Age and life expectancy clocks based on machine learning analysis of mouse frailty

Michael B. Schultz, Alice E. Kane, Sarah J. Mitchell and 7 more

Nature Communications · 2020 · CC BY

doi:10.1101/2019.12.20.884452

Abstract

The identification of genes and interventions that slow or reverse aging is hampered by the lack of non-invasive metrics that can predict the life expectancy of pre-clinical models. Frailty Indices (FIs) in mice are composite measures of health that are cost-effective and non-invasive, but whether they can accurately predict health and lifespan is not known. Here, mouse FIs are scored longitudinally until death and machine learning is employed to develop two clocks. A random forest regression is trained on FI components for chronological age to generate the FRIGHT ( Fr ailty I nferred G eriatric H ealth T imeline) clock, a strong predictor of chronological age. A second model is trained on remaining lifespan to generate the AFRAID ( A nalysis of Frai lty and D eath) clock, which accurately predicts life expectancy and the efficacy of a lifespan-extending intervention up to a year in advance. Adoption of these clocks should accelerate the identification of longevity genes and aging interventions.

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