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Prevalence and risk factors of dementia based on BPS
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Peng-xin GENG1, Guo-lei LI1, Tao LIU2, Shuang-xin DU1, Xin-yu WANG1, Hong-qing AN1
Modern Preventive Medicine | 2025, 52(23) : 4236 - 4242
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Modern Preventive Medicine | 2025, 52(23): 4236-4242
• Epidemiology and Statistical Methods •
Prevalence and risk factors of dementia based on BPS
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Peng-xin GENG1, Guo-lei LI1, Tao LIU2, Shuang-xin DU1, Xin-yu WANG1, Hong-qing AN1
Affiliations
  • Shandong Second Medical University, Weifang, Shandong 261053, China
Published: 2025-12-10 doi: 10.20043/j.cnki.MPM.202501192
Outline
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Objective

Based on biopsychosocial (BPS) perspective, this study explores the prevalence of dementia and related risk factors, providing a reference for the prevention and control of dementia.

Methods

Using the 2018 China Health and Retirement Longitudinal Study (CHARLS) data, a statistical analysis was conducted on individuals aged 60 and above. Lasso regression was employed to select variables, and binary logistic regression was used to analyze risk factors for dementia. The dataset was divided into a 7:3 ratio to construct a logistic nomogram prediction model for the risk of dementia, and the model was validated using receiver operating characteristic curve (ROC), decision curve analysis (DCA), and calibration curves.

Results

A total of 3 464 study subjects were included, with 2 425 in the training set and 1 039 in the test set. The prevalence of dementia was 23.96%. Through lasso regression and logistic regression analysis, five risk factors were identified: gender (OR=3.918, P<0.001), age (OR=1.398, P<0.001), education level (OR=1.589, P<0.001), napping (OR=0.799, P=0.013), and life insurance (OR=0.613, P=0.016). The evaluation of the logistic nomogram prediction model for the risk of dementia showed that the area under the curve (AUC) for the training set was 0.729 (95% CI: 0.706-0.752) and the AUC for the test set was 0.713(95% CI: 0.677-0.749). The DCA threshold probability for the training set was 10%-84%, and the threshold probability for the test set was 15%-78%. The calibration curves for both the training and test sets showed good agreement with the prediction curve.

Conclusion

Gender, age, education level, napping, and life insurance are the main risk factors for dementia and society should focus on and effectively intervene in these factors. In addition, the constructed nomogram prediction model for the risk of dementia has good predictive performance and can be used for external prediction.

BPS perspective  /  Dementia  /  Risk factors  /  Nomogram
耿鹏馨, 李国磊, 刘涛, 杜爽心, 王馨瑜, 安洪庆. BPS视角下老年痴呆患病状况及风险因素分析. 现代预防医学, 2025 , 52 (23) : 4236 -4242 . DOI: 10.20043/j.cnki.MPM.202501192
Peng-xin GENG, Guo-lei LI, Tao LIU, Shuang-xin DU, Xin-yu WANG, Hong-qing AN. Prevalence and risk factors of dementia based on BPS[J]. Modern Preventive Medicine, 2025 , 52 (23) : 4236 -4242 . DOI: 10.20043/j.cnki.MPM.202501192
Year 2025 volume 52 Issue 23
30
14
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doi: 10.20043/j.cnki.MPM.202501192
  • Receive Date:2025-01-13
  • Online Date:2026-11-02
  • Published:2025-12-10
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  • Received:2025-01-13
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    Shandong Second Medical University, Weifang, Shandong 261053, China
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表12种不同金属材料的力学参数
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小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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