at other things as ways of helping you make a pragmatic decision,

01:34:14.980 that's fine. So if I say, oh, I trust Peter, and he's a smart guy, and I know he studies a lot,

01:34:19.940 and he tells me I should eat this, that's maybe a very pragmatic way of making a decision. But it's

01:34:24.540 not science. The science is the data, the methods, and the logic connecting the data to conclusions,

01:34:29.960 not whether I trust you or not. So all these ad hominem things were said. They attacked her. It was

01:34:34.840 very, very vociferous, very inappropriate. But there are many other people who've been attacked.

01:34:40.360 I've been attacked. I know lots of other people have been attacked for their beliefs. When Nina

01:34:44.740 T. Kaltz had an editorial, I think in Lancet, talking about some elements of nutrition and fat

01:34:51.120 and carbohydrate, many people wrote in, tried to get it retracted. This is a sort of regular occurrence now.

01:34:56.920 When Brad Johnson published on Red Meat roughly two years ago, big meta-analysis showing

01:35:02.480 that the association between red meat consumption and negative health outcomes was not strong and

01:35:09.980 compelling, as his words, again, vigorously attacked. People tried to get it retracted before

01:35:16.100 it was published, which thankfully didn't work, instead of just engaging with the data,

01:35:21.920 the methods, and the logic. It's terrible this happens. It doesn't only happen in obesity and

01:35:27.160 nutrition, but it happens a lot in obesity and nutrition.

01:35:29.520 It does seem to happen disproportionately in this field. Why is that?

01:35:34.820 I think it has to do with the everyday experience. Back to my example from early in our discussion

01:35:40.540 about the beta cell of the pancreas. If you are at a cocktail party in your neighborhood and you say

01:35:46.640 to somebody, well, I study diabetes and here's what I think about the beta cell of the pancreas and how

01:35:53.680 this might work. Unless that person also studies it, they're not going to challenge you. They probably

01:35:58.700 don't know what the beta cell of the pancreas is. They don't have strong feelings about it. They

01:36:02.600 don't see it every day. All of us eat every day. Almost all of us eat almost every day.

01:36:08.540 Food is culture. It's family. It's love. It's economy. It's commerce. It's political beliefs. It's

01:36:15.900 philosophical belief. It's ethical beliefs about whether you're a vegetarian or not. It's the

01:36:19.960 sustainability of the environment. It's so connected to so many emotional things.

01:36:24.300 And we all have that everyday experience. And we have to make decisions every day. Each of us

01:36:30.600 decides what to eat, what to feed our kids, whether wear a seatbelt or not, all these things. We make

01:36:37.020 these decisions and then we may want to justify them. We may need to believe they're good. We may

01:36:42.400 mistake our experience for expertise. And that's why I think when you get into any fields where people

01:36:48.640 have everyday experience, whether it's human sexuality, whether it's relationships, whether

01:36:55.640 it's child rearing, TV watching, book reading, music, eating. These are things people have very

01:37:02.700 strong feelings about and often will opine about, often quite aggressively in the absence of data.

01:37:08.540 It is not only obesity. Statements about violence in media, statements about how to best teach

01:37:14.580 mathematics to children. People have very strong feelings that are often quite in contrast to what

01:37:19.240 the data show. So is there a path forward here from a nutritional standpoint? I mean, I think you've

01:37:24.800 already touched on a few of them, but we've already seen some pretty exciting pharmacologic things come

01:37:29.780 along. I think semaglutide, the study that we've talked about before on this podcast, really kind of a

01:37:35.660 remarkable drug. It's certainly the most impressive thing that I've seen clinically for obesity.

01:37:42.360 We're making great progress there. We talk about surgical treatments. 25 years ago when I was in

01:37:48.780 medical school, I remember the first gastric bypass I ever saw done as a medical student

01:37:54.600 watching a surgical rotation. And this was an open, this was done before they were doing them

01:38:01.000 laparoscopically. It was done as an open Roux-en-Y gastric bypass on a 400-pound man who would die 40 days

01:38:09.000 later in the hospital of sepsis. He never got out of the hospital. He had an anastomotic leak.

01:38:13.360 That was a very dangerous operation. 25 years ago today, that operation is done almost without

01:38:19.060 exception, laparoscopically. It is an incredibly safe procedure and it has also remarkable efficacy.

01:38:26.200 So on this surgical front, on this pharmacologic front, we have made amazing progress. We haven't

01:38:31.980 made progress on the nutritional front. Are we going to?

