Journal Club with Dr. Peter Attia | Metformin for Longevity & The Power of Belief Effects

Andrew Huberman 0:00 (mellow music) Welcome to the Huberman Lab Podcast, where we discuss science and science-based tools for everyday life. I'm Andrew Huberman, and I'm a professor of neurobiology and ophthalmology at Stanford School of Medicine. Today marks the first journal club episode between myself and Dr. Peter Attia. For any of you that are not familiar with Dr. Peter Attia, he is a medical doctor, an MD, who is an expert in all aspects of health and lifespan. He is the author of a best-selling book entitled Outlive, which is a phenomenal resource on all things healthspan and lifespan. And he is the host of the very popular podcast, The Drive, where he interviews various experts in all domains of medicine, and scientists as well. Today, Peter and I hold our first online collaborative journal club. For those of you that aren't familiar with what a journal club is, a journal club is a common practice in graduate school and/or medical school, whereby students get together to discuss one or two papers, to critique those papers, and to really compare their own conclusions of those papers with the conclusions of the authors and to highlight any key takeaways. Peter and I have been wanting to do a journal club together for a very long time, and we decided to do that journal club and to record it for you. So today, you will be sitting in on the first Huberman-Attia Journal Club. By the way, it could just have easily been called the Attia-Huberman Journal Club. And we will discuss two papers. First, Peter is going to discuss a paper on metformin, which is a drug that many people are interested in for its potential role in longevity. I want to highlight potential there. He's going to compare that paper to previous findings on metformin, and by the end of that discussion, he will advise as to whether or not he himself would take metformin and whether or not other people might be well-advised or ill-advised to take metformin based on the data in that paper and at this time. Then I present a paper which is about the placebo effect. Uh, I have to imagine that most of you have heard of the placebo effect, but what's interesting about the paper that we discuss today is that it shows that the placebo effect can actually follow a dose response. So it's not just all or none. It actually is the case that you can scale the degree of placebo effect depending on whether or not you're thinking you're taking low doses, moderate doses, or high doses of a particular drug. And the particular drug that's discussed in the paper that I cover is nicotine. So for those of you that are interested in cognitive enhancement by way of pharmacology, or frankly, for people who are simply interested in how our beliefs can shape our physiology, I think you'll find that discussion to be very interesting. So by the end of today's episode, you will not only have learned about two novel sets of findings, one in the realm of longevity as it relates to metformin, and another in the realm of neurobiology and placebos or placebo effects, but you will also learn how a journal club is conducted. I think you'll see in observing how we parse these papers and discuss them, even arguing in them at times, that what scientists and clinicians do is they take a look at the existing peer-reviewed research, and they look at that peer-reviewed research with a fresh eye, asking, "Does this paper really show what it claims to show or not?" And in some cases, the answer is yes, and in other cases, the answer is no. What I know is for certain is that by the end of today's episode, you will learn a lot of science, you'll learn a lot about health practices, some of which you may want to apply or avoid, and you'll learn a lot about how science and medicine is carried

Andrew Huberman 3:27 out. Before we begin, I'd like to emphasize that this podcast is separate from my teaching and research roles at Stanford. It is, however, part of my desire and effort to bring zero-cost-to-consumer information about science and science-related tools to the general public. In keeping with that theme, I'd like to thank the sponsors of today's podcast. Our first sponsor is Helix Sleep. Helix Sleep makes customized mattresses to give you the best possible night's sleep. Now, sleep is the foundation of mental health, physical health, and performance. When we are sleeping well and enough, mental health, physical health, and performance all stand to be at their best. One of the key things to getting a great night's sleep is to make sure that your mattress is tailored to your unique sleep needs. Helix Sleep has a brief two-minute quiz that if you go to their website, you take that quiz and answer questions such as, do you tend to sleep on your back, your side, or your stomach? Do you tend to r-run hot or cold in the middle of the night? Maybe you don't know the answers to those questions, and that's fine. At the end of that two-minute quiz, they will match you to a mattress that's ideal for your sleep needs. I sleep on the Dusk, D-U-S-K, mattress, and when I started sleeping on a Dusk mattress about two years ago, my sleep immediately improved. So if you're interested in upgrading your mattress, go to helixsleep.com/huberman, take their two-minute sleep quiz, and they'll match you to a customized mattress for you. And you'll get up to $350 off any mattress order and two free pillows. Again, if interested, go to helixsleep.com/huberman for up to $350 off and two free pillows. Today's episode is also brought to us by Levels. Levels is a program that lets you see how different foods and behaviors affect your health by giving you real-time feedback using a continuous glucose monitor. One of the most important factors impacting your immediate and long-term health is the way that your body manages its blood glucose or sometimes referred to as blood sugar levels. To maintain energy and focus throughout the day, you want to keep your blood glucose steady without big spikes or dips. Using Levels, you can monitor how different types of foods and different food combinations, as well as food timing and things like exercise, combine to impact your blood glucose levels. I started using Levels a little over a year ago, and it gave me a lot of insight into how specific foods were spiking my blood sugar and then leaving me feeling tired for several hours afterwards, as well as how the spacing of exercise and my meals was impacting my overall energy. And in doing so, it really allowed me to optimize how I eat, what I eat, when I exercise, and so on, such that my blood glucose levels and energy levels are stable throughout the day. If you're interested in learning more about Levels and trying a continuous glucose monitor yourself, go to levels.link/huberman. Right now, Levels is offering an additional two free months of membership. Again, that's levels.link, L-I-N-K, /huberman to get two free months of membership.And now for my journal club discussion with Dr. Peter Attia.

Andrew Huberman 6:11 Peter, so good to have you here.

Peter Attia 6:14 So great to be here, my friend.

Andrew Huberman 6:16 This is something that you and I have been wanting to do for a while, and it's basically something that we do all the time, which is to peruse the literature and find papers that we are excited about for whatever reason, and oftentimes that will lead to a text dialogue or a phone call, or both. But this time, we've opted to try talking about these papers that we find particularly exciting in real time for the first time as this podcast format. First of all, so that people can get some sense of why we're so excited about these papers. We, we do feel that, um, people should know about these findings. And second of all, that it's an opportunity for people to learn how to dissect information and think about the papers they hear about in the news, the papers they might download from PubMed if they're inclined, also just to start thinking like scientists and clinicians and get a better sense of what it looks like to pick through a paper, um, the good, the bad, and the ugly. So we're flying a little blind here, which is fun. Um, I'm definitely excited for all the above reasons.

Peter Attia 7:22 Y- yeah, no, this is, uh, uh, you and I have been talking about this for some time, and, and, um, you know, actually we used to run a journal club inside the practice where once a month one person would, um, just pick a paper and we would go through it in kind of a formal journal club presentation. We've gotten away from it for the last year just because we've been a little stretched thin. I think it's something we need to resume because it's, uh, it's a great way to learn and it's a skill. You know, people probably ask you all the time, 'cause I know I get asked all the time, "Hey, w- w- what are the dos and don'ts of interpreting, you know, scientific papers? Is it enough to just read the abstract?" Um...

Andrew Huberman 7:59 No.

Peter Attia 7:59 And, and, you know, usually the answer is, well, no. Um, but the how-to is, is tougher. And I think the two papers we've chosen today illustrate two opposite ends of the spectrum. You know, you're going to obviously talk about something that we're going to probably get into the technical nature of the assays, the limitations, et cetera. And the paper ultimately I've chosen to present, although I apologize, I'm surprising you with this, up until, you know, a few minutes ago, is, is actually a very straightforward, simple epidemiologic paper that I think has important significance. I had originally gone down the rabbit hole on a, a much more nuanced paper about ATP binding cassettes in cholesterol absorption, but ultimately, I thought this one might be more interesting to a broader audience.

Andrew Huberman 8:38 Hm.

Peter Attia 8:38 By the way, I got to tell you this funny story. So I had a dream last night about you, and, um, in this dream... (laughs) You were obsessed with making this certain drink that was like your elixir, and it had all of these crazy ingredients in it.

Andrew Huberman 8:53 Supplements?

Peter Attia 8:53 Supp- tons of supplements in it.

Andrew Huberman 8:55 (laughs)

Peter Attia 8:55 But the one thing I remembered when I woke up, 'cause I forgot most of them, I was really trying so hard to remember them, one thing that you had in it was dew. Like, you had to collect a certain amount of dew off the leaves every morning to put into this drink.

Andrew Huberman 9:09 (laughs)

Peter Attia 9:09 It was so... It was like-

Andrew Huberman 9:11 It sounds like something that I would do. (laughs)

Peter Attia 9:13 And, and so... But here's the best part. You had, you had like a thermos of this stuff that had to be with you everywhere, and all of your clothing had to be tailored with a special pocket that you could put the thermos into so that you were never without the special Andrew drink. And again, you know how dreams, when you're having them, seem so logical and real, and then you wake up and you're like, "That doesn't even make sense." Like, "Why would he want the thermos in his shirt? Like, that, that would warm it up." Like, you know, all these... But, but boy, it was a realistic dream, and there were lots of things in it, including dew. Spas- special dew off the leaves every morning.

Andrew Huberman 9:51 I love it. Well, it's not that far from reality. I'm a big, um, fan of yerba mate. I'm drinking it right now, in fact.

Peter Attia 9:59 As am I.

Andrew Huberman 9:59 Um, uh, in its many forms, usually the loose leaf. I don't tend to drink it out of the gourd. My dad's Argentine, so that's where I picked it up. I started drinking it when I was like five years old or younger, which I don't recommend people do. It's heavily caffeinated. Don't drink the smoked versions either, folks. I think those are potentially carcinogenic. But this thing that you describe of, of carrying around the thermos close to the body, if you are ever in Uruguay or if you ever spot grown men in a restaurant anywhere in the world carrying a thermos with them and to their meals and, and hugging it close, chances are they're Uruguayan.

