Affiliation:
1. Center for Clinical Big Data and Analytics, Second Affiliated Hospital and Department of Big Data in Health Science, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China
2. Department of Pathology, Yale School of Medicine, New Haven, Connecticut
Abstract
Abstract
Background
This study aimed to: (i) develop 2 composite aging measures in the Chinese population using 2 recent advanced algorithms (the Klemera and Doubal method and Mahalanobis distance); and (ii) validate the 2 measures by examining their associations with mortality and disease counts.
Methods
Based on data from the China Nutrition and Health Survey (CHNS) 2009 wave (N = 8119, aged 20–79 years, 53.5% women), a nationwide prospective cohort study of the Chinese population, we developed Klemera and Doubal method-biological age (KDM-BA) and physiological dysregulation (PD, derived from Mahalanobis distance) using 12 biomarkers. For the validation analysis, we used Cox proportional hazard regression models (for mortality) and linear, Poisson, and logistic regression models (for disease counts) to examine the associations. We replicated the validation analysis in the China Health and Retirement Longitudinal Study (CHARLS, N = 9304, aged 45–99 years, 53.4% women).
Results
Both aging measures were predictive of mortality after accounting for age and gender (KDM-BA, per 1-year, hazard ratio [HR] = 1.14, 95% confidence interval [CI] = 1.08, 1.19; PD, per 1-SD, HR = 1.50, 95% CI = 1.33, 1.69). With few exceptions, these mortality predictions were robust across stratifications by age, gender, education, and health behaviors. The 2 aging measures were associated with disease counts both cross-sectionally and longitudinally. These results were generally replicable in CHARLS although 4 biomarkers were not available.
Conclusions
We successfully developed and validated 2 composite aging measures—KDM-BA and PD, which have great potentials for applications in early identifications and preventions of aging and aging-related diseases in China.
Funder
National Institute for Nutrition and Health
China Center for Disease Control and Prevention
Carolina Population Center
University of North Carolina at Chapel Hill
NIH
Fogarty International Center
China-Japan Friendship Hospital
Beijing Municipal Center for Disease Prevention and Control
National Institute on Aging
National Natural Science Foundation of China
World Bank
Fundamental Research Funds for the Central Universities
Publisher
Oxford University Press (OUP)
Subject
Geriatrics and Gerontology,Ageing
Cited by
35 articles.
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