01:38:34.380 Not clear. New York Times reporter called me a few years ago and this was in response. There

01:38:40.420 was an article about, I think it was President Taft, who was very obese and somebody had found

01:38:46.620 some letters between him and his physician talking about diet. And it was almost, you could have picked

01:38:52.180 those letters up and said this was between a president and his or her physician today and

01:38:57.500 they would make equal sense. And the reporter said to me, this is a very bright science nutrition

01:39:03.240 journalist, said to me, David, why have we made so little progress? Why have we not been able to find

01:39:09.680 the diet that reliably causes sustained weight loss? And I said, your question is premised on the idea

01:39:17.760 that there is a diet that reliably causes sustained weight loss. Why should we believe that that's

01:39:24.760 true? So that's an important thing to think about. I think one of the things that is perhaps a

01:39:31.280 misperception and maybe a very problematic one in our field around nutrition and weight loss or food

01:39:36.820 intake and weight loss is that there is a good diet with respect to weight loss, particularly such that

01:39:43.580 for most, if not all people, if you just ate the right way, you wouldn't have to count your calories,

01:39:51.540 you wouldn't have to be uncomfortable and hungry, you wouldn't have to feel deprived,

01:39:56.640 and yet you would maintain a good healthy weight. I know of no reason to believe that's true.

01:40:01.760 I know lots of people who argue, is it diet A or diet B? So this one thinks it's low carb and this one

01:40:07.740 thinks it's high carb or low fat and this one thinks it's don't eat at night and this one thinks it's

01:40:11.920 whatever it is, eat paleo, you know, et cetera. Maybe the null hypothesis is doesn't matter that

01:40:18.520 much. There isn't such a diet for many people. Now, for some people, they do maintain a normal,

01:40:23.660 healthy, desirable weight without trying to restrict their energy. But maybe for others,

01:40:28.420 it's just not the case. I think the paths forward are manifold. And I think in some cases,

01:40:33.260 we are on the good path. And in some cases, we are wandering in the drunkard's walk. We're on the

01:40:41.280 good path, I think, on surgery and pharmaceuticals. Clearly a long way to go, but they've gotten much

01:40:48.560 better. I'd love to see more funding for those for good research. And I think we need to, we are on a

01:40:57.040 good path, but I think we need to get on a much better path about, as a society, making those

01:41:02.560 available to people. If you have cancer, we're willing to treat you. If you have obesity, maybe

01:41:08.680 not. So if you're rich, you can pay for that. If you're not rich, what do you do? So I'd love to see

01:41:14.220 more access to care. And I think we're on a better path, but we need to be on a better path still.

01:41:18.920 I think we're on a good path on stigma. A long way to go. But I think as a society, we've woken up

01:41:25.740 to say stigmatizing obese people is not okay. Do you think the pendulum's gone too far? I read

01:41:32.460 something, this might've been a joke, but I literally read that Adele was actually shamed 0.93

01:41:37.080 for losing weight. I don't know if that's true, but if it was, it would certainly suggest that

01:41:41.180 the pendulum has gone a little too far in the other direction. I would certainly say that that's

01:41:45.060 ridiculous. I don't know if she was or wasn't, but if she was, that's ridiculous. To me, the take-home 0.98

01:41:50.180 message is shaming people about their body habitus is not good in either direction. It's not a

01:41:56.240 directional thing. It's just shaming people about their body habitus is not okay. Sometimes the

01:42:00.800 counter-argument is, but it's good for them because it'll help them want to lose weight.

01:42:06.220 And sometimes the argument made against the shaming is empirical. It's the evidence shows that

01:42:13.060 people who experience a lot of weight shaming gain more weight and then the causal thing is thrown in.

01:42:17.680 So therefore you shouldn't do it. I'm like, you mean if shaming didn't cause weight gain,

01:42:22.800 it would be okay to be immoral and cruel to others? No. I mean, if you replace things like

01:42:29.040 sex and race in that sentence, you'd be like, no, it's not okay to shame and denigrate people because

01:42:35.220 of their sex, race, age, body habitus, regardless of whether it causes weight loss or weight gain.

