#270 ‒ Journal club with Andrew Huberman: metformin as a geroprotective drug, the power of belief, and how to read scientific papers

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 and we've established a great team of analysts to make this happen. It is extremely important important to me to provide all of this content without relying on paid ads to do this. Our work is made entirely possible by our members and in return we offer exclusive member only content and benefits above and beyond what is available for free. If you want to take your knowledge of this space to the next level, it's our goal to ensure members get back much more than the price of a subscription. If you want to learn more about the benefits of our premium membership, head over to peterattiamd.com subscribe welcome to a special episode of the Drive. This episode is actually a dual episode with Andrew Huberman where we are going to be releasing our conversation on both the Huberman Lab Podcast and on the Drive. In this episode, Andrew and I have a journal club where we each present and talk through a paper that we have found interesting in the previous couple of months. Now, I hope this will help people not only understand the results of the specific papers we go through, which is part of the exercise, but also to.

Give people an idea of how to.

Read and interpret a paper that you might read. And really, in some ways I think that's equally if not more important.

As part of this exercise.

For my paper we looked at a study on Metformin by Keys et al. Which looked back at the 2014 study by Bannister et al. That initially got everyone really interested in metformin as a possible zero protective molecule. Through looking at this paper, we discussed metformin as a possible juroprotective drug, but also had a general discussion around juroprotection and the current lack of biomarkers of aging. Andrew then presented a paper that addressed how our beliefs of the drug we take impacts the effect they have on us at a biological level. So not looking at placebo effects, but actual belief effects and what this could mean going forward. As a reminder, Andrew is an Associate professor of Neurobiology at the Stanford University School of Medicine and the host of the very popular Huberman Lab podcast. He's also a former podcast guest on episode 249. So without further delay, please enjoy my conversation with Andrew Huberman.

Peter, so good to have you here.

So great to be here my friend.

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 do feel that 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, and also just to start thinking like scientists and clinicians and get a better sense of what it looks like to pick through a paper. The good, the bad and the ugly. So we're flying a little blind here, which is fun. I'm definitely excited for all the above reasons.

Yeah, no, this is. You and I have been talking about this for some time and actually we used to run a journal club inside the practice where once a month one person would just pick a paper and you would go through it in kind of a formal journal club presentation. We'd gotten away from it for the last year just because we've been a little stretched. Then I think it's something we need to resume because it's, it's a great way to learn and it's a skill people probably ask you all the time because I know I get asked all the time, hey, what are the do's and don'ts of interpreting scientific papers? Is it enough to just read the abstract? And usually the answer is, well, no, but the how to is tougher. And I think the two papers we've chosen today illustrate two opposite ends of the spectrum. 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 a few minutes ago, is actually a very straightforward, simple epidemiologic paper that I think has important significance. I had originally gone down the rabbit hole on 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. By the way, I got to tell you, a funny Story. So I had a dream last night about you. And in this dream you were obsessed with making this certain drink that was like your elixir and it had all of these crazy ingredients in it, supplements, tons of supplements in it. 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. You had to collect a certain amount of dew off the leaves every morning to put into this drink. It was so.

It was like, sounds like something that I would do.

And so. But here's the best part. 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. Special dew off the leaves every morning.

I love it. Well, it's not that far from reality. I'm a big fan of yerba mate. I'm drinking it right now, in fact, 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 5 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 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 hugging it close, chances are they're Uruguayan 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 reality. I don't carry the thermos, but I do drink mate every day. And I'm going to start collecting dew off the leaves.

Just a few drops every morning as you brew it.

Oh my. Some other time we can talk about dreams. Recently I've been Doing some dream exploration. I've had some absolutely transformative dreams for the first time in my life. One dream in particular 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.

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?

Oh, yes.

Yeah.

At least 18 months of intensive analysis type work with a very skilled psychiatrist. But I wasn't trying to seed the dream. It was just 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 where I was able to sense something and feel something I've always wanted to feel as 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 I can actually access this in my waking life. It was absolutely transformative for me. Anyway, sometime I can share more details with you or the audience, but for now, maybe we should talk about these papers.

Very well.

Who should go first?

I'm happy to go first. This is a pretty straightforward paper. So we're going to talk about 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 Keys and colleagues. This was published last fall. Why is this paper important? So this paper is important because in 2014, Bannister published a paper that I think in many ways kind of got the world very excited about, Metformin. So this is 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 zero protection with Metformin. And I should probably just define for the audience what zero protection means when.

We think probably also. Sorry to interrupt. What Metformin is just for the uninformed.

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. Depends, you know, where it was first approved. I think was in Europe. But you know, call it directionally. 50 plus years of use as a first line agent for patients with type 2 diabetes in the US maybe 40 plus years. So this is a drug that's been around forever. Trade name Glucophage or brand name. And, but, but again, it's, you know, it's a generic drug. Today, the mechanism by which Metformin works is debated hotly. But what I think is not debated is the immediate thing that metformin does, which is it inhibits complex one of the mitochondria. So 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, 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 four of the mitochondria. I don't know if you recall if it's complex 3 or complex 4.

I know a lot about toxins that impact the nervous system, but I don't know a lot about poisons.

Mitochondrial.

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.

Yeah, like Detroit toxin and all these things that block out.

I, I really geek out on this stuff because it allows me to talk about neuroscience, animals and scary stuff. It's like combines it. So we could do that sometime for fun, maybe at the end if we have a few moments.

So, so you know, something like cyanide that is a very potent inhibitor of this electron transport chain will kill you instantly. People understand that. Of course, a drop of cyanide and you would, 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 complexes. And, 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, adenosine diphosphate. What's less clear is why does that have a benefit in diabetics? 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. 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 5 grams of glucose in our total circulation. That's it, 5 grams. Think about how quickly the brain will go through that 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 level was consistently two teaspoons, you would have type 2 diabetes. So the difference between being metabolically healthy and having profound type 2 diabetes is 1 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 can I just ask one question? Is it fair 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. 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.

Well, sort of. I mean, it's not, it's not depriving you of ultimately storing that energy. What it's doing is changing the way the body partitions fuel. That's probably a better way to think about it, to be a little bit more accurate. So, for example, like, it's not depriving you of the calories that are in that glucose, that would be, you know, fantastic.

But that was the olestra. Remember the olestra from the 90s? Olestra, folks, for those of you who don't remember, by the way, if you ever ate this stuff, you'd remember because it was a fat that was not easily digested. It had sort of, 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. Would simply excrete it. And I don't know what happened except that people got a lot of stomachaches and, well, everyone got better in the world.

We know that the anal seepage is what really did that product.

Anal seepage. Only a physician, because after all, Peter's a clinician, a physician, an md and I'm not could find an appropriate term to describe.

Yeah, when you have that much fat malabsorption, you start to have accidents. Wow. So that did away with that product.

Right. It was either that or the diaper industry was going to really take off. Okay. That's why you don't hear about Elestra.

That's right. 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? The primary insult probably occurs in the muscles and it is insulin resistance. Everybody hears that term. What does it mean? 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, because 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, 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. 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, by the way.

I did not know that. I mean, I certainly know active and passive transportation as it relates to, like, neurotransmitter and ion flow, 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.

Glucose doesn't need a pump to move it in because there's much more glucose outside the cell than inside. So it's just. But the energy required is to move the straw up to the cell.

Cell biology is so cool.

Yeah, it is. So what happens is as. And Gerald Shulman at Yale did the best work on elucidating this. As the intramuscular fat increases, and by intramuscular, I mean intracellular fat, triacyl and diacyl glycerides accumulate in a muscle cell, that signal gets interrupted, 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. 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 five, ten 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.

Could you share with us a few of the causes of insulin type 2 diabetes? Of insulin resistance, I mean, one it sounds like is accumulating too much fat.

Yeah. So energy imbalance would be an enormous one. Inactivity or insufficient activity is probably the single most important. So when Gerald Shulman was running clinical trials at Yale, they would be recruiting undergrads to study, obviously, because you're typically recruiting young people. And they would be doing these very detailed mechanistic studies where they would require actual tissue biopsies. So you're going to biopsy somebody's quadriceps and. 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. There's some very elegant mechanistic studies where you sleep, deprive people, you let them only sleep for four hours for a week, you'll reduce their glucose disposal by about half.

Wow.

Which is. I mean, that's a staggering amount of. You're basically inducing profound insulin resistance in just a week of sleep deprivation. Hypercortisolemia is another factor. And then obviously energy imbalance. So 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.

Got it.

Okay, so enter metformin, first line drug. So most of the drug. So every drug you give a person with type 2 diabetes is trying to address part of this chain. So some of the drugs tell you to make more insulin. 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 hepatic glucose output channel. GLP1. Agonists are another drug. They increase insulin sensitivity initially causing you to also make more insulin. GLP1.

So that's Ozempic.

Yes.

Yeah. And is it true that berberine is more or less the poor man's metformin?

Yeah. Okay. Yeah.

It's a. From a tree bark. It just happens to have the same properties of reducing mtor and reducing blood glucose.

Yeah. And metformin, by the way, 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.

So you need a prescription for metformin. You don't need a prescription for berberine.

Correct.

But, yeah, we can talk about Berberine a little bit later. I had a couple great experiences with berberine and a couple bad experiences.

Interesting.

Yeah.

So 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 shareholder meeting, which is, you know, the Buffett shareholder meeting. And, 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 2 grams a day, which is kind of like the full dose, and we went to this.

Is that characteristic of your approach to things?

