# Vadim Gladyshev and the Science of Aging

An episode of [Ground Truths](https://invisiblenetwork.ai/podcast/1728526108), 27 Oct 2026, 50 min.

Audio: https://api.substack.com/feed/podcast/222114108/a032e7130b46e2fdca9e41d9e6e0bc86.mp3

Vadim Gladyshev, PhD, Professor of Medicine at Brigham and Women's Hospital, Harvard Medical School, elected to the National Academy of Science, is a prolific science of aging researcher. We covered many topics including: —The nature of aging, and how it is different from biological aging (with glossary below) —The clocks of biological aging, which he considers biomarkers proteomic organ clock paper transcriptomic clock paper mAge (multimodal) clock with wearable preprint facial clock (and others to come such as voice, videos) metabolomic clock paper (from a different group) —Transplant of heart from old to young and young to old (preprint) and Rejuvenation Nature News and Views on that study by Chris Simms And how this could markedly expand transplant donor organs Parabiosis studies in mice —Effect of drugs on biological aging —slowing of biological aging with anti-fibrotic drug by proteomic clocks —studies of semaglutide in 5 randomized trials vs placebo, organ proteomic clocks —AI and aging biology —Future of aging Ground Truths has 217,000 subscribers from every US state and 214 countries. There are over 320,000 followers of Ground Truths so more than 100,000 folks who can easily convert to be free subscribers. If you’re one of them, please consider doing so. Your subscription to these free essays and podcasts makes my work in putting them together worthwhile. If you’re not a subscriber, please join! If you found this interesting PLEASE share it! The proceeds from all voluntary paid subscriptions go to support our summer internship program. It enabled us to accept and support a record number of 62 summer interns that joined us in 2026! These are high school, college and medical students selected from thousands of applicants. We couldn’t do this expanded program without the funds coming in through Ground Truths. Thank you! And big thanks to jojofinance, VivoSage, Burak Yedek, Vau Geha, José Pedro Castro, and 800 others for tuning into my live video with Vadim Gladyshev! Join me for my next live video in the app. Impressive since we had technical audio issues for the first 10 minutes and the attendees rose from 500-800! I edited out the video/audio the first 10 minutes when we couldn’t get started so you wouldn’t have to go through the horrible echo or silence while we waited for Vadim’s return. Get full access to Ground Truths at erictopol.substack.com/subscribe

## Transcript

[0:00] SPEAKER_02: Hadeem, hello.
[0:02] SPEAKER_00: Hello.
[0:04] SPEAKER_02: Are we okay now without an echo?
[0:07] SPEAKER_02: Now I'm on the phone, yeah.
[0:09] SPEAKER_02: Good.
[0:10] SPEAKER_02: Sorry about that, everybody.
[0:12] SPEAKER_02: We've got 500 folks joined and they stood with us while we got this worked out.
[0:17] SPEAKER_02: So let me start things off.
[0:18] SPEAKER_02: I've already introduced you and discussed that you really are one of the leaders in
[0:23] SPEAKER_02: the science of aging.
[0:24] SPEAKER_02: And maybe we'll start off giving us a general impression about your views on
[0:28] SPEAKER_02: the nature of aging.
[0:32] SPEAKER_01: Yeah, I guess this is the most important question in the field.
[0:37] SPEAKER_01: Okay, so let's see how to start.
[0:41] SPEAKER_01: So maybe this way.
[0:44] SPEAKER_01: A few years ago,
[0:44] SPEAKER_01: I chaired a conference,
[0:46] SPEAKER_01: Gordon Research Conference on Aging, and I gave a questionnaire to...
[0:51] SPEAKER_01: Experts in the field,
[0:52] SPEAKER_01: over 100 people responded,
[0:54] SPEAKER_01: and the questions were like the most basic questions in the field.
[0:57] SPEAKER_01: Like, what is aging?
[0:58] SPEAKER_01: What causes aging?
[1:00] SPEAKER_01: Is aging a disease?
[1:03] SPEAKER_01: When does aging begin?
[1:04] SPEAKER_01: Questions like that.
[1:05] SPEAKER_01: And there was no consensus on any question.
[1:08] SPEAKER_01: So over 100 people responded, and there was over 100 definitions of aging.
[1:13] SPEAKER_01: So I tried to cluster them, tried to understand what is the thinking in the field,
[1:21] SPEAKER_01: It turns out that for many people, aging is the increased chance of dying.
[1:26] SPEAKER_01: Some, like, something ages if it's more likely to die tomorrow than today.
[1:31] SPEAKER_01: For other people,
[1:32] SPEAKER_01: some, a fraction of people think it's a period after completion of development.
[1:36] SPEAKER_01: Some other people think it's the increase in biological age or increase, increase in damage.
[1:43] SPEAKER_01: Some other people think it's a loss of function, like,
[1:46] SPEAKER_01: so Basically,
[1:47] SPEAKER_01: when we say that we study aging,
[1:49] SPEAKER_01: it actually means completely different thing for different people.
[1:52] SPEAKER_01: So the word aging, the meaning of the word aging is different.
[1:57] SPEAKER_01: So for example, I study aging and you study aging, but we may study different things.
[2:03] SPEAKER_01: So that's one aspect.
[2:05] SPEAKER_01: So for me, I would say aging is the accumulation of negative consequences of being alive.
[2:12] SPEAKER_01: So I will explain what this is.
[2:14] SPEAKER_01: So We live, we have a set of functions that's our life, and these functions are imperfect.
[2:22] SPEAKER_01: So they, because no function can be, can be made, designed or evolved perfect.
[2:28] SPEAKER_01: And therefore, because of non-zero likelihood of producing damage, damage will be produced.
[2:36] SPEAKER_01: Damage in a broader sense would be byproducts of metabolism,
[2:40] SPEAKER_01: mutations,
[2:41] SPEAKER_01: you know,
[2:41] SPEAKER_01: different Changes in different levels of hormones or maybe a number of cells in
[2:46] SPEAKER_01: a tissue.
[2:46] SPEAKER_01: So it's not just molecular damage, but really multi-scale negative consequences of being alive.
[2:55] SPEAKER_01: And of course, the exposures like from environment contribute as well.
[2:59] SPEAKER_01: And that thing, negative consequences, they accumulate.
[3:03] SPEAKER_01: There are a variety of, let's call it damage, but it's not really damage, I'll explain.
[3:08] SPEAKER_01: It grows with time.
[3:10] SPEAKER_01: So it's very hard to deal with that.
[3:12] SPEAKER_01: But nature is able to do it.
[3:14] SPEAKER_01: So the way nature does it, it all functions.
[3:18] SPEAKER_01: So let's say, consider a protocell when life just started.
[3:23] SPEAKER_01: It was just probably a simple micelle with some kind of components inside.
[3:29] SPEAKER_01: There was no DNA, yet no proteins, no RNA.
[3:32] SPEAKER_01: And of course, These processes were very inefficient.
[3:36] SPEAKER_01: So the only way for them to survive was to divide.
[3:41] SPEAKER_01: So cells divided.
[3:42] SPEAKER_01: These micelles, protocells divided.
[3:44] SPEAKER_01: And over time, biology evolved functions.
[3:50] SPEAKER_01: Initially RNA and dead proteins and DNA.
[3:53] SPEAKER_01: So these functions essentially can be viewed as anti-aging.
[3:57] SPEAKER_01: So we have natural aging, kind of a default state.
[4:00] SPEAKER_01: Mm-hmm.
[4:01] SPEAKER_01: But then the biology kind of works against it.
[4:04] SPEAKER_01: But this means that biology works as much as it needs to do, but it cannot do it perfectly.
[4:16] SPEAKER_01: So all functions are imperfect.
[4:18] SPEAKER_01: You see?
[4:19] SPEAKER_01: And so therefore,
[4:20] SPEAKER_01: these functions,
[4:21] SPEAKER_01: they create negative consequences, and these negative consequences grow.
[4:25] SPEAKER_01: And that's, to me, aging.
[4:27] SPEAKER_02: Now, in your recent paper in cell metabolism,
[4:32] SPEAKER_02: you had a really great glossary that I'll link with.
