The Peter Attia Drive - #386 - Aging clocks—what they measure, how they work, and their clinical and real-world relevance

00:00:00.000 Hey, everyone. Welcome to the Drive podcast. I'm your host, Peter Atiyah. This podcast,

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00:01:04.220 Welcome to a special episode of The Drive. In this episode, I take a different approach

00:01:10.040 where I walk through a single topic in depth. And this is a topic that many of you have been

00:01:15.440 asking about, aging clocks. So in this episode, I explain what aging clocks are and the difference

00:01:21.300 between chronological age and biological age, along with the difference between those and

00:01:26.340 something called the pace of aging, how epigenetic clocks work, and what they may actually be

00:01:31.400 measuring. I'm going to talk about a randomized control trial that used three very simple

00:01:35.940 interventions and tested four of the most common aging clocks. I'm going to also talk about another

00:01:41.980 study that used brain imaging via MRI to study the pace of aging and see what could be gleaned

00:01:50.800 about not just the risk of dementia, but also mortality. I'll discuss the biggest limitation

00:01:56.080 in the field, which is whether changing a clock actually changes meaningful clinical outcomes.

00:02:02.420 So without further delay, I hope you enjoy this special episode of The Drive.

00:02:11.480 So if you wanted to run the perfect anti-aging trial, you know, the end points would be really

00:02:17.740 obvious. You'd want to see fewer heart attacks, fewer cancers, fewer dementia diagnoses, and

00:02:24.080 ultimately fewer deaths. So we would call these hard outcomes real outcomes that matter. These

00:02:29.480 are the clinical outcomes that we all care about. Now, of course, the reason we don't see these

00:02:33.900 trials is that they would take a very long time. These would literally be 20-year trials, and with

00:02:41.340 that would come enormous complexity and cost. Furthermore, it would be very difficult to

00:02:48.160 ensure that whatever intervention you put in place was being put in place for the duration

00:02:53.440 of this time. I mean, that would be not that hard to do if it was a drug trial, because it's

00:02:58.040 relatively easy to take a drug, but it would be more challenging for a lifestyle trial.

00:03:02.640 Okay. So every few years, the fields of geroscience and medicine and cardiovascular

00:03:09.580 disease, et cetera, they, they go looking for a proxy or a shortcut. So some intermediate marker

00:03:16.740 that could move faster than these hard outcomes, but that would still predict the hard outcome

00:03:22.440 reliably. And I think over the past few years, what we've really seen is that aging clocks

00:03:27.060 are the most interesting and popular proposed shortcut. Again, I don't use the word shortcut

00:03:34.600 with a, with a sort of negative connotation. It's like, this is what we need. We do need a

00:03:38.460 shortcut. We need a proxy. So again, the idea here is pretty compelling, right? Imagine you

00:03:43.280 could have a single number that would predict your actual aging or your actual biological age

00:03:49.840 that's different from your chronological age, or maybe a rate of aging that reflects a new

00:03:55.140 trajectory you're on. This could be very valuable in designing clinical trials or looking at

00:04:00.880 interventions. And even at the individual level, understanding if you've made a change and is it

00:04:07.620 making a difference. So think about this as a foray into precision medicine. Now, there's a

00:04:13.080 little bit of a problem in my mind because these aging clocks are being marketed as the latest and

00:04:18.960 maybe best way to keep tabs on your health. Lots of people are ordering them. They're available to

00:04:24.820 anybody. They're sold by longevity docs who promise to improve your biologic age with

00:04:30.820 groundbreaking, you know, combinations of peptides or other elixirs. But I think it's worth looking

00:04:37.780 into these a little more closely to understand what the science can actually tell us. And I

00:04:43.080 think the best way to do this is to look closely at two studies, two very interesting studies,

00:04:49.760 that can help us get at the fundamental questions that we really want to be asking around this,

00:04:55.840 which is what is the clinical utility of an aging clock? So before we do that, though,

00:05:02.160 I just want to kind of make sure everybody's starting from the same footing in terms of

00:05:06.940 understanding the biological stuff that we're talking about. So what is an aging clock? Well,

00:05:12.480 basically at its core, it's a prediction model. But let's take a step back. Your chronological age

00:05:19.200 is also a prediction model. You see, if I told you that in front of me there is a 20-year-old

00:05:25.860 and there is a 70-year-old, and I asked you to predict which one of those people is going to die

00:05:32.180 first, I think everybody would, knowing nothing else, make the correct prediction. Now, we could

00:05:38.660 layer onto that certain other factors. So if I said, okay, well, I actually now have two 70-year-olds

00:05:45.020 in front of me and one of them has cancer and the other one does not. Do you predict which one of

00:05:51.040 those is going to live longer than the other? And again, without knowing anything beyond what I told

