Literature DB >> 32598446

Comparing Biological Age Estimates Using Domain-Specific Measures From the Canadian Longitudinal Study on Aging.

Chris P Verschoor1,2,3, Daniel W Belsky4, Jinhui Ma3, Alan A Cohen5, Lauren E Griffith3, Parminder Raina3.   

Abstract

Many studies have shown that estimates of biological age (BA) can predict health-related outcomes in older adults. Often, researchers employ multiple measures belonging to a variety of biological/physiological systems, and assess the validity of BA estimates by how well they approximate chronological age (CA). However, it is not clear whether this is the best approach for judging a BA estimate, or whether certain groups of measures are more informative to this end. Using data from the Canadian Longitudinal Study on Aging, we composed panels of biological measures based on the physiological systems/domains they belong to (blood, organ function, physical/cognitive performance), and also composed a panel of measures that optimized the association of BA with CA. We then compared BA estimates for each according to their association with CA and health-related outcomes, including frailty, multimorbidity, chronic condition domains, disability, and health care utilization. Although BA estimated using all 40 measures (r = 0.74) or our age-optimized panel (r = 0.77) most closely approximated CA, the strength of associations to health-related outcomes was comparable or weaker than that of our panel composed only of physical performance measures (CA r = 0.59). All BA estimates were significantly associated to the outcomes considered, with exception to the neurological and musculoskeletal disease domains, and only varied slightly by sex. In summary, while the approximation of CA is important to consider when estimating BA, the strength of associations to prospective outcomes may be of greater importance. Hence, the context in which BA is estimated should be influenced by an investigator's specific research goals.
© The Author(s) 2020. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  Biological age; Biological measures; Biomarkers; CLSA; Healthy aging

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Year:  2021        PMID: 32598446      PMCID: PMC7812432          DOI: 10.1093/gerona/glaa151

Source DB:  PubMed          Journal:  J Gerontol A Biol Sci Med Sci        ISSN: 1079-5006            Impact factor:   6.053


  33 in total

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6.  Eighty-year trends in infant weight and length growth: the Fels Longitudinal Study.

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8.  The Canadian longitudinal study on aging (CLSA).

Authors:  Parminder S Raina; Christina Wolfson; Susan A Kirkland; Lauren E Griffith; Mark Oremus; Christopher Patterson; Holly Tuokko; Margaret Penning; Cynthia M Balion; David Hogan; Andrew Wister; Hélène Payette; Harry Shannon; Kevin Brazil
Journal:  Can J Aging       Date:  2009-09

9.  Association of Blood Chemistry Quantifications of Biological Aging With Disability and Mortality in Older Adults.

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Journal:  J Gerontol A Biol Sci Med Sci       Date:  2020-09-16       Impact factor: 6.053

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3.  Advanced biological age is associated with improved antibody responses in older high-dose influenza vaccine recipients over four consecutive seasons.

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4.  Disparities in the pace of biological aging among midlife adults of the same chronological age have implications for future frailty risk and policy.

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