#167 - Gary Taubes: Bad science and challenging the conventional wisdom of obesity

Hey everyone. Welcome to the Drive podcast. I'm your host, Peter Attia. This podcast, my website, and my weekly newsletter all focus on the goal of translating the science of longevity into something accessible for everyone. Our goal is to provide the best content in health and wellness, full stop, and we've assembled a great team of analysts to make this happen. If you enjoy this podcast, we've created a membership program that brings you far more in depth content. If you want to take your knowledge of this space to the next level, at the end of this episode, I'll explain what those benefits are. Or if you want to learn more now, head over to peterattiamd.com subscribe now. Without further delay, here's today's episode. My guest this week is Gary Taubes. Gary is an investigative science and health journalist. He's the author of multiple books, including the Case for Keto, the Case Against Sugar, why We Get Fat, and what to Do About It, Good Calories, Bad Calories, Bad Science, and Nobel Dreams. Most of you who recognize Gary's name will undoubtedly recognize him from his work in nutrition, and that's covered obviously in the most recent of his books. However, we spend the first part of this discussion really talking about his background, how he got into journalism as a physics major, and how he spent the first, really decade and a half of his career writing about science and how he was particularly drawn to bad science or what would be called pathologic science. Gary almost stumbled into health sciences in the late 90s, and really, he's never looked back since. In this podcast, we probably spend about two thirds of it covering his career up until that point and about the last third getting into this. Gary provides a great history of the overview of obesity research and provides his explanation for why the conventional wisdom today is incorrect. Whether or not you come away from this episode believing that Gary is right or wrong, I hope you do come away from this episode at least acknowledging that there perhaps remains some uncertainty with respect to our understanding of obesity and its causes. So without further delay, please enjoy my conversation with Gary Taubs. Hey, Gary, how you doing?

I'm good, Peter. How are you?

I'm good as well. I feel like it's been a very long time. I don't know the last time I actually saw you. It's been how many years?

Probably three or four. San Francisco. We inverta offices.

Yeah. God, that's maybe four or five years ago now. Who knows?

We were certainly younger.

Speak for yourself. Come on.

Oh, yeah, that's true. You may be aging the other way. I forget.

There'S so much to talk about. I want to start with some of the stuff that probably a lot of people forget about you, which is long before you were writing about nutrition, you were writing about other disciplines of science. And I'm curious how you got interested in this whole thing. So remind me what you studied in college. You went to Harvard and did you study physics? Was that your undergrad?

Yeah, I was a physics major. Actually started off as astrophysics major and then got a C minus in quantum physics and my advisor suggested I change my major. So we switched to applied physics and I spent my senior year taking writing courses for the most part, but yeah.

What drew you to physics in the first place? Were you a good student in high school?

Yeah. Well, you gotta be a good student in high school to get into Harvard. I read science fiction books growing up. This was the 60s, you know, we all wanted to be astronauts. Now our kids want to be billionaires. Back then they wanted to be astronauts. So I read science fiction books. I studied astrophysics. I have an older brother, as you know, who's very smart and was studying physics and I was competing with him. So that's what I did. Problem is my brain couldn't wrap itself around the concept of Hamiltonian was beyond my ability to comprehend. So that was the end of that.

So the whole time you're studying physics, you're thinking, I want to now apply this. And I think you went to Stanford thereafter to do a master's in aerospace. If my memory serves me correctly, I did.

And I was still thinking astronaut. Weirdly enough, at Stanford I actually lived with a few naval pilots in the same housing apartment building. And I realized one, that there really wasn't any need for a 6 foot 2, 220 pound astronaut when you could send up a 5 foot 950 pound astronaut who was in better shape and more fuel efficient. And B, that I wouldn't survive very well any kind of military hierarchy that required blind acknowledgment to superiors. So started aiming towards journalism from there.

Where was that seed planted? Of call it insatiable skepticism and a refusal to bend to authority on one level.

I once had a editor at Discover magazine who asked me sarcastically or facetiously whether my brother was as, quote, well adjusted as I was. And my response was I didn't have to grow up in his shadow. My youngest son now is 12 years old and he's a basketball obsessed and he plays with the local AAU basketball team. And I noticed that the best players are the ones who have older brothers who play, they're the scrappiest or the toughest. They refuse to give up a rebound. And I think because they've been playing with their older brothers their whole life. I mean, the ones that can compete, quit and go into another sport, and the ones that can sort of refuse to give in. And so on some level, that probably started my skepticism. But then when I was, you know, so I go to journalism school, I get, I want to be an investigative journalist. I had read all the President's Men by Woodward and Bernstein. I was fascinated by it. The only job I could get out of journalism school was I had two semi offers. One was at the Dallas Morning news and in 1979 I wasn't willing to move to Dallas. The other was at CNN in Atlanta where they didn't allow cigarette smoking in the newsroom. And I was a smoker. So I stayed in New York and took a job with Discover magazine. And three or four years in, I did maybe two or three years in, I did a piece, reported an article on the Shroud of Turin. So at the time, researchers from Los Alamos were taking this high tech imaging equipment to Turin to study the Shroud. And they had concluded, so this is supposedly the burial shroud of Christ. That it appeared in the historical record right around the 11th or 12th century, right when there's a big market in sort of fake religious artifacts. So there's a strong prior belief that this is a fake religious artifact. But the Los Alamos researchers take their high tech imaging equipment to Turin and they say, and they get permission from the church to do it. And they say they can't understand how this was made, even though it's also been carbon dated to the 11th and 12th century. I remember a Sunday evening, I was living in my studio apartment in New York. I was reading their reports that they had published. And I forget what it was that struck me as profoundly unsupported by the data. But I actually called the researcher in Los Alamos at home to say, how could you possibly interpret this from what you did? And it was just the step one in realizing and maybe because I had gone to Harvard and I'd gone to Stanford and I knew a lot of people who went into science personally. There's nothing fundamentally different about somebody who goes into science and somebody who goes into journalism other than we have sort of different mechanisms of wanting to understand what truth is.

What was his response?

I don't remember. I don't remember. But if he certainly wasn't able to satisfactorily defend the interpretation. It was also an interesting conclusion about the observation about negative results, which comes up all the time. When somebody gets a negative result, when they're unable to determine how the shroud was created. Does that mean that the shroud was created by supernatural means that are beyond the ability of the equipment to detect? Or does it mean that the equipment that they used are simply inadequate for doing the job? And in effect, almost 40 years later, we're having the same debate online this week about the cause of obesity. Are you doing the right experiments? Have you refuted the hypothesis or are the experiments flawed? And that's always a fundamental issue in science.

Did you know the harm of cigarette smoking when you were a smoker?

That's a good question. My mother died of lung cancer and she was a smoker. But that was about 10 years after I started. You know, it's interesting. So I started smoking in 1978 when I went out to Stanford for grant graduate school. I was depressed.

The sunshine in Palo Alto wasn't helping you? You miss those dark Boston winters?

You know, it's interesting. I mean, this is getting deeply into my psychopathology here. I've suffered mild depressions my whole life. As you know. You go from Harvard, where I play football. I'm well liked. I built a sort of four year career at the school. You think of yourself in a particular way and then you go someplace completely new where nobody knows. I didn't handle that well. So I started smoking to, I don't know, maybe to spite the kids who seemed so happy and complacent in the Stanford sunshine when I was definitely not happy and complacent. And then it took me 20 years to quit. I don't know if children. And we discussed this when we were doing nusi. I don't know if kids ever think in terms of the things they do being dangerous. We both did some very dangerous things when we were younger. We were both boxers.

I want to actually talk about your boxing history because the article you wrote in Playboy is such a great one. But I don't know if I ever told you this story, but the entire time I was boxing, I never thought it was dangerous. I had this true mental block that was I don't get hit. So it's, it's not dangerous. Like nothing's happening to me. I'm evading and I'm hitting and da, da, da, da, da. Which of course was categorically untrue because my style of boxing was actually to get hit, tire out opponents, and eventually Overcome them due to superior conditioning. But it never seemed to hurt. And it wasn't until I suffered a very, very severe concussion when I was 20. In this. I still remember it very well. I was actually hospitalized for two days, had a CT scan, had significant bruising, had significant cerebral contusions, and had a headache that lasted for three months. This was the hardest I've ever had my bell rung. And it was only at that point that I thought, oh, my God, you actually get hit in this sport. And by that point, I'd already decided I didn't want to be a professional boxer. That was the irony of it is I was already in college, and I was now only boxing as something fun to do. But it literally took that event for me to go, this sport is crazy. And from that point on, I look at it very differently. But while I was doing it, I.

Never thought that I only got into it competitively after my best friend got killed. Boxing. Yeah, I mean, there's a lot of things. I was a football player in high school and college, and when you're a football player in high school and college, back then, getting your bell rung, it just was what it was. I mean, I clearly had at least one concussion in high school football. My best friend in college, who was actually a lot of kids, best friends, a very charming, wonderful young man of Puerto Rican descent from New York, was an amateur boxer in New York, went back to boxing after his football career came to an end due to knee injuries and got our senior year, he had his first fight, and he got killed in the ring. He was knocked out, his head hit the mat. We were in Lowell, Massachusetts, watching the fight at the time, and, you know, he never woke up. My memory is of them unplugging him a week later. It did not stop me from then moving to New York when school was over. Actually, three years later when I got to New York, starting to box because I was bored.

Did you think about him when I did it?

Yeah, sure. But I never thought that what happened to him could happen to me until I got knocked out in the Golden Gloves, at which point there's this awareness when you get knocked unconscious, when you wake up, that some people never wake up from that. That that might have been the moment in which your candle was blown out, in effect, and there's no relighting it. Talking about this is interesting because a lot of what we do in life and talk about when you're talking about prevention of chronic illnesses and prevention of disease and prevention of addiction, it's Getting young men and women who don't think and don't process risk the same way we do as we get older. I always thought it was fascinating that the less life we have, less to live, the more risk adverse we become. If something bad happens, you're actually losing less of your life than you are when you're younger. And I'm sure that behavioral psychologists like Kahneman and Tversky could say things, or do say things about that.

Tell me about the Playboy article that you wrote after that Golden Gloves tournament. I know you still have. At least the last time I was at your house may have been eight years ago. You actually had a framed photo of you lying prostrate in the ring.

You know, I still do. It's in our coat entryway. I often thought of putting it up here instead of this picture because I refer to it as my hubris protection. So while I was working at Discover magazine and I was boxing. So this was 1983, 84, by the.

Way, not too long after Duck who Kim was killed by Ray Mancini. I mean, the early 80s was a very difficult period in boxing. There were a number of high profile deaths in the ring.

Yeah, he was killed while I was boxing. And we actually did a story on it at Discover about what happens to the brain. A good friend of mine, a New York Times reporter now named Denise Grady, was writing that story. And while I was boxing and getting ready to box in the Golden Gloves, and she would come and then slip like articles under my door about whatever we were calling the traumatic event back then. Yeah, I was just a young man. I was boxing. I had a friend, a good friend, who was Norman Maller's nephew. And then Norman Maller had a group of young men, not even that young, who met at a gym that's no longer there on 14th street in Manhattan, back when that was a very questionable part of town. And we would go down, I think it was every Saturday morning, we would box for a few hours and then we would go out to lunch. And, yeah, from that I got the idea, why don't I box in the Golden Gloves and write about it? So I originally was doing it for Sports Illustrated, but then I realized that I was technically too old to do it, that I missed the cutoff by about two or three months, and that if I wanted to box, I would have to revise my birth certificate. And so when I realized that, I talked to the editors at Sports Illustrate and I said, you folks, you do investigative journalism. We were in the same building, Discover and Sports Illustrated were both owned by Time Incorporated at the time. So I told the editors, you do, you know, hard hitting investigative journalism. If I want to continue to do this article, I'm going to have to fake my age. And I think that's a problem. And the editor has said, well, it is now that you told us. So we pulled it from Sports Illustrated and one of the editors I worked with had a good friend at Playboy. So I pitched it to Playboy and my sister in law got angry at me for writing for a magazine that doesn't portray women in the most ideal light, depending on how you look at it. And she had a point. But I said, some of the best writers in America write for Playboy and maybe later I'll have the platform by which I can be too good for this approach. Approach. Anyway, we did it. I won my first fight against a policeman from Staten island who beat the crap out of me in the first round. And then I realized that if I punched back, that might help. And I knocked him out in the second round. And then second fight was a week later and I got knocked out, and I think a minute and 37 seconds by the fellow who ended up winning our division, who was not particularly good. But I had never really had enough time to work on defense. You had been boxing for many years competitively, I had been doing this for about four months at any level other than a Saturday morning hobby. But it was a reminder that anytime I think I'm. Some of us grow up with this kind of James Bond complex. I think it's fair to say between the watches and the fast cars and the archery and things like that, you might have it also that we think we're so cool, we can do anything. And that was my reminder that I'm certainly not. And that there are things I should stay away from if I want to have a long and healthy life. Among them being boxing. And I haven't gone back into. I never went back into the ring after that.

What's the process like of learning to write? I was rereading an article yesterday that you had recently written. And don't get too flattered by this, but I forgot how well you write. I think I write reasonably well. I've learned to write better over the years. And I think a lot of that came from how often I used to write, how often you used to rip what I used to write apart. Back when we were at Nusi, I was doing so much of the writing and you were doing so much of the editing and revising of internal stuff. That I would write. And that process, of course, I think made me much better. And I can now see it when I look at the writing of other people. But your writing today, I think is really excellent. At least it's a style that I find really good. You can write about science in a way that's very readable. How does one learn that craft? When you show up at Columbia, which ostensibly is the best journalism school in the country, I assume you're coming in incredibly raw. Did you believe you had some talent, some hidden talent for writing, or is that not even a part of what they're selecting at a top journalism school? Is it more the thought process they're selecting for? And the writing is the easy thing to teach on top?