Peter Attia 10:34 Mm.

Andrew Huberman 10:35 A- and they're drinking yerba mate. They drink it usually after their meals. It's supposed to be good for your digestion. So it's not that far from-

Peter Attia 10:41 There you go.

Andrew Huberman 10:41 ... from reality. I don't carry the thermos, but I do drink mate every day, and, um, I'm going to start collecting dew off the leaves, uh-

Peter Attia 10:49 Just a few drops every morning.

Andrew Huberman 10:50 (laughs)

Peter Attia 10:51 Just, just, as you ruin it.

Andrew Huberman 10:52 Oh my. Um, some other time we can talk about dreams. Recently, I've, I've been doing some dream exploration. I've had some absolutely transformative dreams for the first time-

Peter Attia 11:02 Mm.

Andrew Huberman 11:02 ... in my life. One dream in particular that has, that allowed me to feel something I've never felt before and has catalyzed a large number of important decisions in a way that no other experience, waking or sleep, has ever impacted me. And this was drug-free, et cetera. Um-

Peter Attia 11:19 And, and do you think you could have had that dream... We don't have to get into it if you don't want to talk about it now, but was there a lot of work you had to do to prepare for that dream to have taken place?

Andrew Huberman 11:29 Oh, yes. Yeah.

Peter Attia 11:30 Okay.

Andrew Huberman 11:30 Um, at least, uh, 18 months of intensive, um, analysis type work, um, with a very skilled psychiatrist. But I wasn't trying to seed the dream.

Peter Attia 11:42 Yeah, yeah.

Andrew Huberman 11:42 It was just I, I was at a sticking point with a certain process in my life, and then I was taking a walk while waking and realized that my brain, my subconscious was going to keep working on this. I just decided it's going to keep working on it, and then two nights later I traveled to a meeting in Aspen and I had the most profound dream ever, uh, where I was able to sense something and feel something I've always wanted to feel as, uh, so real within the dream. Woke up, knew it was a dream-... and realized this is what people close to me that I respect have been talking about, but I was able to feel it, and therefore-

Peter Attia 12:20 Mm.

Andrew Huberman 12:20 ... I can actually access this in my waking life.

Peter Attia 12:23 Mm-hmm.

Andrew Huberman 12:23 It was a, it was, it was absolutely transformative for me. Um, anyway, sometime I can share more details with you or the audience, but for now, maybe we should talk about these papers.

Peter Attia 12:33 Very well.

Andrew Huberman 12:35 Um, who should

Andrew Huberman 12:36 go first?

Peter Attia 12:38 I, I'm happy to go first.

Andrew Huberman 12:39 Great, yeah.

Peter Attia 12:39 This one's, this one's... This is a pretty straightforward paper. So, so we're gonna talk about a, a paper titled Reassessing the Evidence of a Survival Advantage in Type 2 Diabetics Treated with Metformin Compared with Controls without Diabetes: A Retrospective Cohort Study. This is by Matthew Thomas Keyes and colleagues. This was published, uh, last fall. (smacks lips) Um, why is this paper important? So this paper is important because in 2014, uh, Bannister published a paper that I think in many ways kind of got the world very excited about metformin. So this was almost 10 years ago, and I'm sure many people have heard about this paper, even if they're not familiar with it, but they've heard the concept of the paper. And in many ways, it's the paper that has led to the excitement around the potential for geroprotection with metformin. And I should probably just define for the audience what geroprotection means. When we think of-

Andrew Huberman 13:36 And probably also, sorry to interrupt, what metformin is-

Peter Attia 13:38 Yeah.

Andrew Huberman 13:38 ... just for the uninformed.

Peter Attia 13:39 Yeah. That, that's a great point. So I'll start with the, with the latter. So metformin is a drug that has been used for many years. Uh, depends, you know, where it was first approved, I think was in Europe. Um, but, you know, call it directionally 50 plus years of use as a first-line agent for patients with type 2 diabetes. Uh, in the US, maybe 40 plus years. So this is a drug that's been around forever, trade name, uh, Glucophage, um, or brand name and, uh, but, but again, it's, you know, it's a generic drug today. The mechanism by which metformin works is debated hotly, um, but what I think is not debated is the immediate thing that metformin does, which is it inhibits complex 1 of the mitochondria. So a- again, maybe just taking a step back. So the mitochondria, as everybody thinks of those as the cellular engine for making ATP. So the most efficient way that we make ATP is through oxidative phosphorylation, where we take either fatty acid pieces or a breakdown product of glucose once it's partially metabolized to pyruvate. We put that into an electron transport chain, and we basically trade chemical energy for electrons that can then be used to make phosphates onto ADP. So it's, you know, you think of everything you do. Eating is taking the chemical energy in food, taking the energy that's in those bonds, making electrical energy in the mitochondria. Those electrons pump a gradient that allow you to make ATP. To give a sense of how primal and important this is, if you block that process completely, you die. So everybody's probably heard of cyanide, right? Cyanide is something that is incredibly toxic even at the smallest doses. Cyanide is a complete blocker of this process, and if my memory serves me correctly, I think it blocks complex IV of the mitochondria. I don't know if you recall if it complex III or complex IV.

Andrew Huberman 15:36 I, I know a lot about toxins that impact the nervous system, but I don't know a lot about poisons.

Peter Attia 15:40 The mitochondria, yeah.

Andrew Huberman 15:40 But if ever you want to have some fun, we can talk about all the dangerous stuff that animals make and insects make and how they kill you. (laughs) That's like a favorite topic.

Peter Attia 15:47 Yeah, like the prototoxin and all these things that block sodium channels. Yeah, yeah, yeah.

Andrew Huberman 15:50 Oh, alpha-latrotoxin, bungaro- to... I, I really geek out on this stuff 'cause it allows me to talk about neuroscience, animals-

Peter Attia 15:57 (laughs)

Andrew Huberman 15:57 ... and scary stuff. It's like-

Peter Attia 15:58 Yeah.

Andrew Huberman 15:58 ... combines it. So we could do that sometime for fun, maybe at the end if we have a few moments.

Peter Attia 16:02 So, so, you know, something like cyanide that is a very potent inhibitor of this e- electron transport chain will kill you instantly. People understand that, of course, a drop of cyanide and you would m- you would be dead, literally instantaneously. So metformin works at the first of those complexes. I believe there are four, if my memory serves correctly, four electron transport chain, uh, complexes. And, um, but of course, it's not a complete inhibition of it. It's just kind of a weak blocker of that and the net effect of that is what? So the net effect of that is that it changes the ratio of adenosine monophosphate to adenosine diphosphate. Um, what's less clear is why does that have a benefit in diabetics? Um, because what it unambiguously does is reduces the amount of glucose that the liver puts out. So hepatic glucose output is one of the fundamental problems that's happening in type 2 diabetes. Y- you may recall, I think we talked about this even on a previous podcast, you and I sitting here with normal blood sugar have about five grams of glucose in our total circulation. That's it. Five grams. Think about how quickly the brain will go through that.

Andrew Huberman 17:07 Mm-hmm.

Peter Attia 17:07 Within minutes. So the only thing that keeps us alive is our liver's ability to titrate out glucose and if it puts out too much, for example, if the glucose can... If the glucose level was consistently two teaspoons, you would have type 2 diabetes. So the difference between being metabolically healthy and having, you know, profound type 2 diabetes is one teaspoon of glucose in your bloodstream. So the ability of the liver to tamp down on high glucose output is important. Metformin seems to do that. So-

Andrew Huberman 17:38 Can I just ask, uh, uh, one question? Is it fair, um, to provide this overly simplified summary of the biochemistry, which is that when we eat, the food is broken down, but the breaking of bonds creates energy that then our cells can use in the form of ATP? And the mitochondria are central to that process, and that metformin is partially short-circuiting the energy production process.

Peter Attia 18:03 Yep.

Andrew Huberman 18:03 And so even though we are eating when we have metformin in our system, presumably there is going to be less net glucose. The bonds are gonna be broken down, we're chewing, we're digesting, but less of that is turned into blood sugar, glucose.

Peter Attia 18:19 Well, sort of. I mean, the, it, it's not, um...

Guest (unidentified, brief interjection) 18:22 ... it's not depriving you of ultimately storing that energy. What it's doing is changing the way the body, um, partitions fuel. That's probably a better way to think about it to be a little bit more accurate.

Peter Attia 18:35 Okay.

Guest (unidentified, brief interjection) 18:35 So, um, for, for example, like, it's not depriving you of the calories that are in that glucose. That would be, you know, fantastic, but that's not-

Peter Attia 18:43 That was the, that was the, uh, Olestra project.

Guest (unidentified, brief interjection) 18:46 Olestra, that's right, yeah.

Peter Attia 18:46 Remember the Olestra from the '90s? Olestra, folks, for those of you who don't re- remember... Um, by the way, if you ever ate this stuff, you'd remember.

Guest (unidentified, brief interjection) 18:53 (laughs)

Peter Attia 18:53 Uh, because it was a fat that was, um, not easily digested. It had sort of i- in- sort of analogous to plant fiber or something like that, so it was being put into potato chips and whatnot. And the idea was that people would, um, would simply excrete it. Um, and I don't know what happened, except that people got a lot of stomachaches. And, um-

Guest (unidentified, brief interjection) 19:14 Well, uh, the anal seepage.

Peter Attia 19:15 ... everyone got fatter in the world. We know that.

Guest (unidentified, brief interjection) 19:17 The, the anal seepage is what really did that product in.

Peter Attia 19:19 Anal seepage. Only a physician, because after all, Peter's a clinician, a physician, an MD and I'm not, um, could find a, um, an appropriate term to describe-

Guest (unidentified, brief interjection) 19:32 Yeah. When you have that much-

Peter Attia 19:34 ... what this is.

