Age and Life Expectancy Clocks Based on Machine Learning Analysis of Mouse Frailty

Overview

Journal Nat Commun

Specialty Biology

Date 2020 Sep 16

PMID 32934233

Citations 62

Authors

Michael B Schultz

Alice E Kane

Sarah J Mitchell

Michael R MacArthur

Elisa Warner

David S Vogel

James R Mitchell

Susan E Howlett

Michael S Bonkowski

David A Sinclair

Affiliations

Soon will be listed here.

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 (Frailty Inferred Geriatric Health Timeline) clock, a strong predictor of chronological age. A second model is 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 longevity genes and aging interventions.

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