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Aging metrics incorporating cognitive and physical function capture mortality risk: results from two prospective cohort studies

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 p...

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Autores principales: Cao, Xingqi, Chen, Chen, Zhang, Jingyun, Xue, Qian-Li, Hoogendijk, Emiel O., Liu, Xiaoting, Li, Shujuan, Wang, Xiaofeng, Zhu, Yimin, Liu, Zuyun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9052591/
https://www.ncbi.nlm.nih.gov/pubmed/35484496
http://dx.doi.org/10.1186/s12877-022-02913-y
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author Cao, Xingqi
Chen, Chen
Zhang, Jingyun
Xue, Qian-Li
Hoogendijk, Emiel O.
Liu, Xiaoting
Li, Shujuan
Wang, Xiaofeng
Zhu, Yimin
Liu, Zuyun
author_facet Cao, Xingqi
Chen, Chen
Zhang, Jingyun
Xue, Qian-Li
Hoogendijk, Emiel O.
Liu, Xiaoting
Li, Shujuan
Wang, Xiaofeng
Zhu, Yimin
Liu, Zuyun
author_sort Cao, Xingqi
collection PubMed
description 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12877-022-02913-y.
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spelling pubmed-90525912022-04-30 Aging metrics incorporating cognitive and physical function capture mortality risk: results from two prospective cohort studies Cao, Xingqi Chen, Chen Zhang, Jingyun Xue, Qian-Li Hoogendijk, Emiel O. Liu, Xiaoting Li, Shujuan Wang, Xiaofeng Zhu, Yimin Liu, Zuyun BMC Geriatr Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12877-022-02913-y. BioMed Central 2022-04-28 /pmc/articles/PMC9052591/ /pubmed/35484496 http://dx.doi.org/10.1186/s12877-022-02913-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Cao, Xingqi
Chen, Chen
Zhang, Jingyun
Xue, Qian-Li
Hoogendijk, Emiel O.
Liu, Xiaoting
Li, Shujuan
Wang, Xiaofeng
Zhu, Yimin
Liu, Zuyun
Aging metrics incorporating cognitive and physical function capture mortality risk: results from two prospective cohort studies
title Aging metrics incorporating cognitive and physical function capture mortality risk: results from two prospective cohort studies
title_full Aging metrics incorporating cognitive and physical function capture mortality risk: results from two prospective cohort studies
title_fullStr Aging metrics incorporating cognitive and physical function capture mortality risk: results from two prospective cohort studies
title_full_unstemmed Aging metrics incorporating cognitive and physical function capture mortality risk: results from two prospective cohort studies
title_short Aging metrics incorporating cognitive and physical function capture mortality risk: results from two prospective cohort studies
title_sort aging metrics incorporating cognitive and physical function capture mortality risk: results from two prospective cohort studies
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9052591/
https://www.ncbi.nlm.nih.gov/pubmed/35484496
http://dx.doi.org/10.1186/s12877-022-02913-y
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