#207 - AMA #35: "Anti-Aging" Drugs — NAD+, metformin, & rapamycin
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Summary
In this episode, Dr. Matt Kaberlein joins Dr. Peter Atiyah to discuss aging biomarkers and their impact on the field of aging research. Dr. Atiyah discusses the role of three molecules in aging research: NAD, NR and NMN, and metformin.
Transcript
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Hey everyone, welcome to a sneak peek, ask me anything or AMA episode of the drive podcast.
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I'm your host, Peter Atiyah. At the end of this short episode, I'll explain how you can
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access the AMA episodes in full, along with a ton of other membership benefits we've created,
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or you can learn more now by going to peteratiyahmd.com forward slash subscribe.
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So without further delay, here's today's sneak peek of the ask me anything episode.
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Welcome to ask me anything episode 35. This is a special AMA where in addition to being joined by
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Nick Stenson, I'm also joined by a previous guest, Matt Kaberlein. That's been a previous guest on
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the podcast twice actually, with the most recent one being believe in September of last year.
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He's a professor of laboratory medicine and pathology, an adjunct professor of genomic
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science, and an adjunct professor of oral health science at the University of Washington. His
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research interests are focused on the basic mechanisms of aging in order to facilitate
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translational interventions that promote healthspan and improve quality of life.
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I wanted Matt to join for this one because I knew we were going to go into a lot of different
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subjects where he would provide insight into the questions that many of you have asked.
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So in this episode, we focus on answering questions around the field of aging, but specifically
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looking at three geoprotective molecules. And these are the three molecules that I get asked
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about the most. The first is all things that have to do with NAD, and that usually implies its
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precursors, NR and NMN, but also sometimes NAD itself. The second being rapamycin, and the third
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being metformin. Now, if you were to do a search and find out which of those gets asked about the
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most, it's metformin ends down the most. And that's followed actually by rapamycin. And I think after
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that is NR, NAD, and NMN, but I might have that a little bit backwards. In this podcast, we focus on a
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number of questions in the field of aging. So it starts with a bit of a discussion around biomarkers of
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aging, what are they, how good are they, and what do they tell us about the field. We talk about how
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studying aging can be done in various animals, and we get into the specifics, meaning the benefits and
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disadvantages of studying these in yeast, worms, flies, mice, dogs, and ultimately, of course, humans,
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the species of interest. We talk about how to think about the various studies that are being done around
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the idea of lifespan and healthspan. We talk about epigenetic clocks. And then from there, we really dive
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into the meat of this, getting into everything that has to do with NAD, and then ultimately its
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precursors, NR and NMN. It's a molecule, as I said, I get asked about this a lot. And so we had no
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shortage of questions and nuances to get into here. After speaking about NAD in detail, we then look at
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rapamycin and metformin, though probably not in as much detail because we've spent lots of time
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talking about those molecules on other podcasts. However, Matt felt, and I agreed when it was all said and
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done, that it was going to be beneficial to at least include these other molecules as a means to
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compare what we know and what we don't know about NAD to molecules for which we have much more data,
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at least in the case of rapamycin, as it pertains to longevity, but in humans, as it pertains to
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metformin. The goal of this podcast was to help you not only understand these molecules and see how
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they stack up against each other, but also to help you think about the new information around these and
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what we might want to expect to see or look to see as we make decisions about the use of these
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things in the future. If you're a subscriber and you want to watch the full video of this podcast,
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you can find it on the show notes page where, of course, you also find the show notes. And if
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you're not a subscriber, you can watch a sneak peek of this video on our YouTube page. So without
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Hey, Nick, how are you doing today? I'm doing good. How are you doing?
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Very well. We're going to do things a little different today, huh?
