ArticleDiabetes, metabolic syndrome and obesity : targets and therapy2020
Nomogram for the Risk of Diabetic Nephropathy or Diabetic Retinopathy Among Patients with Type 2 Diabetes Mellitus Based on Questionnaire and Biochemical Indicators: A Cross-Sectional Study.
Article in Diabetes, metabolic syndrome and obesity : targets and therapy, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence based prediction models for individuals at risk of multiple diabetic complications: A systematic review of the literature.Journal of nursing management · 2022Pooled it
- A nomogram prediction model incorporating noninvasive lens AGEs and conventional biochemical indicators for assessing and predicting diabetic kidney disease.Scientific reports · 2026Article
- Machine learning-based prediction of diabetic peripheral neuropathy: model development and clinical validation.Frontiers in endocrinology · 2025Article
- A machine learning model for predicting the risk of diabetic nephropathy in individuals with type 2 diabetes mellitus.Frontiers in endocrinology · 2025Article
- Evaluating Feature Selection Methods for Accurate Diagnosis of Diabetic Kidney Disease.Biomedicines · 2024Article
- Clinical study of different prediction models in predicting diabetic nephropathy in patients with type 2 diabetes mellitus.World journal of diabetes · 2024Article
- Association between blood urea nitrogen levels and diabetic retinopathy in diabetic adults in the United States (NHANES 2005-2018).Frontiers in endocrinology · 2024Article
- Comprehensive machine learning models for predicting therapeutic targets in type 2 diabetes utilizing molecular and biochemical features in rats.Frontiers in endocrinology · 2024Article
- A closer association between blood urea nitrogen and the probability of diabetic retinopathy in patients with shorter type 2 diabetes duration.Scientific reports · 2023Article
- A risk prediction model for type 2 diabetes mellitus complicated with retinopathy based on machine learning and its application in health management.Frontiers in medicine · 2023Article
- Acarbose Protects Glucolipotoxicity-Induced Diabetic Nephropathy by Inhibiting Ras Expression in High-Fat Diet-Fed db/db Mice.International journal of molecular sciences · 2022Article
- Article
- Nomogram-based prediction of clinically significant macular edema in diabetes mellitus patients.Acta diabetologica · 2022Article
- Diabetic retinopathy risk prediction in patients with type 2 diabetes mellitus using a nomogram model.Frontiers in endocrinology · 2022Article
- Development and Validation of a Model That Predicts the Risk of Diabetic Nephropathy in Type 2 Diabetes Mellitus Patients: A Cross-Sectional Study.International journal of general medicine · 2022Article
- Association Between Serum Albumin Level and Microvascular Complications of Type 2 Diabetes Mellitus.Diabetes, metabolic syndrome and obesity : targets and therapy · 2022Article
- Construction of Predictive Model for Type 2 Diabetic Retinopathy Based on Extreme Learning Machine.Diabetes, metabolic syndrome and obesity : targets and therapy · 2022Article
- Distribution of diabetic retinopathy in diabetes mellitus patients and its association rules with other eye diseases.Scientific reports · 2021Article
- Foveal avascular zone analysis by optical coherence tomography angiography in patients with type 1 and 2 diabetes and without clinical signs of diabetic retinopathy.International ophthalmology · 2021Article
- A Nomogram Model that Predicts the Risk of Diabetic Nephropathy in Type 2 Diabetes Mellitus Patients: A Retrospective Study.International journal of endocrinology · 2021Article
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9 authors.
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Abstract
purposeThis study aimed to develop a diabetic nephropathy (DN) or diabetic retinopathy (DR) incidence risk nomogram in China's population with type 2 diabetes mellitus (T2DM) based on a community-based sample.
methodsWe carried out questionnaire evaluations, physical examinations and biochemical tests among 4219 T2DM patients in Shanghai. According to the incidence of DN and DR, 4219 patients in our study were divided into groups of T2DM patients with DN or DR, patients with both, and patients without any complications. We successively used least absolute shrinkage and selection operator regression analysis and logistic regression analysis to optimize the feature selection for DN and DR. To ensure the accuracy of the results, we carried out multivariable logistic regression analysis of the above significant risk factors on the sample data for both DN and DR. The selected features were included to establish a prediction model. The C-index, calibration plot, curve analysis and internal validation were used to validate the distinction, calibration, and clinical practicality of the model.
resultsThe predictors in the prediction model included disease course, body mass index (BMI), total triglycerides (TGs), systolic blood pressure (SBP), postprandial blood glucose (PBG), haemoglobin A1C (HbA1c) and blood urea nitrogen (BUN). The model displayed moderate predictive power with a C-index of 0.807 and an area under the receiver operating characteristic curve of 0.807. In internal verification, the C-index reached 0.804. The risk threshold was 16-75% according to the analysis of the decision curve, and the nomogram could be applied in clinical practice.
conclusionThis DN or DR incidence risk nomogram incorporating disease course, BMI, TGs, SBP, PBG, HbA1c and BUN can be used to predict DN or DR incidence risk in T2DM patients. The research team has developed an online app based on a clinical prediction model incorporating risk factors for rapid and simple prediction.
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