Evidence map›Paper›PMID 35484496›Full record

ArticleBMC geriatrics2022

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

Xingqi Cao, Chen Chen, Jingyun Zhang, Qian-Li Xue, Emiel O Hoogendijk, Xiaoting Liu, Shujuan Li, Xiaofeng Wang, Yimin Zhu, Zuyun Liu

Open access · goldAbstract read
In one paragraph

Article in BMC geriatrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
2.2field-weighted citation impact, top 14% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 19 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 7 institutions in 3 countries.

Xingqi Cao *Center for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Zhejiang, Hangzhou, China.
Chen Chen *China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health; National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
Jingyun ZhangCenter for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Zhejiang, Hangzhou, China.
Qian-Li XueDepartment of Medicine Division of Geriatric Medicine and Gerontology and Center on Aging and Health, Johns Hopkins Medical Institutions, Baltimore, MD, USA.
Emiel O HoogendijkDepartment of Epidemiology and Data Science, Amsterdam Public Health research institute, Amsterdam UMC - location VU University Medical Center, Amsterdam, the Netherlands.
Xiaoting LiuSchool of Public Affairs, Zhejiang University, Zhejiang, Hangzhou, China.
Shujuan LiDepartment of Neurology, Beijing Chaoyang Hospital, Capital Medical University, Beijing, China.
Xiaofeng WangNational Clinical Research Center for Aging and Medicine, Huashan Hospital, and Human Phenome Institute, Fudan University, Shanghai, China. xiaofengwang71@163.com.
Yimin ZhuDepartment of Epidemiology and Biostatistics, School of Public Health, Zhejiang University School of Medicine, Zhejiang, Hangzhou, China. zhuym@zju.edu.cn.
Zuyun LiuCenter for Clinical Big Data and Analytics of the Second Affiliated Hospital and Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Zhejiang, Hangzhou, China. zuyunliu@zju.edu.cn.
Second Affiliated Hospital of Zhejiang University · CNZhejiang University · CNAmsterdam UMC Location VUmc · NLCapital Medical University · CNChinese Center For Disease Control and Prevention · CNFudan University · CNJohns Hopkins University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAging 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.

methodsWe 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.

resultsIn 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.

conclusionsDespite 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.

Indexed as

FrailtyAgingBenchmarkingCognitionHumansLongitudinal StudiesNutrition SurveysProspective StudiesSyndromeCognitive frailtyCognitive impairmentFrailty indexMotoric cognitive risk syndromePhysical frailty

Identifiers

PMID35484496
PMCPMC9052591
OpenAlexW4225015800

What Socratic holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.