# Steve Horvath: Our Epigenetic Age Clocks

An episode of [Ground Truths](https://invisiblenetwork.ai/podcast/1728526108), 23 Aug 2024, 41 min.

Audio: https://api.substack.com/feed/podcast/147976058/de7d268c55c8cc4edfbf27c40efc8d83.mp3

Steve Horvath made the seminal discovery of the—Horvath Clock— an epigenetic clock based on DNA methylation, which is now being used extensively in medical research and offered commercially for individuals (←we talk about that!). He was on the faculty at UCLA from 2000-2022 as a Professor of Human Genetics and Biostatistics, and now works on anti-aging research at Altos Labs. A perspective on the importance of epigenetic clocks this week’s Nature”This insight is crucial for deriving reliable biological markers of ageing in tissues or blood. Such a feat has been accomplished through the ingenious identification of epigenetic clocks in our genome. But these insights are even more important for revealing targets that enable intervention in the ageing process.” A video snippet on vegetable intake and epigenetic clocks. Full videos of all Ground Truths podcasts can be seen on YouTube here. The audios are also available on Apple and Spotify. Transcript with links to Audio and External Links Eric Topol (00:06): Hello, it's Eric Topol with Ground Truths, and I've got a terrific guest with me today, Steve Horvath. He's a geneticist, a statistician, a mathematician. He's got a lot of background that has led to what is a landmark finding in biomedicine, the Horvath clock. So Steve, welcome. Steve Horvath (00:30): Thank you for having me. Eric Topol (00:33): Well, it's really fascinating. I followed your work for well over a decade since you introduced the pan-tissue clock in 2013, and it's fascinating to go back a bit on that finding, which initially, I guess was in saliva a couple of years prior, and then you found it everywhere you looked, wherever cells had a nucleus and tissues. And what gave you the sense that these markers of methylation on the DNA would give us some clues about the aging process? How did you even come about to make this discovery? Serendipity Steve Horvath (01:17): It was an accidental discovery because before the methylation clock, I had worked very hard on a gene expression clock, a transcriptomic biomarker. I mean, I was at the height of my energy levels. I worked really on weekends, really eight hour days during the week. But all the weekends I had collected a large set of gene expression data and I dredged the data. And for two years and I couldn't get anywhere, there was nothing I could do. But nowadays, of course, you see various publications where people built transcriptomic clocks. But back in the day when we had these arrays, I just couldn't see a signal. And then at some point I got roped into a study of homosexuality where my collaborator at UCLA wanted to see whether there's an epigenetic correlate of sexual orientation in saliva. And so yeah, being a biostatistician, I said, sure, I analyzed the data and I couldn't find any signal for homosexuality. (02:48): But then I just looked for an aging signal in the same, and really within an hour of analyzing the data, I knew that I have to completely drop gene expression. I need to go after methylation. And the signal is so profound, and as you said initially we looked at saliva samples and we thought, isn't it curious? You spit in a cup and you can measure someone's age. And we were of course, hoping that this could become a valuable readout of biologic age, but it took, of course, many years to realize that potential. Nowadays, there's several companies that offer a saliva based methylation clock test. But yeah, many years passed, and it was important to fill in the details and to build the case that methylation clocks are predictive of things we care about time to death or time to various forms of morbidity. So it took many, many years to analyze large cohort studies and to accumulate the evidence that it actually works. Eric Topol (04:16): Yeah, I mean, it was pretty amazing back almost a decade ago when I would see, we would take tissue or blood sample and look at your clock and it would say, age of the person is 75 years. And then we…

## Transcript

[0:06] SPEAKER_00: Hello, it's Eric Topol with Ground Truths, and I've got a terrific guest with me today, Steve Horvath.
[0:14] SPEAKER_00: He's a geneticist, a statistician, a mathematician.
[0:18] SPEAKER_00: He's got a lot of background that has led to what is a landmark finding in biomedicine,
[0:25] SPEAKER_00: the Horvath clock.
[0:27] SPEAKER_00: So, Steve, welcome.
[0:30] SPEAKER_00: Thank you for having me.
[0:33] SPEAKER_00: Well, you know, it's really fascinating.
[0:36] SPEAKER_00: I followed your work for well over a decade since you introduced the pan tissue clock in 2013.
[0:44] SPEAKER_00: And it's fascinating to go back a bit on that finding,
[0:51] SPEAKER_00: which initially,
[0:51] SPEAKER_00: I guess,
[0:52] SPEAKER_00: was in saliva a couple of years prior.
[0:54] SPEAKER_00: And then you found it everywhere you looked, wherever cells had a nucleus and tissues and
[1:01] SPEAKER_00: What gave you the sense that these markers of methylation on the DNA
[1:08] SPEAKER_00: would give us some clues about the aging process.
[1:13] SPEAKER_00: How did you even come about to make this discovery?
[1:17] SPEAKER_01: You know,
[1:17] SPEAKER_01: it was an accidental discovery because before the methylation clock,
[1:25] SPEAKER_01: I had worked very hard on a gene expression clock,
[1:29] SPEAKER_01: a transcriptomic biomarker.
[1:31] SPEAKER_01: I mean, I was at the height of my energy levels.
[1:36] SPEAKER_01: I worked really on weekends, really eight-hour days during the week, but also weekends.
[1:44] SPEAKER_01: I had collected a large set of gene expression data and I dredged the data for two years and I couldn't
[1:56] SPEAKER_01: get anywhere.
[1:57] SPEAKER_01: There was nothing I could do, you know.
