Most important factors that are strongly associated with age are also known as the hallmarks of aging 19 and may be, at least in principle, modified pharmacologically. Typically, however, the physiological indices and the derived quantities such as biological age predictions change from the levels observed in the young organism at a much lower pace than it could be expected from the Gompertzian mortality acceleration. All-cause mortality in humans 17, 18 and the incidence of chronic age-related diseases increase exponentially and double every 8 years 3.
Much of our current understanding of the relationship between aging and changes in physiological variables over an organism’s lifespan originates from large cross-sectional studies 7, 8, 9 and led to development of increasingly reliable “biological clocks” or “biological age” estimations reflecting age-related variations in blood markers 10, DNA methylation (DNAm) states 11, 12 or patterns of locomotor activity 13, 14, 15 (see 16 for a review of biological age predictors). 3, 4, 5) and disease-specific mortality 6. We conclude that the criticality resulting in the end of life is an intrinsic biological property of an organism that is independent of stress factors and signifies a fundamental or absolute limit of human lifespan.Īging is manifested as a progressive functional decline leading to exponentially increasing prevalence 1, 2 and incidence of chronic age-related diseases (e.g., cancers, diabetes, cardiovascular diseases, etc. The observation was immediately confirmed by the independent analysis of correlation properties of intraday physical activity levels fluctuations collected by wearable devices. Extrapolation of this trend suggested that DOSI recovery time and variance would simultaneously diverge at a critical point of 120 − 150 years of age corresponding to a complete loss of resilience. We observed, that the age-dependent population DOSI distribution broadening could be explained by a progressive loss of physiological resilience measured by the DOSI auto-correlation time. To simplify the analysis, we used a log-linear mortality estimate from the CBC variables as a single quantitative measure of the aging process, henceforth referred to as dynamic organism state indicator (DOSI).
We investigated the dynamic properties of the organism state fluctuations along individual aging trajectories in a large longitudinal database of CBC measurements from a consumer diagnostics laboratory.
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