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Predicting cognitive function based on physical performance: findings from the China Health and Retirement Longitudinal Study

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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.

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Availability of data and materials

The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Acknowledgements

We thank the China Center for Economic Research, the National School of Development of Peking University for providing the data, and we also acknowledge all the participants in the survey design and data collection as well as the CHARLS research team for collecting high-quality, nationally representative data.

Funding

This research project was supported by grants from the National Key R&D Program of China (2018YFC2001605), National Natural Science Foundation of China (81571756), Shanghai Municipal Commission of Health and Family Planning Foundation (2020YJZX0105), Shanghai Clinical Research Center for Mental Health (19MC1911100), Shanghai Mental Health Center (CRC2017ZD01), Shanghai Municipal Science and Technology Major Project (No. 2018SHZDZX03) and ZJLab.

Author information

Authors and Affiliations

  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

    Yong Liu, Nannan Gu, Lijuan Jiang, Xinyi Cao & Chunbo Li

  2. Clinical Neurocognitive Research Center, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China

    Xinyi Cao

  3. Center for Excellence in Brain Science and Intelligence Technology (CEBSIT), Chinese Academy of Science, Shanghai, China

    Chunbo Li

  4. Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai, China

    Chunbo Li

  5. Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China

    Chunbo Li

Authors
  1. Yong Liu
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  2. Nannan Gu
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  3. Lijuan Jiang
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  4. Xinyi Cao
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  5. Chunbo Li
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Contributions

The data used in this paper are from China Health and Retirement Longitudinal Study (CHARLS). We applied the permission for the date access (http://charls.pku.edu.cn/zh-CN) and got the access to use it. Prof. Yaohui Zhao (National School of Development of Peking University), John Strauss (University of Southern California) and Gonghuan Yang (Chinese Center For Disease Control And Prevention) are the principle investigator of CHARLS, and they make the data available online for academic use freely. YL, XYC and CBL designed this study. YL and XYC acquired the data. YL performed the statistical analysis, assisted by XYC and reanalyzed by LJJ. YL drafted the manuscript. XYC and CBL reviewed the manuscript and all the authors approved the final version for submission.

Corresponding authors

Correspondence to Xinyi Cao or Chunbo Li.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Statement of human and animal rights

This study including human participants has been performed in accordance with the ethical standards laid down in the Declaration of Helsinki and its later amendments.

Ethical approval

Ethics approval for the data collection in CHARLS was obtained from the Biomedical Ethics Review Committee of Peking University (IRB00001052-11015).

Informed consent

Each participant included in this study signed a written informed consent form before taking part in the survey.

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Supplementary Information

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Liu, Y., Gu, N., Jiang, L. et al. Predicting cognitive function based on physical performance: findings from the China Health and Retirement Longitudinal Study. Aging Clin Exp Res 33, 2723–2735 (2021). https://doi.org/10.1007/s40520-021-01810-5

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  • DOI: https://doi.org/10.1007/s40520-021-01810-5

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