[2:01] SPEAKER_01: But nowadays, of course, you see various publications where people build transcriptomic logs.
[2:07] SPEAKER_01: But back in the day when we had these arrays, I just couldn't see a signal.
[2:14] SPEAKER_01: And then at some point I got roped into a study of homosexuality,
[2:22] SPEAKER_01: you know,
[2:23] SPEAKER_01: where my collaborator at UCLA wanted to see whether there is an epigenetic
[2:32] SPEAKER_01: correlate of sexual orientation in saliva,
[2:35] SPEAKER_01: you know.
[2:36] SPEAKER_01: And so, yeah.
[2:39] SPEAKER_01: Being a biostatistician, I said, sure.
[2:42] SPEAKER_01: I analyzed the data and I couldn't find any signal for homosexuality.
[2:48] SPEAKER_01: But then I just looked for an aging signal, you know, in the same way.
[2:53] SPEAKER_01: And really within an hour of analyzing the data, I knew that I have to completely drop gene expression.
[3:02] SPEAKER_01: I need to go after methylation.
[3:05] SPEAKER_01: And the signal is so profound, you know.
[3:09] SPEAKER_01: And as you said,
[3:10] SPEAKER_01: initially,
[3:11] SPEAKER_01: we looked at saliva samples and we thought,
[3:16] SPEAKER_01: isn't it curious you spit in a cup and you can measure someone's age?
[3:22] SPEAKER_01: And we were, of course, hoping that this could become a valuable readout of biologic age.
[3:29] SPEAKER_01: Right.
[3:31] SPEAKER_01: But it took, of course, many years to realize that potential.
[3:38] SPEAKER_01: Nowadays, there are several companies that offer a saliva-based methylation clock test.
[3:46] SPEAKER_01: But yeah.
[3:48] SPEAKER_01: many years passed and it was important to fill in the details and to build the case
[3:55] SPEAKER_01: that methylation clocks are predictive of things we care about,
[4:01] SPEAKER_01: like time to death or time to various forms of morbidity.
[4:06] SPEAKER_01: So it took many,
[4:07] SPEAKER_01: many years to analyze large cohort studies and accumulate the evidence that it
[4:14] SPEAKER_01: actually works.
[4:16] SPEAKER_00: Yeah,
[4:17] SPEAKER_00: I mean,
[4:17] SPEAKER_00: it was pretty amazing,
[4:18] SPEAKER_00: you know,
[4:19] SPEAKER_00: back almost a decade ago,
[4:21] SPEAKER_00: when I would see,
[4:23] SPEAKER_00: we would take tissue or blood sample and look at your clock.
[4:29] SPEAKER_00: And it would say, you know, age of the person is 75 years.
[4:34] SPEAKER_00: And then we look at the actual age of the person with 75 years to say, wait a minute, how can this be?
[4:39] SPEAKER_00: So
[4:41] SPEAKER_00: mean this the the plausibility of this discovery if you look back i mean you know
[4:46] SPEAKER_00: you say well this is just kind of the rust of the pipes or what what what do you
[4:52] SPEAKER_00: how do you process that the methylation is such a a marker potentially of a
[4:59] SPEAKER_00: person's biological age of course we're going to get into how it could be a way to
[5:06] SPEAKER_00: intervene to change the aging process but
[5:11] SPEAKER_00: Is it,
[5:11] SPEAKER_00: would it be fair to say that it's epigenetic clocks are not the same as biologic
[5:17] SPEAKER_00: aging or how do you put all that together?
[5:21] SPEAKER_01: Yes, for sure.
[5:23] SPEAKER_01: An epigenetic age estimate is certainly not the same as a biologic age estimate.
[5:29] SPEAKER_01: And the reason why I say it is because biologic age is really determined by so many
[5:38] SPEAKER_01: things and by so many organs.
[5:41] SPEAKER_01: And as I mentioned, initially we had a clock for saliva, later for blood, and so on.
[5:49] SPEAKER_01: And so if you only have an epigenetic readout of a certain cell type,
[5:54] SPEAKER_01: you know,
[5:55] SPEAKER_01: it's really too limited to assess the whole organismal state,
[6:02] SPEAKER_01: you know.
[6:04] SPEAKER_01: And arguably you would want to measure also proteomics readouts, you know.
[6:12] SPEAKER_01: and many other data modalities.
[6:14] SPEAKER_01: So I typically avoid the terminology biologic age,
[6:23] SPEAKER_01: because to begin with,
[6:25] SPEAKER_01: we don't have a definition of it,
[6:27] SPEAKER_01: you know,
[6:28] SPEAKER_01: decades of discussions,
[6:30] SPEAKER_01: nobody really has a precise definition of it.
[6:33] SPEAKER_01: Well,
[6:36] SPEAKER_00: be from the first generation uh or at the clock then became this newer second
[6:42] SPEAKER_00: generation grim age pheno age the lean pace age how has that helped to advance the
[6:52] SPEAKER_00: field because as you touched on they're measuring different things and what is what
[6:59] SPEAKER_00: is it meant by a kind of a second generation clock
[7:03] SPEAKER_01: Yeah,
[7:03] SPEAKER_01: so a second generation clock truly aims to predict mortality or morbidity risk as
[7:13] SPEAKER_01: opposed to simply chronologic age or what is known as calendar age.
[7:21] SPEAKER_01: And fortunately, there's no doubt that the second generation clocks can do that.