Well, I thought I could write because I used to write letters to friends and girlfriends that I thought were clever and they liked. So there was always this sense that somewhere inside me, I was a writer. When I first started taking writing classes, it was actually at night school at Harvard. After I got my master's degree from Stanford, I went back to Boston, and I don't even remember what I was doing for a living. But over the course of a semester, I took two writing courses. One taught by a science writer from the Boston Herald who I later worked with at Discover magazine, and one taught by fiction editor from the Atlantic named Michael Curtis, who had been a roommate of Thomas Pynchon and Dick Farina at Cornell in the early 1960s and was already a famous fiction editor at the Atlantic. And the very first article I wrote for him could probably remember the subject if I had to. But what I do remember is it came back with the words puerile written in 1 inch high letters on the front, underlined twice. And I had to look up puerile, which meant childish and amateurish and thoughtless. Over the course of a semester, Michael Curtis started to hammer me into being a functional writer by constant and relentless criticism of what I did and I appreciated. In fact, the Boston Herald science editor was so nice, he would read people's work, and he believed you should never say anything negative about their work. And I gained absolutely nothing from being in his class. He was a lovely man, but I don't think anyone else did either, except maybe unjustified sense of their own talent. Curtis hammered into me. And then when I got to journalism school, it was just about basically teaching you to write in a report. And so it's a steady learning process. And then as a journalist, when I started at Discover, I started as a writer. So Time Inc. Back then had reporters who did the information gathering. And then we would write files and the files would be given to the writers. And the writers were true craftsmen. And I started off, I don't know, the first story I remember doing was about some type of particle accelerator at Michigan State. I had the writers there, friends who were writers, would read this draft and then relentlessly critique it until I ended up with something that was worth submitting to the editors. And then the editors would do the same thing before it would be published. And as I became a writer and got older, I did that same thing for other reporters who came in under me. And there are a few very well respected journalists who I think benefited from me being relentlessly critical. And then the actual process of writing is just, you know, I told you this years ago, Calvin Trillin, the famous New Yorker writer, had a phrase he called the first draft, the vomit out, where you just get everything down and paper and it kind of relieves the pressure of a first draft. So you get everything down on paper, the computer, and then you just keep rewriting it and rewriting it and rewriting it until it reads like something I would have wanted to read if it hadn't been written by me. And because I read a lot and I read a lot of very talented authors, I know what good writing looks like and feels like. And you just continue to revise and edit until it gets there.

It's who's the best living science writer in your opinion?

I don't think I could answer that question. There are a lot of people who have a lot of different skills. It's like saying who's the best living football player or the best. My 12 year old is always asking me who I think the five best basketball plays was. Wilt Chamberlain better than Steph Curry? Well, they were different. There are science journalists, jeez, there's a young man at the Atlantic now whose name I'm going to forget. Henry Somebody who writes a thoughtful, well reported article on a different subject every three or four days. And I often read his stuff and I think, could I have done that when I was his age and, you know, worked 70, 80 hours a week? Possibly, but I don't think so and I don't think I could have done it as well when I was growing up. The best out there was Jim Glick who wrote Remember Chaos? And the Feynman biography, the name of which I forget at the moment. There were a whole series of science journal, a whole collection of science journalists who came of age in the 1980s when science magazines had first started. There was a magazine called science. Well, science 80. Then it was science 81, science 82. There was discover some of those like Charles Mann and Steve Hall, John Tierney.

Chaos in the Blood.

Chaos in the Blood, yeah. Just exquisitely beautiful, thoughtful writers. In fact, I can't. Charles Mann, he's a friend and Steve's a friend. And I call Charles, goes by Cam. And I can't read Cam's books because they're so painfully. The writing is so painfully beautiful that I feel inadequate when I read them. It's very difficult. The same goes for Steve. I think what they do, they do much better than I do. But we're now these senior figures in the field.

What is it about their work though? You once told me that great science writing isn't really about the science. What is it about Steve, for example, that you read and think he's doing that so well?

It's a combination of comprehensive, rigorous reporting. So you know you have the story, although you can never really judge if somebody else has a story write unless they're writing about your field. The prose style, the ability to tell a story around a complicated subject. I was going to say I just read the Ghost Map by Steven Levy. Have you ever read that?

No.

It's an extraordinary book. I hope that's the best book he ever wrote. Because if he's written better books, then I'm in the wrong field. And I know many people say that anyway, but on one level, just the pros. So the ability to write a sentence and a paragraph that other people want to read, but then the story that you tell and the digressions to the story and keeping it always interesting and making it both bigger than other people might see it and yet simultaneously getting the details of the lives and the times. And I'm sure every genre has its own challenges, but science has this challenge that you're inherently writing about a subject that's very complex. It's hard for even the people who the scientists themselves to wrap their heads around. So I'm still going to have to think about who the best science who I think the best science writer is today, if there is one, and there's so many out there today. The question you always ask is what books out there like the Ghost Map. And again, on one level, it's about the John Snow and the cholera epidemic in England and whatever, 1864 in London, and how John Snow and his compatriots figured out that this was cholera was not bad air, but infectious agent in the water supply. But then it's about the birth of modern cities, about urbanization, about the problems that cities faced coming together, about cities into the future, and then fundamentally about the scientific process. The challenge of an outsider doing science, which might be one reason why it resonated so much with me. You start with a small story and you turn it into something large and intricate and meaningful. Extraordinary book.

So at what point during your time at Discover did you become interested with the subject matter that would eventually become your first book?

My background was in physics, so it was natural for me to write about physics. And a Harvard physicist named Carlo Rubia, working at the European center for nuclear nuclear research, CERN in Geneva, had his research collaboration of 150 people, physicists known as UA1 had discovered two fundamental particles that were sort of the two of the three last remaining. Well, two of the four last remaining particles of the standard model of physics. So physics, the electricity and magnetism had been united with the weak force to the electroweak theory. And that was a great step forward in the 1960s and 70s. And by 1973, that theory had mostly been confirmed and it predicted the existence of two particles that transmit, or particles that carry the electroweak force. This was a W and Z boson. And Rubia's group, Rubia, who was this very controversial physicist who taught at Harvard, had convinced Europe to, in effect, spend the money to build an accelerator that had the power to detect W and Z particles. And when they announced the discovery, we did an article on it at Discover. I forget who, I must have written that. But then Discover was part of Time magazine and Time has their man of the Year cover, right? So Discover had this concept of the Scientist of the Year. And because of this great discovery, and everyone knew that Rubia was going to get the Nobel Prize for Discover, made Rubia the Scientist of the Year. So I get to fly to Geneva. Actually, I flew to Belgium, rented a BMW, drove through Paris, went to Geneva. Time had a very good expense account back then. My life has been downhill ever since. Anyway, I spent time with Ruby in Geneva. I interviewed his friends, I wrote him up as a profile for the the Scientist of the Year. Time, Inc. Flew him to New York at the Explorers Club. He was given the award.

And this was before Stockholm or after Stockholm?

This was before Stockholm.

Okay. He was awarded the Nobel Prize. What? And it's always the fall. So was it the fall of 84, 85? Yeah, it was 85. Okay. They published the two bosons in January or February of 83. Is that right? Or was it earlier? 82.

Must a been probably 83. Okay. So anyway, the point is, not only does Ruby get this award, he gets a nice gold Rolex from the Explorers Club. So he's very grateful to Gary Taubes for he thinks somehow, and I probably was the one who suggested him for Scientist of the year. So that April, which would be April of 84, he comes to Washington. There's the big physics meeting every year in Washington and Rubia gives a presentation announcing that he's now onto the next step, which is physics beyond the standard model. And he shows what they've got and he shows a few events and we could talk about that. And the message is that all he has to, it's the biggest discovery in physics in 40 years, and all he has to do is turn on the accelerator, which will happen that autumn, and gather more data and nail it down and it's Nobel Prize number two. And so I was at that talk. So I came up to him afterwards, I said, look, it's, you know, this is terrifically exciting and it's rare that anyone predicts a great discovery in advance. So can I write a book about this? Maybe I can come to CERN live with you guys and document this as it happens. And Rubia liked me. He thought of me as his personal scribe. So he said yes. And I put together. Well, first I put together a proposal for the Atlantic that they rejected because Charles Mann and his co authors at the time, Bob Kreese, had just put their own proposal in to do a piece on high energy physics. So when I was always following behind man and Kreese, if you're going to follow behind two writers, those are two very good writers to lose out to. Anyway, so I pitched it as a book deal instead. Actually, I was approached by a publisher who was publishing a book by Shelley Glashow, who's a Nobel laureate at Harvard, who I knew. And they had asked Shelley who the good science writers were. And Shelley said, you should get Taubes, he's probably the best. So they approached me. I had this proposal from the Atlantic that they rejected. I sent it to the publisher and they said they'd be willing to pay me to do the book. So I left Discover magazine and flew to Geneva and got a room at the hostel on the lab and was today we'd say I was embedded with the physicist for the the next nine months and thought I was going to write about a great discovery and pretty quickly realized that this was far less compelling, the evidence were far less compelling than Rubia had claimed in Washington and that there was a very good story here, and it was probably not the story of a great discovery. You know, when you ask what informed my approach to science and science journalism, that was the learning experience, because. So there's 150 physicists on the experiment. So the gist of it is you've got, well, an atom smasher, part of a collider. So you're colliding subatomic particles together at speeds as close as you can get them to the speed of light. The faster the particles go, the greater the energy in the collisions. And then you're looking in the collisions for the signs for particles that might be made that your theory of physics, the standard model, does not predict. So in that sense, you're testing the standard model. And as the accelerators are able to put more and more energy into these collisions, you're able to test your predictions of the standard model further and further out. And then you've got to understand the predictions, which is part of what we're dealing with here. But so you've got this huge particle accelerator. If I remember correctly, it was maybe four miles in circumference. It straddles the French Swiss border. At points on the accelerator where you're colliding the two beams together, you build a detector and a dimension detector. These things are with the size of a mansion, three, four stories high. That's where the money that's, you know, they're as expensive. So the accelerator itself might cost $50 million or $100 million today. They cost billions. Back then, it was hundreds of millions. And the detector, which I always thought of as kind of a camera that's created to photograph, to be able to detect the passage or the emittance of all the various particles that you will come out of these collisions. So you've got photon detectors to detect photons that come out. You've got muon detectors to detect muons that come out. And so you collide it. And of the billions of collisions you're making, I don't know, every day you're looking for one collision that creates one particle that you're standard model doesn't predict.

And just to put that in context, as you said, you could have billions of collisions per day. But I think if I recall, I mean, Rubia would say, look, you might get 20 a year that are relevant. And so you just think about the signal to noise ratio there. And again, I think just for folks to understand, you're trying to look at, so you'd have a Prediction of the mass effect of that collision. So you have to have some conservation of mass. You have to have some conservation, conservation of charge. And you're trying to sort of coalesce all of these different things to look for discrepancies that are occurring at almost an infinitesimal fraction of the number of collisions. It's very hard to wrap your mind.

Around that are predicted to be occurring or not occurring.

That's right. They may not even occur.

Yeah, you've got the particles colliding. Everything that comes out of that collision you have to detect. Because if you miss something, it's beginning to sound a lot like calorimetry, which is a subject we should get to later. If you miss something, then that collision is worthless to you. And you're likely to be fooled because you're likely to think some particle was created that wasn't created, because you do have to have conservation of energy. So if a certain amount of energy goes off in one direction, you better get the same amount of energy going off in the other direction. And ideally, with every particle you detect, and you detect the energy in the particles, that's part of what the detector does. And you recreate this collision. You know that everything's balanced. If it's not balanced, does that mean that your detector failed or that a particle was created that your detector couldn't see? Because it's beyond the standard model. Now you have to know your detector to better than parts per billion issues because you have to be able to estimate whether or not you're detector failed. So on some level, this idea that science is about signal to noise, you have to be able to predict the noise almost perfectly to understand the noise almost perfectly. And this is a sort of repeating theme in everything I've written about, even if I don't phrase it like that. In Nusi, we used to talk about signal to noise problems all the time. If you don't understand your background, which in high energy physics is the standard model, predictions of what you would expect to see if there's nothing new out there and what you would expect to see from the understood flaws of the equipment. So that's the issue, the problem we're confronting. So you've got parts per billion effects that you're looking for, or parts per trillion effects. And you have to understand the flaws in your detector to better than that. And so as what's his name Rumsfeld would have said, you got your known unknowns and your, your unknown unknowns, and you're constantly trying to figure out your unknown Unknowns and you never fully figure out your unknown unknowns. So physicists, we read about this, there's a talk recently of a new particle possibly being discovered. And physicists say, well, they haven't discovered it yet because it's only a three sigma effect. And a three sigma effect is what? You know this better than I do.

A point 000 probably 00015 or something like that. But our threshold would be six sigma to really say.

So the reason for the six sigma or actually five sigma is because you want to make a lot of room for the unknown unknowns. You want to say this is how I perceive it. We can calculate the sigma is a.

Calculation of the standard deviation. Yeah. The difference from the mean.

Yeah. But there's going to be stuff you don't know. There's going to be stuff that's going to fool you that you don't know. So you want to have an event that's so absolutely fundamentally frigging outside the range of what you think you know, that you can confidently assume that what you don't know, the unknown unknowns can't explain it either. And it's new physics.

Before you go further, there's one thing I want you to explain to listeners because it's a beautiful part of physics, but it doesn't exist in every other field of science, which is the relationship between the theoretical and the experimental physicists. I think it's worth pointing out here how that is a collaborative nature in physics.

Yeah. Physicist divides up into theory and experiment. It wasn't always quite so clear. But even if you think of Einstein was a theorist, Eddington, who did the seminal test of the theory of relativity, was an experimentalist. So as physics evolves, you have people who wrestle with the theory and then they have to come up with a theory that makes predictions that can be experimentally tested. And then you have experts who are experimentalists whose job is to do the experiment. And then there's got to be crosstalk between the theorists and the experimentalists because the experimentalists have to know precisely what they're looking for. And they need the help of the theorists to tell them how this might manifest itself. And the experimentalists have to understand their detectors, their experiments to this sort of superhuman ability. That's one of the defining characteristics of an experimentalist, is the awareness of an exquisite physicist, is the awareness of how their equipment could fool them. And so this is how physics has always developed. And then you get to the point that theorists job is to come up with a good theory and Make a prediction. And the experimentalists job is to test that in the fields in biology and medicine, you don't have that diversion, which I think is a problem. And we talked about this through our years. You've got the theorists and the experimentalists are merged into one person. Everyone's a theorist, everyone should be capable of doing the experiment. And I think they're two entirely different skill sets. And they both require such a fine level of detail and thinking and rigor that I'm not sure that, you know, after a certain point, like say from 1900 onward, I don't think one person could encompass both aspects of, you know, they're almost different endeavors. So to get back to cern, because this is where it's relevant. When I get to cern, I move into the hostel, I'm hanging out with the physicists. Once Rubia published the W and Z discoveries and everyone knew this was the place to be in physics. Even before that, he collected a group of very ambitious young, mostly men, physicists from some of the best physics schools in the world who came to work with him to analyze the data.

Has UA1 and UA2 collapsed at this point?