[4:35] SPEAKER_02: And it was nice because as you just defined, aging is an irreversible process.
[4:42] SPEAKER_02: But biological age,
[4:44] SPEAKER_02: if you will,
[4:46] SPEAKER_02: as aging associated biological changes, which could be at least
[4:51] SPEAKER_01: modulated, right?
[4:52] SPEAKER_01: Exactly.
[4:54] SPEAKER_01: Of course, because we have biology and biology may be modulated.
[4:58] SPEAKER_01: Moreover,
[4:59] SPEAKER_01: often it's discussed that aging is kind of stochastic
[5:03] SPEAKER_01: and stochastic means like all kinds of damage you cannot do much about.
[5:07] SPEAKER_01: It's not exactly the case because when we have functions,
[5:13] SPEAKER_01: they are designed for a particular goal.
[5:17] SPEAKER_01: For example, a particular enzyme would produce a particular product.
[5:22] SPEAKER_01: You know, reacting with a substrate, producing a product.
[5:25] SPEAKER_01: But then it also would produce negative consequences like a byproduct.
[5:29] SPEAKER_01: But that would not be a random byproduct.
[5:32] SPEAKER_01: It would be a particular byproduct or byproducts gerated by a particular enzyme.
[5:37] SPEAKER_01: So therefore, this damage also will be structured.
[5:40] SPEAKER_01: It's just not any damage.
[5:42] SPEAKER_01: And so that's why we have differences in aging patterns across different types
[5:48] SPEAKER_01: within the organism, difference across different species.
[5:51] SPEAKER_01: So it has this, we call it programmatic nature.
[5:55] SPEAKER_01: Aging, of course, is not programmed.
[5:57] SPEAKER_01: There's no program.
[5:57] SPEAKER_01: There's no goal in aging.
[6:02] SPEAKER_01: There's no benefit in aging.
[6:03] SPEAKER_01: But nevertheless, there's appearance of a program, quasi-program.
[6:08] SPEAKER_01: It's because of that features,
[6:10] SPEAKER_01: because of the Particular forms of negative consequences are generated by
[6:14] SPEAKER_01: a particular function.
[6:16] SPEAKER_02: Oh, that's a really nice way to frame it.
[6:18] SPEAKER_02: Now, a lot of work,
[6:20] SPEAKER_02: as you know,
[6:20] SPEAKER_02: has been in the area of clocks,
[6:23] SPEAKER_02: or as you would call them,
[6:25] SPEAKER_02: or biomarkers of the aging process, getting at biologic aging.
[6:31] SPEAKER_02: And as you say very nicely, this is not a direct, it's a latent variable.
[6:37] SPEAKER_02: There are no Ground Truths here.
[6:39] SPEAKER_02: Um, And this kind of got started with Steve Horvath's work with the methylation clocks.
[6:45] SPEAKER_02: And then recently,
[6:46] SPEAKER_02: you've published multiple different papers called TH for transcriptomic age,
[6:53] SPEAKER_02: where you looked at four different mammals, including humans.
[6:56] SPEAKER_02: You also did proteomic clocks, organ clocks, cell clocks, MH, multimodal age with wearables.
[7:06] SPEAKER_02: And This is extraordinary body of work that you published just in recent months.
[7:11] SPEAKER_02: So maybe you could take us through these different clocks that you've worked on
[7:16] SPEAKER_02: because they don't seem to measure the same thing.
[7:19] SPEAKER_02: And they each are providing some new insights about biologic aging.
[7:26] SPEAKER_01: Yes.
[7:27] SPEAKER_01: So when we discuss clocks,
[7:30] SPEAKER_01: I think it's nice that we started discussing on the nature of aging
[7:34] SPEAKER_01: because it's just naturally linked to Because depending on how you view
[7:40] SPEAKER_01: the aging process,
[7:41] SPEAKER_01: you would design or develop biomarkers or aging clocks for that feature.
[7:47] SPEAKER_01: So you could, for example, train the clock for chronological age.
[7:50] SPEAKER_01: You can train the clock for functional change.
[7:54] SPEAKER_01: You can train the clock for time to death or time to event,
[7:58] SPEAKER_01: like mortality clocks or rate of change.
[8:01] SPEAKER_01: And so all these different features are represented by different aging clocks.
[8:06] SPEAKER_01: And on top of that, there is clocks based on different modalities.
[8:11] SPEAKER_01: And you mentioned some of the clocks.
[8:13] SPEAKER_01: So, for example, the field started with, as you said, Steve Horvath, with epigenetic clocks.
[8:19] SPEAKER_01: And I think it was a major advance in the field, perhaps revolution even.
[8:25] SPEAKER_01: Now it's become...
[8:27] SPEAKER_01: All various other modalities have been added.
[8:30] SPEAKER_01: Like, for example,
[8:31] SPEAKER_01: we published a couple of months ago a large paper on transcriptomic clocks
[8:36] SPEAKER_01: because many people, what they do is just do early sequencing.
[8:40] SPEAKER_01: And it's very convenient to analyze them and predict biological age
[8:44] SPEAKER_01: and also interpret because there are particular genes there.
[8:47] SPEAKER_01: Also, proteomic clocks are very useful because,
[8:50] SPEAKER_01: at least in humans,
[8:51] SPEAKER_01: we could collect plasma And plasma has proteins and proteins,
[8:57] SPEAKER_01: they come from different organs and tissues.
[9:01] SPEAKER_01: They are made there and secreted.
[9:03] SPEAKER_01: And by identifying where they come from and quantifying their changes with age
[9:11] SPEAKER_01: or changes with the chance of dying,
[9:16] SPEAKER_01: We could tell about aging processes across the body.
[9:20] SPEAKER_01: So this is very useful.
[9:21] SPEAKER_01: Now, you also mentioned variables.
[9:25] SPEAKER_01: Wearables also can be used to quantify biological age, and now we combine in a multimodal way.
[9:31] SPEAKER_01: This MH tool is where we combined initially protomics and wearables,
[9:37] SPEAKER_01: but now we combine up to eight different modalities.
[9:40] SPEAKER_01: So because biological age can be quantified through epigenetic changes,
[9:44] SPEAKER_01: through protomic changes,
[9:46] SPEAKER_01: transcriptomic changes, we could do long-credit sequencing, for example, EHRs.
[9:51] SPEAKER_01: We can do photos and videos and voice and then trying to combine them all.
[9:57] SPEAKER_01: Because any single modality is just kind of one slice in the aging space.
[10:02] SPEAKER_01: If there is aging space, any clock cannot give a perfect measure, no matter what you do.
[10:09] SPEAKER_01: So the more modalities, the better.
[10:12] SPEAKER_01: And it needs to be multi-modal, multi-scale, and also...
[10:19] SPEAKER_01: As I said, it depends on what you want to quantify.
[10:22] SPEAKER_01: If you want to quantify chronological age,
[10:24] SPEAKER_01: you would use the kind of initial form of clock trained for chronological age.
[10:31] SPEAKER_01: But in terms of predicting diseases, we find that mortality clocks are much better.
[10:36] SPEAKER_01: So, for example,
[10:38] SPEAKER_01: we develop organ-specific mortality clocks,
[10:40] SPEAKER_01: and they are perfectly, not perfectly, but really nicely predict diseases.
[10:47] SPEAKER_01: For example,
[10:48] SPEAKER_01: we look at people with dementia,
[10:51] SPEAKER_01: for example,
[10:52] SPEAKER_01: and we find that their biological age of their organs are somewhat normal.
[10:58] SPEAKER_01: Okay, it's just their brain is older.
[11:00] SPEAKER_01: Or this can be quantified by clocks.
[11:03] SPEAKER_01: Or, for example, if we use a lung clock, It actually predicts COPD.
[11:10] SPEAKER_01: It's a lung disease.
[11:11] SPEAKER_01: So for example, people with liver cirrhosis, they are older in the liver.
[11:15] SPEAKER_01: So it seems like diseases, they
[11:21] SPEAKER_01: are associated with the increased biological age.