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Yeah, we have a little different setup for this one. So what happened was back in February, 2021,
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you and Bob did an AMA where you looked at one specific topic that was covered on multiple podcasts,
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in particular, the Shulman episode, and kind of went back and tried to simplify that conversation
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around insulin resistance. And what we heard from subscribers was a lot of people really enjoyed
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that type of podcast. We had a lot of requests to do more of it. And so what we did for this one is
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we just kind of been collecting a ton of questions around the science of aging and in particular,
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three geoprotective molecules that I know we see the most questions come through. And I know you hear
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the most from your patients, which is NAD, rapamycin, metformin. We had no shortage of podcasts on this
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with Matt Caberman, Steve Austed, Nir Barzilai, Joan Manik, David Sinclair, Lloyd Clickstein,
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David Savantini, you kind of name it. We've had a ton of podcasts on it. So what we did is compile
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all those questions in hopes of having a one-stop shop for people to really understand these topics
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and how they can think about them just with these molecules. And then also in the future,
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as new information comes out, that's kind of what we're looking at today, which leads us to a little
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bit of a different thing we're doing, which is in addition to me asking you questions, we also thought
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there'd be no better person to ask back on the podcast for the third time than Matt Caberman.
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And we reached out to Matt and he graciously said yes. And so we're doing a three-person AMA today,
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which we've never done before. So we'll see how it goes, but thank you, Matt, for joining us for this
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one. Thanks for having me back. Looking forward to it. So this is an ambitious way to go about this.
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And truthfully, when we first kicked around this idea a couple of weeks ago, my vote was to talk
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exclusively about NAD and its precursors. I felt that there was so much information there that to try to
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do anything beyond that would frankly be counterproductive. We just wouldn't be able to
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cover it in the depth. Now, Matt, you had very strong feelings that as much detail as we want to go
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into around NAD and its precursors, NR and NMN, you really felt strongly that we needed to look at
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rapamycin and metformin. What was your rationale for that? Yeah, well, I mean, I think as Nick said,
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those three molecules often get talked about together in the field and by people who are following the
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field as certainly three of the leading candidates for Jera protectors. And so I think there's some
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value in almost a compare and contrast between the three and really take a look at the state of the
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data that we've got today so that you can really sort of understand what is the evidence for each of
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these classes of molecules, maybe where are some of the challenges as we think about moving from the
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laboratory into the real world, into the clinic in terms of testing them. So I thought it would be
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helpful to at least cover those three classes of molecules together so that we can kind of take a look
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and compare them against each other. Well, you won. I lost. No, I'm kidding. I agree with that logic.
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We're all winners here, Peter. Yeah. So I think we will do that. So Nick, where do you want to start
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this thing? Yeah. So as we were thinking about it, I think what we need to do is just answer some
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general questions around aging and studies of aging, because I think that's going to be really helpful
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for people as they hear what you and Matt have to say to break down NAD, rapamycin, metformin.
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And so maybe what we'll start with is just if you can remind people at the highest level, are there
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any biomarkers of aging that we can look at when we look at these molecules? Well, certainly what I
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would say is when you contrast aging with a field like lipidology, our hands are a little bit tied.
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If your objective is to lower ApoB, because ApoB plays a causative role in atherosclerotic
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cardiovascular disease, you have the perfect biomarker. It's ApoB. So even though you have
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multiple different ways that drugs can go about lowering that, they can inhibit synthesis primarily,
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they can increase clearance, they can impute absorption, all of these things. You have a very
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clear biomarker that you can track. And of course that's true for a number of drugs, but when it comes
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to this field of aging, it really is difficult. I'm guessing Matt, that there are going to be some
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people who will argue that we have remarkable biomarkers for aging. And then you'll have
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others, and I'm probably more in this camp, that would argue actually we don't really have any good
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biomarkers for aging. Where do you sit on this, Matt? I think you're right. And I think one of the
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things that you have to consider is really what do you want a biomarker to do? We're obviously talking
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about biomarkers of biological aging. What I think you really want is something you can measure
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that is predictive at either the individual or the population level of future health outcomes.
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Mortality certainly, but also functional outcomes, disease risk, things like that.
