Epigenetic clocks and other biomarkers of aging

What is an aging biomarker and why is it needed?

We all know someone who is “perfectly preserved” for his age, and someone who is “old beyond his years”:


Actually, aging biomarkers are exactly what we need in order to be able to objectively say: yes, you are 50, but your health is at the age of 35. But you, a young man, should take a closer look at your health - your biological age is 10 years higher than the chronological , and this is fraught with a 48% increase in the risk of death. What does aging and the likelihood of death have to do with it? And despite the fact that in humans, like in most mammals, aging is accompanied by an exponential risk of mortality:




And if in 30 years the annual probability of dying with you is 1 chance per thousand, then by 80 it rises 100 times. It is this age-related increase in the probability of death that gerontologists call aging. And no, far from “all living things” are aging. There are species that, on the contrary, “get younger” with age - their probability of death decreases, and fertility increases:


So, the goal of the fight against aging is to learn how to roll back the biological age to the level of today's healthy 25-year-old person, and fix it there. And the task of the aging biomarker is to separate the biological age from the chronological (passport) age. That is, reliably show where exactly on this curve you are:

Therefore, a good biomarker should be highly correlated with mortality so that it can be used to determine biological age. For example, if a biomarker shows that your current annual probability of death is 1/1000, then you are biologically 30, and if 1/100, then 60. Regardless of what your passport says. Because we die not by passport, but by health.

And of course, it’s important for us to see the reverse dynamics of aging biomarkers: they “healed” the body with any proven anti-aging therapy (for example, mice with the same fasting or rapamycin) and saw a decrease in biological age.

What is epigenetics?

Epi genetics is a “superstructure” over ( epi = = ) genetics, a mechanism for managing genes. Rather, there are several such mechanisms: methylation of the genes themselves, acetylation or methylation of the histones on which these genes are “wound”, and many other things that fall under the definition of epigenetic control.

Why do genes need to be controlled at all? Firstly, because the body’s DNA is the same in all types of cells, and different sets of genes must be active in the brain cell and skin cell. And also because different genes are responsible for different stages of the organism’s development - the caterpillar and the butterfly have a very different activity profile of these genes. As with us, in fact: in the womb some genes are active, in childhood - others, in old age - third.

And, as it turns out, the profile on / off with age. different genes varies in all people almost the same. And what is even more interesting, it changes in a similar way in mice and other animals. That is, the epigenetic aging of the mouse is similar to the epigenetic aging of a person, only accelerated by 40 times:

We observed tissue-specific age-related changes in DNA methylation [mice], the directional vector of which coincided with the observed changes in humans. These results additionally confirm the view that changes in DNA methylation are associated with chronological age and suggest that these processes are similar in different tissues, as well as between mammalian species.

At the same time, we even have common “gears” with mice in these hours of aging - the same genes that make up their composition :

Differentially methylated regions in mice have a high similarity in the nucleotide sequence to humans, and the nature of their methylation is also significantly similar between the two species.

What is an epigenetic watch?

In fact, a “methylation clock” is simply a set of on / off parameters that best correlates with age. With what age - chronological or biological?

Initially, both with that and with another - after all, in animals in the wild, both chronological and biological are almost identical. They don’t drink or smoke, and they don’t eat at McDonalds. Therefore, initially methylation hours are set (calibrated) according to the chronological age of each species, and only then various methods are tested to speed them up or slow down to check whether those effects that prolong life, at the same time slow down these hours, and those effects that shorten life, Does this watch speed up?

And yes! In smokers, diabetics, AIDS patients or people with Down syndrome (who age much faster), the actual biological age turned out to be higher than their chronological age. And in mice that received various rejuvenating therapy, the biological age was also reduced.

But more on this later. So far I’ll just mention that, in addition to humans, a high correlation between methylation hours and age has been established for a number of animals: rotifers , mice, chimpanzees , and even whales :

And what is so special about this watch?

What is special about methylation watches is that they are highly correlated with mortality. For example, in this large-scale study by Horvat on thirteen thousand people, it was found that for every year ahead of the clock of methylation of chronological age (that is, if you are 45, and the clock shows 46), there is a 2% to 4% increase in the risk of mortality.

Moreover, this observation worked in both directions and had a cumulative effect: for those whose biological clocks were 10 years ahead of their age, the risk of death increased as much as 48% (1.04 ¹⁰ = 1.48), and those who were 5 years younger their age was 18% less at risk of death.

