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

ABSTRACT 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 ( Fr ailty Inferred G eriatric H ealth T imeline) clock, a strong predictor of chronological age. A second model was 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 novel longevity genes and aging interventions.