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Epigenomics
Epigenomics
Epigenomics
1750-1911
1750-192X
Taylor & Francis

39023350
10.1080/17501911.2024.2373682
2373682
Version of Record
Comment
Perspective
Generations of epigenetic clocks and their links to socioeconomic status in the Health and Retirement Study
https://orcid.org/0000-0001-5459-5239
Crimmins Eileen M * a
Klopack Eric T a
Kim Jung Ki a
a Davis School of Gerontology, University of Southern California, Los Angeles, CA 90089-0191, USA
* CONTACT: crimmin@usc.edu
18 7 2024
2024
18 7 2024
16 14 10311042
Aptara29 6 2024
17 7 2024
06 4 2024
21 6 2024
© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits noncommercial reuse, distribution and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed or built upon in any way. The terms on which this article has been published allow the posting of the accepted manuscript in a repository by the author(s) or with their consent.

Aim: This is a brief description of links between nine epigenetic clocks related to human aging and socioeconomic and behavioral characteristics as well as health outcomes.

Materials & methods: We estimate frequently used and novel clocks from one data source, the Health and Retirement Study.

Results: While all of these clocks are thought to reflect “aging,” they use different CpG sites and do not strongly relate to each other. First and fourth generation clocks are not as linked to socioeconomic status or health outcomes as second and third generation clocks.

Conclusion: Epigenetic clocks reflect exciting new tools and their continued evolution is likely to improve our understanding of how exposures get under the skin to accelerate aging.

Plain Language Summary

Biological aging occurs much earlier than mortality and the onset of diseases associated with age that can be clinically diagnosed. In fact, changes in biology that accelerate aging can occur throughout life in response to adverse exposures, behaviors and experiences. One such change is methylation or the attachment of methyl groups to genetic markers to affect their activity. Epigenetic clocks are measures of the amount of methylation that is related to aging. They are called clocks because they are measured in years or ticks of time or in change in years relative to age. We show that not all epigenetic clocks are the same in how they relate to socioeconomic status and health behaviors as well as subsequent mortality and morbidity. There are now four generations of these clocks developed in a little more than 10 years. The second and third generation clocks are more closely associated with lifetime socioeconomic status, health behaviors and health outcomes probably because they have been developed by relating them to health indicators in contrast to epigenetic measures that were developed because of their relation to age. Incorporating epigenetic measures into population studies reflects the beginning of our ability to measure some aspects of aging long before old age; it also provides entry to monitoring, measuring and intervening on biological aging throughout life.

Article highlights

Four generations of epigenetic clocks

First generation: Horvath and Hannum’s clocks trained on chronological age.

Second generation: Levine’s PhenoAge and Lu & Horvath’s GrimAge and GrimAge2 trained on multiple biomarkers and smoking.

Third generation: Belsky measures the pace of epigenetic aging rather than a static age in DunedinPACE.

Fourth generation: Causal clocks use Mendelian randomization to select sites that are putatively causal in general aging, adaptation to aging and age-related damage.

Relationship of the epigenetic clocks to socioeconomic status & health outcomes

Second and third generation clocks correlate more strongly with education, income, race/ethnicity, mortality and multimorbidity.

Lower socioeconomic status is linked to accelerated aging and higher mortality and morbidity.

Different relationships with social & behavioral variables & health outcomes across clocks

Lifestyle factors like smoking and obesity impact epigenetic aging.

Clocks vary in their correlation with risk factors and health outcomes due to many factors including different CpG site inclusion, different populations used for training and different outcomes used for training.

Keywords: 

Causal clocks
DunedinPACE
GrimAge
Hannum
Horvath
PhenoAge
National Institute on Aging 10.13039/100000049 P30 AG017265, R01AG060110 Support for this analysis was provided by the NIH-National Institute on Aging (P30 AG017265, R01AG060110).
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pmcThe development of epigenetic clocks has been one of the most important advances in population aging research. The clocks have provided insights into human aging at a new level of biology and have provided novel measurements of a mechanism linking age related changes in biology and health to social, environmental and behavioral exposures. They are the beginning of integrating a new level of biology into population studies of health and aging. We describe below the major clocks being used in research on aging as well as a number of new clocks, as yet not widely adopted, but for which there are high expectations. The promise of these clocks is that they provide a link between social and environmental factors and health outcomes, so we briefly review relevant literature and use clocks estimated from one data source to examine relationships between social variables and nine epigenetic clocks and between the clocks and health outcomes. After clarifying the differences in these associations, we offer some suggestions as to why they differ and suggestions for what considerations are important in the future.

1. Four generations of epigenetic clocks

The development of the clocks coincided with the development of Geroscience, or the view that a set of molecular and cellular changes underlie all aspects of aging and are related to all age-associated health outcomes [1]. There are said to be nine hallmarks of biological aging, one of which is epigenetic changes, which can be indicated by age-related changes in the methylation of CpG sites, regions of DNA where a cytosine nucleotide is followed by a guanine nucleotide. DNA methylation (DNAm) is a biological process by which methyl groups are added to a DNA molecule, thus changing its activity but not its sequence. Currently, most epigenetic clocks are based on examination of methylation patterns in approximately 450,000–850,000 CpG sites. Steve Horvath produced one of the initial epigenetic clocks which captivated the field of Geroscience because he showed that epigenetic age could be estimated from DNAm across multiple cell types, tissues and species [2]. Around the same time, Gregory Hannum produced a clock based on analysis of blood samples [3]. Using machine learning techniques, both of these clocks were trained to predict age and thus designed to produce an estimate of epigenetic age which could be compared with chronological age; these are the most utilized first generation clocks. The important link to studying aging was that people could be divided into accelerated and decelerated agers depending on how their epigenetic age compared with their chronological age. Persons whose epigenetic age exceeded chronological age are accelerated agers and those whose chronological age was higher than epigenetic age are slow agers or experience decelerated aging.

