4.8 Article

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

Journal

NATURE COMMUNICATIONS
Volume 11, Issue 1, Pages -

Publisher

NATURE PORTFOLIO
DOI: 10.1038/s41467-020-18446-0

Keywords

-

Funding

  1. Glenn Foundation for Medical Research
  2. NIH [2R56AG036712-06A1, R37 AG028730, R01 AG019719, R01 DK100263, R01 DK090629-08, 5T32GM070449]
  3. Epigenetics Seed Grant from Department of Genetics, Harvard Medical School [601139_2018]
  4. NHMRC CJ Martin biomedical fellowship [GNT1122542]
  5. Canadian Institutes for Health Research [PGT 162462]
  6. Heart and Stroke Foundation of Canada [G-19-0026260]

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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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