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So at one level, we absolutely have biomarkers. We can look at each other and to some extent,
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come up with somewhat of a precise measure of biological age. We can look at two people who are
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the same chronological age and humans are actually pretty good at estimating who's in better health.
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So we've evolved to do that. So there must be these underlying molecular biochemical
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signatures that we can find that are predictive of that. And I think it's a work in progress. So
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this has been ongoing since the 1980s, trying to find these molecular biomarkers of aging,
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and it's still a work in progress. It's an interesting time, as you suggested, where we have
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some candidates now. And certainly there are people in the field who are very optimistic. Some would
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argue maybe overly optimistic about how well those candidates work. And it's also an interesting time
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because we're starting to see commercialization of these so-called aging clocks that are being sold
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to the general public. And again, I think you can have a debate about what the evidence is that these
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things are actually measuring biological aging. Are they doing it accurately? But certainly I think
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I feel like we're closer than we were 15 or 20 years ago, but we're still a ways off from that
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definition that I gave of having something that you can measure that in a predictive way at either
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the individual or the population level really tells you with any level of precision what the
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biological aging trajectory is. I think the example you gave is a pretty good one about the eyeball
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test. So if you took two people who are 50 years old and looked at them and one had lots of muscle mass
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and great posture and looked like a physical specimen of health and the other one was slumped over,
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maybe morbidly obese, take the exact opposite of that. It's probably the case that the fitter
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person would look younger. And even if you could look at their face and see the same number of wrinkles
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and assume that they're, well, they're probably the same age, you would still predict sort of a
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younger biologic age of that person. So you're right. There's something in the gestalt that's pretty
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obvious, but truthfully, at least for me, what would be really valuable would be blood-based
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biomarkers, potentially more elaborate, but let's start with the blood where you could do interventions
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for a short period of time. And if in fact, those interventions would, if continued lead to better
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lifespan or health span, and let's just keep it simple and say lifespan, they would show up. So for
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example, if you took an individual and you calorie restricted them for three months, took them down
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to 70% of their weight maintenance, caloric intake, you would like to think that there would be some
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set of biomarkers that would suggest an improvement in their lifespan. What do you think about that idea,
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Matt? Yeah. So, I mean, I agree completely with you that from a pragmatic perspective and a usefulness
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perspective, that's exactly what we want. And I think that's what the field has been searching for
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for a long time. It's a complicated question that you're asking though, because it's one thing to
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hypothesize that there are going to be molecular biomarkers that reflect biological age. Those are
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not necessarily going to be the same biomarkers that reflect rate of aging. And what you're talking
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about, a short-term readout almost has to reflect rate of aging or even potentially this is speculative
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reversal of biological aging. And so my only point is those may not actually be the same markers for
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each of those classes. So I certainly believe that there will be signatures of intervention response
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that are predictive of efficacy. I'm not sure that it's going to be the same as the signatures of
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biological age. If you had asked me 15 or 20 years ago when I was really getting started in this field,
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the kinds of interventions, you mentioned caloric restriction, that's kind of the gold standard that
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we've been studying for many, many years. Are those slowing aging or reversing aging? I would have
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answered they're slowing aging. They are decreasing the rate of decline or damage accumulation. What's been
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really interesting and I think exciting over the last 10 years or so is the observation that at least
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some of these interventions reverse many of the molecular changes that go along with aging. And in many
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cases, the functional changes that go along with aging. So you talked about blood biomarkers. I agree with
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you. That would be great if we had blood biomarkers. I am actually a big fan of functional biomarkers. So
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looking at organ function, tissue function, that's harder to do in people than it is in laboratory animals in
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some ways. But I really feel like those are telling us something fundamental about future health outcomes that
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you can almost take to the bank. There's still some stochasticity involved. There's still some luck with
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staying alive. But if you can make somebody's heart function better, their brain function better,
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you got to feel pretty good about that. And if you can make multiple organs and tissues function
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better with the same intervention, I think you can make a case that you are in fact modulating
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some underlying biology of aging as opposed to only the biology of that tissue and organ.