The second generation of clocks was trained on mortality and health indicators, i.e., to predict length of life and health rather than age. Levine and colleagues introduced PhenoAge in 2018 [4] and Lu and Horvath published GrimAge in 2019 [5]. Levine had earlier developed a measure of biological age or phenotypic age called PhenoAge based on ten clinical chemistry markers. This measure was then regressed on DNAm sites to determine the best CpG site predictors of PhenoAge. These predictors were used to create epigenetic PhenoAge. Lu et al. [5] also had a two stage process for GrimAge which began with developing DNAm measures for 88 plasma proteins; then they integrated the DNAm measures that were highly correlated with the actual protein measure and included them in regressions of mortality on age, sex, eight DNAm protein measures and a DNAm based measure of lifetime smoking. As this was trained on mortality, it received the name “GrimAge.” More recently GrimAge2 has added DNAm components for C-reactive protein (CRP) and hemoglobin A1C (HbA1c) to the original measure which has been shown to be a better predictor of health outcomes than the original GrimAge [6].

The first entry in the third generation of epigenetic clocks was DunedinPoAm38, followed by DunedinPACE [7]. The novelty of this measure is that it is based on change rather than status so it results in a measure that represents the rate of change in epigenetic age rather than current epigenetic age. The measure of the pace of aging was developed based on change in a phenotypic measure including 19 indicators reflecting health status (body mass index, waist–hip ratio, glycated hemoglobin, leptin, blood pressure, cardiorespiratory fitness, forced vital capacity ratio, forced expiratory volume, total cholesterol, triglycerides, high density lipoprotein, lipoprotein(a), apolipoprotein B100/A1 ratio, estimated glomerular filtration rate, blood urea nitrogen, high sensitivity CRP, white blood cell count, mean periodontal attachment loss and number of dental caries affected tooth surfaces). The pace or rate of aging was estimated over 20 years through four observation points for a set of middle aged persons from Dunedin, New Zealand. DNAm was then regressed on this measure to estimate the rate of estimated aging. A “normal” pace or speed of aging would be a 1 year increase in biological aging per year of time or chronological age; so the average for a population would be 1 year of aging per chronological year. Thus, this clock is in a different metric from the others which reflect an absolute epigenetic age at time of measurement, and the value of the epigenetic age for a population should be the mean of the chronological age of that population.

Recently what might be considered a fourth generation of clocks has been introduced – causal clocks. These are clocks composed of CpG sites believed to be causal in overall aging as well as detrimental and adaptive methylation changes. Initially CpG sites are identified by relationships to a principal component of aging based on healthspan, father’s and mother’s lifespan, exceptional longevity, a frailty index and self-rated health using Mendelian randomization. Provided the assumptions of Mendelian randomization hold, these clocks are only composed of sites causally linked to the aging process. Sites are then separated into those upregulated in healthy agers or downregulated in less healthy agers (representing adaptation to aging) or those downregulated in healthy agers and upregulated in less healthy agers (representing aging related damage). Clocks were then developed by being trained on age [8]. All putatively causal sites were used to train CausAge, sites only associated with adaptation to aging were used to train AdaptAge, and sites only associated with age related damage were used to train DamAge [8]. The approach of introducing Mendelian randomization into the development of these clocks has been met with optimism for their ability to determine causal relationships between CpG sites and outcomes [9].

2. Relationship of the epigenetic clocks to socioeconomic status (SES)

There is a fairly extensive existing literature linking SES and epigenetic aging. We briefly describe findings below on how early life SES and later life SES link to epigenetic age. However, we do not think that spending time in school or having more income is the immediate factor affecting epigenetic aging, but SES associated life circumstances, risk exposures and supportive mechanisms which in turn, relate to epigenetic aging. Thus, we also discuss some evidence linking health behaviors and psychological states to epigenetic aging as these are strongly influenced by SES. Our review is brief; Raffington and Belsky [10] provide a more thorough review of the myriad mechanisms through which the social status can affect health both positively and negatively.

2.1. Early life SES & adverse circumstances

Adverse early life conditions have been shown to accelerate epigenetic aging observed in later life [11]. This is true for childhood SES measured by parental education [12] and parental occupation [13]. It is also true that being a member of a racial or ethnic minority group in the US is associated with both SES and accelerated epigenetic aging [14,15]. Adversity in childhood including parental divorce, parental death, deprivation and other difficult childhood experiences have been linked to greater epigenetic age acceleration at later ages [16–20]. These findings suggest that early-life SES and childhood adversities exert a lasting influence on epigenetic aging and underscore the impact of early environmental and social conditions on long-term health and aging.