[11:24] SPEAKER_01: So in fact,
[11:25] SPEAKER_01: the way we think how it happens is that when we develop and then kind of age at
[11:30] SPEAKER_01: the same time, aging
[11:35] SPEAKER_01: Her seeds across organs and tissues, but it's not perfectly synchronized.
[11:38] SPEAKER_01: So some organs and tissues,
[11:41] SPEAKER_01: or sometimes it's different subsets or cell types,
[11:43] SPEAKER_01: or maybe even just one cell sometimes becomes old and older.
[11:50] SPEAKER_01: It cannot be older by itself because it's a part of the organism.
[11:54] SPEAKER_01: It interacts with other organs and systems.
[11:57] SPEAKER_01: Other organs and systems are kind of trying to pull it back to their kind of age,
[12:03] SPEAKER_01: try to assimilate age.
[12:04] SPEAKER_01: But it cannot be perfectly done.
[12:06] SPEAKER_01: And therefore, those more aged tissues become more aged but also more altered.
[12:14] SPEAKER_01: And I think this aged altered state We call a disease.
[12:19] SPEAKER_01: Like, in my universe, there are no diseases.
[12:21] SPEAKER_01: All we have is aging.
[12:24] SPEAKER_02: Dan, as you point out,
[12:25] SPEAKER_02: the proteomic clocks that can tag each organ are predicting diseases in
[12:32] SPEAKER_02: that conditions, in those organs.
[12:35] SPEAKER_02: Or, for example, the tissue or the immune system, that is.
[12:40] SPEAKER_02: And, of course, we've seen the same with proteins from cells, these cell clocks.
[12:45] SPEAKER_02: Can the transcriptomic RNA also do that?
[12:51] SPEAKER_02: Does it also provide organ specificity prediction?
[12:58] SPEAKER_01: To some degree.
[12:59] SPEAKER_01: So, of course,
[13:00] SPEAKER_01: when we do a transcriptomic clock in animals,
[13:03] SPEAKER_01: we do in tissues,
[13:04] SPEAKER_01: and of course,
[13:04] SPEAKER_01: they predict the age of the tissues, and we also do multi-tissue clock.
[13:08] SPEAKER_01: We develop multi-tissue and multi-species clock.
[13:14] SPEAKER_01: But in humans, it's, I mean,
[13:16] SPEAKER_01: in a live organism,
[13:17] SPEAKER_01: we only collect blood and therefore it represents aging of the blood cells.
[13:23] SPEAKER_01: So we could still train a model to predict biological age of the organism based
[13:28] SPEAKER_01: on those patterns.
[13:30] SPEAKER_01: It's just they are less interpretable in terms of organ aging than protomic clocks
[13:35] SPEAKER_01: because protomic, again, plasma that comes from different organs and tissues.
[13:39] SPEAKER_01: In the same way,
[13:39] SPEAKER_01: by the way, for metabolites, metabolites also have different sources in the body.
[13:44] SPEAKER_02: Right,
[13:44] SPEAKER_02: and there was a metabolomics paper that was just recently published that I'll link
[13:50] SPEAKER_02: to from China that also,
[13:53] SPEAKER_02: as you said,
[13:55] SPEAKER_02: complemented this work that we're talking about, all these different clocks.
[13:59] SPEAKER_02: I like the point you're making about the multimodal clocks, I mean,
[14:03] SPEAKER_02: all the layers,
[14:05] SPEAKER_02: because if you really want to get the full holistic view at an individual level,
[14:10] SPEAKER_02: that would be the way to go.
[14:11] SPEAKER_02: Now, I think the question that you also raise is using the face or videos.
[14:18] SPEAKER_02: There was a paper that you also collaborated on,
[14:21] SPEAKER_02: published in Lancet Digital Health,
[14:23] SPEAKER_02: that showed how the face,
[14:25] SPEAKER_02: AI of the face could tell us about cancer survival and other many features.
[14:32] SPEAKER_02: Would you add in as one of the layers of facial age and other video information?
[14:39] SPEAKER_01: Yes, we actually actively work on clocks that predict age from photos and videos and voice.
[14:47] SPEAKER_01: So this paper is one of the examples.
[14:50] SPEAKER_01: That particular paper, we have been collaborators, so it's primarily from the lab of Raymark.
[14:57] SPEAKER_01: Also here at Brigham's Hospital and Harvard Medical School.
[15:01] SPEAKER_01: But we also developed our own blocks.
[15:04] SPEAKER_01: It's still under review and some of the paper is still being written.
[15:08] SPEAKER_01: So, but those biomarkers are very good.
[15:13] SPEAKER_01: They define that they predict age people better than perceived age.
[15:18] SPEAKER_01: So we compare,
[15:19] SPEAKER_01: for example,
[15:19] SPEAKER_01: with how we perceive age people Because when we see a person,
[15:25] SPEAKER_01: we can tell roughly what the age is.
[15:28] SPEAKER_01: Those clocks can do better.
[15:29] SPEAKER_01: And we, of course, train them also not just for chronological age, but also for mortality.
[15:35] SPEAKER_01: There is a mortality signal in the photoclocks.
[15:40] SPEAKER_01: And also predict the rate of aging as well.
[15:45] SPEAKER_01: So because people also age with different traits.
[15:48] SPEAKER_01: Some people might be, you know, for example, older but age slowly.
[15:52] SPEAKER_01: Or maybe younger but age rapidly.
[15:54] SPEAKER_01: All kinds of things that are observed this way.
[15:58] SPEAKER_01: But by themselves,
[16:00] SPEAKER_01: they are not super accurate,
[16:01] SPEAKER_01: but they are being added as one of the components of multimodal blocks.
[16:07] SPEAKER_02: Yeah, and your point about voice is especially notable,
[16:11] SPEAKER_02: too, because that changes in the aging process.
[16:15] SPEAKER_02: PocusPW asks,
[16:17] SPEAKER_02: how will we scale proteomic clocks if samples need
[16:20] SPEAKER_02: to be maintained minus 80 degree centigrade prior to analysis?
[16:26] SPEAKER_01: Yeah, but it's okay,
[16:28] SPEAKER_01: because if samples are collected and frozen,
[16:31] SPEAKER_01: once they are frozen,
[16:31] SPEAKER_01: they are stored, they can be indefinitely frozen, stored really at low temperature.
[16:36] SPEAKER_01: And then they can be analyzed.
[16:38] SPEAKER_01: Of course,
[16:40] SPEAKER_01: one kind of my dream,
[16:41] SPEAKER_01: we don't have it yet,
[16:43] SPEAKER_01: to develop approaches for predicting biological age in real time.
[16:47] SPEAKER_01: So in fact, video, like video and voice and wearables is a component of that.
[16:55] SPEAKER_01: Perhaps in the future,
[16:57] SPEAKER_01: Imaging technologies also would be kind of incorporated into real analysis
[17:02] SPEAKER_01: of biological age in real time.
[17:04] SPEAKER_01: We actually have a paper also on imaging clocks where we predict age
[17:08] SPEAKER_01: from whole body MRIs, DEXA scans, retinal imaging.
[17:12] SPEAKER_01: We segment the body into different parts
[17:14] SPEAKER_01: and incorporate also like imaging plus protomics and so on.
[17:20] SPEAKER_01: But again, so not everybody has access to whole body MRI.
[17:24] SPEAKER_01: So it needs to be some kind of new technologies in the future
[17:26] SPEAKER_01: where imaging would be kind of analyzed in real time,
[17:31] SPEAKER_01: combined with photos,
[17:33] SPEAKER_01: videos, wearables, and other technologies to predict biological age in real time.
[17:38] SPEAKER_01: That would be really nice.
[17:40] SPEAKER_02: Now, one of the other things about these clocks,
[17:43] SPEAKER_02: the biomarkers, if you will, is that they let us study perturbations.
[17:48] SPEAKER_02: Like our diet, drinking alcohol, smoking, hormone replacement therapy, all sorts of things.
[17:57] SPEAKER_02: This is an essential part.
[17:59] SPEAKER_02: Can you comment about that?
