Prediction of future aging-related slow gait and its determinants with deep learning and logistic regression.

Journal: PloS one
Published Date: Jun 17, 2025

Abstract

BACKGROUND: Identification of accelerated aging and its biomarkers can lead to more timely therapeutic interventions and decision-making. Therefore, we sought to predict aging-related slow gait, a known predictor of accelerated aging, and its determinants.

Authors

  • Alison Deatsch
    University of Wisconsin-Madison; 1111 Highland Ave, Madison, WI 53705, United States of America.
  • Michael McKenna
    Magnetic Resonance Imaging and Spectroscopy Section, Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States of America.
  • Jonathan Palumbo
    Magnetic Resonance Imaging and Spectroscopy Section, Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States of America.
  • Qu Tian
    Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States of America.
  • Eleanor Simonsick
    Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, National Institutes of Health, Baltimore, Maryland, United States of America.
  • Luigi Ferrucci
    Translational Gerontology Branch, Longitudinal Studies Section, National Institute on Aging, National Institutes of Health, MedStar Harbor Hospital, 3001 S. Hanover Street, Baltimore, MD, USA.
  • Robert Jeraj
  • Richard G Spencer
    Laboratory of Clinical Investigation, Magnetic Resonance Imaging and Spectroscopy Section, National Institute on Aging, NIH, 251 Bayview Boulevard, Baltimore 21224, Maryland.

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