2.2. Adult SES

Studies have also shown strong associations between epigenetic aging and adult SES measured by education [21,22], income [22,23], occupation [24], assets and neighborhood environment [22]. Numerous studies have shown the significance of these relationships [25] particularly for the second and third generation clocks [10,21,22]. These relationships seem to be geographically robust, in that a geographically broad study of 18 cohorts from 12, primarily European, countries related HorvathAge, HannumAge and PhenoAge to education and concluded that education was an independent predictor of epigenetic age across this set of countries [26], and in another study of individuals from three cohorts in Italy, Australia and Ireland, there was a significant correlation between low SES education and accelerated epigenetic aging [13].

The trajectory of SES throughout life may affect epigenetic aging, suggesting some interplay between early childhood and lifetime experiences [12,13]. Higher SES in both early and later life [27] as well as upward social mobility have been related to slower epigenetic aging [28]. Several studies have examined both the effect of early childhood SES and adulthood SES on epigenetic aging [12,13,22,27], finding that effects tend to vary depending on the specific clocks or measures of SES used. For example, childhood SES significantly influenced epigenetic aging, with strong associations with first-generation clocks like Horvath and Hannum [22], and this was mediated by adult SES. In another study, both childhood and adulthood socioeconomic factors contributed to epigenetic aging in adulthood indicated by PhenoAge, GrimAge and DunedinPoAm38 [27]; and mother’s education significantly linked to some clocks but not others, suggesting the specific aspects of childhood SES may differentially impact epigenetic clocks [22]. This study also suggested that childhood SES does not always serve as a direct proxy for adult SES in relation to all epigenetic clocks, particularly the second and third generation ones like GrimAge or DunedinPoAm [22]. Thus, while early SES-related adversities predispose individuals to faster epigenetic aging, there may be potential for mitigating these effects through improvements in SES across the lifespan.

2.3. Health behaviors

Risky health behaviors such as smoking, heavy alcohol consumption, obesity and poor sleep lead to accelerated epigenetic aging. Exposure to smoking, both in childhood and in adulthood, is related to accelerated aging as indicated by several clocks: Horvath [20], GrimAge, PhenoAge and DunedinPoAm38 [21,29,30]. Studies also suggest that obesity [21], poor diet [30,31] and poor sleep [32] can accelerate epigenetic aging. Exposure to adverse childhood experiences has been shown to result in the subsequent uptake of adverse behaviors that increase epigenetic aging and serve as mediating factors between SES and the epigenetic indicators [19,22,33].

2.4. Mental & psychological states

Recent studies have linked SES related psychological states to epigenetic aging. Studies are fairly consistent in suggesting that elevated stress [34,35], psychological distress and post-traumatic stress disorder symptom severity [36], lower levels of psychological resilience [37], high depressive symptoms [38] and loneliness [39] are related to epigenetic age acceleration. The complexity of the interactions among SES, mental states and health behaviors is indicated by the links between SES of occupation, work-related stress, and risky health behaviors and epigenetic age [24].

3. Evidence linking nine epigenetic clocks to SES

In order to test the generalizations drawn above with one dataset across nine clocks, we provide data that show the association of accelerated aging in each of nine epigenetic clocks described above with years of education, childhood SES, childhood financial hardship, race/ethnicity, past depressive symptoms and a number of health behaviors (Figure 1). We provide results showing the relationship of each variable to accelerated epigenetic aging with controls for only age, sex and cell-type as well as results when all variables are entered simultaneously in these equations so that the effects are for the variable with others controlled. We use multivariate regression equations indicating the effect of accelerated epigenetic aging on risk factors performed in the Health and Retirement Study, a nationally representative study of people age 56 and older [21]. Using one dataset, the same equations and multiple epigenetic measures at the same time, enables comparisons across the clocks which are not possible in literature reviews where all of these things differ (details on the sample and the variable definitions used for analysis are provided in the Supplementary Information).

Figure 1. Ordinary least squares regression coefficients for epigenetic age acceleration, DunedinPACE and accelerated biological age on socioeconomic, psychological and health behavioral measures (N = 3581 for epigenetic clocks and DunedinPACE and N = 2725 for BioAge22): Health and Retirement Study, 2016.

Results in Figure 1 & Supplementary Table S1 indicate that in general whether the variable is entered alone or with other variables makes minor differences in the relationships. The most consistent change between the two relationships is in education. When all variables are in the equations, persons with less than a college education have higher GrimAge and GrimAge2 acceleration and faster DunedinPACE compared with those with college or more education, but there are no significant educational differentials in the Hannum, Horvath, PhenoAge or the causal clocks. So in these equations the expected relationships are found only in the second and third generation clocks. When education is entered alone, having less than a college education is significantly related to PhenoAge and having a high school education or less is associated with AdaptAge and DamAge compared with college or more education.

We should note that while adult higher education levels are related to deceleration in GrimAge, GrimAge2 and DunedinPACE in the equations with all variables, of these three clocks only DunedinPACE is related to low childhood SES and DunedinPACE and GrimAge2 are related to financial hardship with controls for adult social status. On the other hand, low childhood SES is linked to accelerated PhenoAge and faster AdaptAge; clocks that did not have the expected relationships for adult SES. Reports of childhood financial hardship are significantly related in an unexpected direction with the Horvath, Hannum, PhenoAge, CausAge and DamAge acceleration measures when adulthood and childhood SES are controlled.

