The largest of the present challenges facing the use of epigenetic clocks to measure biological age is that there is no established causal connection between what the clock measures, meaning the methylation status of specific CpG sites on the genome, and specific aspects of the burden of age-related damage and dysfunction; e.g. which changes are due to chronic inflammation, which due to mitochondrial dysfunction, etc. Thus the results obtained from an epigenetic clock assay, the raw methylation data or the resulting epigenetic age, are not actionable. There is nothing one can do with that information to guide health practices or choice of intervention.
Various approaches are under development to attempt to connect DNA methylation of CpG sites to underlying mechanisms of aging. The slow and painful method is to investigate each CpG site in isolation, and this has the look of decades of work at the very least. There are other ways forward, however. The one noted here is a clever use of Mendelian randomization to try to link CpG sites to observable traits, followed by splitting up clocks into (probably) harmful methylation changes versus (probably) adaptive methylation changes. Given that as a tool, then one can try to validate whether this assignment of harmful versus adaptive methylation changes is any good using independent data sets and animal studies.
New epigenetic clocks reinvent how we measure age
Existing epigenetic clocks predict biological age (the actual age of our cells rather than chronological) using DNA methylation patterns. However, until now, no existing clocks have distinguished between methylation differences that cause biological aging and those simply correlated with the aging process.
Using a large genetic data set, researchers performed an epigenome-wide Mendelian Randomization (EWMR), a technique used to randomize data and establish causation between DNA structure and observable traits, on 20,509 CpG sites causal to eight aging-related characteristics. The eight aging-related traits included lifespan, extreme longevity (defined as survival beyond the 90th percentile), health span (age at first incidence of major age-related disease), frailty index (a measure of one's frailty based on the accumulation of health deficits during their lifespan), self-rated health, and three broad aging-related measurements incorporating family history, socioeconomic status, and other health factors.
With these traits and their associated DNA sites in mind, researchers created three models, termed CausAge, a general clock that predicts biological age based on causal DNA factors, and DamAge and AdaptAge, which include only damaging or protective changes. Investigators then analyzed blood samples from 7,036 individuals ages 18 to 93 years old from the "Generation Scotland Cohort" and ultimately trained their model on data from 2,664 individuals in the cohort. With this data, researchers developed a map pinpointing human CpG sites that cause biological aging. This map allows researchers to identify biomarkers causative to aging and evaluate how different interventions promote longevity or accelerate aging.
Causality-enriched epigenetic age uncouples damage and adaptation
Machine learning models based on DNA methylation data can predict biological age but often lack causal insights. By harnessing large-scale genetic data through epigenome-wide Mendelian randomization, we identified CpG sites potentially causal for aging-related traits. Neither the existing epigenetic clocks nor age-related differential DNA methylation are enriched in these sites. These CpGs include sites that contribute to aging and protect against it, yet their combined contribution negatively affects age-related traits.
We established a new framework to introduce causal information into epigenetic clocks, resulting in DamAge and AdaptAge-clocks that track detrimental and adaptive methylation changes, respectively. DamAge correlates with adverse outcomes, including mortality, while AdaptAge is associated with beneficial adaptations. These causality-enriched clocks exhibit sensitivity to short-term interventions. Our findings provide a detailed landscape of CpG sites with putative causal links to lifespan and healthspan, facilitating the development of aging biomarkers, assessing interventions, and studying reversibility of age-associated changes.
View the full article at FightAging
