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

0:10Hey, everyone. Welcome to the Drive podcast. I'm your host, Peter Attia. This podcast, my website, and my weekly newsletter all focus on the goal of translating the science of longevity into something accessible for everyone. Our goal is to provide the best content in health and wellness, and we've established a great team of analysts to make this happen. It is extremely important to me to provide all of this content without relying on paid ads. To do this, our work is made entirely possible by our members, and in return, we offer exclusive member-only content and benefits above and beyond what is available for free.

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1:07Peter Attia:In this episode, I take a different approach where I walk through a single topic in depth. And this is a topic that many of you have been asking about, aging clocks. So in this episode, I explain what aging clocks are and the difference between chronological age and biological age, along with the difference between those and something called the pace of aging, how epigenetic clocks work, and what they may actually be measuring. I'm going to talk about a randomized control trial that used three very simple interventions and tested four of the most common aging clocks. I'm going to also talk about another study that used brain imaging via MRI to study the pace of aging and see what could be gleaned about not just the risk of dementia, but also mortality.

1:54Peter Attia:I'll discuss the biggest limitation in the field, which is whether changing a clock actually changes meaningful clinical outcomes. So without further delay, I hope you enjoy this special episode of The Drive.

2:11Peter Attia:So if you wanted to run the perfect anti-aging trial, you know, the end points would be really obvious. You'd want to see fewer heart attacks, fewer cancers, fewer dementia diagnoses, and ultimately fewer deaths. So we would call these hard outcomes real outcomes that matter. These are the clinical outcomes that we all care about. Now, of course, the reason we don't see these trials is that they would take a very long time. These would literally be 20-year trials, and with that would come enormous complexity and cost. Furthermore, it would be very difficult to ensure that whatever intervention you put in place was being put in place for the duration of this time.

2:54Peter Attia:I mean, that would be not that hard to do if it was a drug trial, because it's relatively easy to take a drug, but it would be more challenging for a lifestyle trial. Okay. So every few years, the fields of geroscience and medicine and cardiovascular disease, etc., they go looking for a proxy or a shortcut. So some intermediate marker that could move faster than these hard outcomes, but that would still predict the hard outcome reliably. And I think over the past few years, what we've really seen is that aging clocks are the most interesting and popular proposed shortcut. Again, I don't use the word shortcut with a sort of negative connotation.

3:36Peter Attia:It's like, this is what we need. We do need a shortcut. We need a proxy. So again, the idea here is pretty compelling, right? Imagine you could have a single number that would predict your actual aging or your actual biological age that's different from your chronological age, or maybe a rate of aging that reflects a new trajectory you're on. This could be very valuable in designing clinical trials or looking at interventions. And even at the individual level, understanding if you've made a change and is it making a difference? So this would, you know, think about this as a foray into precision medicine.

4:11Peter Attia:Now, there's a little bit of a problem in my mind because these aging clocks are being marketed as the latest and maybe best way to keep tabs on your health. Lots of people are ordering them. They're available to anybody. They're sold by longevity docs who promise to improve your biologic age with groundbreaking, you know, combinations of peptides or other elixirs. But I think it's worth looking into these a little more closely to understand what the science can actually tell us. And I think the best way to do this is to look closely at two studies, two very interesting studies that can help us get at the fundamental questions that we really want to be asking around this, which is, what is the clinical utility of an aging clock?

4:59Peter Attia:clock. So before we do that though, I just want to kind of make sure everybody's starting from the same footing in terms of understanding the biological stuff that we're talking about. So what is an aging clock? Well, basically at its core, it's a prediction model. But let's take a step back. Your chronological age is also a prediction model. You see, if I told you that in front of me, there is a 20-year-old and there is a 70-year-old, and I asked you to predict which one of those people is going to die first, I think everybody would, knowing nothing else, make the correct prediction. Now, we could layer onto that certain other factors.

5:40Peter Attia:So if I said, okay, well, I actually now have two 70-year-olds in front of me, and one of them has cancer, and the other one does not, do you predict which one of those is going to live longer than the other. And again, without knowing anything beyond what I told you, I think everybody would make the same prediction. And so this idea of using information to predict mortality is not new. It is the entire basis of the actuarial underwriting industry. And there are companies that are exceptionally good at doing this. These are called life insurance companies. And their data are incredibly proprietary, and it's really less so their data and more so what they do with the data that is incredibly proprietary, right?

