Longitudinal analysis of blood markers reveals progressive loss of resilience and predicts ultimate human lifespan limit

Abstract

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. To simplify the analysis, we used a log-linear mortality estimate from the CBC variables as a single quantitative measure of aging process, henceforth referred to as dynamic organism state index (DOSI). 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. 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. The observation was immediately confirmed by the independent analysis of correlation properties of intraday physical activity levels fluctuations collected by wearable devices. 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.

Deep longitudinal phenotyping of wearable sensor data reveals independent markers of longevity, stress, and resilience

“…Finally, we investigated the auto-correlation properties of the BAA fluctuations. The diverging autocorrelation times are typical for systems approaching tipping or dis-integration point [14] and a hallmark of aging [4, 15]. Accordingly, we observed vanishing recovery rate and the exponentially increasing fraction of individuals with long recovery times in subsequent age cohorts.…”

“…BAA and chronic diseases: A ) the negative logarithm of daily step counts, B ) GeroSense BAA C ) questionnaire [22], D ) CBC-based DOSI [4], E ) log-mortality risk trained using combined CBC and Blood biochemistry variables, and F ) Blood-based PhenoAge [2]. The plots are produced for NAHNES users aged 45 − 75 y.o.…”

“…Association of BAA with the future incidence of COVID-19: A ) BAA in the form of the negative logarithm of daily step counts, B ) GeroSense BAA, C ) CBC-based DOSI [4], D ) CBC and Blood biochemistry hazards model, and E ) Blood-based PhenoAge [2]. All data are given for UK Biobank users aged 45 − 75 y.o.…”

“…Moreover, the activity-based models’ signal-to-noise ratio could be improved by averaging over longer motion tracks. After just a few months of averaging, the activity-based model applied to a wristband signal may detect the effects of chronic diseases and smoking at the same level of significance as blood-based PhenoAge from [2] and Dynamic Organism State Indicator (DOSI) from [4]. Same finding held for the association of BAA with the incidence and severity of seasonal infectious diseases (including COVID-19).…”

“…Such capabilities have just recently became available in the form of biological clocks and are increasingly used in the field of quantitative aging research. State-of-art implementations involve machine learning of the associations of the DNA methylation patterns [1] or blood variables [2–4] with either the chronological age or risks of death and diseases. The aging clocks have been used in clinical trials of anti-aging interventions [5].…”