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

Nat Commun. 2020 Sep 15;11(1):4618. 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 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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aging / genetics
  • Aging / physiology*
  • Animals
  • Biological Clocks
  • Female
  • Frailty
  • Humans
  • Life Expectancy
  • Machine Learning
  • Male
  • Mice / genetics
  • Mice / growth & development
  • Mice / physiology*
  • Mice, Inbred C57BL