RT Journal Article
SR Electronic
T1 Age and life expectancy clocks based on machine learning analysis of mouse frailty
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2019.12.20.884452
DO 10.1101/2019.12.20.884452
A1 Schultz, Michael B
A1 Kane, Alice E
A1 Mitchell, Sarah J
A1 MacArthur, Michael R
A1 Warner, Elisa
A1 Mitchell, James R.
A1 Howlett, Susan E
A1 Bonkowski, Michael S
A1 Sinclair, David A
YR 2019
UL http://biorxiv.org/content/early/2019/12/23/2019.12.20.884452.abstract
AB The identification of genes and interventions that slow or reverse aging is hampered by the lack of non-invasive metrics that can predict 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 were scored longitudinally until death and machine learning was employed to develop two clocks. A random forest regression was 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 was 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 novel longevity genes and aging interventions.