Epigenetic clocks are produced by examining age-related changes in DNA methylation, finding combinations of such changes that are consistent across populations, and predict chronological age. These clocks also predict mortality, in the sense that people with higher epigenetic than chronological age tend to have a higher mortality risk, or be more burdened by chronic age-related disease. The challenge here is that it remains very unclear as to what these epigenetic clocks are actually measuring, which of the underlying processes of aging they reflect, and to what degree. That makes it hard to use epigenetic clocks in any meaningful way - the results are not actionable.
There are other issues to be debugged as well. For example, that the first generation epigenetic clocks are unaffected by fitness differences, or that they appear to systemically underestimate age in older individuals. Given that second point, when looking at the results in the paper here, in which slower epigenetic aging is claimed for a cohort of exceptionally long lived individuals, we are left somewhat in the dark regarding the relevance of the data. These and other issues are not insurmountable problems, but they are standing in the way of broader application of epigenetic measures of biological aging.
Many studies are aimed at biomarker discovery and improvement for aging. The need for such characterization is of upmost importance in light of efforts to achieve longer health and lifespans across the world. Such biomarker detection would enable tracking and even reversal of aging processes and allow for drug targeting and development to benefit the already graying population. Molecular and genomic biomarkers for aging are still sparse and inaccurate with the exception of the very recent development of DNAmGrimAge. This DNA methylation biomarker outperforms all previously reported methylation age estimators and serves as a very accurate estimate of chronological age. Although this is expected due to the use of chronological as a surrogate for the age prediction, DNAmGrimAge also serves as an evaluation of health status, indicative of the rate of epigenetic aging. Use of such biomarkers as indication of rate of age acceleration could promote better understanding of the processes underlying progression of aging and replace use of chronological age in clinical assessments relating to those conditions.
We show here that, although accurate in offspring of exceptionally long lived individuals (ELLI) and unrelated controls, DNAmGrimAge underestimates the chronological age of our ELLI participants, predicting a younger epigenetic age. We believe that this represents a slower rate of aging processes occurring in ELLI, enabling them to reach such exceptional chronological age. This is in agreement with the methylation profile of semi-supercentenarians and their offspring, and replicates earlier results in our independent cohort.
Further, the DNA methylation based estimator of telomere length, DNAmTL, showed no correlation with qPCR measurement of telomere length, until adjusted by DNAmGrimAge. This masking effect of the physiological age (measured by DNAmGrimAge) adds support to the slower rate of aging. Telomere length has long been argued for and against use as an age indicator, but it is well-established to be decreased with age. Our qPCR measurements are consistent with previous observations of longer telomeres in ELLI. While telomere length of ELLI was expected to shorten in respect to offspring and controls because of their relatively advanced age, it remained unchanged, indicating a similar telomere length despite almost 30 years average age difference between group participants, demonstrating once again, a decreased aging rate. Taken together with the juvenile methylation rates in ELLI, we suggest that ELLI age slower than the general population through a beneficial methylation profile that may affect telomere length and other aspects of the hallmarks of aging.