Guest (unidentified, brief interjection) 19:34 When you have that much fat malabsorption-

Peter Attia 19:36 Mm-hmm.

Guest (unidentified, brief interjection) 19:36 ... you, you start to have accidents.

Peter Attia 19:38 Wow. And so- So that, that did away with that product.

Guest (unidentified, brief interjection) 19:41 Right.

Peter Attia 19:41 Yeah.

Guest (unidentified, brief interjection) 19:41 It was either that or the diaper industry was gonna really take off.

Peter Attia 19:44 That's right. All right, all right.

Guest (unidentified, brief interjection) 19:44 Okay. That's why you don't hear about Olestra.

Peter Attia 19:46 All right. So

Peter Attia 19:47 here- that's right.

Guest (unidentified, brief interjection) 19:47 (laughs)

Peter Attia 19:47 So, so we've got this drug, we've got this drug Metformin. It's considered a perfect first-line agent for people with type 2 diabetes. So again, what's happening when you have type 2 diabetes, uh, the primary insult probably occurs in the muscles, and it is insulin resistance. Everybody hears that term. What does it mean? Uh, insulin is a peptide. It binds to a receptor on a cell. So let's just talk about it through the lens of the muscle, 'cause the muscle is responsible for most glucose disposal. It gets glucose out of the circulation. High glucose is toxic, we have to put it away, and we want to put most of it into our muscles. That's where we store 75 to 80% of it. When insulin binds to the insulin receptor, uh, ki- uh, tyrosine kinase is triggered inside. So just ignore all that. But a chemical reaction takes place inside the cell that leads to a phosphorylation, so ATP donates a phosphate group. And a transporter, just think of like a little tunnel, like a little straw goes up through the level of the cell, and now glucose can freely flow in. So I'm sure you've talked a lot about this with your audience. We... Things that move against gradients need pumps to move them. Things that move with gradients don't. Glucose is moving with its gradient into the cell, it doesn't need active transport, but it does need the transporter put there. That requires the energy, and that's the job of insulin.

Guest (unidentified, brief interjection) 21:07 By the way, I did not know that. I, I mean, I certainly know active and, and passive transport, uh, as it relates to, like, neurotransmitter and ion flow. Um, but I'd never heard that when insulin binds to a cell that literally a little straw is placed into the membrane of the cell.

Peter Attia 21:21 Yeah, glucose doesn't need a pump-

Guest (unidentified, brief interjection) 21:22 That is cool.

Peter Attia 21:22 ... to move it in, um, because there's much more glucose outside the cell than inside, so it just whoosh. But the energy required is to move the, the straw up to the cell. So-

Guest (unidentified, brief interjection) 21:31 Cell biology is so cool.

Peter Attia 21:32 Yeah, it is. So, so what happens is as ... and Gerald Shulman at Yale did the best work on elucidating this. As the m- intramuscular fat increases, and I, by intramuscular I mean intracellular fat, uh, triacyl and diacylglycerols accumulate in a muscle cell, that signal gets interrupted.

Guest (unidentified, brief interjection) 21:55 Mm.

Peter Attia 21:55 And all of a sudden, I'm making these numbers up, if you used to need two units of insulin to trigger the little transporter, now you need three, and then you need four, and then you need five. You need more and more insulin to get the thing up. That is the definition of insulin resistance.

Guest (unidentified, brief interjection) 22:13 Hm.

Peter Attia 22:13 The cell is becoming resistant to the effect of insulin. And therefore, the early mark of insulin resistance, the canary in the coal mine, is not an increase in glucose. It's an increase in insulin. So normal glycemia with hyperinsulinemia, especially postprandial, meaning after you eat, hyperinsulinemia, is the thing that tells you, hey, you're, you're five, 10 years away from this being a real problem. So fast-forward many steps down the line. Someone with type 2 diabetes has long passed that system. Now, not only are they insulin resistant where they just need a boatload of insulin, which is made by the pancreas to get glucose out of the circulation, but now that system's not even working well, and now they're not getting glucose into the cell. So now their glucose level is elevated, and even though it's continually being chewed up and used up because, again, the brain alone would account for most of that glucose disposal, the liver is now becoming insulin resistant as well. And now the liver isn't able to regulate how much glucose to put into circulation, and it's overdoing it. So now you have too much glucose being pumped into the circulation by the liver, and you have the muscles that can't dispose of it. And it's really a vicious, brutal cascade because the same problem of fat accumulating in the muscle is now starting to happen in the pancreas. And now the relatively few cells in the pancreas called beta cells that make insulin are undergoing inflammation due to the fat accumulation within the pancreas itself, and so now the thing that you need to make more insulin is less effective at making insulin. So ultimately, way, way, way down the line, a person with type 2 diabetes might actually even require insulin exogenously.

Guest (unidentified, brief interjection) 23:53 Could you share with us a few of the causes of type 2 diabetes, of insulin resistance? I mean, one it sounds like is accumulating too much fat.

Peter Attia 24:01 Yeah.

Guest (unidentified, brief interjection) 24:02 Yeah.

Peter Attia 24:02 So energy imbalance would be an enormous one. Inactivity or insufficient activity is probably the single most important. So when Gerald Shulman, um, was running clinical trials at Yale, um, they would be recruiting undergrads to study obviously 'cause you're typically recruiting young people, and they would, you know, be doing these very detailed mechanistic studies where they would require actual tissue biopsies. So you know, you're gonna biopsy somebody's quadriceps and actually look at what's happening in the muscle. Well, I remember him telling me this when I interviewed him on my podcast, he said, "The most important criteria of the people we interviewed is that..." 'cause they were still lean, these weren't people that were overweight, but they had to be inactive.... you couldn't have active people in these studies. So exercising is one of the most important things you're going to do to ward off insulin resistance. But there are other things that can cause insulin resistance. Sleep deprivation has a profound impact on insulin resistance. I think we probably talked about this previously but if you, you know, some very elegant mechanistic studies where you sleep deprive people, you know, you let them only sleep for four hours for a week, you'll reduce their glucose disposal by about half.

Andrew Huberman 25:05 Wow.

Peter Attia 25:06 Which is, I mean, that's a staggering amount of... You, you're basically inducing profound insulin resistance in just a week of sleep deprivation.

Andrew Huberman 25:13 Mm.

Peter Attia 25:13 Hypercortisolemia is another factor and then, obviously, energy imbalance. So where, when you're, when you're accumulating excess energy, when you're getting fatter, if you start spilling that fat outside of the subcutaneous fat cells into the muscle, into the liver, into the pancreas, all those things are exacerbating it.

Andrew Huberman 25:29 Got it.

Peter Attia 25:30 Okay, so enter metformin, first-line drug. So m- most of the drugs... So every drug you give a person with type 2 diabetes is trying to address part of this chain. So some of the drugs m- tell you to make more insulin. That's, that's one of the strategies. So here are drugs like sulfonylureas, they tell the body, "Make more insulin." Other drugs, like insulin, just give you more of the insulin thing. Metformin tackles the problem elsewhere, it tamps down glucose by addressing the glucose dispos- the, uh, hepatic glucose output channel. GLP-1 agonists are another drug. They increase insulin sensitivity, initially causing you to also make more insulin. Um, GLP-1-

Andrew Huberman 26:10 So that's Ozempic?

Peter Attia 26:11 Yes.

Andrew Huberman 26:11 Yeah.

Peter Attia 26:12 Yeah.

Andrew Huberman 26:12 And bur- is it true that berberine is more or less the poor man's metformin?

Peter Attia 26:17 Yeah.

Andrew Huberman 26:17 Okay.

Peter Attia 26:18 Yeah.

Andrew Huberman 26:18 It's, uh, from a tree bark, it just happens to have these same properties of-

Peter Attia 26:21 Yeah, and by the way, metformin-

Andrew Huberman 26:21 ... reducing mTOR and reducing blood glucose?

Peter Attia 26:24 Yeah, and metformin, by the way, o- occurs from a lilac plant in France, like, that's where it was discovered. So it's also... Metformin is also based on a substance found in nature.

Andrew Huberman 26:32 So you, you need a prescription for metformin.

Peter Attia 26:35 Yeah.

Andrew Huberman 26:35 You don't need a prescription for berberine?

Peter Attia 26:36 Correct.

Andrew Huberman 26:37 But, yeah, we can talk about berberine a little bit later. I had a couple great experiences with berberine and a couple bad experiences-

Peter Attia 26:43 Interesting.

Andrew Huberman 26:43 ... with berberine, yeah.

Peter Attia 26:45 So, um, uh, maybe taking one step back from this. In 2011, I became very interested in metformin, personally, just reading about it, obsessing over it and just somehow decided like, "I should be taking this." So I actually began taking metformin, I still remember exactly when I started, I started it in May of 2011, and I realized that because I was on a trip with a bunch of buddies, we went to the Berkshire Hathaway, uh, shareholder meeting, which is, uh, you know, the Buffett, uh, shareholder meeting and, uh, you know, it was kind of like a fun thing to do. And I remember being so sick the whole time because I didn't titrate up the dose of metformin, I just went straight to two grams a day, which is kind of like the full dose and we went to this-

Andrew Huberman 27:28 Is that characteristic of your approach to things?

Peter Attia 27:31 ... Yes, I think that's safe to say.

Andrew Huberman 27:33 Next time I'll give you a thermos of this dew that I collect in the morning. (laughs)

Peter Attia 27:36 Oh, really?

Andrew Huberman 27:37 (laughs)

Peter Attia 27:39 So I remember being so sick that the whole time we were in Nebraska or, or Omaha, I guess, I couldn't... We went to Dairy Queen 'cause you do all the buffet things when you're there, right? Like, I couldn't have an ice cream at Dairy Queen.

Andrew Huberman 27:51 You couldn't?

Peter Attia 27:51 I mean, I couldn't, I was so nauseous.

