Background The epigenetic age can now be extrapolated from one of several epigenetic clocks, which are based on age-related changes in DNA methylation levels at specific multiple CpG sites. Accelerated aging, calculated from the discrepancy between the chronological age and the epigenetic age, has shown to predict morbidity and mortality rate. We assumed that deconvolution of epigenetic age to its components could shed light on the diversity of epigenetic, and by inference, on inter-individual variability in the causes of biological aging. Results Using the Horvath original epigenetic clock, we identified several CpG sites linked to distinct genes that quantitatively explain much of the inter-personal variability in epigenetic aging, with CpG sites related to secretagogin and malin being the most variable. We show that equal epigenetic age in different subjects can result from variable contribution size of the same CpG sites to the total epigenetic age. In a healthy cohort, the most variable CpG sites are responsible for accelerated and decelerated epigenetic aging, relative to chronological age. Conclusions Of the 353 CpG sites that form the basis for the Horvath epigenetic age, we have found the CpG sites that are responsible for accelerated and decelerated epigenetic aging in healthy subjects. However, the relative contribution of each site to aging varies between individuals, leading to variable personal aging patterns. Our findings pave the way to form personalized aging cards allowing the identification of specific genes related to CpG sites, as aging markers, and perhaps treatment of these targets in order to hinder undesirable age drifting.

Epigenetic age not only correlates with chronological age but predicts morbidity and mortality. We assumed that deconvolution of epigenetic age to its individual components could shed light on the diversity of epigenetic, and by inference, biological aging. Using the Horvath original epigenetic clock, we identified several CpG sites linked to distinct genes that quantitatively explain much of the interpersonal variability in epigenetic aging, with secretagogin and malin showing the most dominant effects. The analysis shows that the same epigenetic age for any given chronological age can be accounted for by variable contributions of identifiable CpG sites; that old epigenetic relative to chronological age is mostly explained by the same CpG sites, mapped to genes showing the highest interindividual variability differences in healthy subjects but not in subjects with type 2 diabetes. This paves the way to form personalized aging cards indicating the sources of accelerated/decelerated epigenetic aging in each examinee, en route to targeting specific sites as indicators, and perhaps treatment targets of personal undesirable age drifting.