It's always both. You always need both detectors because if you see something in one, you still need independent replication. Yeah. So if you don't see it in both, you got a problem. That's why even now the Large Hadron Collider at CERN has four, I think four or five different detectors. It's not enough to see something in one detective detector. You always need independent replication. And again, that's true of every science. So you have these young men who are doing the analysis and they're sort of the, they've become, they've moved in, they've come in late, they've become sort of the chosen courtiers of Rubia. They hang with him, they eat with him, they meet with him, they tell him what he wants to hear, which is what's happening with the data. And then you've got the physicists who actually built the damn detector and the technicians who built the detector. And these are, you know, these. I used to think of them as they come from, they don't come from Harvard and Stanford and Oxford and Cambridge. They work at like the red brick universities in the Midland and the uk and they've been there from the beginning. They built this mansion sized piece of $30 million equipment with their hands. There's no mass produced. They know how it can fool them because they built it. So as soon as I get there, I've got these young physicists who are new to the experiment who are all excited about this great discovery. And I'm also hanging with these, the old hands who built the equipment by hand, and they're basically poo, pooing everything and saying, this is crazy. We cannot understand the flaws in our detectors well enough to make the kind of claims that Rubia has been making.

Now, are they voicing that?

Well, they're trying to, but the problem is you need a group leader who's willing to hear that. So once Rubia started talking that he had made a discovery in Washington in 1984, what he wants to hear, he's gone out on the limb, right?

I thought he only predicted it. I didn't think he actually said it had happened.

You know, you've probably read my book more recently than I have. Even if you're claiming that it will happen. If you think of. There's this term that the physicists use, pathological science, which is the scientific of things that aren't so. And my whole career on some level has been writing this right or wrong. Now I think I understand pathological science, and I've studied it as much as any human being alive. One of the defining characteristics of pathological science is people commit themselves publicly to a result based on premature evidence. It's not ironclad. They haven't locked it down. There's still a chance they could be wrong and they don't understand the likelihood of that chance. No one ever does until you get very good at this, when you realize that chance is enormous. Science is supposed to be hypothesis and tests, right? The idea is you're supposed to rigorously test your hypotheses and ideally you're trying to prove that you're wrong. Richard Feynman first principle of science said you must not fool yourself. And you're the easiest person to fool. So you get evidence that you've discovered something new. Your assumption should be be that you're wrong, that somehow your equipment is fooling yourself. And now you try and go out to demonstrate that's true, and you do everything you can to prove it. And if you fail to find out how you're wrong, now you write a paper that says, evidence for the observation of particle X. And you put together a presentation and you go around, you start in your department at whatever university you're at, and you give the presentation to your colleagues. And basically you're asking them to explain to you how you fucked up, if you'll pardon the language, because surely you did. And if they can't explain it to you. You march around the country of the world giving this symposium. You still haven't published the paper, the Evidence for the Observation of Particle X, because you're still working under the assumption that you screwed up. And you just don't understand either your equipment. And as you're traveling around the country, people should be saying to you, well, what about this? Did you think of this? Did you know that the muon detectors, actually, there's a glitch in the detectors that are made in Belgium versus the ones made in Switzerland. And the ones made in Belgium are going to spark once every seven and a half months. Have you checked that? And if you checked it, great. If you didn't, you go back to CERN or wherever you are and you check it. And so you keep giving this lecture. Finally, at the end of, I don't know, 20 seminars, nobody can explain to you how you fucked up. Now you publish the paper with the question question mark, and what you're asking them to do is tell you how you screwed off. Because if you assume that you did and then they tell you, you don't resist it. But if you claim that you didn't and you publish the paper first, now what you're doing instead of that exercise of trying to find out how you assuredly screwed up, you're trying to collect evidence for how you got the right answer. So you go from paying attention to the negative evidence to paying attention to the positive evidence. Again, everyone does this. I'm accused of that all the time. It's like a study comes out that confirms what I believe. I don't question it the same way I will question a study that comes out that appears to challenge it. So once Rubia had gotten to the point where he was predicting a discovery, all he wanted to hear from his physicists was the evidence supporting that. That's the first mistake along the way. So the physicists who wanted to tell them about the problems with the detector and all the ways the detector could fool them and what they weren't thinking about and what they were missing, if those people did their job, they were sort of seen as downers. So after a while, what happens is you get this sort of polarization in the experiment where the principal investigator tends to selectively listen to the people who support what he wants to believe and selectively resist, subtly resist the people who argue against it. And then as the experiment goes on, the more the people who tell him what they want to believe confirm his beliefs, then that confirms his belief that he should not listen to the critics, the skeptics. And this happens in all of science is what we've seen in an extreme way in Covid. It goes on in climate change, it goes on in nutrition and chronic disease. Once you decide you know what the truth is, you tend to stop looking, listening to the people who disagree with you and then you surround yourself with the people. This is also classic group think theory.

At what point did Rubia realize you were no longer his scribe in residence during this 10 month period?

Probably when he read a draft of the book. Carlo was interesting because, well, he had enormous self confidence. So you have to picture he's larger than life, he's Italian, he's overweight, he's got this exuberance.

He's a Nobel laureate.

He's a Nobel laureate. I went to Stockholm with him. You know, I was fitted for my tuxedo with Carlo. I mean, that's how close we were. Once I started realizing there were serious issues with this experiment, I would challenge him on this. So I didn't hide my skepticism. I would say, well, you're saying this, but so and so from Edinburgh or College de France or University of Pisa is telling me this. I wouldn't out them like that, but this is what I'm hearing from your experimenters. And Carlo, my take was that he was something of a pathological liar. So he defined truth as what was convenient for him that he could get people to believe. So I would challenge him on an issue and say, well, you say this, but that's not what I'm hearing from your researchers. And he would say, yeah, well that's a good point. Then he would take a step back and tell me something else, hoping I would believe it. And if I challenged that, he would acknowledge it and then tell me something else. And we just keep going. It never got to the point where I felt from him any awareness that what I was going to write was not the story he wanted to tell. And I think this is a classic problem with people of his sort of quality and strength of ego, which is he couldn't imagine that I could see the universe differently than he did. I don't think he could imagine that anyone could really see the universe differently than he did. When I wrote the book, it was called Nobel Dreams and described what had happened. He had an executive committee at the experiment made up of a dozen or so physicists from all these collaborating institutions and he wanted to sue me and he asked them to join in the suit so that would have more power. They refused and what they didn't tell him is that most of them had read my book and critiqued it in draft as a favor to me. Didn't say that in the book because had I said that in the book, I would have created problems for them professionally. The book was accurate, but he didn't see the universe that way.

Did you like the book itself?

The writing makes me cringe. I can see the book. It could have been. I came out when I was still 29 years old. I guess the story in it is interesting, but I can't read it. My twitter 29 year old prose still makes me cringe.

It came out in what year? 87ish.

Maybe it was February. 87. Yeah. So I would have been not 29, but 30.

Did you go back to Discover at that point or did you immediately jump into the investigative work that would ultimately become the book Bad Science?

No, I went back to discover. I was freelancing for Discover. And actually it's interesting. When the book came out, it was February 1987. I was living in the Upper west side of Manhattan. My publisher's idea of getting me publicity was to get Page six in the New York Post, which is the gossip column. So I heard that Page six was covering it and it was. I remember it was a dark snowy day in New York and dreary. And I went across the street and went down Columbus Avenue and there was scaffolding and there was a newspaper stand and I bought the New York Post. The headline of Page six is Egghead Squabble over Nobel Prize. And in it this Nobel laureate Carlo Rubia is calling me, a 30 year old writer in New York, an asshole. And I'm thinking that my career. And I'm never going to eat lunch in this town again, and my career in journalism is over. And as I continue to report stories, I start talking to researchers about my book and Rubia. And they want to know about Rubia. And they'll say to me, well, if you think Ruby is bad, you should write about so and so. And it turns out that there are characters like him in every area of science. So we could talk about this Pathological science people tend to think of fraud, right? And when you're committing fraud in science, you're knowingly trying to fool other people by manipulating the evidence. But there are two ways you can manipulate the evidence. You could create a signal or you could do an inadequate job of studying your book background. Remember, it's a signal to noise problem. So you could fudge a signal or create a signal. Or move a signal, or you could ignore the background. And if you ignore the background and do a poor job in the background, you'll still end up with a big signal to noise ratio. You haven't technically committed fraud. You're not actively trying to fool anyone else. You're fooling yourself. And that's the kind of science Rubia did. He was ambitious. He was looking for discoveries. Doing a background in analysis is the hard, relentless, rigorous grunt work of science. It's endless and thankless because if you do it right, all you'll do is prove that you were wrong all along. Publicizing a signal and getting a Nobel Prize is the good part. Right.

How do you reconcile that dichotomy? Which is, this is a guy that's not committing fraud, of course, but in your words, he's doing bad science because he's omitting to do the background check. But at the same time, he is the guy, through sheer personality and force of will that discovers these two bosons that, up until the discovery of the Higgs boson, were the last two pieces of this puzzle. And I don't think anybody's disputing the work that went into that Nobel Prize. It's an unnerving revelation.

Right.

It suggests that even at the uppermost echelon of science, you can have people that are committing these omissions.

One of the messages in my book, I didn't go into this in great depth, but the papers that won him the Nobel Prize, and again, I'm Remembering from almost 40 years ago now, those are probably wrong, too.

He rushed it, I think, would be generous.

Yeah, he rushed them, yeah, because he was competing. But the particles had to exist. That discovery had already been made. The neutral currents in 1973 or 74, I think it was Fermilab. So those particles had to exist, and they had to exist, exist with the characteristics that he was looking for. So when he published the discovery based on, I think it was seven events that had these characteristics, no one asked, were these really W particles? In retrospect, most to all of them probably were detector flaws that they didn't understand yet that they still had to work out over the next year when they're trying to discover particles beyond the Standard Model. This was the issue with Rubia. The reason it got the award immediately is there was a political issue behind all this also, which is Since World War II, all the major discoveries in physics had been made in the U.S. the U.S. had spent the big money on particle accelerators. So this was CERN's opportunity, Europe's opportunity to achieve relevance again. And the award was given almost instantaneously because this discovery made Europe basically redistributed the center of gravity of the physics world back to, you know, if nothing else, the center of the Atlantic. When I was reporting the book, one of the things I did was travel around the country and actually around Europe, talking to other physicists about their work, their discoveries and about Rubia and trying to rationalize what they told me with who he was and who I thought he was. In defense of Rubia, none of this gets done without him. No, I'm going to rephrase that. None of it gets done as quickly without him. It all gets done better without him. There are people who, from the experiment, who Rubia ground up and chewed up and spit out, whose careers basically came to an end because of him. And I said this, and I remember in the introduction of the book, I said there were sort of three types of physicists in the. There were those who were at Rubia's level and told me he was brilliant. There were those who knew Rubia well and said he was very smart guy, but they wouldn't want to work for him. And then there were those who worked for him. And there were points in the course of the experiment. I would sit in on group meetings and watch some young French physicists be reduced to tears by Rubia's bullying. And bullying can be very beneficial in science because you're really trying to force. Force someone to think critically and force any sloppiness out of them. But there were times I considered like, look, just ask Ruby to step inside. Don't write the book. Punch him out. Will physics be the better for it? I decided physics might be, but I couldn't afford it. So, you know, that's what you wrestle with as a young writer. The reviewers who didn't like my book, which included Christopher Lehman, Hound at the New York Times, and Jeremy Bernstein, was a New Yorker physicists writer, writing, if I remember correctly, for the Boston Globe. They thought I had no right to write the book I wrote to take on a physicist of this level to. I think it was Bernstein who said this sort of sociology and character flaws of physicists at this level don't interest him in the least. The only thing that's important is this glorious cathedral of knowledge wasn't my interest.

So when did you happen upon Fleischmann and Pons?

I went back to Discover as a freelancer. I had a terrific contract with Discover. I loved living in Paris, where I wrote Nobel Dreams. So I moved back to Paris writing for Discover. And then Time Inc. Sold Discover. Science magazines have trouble making money because it turns out the only people who really read them are high school boys. Or at least back then were high school boys. And they don't spend a lot of money, so they don't support advertising revenue. So timing sold Discover to a low budget publisher and I lost my freelancing gig. And I had a choice of living in Paris and getting by on scrapping to do freelance articles or moving to the States. I had friends in LA who were writing screenplays and making enormous sums of money. And they said, as have been said to screenwriters in the past, come to la, write screenplays, get rich, do a you want. So I moved to Los Angeles, started writing screenplays, and in March 1989 cold fusion broke. And my publisher called me up and said, do you want to write a book about this? And I needed to supplement my income. I thought I could spend a year doing the book and make enough money to write screenplays for two years after that. And then, as is my want, I got obsessed and ended up spending three years on the book and owed my father $40,000 when I was done. I've bonded with many writers over the years, young writers, about how much money we owed our parents. By the time we were done with our first book, it was another fascinating story. I actually read the so called Fusion 2 chemist Stan Pons from the University of Utah and his mentor Martin Fleishman from Southampton University in the uk announcements. They've basically created nuclear fusion in a test tube. March 23, 1989. It's the front page of the Wall Street Journal. Within a day, it's front page news all around the country. The very first article I read in the Los Angeles Times mentioned there was a competing group at Brigham Young University. And as soon as I saw that there was a competing group, I assumed they were wrong. Because as you mentioned with UA1, UA2, you have two experiments competing with each other. So the motivation is to establish reliable knowledge beyond reasonable doubt. That's what you're trying to do in science. But if your motivation is also to beat the other people to the reliable knowledge, you're going to allow one motivation to compromise the other. And now the beyond reliable doubt part is going to be tossed out in an effort to win. That was one of the fundamental problems at CERN with the the work UA1 was doing. UA2 got everything right because once they came in second, they were perfectly happy to do the good job. I think they were doing all along on first principles.

Did you just think it was ridiculous based on your knowledge of physics?

No, I didn't know enough to think that I just smelled bad science.

It's been a while since I read bad science. Actually, I think I read that. Actually, the first one I went back to read. But did they claim it was net energy positive?

Yeah. The idea is, how do you make nuclear fusion While you put heavy water in a test tube with an electrode made of palladium and you plug it into the wall and the deuterium gets sucked into the palladium. And the idea, somehow it gets compressed so much that it fuses and generates, ideally, neutrons, which are one sign of gamma rays or some radioactivity, which would tell you that there was was a nuclear reaction going on, if not at least generating a lot of heat. So Pons and Fleischmann had an explosion in their laboratory with one of these cold fusion cells. It was being run by Pons son. It might have been their garage. I forget the details. And when the cell exploded. Right. If you're looking for trying to create nuclear fusion in a test tube and your cell explodes, Eureka. But even more important to that, it wasn't just that there was a competition with the people. Physicists at Brigham Young, 30 miles, 40 miles down the highway from the University of Utah in Salt Lake City. But the physicists at Brigham Young had studied something called muon catalyzed fusion, which was a type of cold fusion of a sort, and had been shown Pons and Fleischmann's proposal and had started working on this after reading Pons and Fleischmann's proposal. So Pons and Fleischmann thought their discovery was being stolen from. From them by a physicist who should know enough to know whether it's right or not. And as I discussed this at the very end of my research, when I spent about eight hours with the then president of the University of Utah, a very thoughtful, wonderful man named Chase Peterson. And he was explaining to me, you know, we were talking about their thought process, and going public was something that became this huge fiasco. He said, you know, yeah, you know, somebody's. Nothing makes your girlfriend look more attractive than your best friend trying to steal her from you. And so once they decided that these people at Brigham Young were trying to steal it from them, they decided it must be right. They had to throw a press release. They threw a press release, and this enormous phenomena kicks in where everyone in the world tries to replicate it because it seems it holds the promise of infinite free energy. It's the wealth of OPEC, and anyone can study it. It costs like $3,000 worth of materials to put together a cold fusion cell. So you have basically created a bad science generator.

