History of Aging Biomarkers, and their Evolution
Since the 1980s, it has been recognized that valid and reliable biomarkers will be needed to achieve the ancient goal of understanding, slowing, halting or even reversing aging. Instead of using chronological age, which is an imperfect surrogate measure of the aging process, Baker and Sprott proposed the identification of biomarkers that can accurately and rapidly predict the functional capability of a person or organ and how it changes with age — in other words, to identify markers of biological age.
In 1988, the US National Institute on Aging initiated a program with the expressed goal of identifying age-associated biomarkers in model organisms. The eventual successful development of such biomarkers would require considerable technical as well as cultural breakthroughs. The former included the completion of the Human Genome Project, advances in microarray technology and development of biostatistics (in particular, penalized regression models). The latter, which was equally important, was a shift in scientific culture in the form of the open access movement, which led to the availability of large DNA methylation data sets in freely accessible repositories, such as the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Indeed, the first accurate multi-tissue biomarker of aging, which generates an estimated age for multiple tissues or organs of an individual, was developed for the human species through the analyses of publicly available DNA methylation data sets (Horvath 2013).
Recent evidence demonstrates that DNA methylation-based biomarkers satisfy the formerly elusive criteria of a molecular biomarker of aging: they apply to all sources of DNA (sorted cells, tissues and organs) and to the entire age spectrum (from prenatal tissue to tissues of centenarians).
Background Note: Biomarkers broadly are measures of a medical or biological process. Typically they are quantifiable and objective, to ensure they can be used reliably in research studies. There are also biomarkers that are tailored for many different purposes. For instance, surrogate clinical trial endpoints are a special subset of biomarkers approved by FDA that are well-defined and have undergone extensive testing, and as a result when improved by treatment they are able to accurately predict a clinical benefit for patients.
Epigenetic Clocks Are a Simple Yet Compelling Measure of Biological Age
Epigenetic aging clocks based on DNA methylation can now provide a simple yet compelling indication of biological as opposed to chronological age (Horvath & Raj 2018; Jylhava 2017). Beyond being robust predictors of chronological age, epigenetic aging clocks have also been shown to predict morbidity and all-cause mortality independent of established risk factors such as age, smoking, exercise, body mass index (Chen 2016; Levine 2018; Lu 2019). Because epigenetic aging clocks are strong indicators of biological age, their reversal has the potential to lower the risk of multiple chronic diseases, preserve the immune system, and more. Both human studies and preclinical studies indicate that epigenetic clocks are a promising surrogate endpoint for anti-aging clinical trials.
Increasingly these clocks are also being used as a measure of biological age as certain measures – most notably GrimAge (Lu 2019, Li 2020), which is able to predict future morbidity and mortality risk. GrimAge also performs well for predicting functional decline and onset of major age-related diseases, including heart disease, cancer onset, multi-modal measures of brain health, kidney disease, fatty liver, respiratory function, and more (Lu 2019, Hillary 2018, Bergsma
2020, Hillary 2020, McCrory 2020). These peer reviewed and independent studies have shown that epigenetic clocks and GrimAge in particular performs reliably across large diverse populations. Many researchers conclude today that overall epigenetic clocks now represent the most robust measure of biological age (Li 2020, Jylhava et al 2017, Justice & Kritchevsky 2020).
What Makes Up or Drives the Epigenetic Clock?
The advent of epigenetic clocks has witnessed the emergence of reports demonstrating associations between epigenetic age acceleration and many medical conditions and pathologies. These associations highlight a single point, which is that epigenetic clocks capture biological age, to a significant extent. Although the increasing number of such epidemiological reports serve to reinforce this association, they do not explain or reveal the underlying mechanism of epigenetic aging.
DNA methylation is a process where methyl groups are added to DNA to 
influence gene expression, and which are critical for normal human development, defining cell identity, as well as for rejuvenation and reprogramming. Notably, epigenetic clocks are effective across the lifespan (Horvath 2013), working even in utero, in embryonic stem cells, and in induced pluripotent stem cells, which have an epigenetic age of near zero.
Understanding epigenetic aging requires inquiry into (a) the cells that are involved and (b) the molecular pathways that underlie it. These two distinct, but nevertheless connected features form the basis of our research being performed into the underlying mechanisms of the epigenetic clock, and of the aging process itself (Raj & Horvath 2020).
Regardless of the underlying mechanisms, it is difficult to ignore the fact that epigenetic aging begins from very early moments after the embryonic stem cell stage and continues uninterrupted through the entire lifespan. The significance of this is profound because the answer to the question “Why do we age?” has been attributed to many different types of damage, most commonly to “wear-and-tear.” The ticking of the epigenetic clock from the embryonic state challenges this perspective and supports the notion that aging is an unintended consequence of processes that are necessary for the development of the organism and tissue homeostasis thereafter.