The Peter Attia Drive - May 16, 2022


#207 - AMA #35： ＂Anti-Aging＂ Drugs — NAD+, metformin, & rapamycin

Topics
epigenetic clocks epigenetic clocks experiments
Hosted by
Peter Attia
Guests
Nick Stenson
Mentioned
Matt Caberlin Lloyd Clickstein David Savantini David Sinclair Steve Austin Nir Barzilai Joan Manik
#206 - Exercising for longevity： strength, stability, zone 2, zone 5, and more #208 - Tragedy, grief, healing, and finding happiness ｜ Kelsey Chittick

Episode Stats


Length

30 minutes

Words per minute

180.82

Word count

5,581

Sentence count

307

Harmful content

Hate speech

1

sentences flagged


Summary

Summaries generated with gmurro/bart-large-finetuned-filtered-spotify-podcast-summ .

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

Transcript generated with Whisper (turbo).
Hate speech classifications generated with facebook/roberta-hate-speech-dynabench-r4-target .
Topics generated with Qwen2.5-3B-Instruct.
Hosts, guests, and mentioned names generated with spaCy (en_core_web_sm), reconciled against Wikidata.
00:00:00.000 Hey everyone, welcome to a sneak peek, ask me anything or AMA episode of the drive podcast.
00:00:16.500 I'm your host, Peter Atiyah. At the end of this short episode, I'll explain how you can
00:00:20.460 access the AMA episodes in full, along with a ton of other membership benefits we've created,
00:00:25.440 or you can learn more now by going to peteratiyahmd.com forward slash subscribe.
00:00:31.120 So without further delay, here's today's sneak peek of the ask me anything episode.
00:00:39.260 Welcome to ask me anything episode 35. This is a special AMA where in addition to being joined by
00:00:46.200 Nick Stenson, I'm also joined by a previous guest, Matt Kaberlein. That's been a previous guest on
00:00:50.740 the podcast twice actually, with the most recent one being believe in September of last year.
00:00:55.440 He's a professor of laboratory medicine and pathology, an adjunct professor of genomic
00:00:59.280 science, and an adjunct professor of oral health science at the University of Washington. His
00:01:02.840 research interests are focused on the basic mechanisms of aging in order to facilitate
00:01:06.600 translational interventions that promote healthspan and improve quality of life.
00:01:11.340 I wanted Matt to join for this one because I knew we were going to go into a lot of different
00:01:17.160 subjects where he would provide insight into the questions that many of you have asked.
00:01:22.800 So in this episode, we focus on answering questions around the field of aging, but specifically
00:01:28.860 looking at three geoprotective molecules. And these are the three molecules that I get asked
00:01:34.220 about the most. The first is all things that have to do with NAD, and that usually implies its
00:01:42.000 precursors, NR and NMN, but also sometimes NAD itself. The second being rapamycin, and the third
00:01:49.080 being metformin. Now, if you were to do a search and find out which of those gets asked about the
00:01:55.540 most, it's metformin ends down the most. And that's followed actually by rapamycin. And I think after
00:02:02.320 that is NR, NAD, and NMN, but I might have that a little bit backwards. In this podcast, we focus on a
00:02:09.940 number of questions in the field of aging. So it starts with a bit of a discussion around biomarkers of
00:02:14.920 aging, what are they, how good are they, and what do they tell us about the field. We talk about how
00:02:20.260 studying aging can be done in various animals, and we get into the specifics, meaning the benefits and
00:02:26.780 disadvantages of studying these in yeast, worms, flies, mice, dogs, and ultimately, of course, humans,
00:02:31.980 the species of interest. We talk about how to think about the various studies that are being done around
00:02:35.960 the idea of lifespan and healthspan. We talk about epigenetic clocks. And then from there, we really dive
00:02:40.640 into the meat of this, getting into everything that has to do with NAD, and then ultimately its
00:02:45.980 precursors, NR and NMN. It's a molecule, as I said, I get asked about this a lot. And so we had no
00:02:52.200 shortage of questions and nuances to get into here. After speaking about NAD in detail, we then look at
00:02:59.160 rapamycin and metformin, though probably not in as much detail because we've spent lots of time
00:03:04.300 talking about those molecules on other podcasts. However, Matt felt, and I agreed when it was all said and
00:03:09.580 done, that it was going to be beneficial to at least include these other molecules as a means to
00:03:14.740 compare what we know and what we don't know about NAD to molecules for which we have much more data,
00:03:21.060 at least in the case of rapamycin, as it pertains to longevity, but in humans, as it pertains to