When did it become abundantly clear to you what was going on? How long into your investigation?

This is a very different kind of story because this is immediately front page news. So when I jump into it, there are meetings, there are conferences, there are other people discussing this. I probably flew out to Utah within a week of having that conversation with my editor. And Utah is of course, surrounded by journalists who want to know if infinite free energy has been created. Funny coincidence. The Wall Street Journal ran it on the front page. The author was a science writer for the Wall Street Journal named Jeremy Bishop. And he sort of took. So I write a book explaining why Jeremy Bishop was sort of not thinking things through clearly when he put this on the front page. I moved back to New York in 1993 or so. I move on to a block on the Upper west side of Manhattan where Jerry Bishop and I are looking into each other's windows from across the street. Anyway, the world was finding out that this was wrong. I'm following all the attempts to replicate it. In a sense, this was how science is supposed to work, right? Somebody makes a claim of a discovery, and they publish the data necessary to replicate that discovery, but in independent laboratories. And so independent laboratories go out and try to do that. And around the world, chemists and physicists tried to do that. And most people got the right answer, which is cold fusion doesn't exist. But a half dozen laboratories around the world, for reasons that tended to be different in each lab, got the wrong answer. Usually we can talk about that. I mean, there would be a lot of interesting reasons why. So the phenomena keeps going because of the signal being generated from these six labs, While again, all these other labs in the world are working out the background. And some of the best scientists in the world are involved with. This is a major discovery, if it's true. So one of the things that attracted was some of the very best physicists and nuclear physicists in the world. And I had the. And chemists and I had the opportunity to work closely with these people, basically to embed myself with them while they tried to replicate the experiment and did, in effect, the background analysis that Pons and Fleischman and Steve Jones of Brigham Young never had the time to do. So now you get to find out all the ways your equipment could have fooled you. And assuredly did. And that's the science working the way it should work. And so by June of that year, it was clear that this was wrong, that they had Ponzon play the whole thing was wrong. And then I continued reporting it for about nine months to tie up all the loose ends, to explain some the of the other positive results. But it was clear to the world by within two to three months that this was just wrong. And then it faded from the papers.

And then you wrote a book about this, of course. So the book was really less about letting everybody know this was wrong. The purpose of the book was to explain how it became wrong. Right. How did this happen?

Yeah, I became obsessed with how this happened still, what fascinates me so, you know, when I'm writing about nutrition and chronic disease, obesity, diabetes, the question is, how do people establish conventional wisdom, dogma, the ruling theory in any science, and on what evidence? And in this case, cold fusion. I wrote it as kind of. I saw it as a sort of case study in bad science, something my fantasy, something that every graduate student who goes into science would be required to read so they could see the price, all the different ways you're going to fool yourself and the price of doing so, of claiming something to be be true that then later turns out not to be.

What are the hallmarks of pathological science? Again, there was a great paper written about this that wasn't it brought back to light after the cold fusion debacle.

Irving Langmuir, a Nobel laureate chemist, gave a talk at IBM in 1957 on pathological. He called it pathological science, which is a science of things that aren't so. These are things he said it's not about fraud or manipulation data. It's about the scientists not realizing how easily they can be fooled. And there are certain signs and symptoms. The effects tend to be at the very level of the equipment's ability to detect them. There were four criteria that he listed. I should remember them, but I can't.

Yeah, there was a sensitivity analysis in there as well, small perturbations.

But let me give you an example sort of how this plays out. So you've got these test tubes, right? And this is a common thing, and they're supposedly generating excess heat. But the physicists are saying if you've created nuclear fusion, you should be generating gamma rays and neutrons. It should be radioactive. Chemists at the Georgia Tech Research Institute, which is attached to Georgia Tech University, they put together CO fusion cells and they borrow neutron detectors. In fact, you could think of them as Geiger counters from the physics department, and they've got these cold fusion cells, and they plug them in. The cells are bubbling away, and they hold the detectors over the cold fusion cells, and the detectors go off, and then they move the detector away, and the detector stops rattling. And then they bring it back over the cold fusion cell, and it starts. They're detecting neutrons clearly. So they write up a paper, they have a press conference. And at the press conference, one of the local physicists from Georgia Tech says to them, do you realize that these neutron detectors are humidity sensitive? Now, they're chemists. They've never worked with neutron detectors before. So they this at the press conference, and they go, tell us more about that. And they say, well, when you held it over the cell, the cell was bubbling. It's humid. That might have been why it went off. Right. They're doing what scientists have supposed to do, ideally before publication. So they go back to the lab, and now they do. What they should have done all along is they do a control. So a control is same experiment without the deuterium. Just do it with water. So now you're generating. Yeah, now they hold the detector over the control, and it goes off, and they hold it over. And they realize within a day of their press conference, probably 12 hours of their press conference, that they've screwed up. And the reason they screwed up is because they, in effect, chose the wrong control. They thought the control was just not holding it over the cell, but they weren't controlling for the humidity. Gets back to understanding your background. What are all the other possible causes of what we're seeing and we have to go through methodically? And this is what takes time, time, time. And you need the help of every smart person, you know, to point out all the different things that you might have thought for that you. You now have to design an experiment to control for. So everywhere in the country, somebody did an experiment and claimed that they had seen signs of cold fusion. Ultimately, you could explain it away as some aspect of the background that they didn't understand. And it was always. So it was chemists trying to do physics, it was physicists trying to do chemistry. And often, if these cells are really creating nuclear fusion, they got to be generating more heat than they're taking in from the wall. You know, they're plugged into the wall, so they have to generate more heat than they expenditure has to be greater than intake. It's a Chico thing here. And the science of calorimetry is very complicated. So there are people out there who spend their whole lives doing nothing but calorimetry. They were called calorimetrists in the chemical world, and they were capable of doing these experiments, right? When they did the experiments, they saw no excess heat. When somebody who wasn't a professional calorimetrist did the experiment, they inevitably saw a little bit of excess heat, often because they didn't realize that there's some feedback going down the cord. So you're going to get energy coming back into the cell from the power source. If you haven't spent your whole life doing this, you don't know the details of the background.

How long did it take for Pons and Fleischmann to come to grips with their errors?

Well, I don't think they ever did.

One of them's not even alive anymore. So he went to his grave believing.

I think, they both might have passed away by now. Fleischmann would have been at least 50 in 1989. So, you know, it's funny, there's a section of the Twittersphere that likes to point out that I was asked once at a debate in England whether or not I would ever change my mind about the uselessness of the energy balance equation for obesity. And I said, no, I doubt it. So constantly I get these tweets directly at me where people say, look, why are you even discussing with Talbes? He said he would never change his mind anyway. And the reason I said that is because you have phenomena like Pons and Fleischmann. I've witnessed people live through this. It takes a kind of superhuman intellect to be able to say something I believe beyond reasonable doubt and have staked my reputation on is actually wrong. It's always easier for somebody to believe personally that all the other experiments screwed up. People just didn't do the experiment right. And I used to joke that even if cold folks fusion was real, someday, back when I made this joke, I was probably thinking, around 2021, there will be the Stan Ponds Memorial Cold Fusion Nuclear plant on the coast between San Diego and la, pumping out enough energy from three beakers of heavy water to power the entire Southern empire. And I'm going to be crawling around the outside of that perimeter fence with a beard and pieces of Dunkin donuts in the beard and a tattered copy of Bad Science looking for where the damn thing is plugged in because I know it's a con, right? I am never going to be able to change my mind and accept. But then, on the other hand, my argument to the cold fusion people was, if you want us to believe it. Create cold fusion cells, make billions of dollars. The day that my neighbor buys a cold fusion powered car, I'm going to have to start believing it or I'm in trouble. Until then, I think an enormous amount of skepticism, if not closing my mind, Is perhaps the correct thing to do.

Well, I do want to come back to the point about your flexibility, or lack thereof, with respect to current belief systems around nutrition. But let's now really pivot from a career that spans a decade and a half Writing about physics and other means of science to the pursuit of public health. How did your curiosity get piqued there? What took you into a new avenue of science?

The cold fusion book, bad science. I interviewed around 300 people for this book. Horace Freeland Judson, who was one of the great science writers of the late 20th century. He wrote a book about the history of the molecular biology revolution. Anyway, he said I had done more research for the stupidest scientific subject Than any human being alive. I was obsessed with how this had happened, how people made themselves mistakes, how the sociology worked. Again, I became obsessed with pathological science as a concept. And when I was done with the book, I had a lot of friends in the physics community who really respected and appreciated what I did. And some of my friends in the physics community Were involved in this debate about whether or not electromagnetic fields from power lines can cause cancer. And that involved. So on one level, physics, because what we know about physics is they can cause cancer, but because their wavelengths are too big to interact with something as small as cells. And on the other side, you have epidemiology, you have the science of disease, how diseases spread. And researchers saying we could measure the field strength around power lines and somehow make an association between that and the levels of cancer in people living near the power lines. And so my physicist friends were horrified at the level of science they were seeing. And when my book came out, they said, well, look, if you're interested in bad science, maybe you should look at this stuff in public health, because it's terrible. And so the very first article I did, I did an article for the Atlantic on the power line cancer connection. And it was dependent on this field of epidemiology, which I had never paid any attention to, never had any reason to. But I had spent the previous six, seven years learning about how hard science is to get right, and being schooled and tutored and lectured into believing that you have to be extraordinarily rigorous and methodical and relentless, or you'll get the wrong answer. And here was a field where basically people just said, we're going to look at this, we're going to look at that, we're going to get an association, we're going to assume it's causal. They considered doing the kind of rigorous testing of hypotheses to be a luxury that they couldn't afford. So I wrote the Atlantic piece and then I wrote a piece on epidemiology for the journal Science that was infamous. I was used to be that if you wanted to publish critics of the field, first you published Alvin Feinstein, who was at Yale, and then it was Feinstein and Taubes, and now it's Ioannidis and Feinstein and Taubes have kind of been dwarfed by John Ioannidis contribution and Feinstein was a controversial character.

Both great pieces, by the way, which we'll be linking to in the show notes.

Thank you. So Feinstein's piece, you've got a field of science where you can't do the experiments. So that's the first problem. That's a problem with every field where there's an act of controversy. If you can do the experiments easily, like cold fusion, I think I've discovered infinite free energy. Well, tell me how you did it. Let me see if I can do the same thing. And if I can't, we ought to talk. And if he can't and she can't and. And they can't, then you probably screwed up, dude. If you can't do the experiment, then depending how interesting it is, you then develop a field of science around basically a hypothesis. And often as more and more people see the same phenomena, but without using the rigorous experimental techniques of an experimental science, they start to believe that this hypothesis is, is true. And you again, what I described with Rubia with this sort of group thing phenomenon where you collect the people who see the signal and you ignore the people who work on the background. In epidemiology, there was never any significant work on the background. So you could think of epidemiology where you, you have a cohort of people say the Nurses Health Study, the most famous in the US, 110,000 nurses. And you give them credit questionnaires, food frequency questionnaires, and you ask them what they're eating and they tell you what they're eating. And then you follow them and you see who gets sick and who doesn't. And then you look at what the people who get sick tended to be eating versus what the people who didn't get sick tend to be eating. And you have an association between diet and disease. And there's no causal information in that association. But now you hypothesize that the association is causal. Whatever they're eating or not eating causes disease. And there's no way to rigorously test it. So the process of science breaks down.

Let's stop for a second, Gary, and explain to people why the intuitive is not so. Because I think it's worth spending a second on the fact that all of this stuff that we talk about in terms of the rigor of scientific thinking, it's a relatively recent phenomenon. It's less than 400 years old. All of the things you're describing are less than 400 years old. Which means from an evolutionary perspective, they're non existent. It's not wired into our DNA.

Just might add that everything I'm describing is in. So Francis Bacon, 401 years ago, publishes Novum Organum, which is sort of the beginning text in the scientific method. And everything's in there. I had to go back around 2001, 2002. I read bacon because I was thinking maybe I'm just crazy.

But if you think back to 4,000 years ago, 40,000 years ago. Right. If you think back into the roots of our genes, the ability to pattern recognize and make an inference that by definition is presumed to be causal from an association must have been a vital trait for our success. I saw Larry doing X. Larry has the more mating opportunities than me. Doing X must be the reason that Larry has more mating opportunities than me.

That's true. So you make the association and then you test it. I'm going to do X and see if I get more mating. And evolution is going to test it. Right? Because if John now does X and it ends up getting him killed, but.

We didn't codify it, I think that's the difference. We didn't live long enough to see the result because the arc of the information coming back is too long. So you're right. Evolution did follow the experiment, but at the individual level we never got to learn from it. So come along 401 years ago and of course many steps along the way with the introduction of true randomization statistics, which became an important tool, etc. You have this concept that is very foreign to people and while anybody can say very glad, well, correlation doesn't equal causation, I think anybody has heard that and understands what it means. I don't think it's entirely clear to people why control and randomization matter and how ubiquitous bias is. So why is it that people living Next to telephone poles who have a higher likelihood of getting cancer are not getting it because of the telephone poles or the power lines. Rather.

Let's understand our background. So now this is the cruise. So the background is all the ways you can be fooled. Okay, so we see more cancers close to the power lines than we see far away. Can we explain that as something other than the power lines cause cancer? And so what else could explain this? That's the issue. And now you've opened up a door where we have an infinite number of possibilities. That's why right now the probability that your hypothesis correct has gone down enormously as soon as you accept the reality that there are an infinite number of other possibilities that you have to rule out. Some are very likely. You'd refer to, you used to refer to their first order factors and second order. And as you go down, they become less and less likely. And this one, we talked about the physicists needing a five sigma effect. That's because they're going to say, well, we can rule out all the major factors and we want to leave a lot of room for the fourth, fifth, sixth order variables because we can't get to everything. So maybe people live closer to power lines are poorer than people who live farther away from power lines. They might be a slightly lower socioeconomic status. Okay, nobody wants to live next to power lines. They're unsightly, they make noises. So if you can afford not to, you don't. So maybe what you think is being caused by the cancer is actually a result of socioeconomic status. I think a lot of what the epidemiologists publish are false positives that could be explained by socioeconomic status.

