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

Author

Listed:
  • 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
    The University of Sydney)

  • 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
    Northwestern University)

  • David A. Sinclair

    (Paul F. Glenn Center for Biology of Aging Research at Harvard Medical School
    The University of New South Wales)

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.

Suggested Citation

  • 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 & David A. Sinclair, 2020. "Age and life expectancy clocks based on machine learning analysis of mouse frailty," Nature Communications, Nature, vol. 11(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-18446-0
    DOI: 10.1038/s41467-020-18446-0
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    Cited by:

    1. Carolin Thomas & Reto Erni & Jia Yee Wu & Fabian Fischer & Greta Lamers & Giovanna Grigolon & Sarah J. Mitchell & Kim Zarse & Erick M. Carreira & Michael Ristow, 2023. "A naturally occurring polyacetylene isolated from carrots promotes health and delays signatures of aging," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    2. Konstantin Avchaciov & Marina P. Antoch & Ekaterina L. Andrianova & Andrei E. Tarkhov & Leonid I. Menshikov & Olga Burmistrova & Andrei V. Gudkov & Peter O. Fedichev, 2022. "Unsupervised learning of aging principles from longitudinal data," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    3. Cristal M. Hill & Diana C. Albarado & Lucia G. Coco & Redin A. Spann & Md Shahjalal Khan & Emily Qualls-Creekmore & David H. Burk & Susan J. Burke & J. Jason Collier & Sangho Yu & David H. McDougal & , 2022. "FGF21 is required for protein restriction to extend lifespan and improve metabolic health in male mice," Nature Communications, Nature, vol. 13(1), pages 1-14, December.

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