00:03:26.700 metformin. The goal of this podcast was to help you not only understand these molecules and see how
00:03:31.320 they stack up against each other, but also to help you think about the new information around these and
00:03:36.340 what we might want to expect to see or look to see as we make decisions about the use of these
00:03:40.820 things in the future. If you're a subscriber and you want to watch the full video of this podcast,
00:03:44.660 you can find it on the show notes page where, of course, you also find the show notes. And if
00:03:48.800 you're not a subscriber, you can watch a sneak peek of this video on our YouTube page. So without
00:03:52.880 further delay, I hope you enjoy AMA number 35.
00:03:55.960 Hey, Nick, how are you doing today? I'm doing good. How are you doing?
00:04:04.360 Very well. We're going to do things a little different today, huh?
00:04:06.700 Yeah, we have a little different setup for this one. So what happened was back in February, 2021,
00:04:12.920 you and Bob did an AMA where you looked at one specific topic that was covered on multiple podcasts,
00:04:18.740 in particular, the Shulman episode, and kind of went back and tried to simplify that conversation
00:04:23.980 around insulin resistance. And what we heard from subscribers was a lot of people really enjoyed
00:04:29.380 that type of podcast. We had a lot of requests to do more of it. And so what we did for this one is
00:04:34.960 we just kind of been collecting a ton of questions around the science of aging and in particular,
00:04:42.340 three geoprotective molecules that I know we see the most questions come through. And I know you hear
00:04:48.100 the most from your patients, which is NAD, rapamycin, metformin. We had no shortage of podcasts on this
00:04:55.040 with Matt Caberman, Steve Austed, Nir Barzilai, Joan Manik, David Sinclair, Lloyd Clickstein,
00:05:02.020 David Savantini, you kind of name it. We've had a ton of podcasts on it. So what we did is compile
00:05:07.500 all those questions in hopes of having a one-stop shop for people to really understand these topics
00:05:13.940 and how they can think about them just with these molecules. And then also in the future,
00:05:18.320 as new information comes out, that's kind of what we're looking at today, which leads us to a little
00:05:23.820 bit of a different thing we're doing, which is in addition to me asking you questions, we also thought
00:05:29.820 there'd be no better person to ask back on the podcast for the third time than Matt Caberman.
00:05:34.940 And we reached out to Matt and he graciously said yes. And so we're doing a three-person AMA today,
00:05:41.440 which we've never done before. So we'll see how it goes, but thank you, Matt, for joining us for this
00:05:47.220 one. Thanks for having me back. Looking forward to it. So this is an ambitious way to go about this.
00:05:53.240 And truthfully, when we first kicked around this idea a couple of weeks ago, my vote was to talk
00:05:59.240 exclusively about NAD and its precursors. I felt that there was so much information there that to try to
00:06:06.160 do anything beyond that would frankly be counterproductive. We just wouldn't be able to
00:06:11.000 cover it in the depth. Now, Matt, you had very strong feelings that as much detail as we want to go
00:06:16.620 into around NAD and its precursors, NR and NMN, you really felt strongly that we needed to look at
00:06:22.920 rapamycin and metformin. What was your rationale for that? Yeah, well, I mean, I think as Nick said,
00:06:28.060 those three molecules often get talked about together in the field and by people who are following the
00:06:33.620 field as certainly three of the leading candidates for Jera protectors. And so I think there's some
00:06:39.120 value in almost a compare and contrast between the three and really take a look at the state of the
00:06:45.440 data that we've got today so that you can really sort of understand what is the evidence for each of
00:06:51.520 these classes of molecules, maybe where are some of the challenges as we think about moving from the
00:06:57.280 laboratory into the real world, into the clinic in terms of testing them. So I thought it would be
00:07:01.820 helpful to at least cover those three classes of molecules together so that we can kind of take a look
00:07:07.840 and compare them against each other. Well, you won. I lost. No, I'm kidding. I agree with that logic.
00:07:13.320 We're all winners here, Peter. Yeah. So I think we will do that. So Nick, where do you want to start
00:07:17.620 this thing? Yeah. So as we were thinking about it, I think what we need to do is just answer some
00:07:22.840 general questions around aging and studies of aging, because I think that's going to be really helpful
00:07:28.700 for people as they hear what you and Matt have to say to break down NAD, rapamycin, metformin.