[7:29] SPEAKER_01: I often finish a talk on Grimage by telling the audience that I give them a money
[7:36] SPEAKER_01: back guarantee that it will be predictive of mortality in their cohort study.
[7:42] SPEAKER_01: I'm 100% certain that it works.
[7:46] SPEAKER_01: If you analyze 100 people or so,
[7:51] SPEAKER_01: The question is more whether an individual could benefit from such a test.
[7:58] SPEAKER_01: And there are now many providers of various epigenetic clock tests.
[8:04] SPEAKER_01: These biomarkers have different names.
[8:08] SPEAKER_01: But they're quite pricey, a couple of hundred dollars.
[8:14] SPEAKER_01: are needed to get such a measurement.
[8:18] SPEAKER_01: And the question is, is it helpful for the individual?
[8:23] SPEAKER_01: Should you get such a testing?
[8:25] SPEAKER_01: And I would say we are not quite there yet for a variety of reasons.
[8:35] SPEAKER_01: The main reason being we don't have good interventions against accelerated epigenetic age.
[8:45] SPEAKER_01: Because when you think about it,
[8:47] SPEAKER_01: why does a doctor order a test for you,
[8:51] SPEAKER_01: for example,
[8:52] SPEAKER_01: cholesterol levels?
[8:53] SPEAKER_01: Well, because they have a drug against elevated cholesterol levels, the statin.
[9:00] SPEAKER_01: And at the moment, we don't have validated interventions against accelerated epigenetic age.
[9:10] SPEAKER_01: So that's kind of missing.
[9:13] SPEAKER_00: Yeah,
[9:13] SPEAKER_00: we're going to get to that because obviously a lot of things are in the pipeline there,
[9:17] SPEAKER_00: but,
[9:18] SPEAKER_00: um,
[9:20] SPEAKER_00: are you saying then that these people that are getting these consumer tests,
[9:27] SPEAKER_00: uh,
[9:27] SPEAKER_00: that they're getting,
[9:28] SPEAKER_00: um,
[9:29] SPEAKER_00: a test that really wasn't validated at an individual level.
[9:32] SPEAKER_00: So, but predicts their mortality.
[9:35] SPEAKER_00: that it may be,
[9:37] SPEAKER_00: you know,
[9:38] SPEAKER_00: good at a cohort or population level,
[9:40] SPEAKER_00: but maybe it's not so helpful,
[9:43] SPEAKER_00: accurate,
[9:43] SPEAKER_00: or would you say it is accurate?
[9:45] SPEAKER_00: For me, Grimm, age is a good name, because, you know, since this one, you're going to die.
[9:50] SPEAKER_00: How do you make the differentiation between the individual level or beyond?
[9:59] SPEAKER_01: I think it's good to compare to other biomarkers.
[10:05] SPEAKER_01: So take glucose levels, hemoglobin A1c.
[10:10] SPEAKER_01: Nobody doubts that these levels predict mortality risk when you study a couple of hundred people.
[10:18] SPEAKER_01: But how accurate is such a test for an individual?
[10:25] SPEAKER_01: Clearly, there is substantial noise associated with a prediction.
[10:31] SPEAKER_01: Two people could have exactly the same hemoglobin A1C levels, but live very different lifespans.
[10:42] SPEAKER_01: And
[10:44] SPEAKER_01: The same holds for epigenetic clocks, you know.
[10:46] SPEAKER_01: They do predict how long you live.
[10:50] SPEAKER_01: In theory, one could arrive at an estimate of age at death.
[10:56] SPEAKER_01: There's a complicated mathematical formula that allows you to do that.
[11:03] SPEAKER_01: But there would be a substantial error bar associated with it in order of magnitude
[11:09] SPEAKER_01: plus minus five years,
[11:11] SPEAKER_01: you know.
[11:13] SPEAKER_01: And so for the individual,
[11:17] SPEAKER_01: such an estimate is not that important,
[11:21] SPEAKER_01: you know,
[11:22] SPEAKER_01: because the error bar is substantial,
[11:24] SPEAKER_01: you know.
[11:26] SPEAKER_01: But I want to add that the second generation clocks, they do predict mortality risk.
[11:33] SPEAKER_01: There's no question.
[11:35] SPEAKER_00: Well,
[11:36] SPEAKER_00: as you know,
[11:37] SPEAKER_00: the longevity space is now very crowded with all sorts of clubs and,
[11:42] SPEAKER_00: you know,
[11:43] SPEAKER_00: it's like a circus out there.
[11:45] SPEAKER_00: And some of these things are being promoted that really don't have the basis for or,
[11:51] SPEAKER_00: you know,
[11:51] SPEAKER_00: give a false sense to consumers.
[11:54] SPEAKER_00: who want to live forever and be healthy forever,
[11:56] SPEAKER_00: but maybe these markers are not really helping guide them so much.
[12:02] SPEAKER_00: Now, you recently published, you and your group, a fascinating paper
[12:07] SPEAKER_00: So getting away from the individual for a second, but now at the species level.
[12:12] SPEAKER_00: And science advances,
[12:14] SPEAKER_00: and we'll put this diagram with the podcast,
[12:18] SPEAKER_00: but you looked at 348 mammal species for the maximal lifespan with DNA methylation.
[12:26] SPEAKER_00: And it was amazing to see the splay from...
[12:29] SPEAKER_00: the desert hamster all the way to the humpback whale with somewhere along the way the humans.
[12:38] SPEAKER_00: So you could predict maximal lifespan pretty well, right?
[12:43] SPEAKER_01: Yes.
