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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.
The discovery of interventions that slow aging could be accelerated by employing non-invasive biometrics that predict biological age or life expectancy. Here the authors use longitudinal frailty data from naturally aging mice to develop two such tools, that are responsive to interventions.
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1 Paul F. Glenn Center for Biology of Aging Research at Harvard Medical School, Blavatnik Institute, Department of Genetics, Boston, USA (GRID:grid.38142.3c) (ISNI:000000041936754X)
2 Paul F. Glenn Center for Biology of Aging Research at Harvard Medical School, Blavatnik Institute, Department of Genetics, Boston, USA (GRID:grid.38142.3c) (ISNI:000000041936754X); The University of Sydney, Charles Perkins Centre, Sydney, Australia (GRID:grid.1013.3) (ISNI:0000 0004 1936 834X)
3 Harvard T.H. Chan School of Public Health, Department of Genetics and Complex Diseases, Boston, USA (GRID:grid.38142.3c) (ISNI:000000041936754X)
4 University of Michigan, Department of Computational Medicine & Bioinformatics, Ann Arbor, USA (GRID:grid.214458.e) (ISNI:0000000086837370)
5 Voloridge Investment Management, LLC and VoLo Foundation, Jupiter, USA (GRID:grid.214458.e)
6 Dalhousie University, Departments of Pharmacology and Medicine (Geriatric Medicine), Halifax, Canada (GRID:grid.55602.34) (ISNI:0000 0004 1936 8200)
7 Paul F. Glenn Center for Biology of Aging Research at Harvard Medical School, Blavatnik Institute, Department of Genetics, Boston, USA (GRID:grid.38142.3c) (ISNI:000000041936754X); Northwestern University, Department of Dermatology, The Feinberg School of Medicine, Chicago, USA (GRID:grid.16753.36) (ISNI:0000 0001 2299 3507)
8 Paul F. Glenn Center for Biology of Aging Research at Harvard Medical School, Blavatnik Institute, Department of Genetics, Boston, USA (GRID:grid.38142.3c) (ISNI:000000041936754X); The University of New South Wales, Department of Pharmacology, School of Medical Sciences, Sydney, Australia (GRID:grid.1005.4) (ISNI:0000 0004 4902 0432)