00:07:35.200 And so maybe what we'll start with is just if you can remind people at the highest level, are there
00:07:40.640 any biomarkers of aging that we can look at when we look at these molecules? Well, certainly what I
00:07:46.620 would say is when you contrast aging with a field like lipidology, our hands are a little bit tied.
00:07:53.600 If your objective is to lower ApoB, because ApoB plays a causative role in atherosclerotic
00:08:01.560 cardiovascular disease, you have the perfect biomarker. It's ApoB. So even though you have
00:08:06.520 multiple different ways that drugs can go about lowering that, they can inhibit synthesis primarily,
00:08:11.340 they can increase clearance, they can impute absorption, all of these things. You have a very
00:08:15.580 clear biomarker that you can track. And of course that's true for a number of drugs, but when it comes
00:08:22.500 to this field of aging, it really is difficult. I'm guessing Matt, that there are going to be some
00:08:27.500 people who will argue that we have remarkable biomarkers for aging. And then you'll have
00:08:33.560 others, and I'm probably more in this camp, that would argue actually we don't really have any good
00:08:37.420 biomarkers for aging. Where do you sit on this, Matt? I think you're right. And I think one of the
00:08:41.860 things that you have to consider is really what do you want a biomarker to do? We're obviously talking
00:08:48.200 about biomarkers of biological aging. What I think you really want is something you can measure
00:08:53.320 that is predictive at either the individual or the population level of future health outcomes.
00:08:59.920 Mortality certainly, but also functional outcomes, disease risk, things like that.
00:09:04.840 So at one level, we absolutely have biomarkers. We can look at each other and to some extent,
00:09:11.640 come up with somewhat of a precise measure of biological age. We can look at two people who are
00:09:16.480 the same chronological age and humans are actually pretty good at estimating who's in better health.
00:09:22.620 So we've evolved to do that. So there must be these underlying molecular biochemical
00:09:28.340 signatures that we can find that are predictive of that. And I think it's a work in progress. So
00:09:34.540 this has been ongoing since the 1980s, trying to find these molecular biomarkers of aging,
00:09:39.600 and it's still a work in progress. It's an interesting time, as you suggested, where we have
00:09:44.380 some candidates now. And certainly there are people in the field who are very optimistic. Some would
00:09:51.180 argue maybe overly optimistic about how well those candidates work. And it's also an interesting time
00:09:56.200 because we're starting to see commercialization of these so-called aging clocks that are being sold
00:10:01.300 to the general public. And again, I think you can have a debate about what the evidence is that these
00:10:06.880 things are actually measuring biological aging. Are they doing it accurately? But certainly I think
00:10:11.700 I feel like we're closer than we were 15 or 20 years ago, but we're still a ways off from that
00:10:17.300 definition that I gave of having something that you can measure that in a predictive way at either
00:10:23.620 the individual or the population level really tells you with any level of precision what the
00:10:29.500 biological aging trajectory is. I think the example you gave is a pretty good one about the eyeball
00:10:34.900 test. So if you took two people who are 50 years old and looked at them and one had lots of muscle mass
00:10:41.080 and great posture and looked like a physical specimen of health and the other one was slumped over,
00:10:47.440 maybe morbidly obese, take the exact opposite of that. It's probably the case that the fitter
00:10:54.040 person would look younger. And even if you could look at their face and see the same number of wrinkles
00:10:59.180 and assume that they're, well, they're probably the same age, you would still predict sort of a
00:11:03.120 younger biologic age of that person. So you're right. There's something in the gestalt that's pretty
00:11:08.280 obvious, but truthfully, at least for me, what would be really valuable would be blood-based
00:11:15.100 biomarkers, potentially more elaborate, but let's start with the blood where you could do interventions
00:11:22.620 for a short period of time. And if in fact, those interventions would, if continued lead to better
00:11:30.740 lifespan or health span, and let's just keep it simple and say lifespan, they would show up. So for
00:11:35.700 example, if you took an individual and you calorie restricted them for three months, took them down
00:11:41.400 to 70% of their weight maintenance, caloric intake, you would like to think that there would be some