Being a Black or a Hispanic American is linked to lower SES, to more discrimination, to worse health and to less access to healthcare. For this reason we would expect to see accelerated aging among these groups. Our results in Figure 1, when all variables are included in the equation, show that all the second and third generation clocks along with AdaptAge link being Black to accelerated aging; however, we find less accelerated aging among Black participants using the Hannum clock, CausAge and DamAge clocks. Hispanic participants have accelerated aging according to the Dunedin clock and when it is included alone in the equation for GrimAge2 acceleration. The Horvath and PhenoAge clocks indicate slower aging for Hispanic participants.

As we indicate above, it is not just the economic circumstances or the education per se that result in accelerated epigenetic aging but the associations between these fundamental states and the stresses, behaviors, and exposures to adverse circumstances. Figure 1 also shows the links between the epigenetic clocks and a small set of the adversities that have been linked to epigenetic aging in the literature: depressive symptoms, smoking, obesity and heavy drinking. These are all linked to SES and they are included in equations both with controls for all the above mentioned social indicators and singly with only age, sex and cell types controlled. Depressive symptoms are associated with accelerated aging according to the Hannum, GrimAge, GrimAge2, DunedinPACE and AdaptAge clocks (with all variables controlled). Among the second and third generation clocks, they are not related to PhenoAge.

Obesity is significantly related to accelerated aging in both equations, in all clocks except the overall Causal Clock and DamAge. When other variables are in the equation, current smoking links to accelerated aging in GrimAge and GrimAge2, which have a component developed on smoking related methylation, as well as DunedinPACE and DamAge. Drinking is related to faster PhenoAging, GrimAge and GrimAge2 in both equations. So, the evidence supports stronger links between health behaviors and second and third generation clocks. Much remains to be done to clarify how social exposures and their timing affect later life epigenetic age.

4. Relationships to mortality & multimorbidity

One issue is how SES and life circumstances are related to epigenetic age, but the reason epigenetic age is of interest is because it is predictive of poor health outcomes. In order to assess how well health outcomes are linked to each of these clocks, we provide odds ratios from equations regressing mortality in the 4 years after measurement of epigenetic age and the presence of multimorbidity at the time of measurement on individual clocks. Results in Figure 2 indicate that second and third generation clocks are most strongly related to both outcomes. The first and fourth generation clocks have similar very small, often insignificant, odds ratios. This result matches results from existing literature [40].

Figure 2. Logistic regression odds ratios from regression of multimorbidity and mortality on standardized accelerated age from each epigenetic clock and on accelerated biological age (Standardized): Health and Retirement Study, 2016.

5. Different relationships with social & behavioral variables across clocks

Clearly, we cannot make a universal statement about how risk factors and health outcome relate to epigenetic clocks. One might ask why, while all clocks measure epigenetic aging, they are not all related in the same direction and with the same intensity to the same risk factors and the same health outcomes. One answer lies in the fact that most of the clocks are only loosely related to each other. The correlations among the estimates of accelerated aging derived from the clocks after regressing out the effect of age are shown in Figure 3. The strongest correlations are between GrimAge acceleration and GrimAge2 acceleration (r = 0.95) and between DamAge and AdaptAge (r = -0.78) as these clocks are built in a similar fashion; which was also evident in the prior figures where their links to SES, behavioral and health outcome variables were quite similar. The next highest associations are between GrimAge2 and DunedinPACE (r = 0.64) which are both trained on several similar health indicators and then between Horvath, Hannum and overall CausAge accelerated aging which are all trained on age; and between Hannum and Pheno accelerated aging (r = 0.41). The causal clocks are most highly associated with the Horvath and Hannum clocks and have fairly low correlations with most of the others except for PhenoAge.

Figure 3. Correlation coefficients between epigenetic age acceleration (above diagonal) and number of overlapping CpG sites (below diagonal) (n = 4018); correlation coefficients between epigenetic clocks and biological age acceleration (n = 2867): Health and Retirement Study, 2016.

Another difference across the clocks is that they involve widely varying numbers of CpG sites (Horvath: 353 CpG sites; Hannum: 71 CpG sites; PhenoAge: 513 CpG sites; GrimAge and GrimAge2: 1030 CpG sites; DunedinPACE: 173 CpG sites; CausAge: 585 CpG sites (of which only 71.3% were available in the data used here); AdaptAge: 999 CpG sites; and DamAge: 1089 CpG sites). The number of sites overlapping across clocks is shown in Figure 3. There is little overlap in the sites included, aside from the measures that are subsets of, or related to, each other; 41 is greatest for Horvath and PhenoAge and all the others are single digits. So while they all measure biological aging, they use different original markers, which is certainly a source of differences in results.

