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

By Michael B. Schultz, Alice E Kane, Sarah J. Mitchell, Michael R MacArthur, Elisa Warner, James R Mitchell, Susan E Howlett, Michael S. Bonkowski, David A. Sinclair

Posted 23 Dec 2019
bioRxiv DOI: 10.1101/2019.12.20.884452 (published DOI: 10.1038/s41467-020-18446-0)

The identification of genes and interventions that slow or reverse aging is hampered by the lack of non-invasive metrics that can predict 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 were scored longitudinally until death and machine learning was employed to develop two clocks. A random forest regression was trained on FI components for chronological age to generate the FRIGHT (Frailty Inferred Geriatric Health Timeline) clock, a strong predictor of chronological age. A second model was trained on remaining lifespan to generate the AFRAID (Analysis of Frailty and Death) 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 novel longevity genes and aging interventions.

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