Biomarkers of Aging – An Introduction

Published March 5, 2022

It has always been the mission of Suggestic to empower over a billion people to lead healthier, happier and longer lives. We spent years determining how to score health itself, and researching what the factors are which determine an individual’s lifespan.

It led us to value the longevity industry’s effort to determine biological age and we began to experiment ourselves internally with “biomarkers of aging”, including “biological age clocks”.

To our surprise, we found that aging could be slowed, or even reversed (even if just by a couple of years), through dietary intervention alone.

I plan to write a series of posts introducing biomarkers of aging and how nutritional approaches may be applied to the problem of aging (and age-related diseases).

With that view in mind, I recently came across a panel on YouTube titled ‘Ending Age-Related Diseases | Biomarkers of Aging’, featuring many of the key researchers at the forefront of measuring age and the rate of aging. Yet the video only had under 1000 views. To bring it to the forefront and to serve as a preliminary introduction ahead of a series of articles I plan to write, I’ve transcribed it below. Highlights are mine.

 

Vadim Gladyshev:

In recent years quantitative treatment of the aging process all but transformed the [longevity] field; I mean the field really grows exponentially in terms of the contributions, papers and discussions and feature that conferences and panel discussion like this one.

However, there is still a lot of skepticism in the field. Some scientists simply don’t accept the clocks and the use of biomarkers and kind of rely more on, for example, hallmarks of aging or other features of the aging process.

So, today, in the panel discussion, we are going to figure out what is the applicability of the clock, the actual applicability of the clock, what are the best clocks?

And we will hear from the best experts in the field. I think so. Our panel participants Kristen Fortney, she is the CEO of a company BIOAGE. Peter Fedichev, he’s CEO company Gero. Steve Horvath, he’s a professor from UCLA and a pioneer in developing the aging clocks. And Stanislav Skakun, who is the CEO and founder of the biodata platform.

So, welcome everybody [panelists]. What I plan to do is that I will be asking some questions and hopefully you can answer them. And I suggest that we do this in a way so that it’s clear to not just expert scientists who work in the field, but just regular people who’d like to learn about the actual use of the clocks for particular people, for example, in the clinic, but as well as generally, like fundamental science.

So my first question is that many clocks and biomarkers have been developed, as you know, but it’s kind of confusing now in the field because there are just too many, it’s impossible even to read all the papers.

And of course, the presentations feature mostly good examples of the use of the clocks. So could you maybe just summarize, in your opinion, what do you think are the best actionable clocks? What is the actual best use of the clocks based on your experience?

And I don’t know, maybe Kristen, would you like to start?

 

Kristen Fortney:

Yeah, sure. So we’ve looked at clocks in a number of domains. There’s methylomics, proteomics, metabolomics. My personal favorite type of clock is a clock that doesn’t just track your age, but also predicts the future because of course, we already have a variable, which tracks your age very well. It’s called your age.

And the more potent biomarkers predict your future health, your future longevity. So we’re very interested in those clocks that predict all-cause mortality, ideally long-range all-cause mortality.

That’s another important point because if you have a very elderly cohort of people, it’s pretty easy to predict, who’s going to die in one to two years because there’s so many different things going wrong, but long range clocks that predict, you know, a couple of decades out, I think are really capturing what we care about when we’re talking about aging.

And I think that there’s a few different modalities that work. I’ve seen good publications here and, you know, in our own data sets as well in methylomics and in proteomics and metabolomics. And I’m excited to sort of evaluate these in the clinical setting hopefully as early as next year, because again, the mark of a true clock is whether it can capture the extent to which an intervention is rejuvenating an older person.

 

Vadim Gladyshev:

Okay. Thank you very much. Maybe Steve, are you there? Could you maybe continue?

 

Steve Horvath:

Coming to the topic that many publications are coming out, and they describe very exciting clocks in many areas. We’ve mentioned proteomics, metabolomics, any -omics, transcriptomics, and of course, clinical biomarkers. Above all, I need to remind everybody that there is a crisis of replication in the entire biomedical research arena.