6:24Peter Attia:They gather a lot of information about you. They do a blood draw on you. They know your age. They know various factors about you. They take your blood pressure, your weight, and things like that. Relatively rudimentary stuff. But from that, they have these tables, again, highly proprietary, that seem to do a very good job of predicting when you're going to die. And so the question is, would one of these aging clocks be even better. Okay, so let's talk about how these things work. So they typically work by starting with some biological data. And the most common thing that we're going to hear about is epigenetic data.

7:00Peter Attia:So this is DNA methylation. And then they train an algorithm to look at that and predict something age-related. So I think it's worth spending a minute on DNA methylation. I don't want to go far down the rabbit hole on this, but you've undoubtedly heard the term. And so I just want to make sure everybody's playing from the same level. So DNA methylation is a way that we, or the way that the body modifies epigenetically what the DNA expression is. So it doesn't change the sequence of DNA, but it can influence how the genes are turned on or turned off, right? So that's what we call expression of genes.

7:37Peter Attia:So when you modify the epigenome, which is basically when you put a methyl group, so that's a carbon with three hydrogens, when you put it on the backbone of the DNA, that impacts whether or not that section of DNA gets turned into RNA. That's what we mean by expression. You may have heard the term CPG, but not in reference to consumer packaged goods, but a CPG refers to the location where these methylations most commonly take place. If you remember back to high school biology, we have these four nucleotides. The C is the abbreviation for cytosine. And so where these things typically occur is right on the phosphate bond that links the C, the cytosine, with the G, the guanine nucleotide.

8:37Peter Attia:So when we talk about CPGs, that's just kind of another way that people kind of quickly talk about the methylations. So the methylations that occur at these CPG sites will then affect quite strongly the genes that are near to those areas. And why we care about this, of course, why I'm even talking about this is these methylation levels of many sites actually change somewhat predictably as we age. So this is kind of the rationale for all of this, right, is as we age, methylation sites change. Ergo, if we can measure what's happening at methylation sites, can we impute age? Can we impute something better than chronologic age?

9:21Peter Attia:Because remember, chronological age is an awesome predictor as it stands. But we're asking, can we do better? Because chronological age is great at telling you that on average, a 60-year-old is going to live, you know, a shorter duration than a 50-year-old. But we know that that's not true at the individual level. There are plenty of 50-year-olds that are going to have a shorter remaining life than plenty of 60-year-olds. It just depends on the individual health and a whole bunch of other things. So we want to get it that different. Okay. So these patterns are going to shift gradually over time and various factors, behavioral factors, such as smoking, metabolic health, inflammation, actually play a role in that.

10:05Peter Attia:And so for this reason, researchers came to the conclusion, you know, roughly 10, little over 10 years ago, that we could use these as kind of a molecular record keeping of what's going on in the body. And I think that's probably why DNA methylation has created such an important and foundational part of the clock story. So that's why I kind of went a little deep in the weeds there. I think it's important. So, you know, you've probably heard of the Horvath clock. That's one of the earliest first-generation clocks, and that was obviously based on this. So these models were trained on large data sets of DNA methylation, which were measured and collected from thousands of individuals across a wide age range, and they're mostly using cross-sectional cohorts rather than tracking an individual over time.

10:56Peter Attia:Why? Because as exciting as it would be to track an individual over time, those data sets are somewhat limited and they're harder to get. Whereas if you take very large cohorts where you just slice the population, you would get access to 20-year-olds, 25-year-olds, 30-year-olds, 50-year-olds, 60-year-olds, 70-year-olds, 90-year-olds, etc. And the hope would be that, hey, we're going to see what the signature of methylation is, you know, over time. So given that chronological age was the outcome that the model was trying to predict, it's not really surprising that these clocks became very good at estimating age, often within kind of a few years of age.

