Assessment of Epigenetic Clocks as Biomarkers of Aging in Basic and Population Research

The Geoscience paradigm suggests that targeting the aging process could delay or prevent the risk of multiple major age-related diseases, such as cancer, cardiovascular disease, Alzheimer’s disease, diabetes, and osteoporosis, to name only a few(1,2). These diseases have well-established clinical diagnostic and classification criteria, yet despite the fact that aging may be the key driver across these diverse pathologies, aging remains a latent concept with no agreed upon molecular definition. If our goal is to develop interventions to slow the increase in biological aging as a function of chronological time, and thus facilitate health promotion and disease prevention, we need to first come up with clinically valid measures of the underlying biological process and/or classification criteria for what it means to be biologically, rather than chronologically, “aged.” This collection of primary papers and reviews focuses on DNA methylation and its promise as a means to define biological age. As discussed in the primary research paper by Nelson et al. (3), developing biomarkers of aging would serve three important purposes: First, biomarkers can provide both diagnostic and prognostic insight that may inform medical and/or personal decisions; second, they may uncover mechanistic clues that aid basic research; and third, they have the potential to inform intervention strategies. As a result, the quest to develop both valid and reliable biomarkers of aging has been gaining steam over the past decade. While measures of biological age have been developed using a variety of data sources—from proteomics (4), to microbiological profiles of gut microbiota (5), to facial images (6)—at the forefront of the biomarker quest sit the “epigenetic clocks.” These aging measures are based on composite scores that combine information on DNA methylation (DNAm) levels at tens to hundreds of CpG dinucleotide locations across the genome (7). Since the first epigenetic clock(definition) was developed by Bocklandt et al. in 2011 (8), numerous age predictors based on DNAm have been developed and or applied across a variety of mammalian species (9–12). While epigenetic clocks routinely exhibit extremely high precision when it comes to age prediction, many scientists are starting to recognize that perhaps the most important test of these proposed biomarkers is not how well they predict chronological time, but rather how well they predict aging outcomes, above and beyond age itself. With the growing inclusion of DNAm data in many of the existing large human cohort studies, researchers are beginning to test the following—when considering individuals of the same chronological age, do those with higher epigenetic age look phenotypically older on average (eg, have higher mortality rates, greater disease burden, and worse physical and cognitive functioning)? This question is at the heart of this special collection, describing evidence for (or in some cases detracting from) the utility of epigenetic clocks for measuring biological aging in blood. The papers by Bressler et al. (13) and Ryan et al. (14) test the utility of what we consider the first-generation epigenetic clocks—the blood clock by Hannum et al. (2013) (12), and the pan-tissue clock by Horvath (2013) (11). Early epigenetic clocks have been utilized in multiple epidemiological studies (mostly using blood and saliva) to evaluate morbidity and mortality associations. In their systematic review, Ryan et al. (14) conducted a meta-analysis across 61 studies to test for associations between the epigenetic clocks by Hannum and Horvath when it comes to factors pertaining to environment, lifestyle, and health. One of the most robust findings was for body mass index (BMI), in which results suggested that higher BMI was consistently associated with increased epigenetic age acceleration (defined as the residual when clock scores are regressed on age). Frailty was also reliably associated with higher epigenetic age acceleration across three studies (two using Horvath and one using a derivation of Hannum, called EEAA). Unfortunately, for many of the other variables, results were inconclusive. Although many of the studies utilized in the meta-analysis by Ryan et al. (14) did not disclose the racial/ethnic makeup of their samples, the authors speculate that the majority of results are likely based on associations in Caucasians and may not generalize to other groups. The tendency towards Caucasian-specific finings is an issue that has plagued recent genetic association studies; however, there is some evidence that epigenetic clock associations may show less racial/ethnic bias, thanks to the inclusion of more diverse, multiethnic samples when developing these measures. To evaluate this assumption, the paper by Bressler et al. (13) tested associations between cognitive functioning and epigenetic age using a sample of just over 2000 middle-aged to older African Americans from the Atherosclerosis Risk in Communities (ARIC) Study. They found that epigenetic age, using the Hannum clock, was positively associated with a measure of cognitive functioning (the Word Fluency Test), independent of age, sex, and education. They then replicated this finding in a European sample from the Generation Scotland: Scottish Family Health Study. Overall, the results suggest that older epigenetic age, as measured by Hannum but not Horvath, is associated with decreased verbal fluency and that this generalizes to both European and African ancestry samples, and cannot be accounted for by cognitive differences as a function of education, sex, or age. The epigenetic clock by Horvath, and to some extent, the one by Hannum, have been the most widely applied in both epidemiological and basic research. However, this is perhaps due more to their prominence in the field rather than perhaps their utility. The papers by both McCrory et al. (15) and Maddock et al. (16) go one step further by exploring associations with both the firs