It’s not just biomarkers, the scary papers where people claim half of all the articles report false positives, and we could have a one hour discussion. Is it 50% of articles or is it 80% of articles that contain exaggerated claims? Hopefully it’s less, but the bottom line is, and before any of the biomarkers can be used in human clinical trial, we really need to replicate them. And in many different ways, replicate them.

So above all claims have to be validated by several independent groups, several independent cohorts, before we can even take them seriously. That’s one comment. The second comment, of course, we want to have as many biomarkers as we can, so we mentioned hallmarks of aging, ideally from each hallmark of aging, we have one or two or three different biomarkers that represent it.

But this is an ambition, then there’s the reality. So again, it’s up to the people who work in these different arenas to develop biomarkers that can be replicated. Hopefully we will have many. Because there are two ways to look at the literature.

One is we have way too many clocks – hundreds of clocks have been published. But the other way to look at the literature is, it’s very disappointing. How many biomarkers are truly trustworthy in the sense that they have been validated in thousands of different populations?

The best validated biomarkers are, of course, those that are already being used in the clinic. C-reactive protein, albumin levels, various markers of kidney function, liver function and pathology, you know, biomarkers that are being used. Now they clearly predict morbidity, pathology, but very much what Dr. Fortney earlier said, these biomarkers may be terrible at predicting bad outcomes decades away.

Because in my world view, it is attractive to have a biomarker that even applies to a 30 year old. Why? Because we believe that aging starts relatively early, which is another topic. When does aging start? But I think nobody will dispute that aging is already manifesting in a 30 year old, no question about it.

 

Steve Horvath:

You know, and so ideally a biomarker applies to the entire age range from early adult to a 90 year old. And also it would be attractive to have a biomarker of aging that applies to many different populations, but I hope we don’t need to distinguish aging in different populations.

I don’t want, I want a single clock that works in Asians and also in the European ancestry and many other ancestries. Ideally we have biomarkers that cross species barriers, right? Why? Because people use rodent models and it would be nice that you have a biomarker that applies both to rodents and to primates.

And when I surveyed this field, there are very few biomarkers that satisfy these criteria that I mentioned. And that’s the reason why I love methylation, you know, because I described already biomarkers that apply to rodents and primates at the same time. These biomarkers have been validated really in virtually all populations.

I always tell people, I give them a money back guarantee that they can validate GrimAge when it comes to predicting mortality risk. I’ve never seen an epidemiological study that did not validate GrimAge as mortality predictor. So I’ll stop with that.

 

Vadim Gladyshev:

Thank you, Steve. Peter, would you like to continue?

 

Peter Fedichev:

Yes. Thank you. Well, I think that everyone wants biomarkers which predict mortality. If you’re in biotech, maybe the long crunch is not the most important thing for you, but nonetheless, you want to know what is going on and if these biomarkers capture the effects of drugs.

From the fundamental science point of view, what I’d like to stress here is that one way to develop these biomarkers is to explain the data. Everyone wants to have a good predictive biomarker. But I think not the less important issue is to find biomarkers which also explain the process.

So we need to get the understanding, not only to get the exact number, but to actually understand what’s going on. So, for example, I will give you just one example. We know that clocks, which we are using, are associated with chronic diseases, which are very difficult to reverse. So if you take a chronic disease, obviously it sticks for a very long time. But also these clocks respond to lifestyles like diets and smoking. And we know that if you are not yet sick with a chronic disease, then the effect of smoking on biomarkers is totally reversible.

 

Peter Fedichev:

So it clearly gives you an idea that there are two kinds of processes involved there. And the single number, like a clock, captures the combination of the two. So I think that in the future the accuracy is one thing, of course, black box clocks will work and give us physical tools. But on top of that, just to distinguish between slowing down aging process and rejuvenation, we’ll need to find out which components are actually contributing to that single number.