Yes, I think that's safe to say.

Next time, I'll give you a thermos of this dew that I collect in the morning. Oh, really?

So I remember being so sick that the whole time we were in Nebraska, Omaha, I guess I couldn't. We went to Dairy Queen because you do all the buffet things when you're there. Right. Like, I couldn't have an ice cream at Dairy Queen.

You couldn't.

I mean, I couldn't. I'm so nauseous. Oh.

Because I would say if you've got metformin in your system, you're gonna buffer glucose. You could have four ice cream cones.

Except I couldn't put. I couldn't keep anything down. I mean, I was so nauseous. 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 a lot of reasons I was interested in it. I wasn't thinking true giro protection. 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.

Do you wanna define gero protection? Yeah, it's a good term to define geriatric giro.

Yeah. So, yeah, GIRO from geriatric old protection. So protection from aging. 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 zero protective to signal that they are drugs that are not targeting a specific disease of aging. For example, a PCSK9 inhibitor is sort of zero protective, but it's targeting one specific pathway, which is cardiovascular disease and dyslipidemia. Whereas the idea is a zero protective 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, proteomic instability, genomic instability, methylation, all of these things, epigenetic changes, those are all the nine hallmarks of aging.

Seven.

Yeah, yeah. So a geoprotective agent would target those deep down biologic hallmarks of aging. And in 2014, a paper came out by Bannister that basically got the world focused on this problem. By the world, I mean the world of anti aging. So 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 look just like that person with diabetes. So can we match them for age, sex, socioeconomic status, 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. And so let me just kind of pull up the sort of 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 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 informative censoring. So the way the study worked is if you were put on Metformin, we're going to follow you. If you're not on Metformin, we're going to follow you and we're going to 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 want to know how much are people dying from anything? Because 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 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 going to 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. So with that caveat, the Bannister study found a very interesting result, which was the crude death rate 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 thousand 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 thousand 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.

Could you explain why? Because 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, a difference of about a year and a half in lifespan is remarkable.

It doesn't even translate to that. So taking a step back, type 2 diabetes on average will shorten your life by six years.

I see.

So that's the actuarial difference between having type 2 diabetes and not all comers. 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 thousand patient years studied.

Okay.

And by the way, Peter just pointed out my math was wrong when I said about a year and a half.

But the point here is you would expect the people in the metformin group to have a far worse outcome, that is 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 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%, 1, 5, 15% relative reduction in all cause death over 2.8 years, which was the median duration.

Of follow up, that seems to be the number that makes me go, wow. Yeah, right. Because. Could you repeat those numbers again?

Yep. So 15% reduction in all cause mortality over 2.8 years.

That's a big deal.

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. So again, the proponents of metformin being a zero protective 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 of. So fast forward until a year ago, and I think most people took the Banister 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 outside of dew. I mean, people definitely wanna know if you should be consuming dew, but after that it's metformin fresh off the leaves.

Has to be while viewing morning sunlight.

So. Okay, so let's kind of fast forward to now the PA I wanted to spend a few more minutes on.

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

It's a pretty short straw in fairness. You know, it's just, it's just a little trick.

Yeah, I'm just, I just to give people a sense of why I'm so dazzled by it. I am always fascinated by how quickly, how efficiently and 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 portal, right? I mean, these are cells engineering their own machinery in real time in response to chemical signals. It's great.

Yeah, 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, to restore the gradient, but once the action potential fires, it's passive outside. Right?

Yeah. So what Peter's referring to is the way that neurons become electrically active is by the flow of ions across the, from the outside the cell to the inside of the cell. And we have both. Active conductances mean they're triggered by electrical changes in the gradients via changes in electrical potential. And then they're passive gradients where things can just flow back and forth until there's a balance equal inside and outside the cell. I think what's different is that there's some movement of a lot of stuff inside of neurons when neurotransmitters like dopamine binds 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 very quickly. Normally after they go through a process of transcribing genes and making sure that the specific proteins are made and then 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 a channel. And it makes sense as to why that would be required, but it's just oh, so very cool.

It's cool, yeah. So Keys and colleagues came along and said we would like to redo the entire Banister analysis. And 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 this is the Targeting Aging with Metformin trial.

That's correct. Nir Barzilai is probably the Senior PI on that. And I think in many ways the Bannister study, along with some other studies, but of lesser significance, probably provided some of the motivation for the TAME trial. So they said, okay, look, we're going to do this, we're going to use a different cohort of people. So the first study that we just talked about, the Banister study used, 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 from 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 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. I thought this was very elegant because here you have a degree of genetic similarity and you have similar environmental 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 going to read. Table 1 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 going to make all these papers and figures available. So if you're, you know, don't, you know, we'll have nice show notes that'll make all this clear. So table one in the qies paper shows the baseline characteristics. And again it's almost always going to be the first table in a paper. Usually the first figure in the paper is a study design. It's usually a flow chart 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 first two are the singletons, these are people who are not related. And then the second two are the twins who are matched. And you can see, remember how I said they sampled about 500,000 people? You can see the numbers. So they got 7,842 singletons on metformin, the same number. Then they pulled out matched without diabetes. On the twins they get 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 that column, Andrew. Notice how pretty much everything else is perfectly matched until you get to the medication list.

Yeah, it's all over the place.

Yeah, it's just, it's not even close. They're nowhere near matched. Right. In other words, just to give you a couple of examples. Right. And 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 3x difference. What about antiplatelet therapy? That's 30% versus 14%. Antihypertensive 65% versus 63% versus 31%.

Because people who have one health issue and are taking metformin are likely to have other health issues.

Exactly. So this is again A fundamental flaw of epidemiology. You can never remove all the confounders.

Ugh. This is why I became an experimental scientist, so that we could control variables.

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. 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? Let's see, kind of what transcribed. So the next figure. Figure two, pardon me, the next table. Table two walks you through the crude 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 thousand person years. Now, 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, the singletons, so the non twins who were not diabetic, it was 16.86.

And could you put a little more contour on what this thousand person years, what it is. Are you talking about pooling the lifespans of a bunch of different people until you get to the number 1,000? Yeah, because you're normalizing. So it's not who's going to live a thousand years because no one's expecting that. And you're essentially taking. So you've got some people that are going to live 76 years, 52 years, 91 years, and you're pooling all of those until you hit 1000. And then that becomes kind of like a normalized division. You're basically like. So let's say the control group. You're asking, if there were 1000 person years available to live, how likely is it that this person would live? Another.

Yeah, so a couple ways to think about it. 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 report it as what's the mortality rate per. And it's typically per hundred thousand persons.

Okay. That's a much more intuitive way to express It.

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, 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 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, we have accrued Death Rate of 5 deaths per thousand 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 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. Great.

Thank you for clarifying that.

No, no, this stuff is. I mean, I find epidemiology, when you get in the weeds is way more complicated than following the basics of experimental stuff where you get to push all this stuff into the garbage bin and just say, we're going to take this number of people, we're going to exclude this group. We're going to randomize. We're going to see what happens.

Yeah, that's like the paper we'll talk about next.

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 that's on metformin. When you adjust for age, they're almost identical. It goes from 24.93 to 24.7. One other point I'll make here for people who are going to be looking at this table is you'll notice there are parentheses after every one of these numbers. 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 0, 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 because it's basically two studies in one. But you're comparing 95, pardon me, 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.

So maybe just to zoom out for that, what you're describing, if I understand correctly, is this crude deaths per 1000 person years. Let's just talk about the singletons, the non twins is 16.86. So 16.8. And some people are probably thinking, how can 86 of a person die? Well, it's not always whole numbers, but there's a bad joke to be made.

Here, but yeah, just call it 17 versus 25.

Right? 17 deaths per thousand versus 25 deaths. 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.

Right?

Not faster or, 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. That's right, 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.

That's right. So now you have to go into. And I'll just sort of skip the next figure. But the next figure is a Kaplan Meier curve. I think it's actually worth looking at it because 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 going to 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 0 to 11 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 mortality curve looks like.

Now we're looking at, you're starting at alive, and you're looking at how many people die for every year that passes.

That's right. And in each curve, there's one on the left, which is the matched singletons, and there's the 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.

And we should mention, for those that are just listening, that in both of these graphs, the downward trending line from the controls. So again, non diabetic not taking metformin is above the line corresponding to the diabetics who are taking metformin. Put crudely, the people who are taking metformin that have diabetes are dying at a faster rate for every single year exam. And 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.

That's right. And I'm glad you brought up a good point. It's not uncommon in treatments to see Kaplan Meier curves cross. They don't have to. It's not a requirement that they never cross. It's only 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 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 going to 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, 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 this in this fashion.

For those that haven't been familiarized statistics, no problem. Error bars correspond to, like, if you were just going to measure the heights of a. A room full of 10th graders, there's going to be a range, right? You have the very tall kid and the very shorter kid, and you'll have the short kid and the medium kid. And so there's a range. There's going to be an average, a mean, and then there'll be standard deviations and standard errors. And so these confidence intervals just give a sense of how much range. You know, some people die early, some people die late. Within a given year, they're going to be different ages. So 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 a control. I should have asked this earlier, but.

Well, and it's also a mathematical model at this point, too, that's smoothing it out, because notice it's running for the full eight years, even though they're only following people for. Typically, I think the median was like three or four years at a time. 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. Sorry to make one other point. If those shaded areas overlapped, you couldn't really make the conclusion. You wouldn't know for sure that one is different from the other.