Now, the epidemiologist will tell you that they correct for that, don't they? Look at the household incomes.

If they look at the household incomes, there are major studies. The nurses health study, the most famous study in America, never looked at household incomes. The point is, many people don't. So let's say you had to get the health records of people, right, to know how much cancers they had. So how'd you get their health records? Well, we called the people up and we talked to them and asked them if they wanted to be involved in this study. And can we get their health records? Well, the people we called, we got to consider, we're calling them during the day. So maybe the people who answered the phone are the people who aren't working, right? So maybe there's a socioeconomic stat. Maybe there's a bias there because the more affluent people, the, the People who actually have jobs are not home to answer the phone during the day, right? Maybe. So there's a whole world of things like that.

Or maybe the people more likely to participate in the study are the ones that have worse outcomes worth reporting, want.

To participate in the study because God knows, little Jimmy had a brain cancer and they never understood why. And now you're giving them a reason, so they want to be a part of the study. And this is the process that Rubia didn't like to do of doing the background analysis. It's very hard to do because you have to. Each one of these factors is a hypothesis in and of itself. So you have to be able to test the hypothesis. Maybe the biases in the phone sampling process caused what we see. How do we test that? If you can't do an experiment, it's very hard to test. And then there's this factor we were talking about Nobel Dreams. My first book. So Rubia and company are spending the fall and the winter of 1985, 86, trying to understand their background. They understand more and more of their background. They finally come to the revelation that they're probably not seeing anything and other people are doing the same thing also. And there's a meeting in the Aosta Valley in northern Italy where they're going to discuss all of this. And I went to that meeting, and in the meeting, elderly Italian physicist, I forget his first name. His last name was Altarelli. A very cool guy. Altarelli stands up in the meeting, Ruby and everybody is there. And Altarelli begins to explain all the different ways that the equipment could have fooled Rubia into thinking every possible other explanation for the event. So maybe in this case the muon comes off and it gets lodged in the infinitesimal dividing line between the two detectors and you don't see it. Maybe a neutrino comes off on this one and it bounces off the side of the tau detector and you don't see it. And by the time he's done, and the chapter in the book was called the Altarelli Cocktail, and you come up with all these different ways. One of this, one of that, one of these are all the unknown unknowns. You can now imagine them, because around the world, physicists have spent months trying to. And it's clear that if you begin to understand the background and all the unknown unknowns, you could explain everything Ruby and company has seen. It's not a five sigma result, it's not even a one sigma result. That's where the discovery died, was in that meeting in Aosta, again, this is programming my thinking in cold fusion. All these different labs that had published positive results. I told you what happened at the Georgia Tech research lab. They all had a different explanation. There's another version of the Altarelli cocktail. But the point was, whenever they saw something that confirmed their preconceptions, they accepted it. Okay, so there's a selection bias in what you pay attention to, and you're always going to pay less attention to the things you don't want to see. So in epidemiology, people might look for one explanation. Say, I believe socioeconomic status probably explains a good deal of these phenomena, and people just don't measure it, or they don't measure it. Right? But even if it can only explain a quarter of the phenomena, or a tenth, I can guarantee you that there are probably nine other things that could cover the other nine. The difference is in epidemiology, right? You're looking for 95% confidence level. You're willing to claim a causal effect based on there is no causal information, and you're willing to claim a causality based on two sigma, not five, not three, but a 95% confidence level. When, if you actually try to think about it, if you said, let's treat this like the physicists treat their science, we can go through and we can come up with so many possible alternative explanations that there's a very high likelihood that what you're claiming is a discovery is a false positive.

In the case of that example, what would be the alternative experiment? If we're trying to answer the public health question, which is it's inevitable that we have power lines, we have to make an infrastructure decision, which is, can they traverse the areas where people live? One would argue that the length of time it would take to answer that question, which would be to randomize people independent of every variable imaginable, assuming you could get people willing to comport to such an experiment for a long enough period of time to see a result, I mean, you can quickly convince yourself that it's simply impossible to do a true experiment here. And yet an answer to that question should be known. So what is good enough?

Well, and that's the debate. And this is what the field of chronic disease epidemiology has been going through. So infectious disease epidemiology, like with COVID is a very different issue. You can, in effect, do randomized controlled trials like you do with vaccines here. As you just explained, they're effectively impossible, certainly with something like power lines, because you're never going to be able to randomize people to live near power lines or away from power lines. I think they're just beyond the limit of science. So the question is, how do you deal with that issue? The conventional thinking has been to embrace this idea of prudent avoidance. So, yeah, we do that when we wear masks for Covid. But prudent avoidance says you create regulations to try and minimize whatever fields are being emitted from these power lines. So the power lines pay more. They're not avoiding a potential harm. Doesn't come without its own harms, as we know in the COVID debate about what happens with shutdowns. So you can say, well, we can regulate the power lines better, but that means the power companies are going to raise their rates and now people are going to have to pay more for power, which means their socioeconomic status is going to be lower per dollar earned. There are positives and negatives, and these have become social value judgments more than anything. The question is also, what's the incumbent on the researchers? We're both. Every fan of science tends to be a Richard Feynman science fan, if not a Richard Feynman as an individual, has become apparently far more conflicting. Feynman says, and I quote this in the beginning, good calories, bad calories are fundamental. What you need fundamentally in good science is this sort of bending over backwards to be honest about what you know and what you don't know. So as soon as you start doing. And he says this in his 1974 Caltech commencement talk, where he talks about the difference between advertising good science, good science. The it's incumbent upon the investigators to talk about all the limitations in their research. And I think in an ideal article, you know, published journal report, the limitation section would be longer than the results section. Because what we want to know is all the ways they could have fooled themselves, realizing that they very likely did, and then to discuss it with complete and utter honesty, rigor. And I have critics who would say I don't do that. I personally, I try to do that the best I possibly can because I think that's the ethical, moral obligation of doing science or writing about science is not over exaggerating it. If the people, if the scientists covering the power line controversy said, we think we know this, but these are all the things we don't know. Nobody acts. That's the problem. So if they're right, like we think cigarettes cause lung cancer, but we never did a randomized controlled trial. Now the lung cancer effect is huge. That's like a six sigma effect. Even if there's no causal information. So we could kind of believe that's causal because we can't think of any alternative explanation for why smokers have such a hugely increased risk. If you're honest about what you see, that's fulfilling the obligation of the scientists. But now nobody changes their behavior. So if you're right that say power lines cause cancer, people are going to get cancer because you haven't forcefully enough made your argument. And that's the conflict of public health science. How do we get people to change their behavior when we really don't know if they should?

And this is an enormous problem. It's the dietary salt, dietary fat. It's where we're going to be going in the next couple of minutes here. So you've struggled through power lines and cancer. You've come to wrestle with the pitfalls of epidemiology. What brings you into the den of nutrition?

I'm living in la, Venice, near the beach, doing freelance science journalism and working on screenplays. Probably still not making any money. So my friends were wrong about at least my ability to get rich writing screenplays. I call up my editor at science and I say, look, I gotta pay the rent next month. I need a story I could turn over quickly. Do you have anything I could write that'll generate a paycheck? And so researchers have just reported, they're reporting on and the first results on the DASH diet. This dietary approach is to stop hypertension. It's a low fat, lots of fruits and vegetables dietary approach. And the paper's coming out in the New England Journal of Medicine and they have a preprint, they have a pre release copy, it's embargoed. So I should write about this.

And you've never written about nutrition and.

Health science, Never written about nutrition? No.

Okay, so he's pretty desperate if he's giving you this because this is a little outside of your wheelhouse, right?

It's one page in the magazine, it's not a lot going on. So the way you do a one page story, right, you call up the principal investigator. If the article hasn't been published yet, you ask the PI you interview him, you ask the PI for the names of a couple of other people who know about the research. You could comment. Three interviews for one page is doing your job. So it takes a morning to do the interview, an afternoon to write the article. It's a day and a half, I get my rent money. What I didn't know about this article was it had been leaked to science in advance and it had been leaked with a list of researchers who I could interview. This is what year this was, 1998 or 9. My editor gives me the list. He doesn't tell me it was leaked, or if he did, I didn't pay attention. So I get the article. I call up the principal investigator, Larry Appel at Johns Hopkins. I interview him. I ask him for the names of people I could talk to. I call up one of the people on the list. This is a former president of the American Heart association at the University of Alabama, Birmingham. And this person tells me that they can't talk about the paper or they'll lose their funding. And I say, we're talking about a diet trial. The New England Journal of Medicine. Nobody loses their funding for talking about that. It's going to be. Not going to come out until after the. The embargo. The woman, she refuses to talk to me. She won't tell me anything. I said, look, if there's something wrong with this paper, let's go off the record, not for attribution, like Woodward and Bernstein in the garage with Deep Throat. Tell me what's wrong with this paper, what the issue is, because if you don't tell me, I'll never know and I'll report it incorrectly. She refuses even to do that. So then I get off the phone with her. I call up one of the people that Larry Appelwood Johns Hopkins has given me. And he's a researcher who starts yelling at me. This guy is the grand old man of the field. I don't know this. And he starts yelling at me over the phone that there's no controversy over salt and blood pressure. And I say, I'm not calling about salt and blood pressure, Professor. I'm calling about this diet trial that lowered blood pressure in the New England Journal of Medicine. And he continues to berate me that there's no controversy. So I get off the phone with him, and I call up my editor at Science. I said, I had a American Heart association former president refused to talk to me because she would lose her funding, she said, if she did. And then I had this other guy yelling at me that there's no controversy over salt and blood pressure. When I'm not writing about salt and blood pressure, there must be a controversy about salt and blood pressure that I know nothing about. So I'm going to write up this article, get my paycheck, and then, if you don't mind, I'm going to look into this assumed salt blood pressure controversy and see what we're missing. And I spent the next nine months reporting that I interviewed about 85 people for one magazine article. Turns out that it is one of the most vitriolic controversies in the history of medicine. And even though already by 1998, we'd all been eating low salt diets in America for 15 to 20 years, it was clear that the randomized control evidence never really supported the intervention and that it was backed up by a lot of bad epidemiology and researchers assuming that associations were causal that weren't, and there were even questionable associations to begin with. And while I was doing that story, this fellow who was yelling at me, I like to joke he sounded exactly like Walter Matthau over the telephone. By the way, he's still alive. He's about 101 years old. So while I'm running him down as a scientist, that's evidence that maybe he understands nutrition and diet far better than I do. Anyway, while I was interviewing him, this Walter Matthau character, it was clearly the, you know, I had spent 10 years of my life studying bad science was clear. This guy was one of the worst scientists I'd ever interviewed in my life, in the bottom five, at least. And I thought I had interviewed the worst. He took credit not just for getting Americans to eat less salt, but eat less fat as well, for the low fat diet we had all been on since 1984. So at one point I called up my editor and I said, look, this guy was involved in this controversy, the fat controversy in any way. The fat story, there's got to be a story there. The message from Nobel dreams and bad science was that bad scientists never get the right answer. It's just, it's too hard to get the right answer for you to go in being sloppy and slipshod and lazy and ambitious. Ambitious and get it right. Nature isn't that kind. So I said, you know, when I'm done writing about salt, I'm going to write about fat. I have no idea what the story is I've been eating. I've been living in LA, eating my egg whites and probably a 15% fat diet. Dean Ornish would have been proud of me. But if the dogma was based in any substantive way on this fellow's work, there's a story there. And I spent a year writing that piece. I interviewed 140, 45 researchers and administrators for one magazine article, the Big Fat Lie. No, this was the soft science of dietary fat. So this was. The salt story was called the Political Science of Salt. Political in parentheses. And the fat Story was called the Soft Science of Dietary Fat. Soft in parentheses. They both won national association of Science Writers, Science and Society awards.

After you wrote these two articles, one on the soft science of fat and the other on the political science of salt, what made you decide to go even further and write what would become perhaps the biggest and most controversial piece you wrote, at least in the newspaper, I believe it was for the Times magazine, wasn't it?

Yeah, New York Times Magazine. I wanted to write a book when I was done with the two science articles on whatever was happening in medical science that could lead to the these kind of mistakes. Remember, my obsession is pathological science. The nutrition aspect of it is just an interesting vehicle through which to explore it. I realized that if I did a book, then I would go broke. I was married. I had responsibilities. Remember, I had come out $40,000 in debt just doing the cold fusion book. It was clear I didn't work fast and I didn't want to work fast, and that I would not be able to get an advance large enough to cover the time it would take to do the book. So I was living in New York. I was having lunch once a month with an editor from the New York Times Magazine because, among other things, we shared an affinity for the same local French cafe in the village. And we would talk about story ideas. And we decided it might be a good idea to see about to ask the question, what caused the obesity epidemic? And I said to this guy, when I was reporting the dietary fat story for Science, I had had met up with an administrator from the NIH who said, you know, it's interesting, when we told people to go on low fat diets in 1984, we assumed we really didn't have the evidence to support the heart disease connection. And the message of my story is they never got that evidence. But we thought, if nothing else, we'd be telling people to avoid the densest calories in the diet. So if they avoided fat, they'd lose weight and that would take care of the. Obesity and overweight are the greatest risk factors for heart disease. And he said, lo and behold, now we have an obesity epidemic. And apparently people stopped eating fat and eating more carbohydrates, and that got him fatter. So I always had this two hypotheses for what caused the obesity epidemic, which you can see in the data, begins somewhere between 1978 and 1991. And it coincides with two fundamental changes in the American diet. One was the embracing of the idea that a low fat diet is a heart Healthy diet. So carbohydrates in general go from being considered inherently fattening, which is sort of the conventional wisdom up till the 1960s, and then they get transformed into heart healthy diet foods. And you may be too young to remember this, but we all stopped eating butter and started having pasta and bagels every day. And lo and behold, everybody starts getting fatter. The other thing is high fructose corn syrup came in 1977, 78 high fructose corn sirup55 comes in which can replace sugar in Coca Cola and pepsi. And by 1984 it saturates the beverage industry. And this coincides with the beginning of the epidemic. And people like Michael Pollan had suggested that high fructose corn sirup was the cause of the epidemic. So we decided I would write an article about what might have caused the obesity epidemic. It was new enough then that people cared and this would be an important story. And in the course of writing that article, I came upon five studies that had been finished but not yet published. They'd all been discussed in conferences. So I could discuss them in the article which had compared the Atkins diet, which is a low carbohydrate, high fat, eat as much as you want diet, to the kind of low fat, calorie restricted diet the American Heart association was pushing. And then all five trials, the Atkins diet, not only did people lose more weight, but their heart disease risk factors improved. So remember, I'm coming at this kind of from I'm programmed to think from a scientific perspective. I won't say that I'm coming from to it as a scientist because I know people don't like to hear that from a journalist. There are two fundamentals. There are two hypotheses out there. One is that people get fat because they eat too much, and the other is that people get heart disease because they eat high fat foods. And now you run a diet trial where you compare a high fat eat as much as you want diet to a low fat calorie restricted diet. And your two hypotheses would predict that the high fat diet, the Atkins diet, those patients would get fatter because they could eat as much as they want. And clearly they got fat to begin with because they eat too much and that they would have worse heart disease risk factors. And in both cases, the hypothesis failed to pan out. So from a scientific perspective, the first five clinical trials on low carb, high fat diets, actually it was the second through sixth. There was one earlier one that saw the same thing that no one discussed refuted two of the fundamental hypotheses of modern nutrition science.