Basically, I’m calling to understand how many clocks are actually there – mortality clocks, chronological age clocks, how many, what is the minimum number of clocks with different properties and not just different source of signals are there and what kind of biology is behind that? So I think these are the most interesting and challenging issues.

 

Vadim Gladyshev:

Thank you. Thank you. And Stanislav, what are your thoughts on this question?

 

Stanislav Skakun:
Yes. Thanks. Well, I think one of the most important things, and mysterious, about aging is that it’s a very multifaceted process, which runs unevenly in different systems and organs of the organism.

And they are for what Steve is saying and what Peter is saying is very important that we are going to have a lot of biomarkers of aging. We will need to have them stand as close as it’s possible to the aging process itself therefore, so that we can be able to track the causes of aging, which are not yet fully clear to us.

Therefore we will be able to track different velocity of aging, different speed of aging in different systems. So the more comprehensive the clock is, the better. The clock may not be simple.

 

Vadim Gladyshev:

Thank you. I’d like to actually qualify this question, first question, because I really want the listeners to have a very concrete recommendation. Let’s say somebody works on mice and they want to test the effect of a particular intervention that they have given – diet, or maybe they want to know their biological age.

So there is a lot of companies that are out there, and a lot of people and organizations claim that they are able to do it. So what is your advice? Both from the [inaudible] scientist and in the practical use of the biomarkers and clocks? The question is to everybody.

 

Stanislav Skakun:

I will explain why, because I’m tracking several hundred biomarkers every month of myself, therefore I’m having a hard job interpreting this and trying to determine my biological age each month.

So I think the advice that Steve gave that the clinical biomarkers will be at the moment the most precise for tracking your health condition is correct. The thing is that these biomarkers are the most easy to obtain. They will have been mentioned, C-reactive protein, albumin, transaminases, I mean liver and kidney markers, and some other biomarkers which have wide clinical application, but the issue here is that sometimes one measurement is not enough.

So you should have a very longitudinal, a long data of yourself. You should like measure them regularly to avoid oscillation, to avoid chaotic movement of the biomarkers which may occur when you have a cold, when you have some health condition, for example, when it’s gone [the cold infection], the clinical biomarkers, they jump back.

So the best idea right now is to collect some basic biomarkers, but to do this regularly so that you can determine what’s your personal individual norm, what your health usually looks like, and aggregate the data.

I think that the clinical biomarkers would be the best option for a regular person, a non-scientist to use right now.

 

Vadim Gladyshev:

Thank you. Who’d like to continue? Ok, go ahead.

 

Kristen Fortney:

I think that’s a great question, Vadim. It’s a super practical question and it’s one that actually, my company is confronting now because we’re running our first clinical trial next year. And I want to know if this therapy is also going to rejuvenate people and what biomarker do we use?

So my simple answer to the question is that you don’t have to choose, right. Why choose when you don’t have to. A lot of these are mixed modalities, be it proteomics or metabolomics or methylomics, right? You can just, with modern technologies, spend a few hundred dollars that measure billions of different sites if you’re looking at the methylome. And then that’ll be compatible with, you can evaluate several dozens of these biomarkers, which there isn’t really a one true winner yet, but by going to omics profiling, you can keep your optionality open.

And I agree with Steve that the methylome is the best validated so far, which has been done in so many different cohorts. So that’s like a clear decision to make where you can always go back in time and reevaluate as the field evolves.

 

Vadim Gladyshev:

Thank you. And Peter, Steve, I don’t know who would like to continue.

 

Steve Horvath:

Yeah, I mean, when it comes to mouse studies or more generally anti-aging interventions, let me start with the following. With a trivial statement, aging is very complex, right? Many pathways are involved, many different pathologies.

And it’s hard to imagine that there is one biomarker that captures the beneficial effect of any good treatment. I give you an example, blood pressure medication is a phenomenal “anti-aging intervention”. If you take your blood pressure pills, you will live 10 years longer. If you have hypertension.

