ArticleFrontiers in endocrinology2022
Diabetic retinopathy risk prediction in patients with type 2 diabetes mellitus using a nomogram model.
Article in Frontiers in endocrinology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed, 22 citations in OpenAlex.
- Association of Hyperuricemia with the Onset and Progression of Type 2 Diabetic Retinopathy.Journal of clinical medicine · 2026Article
- Article
- A three-parameter online nomogram for diabetic retinopathy risk in primary care: development and external validation in an independent cohort of type 2 diabetes.Frontiers in endocrinology · 2026Article
- A Novel Nomogram for Diabetic Retinopathy Prediction in Young and Middle-Aged Patients with Type 2 Diabetes.Diabetes, metabolic syndrome and obesity : targets and therapy · 2026Article
- Development and Validation of a Nomogram-Based Risk Prediction Model for Diabetic Retinopathy in Elderly Adults with Type 2 Diabetes Mellitus.Diabetes, metabolic syndrome and obesity : targets and therapy · 2025Article
- Development of machine learning predictive model for type 2 diabetic retinopathy using the triglyceride-glucose index explained by SHAP method.Frontiers in endocrinology · 2025Article
- Universal nomogram for predicting referable diabetic retinopathy: a validated model for community and ophthalmic outpatient populations using easily accessible indicators.Frontiers in endocrinology · 2025Article
- Study on the prevalence of diabetic retinopathy in Pudong New Area and the predictive value of estimated glucose disposal rate: a cross-sectional study.Frontiers in endocrinology · 2025Article
- Development and validation of prediction models for diabetic retinopathy in type 2 diabetes patients.PloS one · 2025Article
- Effect of High-Sucrose Diet on the Occurrence and Progression of Diabetic Retinopathy and Dietary Modification Strategies.Nutrients · 2024Review
- Mendelian randomization study and mediation analysis about the relation of inflammatory bowel disease and diabetic retinopathy: the further exploration of gut-retina axis.Frontiers in endocrinology · 2024Article
- Polyunsaturated fatty acids and diabetic microvascular complications: a Mendelian randomization study.Frontiers in endocrinology · 2024Article
- Correlation between glycated hemoglobin A1c, urinary microalbumin, urinary creatinine, β2 microglobulin, retinol binding protein and diabetic retinopathy.World journal of diabetes · 2023Article
- Identification of the shared gene signature and biological mechanism between type 2 diabetes and colorectal cancer.Frontiers in genetics · 2023Article
- Construction and clinical validation of a nomogram-based predictive model for diabetic retinopathy in type 2 diabetes.American journal of translational research · 2023Article
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Authors and funding
11 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: This study aims to develop a diabetic retinopathy (DR) hazard nomogram for a Chinese population of patients with type 2 diabetes mellitus (T2DM). Methods: We constructed a nomogram model by including data from 213 patients with T2DM between January 2019 and May 2021 in the Affiliated Hospital of Zunyi Medical University. We used basic statistics and biochemical indicator tests to assess the risk of DR in patients with T2DM. The patient data were used to evaluate the DR risk using R software and a least absolute shrinkage and selection operator (LASSO) predictive model. Using multivariable Cox regression, we examined the risk factors of DR to reduce the LASSO penalty. The validation model, decision curve analysis, and C-index were tested on the calibration plot. The bootstrapping methodology was used to internally validate the accuracy of the nomogram. Results: The LASSO algorithm identified the following eight predictive variables from the 16 independent variables: disease duration, body mass index (BMI), fasting blood glucose (FPG), glycated hemoglobin (HbA1c), homeostatic model assessment-insulin resistance (HOMA-IR), triglyceride (TG), total cholesterol (TC), and vitamin D (VitD)-T3. The C-index was 0.848 (95% CI: 0.798-0.898), indicating the accuracy of the model. In the interval validation, high scores (0.816) are possible from an analysis of a DR nomogram's decision curve to predict DR. Conclusion: We developed a non-parametric technique to predict the risk of DR based on disease duration, BMI, FPG, HbA1c, HOMA-IR, TG, TC, and VitD.
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