00:11:48.140 set of biomarkers that would suggest an improvement in their lifespan. What do you think about that idea,
00:11:53.500 Matt? Yeah. So, I mean, I agree completely with you that from a pragmatic perspective and a usefulness
00:11:59.140 perspective, that's exactly what we want. And I think that's what the field has been searching for
00:12:03.600 for a long time. It's a complicated question that you're asking though, because it's one thing to
00:12:09.620 hypothesize that there are going to be molecular biomarkers that reflect biological age. Those are
00:12:17.760 not necessarily going to be the same biomarkers that reflect rate of aging. And what you're talking
00:12:23.360 about, a short-term readout almost has to reflect rate of aging or even potentially this is speculative
00:12:29.480 reversal of biological aging. And so my only point is those may not actually be the same markers for
00:12:35.660 each of those classes. So I certainly believe that there will be signatures of intervention response
00:12:43.120 that are predictive of efficacy. I'm not sure that it's going to be the same as the signatures of
00:12:49.640 biological age. If you had asked me 15 or 20 years ago when I was really getting started in this field,
00:12:55.280 the kinds of interventions, you mentioned caloric restriction, that's kind of the gold standard that
00:12:59.440 we've been studying for many, many years. Are those slowing aging or reversing aging? I would have
00:13:06.500 answered they're slowing aging. They are decreasing the rate of decline or damage accumulation. What's been
00:13:12.620 really interesting and I think exciting over the last 10 years or so is the observation that at least
00:13:18.540 some of these interventions reverse many of the molecular changes that go along with aging. And in many
00:13:24.440 cases, the functional changes that go along with aging. So you talked about blood biomarkers. I agree with
00:13:29.400 you. That would be great if we had blood biomarkers. I am actually a big fan of functional biomarkers. So
00:13:34.820 looking at organ function, tissue function, that's harder to do in people than it is in laboratory animals in
00:13:40.320 some ways. But I really feel like those are telling us something fundamental about future health outcomes that
00:13:47.460 you can almost take to the bank. There's still some stochasticity involved. There's still some luck with
00:13:52.120 staying alive. But if you can make somebody's heart function better, their brain function better,
00:13:57.140 you got to feel pretty good about that. And if you can make multiple organs and tissues function
00:14:00.940 better with the same intervention, I think you can make a case that you are in fact modulating
00:14:05.100 some underlying biology of aging as opposed to only the biology of that tissue and organ.
00:14:11.080 Yeah. And frankly, Matt, that's exactly what we do in clinical practice. The reality of it is,
00:14:15.280 and we'll talk about these things, but I'm not looking at epigenetic clocks. I'm just not.
00:14:20.820 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
00:14:26.780 talk about this with some humility. But what gives me great confidence that we're moving in the right
00:14:31.240 direction with a patient, it's basically when all of those functional things improve. So if VO2 max
00:14:36.780 improves, muscle mass improves, strength improves, cardiovascular efficiency improves, phenotypic
00:14:45.100 markers of disease improve. So glucose disposal, insulin signaling, ApoB, lipid markers, inflammatory
00:14:52.080 markers. Maybe those are just biomarkers of aging. I mean, they're certainly my crude version of those
00:14:57.080 things. And again, some of those are things you measure in blood. Some of those are things that you
00:15:01.640 measure non-invasively. Some of those things are imaging related. I think until someone comes up
00:15:08.380 with better tools, this is basically how I think about this problem. But let's talk a little bit
00:15:13.340 about epigenetic clocks because they sure are getting a lot of attention. You want to maybe tell
00:15:17.920 folks what they are specifically, how they work, and what they're aspiring to do? The word epigenetics
00:15:24.520 actually means a lot. It can mean anything that is inherited that's not at the level of your
00:15:30.700 DNA sequence. But mostly when people talk about epigenetic clocks, what they're specifically
00:15:35.380 talking about are chemical modifications either to the DNA or to the histones that pack the DNA.
00:15:42.440 And these chemical modifications control gene expression. So things like methylation and acetylation.
00:15:48.060 What has been observed in laboratory animals and in humans is that there are changes in these