The chips used to develop methylation data differ across the clocks and are another source of variability. Hannum used the Human Methylation 27 Bead Chip; Horvath and the other clocks shown here added data using the 450 Bead chip which results in 17-times as many CpG sites. Current studies, including the Health and Retirement Study used here, are using chips with more than 850K sites. The difference in number and specific sites on chips is what leads to missing sites in some estimated clocks. For instance, because the causal clocks were developed using the 450 Bead chip and our DNAm was based on the EPIC 1 chip, our estimated AdaptAge was missing two out of 999 probes and DamAge was missing two out of 1089 probes. On the other hand, CausAge was missing 168 out of 585 probes (full list of probes, and details on weights, and rankings in Supplementary Table S2). While, there were very few probes missing for AdaptAge and DamAge suggesting that their absence likely minimally affected results, the missing probes for CausAge were more substantial and may have weakened associations involving this measure.

The population used for training epigenetic markers is an additional source of variability. For instance, the DunedinPACE clock is trained on a birth cohort from a city in New Zealand who were middle aged adults at the time of epigenetic measurement; while the Hannum clock was trained in people of all ages but in a relatively small cohort. Framingham Heart Study Offspring have been the data training set for PhenoAge and used along with Atherosclerosis Risk in Communities Study and the Women’s Health Initiative for GrimAge.

The epigenetic measures are in many ways black boxes; the associated CpG sites are chosen by machine learning algorithms rather than for theoretical reasons. Their meaning is somewhat opaque. Our hypothesis is that the epigenetic clocks that relate more strongly to health outcomes and their associated health risks are those that link to the biology that we know is part of the aging process. For this reason, we included a measure of Biological Age based on 22 primarily clinical chemistry measures and some performance tests in all four Figures [41]. The details of this measure are provided in the Supplementary Information but it includes indicators of multisystem physiological dysregulation and organ functioning which might be assessed in a clinical exam such as blood pressure, HbA1C, total cholesterol, CRP, etc. This measure is assumed to be a downstream health indicator influenced by epigenetic age but it is also possible that these measures change at the same time. What is evident is that our measure of clinical Biological Age has similar links as the second and third generation clocks to the risk factors in Figure 1; that Biological Age is even more strongly related to mortality and morbidity than the second and third generation clocks (Figure 2); and that GrimAge, GrimAge2, and DunedinPACE relate most closely to Biological Age (Figure 3 with GrimAge2 having the strongest relationship with Biological Age (r = 0.41), followed by DunedinPACE (r = 0.37), and then GrimAge (r = 0.35). So the epigenetic measures that are most closely related to the social variables and the health outcomes are those related to multisystem biology known to be associated with both risks and outcomes. It should be noted that the original Horvath clock and the Causal Clocks have very little correlation with Biological Age (Figure 3).

This leads to our final assessment of the value of epigenetic clocks in helping us understand human aging. In Figure 4, we present results from regressions similar to those in Figure 2 but Biological Age is added to each of the equations including all variables. The equation with only Biological Age might be thought of as where we were in explanation of health outcomes before the ability to measure DNAm in large populations arrived; and adding DNAm acceleration would indicate how much we gained. The total variance explained in mortality and multimorbidity is decomposed into the variance explained by epigenetic age acceleration, Biological Age acceleration, adult SES, childhood SES, race/ethnicity, depressive symptoms, health behaviors, demographic factors and cell type for the three clocks that are most related to both SES and epigenetic age acceleration. In total, we explain about 25% of the variance in mortality, and about 3% of that is explained by epigenetic age acceleration as indicated by GrimAge2 and DunedinPACE; only 1% of the variation in mortality is explained by PhenoAge acceleration. We should note that Biological Age acceleration explains 7–8% of the variance in these equations. For multimorbidity, our equations explain less of the variance in total, 16–17%, but epigenetic age acceleration still explains either 3 or 1% in the three equations while Biological Age explains 4–5%. With all of these covariates, SES explains a relatively small part of the variance in these outcomes.

Figure 4. R2 decomposition of variance from regressions of mortality and multimorbidity on epigenetic age acceleration, biological age acceleration, demographics, race/ethnicity, adult SES, childhood SES, health behaviors, depressive symptoms and cell type.

6. Future perspective

We can conclude that clocks that were trained on health outcomes indicate that higher adult SES is related to decelerated epigenetic aging and that decelerated epigenetic aging is linked to longer life and less disease. We would not draw the same conclusions from clocks not trained on health outcomes.

Epigenetic clocks trained on health outcomes add to our ability to explain health outcomes, but their overall contribution is relatively minor. They do not eliminate or replace other explanatory variables although they reflect an efficient method of increasing some of understanding of how biology works as they are measured with only a small amount of blood and one assay.

The next decade will provide new measures of epigenetic aging building on those that currently exist. Our evidence is that training clocks on health outcomes rather than age is most likely to provide information about epigenetic changes related to health. We are likely to learn more in the future about why clocks trained on age do not relate to health. Clearly the existing clocks are indexing different components of the aging process and health change related to age. We may never be able to find one clock that fits all; but we should strive to put together indicators of epigenetic methylation changes related to different dimensions of aging that are related to the outcomes we wish to influence. While Geroscience hypothesizes that certain mechanisms are at the root of all aging processes, this is an untested hypothesis. Perhaps aging processes vary in their effect on different organs and systems; and individuals may vary in the risk of system failure resulting in varying clinical outcomes across systems and individuals. We may find that building system specific clocks and combining them in different ways for different analytic tasks might be most appropriate for predicting population health outcomes and designing interventions.

