Li Zhuoyang, Li Yuqian, Mao Zhenxing, Wang Chongjian, Hou Jian, Zhao Jiaoyan, Wang Jianwei, Tian Yuan, Li Linlin
Department of Epidemiology and Health Statistics, College of Public Health, Zhengzhou University, Zhengzhou 450001, China.
Department of Clinical Pharmacology, School of Pharmaceutical Science, Zhengzhou University, Zhengzhou 450001, China.
Nutrients. 2025 Mar 8;17(6):947. doi: 10.3390/nu17060947.
: Diet plays an important role in preventing and managing the progression from prediabetes to type 2 diabetes mellitus (T2DM). This study aims to develop prediction models incorporating specific dietary indicators and explore the performance in T2DM patients and non-T2DM patients. : This retrospective study was conducted on 2215 patients from the Henan Rural Cohort. The key variables were selected using univariate analysis and the least absolute shrinkage and selection operator (LASSO). Multiple predictive models were constructed separately based on dietary and clinical factors. The performance of different models was compared and the impact of integrating dietary factors on prediction accuracy was evaluated. Receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the predictive performance. Meanwhile, group and spatial validation sets were used to further assess the models. SHapley Additive exPlanations (SHAP) analysis was applied to identify key factors influencing the progression of T2DM. : Nine dietary indicators were quantitatively collected through standardized questionnaires to construct dietary models. The extreme gradient boosting (XGBoost) model outperformed the other three models in T2DM prediction. The area under the curve (AUC) and F1 score of the dietary model in the validation cohort were 0.929 [95% confidence interval (CI) 0.916-0.942] and 0.865 (95%CI 0.845-0.884), respectively. Both were higher than the traditional model (AUC and F1 score were 0.854 and 0.779, respectively, < 0.001). SHAP analysis showed that fasting plasma glucose, eggs, whole grains, income level, red meat, nuts, high-density lipoprotein cholesterol, and age were key predictors of the progression. Additionally, the calibration curves displayed a favorable agreement between the dietary model and actual observations. DCA revealed that employing the XGBoost model to predict the risk of T2DM occurrence would be advantageous if the threshold were beyond 9%. : The XGBoost model constructed by dietary indicators has shown good performance in predicting T2DM. Emphasizing the role of diet is crucial in personalized patient care and management.
饮食在预防和控制从糖尿病前期进展为2型糖尿病（T2DM）方面起着重要作用。本研究旨在开发纳入特定饮食指标的预测模型，并探索其在T2DM患者和非T2DM患者中的表现。：本回顾性研究对来自河南农村队列的2215名患者进行。通过单因素分析和最小绝对收缩和选择算子（LASSO）选择关键变量。分别基于饮食和临床因素构建多个预测模型。比较不同模型的表现，并评估整合饮食因素对预测准确性的影响。使用受试者工作特征（ROC）曲线、校准曲线和决策曲线分析（DCA）来评估预测性能。同时，使用组内和空间验证集进一步评估模型。应用SHapley加性解释（SHAP）分析来确定影响T2DM进展的关键因素。：通过标准化问卷定量收集九种饮食指标以构建饮食模型。极端梯度提升（XGBoost）模型在T2DM预测方面优于其他三种模型。验证队列中饮食模型的曲线下面积（AUC）和F1得分分别为0.929 [95%置信区间（CI）0.916 - 0.942]和0.865（95%CI 0.845 - 0.884）。两者均高于传统模型（AUC和F1得分分别为0.854和0.779，<0.001）。SHAP分析表明，空腹血糖、鸡蛋、全谷物、收入水平、红肉、坚果、高密度脂蛋白胆固醇和年龄是进展的关键预测因素。此外，校准曲线显示饮食模型与实际观察结果之间具有良好的一致性。DCA显示，如果阈值超过9%，采用XGBoost模型预测T2DM发生风险将是有利的。：由饮食指标构建的XGBoost模型在预测T2DM方面表现出良好的性能。强调饮食的作用在个性化患者护理和管理中至关重要。
