PMID- 33677738
OWN - NLM
STAT- MEDLINE
DCOM- 20211025
LR  - 20211025
IS  - 1720-8319 (Electronic)
IS  - 1594-0667 (Linking)
VI  - 33
IP  - 10
DP  - 2021 Oct
TI  - Predicting cognitive function based on physical performance: findings from the 
      China Health and Retirement Longitudinal Study.
PG  - 2723-2735
LID - 10.1007/s40520-021-01810-5 [doi]
AB  - BACKGROUND: Physical performance tests are simple means of predicting an 
      individual's risk of cognitive decline. AIMS: This study aimed to assess the 
      predictive value of physical performance tests and develop predictive models for 
      cognitive function. METHODS: Cognitive function was tested biennially and 
      calculated for mental intactness, episodic memory, and global cognition. Using a 
      generalized estimating equation (GEE), we examined each baseline physical 
      performance test as a predictor of cognitive decline. Using a multivariate linear 
      regression model (MLRM), we developed predictive models for cognitive function. 
      Bland-Altman analysis was performed to analyze the agreement between estimated 
      and measured cognition. We validated the predictive model internally with 1000 
      bootstrap resamples. RESULTS: Better physical performance test results, except 
      for standing balance, were associated with a slower cognitive decline over time 
      and better cognitive function at follow-up. Regarding the predictive models, all 
      physical performance tests were included in men; only five chair stands test was 
      included in women. Bland-Altman analysis showed that measured cognition was 
      equivalent to estimated cognition in men (mean bias, 0; 95% limits of 
      agreement, - 8.56 to 8.56) and women (mean bias, 0; 95% limits of 
      agreement - 8.79 to 8.7). Bootstrap analysis showed that predictors were selected 
      in 78.4-100% for men and 64.5-100% for women. DISCUSSION: Bland-Altman and 
      bootstrap analysis demonstrated good agreement and stability of the predictive 
      models. CONCLUSIONS: Physical performance tests are simple, easily obtainable, 
      and clinically relevant markers for cognitive function with aging; predictive 
      models based on physical performance can be used to predict cognitive function.
CI  - (c) 2021. The Author(s), under exclusive licence to Springer Nature Switzerland AG 
      part of Springer Nature.
FAU - Liu, Yong
AU  - Liu Y
AD  - Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, 
      Shanghai Jiao Tong University School of Medicine, 600 Wan Ping Nan Road, 
      Shanghai, 200030, China.
FAU - Gu, Nannan
AU  - Gu N
AD  - Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, 
      Shanghai Jiao Tong University School of Medicine, 600 Wan Ping Nan Road, 
      Shanghai, 200030, China.
FAU - Jiang, Lijuan
AU  - Jiang L
AD  - Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, 
      Shanghai Jiao Tong University School of Medicine, 600 Wan Ping Nan Road, 
      Shanghai, 200030, China.
FAU - Cao, Xinyi
AU  - Cao X
AD  - Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, 
      Shanghai Jiao Tong University School of Medicine, 600 Wan Ping Nan Road, 
      Shanghai, 200030, China. rekixinyicao@163.com.
AD  - Clinical Neurocognitive Research Center, Shanghai Mental Health Center, Shanghai 
      Jiao Tong University School of Medicine, Shanghai, China. rekixinyicao@163.com.
FAU - Li, Chunbo
AU  - Li C
AUID- ORCID: 0000-0001-7827-7198
AD  - Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, 
      Shanghai Jiao Tong University School of Medicine, 600 Wan Ping Nan Road, 
      Shanghai, 200030, China. licb@smhc.org.cn.
AD  - Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), 
      Chinese Academy of Science, Shanghai, China. licb@smhc.org.cn.
AD  - Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, 
      Shanghai, China. licb@smhc.org.cn.
AD  - Brain Science and Technology Research Center, Shanghai Jiao Tong University, 
      Shanghai, China. licb@smhc.org.cn.
LA  - eng
GR  - 81571756/National Natural Science Foundation of China/
PT  - Journal Article
DEP - 20210307
PL  - Germany
TA  - Aging Clin Exp Res
JT  - Aging clinical and experimental research
JID - 101132995
SB  - IM
MH  - Aging
MH  - Cognition
MH  - *Cognitive Dysfunction/diagnosis
MH  - Female
MH  - Humans
MH  - Longitudinal Studies
MH  - Male
MH  - *Retirement
OTO - NOTNLM
OT  - Aging
OT  - Cognition
OT  - Physical performance
OT  - Predictive models
EDAT- 2021/03/08 06:00
MHDA- 2021/10/26 06:00
CRDT- 2021/03/07 20:42
PHST- 2020/11/15 00:00 [received]
PHST- 2021/02/08 00:00 [accepted]
PHST- 2021/03/08 06:00 [pubmed]
PHST- 2021/10/26 06:00 [medline]
PHST- 2021/03/07 20:42 [entrez]
AID - 10.1007/s40520-021-01810-5 [pii]
AID - 10.1007/s40520-021-01810-5 [doi]
PST - ppublish
SO  - Aging Clin Exp Res. 2021 Oct;33(10):2723-2735. doi: 10.1007/s40520-021-01810-5. 
      Epub 2021 Mar 7.