01:42:42.540 Period. It's a moral issue. It's not an empirical issue. We have a long way to go,

01:42:46.740 but I think we're making progress. People are going, yeah, I guess we can generalize this idea

01:42:51.760 that you shouldn't denigrate people because of age, race, sex. Maybe you shouldn't denigrate

01:42:56.500 them for other characteristics that are not moral failings. I think that on the basic science,

01:43:02.780 we're making progress. We could make better progress if we tightened up the rigor of our

01:43:07.520 science. We could make better progress if we had more funding. Many groups, including the

01:43:12.600 National Academy of Sciences, and I'm on a strategic council to increase the rigor of science,

01:43:17.540 all of science, not just obesity and nutrition. But we're making progress. We know so much more

01:43:23.300 about genes and physiology and metabolism and cells with respect to obesity and nutrition than we knew

01:43:30.300 20, 30, 40 years ago. The one area where, as far as I can tell, we are not making progress,

01:43:36.660 and I don't think we are yet on a good path. I don't think we're on a path at all, is this public

01:43:41.560 health, community, school-based, community-based, policy-based approach. I think we are continuing

01:43:48.920 to look for our keys under the lampposts because that's where the light is, as opposed to where

01:43:55.400 the keys might be. I think we are continuing to ignore the data and keep saying the same old

01:44:01.780 hackneyed suggestions that people have been trying for decades and that when you really look at the

01:44:07.540 data have been at best not been shown to work and at worst been shown to not work. And I think there

01:44:14.380 are some people who are patently obfuscating those data. I think the cluster randomized trials we see in

01:44:20.900 the childhood obesity literature bring to mind the phrase that rhymes with cluster muck. And it just,

01:44:26.220 this is, this is cluster muck. This is distorted evidence. This is science gone wrong in the worst

01:44:33.980 sense. I think we've got to clean that up. We've got to clean up the quality of the science we do and

01:44:42.860 start treating this like science just as much as the science of quarks or tires on automobiles or beta cells

01:44:51.660 of pancreases and treat it like real science and take it just as seriously.

01:44:55.980 Can you tell folks briefly what the cluster randomization problem is?

01:45:00.340 Sure. So a cluster randomized trial is a trial which instead of randomizing the individual unit

01:45:06.300 of observation, might be, let's say, a child in a school who you either assign to the treatment group,

01:45:11.980 maybe it's exercise, or the control group, no special exercise. You assign the entire intact unit,

01:45:19.180 such as a classroom or a school or a neighborhood or a family. There's nothing wrong with that.

01:45:25.420 As long as you have, theoretically, at least two clusters assigned to each condition. So you have

01:45:32.560 some ability to estimate a variance. Although, frankly, with only two, you have so little power

01:45:38.020 and robustness. For practical purposes, that would be invalid. But theoretically, it's valid. But then

01:45:44.820 you must analyze the data to take into account both what's called the clustering and the nesting.

01:45:51.300 The clustering is that you have people grouped, and that grouping leads to more similar individuals.

01:45:57.240 So imagine that we did a trial, and the trial was you and your brother randomly assigned to

01:46:02.740 eat low-carb, and me and my brother randomly assigned to eat high-carb. And at the end,

01:46:08.680 we see a difference by an ordinary T-test. And you say, well, hold on a second. Was that really

01:46:14.960 the effect of diet? Or was that the Atiyah brothers are different than the Allison brothers?

01:46:20.700 Well, you have to take that into account. So you need more than one cluster. So if you get the Atiyah 0.99

01:46:26.080 brothers and the Jones brothers and the Allison brothers and the Smith brothers, now, in theory,

01:46:32.300 you can do it. But now you can't treat us like we're eight different people. What you really have

01:46:37.300 is four different clusters, four different sib ships, four different sets of brothers. You've got to

01:46:42.180 take that into account. You have less degrees of freedom. People don't do that reliably. They

01:46:48.380 don't do it correctly often. And that leads to many papers being wrong. We've written countless

01:46:55.240 letters to editors about this. I think we've probably had at least three or four cluster

01:47:01.220 randomized trials retracted as a result of letters we've written, where people have had to come back

01:47:05.860 and just say, the results don't hold up. But until this has changed, we have people out there

01:47:12.200 who understandably say, but I read these papers suggesting that this works. Gardening

01:47:18.980 in schools makes kids thinner. That's not what the results showed, but that's what the paper says.

01:47:26.420 And until we become more rigorous and more honest, we're not on a path.

01:47:30.640 So if I were to ask you now to speculate outside of known things, if you had to guess,

01:47:39.120 and if you presuppose that there is an intervention or a set of interventions that could improve

01:47:44.660 public health outcomes, what would your guesses be? What would you guess to test?