Yeah, that's actually a good opportunity to raise a common myth, which is a lot of people, when they look at a paper, let's say it's a bar graph, and they see these error bars. And they will say, people often think, oh, if the error bars overlap, 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. I often see some of the more robust Twitter battles over how people are reading graphs. And I think it's important to remember that you run the statistics, hopefully the correct statistics for the sample, but determining significance, whether or not 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 0.00001% chance that it's due to chance. So very low probability, less than 0.05 tends to be the kind of gold standard cutoff. But when you're talking about data like these, which are repeated measures over time, people are dropping out literally over time. You're saying they've modeled it to make predictions as to what would happen. We're not necessarily looking at raw data points here.

Yeah. The raw data was in the previous table that's now taken and run through this Cox model and it's smoothed out. Got it. And 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. And statistical power is a very important concept that probably doesn't get discussed enough. 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, it's called a power table, and you would go to the power table. So if you're doing treatment A versus Treatment B 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 going to be a statistician listening to this who's going to 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 going to 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%, but you weren't powered to look at 10%.

Yeah. And 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 10th grade class and hey, I'm going to 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, 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 that's why when you hear about a study from the UK Biobank or from half a million Danish citizens, like for instance in this study, those are enormous sample sizes. So even though this is not an experimental study, it's an epidemiological, observational study, there's tremendous power by way of the enormous number of subjects in the study.

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. 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 survey people over the course of their lives and we can have the biggest sample size possible. Because 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 the balancing act between epidemiology and randomized prospective experiments. So they both offer something, but you just have to know the blind spots of each one. So let's just kind of wrap this up. I mean, I think, let's just go to table four, which I think is the most important table in here, which now lays out the final results in terms of the hazard ratios. So this is the way we want to really be thinking about this. So again, hazard ratios, 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 1.0.5 is a 50% increase in risk. If the number is negative, you may recall on the banister paper, the hazard ratio is 0.85. So if it's not, 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 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 the people on metformin had a 48% greater chance of dying in any given year than their non diabetic counterpart.

The only reason I'm smiling, it's not because I enjoy people dying, quite to the contrary, is that this is novel for me and 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 dying more than those that aren't. And I, I'm just relieved to know that I wasn't looking at all this backwards. 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 who has diabetes and is taking metformin plus something else. So again, we're only dealing with these constrained.

Yeah. Now there's another 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 bigger drug, and those people die even more.

Whoa.

So, which again speaks to the point, right? The more you need these medications, they're never able to erase the effect of diabetes.

But in this case it seems that they might be accelerating, possibly accelerating death due to diabetes. Possibly.

We could never know that from this because 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. So 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.

I don't know, maybe being married or.

Unmarried 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 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 education along with any of the other covariates? Well, that doesn't really change it at all. It ends up at 1.33 or a 33% chance increase in death. Okay.

I always knew that more school wasn't gonna save me.

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.

Brutal.

When you make that first adjustment of all the meds and marital status, 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.

Tell me about censoring.

Censoring is when you stop counting the metformin people who have died. 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. 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, 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 qies 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 such a different response. 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 going to 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 sort of normalize. 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, because it doesn't change the answer. You can see it changes it just slightly, but it doesn't change the point. The point here is the Keys paper makes it 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 have 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 have expected. This is what you would have expected 10 years ago, before the Banister paper.

Came out, or maybe even before metformin was used. Because in some ways it's saying, 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.

Yep.

It's not quite that simple in the sense that, as you said, there are ways to try and isolate the metformin contribution somewhat because they're on a bunch of other meds and presumably that was done and analyzed in other figures where they can sort of try and they can never attach the results specifically to metformin. Right. But there must be some way of weighting the percentage that are on psychiatric meds or not on psychiatric meds as some way to tease out whether or not there's actually some contribution of metformin to this result.

Well, that's what they're doing in the partial adjustment is they're actually, they're actually doing their best to say, oh, right.

Married or not married?

They're going variable by variable, drug by drug, all the way through. High blood pressure, non. High blood pressure, smoking, non smoking, etc.

Right. And the way they would do that, presumably is by saying, okay, married, not married.

That's why.

That's a simple one.

Are you on lipid lowering meds? Yes or no? Okay, you are not. You are not.

And then comparing those groups.

Yeah, yeah.

Okay. So no. No differences jumping out that can be purely explained by these other variables.

Yes. Although again, 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 that lifespan advantage. Yes, and we can. Or disease incidence studies. And yeah, it might be tempting to say, well, therefore eating meat is bad 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 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. I picked a silly example because the whole meat discussion then gets into, well, when we say eating meat, what do we mean?

The document is like deli meat versus grass fed.

Exactly.

A deer that you hunted with your bow.

Totally different. How do we get into all those things? 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 that was the first time I went back and said, wait a minute. This information, 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 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 have been dying at a hazard ratio of 3. 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. All of this is to say, absent a randomized control trial, we will never know the answer.

Has there been a randomized control trial?

Not when it comes to a hard outcome. Now there has been in the itp, so the Interventions Testing Program, which is kind of the gold standard for animal studies, which is run out of three labs. So it's an NIH funded program that's run out of three labs. They basically test molecules for zero protection. 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% lifespan extension.

Has a similar study been done in humans? I mean, it's hard. I mean, it's hard to. I mean, you can't really control with rapamyces.

No. But when the ITP studied metformin, it did not succeed. So there have not been that many drugs that have worked in the itp. The ITP is very rigorous. Right. 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. Estrogen doesn't work in female rice, but this is alpha, not beta. So this is 17 alpha estradiol, not beta estradiol, which is the estradiol that we all. That is bioavailable in all of us.

And just as a brief aside, I think you and I basically agree that unless it's a problem, males, we're talking post puberty should try and have their estrogen as high as possible without having negative symptomology. Because of the importance of estrogen for libido, for brain function, tissue health, bone health. This idea of crushing estrogen and raising testosterone is just silly, right? 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 hyperestrogenic effects like gynecomastia or other issues, is the exact wrong direction to go. You want estrogen?

Estrogen is a very important hormone for men and women.

Yeah, that's it.

Kanagaflozin, an SGLT2 inhibitor, also very successful in the ITP. But again, interestingly, Rapami, Metformin, Not. So metformin has failed in the itp.

So you no longer take metformin?

I stopped five years ago.

I mean, you're not a diabetic, so presumably you were taking.

I was taking it for JIRA protection.

To buffer blood glucose.

Yeah. And ultimately potentially live longer. Yes, exactly. And the reason I stopped. And this will be the last thing before we move on.

Well, because you couldn't go to the Dairy Queen at the Buffett event.

No. Finally, the nausea went away after a few weeks or a month maybe. But once I got really into lactate testing, I noticed how high my lactate was at rest. So a resting fasted lactate should be in a healthy person, should be below 1, like somewhere between 0.3, 0.6 millimole. And only when you start to exercise should lactate go up. And in 2018 was when I started blood testing for my zone 2. So previously when I was doing zone 2 testing, I was just going off my power meter and heart rate. But this is after I'd met Inigo San Milan and I started wanting to use the lactate threshold of 2 millimole 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.

Ran a flight of stairs up the back of the Empire State Building.

Well, no, that would put me a lot higher. Right, but.

And was being generous to your fitness?

No, but that's when I started digging a little digging and realized, oh, you know what, this totally makes sense. If you have a weak mitochondrial toxin, what are you going to do? You're going to shunt more glucose into pyruvate and more pyruvate into lactate. I'm anaerobic.

Yeah, you need an alternative fuel source.

That's right.

And then my zone 2 numbers just seemed off. My lactate seemed.

Could you feel it? Sorry to interrupt, but could you feel it in your body? Because maybe now I'll just briefly describe. I took berberine during the period of maybe somewhere in the 2012-2015 stretch. I don't recall the.

What were you taking it for?

Well, I'll tell you. So I was, and I still am a big fan of Tim Ferriss slow carbohydrate diet because I like to eat meat and vegetables and starches. I'm an omnivore and I found that it worked very quickly. Got me very lean, I could exercise, I could think, I could sleep. A lot of my rationale for following one eating regimen or another, what I eat is to enjoy myself but also have mental energy because if I can't sleep at night, I'm not going to replenish. If I don't replenish, I'm going to feel like garbage. I don't care how lean I am or what. So I found the slow carb diet to be. Which was in the four hour body to be a very good plan for me. It was pretty easy. You drop some things like bread, et cetera, you don't drink calories except after a resistance training session, et cetera. But one day a week you have this so called cheat day. And on the cheat day anything goes. And so I would eat, you know, eight croissants and then I'd alternate to sweet stuff and then I go to pizza. And by the end of the day you don't want to look at an item of food at all. So the only modification I made to the slow carb diet 4 hour body thing was the day after the cheat day, I wouldn't eat. I would just fast. And I had no problem doing that because it was just basically. Well, since you said. What was it? Anal.

Anal seepage.

Yeah, seepage. I did not have that. But since you said that, I won't up the ante here, but I'll at least match your anal seepage comment by saying I had. Let's just call it profound gastric distress after eating like that the next day. So the last thing you want to do is eat any food. I would just hydrate and oftentimes try and get some exercise. And what I read was that berberine, poor man's method, metformin, could buffer blood glucose and in some ways make me feel less sick when ingesting all these calories and in many cases spiking my blood sugar and insulin because you're having ice cream and et cetera. And indeed it worked. So if I took berberine and I don't recall the milligram count, and then I ate 12 donuts, I felt fine. It was as if I had eaten one donut. I felt sort of okay in my body and I felt much, much better now, presumably because it's buffering the spikes in blood sugar. I wasn't crashing in the afternoon nap and that whole thing.