[18:00] SPEAKER_02: Because you've made and others significant findings about the influence of our lifestyle.
[18:08] SPEAKER_01: Yes.
[18:09] SPEAKER_01: Definitely.
[18:10] SPEAKER_01: So, first, if we just directly apply these clocks, it's not particularly useful.
[18:15] SPEAKER_01: So, for example,
[18:16] SPEAKER_01: we test somebody on metformin and ask a question,
[18:21] SPEAKER_01: like, what's the association with, like, mortality?
[18:23] SPEAKER_01: And we find that People who are on metformin,
[18:27] SPEAKER_01: they are a little bit biologically older than controls.
[18:30] SPEAKER_01: But of course, I mean, they are older because they're diseased and then they take a drug.
[18:34] SPEAKER_01: It doesn't say anything about the effect of the drug.
[18:37] SPEAKER_01: So the way we go around it, there are two ways.
[18:41] SPEAKER_01: One, we do it longitudinally.
[18:44] SPEAKER_01: So meaning that,
[18:45] SPEAKER_01: for example,
[18:45] SPEAKER_01: we have two measurements,
[18:46] SPEAKER_01: like anatomic measurements at point A and point B,
[18:49] SPEAKER_01: and exposure changes that happen in between.
[18:52] SPEAKER_01: So we take a sample like 1.8 and then later a person like change that like
[19:00] SPEAKER_01: a diet maybe or start exercising or some other lifestyle change and then measure
[19:05] SPEAKER_01: the second point.
[19:07] SPEAKER_01: Now it's much more causal because we can then quantify the effect of that particular change.
[19:13] SPEAKER_01: If it's done in a large scale, like for example, you use a lot UK Biobank.
[19:16] SPEAKER_01: We have all of us really, I would say, the best datasets.
[19:21] SPEAKER_01: And I know you contributed a lot to all of us.
[19:23] SPEAKER_01: Thank you very much for designing it.
[19:26] SPEAKER_01: Sure.
[19:27] SPEAKER_01: But we also have like our own MGB Biobank and various other datasets where we go large scale.
[19:33] SPEAKER_01: This way we are able to analyze.
[19:35] SPEAKER_01: That's one way.
[19:36] SPEAKER_01: Another way is we do analysis across tissues.
[19:40] SPEAKER_01: So we cannot really compare people in a causal way because people are influenced by
[19:46] SPEAKER_01: so many behaviors that they do, whatever they change.
[19:50] SPEAKER_01: But like across tissues,
[19:52] SPEAKER_01: we can do,
[19:53] SPEAKER_01: so we can analyze,
[19:55] SPEAKER_01: because we have organ-specific locks,
[19:56] SPEAKER_01: and when we test the effect of exposures,
[19:59] SPEAKER_01: different exposures,
[20:01] SPEAKER_01: if you observe that something changes a particular organ or tissue in
[20:05] SPEAKER_01: a specific way, it's much more causal.
[20:08] SPEAKER_01: You see?
[20:09] SPEAKER_01: So this way we are able to analyze the...
[20:15] SPEAKER_01: I'm thinking in the future it's going to be personalized, but not yet.
[20:19] SPEAKER_01: But at least it would affect the aging of particular organs and tissues.
[20:25] SPEAKER_01: And I think that's actually the future, one of the futures, at least.
[20:29] SPEAKER_02: Well,
[20:29] SPEAKER_02: and as you bring up the big question about the causality and that your approach is
[20:34] SPEAKER_02: to get to that, because that's always... Is this just a...
[20:38] SPEAKER_02: A bystander effect or is this actually driving the outcome of the process?
[20:44] SPEAKER_02: So essential stuff.
[20:46] SPEAKER_02: Jody and Pinckney says a question.
[20:49] SPEAKER_02: Is there any commonality in the proteins that accelerate organ-specific aging?
[20:57] SPEAKER_01: There are some proteins that are commonly detected across different aging tissues.
[21:05] SPEAKER_01: That's true.
[21:06] SPEAKER_01: So in terms of causing, like again, causal inferences are very difficult to make.
[21:12] SPEAKER_01: So we typically just use them as biomarkers.
[21:16] SPEAKER_01: Yeah, biomarkers means like the reporters of the effect rather than kind of in a causal way.
[21:23] SPEAKER_02: Right.
[21:24] SPEAKER_02: Graham Creasy asks, can you send out a link to the recording after the Zoom?
[21:29] SPEAKER_02: Absolutely.
[21:30] SPEAKER_02: And with that link will be all the papers.
[21:33] SPEAKER_02: And there are many papers from Vadim to cite here.
[21:37] SPEAKER_02: So you'll have all the links that you can get into if you're interested further.
[21:41] SPEAKER_02: We're going to get into Bob Brown's question, how can aging be slowed or reversed in a minute?
[21:45] SPEAKER_02: But before we get to that,
[21:47] SPEAKER_02: one of the most interesting papers that you published in This is a preprint,
[21:52] SPEAKER_02: but there's already been a nature news and views about it.
[21:55] SPEAKER_02: That's how important it is.
[21:57] SPEAKER_02: This was about transplanting hearts from both in mice and in people,
[22:05] SPEAKER_02: from young to old,
[22:07] SPEAKER_02: and old to young,
[22:10] SPEAKER_02: so that when the young person or mouse got,
[22:16] SPEAKER_02: and also some people,
[22:18] SPEAKER_02: got the old heart,
[22:21] SPEAKER_02: That they assimilated,
[22:23] SPEAKER_02: that is,
[22:24] SPEAKER_02: it changed, if you will, the markers of the old heart into a young heart, okay?
[22:32] SPEAKER_02: It didn't work the other way around.
[22:34] SPEAKER_02: That is,
[22:35] SPEAKER_02: if you put a young heart into an old recipient,
[22:39] SPEAKER_02: mouse or person,
[22:42] SPEAKER_02: but mostly mice and that work,
[22:44] SPEAKER_02: it didn't make the old young, but for the old Donor into a young recipient.
[22:53] SPEAKER_02: There was an extraordinary effect.
[22:56] SPEAKER_02: Can you help explain this and what you think about it?
[23:00] SPEAKER_01: Okay.
[23:01] SPEAKER_01: So you're right.
[23:02] SPEAKER_01: So we have this preprint on...
[23:06] SPEAKER_01: Heart transplantation,
[23:08] SPEAKER_01: but we also work on other tissues, like transplantation of other tissues currently.
[23:13] SPEAKER_01: So maybe I will explain like generalizing across tissues.
[23:17] SPEAKER_01: That would be better.
[23:19] SPEAKER_01: So first,
[23:20] SPEAKER_01: when we place an old organ in the young body,
[23:24] SPEAKER_01: We find that the old organ is generally rejuvenated.
[23:28] SPEAKER_01: It moves in the direction of the younger organism.
[23:31] SPEAKER_01: It does not become truly young because not all the age-related changes can be reversed.
[23:39] SPEAKER_01: But it decreases biological age.
[23:41] SPEAKER_01: So the young body is able to make the old organ a little bit younger.
[23:46] SPEAKER_01: Now, when we place a young organ in the old body, it actually ages.
[23:53] SPEAKER_01: So in that niche where it's located, it ages that organ.
[23:59] SPEAKER_01: So it becomes a little bit older.
[24:00] SPEAKER_01: So in a way,
[24:03] SPEAKER_01: the transplanted organ is subject to age assimilation with the age of the body,
[24:08] SPEAKER_01: although it never reaches the age of the body.
[24:11] SPEAKER_02: Right.
[24:12] SPEAKER_01: And we utilize it in many ways.
[24:13] SPEAKER_01: I think one Big application,
[24:16] SPEAKER_01: and that's why that particular preprint,
[24:18] SPEAKER_01: I think,
[24:18] SPEAKER_01: become very interested is because old hearts,
[24:21] SPEAKER_01: they are discarded primarily,
[24:24] SPEAKER_01: like hearts like about age 65 are not really transplanted.
[24:27] SPEAKER_01: But we find that if they are transplanted in a young body,
[24:30] SPEAKER_01: they may be rejuvenated and partially rejuvenated and may last longer.
