Khamis Amar, Abdul Fatima, Dsouza Stafny, Sulaiman Fatima, Farooqi Muhammad, Al Awadi Fatheya, Hassanein Mohammed, Ahmed Fayha Salah, Alsharhan Mouza, AlOlama Ayesha, Ali Noorah, Abdulaziz Aaesha, Rafie Alia Mohammad, Goswami Nandu, Bayoumi Riad
Hamdan Bin Mohammed College of Dental Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai P.O. Box 505055, United Arab Emirates.
College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai P.O. Box 505055, United Arab Emirates.
J Clin Med. 2024 Dec 5;13(23):7422. doi: 10.3390/jcm13237422.
In type 2 diabetes (T2D), collective damage to the eyes, kidneys, and peripheral nerves constitutes microvascular complications, which significantly affect patients' quality of life. This study aimed to prospectively evaluate the risk of microvascular complications in newly diagnosed T2D patients in Dubai, UAE. Supervised automated machine learning in the Auto-Classifier model of the IBM SPSS Modeler package was used to predict microvascular complications in a training data set of 348 long-term T2D patients with complications using 24 independent variables as predictors and complications as targets. Three automated model scenarios were tested: Full All-Variable Model; Univariate-Selected Model, and Backward Stepwise Logistic Regression Model. An independent cohort of 338 newly diagnosed T2D patients with no complications was used for the model validation. Long-term T2D patients with complications (duration = ~14.5 years) were significantly older (mean age = 56.3 ± 10.9 years) than the newly diagnosed patients without complications (duration = ~2.5 years; mean age = 48.9 ± 9.6 years). The Bayesian Network was the most reliable algorithm for predicting microvascular complications in all three scenarios with an area under the curve (AUC) of 77-87%, accuracy of 68-75%, sensitivity of 86-95%, and specificity of 53-75%. Among newly diagnosed T2D patients, 22.5% were predicted positive and 49.1% negative across all models. Logistic regression applied to the 16 significant predictors between the two sub-groups showed that BMI, HDL, adjusted for age at diagnosis of T2D, age at visit, and urine albumin explained >90% of the variation in microvascular measures. the Bayesian Network model effectively predicts microvascular complications in newly diagnosed T2D patients, highlighting the significant roles of BMI, HDL, age at diagnosis, age at visit, and urine albumin.
在2型糖尿病（T2D）中，眼睛、肾脏和周围神经的共同损伤构成微血管并发症，这会显著影响患者的生活质量。本研究旨在前瞻性评估阿联酋迪拜新诊断的T2D患者发生微血管并发症的风险。在IBM SPSS Modeler软件包的自动分类器模型中使用监督式自动机器学习，以24个独立变量作为预测因子，并发症作为目标，对348例患有并发症的长期T2D患者的训练数据集进行微血管并发症预测。测试了三种自动模型方案：全变量模型；单变量选择模型和向后逐步逻辑回归模型。使用338例无并发症的新诊断T2D患者的独立队列进行模型验证。患有并发症的长期T2D患者（病程约14.5年）比无并发症的新诊断患者（病程约2.5年；平均年龄48.9±9.6岁）年龄显著更大（平均年龄56.3±10.9岁）。贝叶斯网络是在所有三种方案中预测微血管并发症最可靠的算法，曲线下面积（AUC）为77 - 87%，准确率为68 - 75%，灵敏度为86 - 95%，特异性为53 - 75%。在新诊断的T2D患者中，所有模型预测22.5%为阳性，49.1%为阴性。对两个亚组之间的16个显著预测因子应用逻辑回归分析表明，调整T2D诊断时年龄、就诊时年龄和尿白蛋白后的BMI、HDL解释了微血管指标中>90%的变异。贝叶斯网络模型有效预测新诊断T2D患者的微血管并发症，突出了BMI、HDL、诊断时年龄、就诊时年龄和尿白蛋白的重要作用。