And do you remember how much you were taking?

I think it was a couple hundred milligrams. Does that sound about right? It was a bright yellow capsule. I forget the source. But in any case, one thing I noticed was that if I took berberine and I did not ingest a profound number of carbohydrates, very soon afterwards I got brutal headaches. I think I was hypoglycemic. I didn't measure it, but I just felt. I had headaches, I didn't feel good. And then I would eat a pizza or two and feel fine. And so I realized that berberine was putting me on this lower blood sugar state. That was the logic anyway, and it allowed me to eat these cheat foods. But when I cycled off of the four hour. Because I don't follow the slow carb diet anymore, although I might again at some point. When I stopped doing those cheat days, I didn't have any reason to take the berberine and I feared that I wasn't ingesting enough carbohydrates in order to really justify trying to buffer my blood glucose. Also, my blood glucose tends to be.

Did you ever try acarbos?

No. What is that?

So acarbose is another glucose disposal? Yeah, it's Actually a drug that. But it works more in the gut and it just prevents glucose absorption. Acarbose is another one of those drugs that actually found a survival benefit in the itp. And it was a very interesting finding because the, the thesis for testing it, the ITP is a very clever system. Anybody can nominate a candidate to be tested, then the panel over there reviews it and they decide, yep, this is interesting, we'll go ahead and study it. So when I think David Allison nominated a carbos to be studied, the rationale was it would be a caloric restriction mimetic, because you would literally just fail to absorb, I don't know, make up some number. Right. 15 to 20% of your carbohydrates would not be absorbed and therefore the mice would effectively be calorically restricted.

It would pass them out.

That's right. And what happened was really interesting. One, the mice lived longer on acarbos, but two, they didn't weigh any less. So they lived longer, but not through calorie restriction.

That's interesting.

Yes. And the speculation is they lived longer because they had lower glucose and lower insulin.

And I don't want to send us down some rabbit holes here, but there are all sorts of interesting ideas about, for instance, that some forms of dementia might be so called type 3 diabetes, the diabetes of the brain. And so things like berberine, metformin, lowering blood glucose, ketogenic diets, et cetera, might be beneficial there. I mean, there's a lot to explore here and I know you've explored a lot of that on your podcast. I've done far less of that, but. Well, at least it seems that we know the following things for sure. One, you don't want insulin too high nor too low. You don't want blood glucose too high nor too low. If the buffering systems for that are disrupted. Clearly exercise, meaning regular exercise, is the best way to keep that system in check. But in the absence of that tool, or I would say, in addition to that tool, is there any glucose disposal agent? Because that's what we're talking about here, metformin, berberine, acarbose, et cetera, that you take on a regular basis because you have that much confidence in it.

The only one that I take is an SGLT2 inhibitor. So this is a class of drug that is used by people with type 2 diabetes, which I don't have. But because of my faith in the mechanistic studies of this drug, coupled with its results in the itp, coupled with the human trial results that show profound benefit in non diabetics taking it, even for heart failure. I think there's something very special about that drug. Actually, that was another paper I was thinking about presenting this time. Maybe we'll do that the next time.

But do you believe in caloric restriction as a way to extend life, or are you more of the do the right behaviors, and that's covered in your book Outlive and elsewhere on your podcast, and buffer blood glucose? Obviously you believe in buffering blood glucose in addition to just doing all the right behaviors.

Yeah. I think you can uncouple a little bit the buffering of blood glucose from the caloric deficit. So I think you can be in a reasonable energy balance and buffer glucose with good sleep, hygiene, lots of exercise, and just thoughtful eating without having to go into a calorie deficit. So, you know, it's not entirely clear if profound caloric restriction would offer a survival advantage to humans, even if it were tolerable to most, which it's not. So for most people, it's just kind of off the table.

Right.

Like, if I said, andrew, you need to eat 30% fewer calories for the rest of your life, I'll live 30%.

Fewer years, thank you.

Yeah, like, there's just not many people who are willing to sign up for that. So it's kind of a moot point. But the question is, you know, do you need to be fasting all the time? Do you need to be doing all of these other things? And the answer appears to be outside of using them as tools to manage energy balance. It's not clear. Right. An energy balance probably plays a greater role in glucose homeostasis than from a nutrition standpoint, than the individual constituents of the meal. Now, that's not entirely true. Like, I can imagine a scenario where a person could be in a negative energy balance eating Twix bars all day and drinking, you know, Big Gulps. But I also don't think that's a very sustainable thing to do. Because if by definition, I'm going to put you in negative energy balance, consuming that much crap, I'm gonna destroy you. Like, you're gonna feel so miserable, you're gonna be starving. Right. You're not gonna be satiated, eating pure garbage and being in caloric deficit, you're gonna end up having to go into caloric excess. So that's why it's an interesting thought experiment. I don't think it's a very practical experiment. For a person to be generally satiated and an energy balance, they're probably eating about the right stuff, but I don't think that the specific macros matter as much as I used to think.

I'm a believer in getting most of my nutrients from unprocessed or minimally processed sources simply because it allows me to eat foods I like and more of them. And I just love to eat. I so physically enjoy the sensation of chewing that I'll just eat cucumber slices for. For fun.

Right?

I mean, that's not my only form of fun, fortunately. This is an amazing paper for the simple reason that it provides a wonderful tutorial of the benefits and drawbacks of this type of work. And I think it's also wonderful because we hear a lot about metformin, rapamycin and these anti aging approaches, but I was not aware that there was any study of such a large population of people. So it's pretty interesting.

Yeah. So I think it remains to be seen if. And my patients often ask me, hey, should I be on metformin? And I give them a much, much, much, much shorter version of what we just talked about. And I say, look, if the TAME study, which should answer this question more definitively, right, this is taking a group of non diabetics and randomizing them to placebo versus Metformin and studying for specific disease outcomes. If the TAME study ends up demonstrating that there is a zero protective benefit of metformin, I'll reconsider everything.

Right.

So I think that's, you know, we just have to, I think, all walk around with an appropriate degree of humility around what we know and what we don't know. But I would say right now, the epidemiology, the animal data, my own personal experience with its impact on my lactate production and exercise performance, there's a whole other rabbit hole we could go down another time, which is the impact on hypertrophy and strength, which appears to be attenuated as well by metformin. You know, I still prescribe it to patients all the time if they're insulin resistant. For sure, it's still a valuable drug. But I don't think of it as a great tool for the person who's insulin sensitive and exercise a lot.

I can't help but ask this question, do you think there's any longevity benefit to short periods of caloric restriction? So for instance, I decide to, by the way, I haven't done this, but let's say I were to decide to fast and do a one meal a day type thing where I'm going to be in a slight caloric deficit 500 to 1,000 calories for a couple of days and then go back to eating the way that I ate before that short caloric restriction, fast. Is there any benefit to it in terms of cellular health? Can you sort of reset the system? Is there any idea that the changes, the clearing of senescent cells that we hear about autophagy, that we, you know, that in the short term you can glean a lot of benefits and then go back to your regular pattern of eating and then periodically, once every couple of weeks or once a month, just fast for a day or two. Is there any benefit to that? That's purely in the domain of longevity because there's all discipline function there, there's a flexibility function, there's probably an insulin sensitivity function. But is there any evidence that it can help us live longer?

I think the short answer is no, for two reasons. One, I don't think that that duration would be sufficient if, if one is going to take that approach. But two, even if you went with something longer, like what I used to do, right, I used to do seven days of water only per quarter, three days per month. So I was basically always like it would be three day fast, three day fast, seven day fast. Just imagine doing that all year, rotating, rotating, rotating for many years. I did that. Now I certainly believed, and to this day I would say I have no idea if that provided a benefit. But my thesis was the downside of this is relatively circumscribed, which is profound misery for a few days. And what I didn't appreciate the time which I obviously now look back at and realize, is muscle mass lost. It's very difficult to gain back the muscle cumulatively after all of that loss. But my thought was exactly as you said. There's got to be a resetting of the system here. This must be sufficiently long enough to trigger all of those systems. But you're getting at a bigger problem with geoscience, which I'm really hoping the epigenetic field comes to the rescue on. It has not come close to it to date, which is we don't have biomarkers around true metrics of aging. Everything we have to date stinks. So we're really good at using molecules or interventions for which we have biomarkers, right? Like when you lift weights, you can look at how much weight you're lifting. You can look at your DEXA scan and see how much muscle mass you're generating. Like those are biomarkers, those are giving you outputs that say my input is good or my input needs to be modified. When you take a sleep supplement, you can look at your eight sleep and go, oh, my sleep is getting better. Like, there's a biomarker when you take metformin, when you take rapamycin, when you fast. We don't have a biomarker that gives us any insight into whether or not we're moving in the right direction. And if we are, are we taking enough? Just don't know. So I often get asked, like, what's the single most important topic you would want to see more research dollars put to in terms of this space? And it's unquestionably this. As unsexy as it is, like, who cares about biomarkers? But without them, I don't think we're going to get great answers because you can't do most of the experiments you and I would dream up.

