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

**Peter Attia** (0:11)
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Welcome to a special episode of The Drive. 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. 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.
So if you wanted to run the perfect anti-aging trial, the endpoints 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. 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, et cetera, 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. 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. 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 ground baking 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? So before we do that, though, I just want to kind of make sure everybody's starting from the same, 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. 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? 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. 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. 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 package 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. 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? It 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? Because remember, chronological age is an awesome predictor as it stands. But we're asking, can we do better? Because chronologic age is great at telling you that on average, a 60-year-old is going to live 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. 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 datasets 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. Why? Because as exciting as it would be to track an individual over time, those datasets 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, et cetera. And the hope would be that, hey, we're going to see what the signature of methylation is 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 a few years of age. 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, 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?