00:05:55.460 you, I think everybody would make the same prediction. And so this idea of using information

00:06:01.980 to predict mortality is not new. It is the entire basis of the actuarial underwriting industry. And

00:06:09.520 there are companies that are exceptionally good at doing this. These are called life insurance

00:06:14.540 companies. And their data are incredibly proprietary, and it's really less so their

00:06:19.020 data and more so what they do with the data that is incredibly proprietary, right? They gather a

00:06:25.580 lot of information about you. They do a blood draw on you. They know your age. They know various

00:06:31.620 factors about you. They take your blood pressure, your weight, and things like that, relatively

00:06:36.020 rudimentary stuff. But from that, they have these tables, again, highly proprietary, that seem to

00:06:42.040 do a very good job of predicting when you're going to die. And so the question is, would one of these

00:06:47.880 aging clocks be even better? Okay, so let's talk about how these things work. So they typically

00:06:52.520 work by starting with some biological data. And the most common thing that we're going to hear

00:06:58.860 about is epigenetic data. So this is DNA methylation. And then they train an algorithm

00:07:05.180 to look at that and predict something age-related. So I think it's worth spending a minute on DNA

00:07:11.920 methylation. I don't want to go far down the rabbit hole on this, but you've undoubtedly

00:07:15.700 heard the term. And so I just want to make sure everybody's playing from the same level.

00:07:19.460 So DNA methylation is a way that we, or the way that the body modifies epigenetically what the

00:07:26.440 DNA expression is. So it doesn't change the sequence of DNA, but it can influence how the

00:07:32.740 genes are turned on or turned off, right? So that's what we call expression of genes. So

00:07:38.300 when you modify the epigenome, which is basically when you put a methyl group, so that's a carbon

00:07:45.600 with three hydrogens, when you put it on the backbone of the DNA, that impacts whether or not

00:07:54.980 that section of DNA gets turned into RNA. That's what we mean by expression.

00:08:00.060 You may have heard the term CPG, but not in reference to consumer packaged goods,

00:08:05.640 but a CPG refers to the location where these methylations most commonly take place. If you

00:08:13.400 remember back to high school biology, we have these four nucleotides. The C is the abbreviation

00:08:21.920 for cytosine. And so where these things typically occur is right on the phosphate bond that links

00:08:32.740 the C, the cytosine, with the G, the guanine nucleotide. So when we talk about CPGs, that's

00:08:40.940 just kind of another way that people kind of quickly talk about the methylations. So the

00:08:45.420 methylations that occur at these CPG sites will then affect quite strongly the genes that are

00:08:51.300 near to those areas. And why we care about this, of course, why I'm even talking about this, is

00:08:57.460 these methylation levels of many sites actually change somewhat predictably as we age. So this

00:09:04.480 is kind of the rationale for all of this, right? Is as we age, methylation sites change. Ergo,

00:09:12.240 if we can measure what's happening at methylation sites, can we impute age? Can we impute something

00:09:20.260 better than chronologic age? Because remember, chronological age is an awesome predictor as it

00:09:25.940 stands. But we're asking, can we do better? Because chronologic age is great at telling

00:09:31.200 you that on average, a 60-year-old is going to live, you know, a shorter duration than a 50-year-old.

00:09:37.300 But we know that that's not true at the individual level. There are plenty of 50-year-olds that are

00:09:41.940 going to have a shorter remaining life than plenty of 60-year-olds. It just depends on the individual

00:09:46.500 health and a whole bunch of other things. So we want to get at that difference. Okay.

00:09:49.900 it. So these patterns are going to shift gradually over time. And various factors,

00:09:59.120 behavioral factors, such as smoking, metabolic health, inflammation, actually play a role in

00:10:04.700 that. And so for this reason, researchers came to the conclusion roughly 10, little over 10 years

00:10:12.780 ago, that we could use these as kind of a molecular record keeping of what's going on

00:10:19.500 in the body. And I think that's probably why DNA methylation has created such an important

00:10:27.660 and foundational part of the clock story. So that's why I kind of went a little deep in the

00:10:33.800 weeds there. I think it's important. So, you know, you've probably heard of the Horvath clock.

00:10:38.020 That's one of the earliest first generation clocks, and that was obviously based on this.

00:10:42.780 So these models were trained on large data sets of DNA methylation, which were measured

00:10:48.420 and collected from thousands of individuals across a wide age range. And they're mostly

00:10:53.800 using cross-sectional cohorts rather than tracking an individual over time. Why? Because

00:10:57.520 as exciting as it would be to track an individual over time, those data sets are somewhat limited

00:11:02.700 and they're harder to get. Whereas if you take very large cohorts where you just slice the

00:11:07.500 population, you would get access to 20 year olds, 25 year olds, 30 year olds, 50 year olds, 60 year

00:11:12.680 olds, 70 year olds, 90 year olds, et cetera. And the hope would be that, Hey, we're going to see

00:11:17.760 what the signature of methylation is, you know, over time. So given that chronological age was

00:11:24.700 the outcome that the model was trying to predict, it's not really surprising that these clocks

00:11:28.880 became very good at estimating age often within kind of a few years of age. But at the same time,

00:11:35.260 And the fact that patterns of DNA methylation change consistently across ages of individuals,

00:11:43.240 that had a biological interest to it.