[12:43] SPEAKER_01: So I collected this very large data set over seven years.
[12:49] SPEAKER_01: And one of the reasons was to understand the mystery of maximum lifespan.
[12:58] SPEAKER_01: Sorry, the bowhead whale can live over 211 years, whereas certain mice only three or four years.
[13:09] SPEAKER_01: And my question was, can methylation teach us something about maximum lifespan?
[13:15] SPEAKER_01: And the answer is a resounding yes.
[13:19] SPEAKER_01: The methylation profiles very much predict the maximum lifespan of a species.
[13:28] SPEAKER_01: And
[13:29] SPEAKER_01: And maybe to use a metaphor to explain the patterns,
[13:35] SPEAKER_01: so one can visualize methylation around the DNA molecule like a landscape.
[13:43] SPEAKER_01: You want that certain regions exhibit high levels of methylation.
[13:50] SPEAKER_01: These regions must be really shut down, and other parts of the DNA
[13:56] SPEAKER_01: are supposed to exhibit very low methylation, for example, at transcriptional start sites.
[14:03] SPEAKER_01: And the long-lived species have very hilly landscapes,
[14:11] SPEAKER_01: high hills of methylation and steep valleys of low methylation,
[14:17] SPEAKER_01: whereas shorter-lived species have a flatter landscape.
[14:24] SPEAKER_01: That was one of the insights of that study.
[14:31] SPEAKER_01: The other perhaps paradoxical insight was that the locations in our DNA
[14:40] SPEAKER_01: that gain methylation with chronologic age,
[14:47] SPEAKER_01: these regions often differ from regions that determine the maximum lifespan of our species.
[14:55] SPEAKER_01: So that's a bit perhaps paradoxical and counterintuitive, but it just shows that the DNA encodes
[15:06] SPEAKER_01: our species characteristics at different locations from our mortality risk.
[15:12] SPEAKER_00: All right, all right.
[15:14] SPEAKER_00: No, and I mean, it's fascinating.
[15:20] SPEAKER_00: I can imagine how it could take seven years to pull all that data together.
[15:23] SPEAKER_00: It's amazing now.
[15:25] SPEAKER_00: One of the issues,
[15:26] SPEAKER_00: of course,
[15:27] SPEAKER_00: is if you're trying to gauge the biologic age,
[15:32] SPEAKER_00: which we already established is somewhat different than epigenetic age or clock,
[15:38] SPEAKER_00: there are many different ways to do that.
[15:41] SPEAKER_00: And you mentioned transcriptoma clocks, which are not as well perhaps developed.
[15:46] SPEAKER_00: Obviously,
[15:47] SPEAKER_00: none of these others are developed like the Horvath clocks and the newer generation clocks,
[15:52] SPEAKER_00: but
[15:53] SPEAKER_00: There's immuno-aging clocks like IMAGE and IAGE.
[15:57] SPEAKER_00: There's proteomic clocks.
[15:59] SPEAKER_00: There's organ clocks with high-throughput proteomics, thousands of proteins.
[16:04] SPEAKER_00: Do you see these as complementary, you know, like orthogonal, where they each kind of add to the story?
[16:11] SPEAKER_00: Or do you really, you know, see the methylation as distinct?
[16:20] SPEAKER_01: Well,
[16:21] SPEAKER_01: I think ideally you measure all of the above,
[16:25] SPEAKER_01: you know,
[16:25] SPEAKER_01: to really get a very granular understanding of different facets of aging,
[16:34] SPEAKER_01: you know.
[16:35] SPEAKER_01: And however, scientists always like to find deep connections between different readouts.
[16:45] SPEAKER_01: For example, it would be wonderful if we could use proteomics instead of methylation.
[16:55] SPEAKER_01: Or my group has worked on the opposite.
[16:57] SPEAKER_01: So we can actually estimate protein levels in the plasma based on methylation.
[17:06] SPEAKER_01: For about 10% of all plasma proteins, you can estimate their levels based on methylation, you know.
[17:15] SPEAKER_01: So yeah,
[17:17] SPEAKER_01: people who are interested in these deeper programs that ideally link everything,
[17:25] SPEAKER_01: some sort of aging program that underlies these different manifestations of aging,
[17:32] SPEAKER_01: they will want to reduce everything.
[17:35] SPEAKER_01: But until we have a deeper understanding, I think let's err on the side of measuring too much.
[17:44] SPEAKER_00: Well,
[17:45] SPEAKER_00: what's interesting,
[17:46] SPEAKER_00: as you mentioned,
[17:47] SPEAKER_00: I didn't realize you could basically impute the protein story from the methylation.
[17:53] SPEAKER_00: But one of the issues is if you want to do 11,000 plasma proteins, it could cost $1,000.
[17:59] SPEAKER_00: But if you want to do a bisulfite methylation, you might do that very inexpensively.
[18:07] SPEAKER_00: So there's a practical part of this, too.
[18:10] SPEAKER_00: And the immune characterization is even more expensive and difficult from a practical standpoint.
[18:17] SPEAKER_00: So we go back to that initial work that you did and how you got into an area that
[18:24] SPEAKER_00: is practical and expensive compared to some of the alternatives.
[18:28] SPEAKER_00: But as you say, you know, they may have features that are also helpful now.
[18:35] SPEAKER_00: This is now the craze, this epigenetic clocks.
[18:38] SPEAKER_00: And I want to mention,
[18:39] SPEAKER_00: you probably didn't see it because it's not a journal that you would look at,
[18:42] SPEAKER_00: but just yesterday,
[18:45] SPEAKER_00: July 27th,
[18:46] SPEAKER_00: there were 12 papers published in JAMA Network Open.