[24:35] SPEAKER_01: I think if what we find would be validated,
[24:40] SPEAKER_01: confirmed,
[24:41] SPEAKER_01: Perhaps some lives may be spared because the old hearts could be utilized,
[24:49] SPEAKER_01: those which are currently discarded.
[24:50] SPEAKER_01: I think before doing that, I think we need to understand better the process.
[24:55] SPEAKER_01: Right.
[24:56] SPEAKER_02: Well, I mean,
[24:56] SPEAKER_02: this is such exciting work because the term rejuvenation has been thrown around
[25:02] SPEAKER_02: a lot in the field of aging, science of aging.
[25:05] SPEAKER_02: But here you actually did show rejuvenation,
[25:09] SPEAKER_02: as you said,
[25:09] SPEAKER_02: not full,
[25:11] SPEAKER_02: Not to bring that heart transplanted into a young recipient,
[25:17] SPEAKER_02: an old heart and a young recipient to be the same youth,
[25:21] SPEAKER_02: but to move in the right direction.
[25:23] SPEAKER_02: So this work seemed a bit different.
[25:27] SPEAKER_02: You've also done work with parabiosis with an old and a young mouse connected.
[25:32] SPEAKER_02: And when you did that, the effects were a bit different, would you say?
[25:39] SPEAKER_01: It's just the design of the experiment was different.
[25:42] SPEAKER_01: In fact, this, to me,
[25:43] SPEAKER_01: it's the same project,
[25:44] SPEAKER_01: actually,
[25:44] SPEAKER_01: because we started with heterochronic parabiosis, where we connect the two mice.
[25:49] SPEAKER_01: They have joint circulation, they exchange all of the factors.
[25:53] SPEAKER_01: And then we observed that the old mice are rejuvenated.
[25:57] SPEAKER_01: After we separate them,
[25:59] SPEAKER_01: we keep them for three months, joint,
[26:01] SPEAKER_01: we separate them,
[26:02] SPEAKER_01: and two months later, they are still rejuvenated, and eventually they live longer.
[26:07] SPEAKER_01: So old mice, sorry,
[26:10] SPEAKER_01: young mice after disconnected from old mice,
[26:13] SPEAKER_01: at first they age rapidly, but then we disconnect, they recover.
[26:16] SPEAKER_01: Mostly they recover.
[26:17] SPEAKER_01: That's also very interesting.
[26:19] SPEAKER_01: And then,
[26:20] SPEAKER_01: so our organ transplantation work was really to explain the effect of parabiosis
[26:25] SPEAKER_01: because parabiosis is,
[26:29] SPEAKER_01: for us, it's like a gold standard, like a rejuvenation standard.
[26:33] SPEAKER_01: But it's very hard to recapitulate it.
[26:35] SPEAKER_01: So can we recapitulate by transplanting organs?
[26:37] SPEAKER_01: Can we rejuvenate the body itself by transplanting certain organs?
[26:42] SPEAKER_01: And so far, we find it's very hard.
[26:45] SPEAKER_01: So when we transplant like a heart,
[26:47] SPEAKER_01: like young heart in the old body, the body itself is not really rejuvenated.
[26:52] SPEAKER_01: So,
[26:54] SPEAKER_01: but we're still trying with other organs and there is still some leads
[26:59] SPEAKER_01: that we explore and I think there is potential.
[27:03] SPEAKER_02: Yeah, so that was why I was referring to maybe there's a difference here,
[27:07] SPEAKER_02: because when you had the young and the old mice connected,
[27:12] SPEAKER_02: it didn't exactly make the old mouse young,
[27:16] SPEAKER_02: or it had an effect,
[27:18] SPEAKER_02: as you said,
[27:18] SPEAKER_02: which was unlike, I'm sorry, it did, unlike in the heart transplant, right?
[27:24] SPEAKER_01: That's right.
[27:25] SPEAKER_01: So actually the whole monos got rejuvenated.
[27:27] SPEAKER_01: But of course, in the case of parabiosis, we connect mice for three months.
[27:31] SPEAKER_01: It's like 10% of their life.
[27:34] SPEAKER_01: So it's a very extensive treatment,
[27:37] SPEAKER_01: which would be hard to recapitulate like by organ transplantation or injection
[27:42] SPEAKER_01: of young blood.
[27:43] SPEAKER_01: No, it's not.
[27:44] SPEAKER_01: It's a very severe kind of rejuvenation process.
[27:46] SPEAKER_01: And even then, The regination effect is not very strong.
[27:50] SPEAKER_01: It's detectable, it's significant.
[27:52] SPEAKER_01: And mice live a little bit longer, but it's not like, not twice longer, maybe 10% longer.
[27:56] SPEAKER_02: Well, that's an important perspective, right?
[27:58] SPEAKER_02: And also, over these years, this parabiosis has been, you know, an area of fascination.
[28:04] SPEAKER_02: And unfortunately,
[28:05] SPEAKER_02: it's even led to clinics that do young blood, plasmapheresis, and all this stuff.
[28:10] SPEAKER_02: But we've never understood the factor or factors that account for this.
[28:16] SPEAKER_02: You've done some work to try to isolate that or understand what is accounting for
[28:21] SPEAKER_02: the success of chirobiosis?
[28:25] SPEAKER_01: Well, we've tried for organ transplantation.
[28:27] SPEAKER_01: That's really our attempt.
[28:29] SPEAKER_00: Yeah.
[28:30] SPEAKER_01: So we've done initially,
[28:31] SPEAKER_01: for example,
[28:32] SPEAKER_01: like our experiment is still unpublished where we transplanted bone marrow first.
[28:37] SPEAKER_01: Like bone marrow was like used rice to cells in the blood.
[28:41] SPEAKER_01: And we found that...
[28:43] SPEAKER_01: Young bone marrow in the old body does not rejuvenate the body.
[28:48] SPEAKER_01: So it's just insufficient.
[28:49] SPEAKER_01: So it doesn't explain the effect of parabiosis through cells in the blood.
[28:53] SPEAKER_01: But of course,
[28:54] SPEAKER_01: when mice are connected,
[28:56] SPEAKER_01: old mice not just only get young blood,
[28:58] SPEAKER_01: but they have access to younger organs of the young mums.
[29:01] SPEAKER_01: Like, for example, the young liver or young kidney through circulation.
[29:05] SPEAKER_01: So the effect is much more profound.
[29:07] SPEAKER_01: It's very hard to recapitulate it by simple interventions.
[29:12] SPEAKER_01: As I said,
[29:12] SPEAKER_01: like plasmapheresis,
[29:15] SPEAKER_01: I don't think,
[29:17] SPEAKER_01: I'm skeptical a little bit of plasmithrasis, or injection of young blood.
[29:22] SPEAKER_01: The blood changes so quickly,
[29:23] SPEAKER_01: and I don't think it would provide a strong effect like parabois does.
[29:28] SPEAKER_02: Right, yeah, no,
[29:29] SPEAKER_02: and it hasn't held back people like Brian Johnson to get his son's blood transfer,
[29:34] SPEAKER_02: but yeah.
[29:36] SPEAKER_02: Just going back a bit,
[29:37] SPEAKER_02: Nandini Sadegopan asked,
[29:41] SPEAKER_02: Given that different cell types age at different rates,
[29:44] SPEAKER_02: do you think cell type specific epigenetic clocks are essential
[29:50] SPEAKER_02: for understanding biological aging?
[29:53] SPEAKER_02: Or do you think pan tissue clocks can adequately capture these changes?
[30:01] SPEAKER_01: I think both are important.
[30:04] SPEAKER_01: And that's why the field has developed all kinds of clocks and both multi-tissue,
[30:10] SPEAKER_01: multi-species clock and also cell-type specific clock.
[30:12] SPEAKER_01: Typically cell-type specific clocks are more accurate because they're trained on
[30:17] SPEAKER_01: that particular organ and tissue.
[30:19] SPEAKER_01: But maybe I will extend this point a little bit in the sense that
[30:26] SPEAKER_01: When we age, we age unequally in different organs.