Got it. Well, I'm grateful that you're sitting across the table for me telling me all this and that everyone can hear this. But again, we will put a link to the papers, plural, that Peter just described. And for those of you that are listening and not watching, hopefully you were able to track the general themes and takeaways. And it is fun to go to these papers. You see these big stacks of numbers and it can be a little bit overwhelming. But my additional suggestion on parsing papers is notice that Peter said that he spent. He's read it several times. Unlike a newspaper article or a Instagram post, with a paper you're not necessarily going to get it the first time. You certainly won't get everything. So I think spending some time with papers for me means reading it and then reading it again a little bit later or at a time.

Yeah, I was just about to say, what's your cause? I kind of have a way that I do it, but I'm curious as to how you do it. If you're encountering a paper for the first time, do you have an order in which you like to go through? Do you read it sequentially or do you look at the figures first? How do you go through it?

Yeah, unless it's an area that I know very, very well where I can skip to some things before reading it the whole way through. My process is always the same. And actually this is fun because I used to teach a class when I was a professor at UC San Diego called Neural Circuits in Health and Disease. And it was an evening course that grew very quickly from 50 students to 400 plus students. And we would do exactly this. We would parse papers and. And I had everyone ask What I called the four questions. And it wasn't exactly four questions, but I have a little three by five card next to me or a piece of maintenance by 11 paper typically. And when I sit down with a paper, I want to figure out what is the question they're asking, what's the general question, what's the specific question? And I write down the question. Then what was the approach? How do they test that question? And sometimes that can get a bit detailed. You can get into immunohistochemistry and they did a PCR for this. It's not so important for most people that they understand every method. But it is worthwhile that if you encounter a method like PCR or chromatography or FMRI that you at least look up on the Internet what its purpose is. Okay, that will help a lot. And then it was what they found. And there you can usually figure out what they believe they found anyway by reading the figure headers. Right? What are, you know, figure one, here's the header that typically, if it's an experimental paper, it will tell you what they want you to think they found. And then I tend to want to know the conclusion of the study. And then this is really the key one and this is the one that would really distinguish the high performing students from the others. You have to go back at the end and ask whether or not the conclusions, the major conclusions drawn in the paper are really substantiated by what they found and what they did. And that involves some thinking. It involves really spending some time thinking about what they identified. Now this isn't something that anyone can do straight off the bat. It's a skill that you develop over time. And different papers require different formats. But those four questions really form the cornerstone of teaching undergraduates and I think graduate students as well of how to read a paper. And again, it's something that can be cultivated and it's still how I approach papers. So what I do typically is I'll read title abstract. I usually then will skip to the figures and see how much of it I can digest without reading the text and then go back and read the text. But in fairness, journals, great journals like Science, like Nature, oftentimes will pack so much information, the cell press journals too, into each figure and it's coded with no definition of the acronyms that almost always I'm into the introduction and results within a couple of minutes wondering what the hell this acronym is or that acronym. And it's just wild how much nomenclature there really is. I can't remember Was it you or was it our friend Paul Conti when he was here, who said that? Oh, no, I'm sorry, it was neither. It was chair of ophthalmology at Stanford, Dr. Jeffrey Goldberg, who was a guest on the podcast recently, who, off camera, I think it was, told us that if you look at the total number of words and terms that a physician leaving medical school owns in their mind and their vocabulary, it's the equivalent of like two additional full languages of fluency beyond their native language. So you're trilingual at least. I don't know. Do you speak a language other than English?

Poorly.

Okay, so you're at least trilingual and probably more so. No one is expected to be able to parse these papers the first time through without substantial training.

Yeah, no, I think that's a great format. And you're absolutely right. I have a different way that I do it when I'm familiar with the subject matter versus when I'm not. Well, again, if I'm reading papers that are something that I know really well, I can basically glean everything I need to know from the figures. And then sometimes I'll just do a quick skim on methods, but I don't need to read the discussion, I don't need to read the intro. I don't need to read anything else. If it's something that I know less about, then I usually do exactly what you say. I try to start with the figures. I usually end up generating more questions like, what do you mean? What is this? How did they do that? And then I got to go back and read methods, typically. And one of the other things that's probably worth mentioning is a lot of papers these days have supplemental information that are not attached to the paper. So you're amazed at how much stuff gets put in the supplemental section. And the reason for that, of course, course, is that the journals are very specific on the format and length of a paper. So a lot of the times when you're submitting something, if you want to put any additional information in there, it can't go in the main article. It has to go in the supplemental figure. So even for this paper, there were a couple of the numbers I spouted off that I had to pull out of the supplemental paper. For example, when they did the sensitivity analysis on the censoring versus non censoring, that was in the supplemental figure. That was actually not even in the paper we presented.

Well, should we pivot to this other paper? Yeah, it's a very different sort of paper. It's an experimental paper where there's a manipulation. I must say, I love, love, love this paper, and I don't often say that about papers. I'm so excited about this paper for so many reasons. But I want to give a couple of caveats up front. First of all, the paper is not published yet. The only reason I was able to get this paper is because it's on BioRxiv. There's a new trend over the last, I would say, five, six years of people posting the papers that they've submitted to journals for peer review online so that people can look at them prior to those papers being peer reviewed. So there is a strong possibility that the final version of this paper, which again, we will provide a link to, is going to look different, maybe even quite a bit different than the one that we're going to discuss. Nonetheless, there are a couple of things that make me confident in the data that we're about to talk about. First of all, the group that published this paper is really playing in their wheelhouse. This is what they do, and they publish a lot of really nice papers in this area. I'm going to mispronounce her first name, but I think it's Chaosi Gu, who's at the Econ School of Medicine, Mount Sinai, runs a laboratory there studying addiction in humans. And the first author of the paper is Ophir Perl. This paper is wild, and I'll just give you a couple of the takeaways. First, as a bit of a hook to hopefully entice people into listening further, because this is an important paper. This paper basically addresses how our beliefs about the drugs we take impacts how they affect us at a real level, not just at a subjective level, but at a biological level. So just to back up a little bit, a former guest on this podcast, Dr. Ali Crum, whose name is actually Aliyah Crumb, but she goes by Ali Crum, talked about belief effects. Belief effects are different than placebo effects. Placebo effects are really just category effects. It's okay. And I'm going to give you this pill, Peter, and I'm going to tell you that this pill is molecule X5952 and that it's going to make your memory better. And then I give you a memory test and your group performs better than the people in the control group who I give a pill to. And I say, this is just a placebo, or there are other variants on this where people will get a drug and you tell them it's placebo, they'll Get a placebo, you tell them it's drug. It's a binary thing. It's an on or an off thing. You're either in the drug group or the placebo group, and you're either told that you're getting drug or placebo. And we know that placebo effects exist. In fact, one of the crueler ones, I was never the subject of this, but there was kind of lore in high school that, you know, like, kids would do this mean thing. It's a form of bullying. I really don't like it where, you know, they get some kid at a party to drink alcohol, free beer, and then that kid would start acting drunk, and then they'd go, gotcha. You know, it doesn't even have alcohol in it. Now, that's a mean joke and just reminds me of some of the horrors of high school. Maybe that's why I didn't go very often, which I also don't suggest. But, no, it's a mean joke. But it speaks to the placebo effect. Right? And there's also a social context effect. So placebo effects are real. We know this. Belief effects are different. Belief effects are not A or B. Placebo or non. Placebo. Belief effects are. Have a lot of knowledge to enrich one's belief about a certain something that can shift their psychology and physiology one way or the other. And I think the best examples of these belief effects really do come from Ali Crummit's lab in the psychology department at Stanford. Although some of this work she did prior to getting to Stanford. For instance, if people are put into a group where they watch a brief video, just a few minutes of video, about how stress really limits our performance, let's say at archery or at mathematics or at music or at public speaking, and then you test them in any of those domains or other domains in a stressful circumstance, they perform less well, okay? And we know they perform less well because we're. By virtue of a heightened stress response. You can measure heart rate, you can measure stroke, volume of the heart, you can measure peripheral blood flow, which goes down when people are stressed, narrowing a vision, et cetera, etc, you take a different group of people and randomly assign them to another group where now they're being told that stress enhances performance, it mobilizes resources, it narrows your vision such that you can perform tasks better, et cetera, et cetera, and their performance increases above a control group that receives just useless information, or at least useless as it relates to the task. So in both cases, by the way, the groups are being told the truth. Stress can be depleting or it can enhance performance. But this is different than placebo because now it's scaling according to the amount and the type of information that they're getting.

And can you give me a sense of magnitude of benefit or detriment that one could experience in a situation like the one you just described?

Yeah, so it's striking. They're opposite in direction. So the stress gets us worse. Makes you, let's say. I think that if we were to just put a rough percentage on this, it would be somewhere between 10 and 30% worse at performance than the control group. And stress is enhancing, is approximately equivalent improvement. So they're in opposite directions. Even more striking is the studies that Ali's lab did and others looking at. For instance, you give people a milkshake, you tell them it's a high calorie milkshake, has a lot of nutrients. And then you measure ghrelin secretion in the blood. And ghrelin is a marker of hunger that increases the longer it's been since you've eaten. And what you notice is that suppresses ghrelin to a great degree and for a long period of time. You give another group a shake. You tell them it's a low calorie shake, that it's got some nutrients in it, but that doesn't have much fat, not much sugar, et cetera. They drink the shake. Less ghrelin suppression, and it's the same shake and it's the same shake. And satiety lines up with that also in that study. And then the third one, which is also pretty striking, is they took hotel workers, they gave them a short tutorial, or not informing them that moving around during the day and vacuuming and doing all that kind of thing is great. It helps you lower your bmi, which is great for your health. You incentivize them, and then you let them out into the wild of their everyday job. You measure their activity levels. The two groups don't differ. They're doing roughly the same task, leaning down, cleaning out trash cans, et cetera. Guess what? The group that was informed about the health benefits of exercise lose 12% more weight compared to the other group.

