用常规化验指标预测糖尿病多器官功能障碍，模型准确率接近100%
Explainable Retinal Imaging for Prediction of Multi-Organ Dysfunction in Type 2 Diabetes
- 基于1195名患者数据，构建系统性异常指数预测多器官失调
- 梯度提升模型AUC达1.000，显著优于逻辑回归的0.925
- 揭示高血糖、肾损伤等是主要风险因素，结果可解释
背景：2型糖尿病（T2DM）被越来越多认为是一种系统性疾病，涉及代谢、肾脏、脂质和炎症通路的协同失调。现有临床评估常无法捕捉这种多维度负担。方法：对1,195名患者的常规实验室生物标志物进行回顾性研究，构建系统级异常指数以量化器官特异性功能障碍，多系统受累定义为两个或以上系统出现异常。训练了逻辑回归、随机森林和梯度提升等监督学习模型来预测多系统失调。通过SHapley Additive exPlanations（SHAP）实现模型可解释性。结果：梯度提升模型表现出近乎完美的判别能力（AUC = 1.000），显著优于逻辑回归（AUC = 0.925）。特征归因分析显示，高血糖、肾功能损害、血脂异常和炎症是多系统风险的主要驱动因素。部分依赖分析中观察到的剂量-反应关系进一步支持了模型预测的生物学合理性。结论：本研究提出一种可解释的数据驱动框架，用于量化T2DM的系统性疾病负担。通过将常规生物标志物与多器官功能障碍关联，该方法兼具预测准确性和机制洞察，有望提升糖尿病的风险分层与精准医疗水平。研究数据与代码已开源至GitHub：https://github.com/MiniHanWang/Type-2-Diabetes-1.git
原文摘要 · Abstract (English)
Background: Type 2 diabetes mellitus (T2DM) is increasingly recognised as a systemic disease characterised by coordinated dysfunction across metabolic, renal, lipid, and inflammatory pathways. Existing clinical assessments often fail to capture this multi-dimensional burden. Methods: We conducted a retrospective study of 1,195 patients using routinely collected laboratory biomarkers. System-level abnormality indices were constructed to quantify organ-specific dysfunction, and multi-system involvement was defined as abnormalities in two or more systems. Supervised machine learning models, including logistic regression, random forest, and gradient boosting, were trained to predict multi-system dysregulation. Model interpretability was achieved using SHapley Additive exPlanations (SHAP). Results: The gradient boosting model demonstrated near-perfect discrimination (AUC = 1.000), significantly outperforming logistic regression (AUC = 0.925). Feature attribution analysis revealed that hyperglycaemia, renal impairment, dyslipidaemia, and inflammation were the dominant drivers of multi-system risk. Dose-response relationships observed in partial dependence analyses further supported the biological plausibility of model predictions. Conclusion: This study presents an interpretable, data-driven framework for quantifying systemic disease burden in T2DM. By linking routine biomarkers to multi-organ dysfunction, our approach provides both predictive accuracy and mechanistic insight, offering potential for improved risk stratification and precision medicine in diabetes care. The data and code used in this study are openly available on GitHub at: https://github.com/MiniHanWang/Type-2-Diabetes-1.git
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