00:11:45.580 And it suggested that there might be certain areas in the genome where methylation shifts

00:11:52.300 occur in a predictable way as people get older.

00:11:55.120 Okay, so from a clinical standpoint, what does that tell us?

00:11:58.360 Well, estimating chronological age, which is what these first-generation clocks did,

00:12:03.260 wasn't adding any value because we already know chronological age. So it was more of a proof of

00:12:09.580 concept, but that's when the researchers realized, okay, what we really want to do is come up with

00:12:14.660 something that could be better than chronological age. And that's where this idea of biological age

00:12:19.020 could come from. And I just want to explain like sort of an extreme example of what this would

00:12:24.600 look like. So again, let's say you took two 60 year olds who I didn't tell you anything else 0.73

00:12:31.880 about them other than they're both 60 and they're, let's just say they're both the same sex. So two

00:12:35.580 60 year old women. So an actuarial table would say based on their chronological age, these women 0.98

00:12:43.960 both would have a life expectancy of, I'm making this up, 27 years. So if you're 60, your life

00:12:49.960 expectancy is 27 years, you're expected to live to 87. But if we could look at the methylation of

00:12:55.520 these two women and one of them came back and we were told, yes, but her biologic age is 65 and

00:13:02.440 the other one's biologic age is 55, the question is, does that delta of 10 years between them

00:13:11.460 actually translate to 10 years difference in lifespan? And so that's what these next generation

00:13:18.040 of clocks set out to do. Instead of predicting age, they started training on more clinically

00:13:24.600 meaningful outcomes. So they were trained on physiological biomarkers, data sets that would

00:13:31.080 be able to track mortality, and basically even things like rate of physiologic decline or pace

00:13:39.140 of aging. So the last category, this pace of aging one, I think is particularly interesting

00:13:44.220 because it aligns with what most people think they're getting when they look at a biologic

00:13:50.240 clock, which is not just how old am I, but how quickly am I aging right now? This topic came up,

00:13:55.320 by the way, in a podcast with Rich Miller, which was when they went back and looked at data from

00:14:02.020 the various ITP winners. So recall Rich Miller is the guy who oversees one of the parts of the

00:14:10.180 interventions testing program at the University of Michigan, where they take drugs like rapamycin,

00:14:15.200 metformin, nicotinamide, riboside, et cetera, they put them into mice over the duration of

00:14:21.140 their life. And then they look to see which of these drugs extend life. Well, they took all of

00:14:25.420 the ITP winners plus other things that were known to improve lifespan of mice, such as caloric

00:14:30.820 restriction. And they identified roughly a dozen or so, not epigenetic changes, but actual things

00:14:39.520 that showed up, proteins that showed up in those animals, something we would refer to as the

00:14:43.180 proteome, and they were able to generate in those mice predictive age rate calculators. So again,

00:14:51.740 a little too soon to tell if that's going to pan out in humans, but very interesting.

00:14:55.780 So many of these second-generation clocks were designed to predict outcomes like mortality by

00:15:02.200 incorporating methylation patterns that correlated with things like smoking exposure, inflammation,

00:15:06.460 and things like that. And while that's useful, it's important to understand that that kind of

00:15:11.200 changes how we interpret the output. So if a clock is particularly capturing a biological

00:15:16.840 fingerprint of something like smoking history or cardiometabolic health, then a shift in that

00:15:22.700 clock might reflect an improvement in that pathway. But it might reflect something really

00:15:28.280 uninteresting, like you're just recovering from a cold or you have lingering inflammation from

00:15:32.760 a heavy workout. So this is kind of where the excitement around aging clocks collides with

00:15:39.920 the reality of measurements, because there's basically two types of noise you have to consider

00:15:45.740 when you look at these clocks. You have to consider biological noise, which is the example I just gave

00:15:52.220 of how do I know that what you did in the day or week before didn't transiently impact what I'm

00:15:59.680 measuring, but truly has no impact on your health versus the measurement noise, which is how hard

00:16:07.540 is it to actually measure these things? So even in a very high quality lab, DNA methylation

00:16:13.160 measurements are not very stable. So variations can arise from a lot of things that how the sample

00:16:19.540 is handled and stored, differences in the methods for DNA extraction, you know, the efficiency of

00:16:26.320 the conversion steps that are used to read the methylation patterns, the batch effects on

00:16:32.920 methylation arrays themselves, and even differences in the mixture of immune cells present in the

00:16:39.060 blood at the time that the blood is drawn. Because remember, these are all being done not on tissue,

00:16:42.600 but on cells in the blood. And then on top of that, you know, the clocks are typically measuring

00:16:47.600 hundreds of thousands of methylation sites across the genome, and then trying to collapse all of

00:16:53.980 that information into a single summary score. And again, we love when we can do that, when we can

00:17:01.320 convert lots of data into a number, but we have to understand that we run risks of doing that.

