Duan Juncheng, Nayan Norshita Mat
Institute of IR4.0, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia.
Healthcare (Basel). 2025 Aug 15;13(16):2007. doi: 10.3390/healthcare13162007.
China has the largest number of patients with type 2 diabetes (T2D) worldwide, and the chronic complications and economic burden associated with T2D are becoming increasingly severe. Developing accurate and widely applicable risk prediction models is of great significance for the early identification of and intervention in high-risk populations. However, current Chinese models still have many shortcomings in terms of methodological design and clinical application. This study conducts a systematic review and narrative synthesis of existing risk prediction models for type 2 diabetes in China, aiming to identify issues with existing models and provide references with which Chinese scholars can develop higher-quality risk prediction models. This study followed the PRISMA guidelines to conduct a systematic search of the literature related to T2D risk prediction models in China published in English journals from October 2019 to October 2024. The databases included PubMed, CNKI and Web of Science. Included studies had to meet criteria such as clear modeling objectives, detailed model development and validation processes, and a focus on non-diabetic populations in China. A total of 20 studies were ultimately selected and comprehensively analyzed based on model type, variable selection, validation methods, and performance metrics. The 20 included studies employed various modeling methods, including statistical and machine learning approaches. The AUC values of the models ranged from 0.728 to 0.977, indicating overall good predictive capability. However, only one study conducted external validation, and 45% (9/20) of the studies binned continuous variables, which may have reduced the models' generalization ability and predictive performance. Additionally, most models did not include key variables such as lifestyle, socioeconomic factors, and cultural background, resulting in limited data representativeness and adaptability. Chinese T2DM risk prediction models remain in the developmental stage, with issues such as insufficient validation, inconsistent variable handling, and incomplete coverage of key influencing factors. Future research should focus on strengthening multicenter external validation, standardizing modeling processes, and incorporating multidimensional social and behavioral variables to enhance the clinical utility and cross-population applicability of these models. Registration ID: CRD420251072143.
中国是全球2型糖尿病（T2D）患者数量最多的国家，且与T2D相关的慢性并发症和经济负担日益严峻。开发准确且广泛适用的风险预测模型对于高危人群的早期识别和干预具有重要意义。然而，目前的中国模型在方法设计和临床应用方面仍存在诸多不足。本研究对中国现有的2型糖尿病风险预测模型进行系统综述和叙述性综合分析，旨在找出现有模型存在的问题，并为中国学者开发更高质量的风险预测模型提供参考。本研究遵循PRISMA指南，对2019年10月至2024年10月在英文期刊上发表的与中国T2D风险预测模型相关的文献进行系统检索。数据库包括PubMed、CNKI和Web of Science。纳入的研究必须满足以下标准：建模目标明确、模型开发和验证过程详细，且聚焦于中国的非糖尿病人群。最终共筛选出20项研究，并基于模型类型、变量选择、验证方法和性能指标进行综合分析。这20项纳入研究采用了多种建模方法，包括统计和机器学习方法。模型的AUC值在0.728至0.977之间，表明总体预测能力良好。然而，只有一项研究进行了外部验证，45%（9/20）的研究对连续变量进行了分箱处理，这可能降低了模型的泛化能力和预测性能。此外，大多数模型未纳入生活方式、社会经济因素和文化背景等关键变量，导致数据代表性和适应性有限。中国T2DM风险预测模型仍处于发展阶段，存在验证不足、变量处理不一致以及关键影响因素覆盖不完整等问题。未来的研究应着重加强多中心外部验证、规范建模过程，并纳入多维社会和行为变量，以提高这些模型的临床实用性和跨人群适用性。注册编号：CRD420251072143。