01:47:51.700 General education, not nutrition education, general education. There is provocative data. I don't want

01:47:59.460 to say definitive data. There are provocative data strongly suggesting that general education,

01:48:07.000 especially for girls and women, leads to lower BMIs, lesser rates of obesity, and lesser diabetes

01:48:14.320 than less education. So there are some studies in Europe of policies where someone puts in a policy

01:48:20.320 and it effectively gives a cohort of people more education. And then you see in that cohort,

01:48:26.440 less obesity, especially among women. There's a famous study by the Rameys, who were a husband-wife 0.76

01:48:32.720 investigative team who actually worked at UAB when I first got there. And they started the study

01:48:37.900 decades ago at UNC. And it was sort of head start on steroids. They called it the ABC-A-Darian or Beck-A-Darian

01:48:45.680 study. And they gave these kids the super head start program. And it was mostly just general

01:48:52.240 education. There may have been a little nutrition education, but mostly just general education.

01:48:55.840 It wasn't a weight loss study. It wasn't intended to be. 30 years later, they followed them up. There's

01:49:00.560 a paper in science on this. Guess what? The women have less obesity. The Moving to Opportunity study,

01:49:07.340 funded by the Department of Housing and Urban Development, took families who lived in so-called

01:49:12.500 poor neighborhoods. And they gave them either, randomly assigned them, either to control,

01:49:19.840 but they basically got nothing, or to housing vouchers.

01:49:22.220 But the housing vouchers required that they move to less poor neighborhoods.

01:49:27.980 And what they then found years later in follow-up, again, published in Science and New England Journal

01:49:33.580 of Medicine, is that there was less obesity and diabetes in those assigned to move to the less

01:49:41.700 poor neighborhoods and given the financial wherewithal to do so. So I could go on. But these

01:49:48.100 are things that suggest to me that education, general education, may help. And I think that may speak to

01:49:55.820 this whole socioeconomic thing we started about way earlier. What is it about higher socioeconomic

01:50:01.900 status that, at least in some groups, not all, but at least in white women, seems to be associated

01:50:08.020 with less obesity? And I don't know what the causal mechanisms are, but that might be my best. So if somebody

01:50:14.100 said to me, you're going to be the king for a year, and you've got the federal budget, and you can take

01:50:19.580 this big chunk of money, and you can make an impact in obesity and diabetes, I would say, I'm going to

01:50:26.360 divide it into four pots. One pot is going to be surgery, and it's going to be both providing it and

01:50:34.500 continue to study it. The next one's going to be pharmaceuticals, both providing it and continuing to

01:50:39.780 study it. Third pot is going to be some general education, maybe general well-being, safety,

01:50:44.940 security, starting in early childhood to see whether that alone is enough. And it may be really

01:50:51.100 reducing disparities. Back to Confucius. Confucius said, we are not so concerned with an absence of

01:50:57.800 wealth. We are concerned with a disparity of wealth. And so it may be that reducing disparities

01:51:04.740 is really important. And then the fourth pot would be basic research. And I'd like to say, let's look

01:51:10.000 at senolytics, and let's look at microchimerism, and all the things that you talk about so often in

01:51:16.260 your podcast that abut against both metabolism and obesity and nutrition, but also the fundamental

01:51:22.620 of senescence. So I'd love to say, can we use microchimerism to restore people to younger

01:51:28.660 metabolic states? Can we use senolytics to do that? That would be my fourth bucket, those basic

01:51:33.920 science questions. Now, a moment ago, you touched briefly on the National Academy of Science,

01:51:39.980 Engineering, and Medicine. You were on a panel that looked at the question of reproducibility

01:51:46.340 in science, correct? Correct.

01:51:48.060 This was two years ago, I think. The consensus, I believe, was that there was not a crisis,

01:51:54.260 but we shouldn't let our guards down. Am I paraphrasing that correctly?

01:51:58.760 Close. The phrase that Harvey Feinberg, who was the chair of it, I don't know if it was his

01:52:03.660 phrase, but he started using and we followed, is no crisis, but no cause for complacency.

01:52:11.360 And the idea of no cause for complacency or no complacency is we must make things better.

01:52:16.880 The no crisis is a tricky one because it depends what you mean by crisis. And some people say,

01:52:22.260 if you look at dictionary definitions of crisis, the idea is that it's a system about to collapse.

01:52:26.740 So you need to go to the ICU. If you don't get intervention, you're going to die.

01:52:29.880 You're in crisis. There's no evidence that science is in crisis. There's no evidence that science is

01:52:34.920 about to die or it doesn't work anymore. Not at all. Another way to say things are getting much,

01:52:41.000 much worse rapidly. There's also no clear evidence for that. There might be some things that are

01:52:45.800 getting worse, but on average, there's a lot of evidence that things are getting better.