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Yeah. And frankly, Matt, that's exactly what we do in clinical practice. The reality of it is,
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and we'll talk about these things, but I'm not looking at epigenetic clocks. I'm just not.
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How do I know if we're moving or how do I believe? I guess you'll never really know if you're going to
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talk about this with some humility. But what gives me great confidence that we're moving in the right
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direction with a patient, it's basically when all of those functional things improve. So if VO2 max
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improves, muscle mass improves, strength improves, cardiovascular efficiency improves, phenotypic
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markers of disease improve. So glucose disposal, insulin signaling, ApoB, lipid markers, inflammatory
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markers. Maybe those are just biomarkers of aging. I mean, they're certainly my crude version of those
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things. And again, some of those are things you measure in blood. Some of those are things that you
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measure non-invasively. Some of those things are imaging related. I think until someone comes up
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with better tools, this is basically how I think about this problem. But let's talk a little bit
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about epigenetic clocks because they sure are getting a lot of attention. You want to maybe tell
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folks what they are specifically, how they work, and what they're aspiring to do? The word epigenetics
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actually means a lot. It can mean anything that is inherited that's not at the level of your
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DNA sequence. But mostly when people talk about epigenetic clocks, what they're specifically
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talking about are chemical modifications either to the DNA or to the histones that pack the DNA.
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And these chemical modifications control gene expression. So things like methylation and acetylation.
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What has been observed in laboratory animals and in humans is that there are changes in these
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epigenetic marks that happen in a predictable way with age. And there are tens of thousands of these
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marks that can be measured at any given time in a cell. And you can create algorithms that predict
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the age-related changes in these epigenetic marks with a pretty high degree of accuracy. So you can sample
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a subset of these specific chemical changes and come up with an algorithm that within plus or minus
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five years will predict a person or an animal's chronological age. And that works really well.
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And that seems to work really well in every organism where people have looked. All the way from very
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early development up into old age, you can create these predictive algorithms. The idea that has emerged
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from that is that you can do that at the population level. And then if you identify individuals whose
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chronological age doesn't match up really perfectly well with their epigenetic age. In other words,
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they lie off of that best fit line that those people may be biologically younger or older than
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their chronological age. And so that's where this idea of these epigenetic clocks has come from
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is you then, at least in principle, can predict a person's biological age depending on how well they
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fit the best fit line for this algorithm. And I think that the evidence in support of that comes mostly
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from longitudinal studies in humans where you can create a training set and a test set and you know
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what the future outcomes were for some of these people. They've been sampled, let's say, over 20
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years. And indeed, you can see a relationship between the people whose predicted biological epigenetic
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age, say, is younger than their chronological age. And then when you look at them 20 years later,
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they have a lower likelihood of developing specific diseases or potentially of dying. So I think that's the
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case that can be made for these epigenetic clocks that they are telling you something about future
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risk. I think in my view, the limitation to these epigenetic clocks, there's several. One is that
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there are about two dozen of them. And honestly, I can't tell from the way people argue with each
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other which are the best and which aren't. But I think more what concerns me is nobody has ever done
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what I would view as the definitive experiment, which is to actually show in the same individual or in the
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same population that you can actually predict future health outcomes. Now, some people will
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argue that the longitudinal data makes that not necessary. I think there are a couple of reasons
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why I don't agree with that. One big one is that the environment that we live in as humans has changed
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dramatically over the last three decades. And we know that environment plays a huge role in epigenetic
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modifications. And so the epigenetic marks that were most relevant for health outcomes 30 years ago
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might not be the most relevant today. So that's one. The other is this is actually a pretty easy
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experiment to do in mice. And it really bothers me that nobody has done it. I was going to ask you
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that. So how many times is someone doing a mouse study that is going to the end of life? Why do we not
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have the definitive lifespan study for each of these epigenetic clocks? I think that's a legitimate
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question. I don't know the answer. I mean, people will tell you that the clocks aren't as good in
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mice. Look, it should be doable. And honestly, it should have been done three, four years ago. So
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it's a black hole in the literature that hasn't been filled yet. And just to be explicit, the
