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

Michael B. Schultz, Alice E. Kane, Sarah J. Mitchell, Michael R. MacArthur, Elisa Warner, David S. Vogel, James R. Mitchell, Susan E. Howlett, Michael S. Bonkowski and David A. Sinclair ()
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Michael B. Schultz: Paul F. Glenn Center for Biology of Aging Research at Harvard Medical School
Alice E. Kane: Paul F. Glenn Center for Biology of Aging Research at Harvard Medical School
Sarah J. Mitchell: Harvard T.H. Chan School of Public Health
Michael R. MacArthur: Harvard T.H. Chan School of Public Health
Elisa Warner: University of Michigan
David S. Vogel: Voloridge Investment Management, LLC and VoLo Foundation
James R. Mitchell: Harvard T.H. Chan School of Public Health
Susan E. Howlett: Dalhousie University
Michael S. Bonkowski: Paul F. Glenn Center for Biology of Aging Research at Harvard Medical School
David A. Sinclair: Paul F. Glenn Center for Biology of Aging Research at Harvard Medical School

Nature Communications, 2020, vol. 11, issue 1, 1-12

Abstract: 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.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-18446-0

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DOI: 10.1038/s41467-020-18446-0

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