[30:29] SPEAKER_01: And therefore, we need interventions that work against our older organs.
[30:35] SPEAKER_01: Organs and systems, not the whole thing.
[30:38] SPEAKER_01: So perhaps it's an opinion, but I think there would be no universal interventions against aging.
[30:45] SPEAKER_01: Interventions would be targeted to particular ageotypes.
[30:50] SPEAKER_01: Like, you know, some of us, they are maybe older in the kidney.
[30:53] SPEAKER_01: It just happened because of genetics or,
[30:55] SPEAKER_01: you know, by chance, simply other people may be older in the heart.
[30:58] SPEAKER_01: Some people are older in multiple organs.
[31:01] SPEAKER_01: And And by identifying these patterns,
[31:08] SPEAKER_01: More age patterns before these people develop even disease, we could better target.
[31:14] SPEAKER_01: So some people might, for example, it's one funny association we find.
[31:17] SPEAKER_01: For example, we look at age of the artery.
[31:21] SPEAKER_01: You know, we quantify specific age of the artery.
[31:24] SPEAKER_01: And then we look for associations.
[31:26] SPEAKER_01: And we find that alcohol consumption rejuvenates the artery.
[31:30] SPEAKER_01: So in general, alcohol consumption is very bad.
[31:32] SPEAKER_01: It's like ages many organs.
[31:34] SPEAKER_01: But in the artery, it's good.
[31:35] SPEAKER_01: So somebody who is older in the artery, Perhaps they may have a wine a couple of times a week.
[31:43] SPEAKER_01: There would be no problem.
[31:43] SPEAKER_01: They might benefit from it,
[31:45] SPEAKER_01: even though many other people would not benefit from it in the same way.
[31:49] SPEAKER_01: So we need to know who can benefit from what rather
[31:52] SPEAKER_01: than giving universal interventions to everybody.
[31:57] SPEAKER_01: I think universal interventions is much less likely to work.
[32:02] SPEAKER_02: I couldn't agree with you more about that.
[32:04] SPEAKER_02: And what you're bringing up is fascinating because...
[32:07] SPEAKER_02: There are organ-specific effects like you just outlined
[32:10] SPEAKER_02: that could benefit people once we get this further along.
[32:15] SPEAKER_02: And along this,
[32:17] SPEAKER_02: in the clock review that molecular clocks that Tony Wiscari and I did
[32:22] SPEAKER_02: in Nature Medicine,
[32:24] SPEAKER_02: we noted that the immune system,
[32:27] SPEAKER_02: both the system and the immune cells,
[32:29] SPEAKER_02: seem to be very much correlated,
[32:32] SPEAKER_02: at least,
[32:33] SPEAKER_02: with long-term survival in the UK biobank,
[32:37] SPEAKER_02: Do you find the immune system,
[32:39] SPEAKER_02: immune cells particularly, note,
[32:42] SPEAKER_02: or do they not really separate from other systems or organs as far
[32:48] SPEAKER_02: as their importance?
[32:51] SPEAKER_01: No, we also see consistently the role of the immune system, definitely.
[32:55] SPEAKER_01: I would say immune system and the brain.
[32:58] SPEAKER_01: Right.
[32:58] SPEAKER_01: So that...
[33:00] SPEAKER_01: And also reproductive as well.
[33:02] SPEAKER_01: So those three items that consistently appear in the analysis.
[33:07] SPEAKER_02: I'm glad you found that as well because I think that's
[33:10] SPEAKER_02: an important independent replication really.
[33:13] SPEAKER_02: Now, AI,
[33:15] SPEAKER_02: everyone's talking about AI and obviously a lot of pushback about AI,
[33:18] SPEAKER_02: but how do you see AI helping us in the field of aging, science of aging?
[33:27] SPEAKER_01: Yeah, it's a big topic.
[33:30] SPEAKER_01: I would say it's absolutely critical,
[33:32] SPEAKER_01: already critical, and will be even more critical, of course.
[33:36] SPEAKER_01: The kind of frontier AI companies,
[33:40] SPEAKER_01: they conquered language, conquered mathematics, conquered many other areas.
[33:48] SPEAKER_01: I think they're now an attempt to conquer health.
[33:51] SPEAKER_01: There will be, I expect, a lot of progress in this area.
[33:56] SPEAKER_01: But then I think conquering biology is going to be more difficult.
[34:01] SPEAKER_01: So ultimately, if you want to solve aging and longevity, we need to understand biology.
[34:09] SPEAKER_01: So for that...
[34:11] SPEAKER_01: We need, I think, new type of data.
[34:13] SPEAKER_01: So we are a little bit low on the data.
[34:16] SPEAKER_01: Data are very biased based on our biases in the way we choose targets to study
[34:22] SPEAKER_01: in certain enzymes and proteins.
[34:24] SPEAKER_01: Some proteins are overstudied and other proteins just never been studied at all.
[34:28] SPEAKER_00: Right, right.
[34:31] SPEAKER_01: So we need new data, which is like multiscale data.
[34:36] SPEAKER_01: Potentially,
[34:37] SPEAKER_01: preferentially,
[34:38] SPEAKER_01: I would say, Kind of in life organisms because most of the biology has been done.
[34:44] SPEAKER_01: It's done with dead organisms.
[34:46] SPEAKER_01: We collect samples or kill animals.
[34:49] SPEAKER_01: So we do not really learn biology this way.
[34:52] SPEAKER_01: And we learn some of biology, but not exactly the entirety of biology.
[34:58] SPEAKER_01: But we need new data.
[35:00] SPEAKER_01: And I think with new data,
[35:02] SPEAKER_01: then we would be able to build I don't know,
[35:04] SPEAKER_01: foundation models, work models that would understand biology.
[35:08] SPEAKER_01: And from that,
[35:09] SPEAKER_01: from those models,
[35:10] SPEAKER_01: then we would naturally see as a byproduct,
[35:13] SPEAKER_01: like biomarkers of aging and interventions that we could study.
[35:18] SPEAKER_01: Because interventions,
[35:21] SPEAKER_01: most of the biomedical science Operates at a state of identifying target,
[35:27] SPEAKER_01: like you need a particular target, like a protein, a gene.
[35:31] SPEAKER_01: And then we develop a molecule against it, like inhibitor or activator.
[35:36] SPEAKER_01: But biology is so complex and the humans are so stable kind of organisms,
[35:43] SPEAKER_01: very hard to interfere with that.
[35:45] SPEAKER_01: In terms of longevity.
[35:47] SPEAKER_01: So I think in the future,
[35:48] SPEAKER_01: it's going to be first we need AI to learn biology,
[35:52] SPEAKER_01: learn humans specifically, and then identify complex interventions.
[35:59] SPEAKER_01: It's not going to be like one drug.
[36:01] SPEAKER_01: It could be a combination of things that maybe could shift biology in the direction
[36:08] SPEAKER_01: of longevity.
[36:09] SPEAKER_01: Why I'm kind of excited about it is because of what we observe in nature.
[36:17] SPEAKER_01: So in nature, nature is able to change lifespan very dramatically.
[36:21] SPEAKER_01: So when you look across species,
[36:23] SPEAKER_01: like across mammals, there's over a hundredfold difference in lifespan.
[36:27] SPEAKER_01: Between like shortest-lived are shrews,
[36:29] SPEAKER_01: they live maybe one or two years,
[36:31] SPEAKER_01: and some whales, they can live more than 200 years.
[36:35] SPEAKER_01: And even within lineages,
[36:37] SPEAKER_01: like within primates or within rodents,
[36:39] SPEAKER_01: like look within rodents,
[36:41] SPEAKER_01: like named more red, like the size of a mouse lives more than 40 years.
[36:45] SPEAKER_01: So it's like tenfold difference in lifespan.
[36:47] SPEAKER_01: So it's nature can do it easily.
[36:50] SPEAKER_01: How does it do it?
[36:50] SPEAKER_01: We have no idea.
[36:52] SPEAKER_01: And that's one of the questions that we try to understand.