01:52:49.720 My own belief is science is better and more rigorous than it's ever been in history.

01:52:52.860 And so in that sense, no, I still don't think there's a crisis. But there's a third way,

01:52:57.100 which is, I think, the spark for many social movements. If you think about what often happens

01:53:00.920 as a social movement is you have a group of people that, for example, are often, when it's around

01:53:05.280 oppression, and they're oppressed, and they're sufficiently oppressed that people are unwilling

01:53:10.760 to make too much noise. And the status quo is only very slowly and grudgingly move. And then at some

01:53:17.520 point, people feel a little bit more confident and a little less oppressed, and they start to speak.

01:53:24.540 And people say, this state of affairs is not acceptable anymore. And if you were to say in

01:53:29.660 that later point in time, but it's much better than it was in the earlier time, someone might say,

01:53:35.120 it may be much better, but we ain't taking it anymore. Things may be much better than they used to be.

01:53:41.160 Things in 2021 are probably much better for many situations than they were in 1921.

01:53:47.020 And much better then than they were in 1821. But there comes a point where you say,

01:53:50.980 this lead in gasoline, or this lead in our paint, or this way in which we use heating,

01:53:57.060 or these kinds of catalytic converters on cars, or this way in which we burn up fossil fuel,

01:54:02.140 or this way in which we treat different age, race, sex groups, it's just not okay anymore.

01:54:07.420 Yes, it may be better than it was years ago. The world has spoken. We won't take it anymore.

01:54:12.300 I think that's where we are sciencing. Yes, science is probably better than it's ever been,

01:54:16.120 but we also see all the flaws, and it must get better. And that's why Marsha McNutt,

01:54:21.820 the president of the National Academy of Sciences, instantiated this new strategic council

01:54:26.880 on trust and integrity in rigor and science, and very generously appointed me as one of the three

01:54:33.000 co-chairs, Marsha France-Cordova, who's the director emeritus of the National Science Foundation,

01:54:38.820 and I have the three co-chairs, and are working on trying to see what we as the academies can do to

01:54:44.880 try to help a little bit. And many other organizations are also trying to help on this.

01:54:50.120 How much of the situation in science is intrinsic, is within science itself,

01:54:57.040 and how much of it is a result of the public, inclusive of the media's interface with science?

01:55:05.620 I think it's both. I think science is hard. Michael Strevin's book, which I mentioned earlier,

01:55:12.140 The Knowledge Machine, does a wonderful treatise on that. And I think we have to recognize that.

01:55:17.760 But I think we have to make the distinction between normative errors and non-normative errors.

01:55:23.660 And let's take an example of each. So a few hundred years ago, Galileo is under house arrest.

01:55:29.380 The Pope has said, bad writing about Copernicus, stay locked up in your house, but we will kill you. 0.59

01:55:34.540 And from his house, Galileo directs an experiment, or not really an experiment, a study. And he has 0.98

01:55:42.280 two colleagues go out to two tops of mounds or hills or mountains, far apart, each holding a

01:55:50.400 lantern with shutters and a synchronized watch or timepiece. And he says to them, at a predetermined

01:55:58.000 moment, you guys open your shutters and you record when you see the other guy's light.

01:56:03.740 And we'll figure out whether light travels instantaneously or not, right? Is there some

01:56:08.560 delay until the light gets to you? And they conclude that it's instantaneous. They can't

01:56:13.780 discern any time. Now we know today, of course, that that's wrong. Ole Romer probably is the first

01:56:19.600 person who convincingly shows it's wrong when he shows that a moon comes around at a time different

01:56:24.540 than his mentor, Dominic Cassini, predicted. But at the time, that's the answer they get.

01:56:30.060 With their instrumentation, they couldn't have done better. I would call that a normative error.

01:56:35.220 I would not mock Galileo. I don't think Galileo did anything wrong. I think we look at him as

01:56:39.980 brilliant. My God, great question, great worry of working on it, but things move along.

01:56:45.480 Similarly, when Linus Pauling says DNA is a triple helix before Watson and Crick come out,

01:56:52.940 it's a double helix. He didn't have good x-ray crystallography data. He's working at the edge,

01:56:57.820 normative error. Then there are other things. Very famous non-normative error is people working

01:57:04.580 out the size of the thymus gland of children. They worked it out from cadavers in the early 20th

01:57:10.400 century. Guess what? People don't become cadavers at random. Poorer people tend to become cadavers.

