Institute of Wenzhou, Second Affiliated Hospital, and School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
School of Public Affairs, Zhejiang University, Hangzhou, Zhejiang, China.
J Am Med Dir Assoc. 2023 Dec;24(12):1959-1966.e7. doi: 10.1016/j.jamda.2023.08.016. Epub 2023 Sep 14.
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.
A narrative review and cohort study.
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).
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.
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.
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.
轻度认知障碍（MCI）是正常认知老化和痴呆之间的过渡阶段，增加了认知能力进行性下降的风险。MCI 的早期预测可能有助于识别社区中的脆弱个体，并规划初级和二级预防措施，以降低 MCI 的发病率。
叙述性回顾和队列研究。
我们综述了基于社会人口因素评估的 MCI 预测。我们纳入了来自 3 项研究的数据：中国健康与退休纵向研究（CHARLS）2011/2012 年波次的 8915 名参与者、中国健康长寿纵向调查（CLHLS）2011 年的 9765 名参与者和 2014 年如皋长寿与衰老研究（RuLAS）的 1823 名参与者。
我们在 2019 年 1 月 1 日至 2022 年 12 月 30 日期间在 PubMed、Embase 和 Web of Science 核心合集进行了检索。使用多变量 Cox 比例风险回归模型构建复合风险评分。使用受试者工作特征（ROC）曲线评估评分的性能。此外，该复合风险评分在 CLHLS 和 RuLAS 两个纵向队列中进行了验证。
我们从 892 篇可用文献中得出 20 篇文章的结论。结果表明，以前的模型存在几个缺陷，包括过度依赖横断面数据、预测效用低、测量不便和不适用于发展中国家。我们的实证工作表明，CHARLS 中 5 年 MCI 预测的曲线下面积为 0.861，CLHLS 为 0.797，RuLAS 为 0.823。我们为这个复合风险评分设计了一个可公开访问的在线工具。
关注这些与 MCI 发病率相关的社会人口因素，可以有益地纳入当前的工作，为 MCI 发病前的早期预测和减轻疾病负担奠定基础。