[36:55] SPEAKER_01: The same way across cell types.
[36:57] SPEAKER_01: Some cells in our body, they live only a couple of days, cells in the blood.
[37:01] SPEAKER_01: Some cells and neurons are cardiomyocytes.
[37:03] SPEAKER_01: They live whole life.
[37:05] SPEAKER_01: They have the same origin, the same genome.
[37:08] SPEAKER_01: They have like a trillion fold difference in lifespan.
[37:11] SPEAKER_02: Yeah, and they have a different transcriptome.
[37:15] SPEAKER_02: They have a different epigenome.
[37:16] SPEAKER_02: That's right.
[37:17] SPEAKER_02: And it's just so hard to deal with the complexity.
[37:20] SPEAKER_02: But what I like about what you answered there, our biggest bottleneck are the data sets.
[37:26] SPEAKER_02: We have the AI and the computing capabilities,
[37:28] SPEAKER_02: but we just need much better data sets to do some of this unraveling.
[37:35] SPEAKER_02: It is ultra complex for sure.
[37:38] SPEAKER_02: Now, the next thing I want to tap into before we get towards close is about our drugs.
[37:48] SPEAKER_02: There was a recent paper that you collaborated on with the folks at Insilico
[37:53] SPEAKER_02: where they looked at six proteomic clocks for their Anti-fibrotic drug
[37:58] SPEAKER_02: and they all look favorable.
[38:00] SPEAKER_02: And so the question is,
[38:01] SPEAKER_02: is that drug or GLP-1 drugs,
[38:05] SPEAKER_02: are these drugs going to have favorable effects in modulating
[38:11] SPEAKER_02: the age-associated biologic changes?
[38:16] SPEAKER_01: Yes.
[38:16] SPEAKER_01: I mean, this particular paper,
[38:18] SPEAKER_01: of course, we had a hypothesis that CLOX may be used in clinical trials.
[38:24] SPEAKER_01: We utilized CLOX Clinical trials have been completed already and we applied different clocks.
[38:33] SPEAKER_01: And we found that across different clocks, biological age has been reduced.
[38:39] SPEAKER_01: So, of course, we have to keep in mind that these are the diseased people.
[38:44] SPEAKER_01: So people are diseased and they take drug against that disease.
[38:48] SPEAKER_01: So, of course,
[38:49] SPEAKER_01: they mitigate the consequence of that disease and they feel better and they Probably
[38:55] SPEAKER_01: a little bit biologically younger just because of this damaging factor,
[39:00] SPEAKER_01: disease factor.
[39:01] SPEAKER_01: Of course,
[39:01] SPEAKER_01: we have to test whether the factor is persistent
[39:07] SPEAKER_01: or not and how it works at
[39:09] SPEAKER_01: the level of organ clocks.
[39:10] SPEAKER_01: I think this paper is important that you mentioned because it was the first paper.
[39:15] SPEAKER_01: But now we actually, for example, another study is still under review, but we work with NOVA.
[39:22] SPEAKER_01: So NOVA was interested in applying our plugs to test the effect of GLP-1s
[39:30] SPEAKER_01: across clinical trials that they run.
[39:32] SPEAKER_01: And just earlier this month,
[39:35] SPEAKER_01: we had a conference called ARDD,
[39:37] SPEAKER_01: Aging Infection Tract Discovery, and they presented the data.
[39:41] SPEAKER_01: It's our collaborative study.
[39:43] SPEAKER_01: And the findings are that GLP-1s,
[39:47] SPEAKER_01: they actually reduce biological age,
[39:51] SPEAKER_01: but interestingly, in the organ-specific manner.
[39:53] SPEAKER_01: So our clocks that,
[39:55] SPEAKER_01: we have a heart clock, for example, we found that the effect goes through the heart.
[39:59] SPEAKER_01: So heart is the first to rejuvenate.
[40:03] SPEAKER_01: Interestingly,
[40:04] SPEAKER_01: the adipose tissue a little bit aged initially,
[40:07] SPEAKER_01: which also is kind of reasonable because people lose weight.
[40:10] SPEAKER_01: So during this period,
[40:11] SPEAKER_01: maybe the adipose tissue becomes a little bit older,
[40:14] SPEAKER_01: but then they recover in terms of the age of the adipose.
[40:18] SPEAKER_01: That illustrates the beauty of organ-specific clocks, by the way.
[40:22] SPEAKER_02: Yeah, I was really struck by that.
[40:24] SPEAKER_02: It was a preprint, as you mentioned, and then presented at the conference.
[40:28] SPEAKER_02: Five randomized placebo-controlled trials looking at organ clocks.
[40:32] SPEAKER_02: And of course, as you said, the heart clock had the most pronounced effect.
[40:37] SPEAKER_02: The artery clock also showed benefit.
[40:39] SPEAKER_02: The kidney clock, these were the ones that you saw the trend towards a youthful effect.
[40:46] SPEAKER_02: But these,
[40:48] SPEAKER_02: I don't know about the drugs we have today,
[40:50] SPEAKER_02: whether it's an antifibrotic or the GOP-1s that were tested,
[40:54] SPEAKER_02: of course, in people with obesity.
[40:56] SPEAKER_02: At least in those five trials.
[40:59] SPEAKER_02: Are they going to be potent enough to be considered anti-aging drugs in the future?
[41:04] SPEAKER_02: Or are we going to see even much more potent drugs?
[41:10] SPEAKER_01: Actually, I'm not sure.
[41:12] SPEAKER_01: I mean, there would be anti-aging drugs for diseased people, I think.
[41:19] SPEAKER_01: Like drug,
[41:20] SPEAKER_01: in general,
[41:20] SPEAKER_01: drug that is efficient against a particular disease or unhealthy condition is going
[41:25] SPEAKER_01: to be Anti-aging for this population.
[41:28] SPEAKER_01: But would it work across the healthy population?
[41:32] SPEAKER_01: I'm not sure.
[41:33] SPEAKER_01: So now, of course,
[41:34] SPEAKER_01: GLP-1s are so popular,
[41:36] SPEAKER_01: all younger people,
[41:37] SPEAKER_01: they take it, like,
[41:39] SPEAKER_01: even though micro doses,
[41:40] SPEAKER_01: but actually we don't know how it would work in healthy people.
[41:45] SPEAKER_01: There are no data yet.
[41:48] SPEAKER_01: So in the future,
[41:49] SPEAKER_01: I think it's important to To test this,
[41:52] SPEAKER_01: whether it's truly underaging or not in healthy individuals.
[41:57] SPEAKER_01: But regardless of that,
[41:58] SPEAKER_01: I think that changing biological age,
[42:02] SPEAKER_01: human biological age is not easy in healthy individuals,
[42:07] SPEAKER_01: like extending it even further.
[42:10] SPEAKER_01: So I kind of think that there are,
[42:14] SPEAKER_01: I classify in general drugs like in three types in terms of their potential
[42:18] SPEAKER_01: to extend lifespan.
[42:19] SPEAKER_01: One type is drugs that kind of slightly change metabolic state of the organism.
[42:27] SPEAKER_01: They are like, you know, metformin, rapamycin, GLP-1s, things like that.
[42:31] SPEAKER_01: So they have a potential to slightly change lifespan.
[42:36] SPEAKER_01: But in the best case scenario, one to three years maybe.
[42:41] SPEAKER_01: I think the organ specific where we identify individuals who can benefit
[42:45] SPEAKER_01: from particular interventions, particular drugs, there is much more potential.
[42:50] SPEAKER_01: I would maybe put it like five to 10 years potential of lifespan extension in those people.
[42:57] SPEAKER_01: But what we really want is radical change in lifespan.
[43:01] SPEAKER_01: Radical means more than 10 years.
[43:04] SPEAKER_01: And for that, we don't have interventions yet.
[43:08] SPEAKER_01: Our approach is like probiosis, rejuvenation.
[43:12] SPEAKER_01: This is an attempt to identify and like world models,
[43:17] SPEAKER_01: like biological world models is really an attempt to identify complex interventions.
[43:21] SPEAKER_01: Learn from nature how we can do it.