00:17:06.620 So again, I'm not saying any of these things aren't challenges that maybe couldn't be overcome,

00:17:12.100 but I just want to make sure everybody understands like how, how technically complicated this is. So

00:17:17.380 again, just keep in mind biological noise and then technical or measurement noise. So basically

00:17:23.940 I think the reason these clocks are exciting is that they're kind of offering three things that

00:17:31.900 people want. So the first one I just kind of alluded to, which is compression. So aging is

00:17:37.280 very multi-dimensional. There are so many things that are going on. Your immune system is declining,

00:17:42.340 your fuel partitioning skills are declining, vascular health is declining, brain functions,

00:17:46.520 all these things are declining as we age on average, but the clocks are attempting to take

00:17:52.240 this and compress it into a single number. On the one hand, that's exciting. We love when we can do

00:17:58.040 things like that. BMI is a great example. Again, it's only combining height and weight, but it's

00:18:02.560 turning it into a single number. But we have to be mindful of the fact that the more you compress

00:18:09.320 something complicated, and BMI is a great example, it's only taking height and weight and compressing

00:18:14.200 it into a single number and trying to be a proxy for muscle mass, body fat, and things of those

00:18:19.060 nature. And here's the thing. On average, it's pretty good, right? At the population level,

00:18:24.240 BMI is pretty good. If I told you that in one city, the BMI average was 24, and in the other

00:18:31.140 city, the BMI average was 29, and I said, tell me which of those two cities do you think is healthier?

00:18:37.120 Again, knowing nothing else, you're all going to say 24, and you're almost assuredly right. 0.85

00:18:41.840 But if I told you I have an individual who's got a BMI of 24, and I have an individual whose BMI

00:18:47.380 is 29, tell me which one's healthier. Truth of the matter is, it's going to be tough. There are some

00:18:54.080 really unhealthy people with normal BMIs, and there are some really healthy people, usually quite

00:18:58.860 muscular, that have quite high BMIs. So this idea sort of falls apart, and you can imagine how much

00:19:04.460 more susceptible we would be to that when we're taking a much larger and more multi-dimensional

00:19:09.920 problem. Okay, the second big thing we want out of clocks is not just compression, but it's speed.

00:19:15.160 I kind of alluded to this at the outset, right? It would be amazing if we could do clinical trials

00:19:19.620 for a year and get the type of benefits in terms of insight that we would get if we were doing

00:19:26.080 these clinical trials for 20 years. The third one I also kind of alluded to already, which is at the

00:19:31.680 individual level, we want feedback. I want to know that if I changed my diet from this to that after

00:19:39.180 three months or six months, was that the right change to make? I want to know that if I'm taking

00:19:45.740 this supplement, which supposedly reduces inflammation and supposedly does several other

00:19:52.360 factors that are targeting hallmarks of aging, I want to know if it's doing it. So bottom line is,

00:19:59.340 this is why we want them. Question is, does it work? Okay. So the best way we could think to

00:20:05.840 talk about this is internally, meaning our research team, we looked at a couple of studies

00:20:11.520 that we thought really highlighted a couple of the important points here. And so that's what I

00:20:18.240 want to talk about here, these two studies. Okay. So the first study looked at an intervention.

00:20:24.840 It was a very simple intervention. They used omega-3 supplementation, vitamin D supplementation,

00:20:30.360 and exercise. And then they ask the question, will those simple interventions, and I'll talk

00:20:37.880 about them in a little bit more detail, move the needle on the four most common epigenetic

00:20:42.920 aging clocks in a randomized fashion? So, randomized people to these things measure on.

