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.

Highlights d Mortality from age-related diseases is U-shaped with the nadir below reproductive age d Quantitative biomarkers of aging change continuously throughout life d Mutation burden causes early-life mortality and contributes to selection d Aging is best defined by damage rather than mortality and starts very early in life

Aging involves coordinated yet distinct changes in organs and systems throughout life, including changes in essential trace elements. However, how aging affects tissue element composition (ionome) and how these changes lead to dysfunction and disease remain unclear. Here, we quantified changes in the ionome across eight organs and 16 age groups of mice. This global profiling revealed novel interactions between elements at the level of tissue, age, and diet, and allowed us to achieve a broader, organismal view of the aging process. We found that while the entire ionome steadily transitions along the young‐to‐old trajectory, individual organs are characterized by distinct element changes. The ionome of mice on calorie restriction (CR) moved along a similar but shifted trajectory, pointing that at the organismal level this dietary regimen changes metabolism in order to slow down aging. However, in some tissues CR mimicked a younger state of control mice. Even though some elements changed with age differently in different tissues, in general aging was characterized by the reduced levels of elements as well as their increased variance. The dataset we prepared also allowed to develop organ‐specific, ionome‐based markers of aging that could help monitor the rate of aging. In some tissues, these markers reported the lifespan‐extending effect of CR. These aging biomarkers have the potential to become an accessible tool to test the age‐modulating effects of interventions.

Two recent stimulating publications have examined the causes of cancer, comparing “bad luck” vs. environment as main risk factors for cancer incidence. However, bringing aging into the picture might question the entire debate.

It was previously argued that the phenomenon of quantum gravitational decoherence described by the WheelerDeWitt equation is responsible for the emergence of the arrow of time. Here we show that the characteristic spatiotemporal scales of quantum gravitational decoherence are typically logarithmically larger than a characteristic curvature radius R −1/2 of the background space-time. This largeness is a direct consequence of the fact that gravity is a non-renormalizable theory, and the corresponding effective field theory is nearly decoupled from matter degrees of freedom in the physical limit M P → ∞. Therefore, as such, quantum gravitational decoherence is too ineffective to guarantee the emergence of the arrow of time and the "quantum-to-classical" transition to happen at scales of physical interest. We argue that the emergence of the arrow of time is directly related to the nature and properties of physical observer.

Age-associated molecular changes are deleterious and causally linked with aging and may affect life span through diet.