[43:24] SPEAKER_01: Or maybe combine some engineering approaches.
[43:27] SPEAKER_01: I'm enthusiastic that it may be possible in the future, but currently we don't have.
[43:32] SPEAKER_01: None of the approaches I know that has been studied I think has a potential
[43:39] SPEAKER_01: to begin new approaches.
[43:41] SPEAKER_02: The one that gets the most attention,
[43:43] SPEAKER_02: of course,
[43:43] SPEAKER_02: is the partial cellular reprogramming with Yamanaka factors,
[43:49] SPEAKER_02: whether it's all four These transcriptome,
[43:52] SPEAKER_02: transcript factors or transcription factors or three of them as being used by life,
[43:58] SPEAKER_02: biosciences.
[43:59] SPEAKER_02: Do you think that one has real potential?
[44:03] SPEAKER_01: There is a potential.
[44:05] SPEAKER_01: I'm a little bit worried that the paper,
[44:08] SPEAKER_01: like this kind of paper is a big milestone in the field of campo,
[44:12] SPEAKER_01: like from Juan Carlos Bremontes' lab, published 10 years ago.
[44:18] SPEAKER_01: And as of today, We don't really have, I mean, really advances, major advances in that area.
[44:26] SPEAKER_01: So it's still a promising approach, I think.
[44:31] SPEAKER_01: I'm a little bit afraid that much of the focus is now
[44:35] SPEAKER_01: on rejuvenating particular organs, like doing an organ-specific manner.
[44:40] SPEAKER_01: But our transplantation approach is exactly to address the point.
[44:44] SPEAKER_01: Let's say you reprogram through OSK, OSKM.
[44:48] SPEAKER_01: Like an organ.
[44:49] SPEAKER_01: It's younger.
[44:50] SPEAKER_01: Let's say it's younger.
[44:51] SPEAKER_01: The question is, what happens next?
[44:52] SPEAKER_01: So when we transplant our young organ in the old body,
[44:56] SPEAKER_01: we already kind of achieved that because it's young.
[44:58] SPEAKER_01: So the question, what happens next?
[44:59] SPEAKER_01: And we find that it's very hard to rejuvenate the biological age of
[45:03] SPEAKER_01: the whole organs, even if the organ is young.
[45:07] SPEAKER_01: So that's why the partial reprogramming in an organ-specific manner,
[45:11] SPEAKER_01: it would potentially help People who are older in that particular organ or system,
[45:17] SPEAKER_01: but would it actually extend lifespan of the whole organism is still unclear.
[45:22] SPEAKER_02: Yeah, it seems like that's going to be a dicey question and with some risks involved
[45:27] SPEAKER_02: that are not trivial.
[45:29] SPEAKER_02: The way I look at it, Vadim,
[45:31] SPEAKER_02: is that if you have evidence of clocks that are accelerated for aging
[45:38] SPEAKER_02: and you have some particular organ that not only could be used as you just outlined,
[45:43] SPEAKER_02: but also As a signal for prevention.
[45:49] SPEAKER_02: That is, you see the heart or an artery clock is accelerated.
[45:54] SPEAKER_02: You start to hone in on that to prevent the diseases that are likely to be as a result.
[46:00] SPEAKER_02: So there's another side of all this,
[46:02] SPEAKER_02: which is also the idea of trying to reverse aging specifically,
[46:07] SPEAKER_02: or even as a way to understand a person's system that's Out of kilter,
[46:13] SPEAKER_02: if you will, that deserves attention.
[46:16] SPEAKER_02: Now, the last thing I want you to do here today as we wrap up is your general view.
[46:22] SPEAKER_02: You have been leading this field in so many ways,
[46:26] SPEAKER_02: and you've made many different contributions,
[46:29] SPEAKER_02: and many, as you outlined, are in the hopper to get published, too.
[46:33] SPEAKER_02: You think ultimately we're going to have a substantial way to reverse
[46:38] SPEAKER_02: the biological aging process?
[46:40] SPEAKER_02: Not aging per se, but the associated changes.
[46:44] SPEAKER_02: Do you think this is likely in the years ahead?
[46:48] SPEAKER_01: First, I would say, well,
[46:50] SPEAKER_01: thank you for your kind words about our contributions,
[46:52] SPEAKER_01: but of course, I mean, science is a collective effort.
[46:57] SPEAKER_01: We benefit from Studies done by other people and maybe they benefit from our studies
[47:04] SPEAKER_01: and it's a collective effort.
[47:07] SPEAKER_01: And there are many great labs in the field from which we learn and which we respect a lot.
[47:12] SPEAKER_01: And many of them you interviewed, for example.
[47:16] SPEAKER_01: So I would not overestimate our contribution in that sense.
[47:21] SPEAKER_01: But then in terms of biological age,
[47:24] SPEAKER_01: reversibility, I think biological age is partially reversible.
[47:29] SPEAKER_01: So aging itself is irreversible because there are so many specific changes that happen.
[47:36] SPEAKER_01: It's impossible to go back.
[47:39] SPEAKER_01: But we could still target a biological state of the organism to make it younger.
[47:45] SPEAKER_01: That younger would not be the same younger where the organism was before,
[47:49] SPEAKER_01: but it may be younger and the lifespan may be extended.
[47:52] SPEAKER_01: There are Like some of the approaches we discussed today,
[47:57] SPEAKER_01: like parabiosis,
[47:58] SPEAKER_01: for example,
[47:58] SPEAKER_01: but there are other approaches that,
[48:00] SPEAKER_01: for example, we discovered embryonic rejuvenation.
[48:02] SPEAKER_01: We are very excited about it because it's actually the only natural rejuvenation process.
[48:07] SPEAKER_00: Mm-hmm.
[48:08] SPEAKER_01: And there are other ways,
[48:10] SPEAKER_01: like we also discussed in the case of diseases,
[48:12] SPEAKER_01: disease conditions,
[48:13] SPEAKER_01: certain treatments that work against that disease also have the opportunity
[48:18] SPEAKER_01: to partially rejuvenate a particular organ of the system.
[48:21] SPEAKER_01: So there are ways to rejuvenate, and therefore, I think, biological age is partially reversible.
[48:29] SPEAKER_02: Right.
[48:30] SPEAKER_02: Well, I like your optimism,
[48:33] SPEAKER_02: especially as an older chap,
[48:35] SPEAKER_02: I'd be interested in To see these be successful,
[48:38] SPEAKER_02: but I think it could have, of course, a dramatic impact if all this takes hold.
[48:43] SPEAKER_02: And certainly we know it could at a specific level,
[48:46] SPEAKER_02: not just at a whole organism, whole body level.
[48:49] SPEAKER_02: So look, we got a little bit of a late start, but we got past the technical problem.
[48:57] SPEAKER_02: Vadim, it's been a pleasure to have this conversation with you.
[49:00] SPEAKER_02: I do regard your efforts and leadership as especially important in this very critical field.
[49:09] SPEAKER_02: And you mentioned some of the other folks who are active,
[49:11] SPEAKER_02: but really it's great to have this chance to meet up with you,
[49:15] SPEAKER_02: discuss a lot of your recent work,
[49:18] SPEAKER_02: forthcoming work,
[49:20] SPEAKER_02: and we'll definitely want to connect again in the future because you're moving at
[49:23] SPEAKER_02: a very accelerated pace.
[49:27] SPEAKER_01: Thank you very much, Eric.
[49:28] SPEAKER_01: I really appreciate the invitations to discuss it in your podcast.
[49:34] SPEAKER_01: So thank you very much.
[49:35] SPEAKER_01: It's been a pleasure.
[49:36] SPEAKER_02: Thanks, everybody, for joining.
[49:38] SPEAKER_02: And again,
[49:38] SPEAKER_02: we're sorry for the initial technical glitch,
[49:41] SPEAKER_02: but I think we were able to make up for it with a lot of great discussion.
[49:44] SPEAKER_02: Sorry we couldn't get to all the questions.
[49:46] SPEAKER_02: Thank you.
[49:47] SPEAKER_00: Bye for now.

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