00:20:48.080 Second study that we're going to talk about takes a different approach, but asks if we can estimate

00:20:53.560 a person's pace of aging using structural features from a single brain MRI. So different

00:21:01.420 approach, but let's talk about them both. Okay, so let's start with the first study. This was

00:21:07.680 referred to as the DO-HEALTH study, and I think it's a reasonable use case example, okay? So again,

00:21:15.800 if an aging clock is going to be useful, the best case scenario is probably that it can detect

00:21:21.180 biological signals in a randomized control trial. So if you think back to, again, the problem we

00:21:26.300 started with, the endpoint you really care about is something like mortality or incidence of disease

00:21:32.780 like dementia or cardiovascular disease or cancer. And the interventions in this trial, which are,

00:21:38.180 you know, very reasonable things to propose, you're not going to figure out in a year or two

00:21:43.320 years or three years if they're having an impact on those things. But if you have a biological

00:21:47.000 clock that is true to those things, you're going to be in the, in the end zone. So what did the

00:21:52.020 study do? So the study took, you know, it was, it was a European large study. It was a two by two

00:21:57.540 by two factorial. So that means they tested these three interventions that I talked about. So

00:22:03.680 giving vitamin D, giving EPA, DHA, and assigning exercise. They tested them individually and in

00:22:10.880 combination. So each individual was then randomized to one of eight groups for the duration of the

00:22:16.280 study, which is a three-year duration study. The measurements were collected at baseline and then

00:22:20.580 at three years. So you've got a blood level at time zero and then at three years. So they looked

00:22:25.620 at nearly 800 generally healthy older adults. So everyone was 70 plus, mean age 75 from Switzerland.

00:22:35.560 About half of these individuals met the criteria for what we would call healthy aging. So they

00:22:40.880 were basically free of chronic diseases, disabilities, cognitive impairment, any other

00:22:45.080 limitations, and they were quite active. About 88% of these people reported regular physical

00:22:51.040 activity, and about 60% of these people reported exercising more than three days per week prior to

00:22:57.020 enrollment. Okay, so again, the interventions, 2,000 IU of vitamin D, a relatively modest amount

00:23:05.140 of EPA and DHA, so one gram a day that contained 330 milligrams of EPA, 660 milligrams of DHA,

00:23:14.020 and this was from marine algae. And then adherence for both the vitamin D and omega-3 levels was

00:23:21.780 assessed by changes in serum level. So they did have the ability to kind of go up or down based

00:23:26.800 on what they were measuring. And then on the activity front, they had a simple home-based

00:23:32.840 exercise regimen that consisted of mostly strength training, 30 minutes, three times a week.

00:23:39.620 And this was added on top of whatever you were doing at baseline. So whatever you're doing at

00:23:44.000 baseline, fine, but we add this. And then compliance was tracked with exercise diaries

00:23:48.960 and follow-up. So my first thought when I looked at this was these are kind of modest interventions.

00:23:53.940 You know, a gram of omega-3 is pretty low. 2,000 IU of vitamin D is not going to do much. And 90

00:23:59.780 minutes of exercise in people who are mostly already exercising, you know, I guess it depends

00:24:05.920 on what they're already doing. But truthfully, I would have thought that was the most interesting

00:24:09.220 thing. But none of these are Herculean things, especially when you combine it with the fact

00:24:13.660 that most of these participants were relatively healthy and physically active. So it's not clear

00:24:17.920 what you would see here. But that said, I think this trial was designed well, and it's a useful

00:24:24.040 way to test whether these aging clocks can detect subtle changes over time. So let's talk about the

00:24:29.760 tests, right? So again, time zero and time three years, they measured DNA methylation, and then

00:24:35.100 they applied these clocks. So let's talk about the clocks that they used, because I mentioned that

00:24:38.960 they used four next-gen clocks. So the first one is called PhenoAge. So this test uses methylation

00:24:45.120 data from about 500 CPG sites, and it's trained to reproduce a clinical biomarker score that

00:24:51.360 predicts mortality risk. So that biomarker score that I incorporate takes measurements beyond just

00:24:57.840 the CPGs. It looks at albumin, glucose, C-reactive protein, kidney function, white blood cell count,

00:25:04.740 and then the clock reflects the physiologic health rather than just sort of their chronological aid.

00:25:10.680 The next one is called GrimAge. This uses methylation data from about a thousand CPG

00:25:16.400 sites to estimate the level of plasma proteins that are linked to aging, such as GDF15, leptin,

00:25:25.940 PAI1, and also smoking exposure. So these methylation-derived biomarkers are then combined

00:25:33.300 with chronological age and sex to predict a time to death. So this is kind of an interesting one,

00:25:40.280 right? This is that one that would try to get at what I was saying earlier, which is

00:25:44.220 if you have a 60-year-old, the actuarial time to death expectation might be, you know,

00:25:51.400 I'd made up a number, but say 35 or 37 years. If you did a grim age on that person and it said

00:25:58.420 20 years, that would suggest to you that this person is much less healthy than the average

00:26:03.940 60-year-old. And if it said, no, 40 years, you would say, oh, this person's healthier than the

00:26:09.460 average person that you would expect to see that age. Okay. Then there's another one called Grim

00:26:13.640 Age 2, which is the same as Grim Age. It just has an updated set of biomarkers. So it's using

00:26:19.980 C-reactive protein and A1C along with some other refinements. And then you have another one called

00:26:26.320 the Dunedin-PACE estimate, which is really trying to estimate the rate of aging rather

00:26:33.620 than biological age.

