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Age and life expectancy clocks based on machine learning analysis of mouse frailty.
- Source :
-
Nature communications [Nat Commun] 2020 Sep 15; Vol. 11 (1), pp. 4618. Date of Electronic Publication: 2020 Sep 15. - Publication Year :
- 2020
-
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.
Details
- Language :
- English
- ISSN :
- 2041-1723
- Volume :
- 11
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- Nature communications
- Publication Type :
- Academic Journal
- Accession number :
- 32934233
- Full Text :
- https://doi.org/10.1038/s41467-020-18446-0