Research Paper|Volume 13, Issue 20|pp 23471—23516

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

Eric D. Sun¹, Yong Qian¹, Richard Oppong¹, Thomas J. Butler¹, Jesse Zhao¹, Brian H. Chen², Toshiko Tanaka¹, Jian Kang³, Carlo Sidore⁴, Francesco Cucca⁴, Stefania Bandinelli⁵, Gonçalo R. Abecasis³, Myriam Gorospe⁶, Luigi Ferrucci¹, David Schlessinger⁶, Ilya Goldberg⁷, Jun Ding¹

¹Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, Baltimore, MD 21224, USA
²Department of Epidemiology, The Herbert Wertheim School of Public Health and Human Longevity Science, UC San Diego, La Jolla, CA 92093, USA
³Department of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA
⁴Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche, Monserrato, Italy
⁵Geriatric Unit, Azienda Sanitaria di Firenze, Florence, Italy
⁶Laboratory of Genetics and Genomics, National Institute on Aging, Baltimore, MD 21224, USA
⁷ViQi, Inc., Santa Barbara, CA 93111, USA

Received: February 18, 2021Accepted: September 29, 2021Published: October 29, 2021

https://doi.org/10.18632/aging.203660

Copyright: © 2021 Sun et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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.