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experiment you want to do, right, is you take a cohort of mice at, say, 20 months. You measure their
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epigenetic age in blood. You do a few interventions that we know should extend lifespan. You measure their
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epigenetic age in blood six months later. And then you see at an individual and population level
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what the survival is. And you can do end of life pathology. And so if the clocks are working,
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you should absolutely be able to detect that signature well in advance of end of life. If
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somebody did that experiment and it worked, I would be convinced. That would make me really be a believer
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in the epigenetic clocks, particularly if you could do it at the individual level. But it hasn't been
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done yet. So it's a little bit unclear. That's a big ask to do it at the individual level. I think it is
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one thing to do it at the population level, but the question is how will it port to the individual
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level? We use this term, and you've already alluded to this, we use this term broadly. Sometimes when
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a person says epigenetic clock, they mean literally a set of biomarkers that look at methylation
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patterns on DNA. And other times when people say epigenetic clock, they mean an algorithm that looks
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at 15 biomarkers that can include obviously the methylation pattern on DNA, but can include things
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like vitamin D level, fasting glucose level, traditional biomarkers. Do you have a point
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of view on the difference between these? Well, I think what you just said is accurate.
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They're measuring different things. My personal intuition, so I would call that more of a general
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aging clock, putative aging clock, I guess I should say. The putative aging clocks that incorporate
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things beyond epigenetics are much more likely to actually work in a useful way in humans. And I think
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one reason to believe that is if you look at what people call the hallmarks of aging, right? These
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sort of famous nine things, molecular processes that seem to contribute to aging, only one of them
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is epigenetics. And so I think you run the risk with the epigenetic clocks that you're only informing
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on a subset of the biological aging processes. And if you look more broadly, you're much more likely to
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get a holistic picture at the whole individual level. I want to come back to something you said,
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though. You said it's kind of a heavy lift or a hard ask to get these clocks to work at the
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individual level. That may be true, but I think in order for them to be useful, that's what you want,
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right? And that's exactly right. The fact that it would be so hard to do speaks to exactly why
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you would love to see it done. I still come back to what we talked about earlier. I find it hard to
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believe. I hope I'm wrong because this would be a really efficient way to do things, but I just have
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a hard time believing that there's going to be an epigenetic signature that I think will be more
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valuable than some of the most tried and true phenotypic tests. VO2 max, zone two threshold,
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grip strength, muscle mass, fat free mass index, all of these sorts of things that are so highly,
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and I believe causally linked to longevity. So I guess if nothing else, it will be interesting to see
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how tight that association can be. I would agree with you about the epigenetic marks like methylation
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specifically. I'm a little bit more optimistic that you can create the kind of more broad aging clock
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or aging signature. But do you think it can be done out of an existing collection of biomarkers,
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or do you think we're going to have to go deeper into the proteome and metabolome to find things we
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don't even know exist yet? In other words, find other molecules that we basically haven't
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identified yet. I honestly don't know. It wouldn't surprise me if just given the state of knowledge
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today that there are a subset of the things that people in the field are thinking about that can
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actually be extremely predictive at the individual level. It's never going to be perfect. You can always
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do better. But all of the things you mentioned, all of the functional outcomes that we know are
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important for health, there is underlying biology that drives that. And I think we've got certainly an
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incomplete, but a pretty good idea of what a lot of the processes are that are driving that
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loss of function and that degeneration. Time will tell, but I feel like the candidates we've got are
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pretty good. And they may not be as precise as you can get if you can do a full functional workup on a
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person, but they might be good enough to tell you some information about likely efficacy of lifestyle
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changes or drug interventions or things that people might want to incorporate to potentially maximize
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their health span. Last point on this before we get into the more substantive attempts to answer some
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questions. One of the things I'm always mindful of here, and I've seen this a lot with early cancer
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screening diagnostic companies, is changing the definition of what something means in order to fit
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a diagnostic test. I've been pitched on these so many times, literally at least three, if not four times,
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where a company comes along and says, hey, we've got a biomarker that is an early detection of
00:24:11.860
cancer. And I say, okay, show me the data. And they say, look at this sample set where we predicted
00:24:18.360
so many cancers in patients. And we have zero false positives and we have zero false negatives.