Now, do we believe that blood pressure pills affect omics data that we mentioned? Do you think blood pressure pills effects transcriptomics, proteomics? Methylome, you know? Maybe, maybe not. Probably not. And so what I’m saying is the biomarker has to be adapted to the specific treatment. Having said this, what all of us want is a biomarker that captures a lot of the territory.

And especially if you have an intervention where you don’t quite know what to measure. But with blood pressure, it’s obvious. You get blood pressure medication, of course you measure blood pressure. If you have an HIV intervention, you measure CD4 T-cell count. So often it’s obvious what the biomarker is, but in some cases it’s not obvious what to measure.

So for example, metformin, what should we measure, beyond glucose levels. Or rapamycin, same question. And so then you have to hedge your bets, and like everybody else here on this panel, we are all serious scientists. We always want more data in order to maximize our chances to detect something significant. And I’ll stop with that.

 

Peter Fedichev:

Yes. Well, I, of course I appreciate what has been said here that aging is complex and so on, but I think Vadim’s question was on the practical side. And when you’re doing practical things, sometimes bad decisions now are better than more sophisticated solutions in the future.

So I think in many ways, aging is much simpler in mice. I would say that we have a practical issue. If you wanted to detect an effect of drug in an experiment in mice, you take any quantity, which correlates to the remaining lifespan, which is the risk of death. You measure it two weeks after the treatments. And I guarantee you that two weeks after the treatment, all the high frequency effects disappear. And if there is an effect on all-cause mortality in the future, it is there or not.

So what I’m saying is that if you want to predict a point in the future, death in the future, you have to wait a little bit after your experiment to see this. And if the effect is still there, also probably it will remain in this animal for a very long time. [indistinct] And you can detect such an intervention just by observing the animal long enough time after the experiment, after the intervention, if it’s there. [indistinct] So that’s my educated guess.

 

Vadim Gladyshev:

Thank you. Just before we continue with my questions, actually there’s a question from the audience and I would like you to answer really short, you know, in the short way. Maybe one word or one sentence. So the question is which biomarkers would you use for rapamycin in human trial and also for metformin? Like all of you just, just tell the one.

 

Steve Horvath:

Maybe I’ll start. For rapamycin, I would measure skin cells or buccal cells, anything related to keratinocytes. Why? Because in vitro studies show a beneficial effect of rapamycin in human keratinocytes.

 

Vadim Gladyshev:

But which biomarker, specifically, not just which tissue?

 

Steve Horvath:

Oh. Epigenetic clocks. Thank you. So we, we have an epigenetic clock called the skin & blood clock. It applies to skin cells. It works beautifully in keratinocytes. The other biomarkers, my original pan-tissue clock. I would, again, apply to buccal swaps. So that, that’s what I would use for that.

 

When it comes to metformin, I would use the GrimAge clock. It’s a blood-based clock. We do not yet know whether metformin reverses the epigenetic age of blood, the jury is out, but that’s what I would use.

 

Peter Fedichev:

We have tried rapamycin in mice, so [inaudible]. So I would try PhenoAge in humans, and not mice, and study, but of course I don’t hold the answer.

 

Vadim Gladyshev:

I would track insulin resistance because one of the side effects of rapamycin treatment is the increase in insulin resistance. So insulin and glucose, and on the other hand that will track some proxy mTOR markers like insulin, like growth factor one (IGF-1) which will definitely go down during this treatment. But shouldn’t go down too much. So I would track these two groups of markers.

 

Kristen Fortney:

So you definitely want those markers just to see that the rapamycin was having an effect at all, but then you’d also want to layer on top some kind of omics marker to see that it was actually relevant for longevity. So my answer is basically the same as for our trial. You would do something like a methylome or a proteome. You keep your options open.

Didn’t someone publish that, in mice at least, taking rapamycin reversed some of these clocks. Am I misremembering? Is it one of you guys?