Andrew Huberman 27:53 Oh, 'cause I would say if you've got metformin in your system, you're gonna buffer glucose, you could have four ice cream cones and probably feel fine.

Peter Attia 27:57 Except I couldn't put, I couldn't keep anything down. I mean, I was so nauseous.

Andrew Huberman 28:00 That's uncomfortable.

Peter Attia 28:01 So, so clearly metformin has this side effect initially, which is a little bit of appetite suppression but regardless, that's the story on metformin. There were, there are a lot of reasons I was interested in it. Um, I wasn't thinking true geroprotection, that term wasn't in my vernacular at the time but what I was thinking is, "Hey, this is gonna help you buffer glucose better, it's gotta be better." And this was sort of my first foray into, you know, self-experimentation.

Andrew Huberman 28:23 Do you wanna define geroprotection? Um-

Peter Attia 28:25 Yeah, yeah, it's a good term to define.

Andrew Huberman 28:27 ... ge- geriatric, gero?

Peter Attia 28:28 Yeah.

Andrew Huberman 28:29 Yeah.

Peter Attia 28:29 So, so yeah, gero from, from geriatric, old, protection, so protection from aging.

Andrew Huberman 28:34 Mm-hmm.

Peter Attia 28:34 And when we talk about a drug like metformin or rapamycin or even NAD, NR, these things, the idea is we're talking about them as geroprotective to signal that they are drugs that are not targeting a specific disease of aging. For example, a PCSK9 inhibitor is sort of geroprotective but it's targeting one specific pathway, which is cardiovascular disease and dyslipidemia. Whereas the idea is a geroprotective agent would target hallmarks of aging. There are nine hallmarks of aging, please don't ask me to recite them. I've never been able to get all nine straight but people know what we're talking about, right? So decreased autophagy, increased senescence, decreased nutrient sensing or defective nutrient sensing, uh, proteomic instability, genomic instability, uh, methylation, all of these things, epigenetic changes. Those are all the nine hallmarks of aging.

Andrew Huberman 29:25 You got s- you got seven.

Peter Attia 29:26 Yeah, yeah, yeah.

Andrew Huberman 29:26 Yeah.

Peter Attia 29:27 So a geroprotective agent would target those deep down biologic hallmarks of aging. And in 2014, um, a paper came out by Bannister that basically got the world focused on this problem. By the world, I mean the world of, of anti-aging. So what, what Bannister and colleagues did was they took a registry from the UK and they got a set of patients who were on metformin with type 2 diabetes but only metformin. So these were people who had just progressed to diabetes, they were not put on any other drug, just metformin and then they found, from the same registry, a group of matched controls. So this is a standard way that epidemiologic studies are done because again, you don't have the luxury of doing the randomization so you're trying to account for all the biases that could exist by saying, "We're gonna take people who, uh, look just like that person with diabetes, so can we match them for age, sex, socioeconomic status, um, blood pressure, BMI, everything we can and then let's look at what happened to them over time." Now again, this is all happening in the future so you're looking into the past. It's retrospective in that sense.

Andrew Huberman 30:45 Mm-hmm-

Peter Attia 30:46 And so let me just kind of pull up the, the sort of, uh, table here so I can kind of walk through. And this is not in the paper we talked about, but I think this is an important background. So (clears throat) they did something that, at the time, I didn't really notice. I didn't notice what they did. I probably did and I forgot, but I didn't notice this until about five years ago when I went back and looked at the paper. And they did something called, um, uh, informative censoring. So the way the study worked is if you were put on metformin, we're gonna follow you. If you're not on metformin, we're gonna follow you, and we're gonna track the number of deaths from any cause that occurred. This is called all-cause mortality, or ACM, and it's really the gold standard in a trial of this nature or a study of this nature or even a clinical trial. You wanna know how much D- are people dying from anything 'cause we're trying to prevent or delay death of all causes. Informative censoring says if a person who's on metformin deviates from that inclusion criteria, we will not count them in the final assessment. So how are the ways that that can happen? Well, one, the person can be lost to follow-up. Two, they can just stop taking their metformin. Three, and more commonly, they can progress to needing a more, uh, significant drug. So all of those patients were excluded from the study. So think about that for a moment. This is, in my opinion, a significant limitation of this study because what you're basically doing is saying, "We're only gonna consider the patients who were on metformin, stayed on metformin, and never progressed through it, and we're gonna compare those to people who were not having type 2 diabetes." So an analogy here would be, imagine we're gonna do a study of two groups that we think are almost identical. One of them are smokers, and the other are identical in every way but they're not smokers, and we're gonna follow them to see which ones get lung cancer. But every time somebody dies in the smoking group, we stop counting them. When you get to the end, you're going to have a less significant view of the health status of that group.

Andrew Huberman 32:55 Mm-hmm.

Peter Attia 32:56 So with that caveat, the Banister study found a very interesting result, which was the crude death rate, um, was... And by the way, the way these are done, this is also one of the challenges of epidemiology, is the math gets much more complicated. You have to normalize death rate for the amount of time you study the people, so everything is normalized to 1,000 person years. So the crude death rate in the group of people with type 2 diabetes who were on metformin, including the censoring, was 14.4, so 14.4 deaths occurred per 1,000 patient years. If you looked at the control group, it was 15.2. This was a startling result, and I remember reading this in, again, 2014 and being like, "Holy crap, this is really amazing."

Andrew Huberman 33:52 Is there, um... Could you explain why? 'Cause I, I, I hear those numbers, and they don't seem that striking. It's a difference of about a year and a half. Now, of course, um, a difference of about a year and a half in lifespan-

Peter Attia 34:04 Well, a-

Andrew Huberman 34:05 ... is, is, well, is remarkable but-

Peter Attia 34:06 And, and actually, it doesn't even translate to that, so, so taking a step back, diabe- type 2 diabetes, on average, will shorten your life by six years.

Andrew Huberman 34:12 I see.

Peter Attia 34:12 So that's the actuarial difference between having type 2 diabetes and not, all comers. Uh, but you're right. This is not a huge difference. It's only a difference of a little less than one year of life per 1,000 patient years studied.

Andrew Huberman 34:24 Okay there, and by the way, uh, Peter just pointed out my, my math was wrong when I said-

Peter Attia 34:27 (laughs) Yeah.

Andrew Huberman 34:27 ... about a year and a half but-

Peter Attia 34:29 But, but the point here is you would expect the people in the metformin group to have a far worse outcome, i.e. to have a far worse crude death rate. And the fact that it was statistically significant in the other direction, and it turned out on the, what's called a Cox proportional hazard, which is where you actually model the difference in lifespan, the people who took metformin and had diabetes had a 15%, one five, 15% relative reduction in all-cause death over 2.8 years, which was the median duration of follow-up compared to the non-

Andrew Huberman 35:05 Well, that seems to be the, the number that makes me go, "Wow."

Peter Attia 35:08 Yeah.

Andrew Huberman 35:09 Right? That, um, because could you repeat those numbers again?

Peter Attia 35:13 Yeah. So 15% reduction in all-cause mortality over 2.8 years.

Andrew Huberman 35:19 That's a big deal.

Peter Attia 35:21 It is, and again, there's no clear explanation for it unless you believe that metformin is doing something beyond helping you lower blood glucose because the difference in blood glucose between these two people was still in favor of the non-diabetics.

Andrew Huberman 35:42 Oh.

Peter Attia 35:42 So again, the proponents of metformin being a geroprotective agent, and I put myself in this category at one point. I would put myself today in the category of undecided, but at the time, I very much believed this was a very good suggestion that metformin was doing other things. You mentioned a couple already. Metformin is a weak inhibitor of mTOR. Metformin reduces inflammation. Metformin potentially tamps down on senescent cells and their secretory products. You know, there are lots of things metformin could be doing that are off target, and it might be that those things are conferring the advantage.

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Peter Attia 37:16 So fast-forward until a year ago, and I think most people took the Bannister study as kind of the best evidence we have for the benefits of metformin, and I'm sure you've had lots of people come up to you and ask you, "Should I be on metformin? Should I be on metformin?" I mean, I probably get asked that question almost as much as I'm asked any question, o-outside of Dew. I mean, people definitely want to know if you should be consuming Dew. But, but after that, it's metformin.

Andrew Huberman 37:40 Fresh off the leaves.

Peter Attia 37:41 Has to be.

Andrew Huberman 37:41 While viewing morning sunlight.

Peter Attia 37:43 So okay, so let's, so let's kind of fast-forward to now, the, the paper that I wanted to spend a few more minutes on.

Andrew Huberman 37:48 Yeah, and thanks for that background. I, I'm still, uh, dazzled by the, uh, insertion of the straw by way of, of, uh, insulin. I, I, I don't think I've ever heard that described. I need to, I need to go get a better textbook.

Peter Attia 38:02 It's a pretty short straw, in fairness.

Andrew Huberman 38:04 Okay.

Peter Attia 38:04 You know? It's just, it's just a little transport-

Andrew Huberman 38:05 Yeah, I'm just... I, uh, just, um, b- to give people a sense of why I'm so dazzled by it, I am always fascinated by how quickly, how efficiently, and, um, how specifically biology can create these little protein complexes that do something really important. I mean, you're talking about an on-demand creation of a little, of a portal, right? I mean, th- these are cells engineering their own machinery in real time in response to chemical signals.

Peter Attia 38:32 But, but remind me-

Andrew Huberman 38:33 I mean, this, it's, it's great. It's, it's-

Peter Attia 38:34 Yeah, but, but, but I'm sort of rusty on my neuroscience, but an action potential works in reverse the same way. Like, you need the ATP gradient to restore the, uh, to, to restore the gradient, but once the action potential fires, it's passive outside, right?

