期刊
NATURE COMMUNICATIONS
卷 11, 期 1, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41467-020-18446-0
关键词
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资金
- Glenn Foundation for Medical Research
- NIH [2R56AG036712-06A1, R37 AG028730, R01 AG019719, R01 DK100263, R01 DK090629-08, 5T32GM070449]
- Epigenetics Seed Grant from Department of Genetics, Harvard Medical School [601139_2018]
- NHMRC CJ Martin biomedical fellowship [GNT1122542]
- Canadian Institutes for Health Research [PGT 162462]
- Heart and Stroke Foundation of Canada [G-19-0026260]
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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