Researchers here report on an improved version of the epigenetic clock. A few carefully defined patterns of DNA methylation, including the original epigenetic clock, correlate quite closely with age. The current commercial implementation of the epigenetic clock, MyDNAge, has a margin of error of two years or so. While the consensus is that the clock reflects biological age, it is still the case that we might ask what exactly is being measured. The answer to that question remains to be established. It is plausible that DNA methylation changes with age are a reaction to all of the forms of cell and tissue damage that drive aging, but this is by no means certain - it could be more specific than that, tied to only some of the causes of aging.
One of the major goals of geroscience research is to define "biomarkers of aging", which can be thought of as individual-level measures of aging that capture inter-individual differences in the timing of disease onset, functional decline, and death over the life course. While chronological age is arguably the strongest risk factor for aging-related death and disease, it is important to distinguish chronological time from biological aging. Individuals of the same chronological age may exhibit greatly different susceptibilities to age-related diseases and death, which is likely reflective of differences in their underlying biological aging processes. Such biomarkers of aging will be crucial to enable evaluation of interventions aimed at promoting healthier aging, by providing a measurable outcome, which unlike incidence of death and/or disease, does not require extremely long follow-up observation.
One potential biomarker that has gained significant interest in recent years is DNA methylation (DNAm). Chronological time has been shown to elicit predictable hypo- and hyper-methylation changes at many regions across the genome, and as a result, the first generation of DNAm based biomarkers of aging were developed to predict chronological age. The blood-based algorithm by Hannum and the multi-tissue algorithm by Horvath produce age estimates (DNAm age) that correlate with chronological age for full age range samples. Nevertheless, while the current epigenetic age estimators exhibit statistically significant associations with many age-related diseases and conditions, the effect sizes are typically small to moderate. One explanation is that using chronological age as the reference, by definition, may exclude CpG sites whose methylation patterns don't display strong time-dependent changes, but instead signal the departure of biological age from chronological age. Thus, it is important to not only capture CpG sites that display changes with chronological time, but also those that account for differences in risk and physiological status among individuals of the same chronological age.
Previous work by us and others have shown that "phenotypic aging measures", derived from clinical biomarkers, strongly predict differences in the risk of all-cause mortality, cause-specific mortality, physical functioning, cognitive performance measures, and facial aging among same-aged individuals. What's more, in representative population data, some of these measures have been shown to be better indicators of remaining life expectancy than chronological age, suggesting that they may be approximating individual-level differences in biological aging rates. As a result, we hypothesize that a more powerful epigenetic biomarker of aging could be developed by replacing prediction of chronological age with prediction of a surrogate measure of "phenotypic age" that, in and of itself, differentiates morbidity and mortality risk among same-age individuals.
Using a novel two-step method, we were successful in developing a DNAm based biomarker of aging that is highly predictive of nearly every morbidity and mortality outcome we tested. Training an epigenetic predictor of phenotypic age instead of chronological age led to substantial improvement in mortality/healthspan predictions over the first generation of DNAm based biomarkers of chronological age. In doing so, this is the first study to conclusively demonstrate that DNAm biomarkers of aging are highly predictive of cardiovascular disease and coronary heart disease. The new measure, DNAm PhenoAge, also tracks chronological age and relates to disease risk in samples other than whole blood. Finally, we find that an individual's DNAm PhenoAge, relative to his/her chronological age, is moderately heritable and is associated with activation of pro-inflammatory, interferon, DNA damage repair, transcriptional/translational signaling, and various markers of immunosenescence: a decline of naïve T cells and shortened leukocyte telomere length.