 

Peter Fedichev:

[Inaudible] Yes, we have a pre-print [inaudible]

 

Vadim Gladyshev:

Okay. Yeah. Thank you. Let me ask you a little bit more detailed question about the biomarkers and clocks. What do you think they actually measure? Can you give an actual molecular basis and how that relates to the aging process? In a simple way, just so that it’s understandable for everybody. Too tough question?

 

Kristen Fortney:

There’s so many different things going wrong as you get older, and it’s just one number that summarizes all of them. I think Peter touched on this earlier. There are some clocks that are just combining all this information and you don’t quite know what the mechanism is, and if all you want is just a metric, that’s really good at predicting the future that reflects intervention, you might not be able to figure that out.

Certainly you can build more specific clocks around specific molecular hypothesis, but I think it’s often a hard question.

 

Steve Horvath:

Yeah. Just to echo that, some biomarkers could be called integrators/ They integrate different innate pathways of aging.

And therefore they don’t have a simple explanation from the view of epigenetic clocks, I can tell you that some of our epigenetic clocks, for example, relate weekly to blood cell composition and immunosenescence. Emphasis on weekly, but they capture it.

Then other epigenetic clock relate to hematopoietic stem cell biology, and then there are other clocks which are much harder to interpret, where we believe they measure epigenetic stability, epigenomic maintenance.

So in certain ways they measure to what extent cells maintain their cellular identity, because the epigenome very much plays a role in maintaining a cellular identity, but I already mentioned now several pathways, you know, from immunosenescence to cellular identity to stem cell biology.

And you can see that it’s complicated. And the reason is perhaps because aging is complicated. So these biomarkers are integrators. Then there are other beautiful biomarkers that have a very simple explanation. Telomere lengths, everybody loves telomere length because it’s so well understood. And the unfortunate thing about telomere length is that it’s such a dismal predictor of morbidity and mortality, so that’s the problem with that.

So as a biomarker person, you have to weigh things. Do I want to use a biomarker that works well in epidemiological studies? But am I willing to give up on biologic interpretability? Or conversely you have a beautiful biomarker that relates to cellular processes, but then it may fail in epidemiological studies. It’s a tough call.

 

Peter Fedichev:

Well, once again, I don’t think that everyone understands, so the answer to this question in entirety, at this moment. I can try to give me an engineering, a plausible engineering explanation for that? I think nature didn’t build a clock inside us to help us with clinical trials. So that was not the point.

So what I think actually happens is that, well, all-cause mortality is organism-level phenomena. So I think that our body has molecular pathways, which regulate whole organism response to stresses.

So what actually these biomarkers, like mortality markers, measure in healthy people that’s important is that these are just stress responses. So that’s why biological age is higher in people who smoke. And that’s why it goes back to normal once you cease smoking.

When you have diseases, this is just another source of stress and that’s, anecdotally, is a stress age actually tracks the total number of diseases which you have developed. So we might be aware of measuring stress, the integrated stress, the whole body response to stress, which essentially by a chance also measures the total number of diseases which we have developed, which of course correlates to our age.

 

Stanislav Skakun:

I think I want to add to what Peter is saying. Maybe most of the clocks are looking at the aging and mortality as a sort of gradual process, which is developing in time, in some long periods and like is putting us biologically speaking in these or that age category.

But sometimes there are two things. One is stress resistance, resilience of the organism. And another one is the exact stressor incoming. And therefore we would very much like biological clocks to predict mortality, so understand how much time is left.

But stresses do not come in a gradual and dosed manner, they come sometimes unexpectedly like a black swan, in a fragile environment. They cause too much damage to us.

So this may be bringing us to some limitation of the biological clock method. So they are measuring aging as a gradual process, but mortality is not gradual. It’s a discrete happening and which leaves us with the limitation here.

 

Vadim Gladyshev:

Yeah, Peter, go ahead.

 

Peter Fedichev:

I’m sorry for jumping in without my proper turn. Well, I think what Stanislav is saying actually it comes from the observations. So we have lots of longitudinal data on humans and what we see is that any biological age you like actually oscillates in healthy humans and the amplitude of the oscillation is also the biomarker of age.

