The level of skin advanced glycation end products in diabetic retinopathy patients and its predictive value
10.3760/cma.j.cn511434-20241025-00393
- VernacularTitle:糖尿病视网膜病变患者皮肤晚期糖基化终末产物水平及其预测价值
- Author:
Zhirou HU
1
;
Menghua WANG
1
;
Hongtao DONG
1
;
Ge YANG
1
;
Qiuming LI
1
Author Information
1. 郑州大学第一附属医院眼科, 郑州 450052
- Publication Type:Journal Article
- Keywords:
Diabetic retinopathy;
Advanced glycation end products;
Prediction model
- From:
Chinese Journal of Ocular Fundus Diseases
2025;41(7):542-547
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To observe the correlation between the level of advanced glycosylation end products (AGE) in skin and diabetic retinopathy (DR), and establish and preliminatively verify the nomogramolumbaric model for predicting the risk of DR.Methods:A clinical case-control study. A total of 346 patients with type 2 diabetes mellitus (T2DM) who were admitted to the Department of Endocrinology and Ophthalmology of the First Affiliated Hospital of Zhengzhou University from January 2023 to June 2024 were included in the study. Among them, 198 were males and 148 were females. The mean age was (54.77±10.92). According to whether the patients were accompanied by DR, the patients were divided into the non-DR group (NDR group) and the DR group (DR group), 174 and 172 cases, respectively. All patients underwent skin AGE detection using a noninvasive diabetes detector. Diabetes duration, hemoglobin A1c (HbA1c), fasting plasma glucose, Urea, creatinine (Crea), uric acid, total cholesterol, triglyceride, estimated glomerular filtration rate (eGFR), urinary albumin concentration (UALB), and body mass index (BMI) were collected in detail. Univariate analysis and multivariate logistic regression analysis were used to determine the independent risk factors for T2DM concurrent DR, and to construct a nomogram prediction model for DR risk. Receiver operating characteristic curve (ROC curve), calibration curve and decision curve (DCA) were used to evaluate the model.Results:Hypertension prevalence rate ( χ2=3.892), Diabetes duration ( Z=?7.708), BMI ( Z=?2.627), HbA1c ( Z=?4.484), Urea ( Z=?4.620), Crea ( Z=?3.526), UALB ( Z=?6.999), AGE ( Z=?8.097) in DR group were significantly higher than those in NDR group, with statistical significance ( P<0.05); eGFR was lower than that in NDR group, the difference was statistically significant ( Z=?6.061, P<0.05). Logistic regression analysis showed that AGE, diabetes duration, HbA1c, UALB and eGFR were independent risk factors for DR ( P<0.05). Based on the results of multi-factor regression analysis, a nomogram prediction model was constructed. The area under ROC curve of the model was 0.843, 95% confidence interval was 0.802-0.884, sensitivity and specificity were 79.1% and 75.9%, respectively. The calibration curve was basically consistent with the ideal curve. The results of DCA analysis showed that when the model predicted the risk threshold of patients with DR between 0.17 and 0.99, the clinical net benefit provided by the nomogram model was> 0. Conclusions:Skin AGE level is an independent risk factor for DR. The nomogram prediction model based on AGE, diabetes duration, HbA1c, eGFR and UALB can accurately predict the risk of DR, and has good clinical practicability.
