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Unsupervised learning of aging principles from longitudinal data

Author

Listed:
  • Konstantin Avchaciov

    (Gero PTE. LTD.)

  • Marina P. Antoch

    (Department of Pharmacology and Therapeutics, Roswell Park Comprehensive Cancer Center)

  • Ekaterina L. Andrianova

    (Genome Protection, Inc.)

  • Andrei E. Tarkhov

    (Gero PTE. LTD.)

  • Leonid I. Menshikov

    (Gero PTE. LTD.)

  • Olga Burmistrova

    (Gero PTE. LTD.)

  • Andrei V. Gudkov

    (Genome Protection, Inc.
    Department of Cell Stress Biology, Roswell Park Comprehensive Cancer Center)

  • Peter O. Fedichev

    (Gero PTE. LTD.)

Abstract

Age is the leading risk factor for prevalent diseases and death. However, the relation between age-related physiological changes and lifespan is poorly understood. We combined analytical and machine learning tools to describe the aging process in large sets of longitudinal measurements. Assuming that aging results from a dynamic instability of the organism state, we designed a deep artificial neural network, including auto-encoder and auto-regression (AR) components. The AR model tied the dynamics of physiological state with the stochastic evolution of a single variable, the “dynamic frailty indicator” (dFI). In a subset of blood tests from the Mouse Phenome Database, dFI increased exponentially and predicted the remaining lifespan. The observation of the limiting dFI was consistent with the late-life mortality deceleration. dFI changed along with hallmarks of aging, including frailty index, molecular markers of inflammation, senescent cell accumulation, and responded to life-shortening (high-fat diet) and life-extending (rapamycin) treatments.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-34051-9
    DOI: 10.1038/s41467-022-34051-9
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    References listed on IDEAS

    as
    1. Johnstone, Iain M. & Lu, Arthur Yu, 2009. "On Consistency and Sparsity for Principal Components Analysis in High Dimensions," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 682-693.
    2. Marco Baggiolini, 1998. "Chemokines and leukocyte traffic," Nature, Nature, vol. 392(6676), pages 565-568, April.
    3. Andreas Mardt & Luca Pasquali & Hao Wu & Frank Noé, 2018. "VAMPnets for deep learning of molecular kinetics," Nature Communications, Nature, vol. 9(1), pages 1-11, December.
    4. 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. "Publisher Correction: Age and life expectancy clocks based on machine learning analysis of mouse frailty," Nature Communications, Nature, vol. 11(1), pages 1-1, December.
    5. Bethany Lusch & J. Nathan Kutz & Steven L. Brunton, 2018. "Deep learning for universal linear embeddings of nonlinear dynamics," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
    6. Jan M. van Deursen, 2014. "The role of senescent cells in ageing," Nature, Nature, vol. 509(7501), pages 439-446, May.
    7. David E. Harrison & Randy Strong & Zelton Dave Sharp & James F. Nelson & Clinton M. Astle & Kevin Flurkey & Nancy L. Nadon & J. Erby Wilkinson & Krystyna Frenkel & Christy S. Carter & Marco Pahor & Ma, 2009. "Rapamycin fed late in life extends lifespan in genetically heterogeneous mice," Nature, Nature, vol. 460(7253), pages 392-395, July.
    8. Andreas Mardt & Luca Pasquali & Hao Wu & Frank Noé, 2018. "Author Correction: VAMPnets for deep learning of molecular kinetics," Nature Communications, Nature, vol. 9(1), pages 1-1, December.
    9. 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.
    10. Marten Scheffer & Jordi Bascompte & William A. Brock & Victor Brovkin & Stephen R. Carpenter & Vasilis Dakos & Hermann Held & Egbert H. van Nes & Max Rietkerk & George Sugihara, 2009. "Early-warning signals for critical transitions," Nature, Nature, vol. 461(7260), pages 53-59, September.
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