Summary DNA methylation levels of certain CpG sites are thought to reflect the pace of human aging. Here, we developed a robust predictor of mouse biological age based on 90 CpG sites derived from partial blood DNA methylation profiles. The resulting clock correctly determines the age of mouse cohorts, detects the longevity effects of calorie restriction and gene knockouts, and reports rejuvenation of fibroblast-derived iPSCs. The data show that mammalian DNA methylomes are characterized by CpG sites that may represent the organism’s biological age. They are scattered across the genome, are distinct in human and mouse, and their methylation gradually changes with age. The clock derived from these sites represents a biomarker of aging and can be used to determine the biological age of organisms and evaluate interventions that alter the rate of aging.

Age predictors based on DNA methylation levels at a small set of CpG sites, DNAm clocks, have been developed for humans and extended to several other species. Three currently available versions of mouse DNAm clocks were either created for individual tissues or tuned toward young ages. Here, we constructed a robust multi-tissue age predictor based on 435 CpG sites, which covers the entire mouse lifespan and remains unbiased with respect to any particular age group. It can successfully detect the effects of certain lifespan-modulating interventions on DNAm age as well as the rejuvenation effect related to the transition from fibroblasts to iPSCs. We have carried out comparative analyses of available mouse DNAm clocks, which revealed their broad applicability, but also certain limitations to the use of tissue-specific and multi-tissue age predictors. Together, these tools should help address diverse questions in aging research.

Somatic mutations have long been implicated in aging and disease, but their impact on fitness and function is difficult to assess. Here by analysing human cancer genomes we identify mutational patterns associated with aging. Our analyses suggest that age-associated mutation load and burden double approximately every 8 years, similar to the all-cause mortality doubling time. This analysis further reveals variance in the rate of aging among different human tissues, for example, slightly accelerated aging of the reproductive system. Age-adjusted mutation load and burden correlate with the corresponding cancer incidence and precede it on average by 15 years, pointing to pre-clinical cancer development times. Behaviour of mutation load also exhibits gender differences and late-life reversals, explaining some gender-specific and late-life patterns in cancer incidence rates. Overall, this study characterizes some features of human aging and offers a mechanism for age being a risk factor for the onset of cancer.