How did you find these? I mean, I know one of them in there is the Minnesota Heart Study, which wasn't published until 1989, right?

Yeah. No, this is Eric Westman's first trial at Duke. There was a group at the VA hospital in Pennsylvania that was doing a trial. There was some researchers in Long island that did a trial.

Why weren't they published?

They just hadn't been published yet. They had been finished and the research we're talking about then once I did my article, it made it both more important for them to get published and simultaneously harder for them to get published because nobody wanted to hear. They wanted to see what these people really had, but not actually have to publish what these people really had. But over the next two or three years, they all came out in the journals.

How did you sort of come to grips with this? This is now challenging your own beliefs. Presumably you had believed that this low fat diet was healthier. Had you ever struggled with your weight?

Yeah, I was a college football player. So in college I tried to be as heavy as humanly possible and eating constantly. I don't know if you did this in college, but we would go out at 11 o' clock at night to Elsie's Diner across the street from the dorm and eat a thousand calories or 1500 calories worth of the biggest sandwiches they had and then go, go home and go to bed. I mean, that's what you, you know, Anyway, I could get up to 240. My boxing weight was 212. And once I turned 30 and simultaneously had moved to California, I just started drifting upward. While I was reporting the science article, I was simultaneously doing a piece for Discover magazine on the mathematics of the stock market. And so I was up at MIT interviewing an economist who ran the laboratory for financial engineering up there about his research, trying to establish whether people like Warren Buffett are brilliant or just lucky. Which is an interesting question because you're looking for the signal of talent over the signal of luck, over the background of luck. And you don't, of course, fully understand your background. So we got to talking about good science and bad science. I told him about the dietary fat article I was writing and he said, oh, well, if you're doing a story on fat, you got to try at. He said his collaborator, Wharton's father, lost £200 on Atkins, and this fellow at MIT is an Asian American. And he said he gave up, basically gave up white rice and lost 40 pounds. So I went back to LA, where I was living at that point. I was unmarried. My parents had passed away, I had no children. If I killed myself on Atkins, the only one who might care were my cats. And so I did Atkins as an experiment and lost 20, 25 pounds in six weeks. And then like everyone else, I sort of drifted off the diet by the time I started this New York Times Magazine piece. If you go back to read the New York Times Magazine article, there's a line in it that says overweight, of course, is caused by taking in more calories than you expand. Which today I think is a meaningless statement. And both wrong and meaningless. There were a lot of ways I had already, because of my research on the dietary fat story, I could already accept the idea that these diets would not raise the risk of heart disease. It was clear to me the dietary fat story was uncompelling at best. There was no compelling evidence to avoid saturated fats. The question about the fundamental cause of obesity and how best to regulate weight was something I didn't really understand when I did that article. But that exceedingly controversial article on the front page of the New York Times Magazine will indeed get you a large book advance if the subject one of natural interest to readers. It did get me a large book advance. People like to say that that's the only reason I came to the controversial conclusions I did, because it would sell more books. What they don't realize is that a large advance in New York was enough to live for four years. And the book took me five years. That's when I started the research for Good Calories, Bad Calories and got obsessed with that story.

And how did your thinking of the nuance around this topic evolved during the writing of Good Calories, Bad Calories?

I mean, it's still evolving. That's what's so bizarre about this, is I still wake up at 3 in the morning thinking, why didn't I say that 13 years ago?

So there's these two issues, right? Which, I mean, I don't think we'll have time to go into both of them. The one being the saturated fat issue, which is that dietary saturated fat drives atherosclerosis, and the other being the cause of obesity being calorie imbalance. And you kind of go after both of these in parallel, but they're really different. They're not mutually exclusive.

Right. But they're related.

They're very related. Yes.

And that's the issue. The dietary heart story was we get heart disease because of the saturated fat content of the Diet elevating LDL cholesterol. And here a guy like Atkins comes along and says, well look, don't eat carbohydrates. So if you want to minimize your risk of heart disease by the dietary fat store, you avoid fats, saturated fats, you replace them with carbohydrates. Back then, now they say monounsaturated or unsaturated fats. And you're now eating a carbohydrate rich diet. And in theory, you're going to minimize your risk of heart disease. The flip side was that carbohydrates are fattening. That's the simplest way to describe what's now called the carbohydrate insulin model. To those of us who gain weight easily, it's the carbohydrates that do it. And we can't eat carbs. But if you don't eat carbs, what are you going to replace those with? And your choices are protein and fat, and protein usually comes with fat attached in real foods. And so you're going to increase your fat consumption one way or the other. And now you're eating a diet that in theory is supposed to kill you. And that was the one of the many arguments against Atkins was it's going to to trigger heart disease and you're going to drop dead. When I first tried it as an experiment, I kept waiting to drop dead and I'm still waiting. I mean, I still have a piece of bacon and pat of butter and I wait for my heart to blow up. So one of the issues I had to deal with in the book is how did we come to believe that dietary fat's the problem? So the first third of the book is the evolution of the it's the soft science of dietary fat. It looks length that how did this theory evolve? What was the real data? What were the problems with the data? Why is it likely wrong? And then the second part of the book is as doing the research, I realized that there had always been a competing hypothesis, which is that the chronic diseases that associate with modern diets and lifestyles are driven by the carbohydrate content of the diet. So this started as a British hypothesis, British nutritionists, and then it should have embraced in the 1960s the research by people like Jerry Reben and others on insulin resistance and all of that targeted the quality and the type of carbohydrates we're eating, the glycemic index and the fructose con. And I didn't know anything about that when I started this Research. And so the first third of good calories, bad calories, is the deconstruction of the fat hypothesis. The second third is the replacement with a carbohydrate centric hypothesis hypothesis. And then the third third or the second half of the book actually is this question of obesity. Because it's clear that whatever causes obesity is so closely associated heart disease and diabetes, type 2 diabetes, that whatever makes us fat also causes these chronic diseases that associate with us. And the question is, what makes us fat? And now doing the research, I also came upon a whole other stream of research that had kind of been ignored. First by Pre World War II European researchers saying obesity is not an energy balance problem, it's not caused by people eating too much, it's a hormonal regulatory disorder. And then Post World War II, by people studying primarily the science of hunger, saying, in effect, hunger is caused by fuel availability. If your liver thinks there's enough fuel available, then you're not going to be hungry. And when your liver starts thinking fuel is being crimped, then it'll release inhibitions on fuel, food seeking behavior. And that work done by people in this field of physiological psychology, all implicated insulin is the hormone determining fuel availability, the primary hormone. And then there was also, and this I think, is what gets Me most, until 1930, virtually no one studied fat tissue. In all these sort of burgeoning fields of physiology and endocrinology, nobody really cared. The assumption was it was inertia, it was for padding and cushioning. And if you starve people, somehow the fat tissue would shrink. They had no way to study fat tissue or fat accumulation. Then, beginning in 1933, with this work of this German emigre, Schonheimer at Columbia, you start having people studying fat tissue. They become aware that mobilization, deposition goes into fat, goes on constantly. Like even when you're in between meals or starving, your body is still depositing calories as fat and mobilizing calories as fat. And through the 1940s and 50s, they begin to work out the details as more and more tools become available to study this problem. And by the mid-1960s, you have very well established science of what we could call intermediary metabolism, which is what your body does with the foods, the proteins, fats and carbohydrates after you eat them. And then what your body continues to do to make fuel available as necessary to your body. And all of this is left out of the science of obesity. As soon as the obesity researchers start saying, we've demonstrated that obesity is caused by Energy balance, imbalance. The impolitical way of saying that is people eat too much. That's a fact of life. We can see it in our animal models. Because when we create an obese animal model, A10 tends to be what they call hyperphagic, which is very hungry. So we have an association between hunger and obesity, and we assume causality. And they just kind of ignore the entire science of fat metabolism. And even to this day, when I try to make that point, it's excruciatingly hard to. Nobody wants to hear it, nobody wants to deal with it. And if I had to prove it to an editor, I could. Ideally, I would have the fact checker fly out to my office at Oakland and say, let's go through these textbooks from 1965 onward and I'll show you the complete and utter absence of any discussion of this science of fat metabolism, fat storage. From the obesity discussion. And from the 1960s onward, the obesity researchers are dominate. Well, obesity research in the 1960s is dominated by psychiatrists and psychologists trying to get fat people to eat less. People like Mickey Stunkard, who was a wonderful man, but his expertise was psychology. And this idea that obesity is a hormonal dysregulation of fat storage, fatty acid oxidation and storage, a fuel partitioning disorder, gets left out of the field.

Who do you most credit for that? Changing that new course in the 1930s, thereabout, where did this idea that is the dominant point of view with respect to obesity being caused by caloric imbalance rather than being the result? Because there has to be luck sometimes, right? Like sometimes there's a dominant personality wins out or something like that.

You think of a field like diabetes where Elliot Joslin opens the first diabetes clinic in the U.S. dedicated diabetes clinic in the U.S. around 1900. And by 1916, he's seen 1,000 patients, which is about 90, 950 more diabetic patients than any other doctor in America. So he writes the first textbook in 1916, and he does another edition in 1917 and 1923 and 28 and 33. And we're now on, I think, edition 14 of Jocelyn's Diabetes Mellitus. Jocelyn became the determinant of truth, the arbitrator of truth in the diabetes world. One researcher, one physician. He's a wonderful physician, not a great scientist because of his position in the field. This doesn't happen in other. I don't know if it happened. It didn't happen in physics because there are too many bright people arguing about the data and not enough People outside care. But in medicine, you get fields in which single individuals, like the famous breast cancer surgeon from Hopkins, Halstead. Yeah, it's maybe a kind of unique phenomena in science, in obesity research. There was no such thing as obesity research pre1930. If, when you read the literature and you go back and look at the literature, there's half a dozen physicians around the world who are writing articles about obesity, and often with some statement of causality. And there's kind of two competing hypotheses. One is fat people just eat too much. And you know that's true because you have an association between gluttony and obesity. Right. They watch Shakespeare. We know Falstaff was obese. He had this zest for living. He ate people out of house and home. So therefore, you assume causality from an association. That's classic pathological science. And the other is that people who suffer from obesity have some kind of hormonal dysregulation, some kind of dysregulation where they're going to get fat regardless of how much they eat. So 1930, Lewis Newberg comes along. He's a physician at the University of Michigan. He does what he considers, considers the first experimental test of the hormonal regulatory disorder hypothesis that you get fat no matter how much you eat or how little. He runs the tests on six or seven people. He discusses it at a medical meeting in Boston. So this guy is now, we'll call him, if not the Newton of the field, the Eddington of modern. You know, this is the guy's name that everyone. If obesity research was a functioning science, every obesity researcher would be insulted if you thought they didn't know who Lewis Newberg was. He publishes a series of papers in 1930 based on 30, 31, based on the one experiment with like seven patients where he basically starves lean people and patients with obesity and says they lose weight at roughly the same rate when starved. Therefore, the obese patients, people. I'm not making this up, got fat because they ate too much. He saw this as refuting the conventional hormonal regulatory hypothesis. Nobody's ever done an experiment in obesity before. So Newberg's papers begin to be taken as gospel. And even though there are European researchers, particularly Julius Bauer, who's one of the pioneers of the science of endocrinology at the University of the Vienna and Austria, which the Germans and Austrians at this period of time are this sort of apex of the height of medical science, he's writing that this is naive and can't possibly be right based on a series of what I think are very logical and undeniable observations. Even back in 1920, as soon as Newberg publishes his article, Bauer co authors an article, the first thing he ever writes in English, explaining why Newberg's argument is inane. This idea that people get fat because they take in more calories than they expend. 1938, the first animal model of obesity is pioneered at the laboratory of a fellow named Ransom, the leading neuroanatomist of the era, and by his graduate student Albert Hetherington. And they take what's called a stereotactic instrument, where you could use it to direct a needle into the brain of an animal. And it been used on dogs. Heatherington pioneers. So he could use it on rats, and he can reproducibly create obesity if they lesion the ventromedial hypothalamus of the rat. Hetherington has a. He's a postdoc. There's a fellow postdoc at the lab named John Brobach, who 1939, gets accepted at Yale for medical school. He's got his doctorate. He goes back to New Haven. He needs to get a job to help finance medical school. He gets a job in a laboratory, and he realizes it's a stereotactic instrument and he can do the same experiments that Hetherington did. And Brobeck starts doing the VMH lesion experiments at Yale. And Brobeck comes, creates what he believes, he discovers what he calls hyperphagic obesity. So you lesion the VMH of the rat, the rat gets obese and it gets crazy hungry. And so you now have the association between hunger and obesity.

I've read that the rats got so hungry that even as you're taking the anesthesia off them, they're basically trying to eat as they're coming out of anesthesia.

I interviewed recently, a researcher, did these experiments in the 70s. He compared it to gasping for breath. So, like, if you are drowning and you suddenly come up above the water and you're going, the animals are eating like that. And it's a fascinating observation because you can explain it pretty easily. And in fact, if you don't allow the anesthesia to wear off, if you allow the animal to eat while it's still under the influence of the anesthesia, it might choke to death. And they often did. So the researchers had to learn to give the animal a couple hours despite its evident starvation. Brobeck has read Newberg. Okay, so Brobeck interprets this experiments as I created hyperphagia, extreme Hunger in the animal. That's the discovery. And the animal got obese. The fact that the animal got obese because it was hyperphagic is just boring. Animals get fat because they eat too much. I know that because that's what Newberg says back in Chicago at Ranson's lab.