00:26:35.300 So this uses the methylation patterns of 173 CPG sites.

00:26:40.660 So unlike the others, which were trained on cross-sectional areas, this is trained on

00:26:46.940 a longitudinal set of data across a population in New Zealand called Dunedin.

00:26:52.920 and that's what allowed them to track over time what the changes were and try to estimate this

00:27:02.060 if you will first derivative okay so we'll put in the show notes a table that just kind of

00:27:08.460 links to all of that so that you you can sort of keep that in mind and keep coming back to it

00:27:12.740 so just to summarize that the first three right which were the pheno age the grim age the grim

00:27:19.800 age too, those are ones that are kind of looking at biological age and trying to see if that can

00:27:24.920 be more predictive than chronological age. And then the Dunedin pace is designed to estimate

00:27:29.940 the pace of aging. Okay. So again, important distinction. And again, what does it matter,

00:27:35.140 right? The biological age might tell you that a person's physiology resembles that of a typical

00:27:40.480 person who's older or younger. That was the example I gave. The pace of aging is trying

00:27:45.140 to answer a slightly different question which is at the time of my measurement how fast is the

00:27:50.940 system deteriorating okay so let's talk about what the study found we'll link to the figures in the

00:27:59.820 show notes so you can go and actually see the figures yourself because I think the figures

00:28:02.820 sort of tell a thousand words right but basically they looked at what each of the four tests showed

00:28:12.640 and they have forest plots for each of them. And they look at each of the four different forest

00:28:17.700 plots for each of the interventions, combinations of, and thereabouts. So what I'll just call out

00:28:23.380 is what was significant. So the pheno-age study found significance, an effect size of 0.2,

00:28:33.600 for just the omega-3 intervention, and then for the omega-3 and the vitamin D intervention,

00:28:38.680 for omega-3 and exercise, and then for all treatments combined. When you looked at the

00:28:45.400 grim age by itself, just this first-gen grim age, it found no significance, no change across the

00:28:51.660 board in anything. When you looked at grim age 2, the only thing that showed significance

00:28:58.240 was omega-3 versus placebo. And when you looked at the Dunedin pace, the only thing that showed

00:29:06.800 significance was omega-3 versus placebo. So the bottom line here is on balance, you know,

00:29:14.840 the most consistent finding was that something about omega-3 supplementation moved the needle

00:29:20.660 in at least three of the four clocks. So the only clock that it didn't move the needle in was the

00:29:26.220 first gen grim age clock. Now that said, even though the omega-3 change showed consistency

00:29:33.200 in three of the four tests, the magnitude of the effect was quite small. So if you translate it

00:29:37.620 into something a little more intuitive, the effect corresponded to about three months of reduced

00:29:43.300 aging over three years, depending on which clock you look at. And again, I still actually think

00:29:48.780 that's a larger magnitude than I would affect, but maybe if you believe these clocks, the

00:29:53.200 translation is for people with really, really low omega-3 intake, you don't need much to move

00:29:59.240 the needle. The vitamin D supplementation, as I said, didn't really impact the clocks at all.

00:30:04.980 But again, given the dosage, I don't think that was surprising. 30% of these participants

00:30:08.660 had a baseline level below 20 nanograms per deciliter. So it's possible that just the 2000

00:30:15.240 IU wasn't enough to get them anywhere. And then exercise also failed to show an independent effect.

00:30:22.520 Again, I think context here matters. Remember, these participants were already physically

00:30:26.400 active at baseline. So it might be that for a study of this size and this duration, you weren't

00:30:33.180 going to see the effect unless you did it in people who were not active. So again, not sure

00:30:39.540 what to make of this. I think that the results of this study, you know, don't answer a ton of

00:30:44.580 questions. These interventions are quite common. Not sure if they were dosed correctly. I don't

00:30:50.420 recall, actually now that I think about it, what they pre-selected as their power analysis. In

00:30:55.220 other words to pick the sample size that they picked they had to assume a certain effect size

00:31:00.660 and it might be that the effect size was it was a real one but it was smaller that would mean it

00:31:05.860 might not be clinically significant but I suppose that the use case here is reasonable right which

00:31:13.540 is were there small molecular shifts over time in a in an otherwise well-controlled setting of

00:31:21.520 course it also begs the question though which of these clocks do you believe right because if you

00:31:27.260 think that grim age is the right clock then it would say none of these things mattered if you

00:31:32.340 pick pheno age you would say gosh everything mattered except for exercise and if you pick

00:31:39.200 grim age too you would say omega-3 was the only thing that mattered and the same with the need

00:31:43.920 pace so i still at least on a personal level don't know that i can tell which of these clocks makes