00:15:55.240 epigenetic marks that happen in a predictable way with age. And there are tens of thousands of these
00:16:01.820 marks that can be measured at any given time in a cell. And you can create algorithms that predict
00:16:09.620 the age-related changes in these epigenetic marks with a pretty high degree of accuracy. So you can sample
00:16:15.960 a subset of these specific chemical changes and come up with an algorithm that within plus or minus
00:16:22.760 five years will predict a person or an animal's chronological age. And that works really well.
00:16:29.140 And that seems to work really well in every organism where people have looked. All the way from very
00:16:34.040 early development up into old age, you can create these predictive algorithms. The idea that has emerged
00:16:39.800 from that is that you can do that at the population level. And then if you identify individuals whose
00:16:45.520 chronological age doesn't match up really perfectly well with their epigenetic age. In other words,
00:16:51.780 they lie off of that best fit line that those people may be biologically younger or older than
00:16:59.180 their chronological age. And so that's where this idea of these epigenetic clocks has come from
00:17:04.060 is you then, at least in principle, can predict a person's biological age depending on how well they
00:17:12.200 fit the best fit line for this algorithm. And I think that the evidence in support of that comes mostly
00:17:18.160 from longitudinal studies in humans where you can create a training set and a test set and you know
00:17:23.300 what the future outcomes were for some of these people. They've been sampled, let's say, over 20
00:17:27.840 years. And indeed, you can see a relationship between the people whose predicted biological epigenetic
00:17:34.940 age, say, is younger than their chronological age. And then when you look at them 20 years later,
00:17:40.180 they have a lower likelihood of developing specific diseases or potentially of dying. So I think that's the
00:17:46.180 case that can be made for these epigenetic clocks that they are telling you something about future
00:17:50.840 risk. I think in my view, the limitation to these epigenetic clocks, there's several. One is that
00:17:56.660 there are about two dozen of them. And honestly, I can't tell from the way people argue with each
00:18:02.020 other which are the best and which aren't. But I think more what concerns me is nobody has ever done
00:18:06.800 what I would view as the definitive experiment, which is to actually show in the same individual or in the
00:18:13.400 same population that you can actually predict future health outcomes. Now, some people will
00:18:18.440 argue that the longitudinal data makes that not necessary. I think there are a couple of reasons
00:18:22.860 why I don't agree with that. One big one is that the environment that we live in as humans has changed
00:18:29.700 dramatically over the last three decades. And we know that environment plays a huge role in epigenetic
00:18:36.140 modifications. And so the epigenetic marks that were most relevant for health outcomes 30 years ago
00:18:41.440 might not be the most relevant today. So that's one. The other is this is actually a pretty easy
00:18:46.260 experiment to do in mice. And it really bothers me that nobody has done it. I was going to ask you
00:18:50.680 that. So how many times is someone doing a mouse study that is going to the end of life? Why do we not
00:18:59.760 have the definitive lifespan study for each of these epigenetic clocks? I think that's a legitimate
00:19:05.840 question. I don't know the answer. I mean, people will tell you that the clocks aren't as good in
00:19:09.840 mice. Look, it should be doable. And honestly, it should have been done three, four years ago. So
00:19:14.760 it's a black hole in the literature that hasn't been filled yet. And just to be explicit, the
00:19:19.860 experiment you want to do, right, is you take a cohort of mice at, say, 20 months. You measure their
00:19:25.740 epigenetic age in blood. You do a few interventions that we know should extend lifespan. You measure their
00:19:32.020 epigenetic age in blood six months later. And then you see at an individual and population level
00:19:38.380 what the survival is. And you can do end of life pathology. And so if the clocks are working,
00:19:43.580 you should absolutely be able to detect that signature well in advance of end of life. If
00:19:50.840 somebody did that experiment and it worked, I would be convinced. That would make me really be a believer
00:19:55.620 in the epigenetic clocks, particularly if you could do it at the individual level. But it hasn't been
00:20:00.500 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
00:20:05.640 one thing to do it at the population level, but the question is how will it port to the individual
