Abstract
It is widely thought that individuals age at different rates. A method that measures “physiological age” or physiological aging rate independent of chronological age could therefore help elucidate mechanisms of aging and inform an individual’s risk of morbidity and mortality. Here we present machine learning frameworks for inferring individual physiological age from a broad range of biochemical and physiological traits including blood phenotypes (e.g., high-density lipoprotein), cardiovascular functions (e.g., pulse wave velocity) and psychological traits (e.g., neuroticism) as main groups in two population cohorts SardiNIA (~6,100 participants) and InCHIANTI (~1,400 participants). The inferred physiological age was highly correlated with chronological age (R2 > 0.8). We further defined an individual’s physiological aging rate (PAR) as the ratio of the predicted physiological age to the chronological age. Notably, PAR was a significant predictor of survival, indicating an effect of aging rate on mortality. Our trait-based PAR was correlated with DNA methylation-based epigenetic aging score (r = 0.6), suggesting that both scores capture a common aging process. PAR was also substantially heritable (h2~0.3), and a subsequent genome-wide association study of PAR identified significant associations with two genetic loci, one of which is implicated in telomerase activity. Our findings support PAR as a proxy for an underlying whole-body aging mechanism. PAR may thus be useful to evaluate the efficacy of treatments that target aging-related deficits and controllable epidemiological factors.
Highlights
The study of possible intrinsic aging rates is itself venerable and continues to be motivated by Peter Medawar’s pioneering question: does aging occur by a fundamental process independent of—though affected by—overt disease? [1]
The measured physiological age is well-correlated with chronological age (R2 > 0.8), and different sets of traits give very comparable estimates of physiological aging rate (PAR)
PAR calculated from the traits is correlated with the epigenetic aging rate (EAR) (R2 = 0.36, r = 0.6), even though the two aging rates are based on separate biological frameworks
Summary
The study of possible intrinsic aging rates is itself venerable and continues to be motivated by Peter Medawar’s pioneering question: does aging occur by a fundamental process independent of—though affected by—overt disease? [1]. A reproducible framework for estimating physiological age as a measurement of intrinsic age progression would permit the analysis and evaluation of treatments that target aging-related debilitation without a need for expensive long-term studies [2,3,4]. Biological aging rate measurements should be relatively stable across time in longitudinal studies [3, 5, 6], account for aging at different biological levels [7,8,9], and ideally be associated with disease risk and mortality [3, 8]. Since there is little variation in human age at natural death [18], true biological age is unlikely to deviate markedly from chronological age— the need for a more tightly correlated measure of biological age
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