00:31:50.280 the most sense. Now, one potential advantage of using an aging clock is that it does give a

00:31:56.940 shared endpoint across different interventions. So if you at least believe that there's internal

00:32:02.820 consistency, you could feel maybe more comfortable that, okay, in an absolute sense, I don't know if

00:32:09.260 these changes are right, but I could figure out that, hey, I'm getting more bang for my buck doing

00:32:15.000 more cardio versus more resistance training or vice versa. So the other thing I think that we

00:32:20.540 don't know here is what the, what the measurement error was. So how much technical noise there was

00:32:26.740 in these. Um, we've talked about this earlier in the podcast, not this one, but previous episodes

00:32:31.540 where, you know, there are lots of folks out there that'll go and buy the same, you know,

00:32:35.680 multiple versions of the same test, take a blood sample, identical, you know, single blood sample,

00:32:41.200 and then spread it across multiple tests, and you get different results. And so, you know,

00:32:46.420 there are various reasons that that could be happening. It's not clear from this paper

00:32:49.880 exactly what their technical spread was, but my guess is there's more noise in these measurements

00:32:57.560 than, say, measuring a blood glucose, where the assay is much easier and much more standardized

00:33:02.480 lab-to-lab. You know, I still think that whether these clocks are actually capturing the biology

00:33:08.840 we care about well enough, I think that's an unresolved question. We can obviously ask

00:33:14.600 different questions with clocks, but with most medical research, we kind of focus on very

00:33:19.660 specific outcomes, right? We're going to look at muscle strength if we're testing resistance

00:33:23.860 training. We're going to look at blood pressure if you're looking at an antihypertensive drug or

00:33:28.540 LDL cholesterol if you're using a lipid-lowering drug. I like what the aging clocks are trying to

00:33:34.080 do because not all things are going to be measured in single parameters. And even if you do lower LDL

00:33:42.800 by itself, it would be nice to know how much of an impact is that having on my overall aging.

00:33:48.660 So I think maybe the most important thing here is that if an aging clock could allow a researcher

00:33:54.280 to ask a long-term question within a shorter trial, that alone to me is reason enough to do

00:34:00.540 this. And then everything else, whether we individually can use them, that those would be,

00:34:04.500 those would be fantastic. I think what I liked about this paper was that the authors didn't kind

00:34:09.280 of use just a single clock that gave them the answer they were hoping for. They pre-specified

00:34:14.500 four clocks. They showed the data for four clocks. We talked about the results, right? Three of the

00:34:19.180 four showed a benefit for omega-3. Again, is that because they're detecting using different CPG

00:34:25.960 sites? Is it because they're using different biomarkers? It's not, it's not, it's not clear.

00:34:29.400 The authors point out that this type of discordance is not unfamiliar. There's a very famous study called the Calorie Trial. This was run out of Pennington many years ago. Eric Robison, who's a previous guest on the podcast, actually was the PI for that. But the data set from Calorie has been used multiple times.

00:34:47.980 This was a calorie restriction study, and it showed that, so the calorie restriction

00:34:54.740 showed a reduction in pace of aging using the Dunedin pace clock, but it didn't affect

00:35:01.520 the pheno age or grim age, which were the, these other first generation clocks.

00:35:05.340 So again, this is not, this is not unusual.

00:35:08.860 So where does this leave us?

00:35:10.640 The first study, the DOE health trial, shows that aging clocks can detect small biological

00:35:16.600 changes in response to an intervention, and in this case it was omega-3 supplementation that

00:35:21.080 appeared to slightly slow several epigenetic clocks over three years. The second study,

00:35:27.400 the Dunedin-PAC-NI paper, showed that scientists could build new types of aging clocks, in this

00:35:33.100 case using structural brain imaging, that appear to capture meaningful patterns related to cognitive

00:35:38.680 decline, frailty, and even overall mortality. So taken together, these studies illustrate both the

00:35:45.140 promise and limitations of aging clocks. On the promising side, these clocks may help researchers

00:35:51.480 detect early biological signals in situations where traditional clinical outcomes would take

00:35:57.100 decades to measure. And I think that's incredibly valuable for aging research. Running a 20-year

00:36:03.660 prevention trial for every possible intervention, you know, simply isn't feasible. And the biomarkers

00:36:10.380 that capture aspects of the aging process could help us prioritize which ideas are worth testing

00:36:16.360 more rigorously and putting more resources into. But at the same time, these clocks are still

00:36:21.720 models, and models come with limitations. In fact, to quote a famous physicist, and it's debated if

00:36:28.020 it was Fermi who said this, all models are wrong, some are useful. But the question is, how useful

00:36:33.700 are these models? Well, different clocks capture different aspects of biology. Small shifts,

00:36:39.960 in the measurements don't necessarily translate into meaningful improvements in health outcomes.