The more stable is your organism, the faster your organism responds to stresses, the less is the range of the fluctuations of your biological age. Not only the mean number which we access in the epidemiological studies, used to train the models for chronological age, but also the range of the fluctuations, as well as the property of your lifestyle, stress is the most common [inaudible].

So what was the amplitude of your stress, the force, the power of your stress amongst your mortality, but also the resilience, how fast your organism comes back from the perturbation, also commands your mortality.

So in the future, once we jumped from epidemiological studies, to longitudinal studies, you will see not that only the level of biological age has been marked, but also the resilience and the power of the noise, the stress, are also markers that you can never get from a single measurement. These are all very important things.

 

Vadim Gladyshev:

Thank you. Next, I’m going to ask a question from the audience and the question is, what do you think of making a focused push to utilize easily collectible, physiological biomarkers to do something useful as a way to be in public and policymakers of the value of biomarkers more generally? Anybody would like to comment on that?

 

Steve Horvath:

Yeah. I mean, I think what we want to do as a field is to engage with regulatory agencies, to convince them that there has been huge progress behind the scenes on aging biomarkers. And we have now entered a new time period where we can rigorously test promising anti-aging interventions. And we can rigorously test and try to repurpose various treatments.

So I think based on the revolutions that have taken place in terms of technology from the human genome project to the most exciting proteomics measurements. And also, great advances in our understanding of the biology of aging.

Now is the time to invest in that, to build also the regulatory structure so that people can form companies that exploit these basic science innovations. And so, I think that it’s very important to have an advocacy group that works on that.

 

Vadim Gladyshev:

Thank you. Yeah. Yep, go ahead.

 

Peter Fedichev:

I think that it will be very important to actually help people explain what are these biomarkers of age? How are they related to their lifestyles? How they are reflecting their mental health, for example, stresses and so on.

 

Peter Fedichev:

I think the roadblock here is invasiveness of the procedures. I think that all of us what do blood work once for fun, maybe two times, but when we want to do a trajectory, when you want to see what’s going on, I think [only a] few people will do it many times, if they are not sick.

 

Peter Fedichev:

I envision that maybe within a few years from now, they will be in a big company which would be using some progress in wearable devices, most obviously. So that people could actually give their information on their biological signals, digitally without actually to going to the doctor.

And then, these kind of people and these kind of companies will eventually test all pharmacology which is prescribable and all kinds of gene sessions, all kinds of interventions. And actually score them, see what works, what not, and maybe even personalize it to everyone of us. I think that would be a cool proposition that might work.

 

Vadim Gladyshev:

Yeah. Thank you. Kristen, would you like to continue?

 

Kristen Fortney:

Sure. Yeah, I would like in the future for there to be a biomarker of aging that we believed in enough such that it could be the primary endpoint in a clinical trial, an approvable endpoint. I do think that future is still far away. I think the major barrier, the needed thing of course, is to show that it works.

So, today any clinical trial you’re doing, it can certainly be a secondary end point, an exploratory endpoint. And then over time, you can show that it predicts these longer-term outcomes that people really matter or care about, like longer-term mortality or hospitalizations. Hard, functional outcomes that people associate with aging.

So, I would like to see more exploratory analyses done in the range of clinical trials, because there certainly are drugs now that should be affecting the aging process. And that’s the kind of evidence-base we need to really build to get these approved.

 

Vadim Gladyshev:

Yeah, I think so as well.

 

Stanislav:

Definitely.

 

Steve Horvath:

Briefly let me jump in. Yeah. I have a little bit of a more charitable view of the fear, from Kristen. So, my analogy is always iPhone version 1 versus iPhone version 10. I think when it comes to biomarkers, we are at the level of iPhone version one. We do have wonderful biomarkers that can already be deployed in human clinical trials.

Yes, five years down the road, we will have much better versions, but the field is ready to use these biomarkers.

