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Unsupervised learning of aging principles from longitudinal data

Avchaciov, Konstantin, Antoch, Marina P., Andrianova, Ekaterina L., Tarkhov, Andrei E., Menshikov, Leonid I., Burmistrova, Olga, Gudkov, Andrei V., Fedichev, Peter O.·2022·Nature Communications
DOI 10.1038/s41467-022-34051-9PMC PMC9626636

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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.

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    From A damage accumulation model identifies distinct aging regimes across species (2026) · cites this paper as [29]

  • In the quasi-steady-state regime, the hazard curve resembles a Gompertz exponential rise over several decades that slows down at very old ages beyond the median lifespan (Fig. 2k).

    “The hazard curve resembles Gompertz exponential rise over several decades which slows down at very old ages beyond the median lifespan (Fig. 2k)2,19,29.”

    From A damage accumulation model identifies distinct aging regimes across species (2026) · cites this paper as [29]

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Metadata

Authors
Avchaciov, K., Antoch, M. P., Andrianova, E. L., Tarkhov, A. E., Menshikov, L. I., Burmistrova, O., Gudkov, A. V., & Fedichev, P. O.
ORCID
Venue
Nature Communications
Publisher
Springer Science and Business Media LLC
Published
2022-11-01
Volume
13
Issue
1
ISSN
2041-1723
DOI
10.1038/s41467-022-34051-9
PMC
PMC9626636
Cited by
25
References
16
Language
en
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