In addition to issues about how to envision and train clocks, the validity of these measures for heterogeneous groups of individuals and across heterogeneous populations needs to be clarified. It is important to clarify the appropriateness of their use across race and ethnic groups as well as in non US and European populations.

As the unreliability of existing measures of methylation at CpG sites has also been noted [42], issues of reliability of measurement will be central in the coming years. While a number of approaches to make the original clocks more reliable have been undertaken, e.g., PACE is a more reliable measure of the Dunedin clock than the original Dunedin clock and Principal Component versions of the Horvath, Hannum, Pheno and GrimAge clocks have been developed and used [21,43], this general lack of reliability remains an impediment to some measurement approaches. One of the most important changes in the near future will be the large number of studies that have repeated epigenetic measures, which are likely to result in major challenges, as well as changes, to epigenetic clocks, some of which will be related to reliability. While the availability of significant longitudinal data will present challenges, it will also offer insights.

We should note that the missing probes for our estimates of CausAge were more substantial and may have weakened associations involving this measure. That being said, results are quite consistent across all three of the causal clocks; though, further studies with greater probe coverage are needed. Many CpG sites included in existing clocks are not going to be included in Illumina’s new chip so updating clocks with new chips will be important for all existing clocks.

In the future, we are likely to link these epigenetic processes to other “omics” in a way that allows more light into the black boxes that characterize the current epigenetic clocks. Technological advancements in the area of omics will lead to increased understanding of the complex interplay among change in DNAm and transcriptomic changes leading to poor health with aging. This will transform the landscape of epigenetic clocks in aging studies and potentially in treatment oriented toward delaying aging and related health outcomes. Importantly, evidence of ability to change epigenetic age by changing behaviors is accumulating [44–46]. Further understanding of the whole process of epigenetic change is important if it is a focus of intervention.

The epigenetic clocks were adopted very quickly by the research community because of their promise in linking the social and the biological aspects of aging and of increasing understanding of aging at a more basic and earlier level. It is only a dozen years since the first clock was developed and epigenetic clocks have been measured in numerous large national studies as well as scores of more localized populations. While their promise seems great as associations with risk factors are often significant, the proportion of variance explained in health outcomes is fairly low [47]. Introduction of new biomarkers generally follows what Colter Mitchell has called the “biomarker hype curve” – extraordinary optimism followed by a drop in favor and then a rebound with more measured expectations [48]. We may have seen the extraordinary optimism and now be heading to a period of second thoughts as the methods and measures for both epigenetic changes with age and the exposures and risks of life circumstances are advanced and developed.

Supplementary Material

Supplementary Information and Tables S1-S2

Supplemental material

Supplemental data for this article can be accessed at https://doi.org/10.1080/17501911.2024.2373682

Author contributions

EM Crimmins and JK Kim wrote the manuscript. JK Kim provided analysis, the figures and table as well as manuscript review and preparation. ET Klopack estimated the causal clocks and reviewed the manuscript.

Financial disclosure

Support for this analysis was provided by the NIH-National Institute on Aging (P30 AG017265, R01AG060110). The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

Competing interests disclosure

The authors have no competing interests or relevant affiliations with any organization or entity with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Writing disclosure

No writing assistance was utilized in the production of this manuscript.
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References

Papers of special note have been highlighted as: • of interest; •• of considerable interest

1. López-Otín C, Blasco MA, Partridge L, et al. The hallmarks of aging. Cell. 2013;153 (6 ):1194–1217. doi:10.1016/j.cell.2013.05.039 23746838
2. Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;14 :R115. doi:10.1186/gb-2013-14-10-r115 24138928
• This is the seminal work in this area.

3. Hannum G, Guinney J, Zhao L, et al. Genome-wide methylation profiles reveal quantitative views of human aging rates. Molecular Cell. 2013;49 (2 ):359–367. doi:10.1016/j.molcel.2012.10.016 23177740
4. Levine ME, Lu AT, Quach A, et al. An epigenetic biomarker of aging for lifespan and healthspan. Aging. 2018;10 (4 ):573–591. doi:10.18632/aging.101414 29676998
5. Lu AT, Quach A, Wilson JG, et al. DNA methylation GrimAge strongly predicts lifespan and healthspan. Aging (Albany NY). 2019;11 (2 ):303–327. doi:10.18632/aging.101684 30669119
6. Lu AT, Binder AM, Zhang J, et al. DNA methylation GrimAge version 2. Aging (Albany NY). 2022;14 (23 ):9484–9549. doi:10.18632/aging.204434 36516495
7. Belsky DW, Caspi A, Corcoran D, et al. DunedinPACE, a DNA methylation biomarker of the pace of aging. Elife. 2022;11. doi:10.7554/eLife.73420
8. Ying K, Liu H, Tarkhov AE, et al. Causality-enriched epigenetic age uncouples damage and adaptation. Nat Aging. 2024;4 (2 ):231–246. doi:10.1038/s43587-023-00557-0 38243142
9. Ryan CP, Belsky DW. Epigenetic clock work ticks forward. Nat Aging. 2024;4 (2 ):170–172. doi:10.1038/s43587-024-00570-x 38291215
10. Raffington L, Belsky DW. Integrating DNA methylation measures of biological aging into social determinants of health research. Curr Environ Health Rep. 2022;9 (2 ):196–210. doi:10.1007/s40572-022-00338-8 35181865
•• Provides a comprehensive introduction to the links between socioeconomic status and epigenetic age.