Andrew Huberman 38:49 Yeah, so what Peter's referring to is, um, the way that neurons become electrically active is by the flow of ions across the cell, th- from the outside of the cell to the inside of the cell, and they have, we have both active conductances, meaning they're triggered by electrical changes in the gradients, uh, by, uh, changes in electrical potential, um, and then there are passive gradients where things can just flow back and forth until there's a balance equal, uh, inside and outside the cell. I think what's, um, what's different is that there's some movement of a lot of stuff inside of neurons when neurotransmitters like dopamine bind to its receptor and then a bunch of ... you know, it's like a bucket brigade that gets kicked off internally. But it's not often that you hear about receptors getting inserted into cells-

Peter Attia 39:28 Yeah, I see your point.

Andrew Huberman 39:28 ... very quickly. Normally you have to go through a process of, of, you know, uh, transcribing genes and making sure that the specific proteins are made and then it... Those are long, slow things that take place over the course of many hours or days. What you're talking about is a real on-demand insertion of, of-

Peter Attia 39:42 Yeah, it works in minutes.

Andrew Huberman 39:42 ... a channel.

Peter Attia 39:43 Yeah.

Andrew Huberman 39:43 And it makes sense as to why that would, uh, be required, but it's just, oh, so very cool.

Peter Attia 39:47 It's cool, yeah. So Keys and colleagues came along and said, "We would like to redo the entire Bannister analysis." Um, and I, I think their motivation for it was, the interest in this topic is through the roof. There is a clinical trial called the TAME trial that is, I think, pretty much funded now and may be getting underway soon. The TAME trial, which is an important trial, is going to try to ask this question prospectively and through random assignment. So...

Andrew Huberman 40:17 So this is the Targeting Aging with Metformin trial, TAME.

Peter Attia 40:20 That's correct.

Andrew Huberman 40:20 Okay.

Peter Attia 40:20 Nir Barzilai, uh, is probably the senior PI on that. Um, and I think in many ways, the Bannister study, along with some other studies, um, but of lesser significance, probably provided some of the motivation for the TAME trial. So they said, "Okay, look, we're gonna do this. We're gonna use a different cohort of people." Um, so the first study that we just talked about, the Bannister study, used, uh, I believe it was, like, roughly they sampled like 95,000 subjects from a UK biobank. Here, they used a larger sample. They did about half a million people sampled-

Andrew Huberman 40:55 Wow.

Peter Attia 40:56 ... from a, a, a Danish health registry, and they did something pretty elegant. They created two groups to study. So the first was just a standard replication of what Bannister did, which, uh, was just a group of people with and without diabetic that they tried to match as perfectly as possible. But then they did a second analysis in parallel with discordant twins. So same-sex twins that only differed in that one had diabetes and one didn't.

Andrew Huberman 41:23 Huh.

Peter Attia 41:24 I thought this was very elegant because here you have a degree of genetic similarity and you have similar environmental, uh, uh, factors during childhood that might give you, you know, allow you to see if there's any sort of difference in signal. So now turning this back into a little bit of a journal club, virtually any clinical paper you're gonna read, table one is the characteristics of the people in the study. You always want to take a look at that. So when I look at table one here, you can see it's... And by the way, just for people watching this, we're gonna make all these papers and figures available, so if you're-

Andrew Huberman 41:59 Right.

Peter Attia 41:59 ... you know, no, don't, you know, w- we'll have nice show notes that'll make all this clear. So table one in the Keys paper shows the baseline characteristics, and again, it's almost always gonna be the first table in a paper. Usually the first figure in the paper is a study design. It's usually a flowchart that says, "These were the inclusion criteria. These are all the people that got excluded. This is how we randomized," et cetera. And you can see here that there are four columns. So the co- the first two are the singletons. These are people who are not related. And then the second two are the twins who were matched. And you can see, remember how I said they sampled about 500,000 people? You can see the numbers. So they got, you know, 7,842 singletons on metformin, the same number then they pulled out matched without diabetes. On the twins they got 976 on metformin with diabetes, and then, by definition, 976 co-twins without them.... and you look at all these characteristics. What was their age upon entry? How many were men? What was the year of indexing when we got them? What medications were they on? What was their highest level of education, marital status, et cetera? The one thing I want to call out here that really cannot be matched in a study like this, so this is a very important limitation, is the medication. So look at, look at that column, Andrew. Notice how pretty much everything else is perfectly matched until you get to the medication list.

Andrew Huberman 43:21 Yeah, it's all over the place.

Peter Attia 43:23 Yeah, it's just... You, it's not even close. They're nowhere, they're nowhere near matched, right? In other words, just to give you a couple of examples, right? On the... And, uh, let's just talk about the singletons because it's basically the same story on the twins. If you look at what fraction of the people with type 2 diabetes are on lipid-lowering medication, it's 45.6% versus 15.4% in the matched without diabetes. It's a three X difference. What about anti-platelet therapy? That's 30% versus 14%. Anti-hypertensives, 65% vers- 63% versus-

Andrew Huberman 43:54 Right.

Peter Attia 43:54 ... 31%.

Andrew Huberman 43:55 Because people who have one health issue and are taking metformin are likely to have other health issues.

Peter Attia 43:59 Exactly. So this is, again, a fundamental flaw of epidemiology. You can never remove all the confounders.

Andrew Huberman 44:08 Ugh. This is why I became an experimental scientist-

Peter Attia 44:11 Yeah.

Andrew Huberman 44:11 ... so that we could control variables.

Peter Attia 44:13 That's right, because without random assignment, you cannot control every variable. Now, you'll see in a moment when we get into the analysis, they go through three levels of corrections, but they can never correct this medication one, so just keep that in the back of your mind.

Peter Attia 44:27 Okay, so the two big things that were done in this experiment, or in this survey or, you know, study, to differentiate it from Bannister was, one, the twin trick, which I think is pretty cool. The second thing that they did was they did a sensitivity analysis with and without informative censoring. So one of the things they wanted to know was, hey, does it really matter if we don't count the metformin patients who progress? So, um, so let's see kind of what, what transcribed. So the next figure, figure two... Uh, pardon me, the next table, table two, walks you through the crude, uh, mortality rate in each of the groups. So the most important row, I think, in this table is the one that says, "Crude mortality per 1,000 person-years." Now, you, you recall that in the previous study, in the Bannister study, those were on the ballpark of about 15 per. Okay, so let's look at each of these. So in the, um, single- the singletons with- without... So the non-twins who were not diabetic, it was 16.86.

Andrew Huberman 45:39 And could you put a little more contour on what this 1,000 person-years-

Peter Attia 45:44 What, what it is?

Andrew Huberman 45:45 ... means? Are you talking about pooling the life spans of a th- of, of a bunch of different people until you get to the number 1,000?

Peter Attia 45:51 Yeah, because l-

Andrew Huberman 45:52 So you're normalizing not... So it's not who's gonna live 1,000 years 'cause-

Peter Attia 45:55 Right, right, right.

Andrew Huberman 45:56 ... no one's expecting that. Um, you're essentially taking... So you've got some people that are gonna live, um, 76 years, 52 years, 91 years, and you're pooling all of those until you hit 1,000, and then that becomes kind of a, uh, a, a, it's like a normalized division. You're basically like... So let's say the, the control group, um... You're asking, "If there were 1,000 person-years available to live, how likely is it that this person would live another 15?"

Peter Attia 46:24 Yeah, so a couple ways to think about it. A simp- so taking a step back, we always have to have some way of normalizing. So when we talk about the mortality from a disease like cancer in the population, we would, we report it as, "What's the mortality rate per..." And it's typically per 100,000 persons.

Andrew Huberman 46:41 Okay.

Peter Attia 46:42 So-

Andrew Huberman 46:42 That's a much more intuitive way to express it, yeah.

Peter Attia 46:44 It is, but the reason we can do it that way is because we're literally looking at how many people died this calendar year-

Andrew Huberman 46:52 Mm-hmm.

Peter Attia 46:53 ... and we divide it by the number of people in that age group. So it's typically what you're doing when you look at aged groups in buckets of, like, decades. So that's why we can say the highest mortality is, like, people 90 and up. Even though the absolute number of deaths is small, it's because there's not that many people there, right? The majority of deaths, in absolute terms, probably occur in the seventh decade. But as you go up, because the denominator is shrinking, you have to normalize to it, so we just normalize to the number of people. Here are all the people that started the year, here are all the people that ended the year, what's the death rate? Why are these done in a slightly more complicated way? Because we do- we, we don't follow these people for their whole lives. We're only following them for a period of observation, in this case, roughly three years. So, to say something like, you know, we have a crude death rate of five deaths per 1,000 person-years, one way to think about that is if you had 1,000 people and you followed them for one year, you'd expect five to die. If you had 500 people and you followed them for two years, you'd expect five to die. If you have 1,000 people and you follow them for one year, you expect five to die. Those would all be considered equivalent mortalities.

Andrew Huberman 48:11 Great. Thank you for clarifying that.

Peter Attia 48:13 Yeah, yeah. No, no, this, this stuff is... I mean, like, I find, I find epidemiology, when you get in the weeds, is way more complicated than following the basics of, um, experimental stuff, where you just... You get to push all the stuff into the garbage bin and just say (laughs) , "We're gonna take this number of people, we're gonna exclude this group, we're gonna randomize, we're gonna see what happens."

Andrew Huberman 48:34 Yeah, that's what... Like the paper we'll talk about next.

Peter Attia 48:36 Yep, yep.

