1st Surgery Clinic 'Victor Babes', University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square No. 2, 300041 Timisoara, Romania.
Center for Neuropsychology and Behavioral Medicine, Discipline of Psychology, Faculty of General Medicine, 'Victor Babes' University of Medicine and Pharmacy Timisoara, Eftimie Murgu Square No. 2, 300041 Timisoara, Romania.
Medicina (Kaunas). 2024 Oct 2;60(10):1617. doi: 10.3390/medicina60101617.
: Diabetic retinopathy (DR) is a leading cause of blindness in patients with type 2 diabetes mellitus (T2DM). Cardiovascular risk factors, such as hypertension, obesity, and dyslipidemia, may play a crucial role in the development and progression of DR, though the evidence remains mixed. This study aimed to assess cardiovascular risk factors as independent predictors of DR and to develop a predictive model for DR progression in T2DM patients. : A retrospective cross-sectional study was conducted on 377 patients with T2DM who underwent a comprehensive eye exam. Clinical data, including blood pressure, lipid profile, BMI, and smoking status, were collected. DR staging was determined through fundus photography and classified as No DR, Non-Proliferative DR (NPDR), and Mild, Moderate, Severe, or Proliferative DR (PDR). A Multivariate Logistic Regression was used to evaluate the association between cardiovascular risk factors and DR presence. Several machine learning models, including Random Forest, XGBoost, and Support Vector Machines, were applied to assess the predictive value of cardiovascular risk factors and identify key predictors. Model performance was evaluated using accuracy, precision, recall, and ROC-AUC. : The prevalence of DR in the cohort was 41.6%, with 34.5% having NPDR and 7.1% having PDR. A multivariate analysis identified systolic blood pressure (SBP), LDL cholesterol, and body mass index (BMI) as independent predictors of DR progression ( < 0.05). The Random Forest model showed a moderate predictive ability, with an AUC of 0.62 for distinguishing between the presence and absence of DR XGBoost showing a better performance, featuring a ROC-AUC of 0.68, while SBP, HDL cholesterol, and BMI were consistently identified as the most important predictors across models. After tuning, the XGBoost model showed a notable improvement, with an ROC-AUC of 0.72. : Cardiovascular risk factors, particularly BP and BMI, play a significant role in the progression of DR in patients with T2DM. The predictive models, especially XGBoost, showed moderate accuracy in identifying DR stages, suggesting that integrating these risk factors into clinical practice may improve early detection and intervention strategies for DR.
糖尿病视网膜病变（DR）是 2 型糖尿病（T2DM）患者失明的主要原因。心血管危险因素，如高血压、肥胖和血脂异常，可能在 DR 的发生和发展中起关键作用，但证据仍存在分歧。本研究旨在评估心血管危险因素作为 DR 的独立预测因子，并为 T2DM 患者 DR 进展建立预测模型。
一项回顾性横断面研究纳入了 377 例接受全面眼科检查的 T2DM 患者。收集了包括血压、血脂谱、BMI 和吸烟状况在内的临床数据。通过眼底摄影确定 DR 分期，分为无 DR、非增生性 DR（NPDR）和轻度、中度、重度或增生性 DR（PDR）。采用多变量 Logistic 回归评估心血管危险因素与 DR 存在之间的关联。应用随机森林、XGBoost 和支持向量机等几种机器学习模型评估心血管危险因素的预测价值并识别关键预测因子。采用准确性、精确性、召回率和 ROC-AUC 评估模型性能。
该队列中 DR 的患病率为 41.6%，其中 34.5%为 NPDR，7.1%为 PDR。多变量分析确定收缩压（SBP）、LDL 胆固醇和体重指数（BMI）是 DR 进展的独立预测因子（<0.05）。随机森林模型显示出中等的预测能力，区分 DR 存在与否的 AUC 为 0.62；XGBoost 表现出更好的性能，ROC-AUC 为 0.68，而 SBP、HDL 胆固醇和 BMI 始终被确定为模型中最重要的预测因子。经过调整后，XGBoost 模型的 ROC-AUC 显著提高，为 0.72。
心血管危险因素，特别是血压和 BMI，在 T2DM 患者 DR 进展中起重要作用。预测模型，尤其是 XGBoost，在识别 DR 分期方面具有中等准确性，提示将这些危险因素纳入临床实践可能有助于改善 DR 的早期检测和干预策略。
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