11. McCrory C, Fiorito G, O'Halloran AM, et al. Early life adversity and age acceleration at mid-life and older ages indexed using the next-generation GrimAge and pace of aging epigenetic clocks. Psychoneuroendocrinology. 2022;137 :105643. doi:10.1016/j.psyneuen.2021.105643 34999481
12. Korous KM, Surachman A, Rogers CR, et al. Parental education and epigenetic aging in middle-aged and older adults in the United States: a life course perspective. Soc Sci Med. 2023;333 :116173. doi:10.1016/j.socscimed.2023.116173 37595421
13. Fiorito G, Polidoro S, Dugué PA, et al. Social adversity and epigenetic aging: a multi-cohort study on socioeconomic differences in peripheral blood DNA methylation. Sci Rep. 2017;7 :16266. doi:10.1038/s41598-017-16391-5 29176660
•• Provides links between social factors and epigenetic aging in a number of cohorts spanning a wide geographic area.

14. Crimmins EM, Thyagarajan B, Levine ME, et al. Associations of age, sex, race/ethnicity, and education with 13 epigenetic clocks in a nationally representative U.S. sample: the Health and Retirement Study. J Gerontol A Biol Sci Med Sci. 2021;76 (6 ):1117–1123. doi:10.1093/gerona/glab016 33453106
15. Yannatos I, Stites SD, Boen C, et al. Epigenetic age and socioeconomic status contribute to racial disparities in cognitive and functional aging between Black and White older Americans. medRxiv. 2023. doi:10.1101/2023.09.29.23296351
16. Farina MP, Klopack ET, Umberson D, et al. The embodiment of parental death in early life through accelerated epigenetic aging: implications for understanding how parental death before 18 shapes age-related health risk among older adults. SSM Popul Health. 2024;26 :101648. doi:10.1016/j.ssmph.2024.101648 38596364
17. Joshi D, Gonzalez A, Lin D, et al. The association between adverse childhood experiences and epigenetic age acceleration in the Canadian longitudinal study on aging (CLSA). Aging Cell. 2023;22 (2 ):e13779. doi:10.1111/acel.13779 36650913
18. Kim JK, Arpawong E, Klopack E, et al. Parental divorce in childhood and the accelerated epigenetic aging for earlier and later cohorts: role of mediators of chronic depressive symptoms, education, smoking, obesity, and own marital disruption. J Popul Ageing. 2023:1–17. doi:10.1007/s12062-023-09434-5
19. Klopack ET, Crimmins EM, Cole SW, et al. Accelerated epigenetic aging mediates link between adverse childhood experiences and depressive symptoms in older adults: results from the Health and Retirement Study. SSM Popul Health. 2022;17 :101071. doi:10.1016/j.ssmph.2022.101071 35313610
20. Schmitz LL, Duffie E, Zhao W, et al. Associations of early-life adversity with later-life epigenetic aging profiles in the multi-ethnic study of atherosclerosis. Am J Epidemiol. 2023;192 (12 ):1991–2005. doi:10.1093/aje/kwad172 37579321
21. Faul JD, Kim JK, Levine M, et al. Epigenetic-based age acceleration in a representative sample of older Americans: associations with aging-related morbidity and mortality. Proc Natl Acad Sci USA. 2023;120 :e2215840120.36802439
22. Schmitz LL, Zhao W, Ratliff SM, et al. The socioeconomic gradient in epigenetic ageing clocks: evidence from the multi-ethnic study of atherosclerosis and the Health and Retirement Study. Epigenetics. 2022;17 (6 ):589–611. doi:10.1080/15592294.2021.1939479 34227900
23. Harris KM, Levitt B, Gaydosh L, et al. The sociodemographic and lifestyle correlates of epigenetic aging in a nationally representative U.S. study of younger adults. bioRxiv. 2024. doi:10.1101/2024.03.21.585983
24. Andrasfay T, Crimmins E. Occupational characteristics and epigenetic aging among older adults in the United States. Epigenetics. 2023;18 (1 ). doi:10.1080/15592294.2023.2218763
25. Oblak L, van der Zaag J, Higgins-Chen AT, et al. A systematic review of biological, social and environmental factors associated with epigenetic clock acceleration. Ageing Res Rev. 2021;69 :101348. doi:10.1016/j.arr.2021.101348 33930583
26. Fiorito G, McCrory C, Robinson O, et al. Socioeconomic position, lifestyle habits and biomarkers of epigenetic aging: a multi-cohort analysis. Aging (Albany NY). 2019;11 (7 ):2045–2070. doi:10.18632/aging.101900 31009935
27. Petrovic D, Carmeli C, Sandoval JL, et al. Life-course socioeconomic factors are associated with markers of epigenetic aging in a population-based study. Psychoneuroendocrinology. 2023;147 :105976. doi:10.1016/j.psyneuen.2022.105976 36417838
28. Graf GH, Zhang Y, Domingue BW, et al. Social mobility and biological aging among older adults in the United States. PNAS Nexus. 2022;1 (2 ):pgac029. doi:10.1093/pnasnexus/pgac029 35615471
