Liang Y L, Wei W Z, Hou Q Z, Huang K K, Liao J Z, Liao J, Yi B
Department of Clinical Laboratory, Xiangya Hospital, Central South University, Changsha 410008, China.
Department of Endocrinology, Xiangya Hospital, Central South University, Changsha 410008, China.
Zhonghua Yu Fang Yi Xue Za Zhi. 2024 Aug 6;58(8):1184-1190. doi: 10.3760/cma.j.cn112150-20240104-00016.
The present study aims to screen and evaluate the early clinical predictors for type 2 diabetes mellitus (T2DM) patients with mild cognitive impairment (MCI) and dementia in Hunan province. A cross-sectional study was conducted from May 2023 to October 2023 to collect data on long-term T2DM patients who settled in Hunan province and were treated in the Department of Geriatrology at Xiangya Hospital of Central South University. The patients were grouped according to the Montreal Cognitive Assessment (MoCA) scale. Basic patient information and multiple serum markers were collected, and differences between groups were compared using one-way ANOVA or Kruskal-Wallis (KW) tests. The multivariate logistic regression analysis was utilized to assess risk factors and Nomogram models were constructed. The logistic regression analysis showed that years of education and serum levels of 1, 5-AG were related factors for the progression of T2DM to T2DM with MCI, and body weight, years of education and FPN levels affected the progression of T2DM with MCI to T2DM with dementia. Based on this, two Nomogram risk prediction models were established. The area under the curve (AUC) of the Nomogram model predicting T2DM progression to T2DM combined with MCI was 0.741, and the AUC of the Nomogram model predicting T2DM combined with MCI progression to T2DM combined with dementia was 0.734. The calibration curves (DCA) of the two models in the training and validation sets were symmetrically distributed near the diagonal line, indicating that the models in the training and validation sets could match each other. In summary, body weight, years of education, and serum HDL-3, FPN, and 1, 5-AG levels are associated with the development of MCI and dementia in T2DM patients. The Nomogram models constructed based on these factors can predict the risk of MCI and dementia in T2DM patients, providing a basis for clinical decision-making.
本研究旨在筛选和评估湖南省2型糖尿病（T2DM）合并轻度认知障碍（MCI）及痴呆患者的早期临床预测因素。于2023年5月至2023年10月进行了一项横断面研究，收集在湖南省定居且于中南大学湘雅医院老年病科接受治疗的长期T2DM患者的数据。根据蒙特利尔认知评估（MoCA）量表对患者进行分组。收集患者基本信息及多种血清标志物，采用单因素方差分析或Kruskal-Wallis（KW）检验比较组间差异。利用多因素逻辑回归分析评估危险因素并构建列线图模型。逻辑回归分析显示，受教育年限和血清1,5-脱水葡萄糖醇（1,5-AG）水平是T2DM进展为合并MCI的T2DM的相关因素，体重、受教育年限和铁调素（FPN）水平影响合并MCI的T2DM进展为合并痴呆的T2DM。基于此，建立了两个列线图风险预测模型。预测T2DM进展为合并MCI的T2DM的列线图模型的曲线下面积（AUC）为0.741，预测合并MCI的T2DM进展为合并痴呆的T2DM的列线图模型的AUC为0.734。两个模型在训练集和验证集的校准曲线（DCA）在对角线附近对称分布，表明训练集和验证集的模型相互匹配。综上所述，体重、受教育年限以及血清高密度脂蛋白3（HDL-3）、FPN和1,5-AG水平与T2DM患者MCI和痴呆的发生有关。基于这些因素构建的列线图模型可预测T2DM患者发生MCI和痴呆的风险，为临床决策提供依据。