00:24:26.200
So I look at their test and I say, well, these are a whole bunch of positives in people that don't
00:24:32.300
have cancer. And they said, no, no, no, no. They have early cancer. I said, well, what do you mean by
00:24:37.140
that? Well, they have cancer, but it's only a few thousand cancer cells. And I said, but do you know
00:24:43.540
if those people go on to get cancer? Because clinically relevant cancer is about a billion cells.
00:24:48.920
That's when it would be one square centimeter. And they said, no, no, it doesn't matter.
00:24:53.060
This person has cancer. And I said, well, look, if a person has a thousand cancer cells in their body,
00:24:59.380
we have no idea if that means they're going to get cancer or if their immune system is going to come
00:25:04.400
along and mop the floor with that cancer. So to tell me you have no false positives just because
00:25:09.180
you captured those is a little bit like moving the goalpost. You shoot the arrow at the side of
00:25:14.480
the barn and you go and draw the target after, right? So I see a little bit of the same thing going on
00:25:19.700
with biologic age clocks where there's pairing an age clock with a supplement or an intervention
00:25:26.640
and we're tuning them to each other. Does that make sense?
00:25:30.660
Yep. I agree completely. And I think certainly something I'm concerned about, I think
00:25:34.060
a fair number of scientists in the field are concerned about is the commercialization of
00:25:38.260
these aging clocks. So you mentioned pairing it with the supplements. That's even a step further.
00:25:44.220
I think even selling to the general public, the idea that with some level of accuracy,
00:25:50.820
we can measure your biological age and you should take action based on that. It's just frankly
00:25:57.120
dishonest. Now, some people will argue that it's a necessary evil in the sense that one,
00:26:02.160
it broadens the appeal of the field to the general public. And two, it's causing people to make
00:26:08.780
healthy lifestyle choices. Maybe when you measure your biological age and it tells you you're 10 years
00:26:14.540
older than your chronological age, you start exercising or you eat better. Maybe that's true.
00:26:19.500
I don't know that that's necessarily true, but it's still dishonest to claim to people
00:26:23.520
that anyone is able to, with any precision, measure your biological age. And there are lots
00:26:29.460
and lots of companies doing that. So to me, that's a problem to begin with. It becomes a bigger problem
00:26:34.140
when the same companies are then also selling a product that they claim will reverse your biological
00:26:39.440
age. That's just snake oil. I don't know any other way to say it. It's just snake oil. Honestly,
00:26:44.400
the FDA should step in and do something about it, in my opinion. I think that was a really good
00:26:50.140
overview kind of around the question of why there's so much complexity around the idea of aging
00:26:56.020
biomarkers. And so maybe what would be really helpful for people is knowing all of that.
00:27:01.200
How do we think about that when we look at these studies that look at geoprotective molecules? So
00:27:07.620
people who aren't in the field don't do these studies day in, day out, aren't always looking at this.
00:27:12.820
A lot of them are going to be wondering, okay, what does that mean as we look at this? So maybe
00:27:18.380
you both can talk about what the takeaway is from everything we just discussed, as well as
00:27:23.440
when we look at studies and models and mice and yeast or humans, whoever that may be, maybe run
00:27:30.940
through one of the strengths, the limitations, how to think about those things.
00:27:35.120
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