I have developed a pan-tissue clock that applies to all human cells and tissues. But there is a question. Is blood a good surrogate tissue for other organs? Is it sufficient to just measure blood methylation? I generated here methylation data from roughly 120 postmortem individuals for whom we have measured different tissues (blood, liver, spleen, bone marrow, and so on). From which we can define this measure of epigenetic age acceleration as a residual of methylation age where you regress out chronological age. And you can ask what’s the correlation of epigenetic age acceleration in blood vs. in bone marrow, and it’s a high correlation. But then you see a surprising result in the liver. Correlation of blood and liver age acceleration in methylation is negligible. You can really see this analysis as a glass half full or half empty way. Either the current clocks show pretty well conservation across tissue types, or maybe blood age acceleration is not really related to heart age acceleration or liver. So there is an opportunity to develop new human biomarkers that really hone in on cytosines that are conserved. Whose aging effects are conserved across tissues. It would be great to have one source of DNA, where you could capture and measure the epigenetic age of all others. The extreme example is the brain, we all want to measure the brain, but what accessible tissue can serve as a surrogate?

I wanted to show you a surprising effect from a different study. The upper row shows the effect of hypertension. Hypertension was associated with age-acceleration in different tissues, but not all organs. It could be that certain age-related diseases will affect epigenetic aging in many organs while others might have only a precise effect on one specific organ. So if you want to predict mortality, is blood methylation sufficient? Or should you measure methylation in multiple tissues and cell types?

Moving on to another topic. Many of you may know that we developed a new array for measuring methylation in all mammalian species. It can be purchased through my non-profit – Epigenetic Clock Development Foundation. If you are interested, reach out to Bobby Brooke – it’s a non-profit so we want to promote the use of clocks.

The array was designed to tolerate mutations across species. Some of the nucleotides in different species are mutated. But if you still want to measure them, you need to have oligonucleotide probes that still bind, and that’s very much done by this array.

We built very accurate clocks for the naked mole rat, an animal that has negligible senescence. But according to epigenetic clocks it definitely ages.

Another application of clocks. This is a dual species clock. You can take this one regression model that measures relative age in both humans and rats. The orange dots correspond to rat samples and the green dots to human samples, and you can see they overlap and overlay. That’s because we defined the relative age (x axis) as chronological age in units of years divided by the maximum lifespan of the species, so it is normalized. And it is just one of these miracles of cytosine methylation that you can build very accurate dual species clocks.

We didn’t stop there and aimed to develop an universal mammalian clock (in hindsight a better name would be pan-mammalian clock). This is one regression model for measuring relative age across all mammalian species.

This is a result of application to over 10,000 tissue samples, labeled by species (over 150 species). And you see the methylation levels predict relative age with the correlation close to 0.95. It is a miracle that this is possible. Why? We are using the maximum lifespan of the species in the denominator, but this maximum lifespan can often only be estimated (think of the debate about the maximum to human lifespan), but even in spite of that substantial noise, methylation allows us to get this very accurate and substantial correlation. And it is even reassuring that even in the worst case (application of pan-mammalian clock to marsupials), this universal clock still works.

This is how the universal clock works in specific species, as you can see it works very well in most species.

Different topic – forget chronological age. If we want to figure out maximum lifespan (a fundamental life history trait), can you predict it based on methylation? And the answer is clear, we can see a correlation of almost 0.9, so this is a rare profound insight that methylation allow us to get.

We have this estimate of the maximum lifespan, now the question is what about growth hormone knockout mice? Their lifespan is very much increased compared to regular mice. And interestingly, this panel shows you that this maximum lifespan predictor shows this expected association, the knockout mice have a predicted maximum lifespan that’s older than the normal mice. The right panel then shows caloric restriction for comparison, and there is no effect. We all know that CR changes at least the average maximum lifespan of the mice, no question, but the question is does it really change the species characteristic? According to this analysis, it does not. It is very important to establish species characteristics such as maximum lifespan from what people often care about, which is the risk estimate of mortality or morbidity. Lots of things relate to mortality but they may not change the maximum lifespan of the species.

Different topic. This is always the quote that I think when we have speakers here from the FDA and we talk about biomarkers. Yes, in 20 years we will have amazing biomarkers, but we need some solutions right now that can be deployed today. Do we have one? Kind of.