Andrew Huberman 48:36 (laughs)

Peter Attia 48:37 So when you adjust for age, and they don't show it in this table, it's only in the text. When you adjust for age, a very important check to do is, what is the crude death rate of the people on metformin who are not twins versus who are twins? Now, in this table, they look different because it's 24.93 for the metformin group and 21.68 for the twin group in... that's on metformin. When you adjust for age, they're almost identical. It's, it goes from 29 point... 24.93 to 24.7.One other point I'll make here for people who are going to be looking at this table is, um, you'll notice there are parentheses after every one of these numbers. What does that, what does that offer in there? Those parentheses are offering the 95% confidence interval. So for example, to take the number, you know, 24.93 is the crude death rate of how many people are dying who take metformin, what it's telling you is we're 95% confident that the actual number is between 23.23 and 26.64. If a 95% confidence interval does not cross the number zero, it's statistically significant. Okay, so the first thing that just jumps out at you, I think, when you look at this is, there's clearly a difference here between the people who have diabetes and those who don't. It complicates the study a little bit 'cause it's basically two studies in one, but you're comparing, um, 95... Pardon me, uh, 24.93 to 16.86, which by the way remains after age adjustment. When you go to the twin group, it's 24.73 to 12.94.

Andrew Huberman 50:23 So maybe just to zoom out for that, what you're describing, if I understand correctly, is this, um, uh, crude deaths per 1,000 person years, let's just talk about the singletons, the non-twins-

Peter Attia 50:34 Yeah, yeah.

Andrew Huberman 50:34 ... is 16.86. So 16.86 people die, and some people are probably thinking, "How can .86 of a person die?" Well, it's not always whole numbers, but, um, there's a, there's a bad joke to be made here, but, um-

Peter Attia 50:47 Y- Yeah, just call it 17-

Andrew Huberman 50:48 S- 17-

Peter Attia 50:48 ... versus 25.

Andrew Huberman 50:49 Right, 17 deaths per 1,000 versus 25 deaths.

Peter Attia 50:54 Yep.

Andrew Huberman 50:54 And the 25 is in the folks that took metformin. Now, that to the naive listener and to me means, oh, you know, metformin basically kills you, (laughs) right? Um, not, uh, faster, or you're, you know, you're more likely to die, but we have to remember that these people have another... They have a major health issue that the other group does not have.

Peter Attia 51:15 That's right.

Andrew Huberman 51:16 Because people weren't assigned drug or not assigned drug, it wasn't placebo drug, it's let's look at people taking this drug for a bad health issue and compare to everyone else.

Peter Attia 51:26 That's right.

Peter Attia 51:28 So now you have to go into... And, and I'll just sort of skip the next figure, but the next figure is a Kaplan-Meier curve. I th- I t- I think it's actually worth looking at it 'cause they show up in all sorts of studies. So if you look at figure one, it's a Kaplan-Meier curve, which is a mortality curve. So you'll see these in any study that is looking at death, and this can be prospective randomized, this can be retrospective, but these are always gonna show up. And I think it's really worth understanding what a Kaplan-Meier curve shows you. So on the x-axis is always time and on the y-axis is always the cumulative survival. So it's a curve that always goes from zero to one, one or 100%, and it's always decreasing monotonically, meaning it can only go down or stay flat, it can never go back up. So that's what a cumulative, uh, mortality curve looks like.

Andrew Huberman 52:22 Now we're looking at you're starting at alive and you're looking at how many people die for every year that passes.

Peter Attia 52:29 That's right. And in each curve, there's one on the left which is the matched singletons, and there's one on the right which are the discordant twins. You have two lines, you have those that were on metformin with type 2 diabetes and you have their matched controls. And in this figure, the matched controls are the darker lines, and the people with type 2 diabetes on metformin, that's the lighter line. You'll also notice, and I like the way they've done it here, they've got shading around each one.

Andrew Huberman 52:58 And we should mention for those that are just listening that in both of these graphs the, um, downward trending line from the controls, so again, non-diabetic, not taking metformin, is above the line f- uh, corresponding to the diabetics who are taking metformin. Um, put crudely, um, the people who are taking metformin that have diabetes are dying at a faster rate for every single year examined. The two lines do not overlap except at the beginning when everyone's alive. It's like a foot race where basically the people with metformin and diabetes are falling behind and dying as they fall.

Peter Attia 53:37 That's right, and I'm glad you brought up a good point. It's not uncommon in treatments, uh, to see Kaplan-Meier curves cross. They don't have to. It i- it's not a requirement that they never cross, it's only a, a, a requirement that they're monotonically decreasing or staying flat. So I've seen cancer treatment drugs where they have like two drugs going head-to-head in a cancer treatment and like one starts out looking really, really bad but then all of a sudden it kind of flattens while the other one goes bad and then it actually crosses and goes underneath. But that's not the case here. So as to your point, the people with diabetes taking metformin in both the matched singletons and the discordants are dropping much faster and they always stay below and I was just gonna say that the shading is just showing you a 95% confidence interval. So you're just putting basically error bars along this. So if this were experimental data, if you were doing an experiment with a group of mice and you were watching their survival and you were, you know, wa- you'd have error bars on this which you're actually measuring. So this is... Because you have much more data here, you're just showing-

Andrew Huberman 54:43 Mm-hmm.

Peter Attia 54:43 ... this in this fashion.

Andrew Huberman 54:43 For those that haven't, um, been familiarized with statistics, no problem, um, error bars correspond to like if you were just gonna measure the heights of a b- of a room full of 10th graders, there's gonna be a range, right? You have the very tall kid and the, and the very, uh, shorter kid, and you'll have the short kid and the medium kid and you'll... And so there's a range. There's gonna be an average, a mean, and then there'll be standard deviations and standard errors. And um, uh, so these confidence intervals just g- give a sense of how much range. You know, some people, um, die, th- die early, some people die late within a given year, they're gonna be different ages, um, so it a-... these error bars can account for a lot of different forms of variability. Here, you're talking about the variability is how many people in each group die. We're not tracking one diabetic taking metformin versus-

Peter Attia 55:30 Right.

Andrew Huberman 55:30 ... um, a control. I, I should have asked this earlier, but, um-

Peter Attia 55:33 Well, and it's also a mathematical model at this point, too, that's smoothing it out.

Andrew Huberman 55:38 Right.

Peter Attia 55:38 'Cause notice it's running for the full eight years, even though they're only following people for, you know, typically, I think the median was like three or four years at a time.

Andrew Huberman 55:46 Mm-hmm.

Peter Attia 55:47 So they're using this quite complicated type of mathematics called a Cox proportional hazard, which is what generates hazard ratios, and basically, any model has to have some error in it, and so they're basically saying this is the error. So you could argue when you look at that figure, we don't know exactly where the line is in there, but we know it's in that shaded area.

Andrew Huberman 56:08 Am I-

Peter Attia 56:09 So if those- if those, sorry to make one other point, if those shaded areas overlapped, you couldn't really make the conclusion.

Andrew Huberman 56:16 Mm-hmm.

Peter Attia 56:16 You wouldn't know for sure that one is different from the other.

Andrew Huberman 56:19 Yeah, that's actually a good opportunity to, um, to, uh, raise a common myth, which is a lot of people, when they look at a paper, let's say it's a bar graph, you know?

Peter Attia 56:30 Yes.

Andrew Huberman 56:30 Um, and they see these error bars, and they will s- say, people often think, "Oh, if the error bars overlap, it's not a significant difference. But if the error bars don't overlap, meaning there's enough separation, then that's a real and meaningful difference." And that's not always the case. It depends a lot on the form of the experiment. Um, I often see some of the- the more robust Twitter battles over, you know, how people are reading graphs, and I think it's important to remember that, um, you run the statistics, uh, hopefully the correct statistics for the, for the sample, um, but determining significance, whether or not the- the result could be due to something other than chance, of course, your confidence in that increases as it becomes typically P-values, P less than .00001 p- percent chance that it's due, um, to chance, right? So very low probability. P less than .05 tends to be the kind of gold standard cutoff. Um, but when you're talking about data like these, which are repeated measures over time, people are dropping out literally, um, over time, you're saying they've modeled it to make predictions as to what would happen. We're not necessarily looking at, you know, raw data points here.

Peter Attia 57:38 Yeah, the raw data was in the previous table. That's now taken and run through this Cox model, and it's smoothed out.

Andrew Huberman 57:46 Got it.

Peter Attia 57:46 And, uh, to your point about the bar graphs, yeah, I think the other thing you always want to understand is just because something doesn't achieve statistical significance, the only way you can say it's not significant is you have to know what it was powered to detect, um, and statistical power is, uh, a very important concept that probably doesn't get discussed enough. Uh, but before you do an experiment, you have to have an expectation of what you believe the difference is between the groups, and you have to determine the number of samples you will need to assess whether or not that difference is there or not. So you use something, uh, it's- it's called a power table, and you- you would go to the power table. So if you- if you're doing treatment A versus treatment B-

Andrew Huberman 58:33 Mm-hmm.

Peter Attia 58:33 ... and you say, "Well, look, I think treatment A is going to have a 50% response, and I think treatment B will have a 65% response," you literally go to a power table that says, "50% response, 15% difference." That gives you a place on the grid, and I want to be 90% sure that I'm right, so 90% power. I'm being a little bit... So there's gonna be a statistician listening to this who's gonna want to kill me, but this is directionally the way we would describe it, and that tells you, this is how many animals or people you would need in this study. You're gonna need 147 in each group. And by the way, if you now do the experiment with 147 and you fail to find significance, you can comfortably say there is no statistical difference, at least up to that 15%. There may be a difference at 10%-

Andrew Huberman 59:22 Mm-hmm.

Peter Attia 59:22 ... but you weren't powered to look at 10%.

Andrew Huberman 59:24 Yeah, and, um, very important point that you're making. Another point that's just a more general one about statistics, in general, the way to reduce variability in a data set is to increase sample size. And that kind of makes sense, right? If you- if I just walk into a tenth grade class and go, "Hey, I'm gonna measure height," and I look up by the first three kids that I see, and I happen to look over there, and it's the three that all play on the volleyball team together. I- my sample size is small, and I'm likely to get a skewed representation, in this case, taller than average. So increasing sample size tends to decrease variation. So the- that's why when you hear about a study from the UK Biobank or from, you know, um, half a million Danish citizens, like, for instance, in this study, that's- those are enormous sample sizes. So even though this is, uh, not an experimental study, it's an epidemiological observational study, um, there's tremendous power by way of the enormous number of subjects in the- in this study.

