Epidemiology, Genetics and Epigenetics of Biological Aging: One or More Aging Systems?

Vast progress was made in the last decade in the development of markers of biological agingBiological aging (BA)—namely estimators of the discrepancy between the hypothetical underlying age and the chronological age of an organism—which can be taken as effective healthy agingAging and public healthHealth screening tools. In particular, the spread of artificial intelligenceArtificial Intelligence applications in biomedical sciences made a substantial contribution also to the field of biogerontologyBiogerontology, with the advent of novel and more accurate clocks based on supervised machine learningMachine Learning algorithms. Despite this promising approach, very little is known about the overlap across the different aging clocksAging clocks, both in terms of mortalityMortality risk predictionRisk prediction and in terms of geneticGenetics, epigeneticEpigenetics and, more generally, biological underpinnings. We will attempt to untangle these aspects by (i) providing a brief introduction to BA clocks developed so far, especially those based on supervised machine learningMachine Learning approaches; then reviewing studies investigating their (ii) epidemiological and (iii) geneticGenetics /epigeneticEpigenetics overlap. Genes intersecting the two overlaps may represent robust candidates as potential molecular targets for the development and validation of future rejuvenation and anti-aging therapies.