TY - JOUR
T1 - Aging metrics incorporating cognitive and physical function capture mortality risk
T2 - results from two prospective cohort studies
AU - Cao, Xingqi
AU - Chen, Chen
AU - Zhang, Jingyun
AU - Xue, Qian-Li
AU - Hoogendijk, Emiel O.
AU - Liu, Xiaoting
AU - Li, Shujuan
AU - Wang, Xiaofeng
AU - Zhu, Yimin
AU - Liu, Zuyun
N1 - Funding Information: This study was supported by the 2020 Milstein Medical Asian American Partnership Foundation Irma and Paul Milstein Program for Senior Health project award (ZL), the Fundamental Research Funds for the Central Universities (ZL), Young Scholar Scientific Research Foundation of National Institute of Environmental Health, China CDC, 2021YSRF01 (CC) and grants of National Nature Science Foundation of China (82171584, ZL; 72004201, XL), and Shanghai Municipal Science and Technology Major Project (2017SHZDZX01, XW). The funders had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript. Funding Information: We would like to thank all respondents of the China Health and Retirement Longitudinal Study (CHARLS), and the US National Health and Nutrition Examination Survey (NHANES). Publisher Copyright: © 2022, The Author(s).
PY - 2022/12/1
Y1 - 2022/12/1
N2 - Background: Aging metrics incorporating cognitive and physical function are not fully understood, hampering their utility in research and clinical practice. This study aimed to determine the proportions of vulnerable persons identified by three existing aging metrics that incorporate cognitive and physical function and the associations of the three metrics with mortality. Methods: We considered three existing aging metrics including the combined presence of cognitive impairment and physical frailty (CI-PF), the frailty index (FI), and the motoric cognitive risk syndrome (MCR). We operationalized them using data from the China Health and Retirement Longitudinal Study (CHARLS) and the US National Health and Nutrition Examination Survey (NHANES). Logistic regression models or Cox proportional hazards regression models, and receiver operating characteristic curves were used to examine the associations of the three metrics with mortality. Results: In CHARLS, the proportions of vulnerable persons identified by CI-PF, FI, and MCR were 2.2, 16.6, and 19.6%, respectively. Each metric predicted mortality after adjustment for age and sex, with some variations in the strength of the associations (CI-PF, odds ratio (OR) (95% confidence interval (CI)) 2.87 (1.74–4.74); FI, OR (95% CI) 1.94 (1.50–2.50); MCR, OR (95% CI) 1.27 (1.00–1.62)). CI-PF and FI had additional predictive utility beyond age and sex, as demonstrated by integrated discrimination improvement and continuous net reclassification improvement (all P < 0.001). These results were replicated in NHANES. Conclusions: Despite the inherent differences in the aging metrics incorporating cognitive and physical function, they consistently capture mortality risk. The findings support the incorporation of cognitive and physical function for risk stratification in both Chinese and US persons, but call for caution when applying them in specific study settings.
AB - Background: Aging metrics incorporating cognitive and physical function are not fully understood, hampering their utility in research and clinical practice. This study aimed to determine the proportions of vulnerable persons identified by three existing aging metrics that incorporate cognitive and physical function and the associations of the three metrics with mortality. Methods: We considered three existing aging metrics including the combined presence of cognitive impairment and physical frailty (CI-PF), the frailty index (FI), and the motoric cognitive risk syndrome (MCR). We operationalized them using data from the China Health and Retirement Longitudinal Study (CHARLS) and the US National Health and Nutrition Examination Survey (NHANES). Logistic regression models or Cox proportional hazards regression models, and receiver operating characteristic curves were used to examine the associations of the three metrics with mortality. Results: In CHARLS, the proportions of vulnerable persons identified by CI-PF, FI, and MCR were 2.2, 16.6, and 19.6%, respectively. Each metric predicted mortality after adjustment for age and sex, with some variations in the strength of the associations (CI-PF, odds ratio (OR) (95% confidence interval (CI)) 2.87 (1.74–4.74); FI, OR (95% CI) 1.94 (1.50–2.50); MCR, OR (95% CI) 1.27 (1.00–1.62)). CI-PF and FI had additional predictive utility beyond age and sex, as demonstrated by integrated discrimination improvement and continuous net reclassification improvement (all P < 0.001). These results were replicated in NHANES. Conclusions: Despite the inherent differences in the aging metrics incorporating cognitive and physical function, they consistently capture mortality risk. The findings support the incorporation of cognitive and physical function for risk stratification in both Chinese and US persons, but call for caution when applying them in specific study settings.
KW - Cognitive frailty
KW - Cognitive impairment
KW - Frailty index
KW - Motoric cognitive risk syndrome
KW - Physical frailty
UR - http://www.scopus.com/inward/record.url?scp=85128944193&partnerID=8YFLogxK
U2 - https://doi.org/10.1186/s12877-022-02913-y
DO - https://doi.org/10.1186/s12877-022-02913-y
M3 - Article
C2 - 35484496
SN - 1471-2318
VL - 22
JO - BMC geriatrics
JF - BMC geriatrics
IS - 1
M1 - 378
ER -