00:20:09.380 level? We use this term, and you've already alluded to this, we use this term broadly. Sometimes when
00:20:15.000 a person says epigenetic clock, they mean literally a set of biomarkers that look at methylation
00:20:21.020 patterns on DNA. And other times when people say epigenetic clock, they mean an algorithm that looks
00:20:26.180 at 15 biomarkers that can include obviously the methylation pattern on DNA, but can include things
00:20:32.380 like vitamin D level, fasting glucose level, traditional biomarkers. Do you have a point
00:20:37.440 of view on the difference between these? Well, I think what you just said is accurate.
00:20:41.780 They're measuring different things. My personal intuition, so I would call that more of a general
00:20:47.320 aging clock, putative aging clock, I guess I should say. The putative aging clocks that incorporate
00:20:52.340 things beyond epigenetics are much more likely to actually work in a useful way in humans. And I think
00:20:58.680 one reason to believe that is if you look at what people call the hallmarks of aging, right? These
00:21:03.380 sort of famous nine things, molecular processes that seem to contribute to aging, only one of them
00:21:09.660 is epigenetics. And so I think you run the risk with the epigenetic clocks that you're only informing
00:21:14.460 on a subset of the biological aging processes. And if you look more broadly, you're much more likely to
00:21:21.680 get a holistic picture at the whole individual level. I want to come back to something you said,
00:21:26.580 though. You said it's kind of a heavy lift or a hard ask to get these clocks to work at the
00:21:30.560 individual level. That may be true, but I think in order for them to be useful, that's what you want,
00:21:36.360 right? And that's exactly right. The fact that it would be so hard to do speaks to exactly why
00:21:41.080 you would love to see it done. I still come back to what we talked about earlier. I find it hard to
00:21:46.660 believe. I hope I'm wrong because this would be a really efficient way to do things, but I just have
00:21:51.100 a hard time believing that there's going to be an epigenetic signature that I think will be more
00:21:57.660 valuable than some of the most tried and true phenotypic tests. VO2 max, zone two threshold,
00:22:06.520 grip strength, muscle mass, fat free mass index, all of these sorts of things that are so highly,
00:22:13.720 and I believe causally linked to longevity. So I guess if nothing else, it will be interesting to see
00:22:19.920 how tight that association can be. I would agree with you about the epigenetic marks like methylation
00:22:26.000 specifically. I'm a little bit more optimistic that you can create the kind of more broad aging clock
00:22:32.140 or aging signature. But do you think it can be done out of an existing collection of biomarkers,
00:22:37.860 or do you think we're going to have to go deeper into the proteome and metabolome to find things we
00:22:43.540 don't even know exist yet? In other words, find other molecules that we basically haven't
00:22:48.220 identified yet. I honestly don't know. It wouldn't surprise me if just given the state of knowledge
00:22:52.900 today that there are a subset of the things that people in the field are thinking about that can
00:22:58.780 actually be extremely predictive at the individual level. It's never going to be perfect. You can always
00:23:03.940 do better. But all of the things you mentioned, all of the functional outcomes that we know are
00:23:08.940 important for health, there is underlying biology that drives that. And I think we've got certainly an
00:23:14.020 incomplete, but a pretty good idea of what a lot of the processes are that are driving that
00:23:18.740 loss of function and that degeneration. Time will tell, but I feel like the candidates we've got are
00:23:24.580 pretty good. And they may not be as precise as you can get if you can do a full functional workup on a
00:23:30.100 person, but they might be good enough to tell you some information about likely efficacy of lifestyle
00:23:36.000 changes or drug interventions or things that people might want to incorporate to potentially maximize
00:23:41.880 their health span. Last point on this before we get into the more substantive attempts to answer some
00:23:48.220 questions. One of the things I'm always mindful of here, and I've seen this a lot with early cancer
00:23:55.080 screening diagnostic companies, is changing the definition of what something means in order to fit
00:24:00.400 a diagnostic test. I've been pitched on these so many times, literally at least three, if not four times,
00:24:06.500 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. 0.99
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.
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