Efficiency analysis of a metabolic profile-based model for predicting the risk of disease progression in patients with diabetic macular edema
10.3980/j.issn.1672-5123.2025.11.25
- VernacularTitle:基于代谢特征的糖尿病性黄斑水肿患者疾病进展风险预测模型效能分析
- Author:
Haiyan YANG
1
;
Xin WANG
1
;
Li LYU
1
Author Information
1. Yanqing District Hospital of Beijing;Yanqing Hospital of Peking University Third Hospital, Beijing 102100, China
- Publication Type:Journal Article
- Keywords:
diabetic macular edema;
metabolic profile;
risk factors;
prediction model
- From:
International Eye Science
2025;25(11):1869-1875
- CountryChina
- Language:Chinese
-
Abstract:
AIM: To screen the predictors of disease progression risk in patients with diabetic macular edema(DME), establish a joint prediction model and analyze its predictive efficiency.METHODS:A retrospective selection was made of 240 patients with DME who were treated in our hospital from July 2021 to October 2024 as the research subjects. The patients were divided into a low-risk group(n=102)and a high-risk group(n=138)based on the central macular thickness(CMT)and whether the fovea was accumulated. The basic information of the patients and relevant examination data such as glycolipid metabolism indicators and liver and kidney function indicators were collected. The indicators with differences in the univariate analysis(P<0.05)were included in the multivariate analysis to screen out the independent influencing factors of disease progression in DME patients and construct a predictive model. The fitting effect of the prediction model was evaluated by the area under the receiver operating characteristic curve, and the prediction model was verified by sensitivity, specificity and Youden index.RESULTS: The general data of the two groups of patients are comparable, and there was no statistically significant difference in blood pressure between the two groups of patients(both P>0.05). Univariate analysis showed that the glycated hemoglobin(HbA1c), fasting blood glucose(FBG), advanced glycation end products(AGEs), total cholesterol(TC), low-density lipoprotein cholesterol(LDL-C), triglycerides(TG), serum creatinine(Scr), uric acid(UA), and cystatin C(Cys-C)at 3 and 6 mo in the high-risk group at baseline, 3 and 6 mo were higher than those in the low-risk group(all P<0.01). The baseline, 3 and 6 mo estimated glomerular filtration rate(eGFR), total bilirubin(TBIL), and 3 and 6 mo high-density lipoprotein cholesterol(HDL-C)in the high-risk group were all lower than those in the low-risk group(P<0.05). Regression analysis showed that baseline HbA1c, 3 mo FBG, 3 mo AGEs, baseline LDL-C, 3 mo TG, 3 mo eGFR, baseline TBIL, and 6 mo TBIL were risk factors for disease progression in DME patients. The area under curve(AUC)of the combined prediction model was 0.909(95%CI: 0.872-0.945), with a sensitivity of 79.00% and a specificity of 94.10%.CONCLUSION: HbA1c, FBG, AGEs, LDL-C, TG, eGFR and TBIL are risk factors for the risk of disease progression in patients with DME. The combined prediction model can provide a reference for predicting the risk of disease progression in patients with DME.