[18:51] SPEAKER_00: Everything from how loss of loved ones changes your epigenetic clock to PTSD,
[18:58] SPEAKER_00: to vegan diets,
[19:00] SPEAKER_00: to inequities.
[19:02] SPEAKER_00: I mean, just incredible.
[19:04] SPEAKER_00: So it is the rage now.
[19:08] SPEAKER_00: It's taken the biomedical community some years to catch up to where you were back a ways.
[19:16] SPEAKER_00: And one of the things, of course, that we know from your prior work
[19:21] SPEAKER_00: that is an intervention that helps give a less accelerated epigenetic clock is exercise.
[19:33] SPEAKER_00: And in fact, that was highlighted in our Lancet essay in the first week of August issue.
[19:40] SPEAKER_00: But can you comment on that and anything else that we know,
[19:44] SPEAKER_00: like plant diets and anything that favorably influence our DNA methylation patterns?
[19:52] SPEAKER_01: Yes.
[19:53] SPEAKER_01: So interestingly,
[19:56] SPEAKER_01: vegetable intake really has a strong effect on grim age and many other epigenetic clocks.
[20:05] SPEAKER_01: And maybe this is obvious to the listener.
[20:09] SPEAKER_01: Everybody knows that vegetable intake is healthy.
[20:13] SPEAKER_01: However,
[20:14] SPEAKER_01: it's very surprising to me as a scientist to contemplate how is it that vegetable
[20:21] SPEAKER_01: intake affects the methylation levels of your blood?
[20:26] SPEAKER_01: How does it affect the hematopoietic stem cells?
[20:29] SPEAKER_01: I just don't understand the mechanism behind it.
[20:34] SPEAKER_01: However, the effect is very strong.
[20:37] SPEAKER_01: So we studied postmenopausal women in the Women's Health Initiative.
[20:43] SPEAKER_01: And for these women, we had blood measures of carotenoid levels.
[20:49] SPEAKER_01: So this is an objective measure of vegetable intake.
[20:53] SPEAKER_01: And the correlations were substantial.
[20:59] SPEAKER_01: So that's one intervention I'm quite certain about.
[21:04] SPEAKER_01: And other interventions that have a strong effect relate to metabolic syndrome.
[21:13] SPEAKER_01: Anything that relates to type 2 diabetes,
[21:19] SPEAKER_01: such as obesity,
[21:22] SPEAKER_01: high glucose levels,
[21:24] SPEAKER_01: that part of the biology very much affects our epigenetic clocks.
[21:32] SPEAKER_01: So disturbed metabolism has a strong effect.
[21:36] SPEAKER_00: Has these findings changed your diet or made you exercise more or anything like that?
[21:44] SPEAKER_01: It has changed my diet.
[21:47] SPEAKER_01: I eat a lot of frozen vegetables.
[21:51] SPEAKER_01: My freezer is full of frozen vegetables.
[21:55] SPEAKER_00: That's great.
[21:57] SPEAKER_00: Well,
[21:58] SPEAKER_00: there's a lot of uses today as we touched on in the last piece as we're waiting for
[22:07] SPEAKER_00: more benchmarking and more work on this.
[22:10] SPEAKER_00: But
[22:11] SPEAKER_00: For example,
[22:13] SPEAKER_00: we have a shortage of donor organs and there are people who might be of calendar
[22:21] SPEAKER_00: age advanced,
[22:23] SPEAKER_00: but their epigenetic clock might put them at a much younger age.
[22:29] SPEAKER_00: Is that ready for use in the transplant world as one application?
[22:37] SPEAKER_01: I haven't seen that yet.
[22:39] SPEAKER_01: I've seen several studies that have explored that idea.
[22:44] SPEAKER_01: The idea is rather obvious, but I haven't seen it implemented in practice.
[22:53] SPEAKER_00: Another one is that we don't,
[22:56] SPEAKER_00: as you've seen from some of these studies on organ clocks,
[23:02] SPEAKER_00: our organs age at different paces.
[23:05] SPEAKER_00: And some people are, you know, accelerated heart agers or brain agers.
[23:12] SPEAKER_00: Would you get that, if you had access to tissue to get methylation, would you see the same thing?
[23:19] SPEAKER_00: Or,
[23:21] SPEAKER_00: you know,
[23:21] SPEAKER_00: this is a course of interest because we're trying to understand high-risk
[23:25] SPEAKER_00: individuals for age-related diseases,
[23:28] SPEAKER_00: whether it's dementia or heart disease or cancer.
[23:33] SPEAKER_00: So is the second generation clocks like PhenoAge just good enough or would you
[23:40] SPEAKER_00: think that the Oregon clocks would give you some added insight?
[23:47] SPEAKER_01: Yeah, I would say this is literally the frontier of research.
[23:53] SPEAKER_01: Several groups attempt to use blood methylation or saliva or skin or fat adipose as
[24:03] SPEAKER_01: surrogates for various other organs.
[24:07] SPEAKER_01: And I've seen very encouraging results.
[24:10] SPEAKER_01: So I do think this idea makes scientific sense.
[24:17] SPEAKER_01: And which comes back to one of the miracles of methylation, that this is even possible.
[24:24] SPEAKER_01: Because if you had written a grant 10 years ago where you said,
[24:31] SPEAKER_01: I will measure blood methylation to assess cognitive functioning,
[24:36] SPEAKER_01: for example,
[24:38] SPEAKER_01: You wouldn't have received any score, you know, no funding, you know.