The other thing I want to emphasize, it’s always plural. There is not one biomarker. We need to discuss what handful of biomarkers should be measured. Or make it 10. All of us agree, measure CRP, albumin, all of these clinical markers. I think I want to say there is consensus that most clinical trials would want to measure methylation, unless you have a good reason not to measure it.

But the point is, I think we are at the stage where we have already these biomarkers available and we shouldn’t wait. Why shouldn’t we wait? Because we are aging at an exponential rate. We need to start these clinical trials now.

 

Kristen Fortney:

I want to cut in, Steve, because we actually weren’t disagreeing. So, definitely, we can measure biomarkers today and we’re going to do that next year in our clinical trial. But what we can’t have yet is an aging biomarker that’s the primary endpoint. Where that’s the thing the FDA looks at and sees it move and says, “Yes, your drug is approved.”

The way that, if you move cholesterol, you can get a drug approved for that. So that’s a biomarker, that’s an approvable endpoint. So, right now you can certainly measure biomarkers, but you still have to see a functional change or a disease change in order to get the drug approved. And I think it’s still going to be a while before that changes.

 

Vadim Gladyshev:

Yeah. Okay. Stanislav, could you please continue?

 

Stanislav Skakun:

I would just absolutely support any idea of an organized push for more testing, more data. That’s just, we need tools for this. I think that we want tools available for people to collect data and to process them, which will facilitate that. That’s more or less what we are working on in this field. So, I totally agree that there is a need for an organized push, not just some people speaking up here and there, but some general strategy.

 

Vadim Gladyshev:

Okay. So we have really just a few minutes left in the panel discussion. So, I would like to actually ask you to express your opinion of what you think is the most important, what you would like to say. Something maybe which we have not covered, but you think it’s important to deliver to the audience. Okay.
Just final statement, everybody. We covered everything? No?

 

Steve Horvath:

No. Maybe I’ll… I feel bad because I’m always the first to answer, but let me try. It’s really wonderful news. Big picture is wonderful news. We have biomarkers that can be used in anti-aging clinical trials. Very promising biomarkers, credible biomarkers.

I also think expert panels can arrive at consensus views. All of us on the panel are quantitative people. So, what I love about our field is we are all quantitative. We are all data driven people. We won’t have big philosophical discussions. We will look at P-values and test, retest variability and replication. So, in that sense, our community is blessed. That we will arrive at consensus.

I think the field is ready to evaluate hundreds of very promising interventions. I think I could point people to various papers that discuss biomarkers. I’m happy to do it. I would mention markers of immunosenescence, flow cytometry. We mentioned all these, traditional biomarkers of organ dysfunction, liver function tests, creatinine for kidney and so on.

And you know what other biomarkers I recommend? GrimAge I would strongly recommend.

 

Vadim Gladyshev:

Thank you.

 

Kristen Fortney:

Yeah. So I’m also very optimist thinking about where we’re going. There’s a ton of clocks now, but look at how far we’ve come in the last decade. Like Steve, when was your paper first published? That was less than 10 years ago. Right.

And now, there’s just an amazing transformation in the field and an understanding of how these clocks are really important for different biologies in different data modalities, be it proteomics, or metabolomics, or methylomics.

So, we’re right in the middle right now of figuring out what’s important. And if we make progress in the next few years like we have in the past decade, I think we’ll get to consensus soon.

 

Vadim Gladyshev:

Peter, Stanislav?

 

Peter Fedichev:

I also think that nothing can be in a place for practice. I think everyone agrees that we have something to play with. So the more we are playing, different angles and things, the more we will understand and I think we will learn a lot.

 

Stanislav Skakun:

I would dread that within the general for people who are not engaged in a science award race, the best option now, the best strategy is to invest in your own data, to collect it. And very soon, the methods, the tools, the platforms, the legislation, will catch up. And when this all becomes available, then you will have your own data, dating back a few years ago. That will be a very valuable asset for you.

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