11:34Peter Attia:But at the same time, the fact that patterns of DNA methylation change consistently across ages of individuals, that had a biological interest to it. And it suggested that there might be certain areas in the genome where methylation shifts occur in a predictable way as people get older. Okay, so from a clinical standpoint, what does that tell us? Well, estimating chronological age, which is what these first generation clocks did, wasn't adding any value because we already know chronological age. So it was more of a proof of concept, but that's when the researchers realized, okay, what we really want to do is come up with something that could be better than chronological age.

12:16Peter Attia:And that's where this idea of biological age could come from. And I just want to explain like sort of an extreme example of what this would look like. So again, let's say you took two 60-year-olds who, I didn't tell you anything else about them other than they're both 60 and they're, let's just say they're both the same sex. So two 60-year-old women. So an actuarial table would say based on their chronological age, these women both would have a life expectancy of, I'm making this up, 27 years. So if you're 60, your life expectancy is 27 years, you're expected to live to 87. But if we could look at the methylation of these two women, and one of them came back and we were told, yes, but her biologic age is 65, and the other one's biologic age is 55, the question is, does that delta of 10 years between them actually translate to 10 years difference in lifespan?

13:15Peter Attia:And so that's what these next generation of clocks set out to do. Instead of predicting age, they started training on more clinically meaningful outcomes. So they were trained on physiological biomarkers, data sets that would be able to track mortality, and basically even things like rate of physiologic decline or pace of aging. So the last category, this pace of aging one, I think is particularly really interesting because it aligns with what most people think they're getting when they look at a biologic clock, which is not just how old am I, but how quickly am I aging right now? This topic came up, by the way, in a podcast with Rich Miller, which was when they went back and looked at data from the various ITP winners.

14:05Peter Attia:So recall Rich Miller is the guy who oversees one of the parts of the interventions testing program at the University of Michigan, where they take drugs like rapamycin, metformin, nicotinamide, riboside, et cetera. They put them into mice over the duration of their life, and then they look to see which of these drugs extend life. Well, they took all of the ITP winners plus other things that were known to improve lifespan of mice, such as caloric restriction, and they identified roughly a dozen or so, not epigenetic changes, but actual things that showed up, proteins that showed up in those animals, something we would refer to as the proteome, and they were able to generate in those mice predictive age rate calculators.

14:50Peter Attia:So again, a little too soon to tell if that's going to pan out in humans, but very interesting. So many of these second generation clocks were designed to predict outcomes like mortality by incorporating methylation patterns that correlated with things like smoking exposure, inflammation, and things like that. And while that's useful, it's important to understand that that kind of changes how we interpret the output. So if a clock is particularly capturing a biological fingerprint of something like smoking history or cardiometabolic health, then a shift in that clock might reflect an improvement in that pathway.

15:25Peter Attia:But it might reflect something really uninteresting, like you're just recovering from a cold or you have lingering inflammation from a heavy workout. So this is kind of where the excitement around aging clocks collides with the reality of measurements, because there's basically two types of noise you have to consider when you look at these clocks. You have to consider biological noise, which is the example I just gave, of how do I know that what you did in the day or week before didn't transiently impact what I'm measuring, but truly has no impact on your health, versus the measurement noise, which is how hard is it to actually measure these things.

16:08Peter Attia:So even in a very high quality lab, DNA methylation measurements are not very stable. So variations can arise from a lot of things that how the sample is handled and stored, differences in the methods for DNA extraction, you know, the efficiency of the conversion steps that are used to read the methylation patterns, the batch effects on methylation arrays themselves, and even differences in the mixture of immune cells present in the blood at the time that the blood is drawn. Remember, these are all being done not on tissue, but on cells in the blood. And then on top of that, you know, the clocks are typically measuring hundreds of thousands of methylation sites across the genome, and then trying to collapse all of that information into a single summary score.

16:57Peter Attia:And again, we love when we can do that, when we can convert lots of data into a number, but we have to understand that we run risks of doing that. So again, I'm not saying any of these things aren't challenges that maybe couldn't be overcome, but I just want to make sure everybody understands how technically complicated this is. So again, just keep in mind biological noise, and then technical or measurement noise. So basically, I think the reason these clocks are exciting is that they're kind of offering three things that people want. So the first one I just kind of alluded to, which is compression.