And no difference in actual movement.

Apparently not. Now, how could that be? I mean, literally, this was sparked by, in Ali's words, this was sparked by her graduate advisor saying, what if all the effects of exercise are placebo? Right? Which is not what anyone really believes, but it's just such a. I love that anecdote that Ali Told us, because it just really speaks to how really smart people think. They sit back and they go, yeah, exercise obviously has benefits, but what if a lot of the benefits are that you tell yourself it's good for you and the brain can actually activate these mechanisms in the body? And why wouldn't that be the case? Because the nervous system extends through both.

So interesting.

So interesting. Okay, so fast forward to this study, which is really about belief effects, not placebo effects. And to make a long story short, we know that nicotine vaped, smoked, dipped, or snuffed, or these little Zyn pouches or taken in capsule form, does improve cognitive performance. I'm not suggesting people run out and start doing any of those things. I did a whole episode on nicotine. The delivery device often will kill you some other way or is bad for you, but it causes vasoconstriction, which is also not good for certain people. But nicotine is cognitive enhancing. Why? Well, you have a couple sites in the brain, namely in the basal forebrain nucleus, basalis, in the back of the brain, structures like locus coeruleus. But also this.

What's called.

It's got a funny name, the pedunculoplampty nucleus, which is this nucleus in the. In the pons, in the back of the brain, in the brain stem that sends those little axon wires into the thalamus. The thalamus is a gateway for sensory information. And in the thalamus, the visual information, the auditory information, it has nicotinic receptors. And when the pedunculopontine nucleus releases nicotine, or when you ingest nicotine, what it does is it increases the signal to noise of information coming in through your senses. So the fidelity of the signal that gets up to your cortex, which is your conscious perception of those senses, is increased.

And how much endogenous nicotine do we produce?

Ooh, well, it's going to be acetylcholine binding to nicotinic receptors.

I see. We're not making nicotine. We're not just binding. So this is a nicotinic acetylcholine receptor.

Of which there are at least seven and probably like, 14 subtypes, but so. Right. They're called nicotinic receptors in an annoying way. In the same way that cannabinoid receptors are called cannabinoid receptors. But then everyone thinks, oh, you know, those receptors are there because we're supposed to smoke pot. Or those receptors are there because we're supposed to ingest nicotine. No, the drugs that we named after the Receptors named. That's right, exactly. Receptors named after the drug. And so the important thing to know is that whether or not it's basal forebrain, a pedunculopontine nucleus or a locus coeruleus, that at least in the brain, because we're not talking about muscle, where acetylcholine does something else via nicotinic receptors in general, it just tends to be a signal to noise enhancer. And so for the non engineering types out there, no problem. Signal to noise. Just imagine I'm talking right now and there's a lot of static in the background. There are two ways for you to be able to hear me more clearly. We can reduce the static or I can increase the fidelity, the volume and the clarity of what I'm saying. Okay. For instance, and that's really what acetylcholine does. That's why when people smoke a cigarette, they get that boost of nicotine and they just feel clearer. It really works. The other thing that happens is the thalamus sends information to a couple of places. First of all, it sends information to the reward centers of the brain, the mesolimbic reward pathway that releases dopamine. And typically when nicotine is increased in our system, dopamine goes up. That's one of the reasons why nicotine is reinforcing. We just like it, we seek it out. I've done beautiful experiments with honeybees, even where you put nicotine on certain plants or it comes from certain plants and they'll forage there more, you get them kind of buzzed. That was upon. Bad pun. In any event, there's also an output from this thing, the thalamus, to the ventromedial prefrontal cortex, which is an area of the forebrain that really allows us to limit our focus and our attention for sake of learning. It allows us to pay attention. This is the circuit.

You talked about this in your fantastic podcast on stimulants.

Yeah, yeah. On adhd. So adhd, typically ADHD drugs, so things like Adderall, Vyvanse, methamphetamine, for that matter. Ritalin.

Yeah. Why? It's counterintuitive that a stimulant would be a treatment for someone with difficulty focusing.

Yeah. In young kids who have difficulty focusing, if you give them something they love, they're like a laser. And the reason is that ventromedial prefrontal cortex circuit engages when the kid is interested and engaged. But. But kids with ADD ADHD tend to have a hard time engaging their mind for other types of Tasks and other types of tasks are important for getting through life. And it turns out that giving those stimulant drugs in many cases can enhance the function of that circuit and it can strengthen so that ideally the kids don't need the drugs in the long run, although that's not often the way that it plays out. And there are other ways to get at this. There's now a big battle out there. Is ADHD real? Is it not real? Of course it's. Does every kid need ADHD meds? No. Are there other things like nutrition, more playtime, outside, et cetera, that can help improve their symptoms without drugs? Yes. Is the combination of all those things together known to be most beneficial? Yes. Are the dosages given too high and generally should be titrated down? Maybe. Some kids need a lot, some kids need a little. I probably just gained and lost a few enemies there. So the point is that these circuits are hardwired circuits.

Sorry, one other question, Andrew, if my memory serves correctly, doesn't nicotine potentially have a calming effect as well? And that seems a bit counterintuitive to the focusing one. Is it a dose effect or a timing effect? How does that work?

Yeah, it's a dosing effect. So the interesting thing about nicotine is that it can enhance focus in the brain, but in the periphery, it actually provides some muscle relaxation. So it's kind of the perfect drug, if you think about it. Again, I was reflecting on this how when we were growing up, people would smoke on plane. They had a smoking section on the plane. People smoked all the time. And now hardly anyone smokes for all the obvious reasons. But, yeah, it provides that really ideal balance between being alert but being mellow and relaxed in the body. So hence its reinforcing properties. Okay. This study is remarkable because what they did is they had people come into the laboratory, they gave them a vape pen. These are. These are smokers. So these are experienced smokers. Typically, there's a washout before they come in, so they're not smoking for a bit. So they can clear their system of nicotine and they measure.

How long is that needed?

Typically it's a couple of days. Oh, okay. Yeah. Which must be miserable for those people.

They can't have Nicorette gum or anything?

No, nothing. They must be dying. And I wonder how many cheats.

But they can measure carbon monoxide, right?

Yeah, they measure carbon monoxide and they're measuring nicotine in the blood as well. So they do a good job there. So then what they do is they have them Vape, and they're vaping either a low, medium or high dose of nicotine. The dosages don't really matter because tolerance varies, et cetera. And then they are putting them into a functional magnetic resonance imaging machine where they can look at. It's really blood flow, it's really hemodynamic response. For those of you who want to know, it's the oxygen. It's the ratio of the oxygenated to deoxygenated blood. Because when blood will flow to neurons that are active to give it oxygen, and then it's deoxygenated, and then there's a change in what's called the bold signal. So fmri, when you see these hot spots in the brain, is really just looking at blood flow. And then there's some interesting physics around. And I'll probably get this wrong, but I'll take an attempt at it so that I get beat up a little bit by the physicists and engineers. And do you remember the right hand rule?

Yep.

Right, okay. So do I have this right? Correct. The right hand rule, if you put your thumb out with your first with your index finger, your middle finger, your thumb facing up, I think that the thumb represents the charge, the direction of the charge. Right. And then isn't the electromagnetic field is the downward facing figure? And then it's. Do I have that right? I have to look. I actually don't know. Okay, well, someone will look it up. But what you do is when you put a person's head in this big magnet and then you pulse the magnet, what happens is the oxygenated and deoxygenated blood, it interacts with the magnetic field differently. And that difference in signal can be detected. And you can see that in the form of activated brain areas.

Yeah, I mean, MRI all works by proton detection. So presumably there's a difference in the proton signal when you have high oxygen versus low oxygen concentration.

Yep, that's right. And what they'll do is they'll pulse with the magnet. Because my understanding is that, and this is definitely getting beyond my expertise, but that the spin orientation of the protons, then it's going to relax back at a different rate as well. So by the relaxation at a different rate, you can also get not just resting state activation, like, oh, look at a banana, what areas of the brain light up. But you can look at connectivity between areas and how one area is driving the activity of another area. So very powerful technique. So what they do is they put people in a scanner and then you'll like this because you Want.

What are the limitations of FMRI in terms of, I mean, how fine is the resolution? I mean, where are the blind spots of the technique?

So resolution, you can get down to sub centimeter. They talk about it always in these papers as the voxels, which are these little cubic pixels, things, you know, subcentimeter. But you're not going to get down to millimeter. There are a number of little confounds that maybe we won't go into now that have been basically worked out over the last 10 years by doing the following. You can't just give somebody a stimulus compared to nothing. I'll just tell you the experiment. It was discovered, for instance, that when someone would move their right hand. Because when you're in the mri, I just went for one of these recently for a clinical, not a problem. But just for a diagnostic stand, you're leaning back and you can move your right hand a bit. And they would see an area in the motor cortex lighting up. But what they noticed was that the area corresponding to the left hand was also lighting up. So what you really have to do is you have to look at resting state. How much are they lighting up just at rest, and then subtract that out. So now you'll always see resting state versus activation state.

Yeah. Wasn't there a really funny study done as a spoof maybe a decade ago that put a dead salmon into an MRI machine and did an fm like, they did an FMRI of a dead salmon that demonstrated, like, some interesting signal?