Let's pause for one sec because that's a very important point. I think this gets to something you've already talked about twice. But I want to make sure the reader doesn't miss the subtlety of this or the listener. Rather, the observation is undeniable. If you lesion the part of the brain in question, the animal eats in an uncontrolled manner. However, if your incoming hypothesis is that overeating leads to obesity, you will interpret it as lesioning that part of the brain leads to overeating. If your hypothesis is that fat accumulation, dysregulated fat accumulation leads to obesity, you would interpret that finding as lesioning that part of the brain leads to dysregulated fat accumulation which then causes overeating. And that's a very subtle difference. And I try to be fair to the people of the era to see how easy it could be to make that mistake.

Well, and now let's put it in the context of remember the cold fusion, what's your control? So if your hypothesis is that eating too much much causes obesity in these animals when they're lesion, the proper control is control for the eating too much. So what I was going to say back in Chicago, Hetherington and Ransom had animals that got obese independent of the hyperphagia. So some of their animals got fat anyway. Actually some of Brobeck's animals also got fat despite not eating any more than lean animals.

Did he ever pair feed his animals with non lesioned animals?

Brobeck did. He did an experiment where he pare fed them with non lesioned animals. And if I remember correctly, it was a dozen animals. Nine of them did not get fatter, but three of them did despite being pair fed. So Brobeck, because he had a pre existing hypothesis, assumed that there must have been something wrong with these three animals or he did the lesion incorrectly. Or maybe they were eating at different times so he left them out of the analysis. He mentions it in the paper, but they're the counter argument. They're the evidence for this counter hypothesis which something about the lesion dysregulates fat accumulation.

What do you think explains the other nine?

You can imagine that if you dysregulate the fat. So let's say we take an animal that normally would grow naturally and we create a dysregulation in their fat tissue such that it's going to store excess fat. It might not be able to do that if you restrict its calories. It's that simple. We all know from personal experience we can lose weight by starving ourselves. So we can somehow starve our predisposition to get fatter by restricting the amount of food we eat. The counter argument to the hyperphagia causes obesity. Evidence is obesity being caused in the absence of hyperphagia. So what Ranson and Hetherington argued. Ransom was the leading neuroanatomist of the day. He had just come from studying diabetes insipidus. And in diabetes insipidus you can also cause it by lesioning a different part of the hypothalamus. You have animals that are extremely thirsty. I forget the technical term. Yeah. So like any diet, they have endless thirst and they're peeing constantly. So the conventional thinking might be the reason they're peeing so much is because they're so thirsty. And the counter hypothesis is that the thirst is a response. The lesion causes them to pee constantly. They're losing body water that makes them thirsty.

That's become unequivocally the case. We now know that diabetes insipidus is the result of the loss of the hormone called antidiuretic hormone, or ddavp. The hyper drinking is in response to replenishing the water that they're losing because they can't concentrate urine.

Exactly. So Branson thinks, well, if they're losing, if you're losing water and drinking in response, maybe in these cases the animal is losing calories, energy into its fat tissue and they're eating in response. The hyperphagia is a response to the loss of energy. And some of his animals, the one that got obese, were also sedentary. So he said maybe the ones that got obese without hyperphasia were sedentary. He said maybe the reason they're sedentary is because again, the losing calories into the fat tissue, the fat tissue has been. Is creating a sort of vacuum and the animal is responding either by eating more or exercising less. So changes in energy balance are a response to the fat tissue being driven to accumulate calories or fat calories. They published this paper three months later. This is 1942. Ransom has a heart attack and dies.

He was eating too much fat.

Clearly. Hetherington, as postdoc, it's the Middle World War II joins the air Force and leaves the research behind. And the only voice left in the field is John Brobach. And so Brobach dismisses their hypothesis, not really even understanding it. It's clear from the literature he still thinks they're trying to blame obesity on sedentary behavior because he's trapped in this energy balance, thinking he doesn't realize that they're trying to blame both the sedentary behavior and the hypophagia on the drive to a accumulate fat, which is the fundamental different paradigm. So bro Beck continues to write about hyperphasia causing obesity. By the 1950s, between Brobeck's work and Newberg's work, the conventional wisdom is all fat people get fat because they eat too much. And there are textbooks that have a statement to that effect in virtually those identical words. By the 1960s, obesity, as I said as being the leading figures in the in the field are psychologists and psychiatrists who are trying to explain why fat people eat too much and how to stop them from eating. And there's a whole world of physiologists and biochemists studying fat metabolism, learning about all the ways it's regulated, learning how its deposition and mobilization of fat go on independent of the nutritional state of the organism. That's a direct quote from one of the seminal papers and that what you should be studying is, is not how much people eat and exercise, but why they accumulate so much fat.

What's the best explanation for why a lesion to the vmh, which is a very central, very, very specific narrow part of the brain, why would that have such a broad peripheral consequence of fat hoarding within all of the adipose tissue throughout the body? What is the mechanistic or best mechanistic explanation for that in that model?

Well, in my model, first observable effect from the VMH lesion is hyperinsulinemia. So you lesion the brain and the animal hyper secretes insulin in response to even thinking about food. Now if you think about it, an animal that's hyper secreting insulin isn't going to be able to. So the insulin is signaling its fat tissue to take up fat and to store it for food. It's in store it for fuel, it's inhibiting the body process of lipolysis. It's also inhibiting the oxidation of fatty acids in the muscle tissue through the malonyl COA pathway. So think about what happens now to these rats or people with tumors. You have the surgery, you hyper secrete insulin, you wake up from the surgery without carbohydrate supplies and unable to oxidize dietary fat. So that would explain the hunger. It's as though you've created a starvation state in the animal instantaneously. You know, normally it's going to take a while for the animal to get to the point where it has to start cannibalizing its own muscle for fuel if you're starving it. But now you've done it instantaneously with the lesion, and that would explain the gasping for food that these animals manifest in the hyper aphasia.

What's the relative increase in insulin that those animals experienced?

You know, I don't have those numbers.

That's an important thing. Right. I mean, if that's the conduit through which a central lesion has such a broad peripheral implication, I would expect an enormous increase in insulin to produce that, a level that wouldn't be otherwise physiologically described. I guess I'm struggling with how that alone could do it.

Well, also, if you think about it, until 1960, they had no way to measure insulin in the bloodstream.

Right. But we could do it today is what I'm saying.

Yeah, I just don't know the number. I mean, it was clearly enormous and it was observed. And post1960, you get yellow and Burson's radio immunoassay. So now you could actually measure insulin levels from blood samples accurately and with sensitivity. And pretty quickly afterwards, that's when they realized that the VMH lesion animals were hyperinsulinemic.

How did the obob mice also give us a clue into both ways to interpret the same observation?

Yeah. So the obob mouse is a mouse that's discovered at the Jackson Laboratories in Maine. It's a mutant that manifests obesity, dramatic obesity. And turns out there's two different depending on what strain you breed these animals into. The DBDB mouse is diabetic, obese and diabetic. The obob mouse is just obese. The work is done at the Jackson laboratory. What's interesting is the assumption is always right that these animals get fat, that they're missing some kind of satiety hormone. The theories through the 1950s, based on Newberg's work, and this idea that obese people get fat not because they partition fuel preferentially into fat tissue, but because they take in more energy than they expand the dominant. The only hypotheses of obesity in the 1950s, there's a lipostat hypothesis from a guy named Kennedy and a glucostat hypothesis from Jean Meir. There's a Thermostat hypothesis with body heat. They're all hypotheses trying to explain eating too much in the animal and none.

Of these pan out.

Not only do they not pan out, you've got these animals that get obese if you think they're caused by eating too much. Again, what's the correct control? Just pear feed the animal. Don't let the animal eat more than a lean animal. So this experiment is done numerous times with leptin, the obob mice. If you take a lean animal, measure how much it's eating, give half of that to the obob mouse, so you're literally semi starving it, it gets obese anyway. One of the fellows who published these results is Douglas Coleman. But Jeff Friedman gets credit for doing the work that identified leptin as a hormone that's missing in the obob animal or dysfunctional. And he did it based on Coleman's parabiosis experiments at Jackson Lab with the OBOB and DBDB animals. So Jeff Friedman comes along, he's a young ambitious researcher. He wants to look for the obesity hormone, the satiety hormone that's missing in the obob mice. He comes with this assumption that what's missing is a signal from the fat tissue to the brain telling the brain not to eat too much. When he discovers leptin, he interprets that signal as being the satiety hormone. It's been identified as satiety hormone ever since. When people write books with titles like the Hungry Brain, they're assuming that the absence of leptin makes the brain hungry. The person gets hungry, eats too much, and that's why they get fat. And yet you always have this observation in the field that the animal gets fat even when it's half starved. The point is, I'm doing this book on diabetes now. I was curious, what is it about the DBDB animal? Why would the absence of a leptin receptor cause diabetes? And I never really thought about it. So you look into the literature. We actually asked Rudi Leibel and Rudi said, well, basically it depends what background you breed the animal onto. And then I went back and I found where Douglas Coleman discussed it. And so the issue is both animals are obese, both animals are hyperinsulinemic from weaning onward. So in the DBDB animals, the background strain can't sustain the hyperinsulinemia, so its pancreas fails and you manifest frank diabetes. The obob strain can continue to keep pumping out the the necessary insulin. But now you've got leptin, basically triggering hyperinsulinemia. And it's always been known these animals are hyperinsulinemic. And now you're back to the Bauer ransom hypothesis. I didn't say, by the way, that when Ranson and Hetherington interpreted the data the way they did, they cited Julius Bauer. So Bro Beck cites Newberg. He's got a preconceived opinion, and Ranson had his own preconceived opinion based on the diabetes insipidus work, and Julius Bauer's paper supported that. But we grew up with one hypothesis, which is the Brobeck eating too much hypothesis, and it influenced how the leptin worked, was interpreted. It's influenced virtually every experiment afterwards. And then some body like me comes along and says, well, wait a minute, all this evidence supports the alternative hypothesis. And you have not been considering that for 67, 70 years. How can you even trust anything you've done when you weren't aware that there was a competing hypothesis? It's an awkward place to be in.

Now, your work has brought a lot of attention to this alternative hypothesis over the past decade, and there have been a number of people who have tried to test it. How would you reconcile the findings that have not demonstrated what would be predicted by the carbohydrate insulin model?

This then gets into the kind of questions we talked about earlier, about judging the value of the experiments, how rigorously they're done, and the biases and preconceptions of the researchers. So. And again, I've been having these arguments on Twitter this week, and I have to stay the hell off Twitter. And you know this. We funded these people at nusi. We funded two groups of researchers, one who was inherently, basically had a hypothesis that dietary fat and fat balance was a driver of obesity, and we. One that had a carbohydrate insulin model like we did. And. And the researchers who believe the conventional wisdom interpreted their results as supporting the conventional wisdom and refuting the carbohydrate insulin model, and the researchers who believe the carbohydrate insulin model interpreted their results as supporting that model.

So let's pause there for a second because I simply don't see how this field can make any progress. And I'm trying to understand how this field can make progress in a way that physics does when, as you point out, everybody in this field is biased. Everybody has a point of view, and everyone seems to do experiments that simply confirm their point of view. There's one other thing that makes this more difficult, which is the inherent messiness of biology as Complicated as physics is, I think biology is more complicated. We don't have a standard model of biology the way we have a standard model of physics. So there are more unknown unknowns in biology. And then secondly, the experiments are far more difficult to control. I think there's more noise in the biologic experiments. And then you couple that with everything we just said. I just don't understand what a path forward looks like towards a reconciliation.

I don't know if reconciliation is the word you're looking for. You want to know which hypothesis is right?

Yeah, I mean, a scientific reconciliation.

We're recapitulating the discussions we had whatever eight, 10 years ago now, when Nusi was starting. One of my favorite stories is background 2009, after good calories, Bad Calories came out, I was invited to lecture at the Pennington Biomedical Research center, which is the largest obesity research center in the country. And I gave my lecture, why we get fat, adiposity 101. And I suggested that the energy balance hypothesis, thinking was, I like to say, not even wrong, stealing from Wolfgang Pauli and then why all the reasons why it should be replaced with this hormonal regulatory disorder of focusing on insulin. And after my talk, one of the faculty raised his hand very politely, a gentleman who was probably then as old as I am now, maybe 65, and he said, excuse me, Mr. Taubes, would it be correct to assume that you think we are all idiots? Because the argument I was making is that they embrace the wrong paradigm. And it was a tough question because partially the ants, I want to say, well, that's one way to look at it. I can't say that. So what I said to them is, I think the problem here is that when you entered the field, there was a paradigm, a way of thinking about obesity that seemed so intuitively obvious, this idea that it's an energy balance disorder, that you never questioned it and certainly your mentors didn't question it. And so you were assumed this had been well tested and well proven and unambiguous, and that it deserved to be dogma. And it hadn't been in. It didn't. And that's been a problem ever since. Now we come along 50 years later, 40 years later, and we have to get people to a huge proportion of the community to entertain the possibility that their fundamental belief system is wrong. So this isn't a subtle shift in thinking. This is you're operating under the wrong paradigm. You think the Earth is flat and it's round, or you think the sun rotates around the Earth. But it's the other way around. 99.9999. Thrown as many nines as you want. Percent of the time, people say that they're quacks.

So why are you not?

That's a good question. I often argue this with my old friend, our old colleague Mark Friedman. And Mark says, well, we're not because we're right. And I say, well, every quack thinks they're right, Mark. That's not evidence that we're not quacks.

That's the same tautology that you argue against.

Yeah, well, the other argument is an even better one. I'm not a doctor, so technically I can't be a quack. The best I can hope for is whack job.

I would offer a more compelling reason. If you turn out to not be a quack, which is the application of the current model is failing, that's probably the most compelling reason to continue to question it, I would say. So. In other words, if we believed that the Earth was the center of the universe and every time we tried to launch a rocket into space, it blew up because we failed to understand gravity and orbits, I would hope we would then say, God, what if the Earth is actually moving even though it doesn't feel like it?

That's where all these discussions start is failure to prevent obesity, failure to treat obesity. The obesity and diabetes epidemics are out of control. Let us question these fundamental hypotheses. How do we test them? I'm thinking at the moment, and having done that and moved to the next stage, where we now have competing researchers arguing, one says, I'm right, the other says I'm right. I side with the one who thinks like I do. How do we get people to care? How do we move this? It's. So this issue, how do you get to move it outside the diet issue? As we discussed over the years, the conventional wisdom comes with a lot of explanations for why people don't lose weight on various dietary interactions interventions. And some of those are true. You know, people don't comply with interventions. They like, dude, nobody sticks with the diet, so maybe everybody's different. And so maybe we have to invest huge sums into individualized nutritional therapy, precision nutrition. Yeah, that's the way the NIH is going. So the idea is, well, these people arguing it's carbohydrates. Yeah, we don't believe that. So we're not going to spend any money researching that. There are people believing it's fat. There are people who think it's meat in an ideal world. And we discussed this for eight years, right? The federal government would say Covid is going to pass and obesity and diabetes are going to go back to being the long pole, using NASA terminology. They're costing a billion dollars a year in direct medical costs. We have to understand how we failed because we have to stop blaming it on industry and blaming it on the individuals. And so let us put together teams of researchers, red teams, blue teams, multiple colors, and hash this out and try and figure out how we failed to control these epidemics. And until we do that, we're not going to reject any reasonable hypothesis.

