Will you age quickly or slowly? Is it possible to predict how long you will live based on your genetics, lifestyle and other traits? In a new study, a team of researchers—from the National Institutes of Health’s National Institute on Aging, University of California San Diego, University of Michigan, Consiglio Nazionale delle Ricerche, Azienda Sanitaria di Firenze, and ViQi, Inc.—sought to answer these questions by developing a novel framework designed to estimate human physiological age and aging rate. Their trending paper was published by Aging (Aging-US) in October 2021, and entitled, “Predicting physiological aging rates from a range of quantitative traits using machine learning”.

“We present machine learning as a promising framework for measuring physiological age from broad-ranging physiological, cognitive, and molecular traits.”

Full blog - https://www.mishablagosklonny.com/2021/11/05/trending-with-impact-machine-learning-predicts-human-aging/

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

Full Text - https://www.aging-us.com/article/203660/text

Correspondence to: Luigi Ferrucci email: [email protected], David Schlessinger email: [email protected], Ilya Goldberg email: [email protected] and Jun Ding email: [email protected]

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.

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