Peter Attia 1:00:21 And that's the way that epidemiology will make up for its deficit. So you could never do a randomized assignment study on half a million people. Um, you'd have, you know... So, so epidemiology makes up for its biggest limitation, which is it can never compensate for inherent biases by saying, we can do infinite duration if we want. Like, we could- we could survey people over the course of their lives, and we can have the biggest sample size possible 'cause this is relatively cheap. The cost of actually doing an experiment where you have tens of thousands of people is prohibitive. I mean, if you look at the Women's Health Initiative, which was a five-year study on, I don't know, what was it? 50,000 women, I mean, that was a billion-dollar study. So this is- this is the balancing act between epidemiology and randomized prospective experiments, and, uh, they- so they both offer something, but you just have to know the blind spots of each one.

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Andrew Huberman 1:02:23 off.

Peter Attia 1:02:24 So let's just kind of wrap this up. I mean, I think, uh, let's just go to table four, which I think is the most important table, um, in, in here, which now lays out the, the, the final results in terms of the hazard ratios. So this is, this is the way we want to really be thinking about this. So again, hazard ratios, um, these are important things to understand. A hazard ratio is a number, and you always subtract one from the hazard ratio, and that tells you if it's a positive number, if it's a number... Sorry, if it's a number greater than one, you subtract one, and that tells you the relative harm. So if the hazard ratio is 1.5, you subtract 1.5 is a 50% increase in risk, um, if the number is negative, you may recall on the Bannister paper, the hazard ratio was 0.85. So if it's less than one, so that means it's a 15% reduction in relative risk. And here you can see all the hazard ratios are positive. So what it's telling you here is... And I'm going to walk through this because it's, there's a lot of information packed here. You've got singletons, you've got twins, they're showing you three different ways that they do it. They do an unadjusted model. If you just look at the singletons with and without metformin and you make no adjustments, the hazard ratio is 1.48, meaning that people on metformin had a 48% greater chance of dying in any given year than their non-diabetic counterpart.

Andrew Huberman 1:03:45 The only reason I'm smiling, it's not because I enjoy people dying, quite, uh, quite to the contrary, is that, um, this is novel for me in that I've read some epidemiological studies before, but it's not normally where I spend the majority of my time. But up until now, I was thinking, "Okay, people taking metformin are, are dying more than those that aren't." I just... And I am just relieved to know that I wasn't, um, looking at all this backwards.

Peter Attia 1:04:07 (laughs) Yeah.

Andrew Huberman 1:04:07 Okay. So they're dying more, but of course, we don't have a group that's taking metformin who doesn't have diabetes, and we don't have a group, um, who, uh, has diabetes and, uh, you know, is taking metformin plus something else. So again, we're, we're only dealing with these constrained populations.

Peter Attia 1:04:24 Yeah. Now, there's the other arm to this study that I'm not getting into because it adds more complexity, which is they also have another group that's got diabetes, takes metformin, and takes sulfonylureas, which is a, a bigger drug, and those people die even more.

Andrew Huberman 1:04:39 Whoa. So... (laughs)

Peter Attia 1:04:40 Which, again, speaks to the point, right?

Andrew Huberman 1:04:43 Right.

Peter Attia 1:04:43 The more you need these medications, they're never able to erase the effect of diabetes.

Andrew Huberman 1:04:49 But in this case, it seems that they might be accelerating, possibly accelerating death due to diabetes. Possibly.

Peter Attia 1:04:56 We, we could never know that from this because we're, we don't see... We would need to see diabetics who don't take metformin, who take nothing, and I would bet that they would do even worse.

Andrew Huberman 1:05:05 Mm-hmm.

Peter Attia 1:05:06 So my, my intuition is that the metformin is helping, but not helping nearly as much as we thought before. So my point is they make another set of adjustments. They say, "Okay, well look, in the first one, in the unadjusted model, we only matched for age and gender." Okay, that's pretty crude. "What if we adjust for the medications they're on, the cardiovascular, psychiatric, pulmonary, dementia meds, and marital status?" I don't know why they threw marital status in there, but they did.

Andrew Huberman 1:05:34 I don't know, maybe being married or unmarried can accelerate the impact of-

Peter Attia 1:05:36 I'm sure it can, but it just seems like a random thing to throw in with all their meds. I would have personally done that adjustment higher up. But nevertheless, if you do that, all of a sudden the, uh, hazard ratio drops from 1.48 to 1.32, which means, yep, you still have a 32% greater chance of dying in any given year. All right, what if we also adjust for the highest level of, um, education along with any of the other covariance? Well, that doesn't really change it at all, it ends up at 1.33 or a 33% chance increase in death. Okay.

Andrew Huberman 1:06:08 I always knew that more school wasn't going to save me.

Peter Attia 1:06:10 Was... It's not doing jack. So now let's do it for the twins. If you do the twin study, which you could argue is a slightly purer study because you at least have one genetic and environmental thing that you've attached, the unadjusted model is brutal, 2.15. That's 115%. Think about this. These are twins who in theory are the same in every way except one has diabetes and one doesn't. And the one with diabetes on metformin still has 115% greater chance of dying than the non-diabetic co-twin.

Andrew Huberman 1:06:42 Brutal.

Peter Attia 1:06:42 When you make that first adjustment of all the meds and marital status, it, you bring it down to a 70% increase in risk. And when you throw education in it, goes up to an 80% chance of risk. Now, they did this really cool thing, which was they did the analysis on with and without censoring. So everything I just said here was based on no censoring.

Andrew Huberman 1:07:05 Tell me about censoring.

Peter Attia 1:07:06 Censoring is when you stop counting the metformin people who have died.

Andrew Huberman 1:07:09 Hmm.

Peter Attia 1:07:10 Okay, so in the singleton group when you unadjust it, and the reason I'm doing the unadjusted is that's where they did the sensitivity analysis. I don't think it really matters that much, it's, you just have to draw a line in the sand somewhere. You'll recall that that was a 48% chance of increased mortality, all cause mortality, if you stop counting, if you, if you... Pardon me, if you don't censor.... meaning if you include everybody, including when people on metformin with diabetes die. If you censor them, it comes down to 1.39. In other words, this is a very important finding, it did not undo the benefits that we saw in the Bannister study. Bannister saw a 15% reduction in mortality when they censored. When Keys censored, it got better, but not that much better. It went from 48 to 39%. In the twins, it went from 115% down to only 97%. So, in some ways, this presents a little bit of an enigma because it's not entirely clear to me, having read these papers many times, exactly why Bannister found such an outlined ... Like, s- such a different response. There's another res- there's another technical detail of this paper, which is they ... You can see on the right side of table four, they did something called a nested case control. But you'll see ... And I was gonna go into a long explanation of what nested case controls are. It's another pretty elegant way to do case control studies, where you sample by year and you, you, you sort of normali- ... You, you don't count all the cases at the end. You count them one by one. I don't think it's worth getting into, Andrew, 'cause it doesn't change the answer. You can see it changes it just slightly, but it doesn't change the point.

Peter Attia 1:09:00 The point here is, the Keys paper makes it und- undeniably clear that in that population, there was no advantage offered by metformin that undid the disadvantage of having type 2 diabetes. This does not mean that metformin wasn't helping them because we don't know what these people would've been like without metformin. It could be that this bought them a 50% reduction in relative mortality to where they'd been. But what it says is, in a way, this is what you would've expected. This is what you would've expected 10 years ago before the Bannister paper came out.

Andrew Huberman 1:09:35 Or maybe even before metformin was used, because in some ways it's saying, um, what is the likelihood that sick people who are on a lot of medication are gonna die compared to not sick people who aren't on a lot of medication?

Peter Attia 1:09:46 Yep.

Andrew Huberman 1:09:47 I wanna ... You know, it's not quite that simple in the, in the sense that, as you said, there are ways to, um, try and isolate the metformin contribution somewhat.

Peter Attia 1:09:57 Yep.

Andrew Huberman 1:09:57 Um, because they're on a bunch of other meds. Um, and presumably that was done and analyzed in, in other figures that ... Where they, they can sort of try and ... They can never attach the results specifically to metformin, right? But, um, there must be some way of weighting the percentage that are on psychiatric meds or not on psychiatric meds. Is there some way to tease out whether or not there's actually some contribution in metformin to this result?

Peter Attia 1:10:22 Well, that's what they're doing in, in b- in the, in the partial adjustment, is they're actually com- They're actually doing their best to say-

Andrew Huberman 1:10:30 Oh, right, married or not married. They're going variable by variable.

Peter Attia 1:10:33 They're going drug by drug all the way through.

Andrew Huberman 1:10:34 Got it.

Peter Attia 1:10:34 High blood pressure, non-high blood pressure, smoking, non-smoking, et cetera.

Andrew Huberman 1:10:37 Right. And the way they would do that, presumably, is, um, by saying, "Okay, married, not married." That's what ... That's a simple one. Um-

Peter Attia 1:10:44 Are you on lipid lower meds? Yes or no. Okay, you are not. You are not. They-

Andrew Huberman 1:10:50 And then comparing those groups.

Peter Attia 1:10:51 Yeah, yeah.

Andrew Huberman 1:10:52 Okay. So, no, no differences jumping out that can be purely explained by these other variables.

Peter Attia 1:10:58 Yes, although, again, this is a, this is a great opportunity to talk about why, no matter how slick you are, no matter how slick your model is, you can't control for everything. There's a reason that, to my knowledge, virtually every study that compares meat eaters to non-meat eaters finds an advantage amongst the non-meat eaters, and we can talk about all lifespan-

Andrew Huberman 1:11:17 Lifespan advantage.