01:57:17.280 Poor children tend to be undernourished. Undernourished children tend to have smaller

01:57:21.280 thymus glands. So when physicians started seeing richer children coming in, dying of sudden infant

01:57:28.160 death syndrome, and they examined them, they go, hmm, it's a big thymus gland. Well, it was actually

01:57:33.480 a normal thymus gland because their norms were from undernourished kids. So let's irradiate these kids 0.98

01:57:39.820 with big thymus glands and prevent sudden infant death syndrome and probably cause lots of cases

01:57:45.320 of thyroid cancer because the thyroid and the thymus are very close to each other. So that's

01:57:51.120 an example of a non-normative error because even 100 years ago, any epidemiologist or statistician

01:57:57.740 could have told you that is bad sampling and bad inference from bad sampling. We need to make that

01:58:04.020 distinction between normative and non-normative errors. And a lot of the errors that we have in

01:58:08.240 nutrition epidemiology today, I don't think can be called normative errors anymore. I think we have

01:58:13.380 to say that was non-normative. The misanalysis of the cluster randomized trials, these are not

01:58:18.960 normative. Any statistician knows how to do it. People are either obfuscating or they're just

01:58:26.220 woefully ignorant and not using professional statisticians when they need to. People using food 0.97

01:58:32.580 infrequency questionnaires to draw causal inference about some of these things we've discussed. These

01:58:38.020 are not normative errors. People should know better. Then there's the stuff about the sort of more general

01:58:43.160 public about believing things and how we promote our ideas. And here's where I think, I think at root

01:58:50.480 is us in the scientific community that have to take responsibility for it, but it branches out beyond

01:58:55.300 us. I think we need to be prepared to lose some battles in order to win the intellectual war. And

01:59:02.520 what I mean by that is we need to be prepared to not use all the rhetorical tools at our disposal at

01:59:10.260 any one point in time to convince somebody that X is true. Even when we're really worked up and think

01:59:17.340 it's important, we believe X is true. We think it's important that others believe X is true because we

01:59:22.460 want them to eat what we think is good. We want them to eat more broccoli and less ice cream.

01:59:26.840 We want them to take their vaccine, wear their mask, wear their seatbelt, stop smoking. We may be right

01:59:33.380 about all those things. Maybe people should eat more broccoli and less ice cream and wear their seatbelt

01:59:37.640 getting vaccinated and wear their mask and seatbelt. But if we use rhetorical devices, such as if you and

01:59:44.700 I are debating and I attack you on ad hominem grounds, or I exaggerate the strength of my evidence,

01:59:50.840 or I don't honestly say that I've shown an association and not causation, then in fact,

01:59:57.380 I may win that battle that day on convincing people to eat broccoli instead of ice cream.

02:00:03.720 But I've lost the battle in helping people think through what good evidence is and elevating our level

02:00:10.040 of dialogue. And I think if we can get to the point where just as today, we changed our dialogue,

02:00:16.420 we think about the things people would say. Think about what a late night comedian would have said

02:00:21.520 30 years ago in making jokes about wives and husbands and race and sex. That would never

02:00:28.880 be considered acceptable today. We are able to change societal norms about dialogue.

02:00:34.700 Can we elevate our societal norms of dialogue on epistemologic and empirical issues so that we can get

02:00:43.380 people to, you don't have to be a genius to say, oh, you're telling me you have a treatment for X?

02:00:49.420 Was there a study? Was the study in humans? Was it a randomized study? Was it a study of the actual outcome

02:00:55.920 you're making a claim about? Was it a study that was long enough for this to be a meaningful outcome?

02:01:01.040 Was there a statistically significant result? Was the result big enough to matter? Was the dose

02:01:06.520 a dose I might realistically take? Those are not all that difficult questions to train ourselves and

02:01:13.220 each other to just reliably ask. And if we just reliably ask those and reliably and honestly answer

02:01:19.040 them, you would go a long way. Yeah, it's interesting. I mean, it's impossible to have this discussion

02:01:24.100 without thinking about COVID because COVID has been such a polarizing scientific phenomenon in a way

02:01:33.640 that I've become quite frustrated in watching it. And I'm trying to maintain a distance from it and ask

02:01:40.760 myself the question, is the reason that the head of the CDC makes assertions about masks or vaccines or

02:01:50.900 mandates because she doesn't think that the people to whom she's speaking are intelligent enough to 0.89

02:02:00.980 appreciate nuance? Or is it because she doesn't actually appreciate the nuance herself? And this

02:02:06.740 gets to exactly the point you made a moment ago, which is the high ground is being lost. I think the

02:02:13.260 public's faith in science in the institution of science is eroding dramatically. And I think COVID has accelerated

02:02:22.700 that in a way that I wouldn't have predicted. I remember having a discussion with a friend in March of 2020.