[24:43] SPEAKER_01: But however,
[24:45] SPEAKER_01: interestingly,
[24:46] SPEAKER_01: blood methylation does relate to cognitive functioning and many other organ functions.
[24:53] SPEAKER_01: And so the proof of concept has been established.
[25:00] SPEAKER_01: Blood methylation relates to fatty liver disease, kidney disease, lung disease.
[25:06] SPEAKER_01: It has all been done in epidemiological studies.
[25:12] SPEAKER_01: However, the question is,
[25:16] SPEAKER_01: how much could a blood methylation measurement help an individual?
[25:20] SPEAKER_01: Should they measure, should I measure my blood methylation to learn about my liver?
[25:27] SPEAKER_01: And I would say we are not there yet, because
[25:35] SPEAKER_01: Arguably, there are wonderful plasma biomarkers to assess organ functions.
[25:44] SPEAKER_01: And in certain ways,
[25:46] SPEAKER_01: one needs to provide evidence that a methylation measurement is superior or
[25:53] SPEAKER_01: complements a plasma measurement.
[25:56] SPEAKER_01: based biomarker, you know, and that's a hard hurdle to take.
[26:02] SPEAKER_00: Right.
[26:02] SPEAKER_00: I imagine someday it may become the norm of assessing people's risk.
[26:07] SPEAKER_00: But as you say, we're not there yet because it's a tough bar to meet, you know, for sure.
[26:14] SPEAKER_00: Now,
[26:16] SPEAKER_00: You were a professor from year 2000 at UCLA in multiple departments in genetics and biostats.
[26:27] SPEAKER_00: And then in more recent times,
[26:29] SPEAKER_00: you joined the Cambridge unit of Altos,
[26:33] SPEAKER_00: which is one of the companies that has gotten the most attention for its diverse
[26:43] SPEAKER_00: efforts towards modulating,
[26:46] SPEAKER_00: rejuvenating the aging process.
[26:49] SPEAKER_00: So you and many top scientists around the world
[26:54] SPEAKER_00: or recruited to Altos.
[26:56] SPEAKER_00: I know some here at the San Diego campus.
[26:59] SPEAKER_00: Was this thinking that it could help accelerate the whole idea of modulating aging in a favorably way?
[27:07] SPEAKER_00: Or where do you see that the biotech world can play a role?
[27:15] SPEAKER_01: Yes.
[27:16] SPEAKER_01: Speaking for myself, I was getting tired of writing scientific papers and not affecting clinical care.
[27:28] SPEAKER_01: I felt I needed to help others.
[27:33] SPEAKER_01: identify or validate rejuvenating interventions because of the great promise.
[27:41] SPEAKER_01: And this is perhaps best done in the setting of a biotech that is focused on translation.
[27:52] SPEAKER_01: And that's why I joined.
[27:54] SPEAKER_01: I'm moving away from biomarker development towards finding interventions that move
[28:01] SPEAKER_01: the needle and ideally rejuvenate multiple organs and cell types at the same time.
[28:08] SPEAKER_00: Right.
[28:10] SPEAKER_00: Now, there's lots of ideas of how we could do that.
[28:14] SPEAKER_00: from senolytics that would get rid of specific senescent cells that are bad actors
[28:19] SPEAKER_00: to epigenetic reprogramming or chemical reprogramming or you know so many
[28:27] SPEAKER_00: anti-inflammatory like the recent paper of interleukin-11 that I'm sure you saw in
[28:34] SPEAKER_00: nature just a couple weeks ago and many many other
[28:37] SPEAKER_00: ways to get there what are you thinking um is this going to be possible because
[28:43] SPEAKER_00: obviously there's lots of naysayers uh is it going to be possible body wide or only
[28:50] SPEAKER_00: you know for specific um ways like for example maybe we could
[28:56] SPEAKER_00: bring back the thymus from its involution,
[28:59] SPEAKER_00: or we could stop ovarian failure in women so that their loss of advantage is
[29:06] SPEAKER_00: delayed many years.
[29:08] SPEAKER_00: Do you think we're going to get to body-wide anti-aging?
[29:13] SPEAKER_01: Yeah, I think of it as divide and conquer, you know.
[29:17] SPEAKER_01: So ultimately, I do believe that we can rejuvenate most cell types and tissues.
[29:29] SPEAKER_01: The question is, how do you roll out this program?
[29:34] SPEAKER_01: Do you look for this one silver bullet that does it?
[29:38] SPEAKER_01: For example,
[29:39] SPEAKER_01: This idea of the interrupted reprogramming based on Yamanaka factor combinations,
[29:47] SPEAKER_01: that looks,
[29:48] SPEAKER_01: of course,
[29:48] SPEAKER_01: very promising in Roden models.
[29:52] SPEAKER_01: But then such silver bullet treatments could be risky for a patient.
[30:01] SPEAKER_01: Keyword malignant transformation,
[30:04] SPEAKER_01: cancer risk,
[30:05] SPEAKER_01: and it could be far safer to focus on one organ system or one tissue.
[30:13] SPEAKER_01: For example,
[30:14] SPEAKER_01: David Sinclair's company,
[30:15] SPEAKER_01: Life Biosciences,
[30:17] SPEAKER_01: looks at optic nerve regeneration for a reason that could be safer.
[30:24] SPEAKER_01: And so, yeah, I'm very happy that companies explore different strategies.