GrimAge, I am quite optimistic about it. I am aware of its many limitations, more than anyone else. However it does have its strengths. There is a very strong literature support for mortality predictions in relation to many age-related diseases, validated by independent groups. Also people started doing longitudinal studies where they relate the rate of change of GrimAge with increased hazard of death, with encouraging results, which might indicate causality. There are also large genetic studies, where we developed polygenic risk scores which were associated with parental longevity. This is important because epidemiologists often use genetic arguments to argue causality. And GrimAge performs exceptionally well when talking about analytical validation. GrimAge has exceptionally high intraclass correlation in all BLOOD data sets. The good news is that we have things we can work with, these are first versions, and we continually develop new versions that improve over the previous versions.

Challenges particularly in the biomarker field, which will be more of a philosophical discussion.

Let’s start with the fundamental challenge of biological age. The problem is that mathematically speaking it is a latent variable, we don’t have a definition, you need to infer it from manifest variables (actual variables). We can use these molecular readouts (omics) to infer it, conversely pathologists would look at cellular changes, tissue pathology, then there are clinical readouts like frailty, and so on. Our field of aging biomarkers needs to find a way to integrate this information. We have one good starting point – the chronological age. Nobody ever debates that biological age increases with chronological age. So we have a starting point. What does it mean? If you have a biomarker that does not correlate with chronological age, you have a problem (think viral load, might predict mortality but is not tied to aging). Personally I am interested in biomarkers that measure innate aging processes.

Here is the challenge in the aging field. There is really no consensus on an established theory of aging. We could spend two days debating very plausible theories of aging, but there is no consensus. Contrast this to the cancer field, where they have well established oncogenes, tumor suppressor, they understand pathways, treatments, and suddenly their theory is far more developed. Then there is another lack of consensus, if you don’t have a theory, you really have a hard time defining biomarkers of aging. So we really don’t have any consensus on proxy endpoints of aging. Again if we contrast it with other medical fields, we clearly have wonderful biomarkers for example for anti-hypertensive medication.

How do we arrive at a consensus in the aging field? There is a medieval solution, developed by the catholic church.

This is what would probably happen if we tried that in the aging field, so we need a different solution.

I believe capitalism will solve this problem. Companies that use effective biomarkers will succeed at developing effective anti-aging interventions. Success will ultimately lead to emulation and adoption.

Now about some open and closed goals of the aging field. I always thought that the old holy grail are aging clocks that apply to all tissues and cell types. In my world, aging happens in most tissues and cell types, so biomarkers should measure aging in all of these tissues and cell types. Then there is another holy grail – we all believe that aging applies to all mammals, so it would be good to have aging clocks that apply to all mammalian tissues, not just humans. The status of these old holy grails is that we have reasonably good solutions based on cytosine methylation. If you contrast that with biomarkers based on transcriptomic data, this will be rough. However my hope is that the field will find other readouts, because cytosine methylation is limited, we need markers of autophagy, mitochondrial function, stem cell function, senescent markers, all of that. So there are still opportunities to address these old goals with new data perhaps.

However, we of course want a causal clock for humans. And there we face this fundamental problem, that we really don’t have validated anti-aging interventions in humans. Without proxy endpoints we cannot validate interventions in humans. And conversely, without interventions we cannot validate the proxy endpoints and biomarkers. We want to test the test, and if we don’t have gold standard interventions in humans, we have a problem. There are a few workarounds listed in the slide I can think of.

People always ask me these questions, so I’ll answer a few of them upfront. Epigenetic changes are causal, but we don’t know to what extent, and in which tissue, and there are different ways to define epigenetic ages. So there is a lot to work out, but the answer to yes/no question is yes.

Now comes another question. Is cytosine methylation causal? Yes and no, ambiguous. You can find plenty of examples to support both the points of view. There are powerful experiments that argue for causal roles for methylation. But also powerful arguments for no – C. elegans doesn’t have cytosine methylation, but C. elegans ages. So if you make the universal assumption that aging is universally conserved between invertebrates and vertebrates, you have a problem. And also many cytosines gain or lose methylation and it doesn’t affect neighboring gene expression, so the transcriptomic consequences are often entirely unclear. Some changes will be causal and some will not be. The challenge of the field is to develop clocks that hone in on these causal changes. We need comprehensive studies between different omics, interventional studies, etc.

There were some conversations about cell composition confounding methylation clocks. The problem is it can confound almost  any omics data. Changes of cell composition can actually be part of the signal. If you ask what is the cellular manifestation of epigenetic ages, well maybe if you change the epigenome, it changes the stem cell function and leads to changes in cell composition – maybe it can be a part of the causal chain. This is in my mind a selling point for pan-tissue clocks, because they are overall quite robust to changes in cellular composition.