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Aging is a complex biological phenomenon influenced by various processes that affect cellular and systemic functions. Several prominent theories attempt to explain its mechanisms, highlighting cellular limitations, oxidative damage, and hormonal changes as central factors in aging.
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A Suppressor Screen for the Characterization of Genetic Links Regulating Chronological Lifespan in Saccharomyces cerevisiae
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Modeling biological age and its link with the aging process.

Hiram Beltrán-Sánchez1, Alberto Palloni2,3, Yiyue Huangfu2

  • 1Fielding School of Public Health and California Center for Population Research, UCLA, Los Angeles, CA 90095, USA.

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|February 6, 2023
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Summary

Biological age (BA) estimates individual physiological aging, offering a better health indicator than chronological age (CA). New structural equation models (SEM) provide more accurate BA predictions, identifying accelerated aging.

Keywords:
agingbiological agebiomarkersmortality

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Area of Science:

  • Gerontology
  • Biostatistics
  • Epidemiology

Background:

  • Individual health status heterogeneity in older adults arises from genetic and environmental factors.
  • Chronological age (CA) is a limited proxy for physiological decline, necessitating better biological age (BA) indicators.
  • Existing BA estimators often rely on restrictive assumptions about the CA-BA relationship.

Purpose of the Study:

  • To propose and validate novel biological age (BA) estimators using structural equation modeling (SEM).
  • To develop BA estimators that do not require arbitrary assumptions about the relationship between chronological age (CA) and BA.
  • To provide tools for empirically testing assumptions in BA estimation.

Main Methods:

  • Utilized the US National Health and Nutrition Examination Survey (1988-1994) dataset.
  • Developed two BA estimators based on SEM, modeling the BA-CA relationship.
  • Compared SEM-based BA estimates against principal components analysis (PCA), multiple linear regression (MLR), and Klemera-Doubal's method (KD).

Main Results:

  • SEM-based BA estimates significantly differed from PCA and MLR.
  • SEM-based BA estimates showed comparable results to KD but with superior predictive power.
  • The proposed SEM approach offers flexibility in modeling the CA-BA relationship and testing assumptions.

Conclusions:

  • The novel SEM-based BA estimators provide a more accurate and flexible approach to assessing biological aging.
  • These estimators can serve as valuable indicators of accelerated aging, improving upon traditional CA metrics.
  • The SEM framework allows for robust validation and interpretation of biological age estimates.