Yong Liu1, Nannan Gu1, Lijuan Jiang1, Xinyi Cao2,3, Chunbo Li4,5,6,7. 1. 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. 2. 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. 3. Clinical Neurocognitive Research Center, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China. rekixinyicao@163.com. 4. 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. 5. Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Science, Shanghai, China. licb@smhc.org.cn. 6. Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai, China. licb@smhc.org.cn. 7. Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China. licb@smhc.org.cn.
Abstract
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.
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.
Authors: Marjon Stijntjes; Marja J Aartsen; Diana G Taekema; Jacobijn Gussekloo; Martijn Huisman; Carel G M Meskers; Anton J M de Craen; Andrea B Maier Journal: J Gerontol A Biol Sci Med Sci Date: 2017-05-01 Impact factor: 6.053