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.

Details

Title
Age and life expectancy clocks based on machine learning analysis of mouse frailty
Author
Schultz, Michael B 1 ; Kane, Alice E 2 ; Mitchell, Sarah J 3 ; MacArthur, Michael R 3   VIAFID ORCID Logo  ; Warner, Elisa 4   VIAFID ORCID Logo  ; Vogel, David S 5 ; Mitchell, James R 3 ; Howlett, Susan E 6   VIAFID ORCID Logo  ; Bonkowski, Michael S 7 ; Sinclair, David A 8   VIAFID ORCID Logo 

 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) 
 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) 
 Harvard T.H. Chan School of Public Health, Department of Genetics and Complex Diseases, Boston, USA (GRID:grid.38142.3c) (ISNI:000000041936754X) 
 University of Michigan, Department of Computational Medicine & Bioinformatics, Ann Arbor, USA (GRID:grid.214458.e) (ISNI:0000000086837370) 
 Voloridge Investment Management, LLC and VoLo Foundation, Jupiter, USA (GRID:grid.214458.e) 
 Dalhousie University, Departments of Pharmacology and Medicine (Geriatric Medicine), Halifax, Canada (GRID:grid.55602.34) (ISNI:0000 0004 1936 8200) 
 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) 
 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) 
Publication year
2020
Publication date
2020
Publisher
Nature Publishing Group
e-ISSN
20411723
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2442688282
Copyright
© The Author(s) 2020. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.