[30:31] SPEAKER_01: Certain companies focus on one condition, fatty liver disease or NASH.
[30:38] SPEAKER_01: Other companies focus on immune system restoration.
[30:43] SPEAKER_01: But I think many people think of one condition as really a first step to establish
[30:52] SPEAKER_01: safety and efficacy,
[30:54] SPEAKER_01: and then hopefully they could translate it to other body systems and organ systems.
[31:02] SPEAKER_00: But is it fair to say you're optimistic that we will be able to change the aging pace in people?
[31:10] SPEAKER_01: Yes, I think, yes, I'm very optimistic.
[31:14] SPEAKER_01: And there are several reasons for this optimism.
[31:19] SPEAKER_01: The first is that dramatic results can be achieved in mice and rats.
[31:26] SPEAKER_01: So we and others have published studies that show that you can reduce the
[31:32] SPEAKER_01: epigenetic age by 30% or so,
[31:35] SPEAKER_01: and you can extend the lifespan of
[31:38] SPEAKER_01: And you cited this very exciting paper by Stuart Kim on IL-11 inhibition that just came out in Nature.
[31:47] SPEAKER_01: So I keep seeing these kinds of headlines and then I want to think that one of
[31:54] SPEAKER_01: these will actually work for humans.
[31:57] SPEAKER_01: So the second thing that makes me optimistic is really this combination of
[32:04] SPEAKER_01: artificial intelligence and biomedical research.
[32:09] SPEAKER_01: Then going forward, robotics.
[32:14] SPEAKER_01: So I can see several ways of accelerating biomedical research.
[32:20] SPEAKER_01: So I'm quite optimistic.
[32:24] SPEAKER_00: Maybe go a little deeper on the AI side.
[32:27] SPEAKER_00: potential to help here.
[32:28] SPEAKER_00: How does AI come into play?
[32:33] SPEAKER_01: So AI can help in so many different ways.
[32:38] SPEAKER_01: The first topic is biomarker development.
[32:44] SPEAKER_01: I,
[32:44] SPEAKER_01: of course,
[32:44] SPEAKER_01: spent 10 years on a certain statistical model for building biomarkers,
[32:50] SPEAKER_01: which is known as penalized regression.
[32:53] SPEAKER_01: It works well.
[32:54] SPEAKER_01: But AI allows the community to build models
[33:00] SPEAKER_01: imaging-based biomarkers.
[33:02] SPEAKER_01: So,
[33:05] SPEAKER_01: for example,
[33:07] SPEAKER_01: based on MRI images,
[33:10] SPEAKER_01: but also cells growing in a dish,
[33:13] SPEAKER_01: we can say this treatment aged the cells growing in the dish or rejuvenated them.
[33:20] SPEAKER_01: So that's one topic, biomarker discovery.
[33:24] SPEAKER_01: The second is, of course, to design small molecules, keyword, these protein design, that's
[33:40] SPEAKER_01: where it has greatly accelerated drug discovery.
[33:47] SPEAKER_01: And there are several companies working in this space.
[33:50] SPEAKER_01: And there's wonderful case studies that look very convincing to me.
[33:59] SPEAKER_01: And the third aspect of AI is another obvious one.
[34:05] SPEAKER_01: AI can read many papers.
[34:08] SPEAKER_01: I mean,
[34:11] SPEAKER_01: you could be a 50-year-old professor who has read papers their entire life,
[34:19] SPEAKER_01: but AI can really read far better and summarize insights better.
[34:27] SPEAKER_00: Yeah, and they're complimentary in terms of the reasoning of that information.
[34:33] SPEAKER_00: So absolutely right.
[34:34] SPEAKER_00: Now, one of the problems we have here is that, you know, aging is not seen as a disease.
[34:43] SPEAKER_00: Of course,
[34:43] SPEAKER_00: we can remember when obesity was not considered a disease,
[34:47] SPEAKER_00: and then there was a drug and everything changed.
[34:50] SPEAKER_00: But here, we don't have a classification as a disease.
[34:55] SPEAKER_00: It's considered a natural process that is highly variable in people.
[35:00] SPEAKER_00: But the question is,
[35:03] SPEAKER_00: we can't do studies that are going to wait 20,
[35:06] SPEAKER_00: 30 years to find out if we promoted healthspan and lifespan.
[35:12] SPEAKER_00: And so we have to rely on these clocks
[35:18] SPEAKER_00: So how do you see this playing out?
[35:20] SPEAKER_00: Do you think that we might see a regulatory approval on a surrogate proxy like,
[35:26] SPEAKER_00: you know,
[35:26] SPEAKER_00: an advanced Horvath clock?
[35:28] SPEAKER_00: Or do you think that's not going to cut it,
[35:31] SPEAKER_00: that you're going to have to show more to get an anti-aging treatment across the
[35:39] SPEAKER_00: regulatory threshold?
[35:42] SPEAKER_01: Yeah, that's a very good question.
[35:44] SPEAKER_01: So I believe that the biomarker community has already assembled enough evidence to
[35:55] SPEAKER_01: offer a battery of tests that could be used as surrogate endpoints of an
[36:06] SPEAKER_01: interventional study.
[36:09] SPEAKER_01: And we could discuss the components of this battery, you know.
[36:15] SPEAKER_01: But I would say we already have biomarkers beyond just methylation, you know, one could...
