Estimating Biological Age in the Singapore Longitudinal Aging Study.

Autor: Zhong X; Social & Cognitive Computing Department, Institute of High Performance Computing, Agency for Science, Technology and Research (A*STAR), Fusionopolis, Singapore., Lu Y; Singapore Immunology Network (SIgN), Agency for Science Technology and Research (A*STAR), Biopolis., Gao Q; Psychological Medicine Department, National University Health System, Yong Loo Lin School of Medicine, National University of Singapore., Nyunt MSZ; Psychological Medicine Department, National University Health System, Yong Loo Lin School of Medicine, National University of Singapore., Fulop T; Geriatrics Division, Department of Medicine, Research Center on Aging, University of Sherbrooke, Quebec, Canada., Monterola CP; School of Innovation, Technology and Entrepreneurship, Asian Institute of Management, Makati, Philippines., Tong JC; Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore., Larbi A; Singapore Immunology Network (SIgN), Agency for Science Technology and Research (A*STAR), Biopolis.; Geriatrics Division, Department of Medicine, Research Center on Aging, University of Sherbrooke, Quebec, Canada.; School of Innovation, Technology and Entrepreneurship, Asian Institute of Management, Makati, Philippines.; Department of Biology, Faculty of Sciences, University Tunis El Manar, Tunisia.; Department of Microbiology and Immunology, Yong Loo Lin School of Medicine, National University of Singapore., Ng TP; Psychological Medicine Department, National University Health System, Yong Loo Lin School of Medicine, National University of Singapore.
Jazyk: angličtina
Zdroj: The journals of gerontology. Series A, Biological sciences and medical sciences [J Gerontol A Biol Sci Med Sci] 2020 Sep 25; Vol. 75 (10), pp. 1913-1920.
DOI: 10.1093/gerona/glz146
Abstrakt: Background: Biological age (BA) is a more accurate measure of the rate of human aging than chronological age (CA). However, there is limited consensus regarding measures of BA in life span and healthspan.
Methods: This study investigated measurement sets of 68 physiological biomarkers using data from 2,844 Chinese Singaporeans in two age subgroups (55-70 and 71-94 years) in the Singapore Longitudinal Aging Study (SLAS-2) with 8-year follow-up frailty and mortality data. We computed BA estimate using three commonly used algorithms: Principal Component Analysis (PCA), Multiple Linear Regression (MLR), and Klemera and Doubal (KD) method, and additionally, explored the use of machine learning methods for prediction of mortality and frailty. The most optimal algorithmic estimate of BA compared to CA was evaluated for their associations with risk factors and health outcome.
Results: Stepwise selection procedures resulted in the final selection of 8 biomarkers in males and 10 biomarkers in females. The highest-ranking biomarkers were estimated glomerular filtration rate for both genders, and the forced expiratory volume in 1 second in males and females. The BA estimates robustly predicted frailty and mortality and outperformed CA. The best performing KD measure of BA was notably predictive in the younger group (aged 55-70 years). BA estimates obtained using a machine learning train-test method were not more accurate than conventional BA estimates in predicting mortality and frailty in most situations. Biologically older people with the same CA as biologically younger individuals had higher prevalence of frailty and 8-year mortality, and worse health, behavioral, and functional characteristics.
Conclusions: BA is better than CA for measuring life span (mortality) and healthspan (frailty). This measurement set of physiological markers of biological aging among Chinese robustly differentiate biologically old from younger individuals with the same CA.
(© The Author(s) 2019. 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.)
Databáze: MEDLINE
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