00:36:45.260 And most importantly, we still don't know whether changing the clock actually changes

00:36:50.000 what we ultimately care about, things like disease risk, disability, or lifespan.

00:36:56.460 This uncertainty matters a lot when we move from research into consumer health.

00:37:02.160 Right now, aging clocks are increasingly being marketed as a tool for individuals,

00:37:07.660 something you can order online, track over time, and use to evaluate your own lifestyle changes.

00:37:14.640 Some of these companies even promise to improve your biological age with supplements that they

00:37:19.980 will conveniently sell to you as well. But based on the current evidence, it's not clear that these

00:37:25.240 numbers actually give consumers any actionable information. If your aging clock changes by a few

00:37:31.540 months, what should you do differently? Should you change your diet, your exercise program,

00:37:36.400 your medications, take more of the supplements? At this moment, the science as it stands does

00:37:45.220 not provide clear answers to those questions. And in many cases, we already have much more

00:37:50.240 reliable metrics that tell us about health and longevity risk. Things like blood pressure,

00:37:56.080 glucose, lipids, whether you're smoking or not, all the various metrics we have around physical

00:38:01.620 fitness, and body composition. These are not particularly glamorous biomarkers, but they have

00:38:08.120 something that aging clocks don't have yet. Decades of evidence linking them directly to real clinical

00:38:15.600 outcomes. In fact, if you take a step back for a moment, we've already solved a large part of the

00:38:21.460 problem that aging clocks are trying to address. There's a particular industry out there that is

00:38:29.740 so good at doing this that their formulas are proprietary. Life insurance companies have been

00:38:37.100 predicting mortality risk for decades using actuarial models based on these various factors.

00:38:44.420 In fact, I recently reached out to a senior member of a life insurance company,

00:38:50.360 And this person shared with me that if they ever see a deviation in expected premium payouts that exceed 1%, it would be considered the most unusual event they could imagine.

00:39:07.360 So that means that at the population level, they have to be able to predict mortality at a degree of accuracy that exceeds anything we can imagine.

00:39:20.360 And I further asked if they were using any of the commercially available or research-grade

00:39:27.600 biological clocks, and the answer was no. So I think that tells you something,

00:39:33.360 that these companies are still doing their jobs surprisingly well, and they don't use

00:39:40.160 any of the aging or biological clocks. They rely instead on the data that we understand.

00:39:47.540 So at least for me, I think about this as if someone were to offer a biological age score, it's worth asking them what that number is actually telling them. Is it telling them something new, or at best, is it just repackaging information you already have or understand?

00:40:06.040 So again, this is not to say that these clocks provide no value. They are fascinating scientifically, and they may become more valuable as research tools over time. They may evolve into clinically meaningful biomarkers over time. But right now, I would say they would be best viewed as experimental tools for studying aging, at least at a broad enough population level, but not as definitive health metrics for individual decision-making.

00:40:35.100 So if you're interested in your own longevity, the takeaway is quite simple. Instead of obsessing over whether your biological age is 42 or 45, it's probably much more productive to focus on the things you already know are going to matter, right? Staying active, eating a balanced diet, getting appropriate sleep, maintaining and measuring clinically validated biomarkers.

00:41:01.240 Now, again, they might not sound as flashy as biological age and the scores that are attached

00:41:05.700 to them, but they remain some of the most powerful tools that we have for improving

00:41:09.640 both lifespan and healthspan. And as aging research continues to evolve, perhaps one day

00:41:15.580 these biomarkers that we get out of these clocks will help guide those efforts even further.

00:41:21.580 But I think for now, it's safe to say that the fundamentals still matter most.

00:41:26.440 Thank you for listening to this week's episode of The Drive. Head over to peteratiamd.com

00:41:33.500 forward slash show notes if you want to dig deeper into this episode. You can also find me

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00:41:45.100 review on Apple Podcasts or whatever podcast player you use. This podcast is for general

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00:41:55.020 or other professional healthcare services, including the giving of medical advice.

00:41:59.840 No doctor-patient relationship is formed. The use of this information and the materials linked to

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00:42:10.840 be a substitute for professional medical advice, diagnosis, or treatment. Users should not disregard

00:42:16.160 or delay in obtaining medical advice from any medical condition they have, and they should

00:42:20.440 seek the assistance of their healthcare professionals for any such conditions. Finally,

00:42:25.360 I take all conflicts of interest very seriously. For all of my disclosures and the companies I

00:42:30.600 invest in or advise, please visit peteratiamd.com forward slash about where I keep an up-to-date

00:42:37.920 and active list of all disclosures.

The Peter Attia Drive - April 06, 2026

#386 - Aging clocks—what they measure, how they work, and their clinical and real-world relevance

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