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Predicting physiological aging rates from a range of quantitative traits using machine learning.

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.

SUBMITTER: Sun ED

PROVIDER: S-EPMC8580337 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Predicting physiological aging rates from a range of quantitative traits using machine learning.

Sun Eric D ED Qian Yong Y Oppong Richard R Butler Thomas J TJ Zhao Jesse J Chen Brian H BH Tanaka Toshiko T Kang Jian J Sidore Carlo C Cucca Francesco F Bandinelli Stefania S Abecasis Gonçalo R GR Gorospe Myriam M Ferrucci Luigi L Schlessinger David D Goldberg Ilya I Ding Jun J

Aging 20211029 20

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 f ...[more]

PMID: 34718232

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Dataset Information

Predicting physiological aging rates from a range of quantitative traits using machine learning.

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Predicting physiological aging rates from a range of quantitative traits using machine learning.

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