17:35Peter Attia:So aging is very multi-dimensional. There are so many things that are going on. Your immune system is declining. Your fuel partitioning skills are declining. Vascular health is declining. Brain functions, all these things are declining as we age on average, but the clocks are attempting to take this and compress it into a single number. On the one hand, that's exciting. We love when we can do things like that. BMI is a great example. Again, it's only combining height and weight, but it's turning it into a single number. But we have to be mindful of the fact that the more you compress something complicated, and BMI is a great example, it's only taking height and weight and compressing it into a single number and trying to be a proxy for muscle mass, body fat, and things of those nature.

18:19Peter Attia:And here's the thing, on average, it's pretty good, right? At the population level, BMI is pretty good. If I told you that in one city, the BMI average was 24, and in the other city, the BMI average was 29, and I said, tell me which of those two cities do you think is healthier? Again, knowing nothing else, you're all going to say 24, and you're almost assuredly right. But if I told you I have an individual who's got a BMI of 24, and I have an individual whose BMI is 29, tell me which one's healthier. Truth of the matter is, it's going to be tough. There are some really unhealthy people with normal BMIs, and there are some really healthy people, usually quite muscular, that have quite high BMIs.

19:00Peter Attia:So this idea sort of falls apart, and you can imagine how much more susceptible we would be to that when we're taking a much larger and more multi-dimensional problem. Okay, the second big thing we want out of clocks is not just compression, but it's speed. I kind of alluded to this at the outset, right? It would be amazing if we could do clinical trials for a year and get the type of benefits in terms of insight that we would get if we were doing these clinical trials for 20 years. The third one I also kind of alluded to already, which is at the individual level, we want feedback. I want to know that if I changed my diet from this to that after three months or six months, was that the right change to make?

19:43Peter Attia:I want to know that if I'm taking this supplement, which supposedly reduces inflammation and supposedly does several other factors that are targeting hallmarks of aging, I want to know if it's doing it. So bottom line is, this is why we want them. question is, does it work? Okay. So the best way we could think to talk about this is internally, meaning our research team, we looked at a couple of studies that we thought really highlighted a couple of the important points here. And so that's what I want to talk about here are these two studies. Okay. So the first study looked at an intervention. It was a very simple intervention.

20:26Peter Attia:They used omega-3 supplementation, vitamin D supplementation, and exercise. And then they asked the question, will those simple interventions, and I'll talk about them in a little bit more detail, move the needle on the four most common epigenetic aging clocks in a randomized fashion? So randomized people to these things measure on. Second study that we're going to talk about takes a different approach, but asks if we can estimate a person's pace of aging using structural features from a single brain MRI. So, different approach, but let's talk about them both. Okay, so let's start with the first study.

21:07Peter Attia:This was referred to as the DO-HEALTH study, and I think it's a reasonable use case example, okay? So again, if an aging clock is going to be useful, the best case scenario is probably that it can detect biological signals in a randomized control trial. So if you think back to, again, the problem we started with, the endpoint you really care about is something like mortality or incidence of disease like dementia or cardiovascular disease or cancer. And the interventions in this trial, which are, you know, very reasonable things to propose, you're not going to figure out in a year or two years or three years if they're having an impact on those things.

21:45Peter Attia:But if you have a biological clock that is true to those things, you're going to be in the end zone. So what did the study do? So the study took, you know, it was a European large study. It was a two by two by two factorial. So that means they tested these three interventions that I talked about. So giving vitamin D, giving EPA, DHA, and assigning exercise. They tested them individually and in combination. So each individual is then randomized to one of eight groups for the duration of the study, which is a three-year duration study. The measurements were collected at baseline and then at three years.

22:21Peter Attia:So you got a blood level at time zero and then at three years. So they looked at nearly 800 generally healthy older adults. So everyone was 70 plus, mean age 75 from Switzerland. About half of these individuals met the criteria for what we would call healthy aging. So they were basically free of chronic diseases, disabilities, cognitive impairment, any other limitations. And they were quite active. About 88 % of these people reported regular physical activity, and about 60 % of these people reported exercising more than three days per week prior to enrollment. Okay, so again, the interventions, 2 ,000 IU of vitamin D, a relatively modest amount of EPA and DHA, so one gram a day that contained 330 milligrams of EPA, 660 milligrams of DHA, and this was from marine algae.

