Sociodemographic Factors Predict Incident Mild Cognitive Impairment: A Brief Review and Empirical Study

医学 纵向研究 老年学 比例危险模型 队列研究 痴呆 接收机工作特性 入射(几何) 累积发病率 认知 队列 内科学 精神科 病理 光学 物理 疾病
作者
Shuyi Jin,Chenxi Li,Jingang Miao,Jia‐Lin Sun,Zhenqing Yang,Xingqi Cao,Kaili Sun,Xiaoting Liu,Lina Ma,Xiufeng Xu,Zuyun Liu
出处
期刊:Journal of the American Medical Directors Association [Elsevier]
卷期号:24 (12): 1959-1966.e7
标识
DOI:10.1016/j.jamda.2023.08.016
摘要

Objectives Mild cognitive impairment (MCI) is a transitional stage between normal cognitive aging and dementia that increases the risk of progressive cognitive decline. Early prediction of MCI could be beneficial for identifying vulnerable individuals in the community and planning primary and secondary prevention to reduce the incidence of MCI. Design A narrative review and cohort study. Setting and Participants We review the MCI prediction based on the assessment of sociodemographic factors. We included participants from 3 surveys: 8915 from wave 2011/2012 of the China Health and Retirement Longitudinal Study (CHARLS), 9765 from the 2011 Chinese Longitudinal Healthy Longevity Survey (CLHLS), and 1823 from the 2014 Rugao Longevity and Ageing Study (RuLAS). Methods We searched in PubMed, Embase, and Web of Science Core Collection between January 1, 2019, and December 30, 2022. To construct the composite risk score, a multivariate Cox proportional hazards regression model was used. The performance of the score was assessed using receiver operating characteristic (ROC) curves. Furthermore, the composite risk score was validated in 2 longitudinal cohorts, CLHLS and RuLAS. Results We concluded on 20 articles from 892 available. The results suggested that the previous models suffered from several defects, including overreliance on cross-sectional data, low predictive utility, inconvenient measurement, and inapplicability to developing countries. Our empirical work suggested that the area under the curve for a 5-year MCI prediction was 0.861 in CHARLS, 0.797 in CLHLS, and 0.823 in RuLAS. We designed a publicly available online tool for this composite risk score. Conclusions and Implications Attention to these sociodemographic factors related to the incidence of MCI can be beneficially incorporated into the current work, which will set the stage for better early prediction of MCI before its incidence and for reducing the burden of the disease.
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