Risk assessment and stratification of mild cognitive impairment among the Chinese elderly: attention to modifiable risk factors
Risk assessment and stratification of mild cognitive impairment among the Chinese elderly: attention to modifiable risk factors
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DOI：
10.1136/jech-2022-219952
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发表时间：
2023-06
影响因子：
6.3
通讯作者：
Qiong Wang;Shuai Zhou;Jingya Zhang;Qing Wang;Fangfang Hou;Xiao Han;G. Shen;Yan Zhang
中科院分区：
文献类型：
--
作者：
Qiong Wang;Shuai Zhou;Jingya Zhang;Qing Wang;Fangfang Hou;Xiao Han;G. Shen;Yan Zhang
早期识别轻度认知障碍（MCI）风险个体对阿尔茨海默病的预防具有重要的公共卫生意义。目的本研究旨在开发和验证MCI的风险评估工具，重点关注可修改的因素和建议的风险分层策略。方法从最近的综述中选择可修改的危险因素，并从文献中获得风险评分或根据Rothman-Keller模型计算风险评分。模拟10 000例受试者的暴露率数据，根据MCI的理论发生率确定风险分层。使用来自基于人群的中国老年队列的横截面和纵向数据集验证了该工具的性能。结果选择了9个可改变的危险因素（社会隔离、受教育程度低、高血压、高血脂、糖尿病、吸烟、饮酒、缺乏运动和抑郁）作为预测模型。对于横断面数据集，曲线下面积（AUC）在训练集中为0.71，在验证集中为0.72。对于纵向数据集，训练集和验证集的AUC分别为0.70和0.64。将0.95和1.86的综合风险评分用作将MCI风险分类为“低”，“中等”和“高”的阈值。结论本研究建立了一个准确度较高的MCI危险评估工具，并提出了MCI危险分层阈值。该工具可能对中国老年人MCI的一级预防具有重要的公共卫生意义。
Background The early identification of individuals at risk of mild cognitive impairment (MCI) has major public health implications for Alzheimer’s disease prevention. Objective This study aims to develop and validate a risk assessment tool for MCI with a focus on modifiable factors and a suggested risk stratification strategy. Methods Modifiable risk factors were selected from recent reviews, and risk scores were obtained from the literature or calculated based on the Rothman-Keller model. Simulated data of 10 000 subjects with the exposure rates of the selected factors were generated, and the risk stratifications were determined by the theoretical incidences of MCI. The performance of the tool was verified using cross-sectional and longitudinal datasets from a population-based Chinese elderly cohort. Results Nine modifiable risk factors (social isolation, less education, hypertension, hyperlipidaemia, diabetes, smoking, drinking, physical inactivity and depression) were selected for the predictive model. The area under the curve (AUC) was 0.71 in the training set and 0.72 in the validation set for the cross-sectional dataset. The AUCs were 0.70 and 0.64 in the training and validation sets, respectively, for the longitudinal dataset. A combined risk score of 0.95 and 1.86 was used as the threshold to categorise MCI risk as ‘low’, ‘moderate’ and ‘high’. Conclusion A risk assessment tool for MCI with appropriate accuracy was developed in this study, and risk stratification thresholds were also suggested. The tool might have significant public health implications for the primary prevention of MCI in elderly individuals in China.