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Author Spotlight: Automated Lifespan Monitoring &#8211; Discovering Aging Dynamics with the Lifespan Machine
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Predicting physiological aging rates from a range of quantitative traits using machine learning.

Eric D Sun1, Yong Qian1, Richard Oppong1

  • 1Longitudinal Studies Section, Translational Gerontology Branch, National Institute on Aging, Baltimore, MD 21224, USA.

Aging
|October 31, 2021
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Summary
This summary is machine-generated.

Scientists developed a machine learning method to calculate physiological aging rate (PAR) from various health traits. This PAR predicts mortality risk and is influenced by genetics, offering a new way to study aging.

Keywords:
aging clockmachine learningmortalitypersonalized medicinephysiological aging ratequantitative trait

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Area of Science:

  • Biogerontology and Computational Biology
  • Genetics and Molecular Biology

Background:

  • Individual aging rates vary, but objective measures independent of chronological age are lacking.
  • Understanding physiological aging is crucial for assessing morbidity and mortality risks.

Purpose of the Study:

  • To develop machine learning frameworks for inferring physiological age and aging rate from diverse biological and psychological traits.
  • To investigate the association of physiological aging rate (PAR) with survival, heritability, and genetic underpinnings.

Main Methods:

  • Utilized machine learning models on biochemical, cardiovascular, and psychological data from two large cohorts (SardiNIA and InCHIANTI).
  • Defined physiological aging rate (PAR) as the ratio of predicted physiological age to chronological age.
  • Conducted correlation analyses with epigenetic aging scores and performed genome-wide association studies (GWAS).

Main Results:

  • Inferred physiological age strongly correlated with chronological age (R2 > 0.8).
  • Physiological aging rate (PAR) significantly predicted survival and mortality.
  • PAR showed moderate heritability (h2~0.3) and correlated with epigenetic age; GWAS identified two genetic loci linked to PAR.

Conclusions:

  • The developed trait-based PAR serves as a robust proxy for whole-body aging mechanisms.
  • PAR's predictive power for mortality and its heritability highlight its biological significance.
  • PAR may be valuable for evaluating interventions targeting aging and related health conditions.