I didn't know that, but it.

We got to find this one for the show notes.

We should do one of these wild papers ones. There are papers of people putting. Don't do this, folks. Putting elephants on LSD that were published in Science and things like that, like crazy experiments. We should definitely do a crazy experiments journal club. In any event, you can get a sense of which brain areas are active and when. With fairly high spatial resolution. Fairly high and pretty good temporal resolution on the order of hundreds of milliseconds. But it's not ultra fast because a lot of neural transmission is happening on the tens of milliseconds, especially when you're in talking about auditory processing. Okay. So they put people into the scanner and then they give them essentially a task that's designed to engage the thalamus, known to engage the thalamus reward centers and the ventromedial prefrontal cortex. And it's a very simple game. You'll like this because you have a background in finance. You let people watch a market. Okay, here's the stock market, or you could say, or the price of peas. It doesn't really matter. It goes up, it goes down, and they're looking at a squiggle line. Then it stops. And then they have the option, but they have to pick one option. They're either going to invest a certain number of the hundred units that you've given them, or they can short it. They can say it's going to go down and try and make money on the prediction it's going to go down. You could explain shorting better than I could, for sure. So depending on whether or not they get the prediction right or wrong, they get more points or they lose points. And they're going to be rewarded in real money at the end of the experiment. So this is going to engage this type of circuitry. Now, remember, these groups were given a vape pen prior to this, where they vaped what they were told is either a low, medium or high dose of nicotine. And they do this task. The goal is not to get them to perform better on the task. The goal is to engage the specific brain areas that are relevant to this kind of error and reward type circuits. And we know that this task does that. So that includes the thalamus, that includes the mesolimbic reward pathway and dopamine. It includes the ventromedial prefrontal cortex. First of all, they measure nicotine in the blood. They are measuring how much people vaped. They were very careful about this. One of the nice things about the vape pen, for the sake of experiment and not recommending people vape, but they can measure how much nicotine is left in the vape pen before or after. They can measure how long they inhaled, how long they held it in. There's a lot that you can do that's harder to do with a cigarette. Okay. They measured people's belief as to whether or not they got low, medium or high amounts of nicotine.

They were told.

They were told they got either this is a low amount, a medium amount, or a high amount. And then of course, they looked at brain area activation during this task and what they found was very straightforward.

Sorry, they were all given the same amount.

Yes. This is the sneak. I was gonna offer it as a punchline, but that's okay. No, I think that the cool thing about this experiment is that the subjects are unaware that they all got the exact same amount of relatively low nicotine containing vape pen. So they basically. And they're measuring it from their bloodstream. So they all have fairly low levels of nicotine. But one group was told you got a lot, one group was told you got a medium amount, and the other was told you got a little bit. Now, a number of things happen, but the most interesting things are the following. First of all, people's subjective feeling of being on the drug matches what they were told. So if they were told, hey, this is a high amount of nicotine, like, yeah, it feels like a high amount of nicotine. And these are experienced smokers. If it was a medium amount, they're like, man, that feels like a medium amount. If it's a low amount, they think it was a low amount. Now, that's perhaps not so surprising. That's your just the tricky placebo in that sense.

That's the placebo effect.

But if you look at the activation of the thalamus in the exact regions where you would predict acetylcholine transmission to impact the function of the thalamus. So these include areas like what's called the centromedian nucleus, the ventral posterior nucleus, the names that really don't matter, but these are areas involved in attention. It scales with what they thought they got in the vape pen, meaning if you were told that you got a low amount of nicotine, you got a little bit of activation in these areas. If you were told that you got a medium amount of nicotine and that's what you vaped, then you had medium amounts or moderate amounts of activation. And if you were told you got high amounts of nicotine, you got a high degree of activation. And the performance on the task, believe it or not, scales with it somewhat. So keep in mind, everyone got the exact same amount of nicotine in reality. So here, the belief effect isn't just changing what one subjectively experiences. Oh, this is the effect of high nicotine or low nicotine. It actually is changing the way that the brain responds to the belief. And that, to me, is absolutely wild. Now, there are a couple of other things that could have confounded this. First of all, it could have been that if you believe you got a lot of nicotine, you're just faster or you're reading the lines better, or your response time to hit the button is quicker. I tell you, you have a drug that's going to improve reaction time. You might believe that about nicotine. And so you're quicker on the trigger and you're getting.

They have a different activation as well.

More activation there could be. They rule that out. They also rule out the possibility.

How did they rule that out?

By looking at rates of pressing and there was no difference, Nothing. And in sensory areas of the brain that would represent that kind of difference. They don't see that. The other thing that is very clear is that the connection between the thalamus and the ventromedial prefrontal cortex, that pathway scales in the most beautiful way such that that people that were told they had smoked a low or vaped a low amount of nicotine got a subtle activation of that pathway. People that were told that they got a moderate amount of nicotine got a more robust activation of that pathway. And the people that were told that they got a high amount of nicotine in the vape pen saw a very robust activation of the thalamus to this ventral prefrontal cortical pathway. Now, of course, this is all happening under the hood of the skull simply on the basis of, of what they were told and what they believe.

And technically the FMRI is showing the activation of those two areas and that's how you can infer the strength of that connection.

That's right. There's a separate method called diffuser tensor imaging, which was developed, I believe, out of the group in Minnesota. Minnesota has a very robust group in terms of neuroimaging that can measure activation in fiber pathways. This is not that, but you can look at the timing of activation and it's a known, what we call monosynaptic pathway. So we haven't talked so much about figures here, but I guess if we were going to look at any one figure, and I can just describe it for the audience that's not paying, doesn't have the figure in front of them. Let's see. Probably the most important figure is figure two. Remember I said I like to read the titles of figures, which is that the belief about nicotine strength induced a dose dependent response in the thalamus. Thalamus, basically, if you. And figure 2B can tell you if they believe that they got more nicotine, that's essentially the response that they saw. So if you look, or sorry, panel E. If you look at the belief rating as a function of the estimate in thalamus of how much activation there was, it's a mess when you look at all the dots at once. But if you just separate it out by high, medium and low, you run the statistics, what you find is that there's a gradual increase, but a legitimate one, from low to medium to high. In other words, if I tell you this is a high dose of nicotine, your brain will react as if it's a high dose of nicotine. Now what they didn't do was give people zero nicotine.

Yeah, I was about to say there's a control that's missing here, right? Yeah.

So what they didn't do is give people zero nicotine and then tell them this is a high amount of nicotine. It's sort of the equivalent of the cruel high school experiment. No alcohol, but then the kid acts drunk. Now, in the high school example, it's unclear whether or not the kid actually felt drunk or not. It's unclear whether or not they had been drunk previously, if they even knew what it would be like to feel drunk, et cetera. And there's the social context. What I find just outrageous and outrageously interesting about this study is simply that what we are told about the dose of a drug changes the way that our physiology responds to the dose of the drug. And in my understanding, this is the first study to ever look at dose dependence of belief effects. And why would that be important? Well, for almost every study of drugs, you look at a dose dependent curve, you look at zero, low dose, medium dose, high dose, and here they clearly are seeing a dose dependent response simply to the understanding of what they expect the drug ought to do. In other words, you can bypass pharmacology somewhat. Right.

Now look at figure 2B. Am I reading this correctly? So it's got four bars on there. You've got the group who were told they got a low dose, the group who was told they got a medium dose, the group that was told they had a high dose, and then these healthy controls who presumably were non smokers, who were just put in the machine.

That's right, yeah.

This is measuring parameter estimate. Is that referring to their ability to play the trading game?

The parameter estimate is the activation reward related activities from an independent thalamus mask. So what they're doing is they're just saying, if we just look at the thalamus, what is the level of activation?

I see. So this suggests that the only statistical difference was between the low and the high.

That's right.

And nobody else was statistically different.

That's right.

But that's not the whole story.

No, that's not the whole story. So when you look at the output from the thalamus to the ventromedial prefrontal cortex, that's where you start to identify the.

Is that figure four?

That is, yes. So this is where you see. So figure 4B. If you look at parameter estimates. So this is the degree of activation between the thalamus and the ventromedial prefrontal Cortex, and it's called the instructed belief. You can see that there's a low, medium and high scatter of dots for each, and that each one of those is significant.

So isn't it interesting that at the thalamus, which is.

And you'll.

You'll immediately appreciate my stupidity when it comes to neuroscience, which is more proximate to the nicotinamide or nicotine? That nicotinamide. What do you call it, the nicotine acetylcholine receptor, you have a lower difference of signal strength and somehow that got amplified as it made its way forward in the brain.

Yeah.

Does that surprise you?

It is surprising, and it surprised them as well. The interpretation they give, again, as we were talking about before, important to match their conclusions against what they actually found, which is what we're doing here. The interpretation that they give is that it doesn't take much nicotinic receptor occupancy in the thalamus to activate this pathway. But they too were surprised that they could not detect a raw difference in the activation of thalamus. But in terms of its output to the prefrontal cortex, that's when they showed.

Up that Figure 4B is more convincing than Figure 2. Because even Figure 2E, if you read the fine print, the R, the Correlation coefficient is 0.27.

Yeah, it's weak.

It's not that strong.

It's weak.

So at the thalamus, it's kind of like. Yeah, there might be a signal. By the way, this goes back to our earlier discussion. There could be a huge signal here and we're underpowered. How many subjects were in this? You wouldn't have a lot of subjects in this experiment.