In another studyshowed a high correlation of methylation hours and the risk of lung cancer in smokers. Moreover, both the risk and the coefficient of reliability of the correlation increased with age:

We also showed that the ability of IEAA to predict lung cancer is highest among people aged 70 years and older. An increase in IEAA per unit was associated with a 2.5-fold increase in lung cancer among a subgroup of people aged 70+, while for the entire cohort aged 50+, it increased the risk by only 50%.

Only 10 methylation sites were identified in this study (for comparison, there were 353 such sites in the “Horvat watch” mentioned above), abnormal methylation of at least six of which increased the risk of mortality by several times - from any cause or from cancer or cardiovascular disease:

And in this study, the Japanese showed generally murderous facts for the mitochondrial theory of aging: defects in the mitochondrial respiration are caused not by the accumulation of breakdowns, but by epigenetic (programmed) changes. And with the epigenetic rollback of such old cells with the help of Yamanaki factors, all mitochondrial respiration defects disappear:

We reprogrammed human fibroblast lines, generating iPSC, and showed that reprogramming fibroblasts from older people restores age-related mitochondrial respiration defects. Therefore, these age-related phenotypes found in elderly fibroblasts are reversibly regulated and are similar to differentiation phenotypes, since both are controlled by epigenetic regulation, and not by mutations in nuclear or mtDNA. Considering that human aging can be considered as a consequence of a programmed phenomenon, it is possible that epigenetic regulation also controls human aging.

The Japanese are talking business. And the Japanese presented the greatest gift to humanity in the form of those very factors, Yamanaki. After all, they not only reset the epigenetic clock (both in humans and in mice ), but also significantly extend the life of animals :

So how to get younger?

The main point of aging biomarkers is to use them to search for the most effective ways to combat it. Therefore, immediately after the methylation clock has established itself as such a potential biomarker, scientists rushed to examine them to see if they reflect the effectiveness of various anti-aging effects. And indeed, this relationship is beginning to manifest itself. Here is a summary of the last study a couple of times mentioned by Horvath, who is one of the leading experts in this direction (blue arrows lower methylation hours, and red ones increase):


Much of the above is expected. A couple of surprises for me were that moderate alcohol consumption and “good” cholesterol reduce methylation hours. Well, there will be a reason to drink for Croatian health.

A much more targeted mouse study to evaluate the effects on rapamycin methylation hours, calorie restriction, and life-prolonging genetic mutations was published not so long ago. And it also confirmed that all these interventions have a rejuvenating effect on methylation hours:

We formulated a model of epigenetic aging in mice and used it to find evidence that known life-extension interventions slow down epigenetic hours in mouse liver.

By the way, these same researchers released a parallel article , where they tried to identify exactly which areas of the genome are subject to age-related changes in methylation. After analyzing 42 million (!) Methylation sites, the authors concluded that the main objects of age-related changes are promoters and enhancers of highly expressed genes.

What confirms the hypothesis of programmed aging for me - it seems that the key age-related change is the change in the expression profile of only a few key gene regulators located at the top of the homeostasis control hierarchy, and this cascade entails all other age-related changes.

Very similar findings that indicate several key genes are described by Vadim Gladyshev and colleagues in his last work :

The significance of different methylation sites was unevenly distributed over methylation hours. Sites formed several different clusters associated with the Hsf4, Kcns2, Map10, Tns2, Wnt3a, and Zscan2 genes. We found that 17 of the 18 CpG sites common to the subsets of methylation hours 1 and 2 were also present among 90 CpG sites of the mDNAm clock. Most of these 17 CpG sites were located inside the introns Ciita, Cd200r4, Rasgef1c, Wnt3a, and Zscan2, and some were grouped.

Slowing the hours of methylation using various anti-aging interventions was also shown in this beautiful work :

It is important to note that we found that biological interventions affect the methylation hours of mice, and therefore we assume that the predictions of the clock reflect not only the chronological, but also the biological age.

I really liked this graph of age-related changes in the methylation level of 329 sites in different tissues:

It shows how some genes are activated with age, others on the contrary, and still others remain unchanged.

By the way, the results of the twin study confirm the correlation of methylation hours with mortality. The higher the biological age of one of the twins, the higher the likelihood that he will die first:

This hypothesis was supported by a classic survival analysis showing an increase in mortality risk of 35% (4-77%) for every 5-year excess of age by methylation hours over chronological age. In addition, a twin analysis of twins revealed a more than double risk of mortality for a twin with a large age by methylation hours, as well as a dose-dependent probability of death, which increased by 3.2 (1.05-10.1) times for every 5 years of age difference by methylation hours between twins, thereby demonstrating a stronger relationship between methylation hours and the likelihood of death in older people, taking into account family factors. In conclusion, our results confirm that methylation hours can be considered a biomarker of aging.