Peter Attia 1:11:18 Yes, and we can ... Or disease, you know, incidence, uh, studies. And yeah, it might be tempting to say, "Well, therefore eating meat is bad," um, until you realize that it takes a lot of work to not eat meat. That's a very, very significant decision that a person ... For most people, that's a very significant decision a person makes. And for a person to make that decision, they probably have a very high conviction about the benefit of that to their health. And it is probably the case that, that they're making other changes with respect to their health as well, that are a little more difficult to measure. Now, there's a million other problems with that, and I picked a silly example because the whole meat discussion then gets into, well, you know, when, when we say eating meat, what do we mean? Like, 'cause I-

Andrew Huberman 1:11:59 What we're talking about is like deli meat versus grass-fed-

Peter Attia 1:12:01 Exactly, and those other things.

Andrew Huberman 1:12:03 ... or a, or a, you know, a deer that you hunted with your bow.

Peter Attia 1:12:04 That, that's right.

Andrew Huberman 1:12:05 Two totally different worlds.

Peter Attia 1:12:05 So how do we get into all those things?

Andrew Huberman 1:12:06 Yeah.

Peter Attia 1:12:06 But my point is, it's very difficult to quantify some of the intangible differences, and I think that even a study that goes to great lengths, as this one does, epidemiologically to make these corrections can never make the corrections. And so for me, the big takeaway of this study is, one, this makes much more sense to me than the Bannister paper, which never really made sense to me. And again, I was first critical of the Bannister paper in 2018, about four years after it came out. That's about the time I stopped taking metformin, by the way. I stopped taking it for a different reason, which we can talk about in a sec. But, um, that was the first time I went back and said, "Wait a minute. This information, this, this, this informative censoring thing is ... That's a little fishy, and I think we weren't looking at a true group of real type 2 diabetics." Now that said, maybe it doesn't matter. In other words, maybe ... And e- even the Keys paper doesn't tell us that metformin wouldn't be beneficial because it could be that those people, if they were on nothing as their matched cohorts were on nothing, would've been dying at, you know, a hazard ratio of three, and this brought it down to 1.5, in which case you would say, "There is some zero protection there." It is putting the brakes on this process.

Andrew Huberman 1:13:17 Mm-hmm.

Peter Attia 1:13:17 All of this is to say, absent a randomized control trial, we will never know the answer, but the-

Andrew Huberman 1:13:22 Has there been a randomized control trial on metformin?

Peter Attia 1:13:24 Not of, not of this nature. Not, not when it comes to a hard outcome.

Andrew Huberman 1:13:26 Really?

Peter Attia 1:13:26 Now, there has been-

Andrew Huberman 1:13:27 Why not?

Peter Attia 1:13:27 ... in the ITP. So the Interventions Testing Program, uh, which is kind of the gold standard for animal-... uh, studies, which is run out of three labs. So it's, it's an NIH-funded program that's run out of three labs. They, um, they basically test molecules for geral protection. Um, the ITP was the first study that really put rapamycin on the map in 2009. That was the study that's fortuitously demonstrated that even when rapamycin was given very, very late in life, it was given to 60-month-old mice, it still afforded them a 15%, uh, lifespan extension.

Andrew Huberman 1:14:03 Has a similar study been done in humans? I mean, it's hard-

Peter Attia 1:14:06 With rapa?

Andrew Huberman 1:14:06 I mean, it's hard to... No, I mean, you can't really control with rapamycin.

Peter Attia 1:14:09 No. Um, but when the ITP studied metformin, it did not succeed. So the, the, there have not been that many drugs that have worked in the ITP. The ITP is very rigorous, right? It's a, it doesn't use an inbred strain of mice. It is done concurrently in three labs with very large sample sizing. And so when something works in the ITP, it's pretty exciting. Rapamycin has been studied several times. It's always worked. Another one we should talk about at a subsequent time is 17-alpha-estradiol. This continues to work in male mice and it produces comparable effects to rapamycin.

Andrew Huberman 1:14:46 Estrogen.

Peter Attia 1:14:47 Doesn't work in-

Andrew Huberman 1:14:48 It makes male-

Peter Attia 1:14:48 ... female rice.

Andrew Huberman 1:14:49 Makes-

Peter Attia 1:14:49 But, but this is alpha, not beta. So this is 17-alpha-estradiol, not beta-estradiol, which is the estradiol that we all... That, that is bioavailable in all of us.

Andrew Huberman 1:14:57 And, and, uh, just as a brief aside, um, I think you and I basically agree that, um, unless it's a problem, h- males, we're talking post-puberty, um, should try and have their estrogen as high as possible without having negative symptomatology because of the importance of estrogen for libido, for brain function, tissue-

Peter Attia 1:15:19 Bone health.

Andrew Huberman 1:15:19 ... health, bone health.

Peter Attia 1:15:20 Yeah. Body composition, maybe.

Andrew Huberman 1:15:20 So this idea of crushing estrogen and raising testosterone is just silly, right? There's not... And let's just leave raising testosterone out of it, but many of the approaches to raising testosterone that are pharmacologic in nature also raise estrogen. A lot of people try and push down on estrogen and that is just, again, unless people are getting, um, hyperestrogenic effects like gynecomastia or other issues s- is the exact wrong direction to go. You want estrogen high.

Peter Attia 1:15:46 Estro, estrogen is a very important hormone for men and women.

Andrew Huberman 1:15:50 Yeah, that's good.

Peter Attia 1:15:51 Um, canagliflozin, an SGLT2 inhibitor, also very successful in the ITP, but again, interestingly, rapami, uh, uh, metformin not. So metformin has failed in the ITP.

Andrew Huberman 1:16:02 So you no longer take metformin?

Peter Attia 1:16:03 I stopped five years ago.

Andrew Huberman 1:16:04 I mean, you're not a diabetic, so presumably you were taking it to bu-

Peter Attia 1:16:06 I was taking it for geral protection.

Andrew Huberman 1:16:08 To buffer blood glucose-

Peter Attia 1:16:09 Y- yeah.

Andrew Huberman 1:16:10 ... in order to potentially-

Peter Attia 1:16:11 And ultimately, ultimately-

Andrew Huberman 1:16:11 ... live longer.

Peter Attia 1:16:11 Yes, exactly. And the reason I stopped, and this will be the last thing before we move on-

Andrew Huberman 1:16:15 Well, 'cause you couldn't go to the Dairy Queen at the Buffett event.

Peter Attia 1:16:18 (laughs) No, finally, the nausea went away after a few weeks-

Andrew Huberman 1:16:20 Ah.

Peter Attia 1:16:20 ... or a month maybe. Um, but once I got really into lactate testing, I noticed how high my lactate was, uh, at rest. So a resting fasted lactate should be, in a healthy person, should be below one, like somewhere between .3, .6 millimole. And only when you start to exercise should lactate go up. And in 2018 was when I started blood testing for my zone two. So previously, when I was doing zone two testing, I was just going off my power meter and heart rate. But this is when, uh, this is after I'd met Inigo San Millan and I started, like, wanting to use the lactate threshold of two millimole as my, as my determinant of where to put my wattage on the bike. And I'm, like, doing finger pricks before I start and I'm like 1.6 millimole and I'm like, "What the hell is going on? I can't be 1.6-"

Andrew Huberman 1:17:11 This is if you ran the, a flight of stairs up the back of the Empire State Building.

Peter Attia 1:17:14 Well, no, that would put me a lot higher, right? But, but, and, and, and when I-

Andrew Huberman 1:17:17 I was being generous to your fitness.

Peter Attia 1:17:19 (laughs)

Andrew Huberman 1:17:19 (laughs)

Peter Attia 1:17:20 No, but, but, but that's when I started doing a little digging and realized, "Oh, you know what? This totally makes sense." If you have a weak mitochondrial toxin-

Andrew Huberman 1:17:29 Mm-hmm.

Peter Attia 1:17:29 ... what are you going to do? You're going to shunt more glucose into pyruvate and more pyruvate into lactate. I'm m- I'm anaerobic-

Andrew Huberman 1:17:37 Yeah, you need an-

Peter Attia 1:17:38 ... at baseline.

Andrew Huberman 1:17:38 ... alternative fuel source.

Peter Attia 1:17:39 That's right. So, and then my zone two numbers just seemed off. My lactate seemed

Andrew Huberman 1:17:44 Could you feel it? Sorry to interrupt.

Peter Attia 1:17:45 No, I-

Andrew Huberman 1:17:45 But could you feel it in your body? 'Cause, uh, maybe now I'll just briefly describe. I took berberine.

Peter Attia 1:17:49 Mm-hmm.

Andrew Huberman 1:17:50 Um, it, I, um, during the period of maybe somewhere in the 2012 to 2015 stretch. I don't recall exactly-

Peter Attia 1:17:57 And, and what were you taking it for?

Andrew Huberman 1:17:58 Well, I'll tell you. So I, I was and I still am a big fan of Tim Ferriss's, uh, slow carbohydrate diet.

Peter Attia 1:18:04 Mm-hmm.

Andrew Huberman 1:18:04 Because I like to eat meat and vegetables and starches. I'm an omnivore. Um, and I found that it worked very quickly, got me very lean. I could exercise. I could think. I could sleep. All, you know, a lot of my, uh, rationale for following one eating regimen or another, what I eat, is to enjoy myself but also have mental energy. I mean, 'cause if I can't sleep at night, I'm not going to replenish.

Peter Attia 1:18:27 Yep.

Andrew Huberman 1:18:27 I'm not... If I don't replenish, I'm going to feel like garbage. I don't care how lean I am or what, you know?

Peter Attia 1:18:30 Yeah.

Andrew Huberman 1:18:31 Um, so I found the slow carb diet to be, um,