Trending With Impact: Machine Learning Used to Estimate Physiological Age



November 4, 2021

Aging-US published this trending research paper as the cover for Issue 13, Volume 20, entitled, "Predicting physiological aging rates from a range of quantitative traits using machine learning” by researchers from the Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, Baltimore, MD; Department of Epidemiology, The Herbert Wertheim School of Public Health and Human Longevity Science, UC San Diego, La Jolla, CA; Department of Biostatistics, University of Michigan, Ann Arbor, MI; Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche, Monserrato, Italy; Geriatric Unit, Azienda Sanitaria di Firenze, Florence, Italy; Laboratory of Genetics and Genomics, National Institute on Aging, Baltimore, MD; ViQi, Inc., Santa Barbara, CA. 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. Sign up for free Altmetric alerts about this article - DOI - Full Text - Correspondence to: Luigi Ferrucci email:, David Schlessinger email:, Ilya Goldberg email: and Jun Ding email: Keywords: physiological aging rate, quantitative trait, machine learning, aging clock, mortality, personalized medicine About Aging-US Launched in 2009, Aging-US publishes papers of general interest and biological significance in all fields of aging research and age-related diseases, including cancer—and now, with a special focus on COVID-19 vulnerability as an age-dependent syndrome. Topics in Aging-US go beyond traditional gerontology, including, but not limited to, cellular and molecular biology, human age-related diseases, pathology in model organisms, signal transduction pathways (e.g., p53, sirtuins, and PI-3K/AKT/mTOR, among others), and approaches to modulating these signaling pathways. Please visit our website at​​ or connect with us on: SoundCloud -​ Facebook - Twitter - Instagram - YouTube -​ LinkedIn -​ Pinterest - Aging-US is published by Impact Journals, LLC please visit​​ or connect with @ImpactJrnls Media Contact 18009220957 MEDIA@IMPACTJOURNALS.COM

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