Unsupervised learning of aging principles from longitudinal data

Konstantin Avchaciov, Marina P. Antoch, Ekaterina L. Andrianova, Andrei E. Tarkhov, Leonid I. Menshikov, Olga Burmistrova, Andrei V. Gudkov and Peter O. Fedichev ()
Additional contact information
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.
Peter O. Fedichev: Gero PTE. LTD.

Nature Communications, 2022, vol. 13, issue 1, 1-14

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-34051-9

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