02:02:33.260 And I very, very naively said to him, I think that what we're about to see with respect to this

02:02:43.180 speed with which medications and vaccines are going to be developed is going to elevate the consciousness of

02:02:52.620 science in the public's eye. I think the public is going to look back in a year and say, wow,

02:02:59.820 science is great. Just as in the 1960s, the best and the brightest kids went into engineering

02:03:08.060 because they were inspired by the space race. I stupidly thought this might be the end of every

02:03:16.220 smart kid becoming an investment banker. And instead we might just see more kids enrolling in science. 0.79

02:03:23.500 How silly of me to not envision what was coming, which was a world in which well-meaning public health

02:03:33.100 officials simply failed to communicate the nuance of science and lost so much credibility.

02:03:41.260 COVID has been unique. One of the things I was saying to my wife recently is I hope I live at

02:03:46.540 least another 20 years for many reasons. But one of the reasons is that I really want to see what the

02:03:52.940 historians say about the period of roughly 2016 to a year or two from now when hopefully we are somewhat

02:04:03.100 out of this pandemic. I can see it in the present. I want to see it in the 2020 hindsight. Let me say

02:04:09.420 two things about your points. One is about motivations and the other is about trust. The motivations issue,

02:04:19.660 I think is an interesting one. I was having a conversation with a colleague many years ago

02:04:24.780 and I was ranting about university accountants and how they drive me crazy with little things.

02:04:31.820 This friend of mine said to me, he says, think about you and your science. He says,

02:04:35.820 you're so fastidious about it. You're so passionate about it. You're so committed to it. One iota of bending

02:04:42.860 the truth is not okay for you. He says, the accountant feels that way about the way the accounting

02:04:49.500 is done. And that's their domain. Different people have their different domains. And if I were to say

02:04:55.660 to you, Peter, what are you professionally? Or you might say any, or what are you just more generally?

02:05:00.140 You might say, you might say I'm a father. You might say I'm a physician. I'm a healer. You might say

02:05:04.700 I'm a scientist. You might say a few other things. When I came to IU, I was at a big retreat and

02:05:09.980 friend of mine was there and we started going around and introducing ourselves.

02:05:13.900 And I was sort of the new kid on the block. And I said, you know, my name is David Allison and

02:05:17.100 I'm a scientist and I study, blah, blah, blah. And he said, you know, the first thing you said,

02:05:21.420 you're like, you're the new dean here. And the first thing you said is I'm a scientist.

02:05:25.500 You didn't say I'm the dean of the school of public health. He says, that shows how you think of

02:05:29.500 yourself. If you were to say to me in your professional role, what's the one thing you must never

02:05:37.500 compromise? Truth. What's my one sacred duty? Pursue and communicate truth to the best of my

02:05:44.540 ability. But if you said to some other people in my field who are equally good people, maybe better

02:05:50.620 people, who knows? And you said to them, what's your one uncompromisable duty? Some of them would say,

02:05:58.300 help people, make their health better, enact justice. Can't say they're wrong. That's their value.

02:06:05.500 And I think that's what comes out a lot in public health. There are true believers who think they

02:06:11.420 know the right answer. They think eating this is better than eating that. In many cases,

02:06:15.980 they're probably right. They think not smoking is better than smoking. I agree. I think they're

02:06:21.180 right. And given those premises, they say, whatever it takes to convince people to eat A and not B,

02:06:28.540 to wear their seatbelt, to not smoke, I'm willing to say it. That's my sacred duty is making better.

02:06:34.380 I think that's part of the problem. What's our identity? Who's speaking? And I think we need to

02:06:39.260 be clear when we are speaking as scientists, then we must not compromise truth. And I think when

02:06:44.140 we're speaking as advocates, that's fine. Just say you're an advocate. Say, I'm not being a scientist

02:06:48.460 right now. I'm just telling you what I want you to do and say whatever I need to say to convince you.