29. Klopack ET, Carroll JE, Cole SW, et al. Lifetime exposure to smoking, epigenetic aging, and morbidity and mortality in older adults. Clin Epigenetics. 2022;14 (1 ):72. doi:10.1186/s13148-022-01286-8 35643537
30. Simons RL, Ong ML, Lei MK, et al. Shifts in lifestyle and socioeconomic circumstances predict change—for better or worse—in speed of epigenetic aging: a study of middle-aged black women. Soc Sci Med. 2022;307 :115175. doi:10.1016/j.socscimed.2022.115175 35820233
31. Quach A, Levine ME, Tanaka T, et al. Epigenetic clock analysis of diet, exercise, education, and lifestyle factors. Aging (Albany NY). 2017;9 (2 ):419–446. doi:10.18632/aging.101168 28198702
32. Kusters CDJ, Klopack ET, Crimmins EM, et al. Short sleep and insomnia are associated with accelerated epigenetic age. Psychosom Med. 2023. doi:10.1097/PSY.0000000000001243
33. Fiorito G, Pedron S, Ochoa-Rosales C, et al. The role of epigenetic clocks in explaining educational inequalities in mortality: a multicohort study and meta-analysis. J Gerontol A Biol Sci Med Sci. 2022;77 (9 ):1750–1759. doi:10.1093/gerona/glac041 35172329
34. Opsasnick LA, Zhao W, Schmitz LL, et al. Epigenome-wide association study of long-term psychosocial stress in older adults. Epigenetics. 2024;19 (1 ):2323907. doi:10.1080/15592294.2024.2323907 38431869
35. Skinner HG, Palma-Gudiel H, Steward JD, et al. Stressful life events, social support, and epigenetic aging in the Women's Health Initiative. J Am Geriatr Soc. 2024;72 (2 ):349–360. doi:10.1111/jgs.18726 38149693
36. Mehta D, Bruenig D, Pierce J, et al. Recalibrating the epigenetic clock after exposure to trauma: the role of risk and protective psychosocial factors. J Psychiatr Res. 2022;149 :374–381. doi:10.1016/j.jpsychires.2021.11.026 34823878
37. Zhang A, Zhang Y, Meng Y, et al. Associations between psychological resilience and epigenetic clocks in the health and retirement study. Geroscience. 2024;46 (1 ):961–968. doi:10.1007/s11357-023-00940-0 37707649
38. Wang H, Bakulski KM, Blostein F, et al. Depressive symptoms are associated with DNA methylation age acceleration in a cross-sectional analysis of adults over age 50 in the United States. medRxiv. 2023. doi:10.1101/2023.04.24.23289052
39. Lynch M, Em Arpawong T, Beam CR. Associations between longitudinal loneliness, DNA methylation age acceleration, and cognitive functioning. J Gerontol B Psychol Sci Soc Sci. 2023;78 (12 ):2045–2059. doi:10.1093/geronb/gbad128 37718577
40. McCrory C, Fiorito G, Hernandez B, et al. GrimAge outperforms other epigenetic clocks in the prediction of age-related clinical phenotypes and all-cause mortality. J Gerontol A Biol Sci Med Sci. 2021;76 (5 ):741–749. doi:10.1093/gerona/glaa286 33211845
41. Crimmins EM, Thyagarajan B, Kim JK, et al. Quest for a summary measure of biological age: the health and retirement study. Geroscience. 2021;43 (1 ):395–408. doi:10.1007/s11357-021-00325-1 33544281
42. Zhang W, Young JI, Gomez L, et al. Critical evaluation of the reliability of DNA methylation probes on the Illumina MethylationEPIC v1.0 BeadChip microarrays. Epigenetics. 2024;19 (1 ):2333660. doi:10.1080/15592294.2024.2333660 38564759
43. Higgins-Chen AT, Thrush KL, Wang Y, et al. A computational solution for bolstering reliability of epigenetic clocks: implications for clinical trials and longitudinal tracking. Nat Aging. 2022;2 (7 ):644–661. doi:10.1038/s43587-022-00248-2 36277076
44. Fiorito G, Caini S, Palli D, et al. DNA methylation-based biomarkers of aging were slowed down in a two-year diet and physical activity intervention trial: the DAMA study. Aging Cell. 2021;20 (10 ):e13439. doi:10.1111/acel.13439 34535961
45. Galow AM, Peleg S. How to slow down the ticking clock: age-associated epigenetic alterations and related interventions to extend life span. Cells. 2022;11 (3 ):468. doi:10.3390/cells11030468 35159278
46. Waziry R, Ryan CP, Corcoran DL, et al. Effect of long-term caloric restriction on DNA methylation measures of biological aging in healthy adults from the CALERIE trial. Nat Aging. 2023;3 (3 ):248–257. doi:10.1038/s43587-022-00357-y. Erratum in: Nat Aging. 2023;3(6):753.37118425
47. Crimmins EM. Social hallmarks of aging: suggestions for geroscience research. Ageing Res Rev. 2020;63 :101136. doi:10.1016/j.arr.2020.101136 32798771
48. Qian W, Zhang C, Piersiak HA, et al. Biomarker adoption in developmental science: a data-driven modelling of trends from 90 biomarkers across 20 years. Infant Child Dev. 2024;33 (1 ):e2366. doi:10.1002/icd.2366 38389732