[36:25] SPEAKER_01: have the readouts of walking speed or muscle function,
[36:30] SPEAKER_01: you know,
[36:30] SPEAKER_01: many,
[36:30] SPEAKER_01: many readouts,
[36:32] SPEAKER_01: and they could be aggregated into an index,
[36:35] SPEAKER_01: you know,
[36:35] SPEAKER_01: to summarize the biologic age,
[36:39] SPEAKER_01: perhaps of the individual.
[36:40] SPEAKER_01: So that already exists.
[36:43] SPEAKER_01: At the same time, this field is undergoing explosive growth.
[36:50] SPEAKER_01: You mentioned every day new papers come out.
[36:53] SPEAKER_01: In the relatively small field of epigenetic clocks, there's so many papers that it's hard to keep track.
[37:02] SPEAKER_01: But I embrace it.
[37:04] SPEAKER_01: I think it's wonderful because clocks get ever more powerful.
[37:08] SPEAKER_01: And so, yeah, I would say there should be different versions.
[37:17] SPEAKER_01: Ideally,
[37:18] SPEAKER_01: a regulatory agency would make an executive decision and say,
[37:25] SPEAKER_01: for the next three years,
[37:27] SPEAKER_01: use the following five biomarkers.
[37:31] SPEAKER_01: Then a few years later, as the science advances, they could come up with an updated version.
[37:37] SPEAKER_01: But even a 90% solution would very much accelerate progress in the whole field of
[37:49] SPEAKER_01: rejuvenating interventions.
[37:51] SPEAKER_01: So I would very much embrace a top-down decision on which biomarkers should be made.
[38:03] SPEAKER_01: Because the bottom-up approach, by the way, simply doesn't work.
[38:06] SPEAKER_01: The minute you put three professors in the room to come up with a decision which
[38:12] SPEAKER_01: biomarker is best,
[38:13] SPEAKER_01: there will be three different opinions.
[38:16] SPEAKER_01: We need an impartial arbiter that makes a decision.
[38:21] SPEAKER_01: Yeah.
[38:23] SPEAKER_00: Now,
[38:24] SPEAKER_00: you know,
[38:25] SPEAKER_00: the drug class that's come on the scene,
[38:27] SPEAKER_00: of course,
[38:28] SPEAKER_00: it was in incubating for decades for diabetes,
[38:32] SPEAKER_00: but now obesity and so many of the obesity related.
[38:38] SPEAKER_00: But now we're seeing the GLP-1 drugs that are showing potential effects in Parkinson's,
[38:44] SPEAKER_00: Alzheimer's and cardiovascular disease and even in obesity related cancers.
[38:51] SPEAKER_00: And I mean, across the board.
[38:53] SPEAKER_00: And you mentioned metabolic derangement as one of the things that accelerate aging.
[38:59] SPEAKER_00: Do you think this class of drugs that has greatly passed our expectations already,
[39:06] SPEAKER_00: and it's being tested,
[39:07] SPEAKER_00: of course,
[39:08] SPEAKER_00: with even more potent drugs,
[39:10] SPEAKER_00: triple receptors and pills and whatnot,
[39:13] SPEAKER_00: will that be a candidate as one of the anti-aging interventions in the future?
[39:19] SPEAKER_01: Yeah, for sure.
[39:20] SPEAKER_01: A couple of months ago,
[39:22] SPEAKER_01: I participated in a conference and one of the speakers showed unpublished results
[39:30] SPEAKER_01: from a study.
[39:33] SPEAKER_01: And they looked good to me.
[39:35] SPEAKER_01: I mean, they registered on epigenetic clocks.
[39:39] SPEAKER_01: This is all unpublished, but it made perfect sense to me.
[39:45] SPEAKER_01: I mentioned the clocks do relate to metabolic health.
[39:48] SPEAKER_01: So I was quite pleased that they registered that intervention.
[39:56] SPEAKER_00: It's fascinating because we could all be taking GLP-1 drugs someday,
[40:01] SPEAKER_00: not for obesity or not for sleep apnea,
[40:04] SPEAKER_00: but for things that are more far-reaching.
[40:10] SPEAKER_00: I didn't know about that unpublished result.
[40:13] SPEAKER_00: That's fascinating.
[40:15] SPEAKER_01: Yeah,
[40:15] SPEAKER_01: you know,
[40:16] SPEAKER_01: I have a joke,
[40:17] SPEAKER_01: which is I wish I was choppy because I would be using these drugs,
[40:21] SPEAKER_01: you know,
[40:21] SPEAKER_01: but I'm a relatively slender,
[40:23] SPEAKER_01: so I don't have any good reason to take them,
[40:26] SPEAKER_01: you know.
[40:28] SPEAKER_00: That says a lot because I don't know anybody who knows more about this process than
[40:32] SPEAKER_00: you and is very candid and frank about it,
[40:35] SPEAKER_00: so...
[40:36] SPEAKER_00: Steve,
[40:36] SPEAKER_00: this has been terrific to have your insights,
[40:41] SPEAKER_00: the work,
[40:41] SPEAKER_00: a body of work that you should be so proud of that extends over many years and many
[40:47] SPEAKER_00: great years and more contributions to come undoubtedly.
[40:51] SPEAKER_00: So thank you for joining us today.
[40:54] SPEAKER_00: And we will follow this continued evolution of our ability not just to track the
[41:01] SPEAKER_00: aging process,
[41:03] SPEAKER_00: but also to to modulate.
[41:04] SPEAKER_00: So thanks very much.
[41:06] SPEAKER_01: Thank you.
[41:07] SPEAKER_01: I really like your podcast, Ground Truths, very informative.
[41:11] SPEAKER_01: So thank you for this.

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