23:16Peter Attia:And then adherence for both the vitamin D and omega-3 levels was assessed by changes in serum level, so they did have the ability to kind of go up or down based on what they were measuring. And then on the activity front, they had a simple home-based exercise regimen that consisted of mostly strength training, 30 minutes, three times a week. And this was added on top of whatever you were doing at baseline. So whatever you're doing at baseline, fine, but we add this. And then compliance was tracked with exercise diaries and follow-up. So my first thought when I looked at this was these are kind of modest interventions.

23:53Peter Attia:You know, a gram of omega-3 is pretty low. 2000 IU of vitamin D is not going to do much. And 90 minutes of exercise in people who are mostly already exercising, you know, I guess it depends on what they're already doing, but truthfully, I would have thought that was the most interesting thing, but none of these are Herculean things, especially when you combine it with the fact that most of these participants were relatively healthy and physically active. So it's not clear what you would see here. But that said, I think this trial was designed well, and it's a useful way to test whether these aging clocks can detect subtle changes over time.

24:27Peter Attia:So let's talk about the tests, right? So again, time zero and time three years, they measured DNA methylation, and then they applied these clocks. So let's talk about the clocks that they used, because I mentioned that they used four next-gen clocks. So the first one is called PhenoAge. So this test uses methylation data from about 500 CPG sites, and it's trained to reproduce a clinical biomarker score that predicts mortality risk. So that biomarker score that incorporate takes measurements beyond just the CPGs. It looks at albumin, glucose, C-reactive protein, kidney function, white blood cell count, and then the clock reflects the physiologic health rather than just sort of their chronological aid.

25:10Peter Attia:The next one is called GrimAge. This uses methylation data from about a thousand CPG sites to estimate the level of plasma proteins that are linked to aging, such as GDF15, leptin, PAI1, and also smoking exposure. So these methylation-derived biomarkers are then combined with chronological age and sex to predict a time to death. So this is kind of an interesting one, this is that one that would try to get at what I was saying earlier, which is, if you have a 60-year-old, the actuarial time to death expectation might be, I've made an up in a number, but say 35 or 37 years. If you did a grim age on that person and it said 20 years, that would suggest to you that this person is much less healthy than the average 60-year-old.

26:04Peter Attia:And if it said, no, 40 years, you would say, oh, this person's healthier than the average person that you would expect to see that age. Okay. Then there's another one called grim age two, which is the same as grim age. It just has an updated set of biomarkers. So it's using C-reactive protein and A1C, along with some other refinements. And then you have another one called the Dunedin-PACE estimate, which is really trying to estimate the rate of aging rather than biological age. So this uses the methylation patterns of 173 CPG sites. So unlike the others, which were trained on cross-sectional areas, this is trained on a longitudinal set of data, across a population in New Zealand called Dunedin.

26:53Peter Attia:And that's what allowed them to track over time what the changes were and try to estimate this, if you will, first derivative. Okay. So we'll put in the show notes a table that just kind of links to all of that so that you can sort of keep that in mind and keep coming back to it. So just to summarize that, the first three, right, which were the Fino age, the Grimm age, the Grimm age too, those are ones that are kind of looking at biological age and trying to see if that can be more predictive than chronological age. And then the Dunedin pace is designed to estimate the pace of aging. Okay. So again, important distinction.

27:33Peter Attia:And again, what does it matter, right? The biological age might tell you that a person's physiology resembles that of a typical person who's older or younger. That was the example I gave. The pace of aging is trying to answer a slightly different question, which is at the time of my measurement, how fast is the system deteriorating? Okay. So let's talk about what the study found. We'll link to the figures in the show notes so you can go and actually see the figures yourself, because I think the figures sort of tell a thousand in words, right? But basically they looked at what each of the four tests showed, and they have forest plots for each of them, and they look at each of the four different forest plots for each of the interventions, combinations of, and thereabouts.