02:06:52.780 I think that's important. The other thing I want to pick up is this trust idea. I hear a lot,

02:06:57.820 especially from people within science and academia, especially from people who are somewhat on the

02:07:03.420 left, but not only on the left side of the political spectrum, who say trust in science

02:07:10.700 is really weighed down in the last few years. I'm not sure that's true. In fact, I don't think it is

02:07:15.260 true. It depends what you mean by science. If what you mean is science as a process of developing and

02:07:23.500 finding knowledge, I know of no evidence that it's down. The results of the Pew Charitable Trusts

02:07:29.180 in their surveys, for example, suggest that it's not. If what you mean by science is trust in

02:07:36.060 individual elements of the scientific community, then I'm not sure it's down either, but it's spread

02:07:44.060 around. So some people think Fauci is trustworthy and some people think Gwyneth Paltrow is trustworthy.

02:07:50.140 Some people think David Allison is, and some people think Peter Artie is, and some people 0.98

02:07:53.660 think we're corrupt and ignorant and confused and terrible. And I think the challenge is that 0.96

02:07:59.900 there are people who cannot distinguish between the statements of a Tony Fauci and the extent to which

02:08:06.460 they are or are not backed by evidence, and the statements of a Johnny Anides and the extent to

02:08:11.980 which they are or not backed, versus the statements of a Gwyneth Paltrow or somebody else. And I think that's

02:08:17.500 the challenge. It's not that people don't trust science. They don't know which voice to trust

02:08:22.780 as a communicator of the science, and therefore they don't trust individual elements of the canon of

02:08:30.700 science. And we see that very strongly in nutrition. There's a nice summary on the Pew Charitable Trust

02:08:38.620 website now indicating that trust in science is high, trust in dietitians is high, trust in medical

02:08:47.020 doctors who talk about nutrition and treating their patients is high, but trust in nutrition scientists

02:08:54.380 compared to dietitians and medical doctors who talk about nutrition and compared to other scientists is

02:09:00.540 low. So in nutrition, we have met the enemy and it is us. We have shot ourselves in the credibility foot

02:09:08.780 with our obfuscation and our exaggeration and our hype. And I think that pocket of trust is gone,

02:09:17.980 even though trust in science overall is not down. David, this was a fantastic discussion. You are always

02:09:24.860 so lucid in the way that you can talk about an idea, including this very one. I think this point

02:09:30.380 that we're ending on is really, really spot on. And I think that the difference between science and

02:09:35.260 advocacy can't be overstated. And I think it would be amazing if people, myself included, all of us had

02:09:41.180 the self-awareness to speak and know which hat we were wearing. Think about the problems that could be

02:09:47.500 solved if, one, you allowed people to wear both hats, but they always had to have a hat on. You

02:09:53.580 couldn't blind the listener from which hat you're wearing. And if you were wearing your scientist hat,

02:09:59.100 you would be delivering the nuanced, hard truth, as messy as it might be, as unclear as it might be,

02:10:06.780 with no regard for how a person feels when they hear it and what action they may or may not take

02:10:12.940 as a result of it. And of course, if you're really just in the business of saying, I want to change

02:10:16.620 your behavior because I think it's in your best interest, I'm going to put the different hat on.

02:10:20.220 I like this idea. I think there's something here. We can come up with a two hat system and

02:10:25.260 everybody gets to own two hats.

02:10:27.820 Every one of us in our lives have to deal with this. We're both fathers. As fathers,

02:10:32.380 you face this all the time. Sometimes your kid says to you, can I do this? And you say,

02:10:37.740 no. And they could say, why? Well, because I think you might get hurt or it's a bad idea or

02:10:42.460 something bad might happen or you won't get that good thing. And they say, do you know? Are you sure?

02:10:47.420 And they have a counter argument. And if you're honest, I don't know. And I'm not sure. I have no

02:10:51.020 randomized controlled trial that climbing that tree is too dangerous. I'm just telling you,

02:10:55.740 I don't think it's a good idea. I have no need to pretend that I did a scientific experiment.

02:11:01.740 I'm your dad. And that tree looks dangerous to me. And I'm telling you to get down from it right now.

02:11:07.580 And then there are other times I'm acting as a scientist. And I say,

02:11:10.300 this is what would constitute adequate evidence. And this is what we know.

02:11:13.820 Just like Confucius said, know what you know, know what you don't know.

02:11:17.820 Brilliant. Thank you, David. It's been a pleasure sitting here with you today.

02:11:21.740 Thank you, my friend. Great to see you, Peter.

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The Peter Attia Drive - February 28, 2022

#197 - The science of obesity & how to improve nutritional epidemiology ｜ David Allison, Ph.D.

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