Summarizing this part, I cannot but say that I believe that in order to become radically younger , you need to learn how to roll back the epigenetic clock directly. So far, we are just beginning to understand how to do this: thanks to the results of the Belmonte group, we have learned to gently knock these watches with a “hammer” in the form of Yamanaki factors. But ideally, I would like to pick up a key for our watch.

What other biomarkers of aging are there?

Among other aging biomarkers, I want to mention locomotor activity, on the basis of which the Russian company Gero recently released a cool application that determines the biological age by the pattern of motor activity from your FitBit.

Also a good predictor of mortality is the thickness of the arterial complex of the intima-media . As for IGF-1, everything is complicated ( 1 , 2 , 3 , 4 , 5 , but 6 , 7 , 8 ), so I will deal with it in a separate post. Well, the grandfather of all biomarkers is the frailty indexgerontologists are still struggling with an unambiguous translation: the “decrepitude index” sounds insulting, and the “fragility index” or “vulnerability index” does not fully convey the original meaning.

By the way, not so long ago I saw the news about a fresh article, boldly claiming that a new version of this index developed by its authors reflects the biological age even better than the “methylation hours”. At least the headline was very ambitious: “ The frailty index outperforms DNA methylation age and its derivatives as an indicator of biological age ”.

But after reading it, I realized that the boldness of the authors' statements is unfounded. Not only is their hypothesis, to put it mildly, doubtful, but their very article refutes this hypothesis. To start them new and improvedThe index is a simple questionnaire with 34 points. Which, in doing so, assigns an equal role to heart attack and cystitis:


And loud statements are not confirmed by their own data. Here is a table of the results of their analysis:


From it we see that the regression by methylation hours (model No. 2) has a higher R ² than the regression by their decrepitude index (model No. 3) —and almost 2 times (0.22 versus 0.13). In this case, the usual chronological age (model No. 1) has R ² almost 4 times higher than their index, and 2 times higher than the methylation hours. I'm not even talking about the fact that R ² in all of their models is extremely low.

How do the authors substantiate their high-profile statements that their FI is better suited for the role of a biological clock than DNAmAge? But with what - a lower p-value in model No. 7, and even limited in age to only eighty-year-olds:

In all Cox regression models throughout the study cohort, which includes age groups from 60 to 103, the chronological age was the best predictor of mortality (Fig. 3a). When Cox regression was limited to only 80 year olds, FI34 was a better predictor of mortality than chronological age (P = 0.035 versus P = 0.054, respectively, Fig . 3b) . This indicates that FI34 is the best indicator of biological age in later years, when accumulation of health deficits accelerates in the oldest.

That is, their lower p-value means better predictive power. A typical fallacy of poorly understanding statistics:

Misconception # 13: Statistical significance is a property of the phenomenon being studied, and thus statistical tests reveal significance.

Not! This misinterpretation progresses when researchers claim that they have found or not found “evidence” of a statistically significant effect. The investigated effect either exists or does not exist. “Statistical significance” is a dichotomous description of the value of P (which is lower than the selected cut-off) and, therefore, is a property of the result of a statistical test, but it is not a property of the effect or population being studied.

Moreover, for their key conclusion, the authors took as a basis model No. 7, where both parameters under consideration (FI and DNAmAge) are presented simultaneously, and even with the chronological age with which they both correlate (that is, they are most likely not independent, violating one of the conditions of regression). Moreover, they narrowed down the sample only to 80-year-olds - that is, to a very narrow segment of that parameter (chronological age), which best explains the variation of the entire data array (as in model No. 1, R ² is several times higher than other models), and then We were pleased to report that the age of such a narrow age segment has a low p-value.

About the fact that the model of their decrepitude index R ²almost 2 times lower than the DNAmAge model, I already mentioned (0.13 versus 0.22). But the fact that adding additional parameters to the model to the already existing chronological age is pointless, is evident from the fact that R ^{2 of} such models (0.48–0.50) is almost identical to the initial one (0.48 of model No. 1).

Well, in conclusion, it is worth noting that the value of the very concept of the decrepitude index proposed by the authors is close to zero. The biomarker is valuable in that it can change in both directions, because aging therapy is designed to reduce biological age. And the proposed decrepitude index is mainly based on historical parameters (whether you had a stroke / heart attack / etc.). Therefore, even if aging therapy rejuvenates your heart, brain and kidneys, this will hardly affect the historical decrepitude. And on a methylation clock or locomotor - completely.

So I congratulate us all on the new biomarkers of aging, and wish us happiness, health, and a very long life!