Sun Jianle, Deng Luojia, Li Qianwen, Zhou Jie, Zhang Yue
Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240, China.
Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA, USA.
BMC Public Health. 2024 Dec 18;24(1):3516. doi: 10.1186/s12889-024-21072-w.
We aimed to assess the effects of body mass index (BMI), activities of daily living (ADL), and subjective well-being (SWB) on cognitive impairment and propose dynamic risk prediction models for aging cognitive decline.
We leveraged the Chinese Longitudinal Healthy Longevity Survey from 1998 to 2018. Cognitive status was measured using the Chinese Mini-Mental State Examination. We employed repeated measures correlation to assess associations, linear mixed-effect models to characterize the longitudinal changes, and Cox proportional hazard regression to model survival time. Dynamic predictive models were established based on the Bayesian joint model and deep learning approach named dynamic-DeepHit. Marginal structural Cox models were adopted to control for time-varying confounding factors and assess effect sizes.
ADL, SWB, and BMI showed protective effects on cognitive impairment after controlling observed confounding factors, with respective direct hazard ratios of 0.756 (0.741, 0.771), 0.912 (0.902, 0.921), and 0.919 (0.909, 0.929). Dynamic risk predictive models manifested high accuracy (best AUC = 0.89). ADL was endowed with the best predictive capability, although the combination of BMI, ADL, and SWB showed the most remarkable performance.
BMI, ADL, and SWB are protective factors for cognitive impairment. A dynamic prediction model using these indicators can efficiently identify vulnerable individuals with high accuracy.
我们旨在评估体重指数（BMI）、日常生活活动能力（ADL）和主观幸福感（SWB）对认知障碍的影响，并提出衰老认知衰退的动态风险预测模型。
我们利用了1998年至2018年的中国老年健康影响因素跟踪调查。认知状态采用中文版简易精神状态检查表进行测量。我们采用重复测量相关性来评估关联，线性混合效应模型来描述纵向变化，以及Cox比例风险回归来模拟生存时间。基于贝叶斯联合模型和名为dynamic-DeepHit的深度学习方法建立动态预测模型。采用边际结构Cox模型来控制随时间变化的混杂因素并评估效应大小。
在控制观察到的混杂因素后，ADL、SWB和BMI对认知障碍显示出保护作用，其直接风险比分别为0.756（0.741，0.771）、0.912（0.902，0.921）和0.919（0.909，0.929）。动态风险预测模型表现出较高的准确性（最佳AUC = 0.89）。ADL具有最佳的预测能力，尽管BMI、ADL和SWB的组合表现最为显著。
BMI、ADL和SWB是认知障碍的保护因素。使用这些指标的动态预测模型可以高效且准确地识别易患个体。