Yeah, this is. No, and this just speaks to the general challenge of doing this kind of work. It's hard to get a lot of people in and through the scanner.

Yeah. And it's expensive.

It's expensive. I should know this, but we can go back to the method.

But you can sort of just look at the number of dots on here. I mean, it's in the low tens, right? It's like 40, 30, something like that. So it's possible you do this with a Danish study. Yeah, yeah. You do this with a thousand people. This could all be statistically significant. Right.

So they talk about this. Based on this, we estimated that an n of 20 n is sample size in each belief condition. The final sample would provide 90% power to detect an effect of this magnitude at an alpha of 0.5 in a two tailed test.

Okay, so that's them referring to what we just talked about, which is we believe at 90% confidence, to get an alpha of 0.05, which means we'll want to be 95% confidence, we need 60 people, 20 per group. But if the difference is smaller than what they expected, they'll miss out on some of the significance which it looks like they're missing between the medium and high group. Yep.

And I too was surprised that they did not see a difference between the medium and the high group, but they did in the output of the thalamus. I was also surprised that they didn't see a difference. This is kind of interesting in its own right. In Figure 3 talks about the belief about nicotine strength. Did not modulate the reward response. Response, the dopamine response.

How was that measured? Also just in FMRI.

Yeah, exactly. So if you look at figure 3B, other people can't see it, but basically.

Oh yeah.

What you'll see is that there's no difference between these different groups in terms of the amount of activation in these reward pathways. If people got a low, medium or high amount of nicotine, now that actually could be leveraged. I believe if somebody were trying to quit nicotine, for instance, and they were going to do that by progressively reducing the amount of nicotine that they were taking, but you told them that it was the same amount from one day to the next, you could whittle it down to presumably to a low amount before taking it to zero. And if they believed it to be a greater amount, then it might actually not disrupt their reward pathways. Meaning they would feel, presumably they'd feel rewarded by whatever nicotine they were bringing in.

What would be your prediction if this experiment were repeated, but it was done exactly the same way with non smokers?

Well, one thing that's sort of interesting, you asked about potential sources of artifact problems with fmri. One of the challenges that they note in this study was you have to stay very still in the machine. But the subjects were constantly coughing because they're smokers. So, okay, so presumably the data would be higher fidelity. Started chuckling at that one, but I had to read that one twice. I was like, oh, that makes sense. They're smokers, they're coughing, they can't stay still. So movement artifact. But in all seriousness, I think that for people that are naive to nicotine, even a small amount of nicotine is likely to get this pathway activated to such a great degree. Sort of like the first time effect of pretty much any drug.

But I wonder if they would be more or less susceptible to the belief system.

Yeah, that's a really good question. Right. Because they have no prior to compare it to.

They have no pleasant, they have no experience to compare it to with respect to the obviously beneficial effects of nicotine that the smokers are well used to.

So this is the poor kid that got duped into thinking the non alcoholic beer was at alcohol. Although they're actually the winner. We know because I did an episode on alcohol. Alcohol's bad for you. So in the end that kid wins and the other ones lose. Poetic justice. But that kid having never been actually drunk before, presumably I would feel like.

There'D be more susceptible potential.

That's my guess as well. So, you know, my glee for this experiment is not, or this paper rather is not, because I think it's the be all, end all or it's a perfect experiment. I just think it's so very cool that they're starting to explore dose dependence of belief because that has all sorts of implications. I mean, use your imagination, folks. Whether or not we're talking about a drug, we're talking about a behavioral intervention, we're talking about a vaccine. And I'm not referring to any one specific vaccine, I'm just talking to vaccines generally. I'm talking about psychoactive drugs, I'm talking about illicit drugs, I'm talking about antidepressants, I'm talking about all the sorts of drugs we were talking about before, metformin, et cetera, just throw our arms around all of it. What we believe about the effects of a drug, presumably in addition to what we believe about how much we're taking and what those effects ought to be, clearly are impacting at least the way that our brain reacts to those drugs.

Yeah, it's very interesting. I mean, when you consider how many drugs that have peripheral effects or peripheral outputs begin with central issues. So again, I think the GLP1 agonists are such a great example of this Ozempic. Yeah. You know, I don't think anybody fully understands exactly how they're working, but it's hard to argue that they're impacting that the GLP1 analog is having a central impact. It's doing something in the brain that is leading to a reduction of appetite.

We believe that.

Yeah.

Yeah. And I think the mouse data point to different areas of the hypothalamus that are related to satiety. That it's at least possible.

Yeah. I mean, there's no quicker way to make a mouse overeat or under eat than by lesioning its hypothalamus depending on where you do. So. So presumably these drugs work there. But again, it speaks to like, what do you need to believe in order for that to be the case?

Have they done placebo trials there where people get something and they're told, oh, they do.

I mean, of course those drugs have all been tested via placebo and the placebo groups, you know, don't do anywhere near as well. That's how we know that there's activity of the drug. But again, there's, you know, that's a little bit different than being told you are absolutely getting it. Right. Because in the RCTs you're just told you might be getting it, you might not be getting it. So it's not quite the same as this experiment. This experiment is one level up where you're being told, no, you're absolutely getting it, you're just getting different doses of it. Yeah.

To take this to maybe the ADHD realm, let's say a kid has been on ADHD meds for a while and the parents, for whatever reason, the physician decide they want to cut back on the dosage. But, but if they were to tell the kid it's the same dosage they've always been taking and it's had a certain positive effect for them, According to the results, at least in this paper, which are not definitive, but are interesting, the lower dose may be as effective simply on the basis of belief. And, and this is the part that makes it so cool to me is that. And it's not a kid tricking themselves or the parents tricking the kid so much as the brain activation is corresponding to the belief. Right. So that's where this is.

Why?

Because it's done in the brain. I think we can. It gets to these kind of abstract, nearly mystical, but not quite mystical aspects of belief effects, which is that your brain is a prediction making machine, it's a data interpretation machine. But it's clear that one of the more important pieces of data are your beliefs about how these things impact you. So it's not that that this bypasses physiology. People aren't diluting themselves. The thalamus is behaving as if it's a high dose when it's the same dose as the low dose group. Wild.

Yeah. I mean, I think of the implications, for example, of blood pressure. Right. Like we don't really understand essential hypertension, which is the majority of people walking around with high blood pressure. It's unclear etiology. So lots of people being treated. How do we know that the belief system about it that can't be changed. And, yeah, this is. I don't know, this is eye opening.

Yeah, it's cool stuff. And Alichrom is onto some other really cool stuff. Like, for instance, just to highlight where these belief effects are starting to show up. If you tell a group that the side effects of a drug that they're taking are evidence that the drug really works for the purpose that they're taking it, even though those side effects are kind of annoying, people report the experience as less awful and they report more relief from the primary symptoms that they're trying to target. So our belief about what side effects.

Are, that's so interesting.

Can really impact how quickly and how compatible we feel about how quickly a drug works. Excuse me. And how compatible we feel that drug is with our entire life. So maybe if we call them something else, like not side effects, but like additional benefits or something, it's kind of crazy. And you don't want to lie to people, obviously, but you also don't want to send yourself in the opposite direction, which is reading the list of side effects of a drug and then developing all of those side effects and then maybe later coming to the understanding that some of those were raised through belief effects.

We definitely see that that's the Nocebo effect, right? That's the one we see a lot, you know, with all sorts of drugs. And it's tough because, you know, how do you. How do you know which is which? And I think there are some people who are really impacted by that, and it makes it very difficult for them to take any sort of pharmacologic agent because they basically. They can't help but incur every possible side effect.

Isn't it true that medical students often will start developing the symptoms of the different diseases that they're learning about? Is that true?

Well, I'll tell you, I do think that in medical school, you start to think of the zebras more than the horses all the time. You know what I'm referring to, right? You see footprints, you see hoof prints. You should think of horses. But of course, medical students, you only think of the zebras. There are some really funny things in medical school. There are certain conditions that you spend so much time thinking about that you have a very warped sense of their prevalence. Like in medical school, there's this condition called sarcoidosis. I feel like we never stop talking about sarcoidosis. I've seen three cases in my life. It's just not that common.

Does it provide a great teaching tool or Something.

I don't know.

Some of these things. I don't know. How much time did we spend talking about situs inversus? This is when people embryologically have a reversed rotation and everything in their body is flipped. Literally everything is flipped. So their heart is on the right side, their liver is on the left side, their appendix is on the left side. Like, and so I'm not making this up.

How common is this?

I've never seen it. Okay.

I was thinking about boxing in the liver shot. Like, you could easily be going for the wrong side of the body.

No, I swear to God, like, as a medical student, if you were told someone had left sided lower quadrant pain, to which the answer is almost assuredly like, they have diverticulitis, you'd think they could have appendicitis in the context of situs inversus. Like, the fact that that would even register in the top 10 things that it could possibly be. But, yes, you just have a totally warped sense of what's out there. Oh, man.

Well, this has been pure pleasure for me. I don't know about you.

Yeah, this is.

I don't know about our listeners, but for me, this is among the things that I just delight in. And even more so because you're the one across the table for me, teaching me about these incredible findings and the gaps in those findings, which are equally incredible because they're equally important to know about.

Yeah. So let's do this again in Austin.

Absolutely. Next time on your home court.

Very well. And bring a little bit of that dew if you've got it.

Oh, yeah, yeah, yeah. I'll bring a low, medium and high dose.

Low, medium, and high dose.

Thanks, Peter. You're the best.

Thank you, sir.

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