But how would you even populate said teams? I mean, to your point, there really aren't that many people that would still stand by this idea that you proposed.

I mean, if we're going to get practical here, Peter, we're not going to make any progress whatsoever. Dreams. It's like populating a jury in a trial. You pick people, unbiased scientists who are good at what they do have demonstrated that they're good at what they do. You can't pick obesity researchers because they have biases. You certainly can't pick nutritionists because they have biases. I question whether any of them are ever taught to think critically enough about scientific progress. I mean, think about the kind of effort that went into Covid, the amount of money that was spent on research to prevent this disease from killing what, one tenth of the number of people who die from chronic diseases related to obesity and diabetes every year. I mean, I'm not. I'm glad they did it, but you make that kind of effort, you can solve this problem. Step one is saying, look, we failed. The conventional wisdom fails. It's clear it fails because we have a obesity and diabetes epidemics that haven't been stemmed in any way. We have to question our assumptions. Who actually does that job? There's a lot of very intelligent critical thinkers out there. The question is, how do you get them to care?

Now, some of them would argue that, hey, we know the answer. And the palatability of food, the availability of food, the affordability of food are driving this equation. And the convenience of modern life is making inactivity even a greater and greater issue. And so you might argue, well, look, we haven't put all the steps in place to address those issues. This answer is going to be one. Through policy, we have to make foods that don't taste as good. You know what I mean?

Yeah, yeah.

I'll give you a funny story. My son came home the other day and he said, mommy, one of the Kids at school today had something for school and I really want it, too. And she was like, okay, what? What is it? He's like, I don't know. It came in a blue bag and it was a triangle. And of course it was Cool Ranch Doritos. So what does Jill go and do? She goes and buys Cool Ranch Doritos for Reese for lunch, for a little snack. And, you know, this is a kid who really likes to eat good food. And so every day he gets a little Ziploc with like five Cool Ranch Doritos. The problem is I can't eat just five Cool Ranch Doritos. So the other day, I'm inhaling half a bag of Cool Ranch Doritos. And all I'm thinking, thinking is this is almost as impressive as the Apollo 11 program from an engineering perspective. Like the way they made this thing, the crunch, the taste, the lingering flavor, it's unbelievable. Like, there's no denying how good this stuff is. So why did I just sit there and eat a whole bag of that thing?

You know, that's a very good question. But here's the counter argument. I gave a talk a couple months ago in Tahoe, and it was just as the ability to give talks in person was winding down. And the fellow who invited me to give the talk, he was from Texas, afterwards, sent me a gift box of wagyu steaks from a butcher in New York. And these are the wagyu's that cost. It's about $170 a pound. One of them was called Wagyu Sashimi. You're just looking at it. You know, it's by weight, as much fat as protein, and by calories and fat farm. It's going to be 80% fat. And I actually made one of these for lunch and I looked up how to do it. So you get the skillet and you use the fat on the edge to grease the skillet and hot, and then it's a minute on each side and it was 8 ounces and I couldn't finish it. It was so filling. My son Harry said he got a taste. He said, this is beef butter, Dad. I still finished it. So it was delicious. However, even if I could afford it, I still have some in the freezer. I did. It wasn't like a Dorito issue. So, yeah, it's not quite as accessible. But the question would then be, did I get fatter? Did I somehow dysregulate my fat tissue by eating that the way you might be doing eating the Dorito? Was it the palability that drives your particular hyperphasia or, or is it the peripheral response to the macronutrient composition that then drives your hyperphagia when it comes to Doritos?

I mean, I just think if you look at the response time, it has to be more central than peripheral. It's barely exiting my esophagus while I'm.

Inhaling these things, but still the response is going to be a response to an expected peripheral change. So you know, your body is. Evolution and homeostasis are very good at what they do. They know what's about to happen, so they're going to prepare your body for that to happen. Think about your experience when you were 205 pounds and swimming, working out three hours a day and eating as healthy as any human being could eat. Question is again, why then and what's changed between then and now? And that wasn't a Dorito induced effect. You know, this idea that some people are just going to get fat are pre programmed. The quote is from George Bernard Shaw. One of the leads said, we're just going to get fat no matter how much we eat. So if that's the case, what triggers that? The Doritos, I would say trigger it. Whereas the Wagyu, which is far more calories, does not. How do we get people to care? How do you get people? Whether right or wrong, there's a lot of people. Clearly the conventional wisdom is what you just argued. We know what causes it. It's too much food available. It's very easy to blame industry. You know, Michael Moss has written two terrific books that get a lot of publicity because he's blaming industry. He's not blaming the scientists, he's not blaming the administrators of the government, he's blaming the industry. It's very easy when I was blaming the industry. People love what I do. As soon as I shift to blaming the scientific community for doing unacceptable science, then I have trouble getting the message heard. I don't know what the answer is. You know, it's something I think about every day. And when people are programmed to assume you're wrong, how do you get them to accept the possibility that you're not and that somebody has to study this and I don't know what the answer is. I do know it's the challenge. I, you know, said this before during the Nusi years. It's like you decide you're going to tilt at windmills for life. You gotta get used to the fact that the windmills are gonna kick you in the ass when you ride by I don't know.

I think people do care, Gary. I mean, I guess it depends how you define care. People certainly care about obesity. Are you really asking the question not how do we get people to care but how do we get people to question?

Yeah, and by people, I mean people in a position of authority who are in the have the ability to put the necessary funds and effort towards addressing this question. I think one of the problems is that the way we fund research in the this country for instance, is a kind of, you know, you get together study groups of mostly like minded individuals and they give out small. These are all the issues we discussed at NUSA. They give out small R01 grants of $500,000 a year for five years to what the Kuhn would have called normal science. So we have our conventional wisdom, our dominant paradigm. We're just going to continue to fund the whatever research people happen to do to create bricks to fit in that paradigm. There is no method by which paradigms are questioned and research programs can shift. There's an assumption that it could happen naturally. The science is self correcting, but it's the way we fund research in this country doesn't actually allow that to happen. If there's money to be made, then capitalism will kick in and people will take advantage of the opportunities that other people might be missing. And that's why you have operations like Virta Health which is doing very well, advocating using nutritional ketosis to treat type 2 diabetes. And you have other people who have started operations like Diet doctor too. There is no mechanism. So when I say people, I mean like how do I get Francis Collins to care? And the journalist in Living in Oakland talking on a podcast. You know, it's just, it's so many levels from influence and even if you did get him to care, could you get the NIH to do anything about it? We're having this conversation the day after there was an article in Science by two researchers who we know very well arguing that this carbohydrate insulin model of obesity is just wrong. Failed. It was interesting. We tested it, it failed. That's what we were talking about when we discussed why not. How do I reconcile my belief beliefs with that? My response was, okay, you leave out this people who tested it that supported the model. You're looking at only a small proportion of the evidence here. But even if you're right, we have a problem. You've avoided the elephant in the living room, which are the obesity and diabetes epidemics. What do we do about it?

The paper that came out Yesterday by John Speakman and Kevin hall is a relatively short paper, but an interesting summation of what would be viewed as a possible refutation of this carbohydrate insulin model. I think of it less as a public health paper. I don't see them trying to address obesity, and I don't see them offering or proposing to offer a solution to obesity. But I think they're really trying to understand a mechanistic thing. And I don't know John, but I do know Kevin. And my view of Kevin is this is a guy who came in as an outsider, so he has at least the benefit of not having some of the limitations that others might have who have come up through the field without the historical context. And, well, maybe Kevin doesn't have that historical context, just as no one would. At least he came in, I think, as a physicist, looking at this from first principles, which you could argue could be an advantage to him. Right?

Yeah. In fact, if you'll recall, that's how I was introduced to Kevin by Mitch Lazar at Penn, because he thought we saw the problem in similar ways, which we did back then. And then I introduced him to you. Smart young researcher, without biases at the time, through the course of our interaction with the Energy Balance Consortium. I think our belief system, it gets complicated. If you recall his initial claim to fame as a researcher, what got him his position at nih, as I understand it, is the model that he has of metabolism and how that influences. Influences body weight. And at one point I had asked him to, you know, he had suggested we could use his model to test my beliefs. And his model rejected them. And I argued. Well, you rejected them because we're working in an area that your model has no data. So why don't we do the experiment to generate that data? And before NUSI hired him, I was arguing to Kevin that he could do this experiment, an NM1 or two at NIH with the metabolic chamber, as soon as they started interpreting their experiment. One of the fundamental problems with pathological science is you establish what you believe based on premature data. So you don't. Rather than entertaining multiple hypotheses as possible, explaining what you see, you kind of collapse down onto one and then you go public with that prematurely, and now you're trying to support that hypothesis rather than test that hypothesis. So once Kevin started to interpret the evidence from the Energy Balance Consortium as a refutation of the carbohydrate insulin model, I think it's basically. And supportive of his computer model, I think it basically began to Create his bias and lock it in. Then the more conflict he and I had, which he witnessed, the more that locked in his biases it might have locked in mind. The other possibility is that he's just right and I'm wrong, which if we could do more experiments, we might find out.

One of the critiques of that paper was on different types of methodologies that are used to measure energy expenditure, which is a very important thing to study and understand when trying to test these hypotheses. You've already alluded to one method, which is indirect calorimetry, where people sit inside of a medical grade, hermetically sealed chamber that can, at very minute levels, measure inhaled carbon dioxide and calculate the consumption of oxygen. And the ratio of those things allows us to calculate very precisely the amount of energy consumed. An alternative method to doing that is something called doubly labeled water, where a person drinks water that has two different types. One is heavily labeled oxygen, the other deuterium. And by collecting urine over a period of days following that ingestion, you can also estimate energy expenditure, but do so in a free living environment. At the outset, you alluded to two experiments that were done in parallel. The experiment that was done by the group that had the hypothesis that energy balance determines obesity used the more precise of these calorimetry. The group that studied this, who had the preconceived idea that the carbohydrate insulin model was correct, did a longer outpatient study and used this other method. One of the critiques of the paper is that the method of doubly labeled water is not valid in people who are carbohydrate restricted. And that explains the difference in these findings because otherwise they're very difficult to reconcile.

Right, right. Even the first Kevin Hall's energy balance paper, remember, they saw a signal, so they're measuring energy two different ways. I think of science differently than Kevin does. You measure energy by the chamber, indirect calorimetry, and by DLW energy expenditure. Remember, the reason we're measuring energy expenditure is because the researchers didn't believe in the course of four weeks you could see an effect on fat mass. So measuring fat mass directly by DEXA wasn't a possibility. And I'm still curious because there was also evidence, remember, back then, DEXA is going to be confounded by water loss. And so can you ever use DEXA reliably for the kind of measurements that are made over, say, two weeks when you're transitioning somebody from a low fat to a very low carbohydrate diet?

I Don't think so.

I don't think so either. And yet that's been done in the last two papers out of nih. So anyway, the point is you've got two ways to measure energy. They give you two different results. One result is more consistent with the energy balance product. The other result is inconsistent with the energy balance and consistent with the carbohydrate insulin model. One way to interpret this data is to say we measured energy expenditure two different ways. We got two different results, therefore our results were not very robust. We tend to trust the chamber, but if the chamber were right, we would have expected to see the same thing with the dlw, and we didn't.

Unless the DLW is wrong and the chamber is the gold standard, which is a possibility.

But the first thinking is we get divergent results here. We have other issues with the experiment non randomized, so let's design a different experiment.

Gary, it's clear that there's probably still enough smoke to question whether there's some fire. With respect to these unanswered questions, do you think this is an answerable question?

Well, I hope so. Clearly I keep doing what I'm doing because I'm hoping I can motivate people to think more deeply about this and to resolve this issue. I don't think progress will ever be made. Meaningful progress will be made on obesity without understanding the fundamental cause and without elucidating how treatments are working. Although I have to say, the GLP1 agonists are fascinating. So there's a public health problem, there's a scientific issue, Things have changed. So in the 20 years that we've done this, when I started this, conventional wisdom was that low carb, high fat diets like Atkins, what we called keto in pre2010 or so, that these diets were deadly, that they would cause heart disease, that they would ultimately make you fatter. Today, for instance, the American Diabetes association recommends these diets for type 2 diabetes, which means 1/10 of the public. They're everywhere, they're viral. The world is saturated with books on keto, and even this article in science we've been discussing acknowledge that they can be beneficial for weight control. And nobody's talking anymore about them causing heart disease. In fact, they're probably the most studied diets in history. And if you go to ClinicalTrials.gov you'll find over 200 trials in the works looking at ketogenic diets for everything from epilepsy, diabetes, cancer. Yeah, cancer. And Alzheimer's. Yeah. Far beyond that. Traumatic brain injury. I mean, you've interviewed a lot of these people. So in one sense, we've made an enormous amount of progress in liberating the intervention that comes out of this way of thinking, such that it can now be used for people. And anyone who struggles with the weight or blood sugar control can now know that they can try these diets, and it's not hard to find a physician to help them, guide them through it. And from the personal perspective, we know that they won't kill you, which is what we assumed going in 20 years ago. And so it would be nice if the research community understood obesity the way I think they should. So it's a way of saying, it would be nice if I was right about all this. But even if I'm dead wrong, the ability to use this dietary intervention to improve your health has now become widespread, mostly accepted. And clearly there are teams of researchers all over the world who find the clinical efficacy of these diets fascinating. So the world is changing. It might not be changing as quickly as I would like, and it may not change as much as I would like, but like I said, even the Science article the other day, there are statements in there that would have been considered unacceptable 20 years ago, including the acceptance that insulin plays a major role in fat accumulation. And so obesity, it's just not the role they were arguing. It's just not the role that I think it is, and David Ludwig thinks it is.

Well, Gary, we've been going at this for a while. This was kind of a tour de force of science and a history of science. In many ways. I'm sure folks found your journey interesting, especially given that many people will know you from your more recent work, and I think many people will not know you from the foundation of work that led to your curiosity here. I've always found that to be the most interesting part of your personal story, by the way, is the way that you stumbled into health sciences almost accidentally. I've always found it interesting and obviously I think a lot of good has come of it. So, Gary, thanks so much for making the time, especially in this case, a lot of time, probably more than your typical podcast.

Thank you for having me. It's been a pleasure and it's great to have an opportunity to not just catch up, but talk at you for three and a half hours.

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Sam.