ArticleAging2020
Nomogram for the prediction of diabetic nephropathy risk among patients with type 2 diabetes mellitus based on a questionnaire and biochemical indicators: a retrospective study.
Article in Aging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 3 of them syntheses that pooled it.
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Who cites it
28 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Machine Learning Models for Prediction of Diabetic Microvascular Complications.Journal of diabetes science and technology · 2024Pooled it
- Risk prediction models for diabetic nephropathy among type 2 diabetes patients in China: a systematic review and meta-analysis.Frontiers in endocrinology · 2024Pooled it
- Prognostic models of diabetic microvascular complications: a systematic review and meta-analysis.Systematic reviews · 2021Pooled it
- ADIPOQ rs1063537 polymorphism correlates with diabetic kidney disease severity in southern Chinese Han patients with type 2 diabetes.Medicine · 2026Article
- Performance of clinical prediction models for chronic kidney disease among people with diabetes: external validation using the Canadian Primary Care Sentinel Surveillance Network (CPCSSN).Diagnostic and prognostic research · 2025Article
- A nomograph model for predicting the risk of diabetes nephropathy.International urology and nephrology · 2025Article
- Shenfushu granules attenuate diabetic kidney disease by inhibitingWorld journal of diabetes · 2025Article
- Development and validation of a model that predicts the risk of diabetic kidney disease in type 2 diabetes mellitus patients: a retrospective study.Frontiers in endocrinology · 2025Article
- Approaches to predict future type 2 diabetes mellitus and chronic kidney disease: A scoping review.PloS one · 2025Article
- Identification of Ferroptosis-related Genes for Diabetic Nephropathy by Bioinformatics and Experimental Validation.Current pharmaceutical design · 2025Article
- FoxO3a: capture the bond between magnesium and diabetic kidney disease.Frontiers in endocrinology · 2025Review
- Setting Ranges in Potential Biomarkers for Type 2 Diabetes Mellitus Patients Early Detection By Sex-An Approach with Machine Learning Algorithms.Diagnostics (Basel, Switzerland) · 2024Article
- Blood urea nitrogen to serum albumin ratio as a new prognostic indicator in type 2 diabetes mellitus patients with chronic kidney disease.Scientific reports · 2024Article
- Clinical study of different prediction models in predicting diabetic nephropathy in patients with type 2 diabetes mellitus.World journal of diabetes · 2024Article
- Development of Serum Lactate Level-Based Nomograms for Predicting Diabetic Kidney Disease in Type 2 Diabetes Mellitus Patients.Diabetes, metabolic syndrome and obesity : targets and therapy · 2024Article
- Non-alcoholic fatty liver disease risk prediction model and health management strategies for older Chinese adults: a cross-sectional study.Lipids in health and disease · 2023Article
- Risk factors and prognostic nomogram for patients with second primary cancers after lung cancer using classical statistics and machine learning.Clinical and experimental medicine · 2023Article
- Article
- Evaluation of Trace Elements Levels and Construction of Auxiliary Prediction Model in Patients with Diabetes Ketoacidosis in Type 1 Diabetes.Diabetes, metabolic syndrome and obesity : targets and therapy · 2023Article
- Ferroptosis: new insight into the mechanisms of diabetic nephropathy and retinopathy.Frontiers in endocrinology · 2023Review
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4 authors.
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Abstract
purposeDevelop a diabetic nephropathy incidence risk nomogram in a Chinese population with type 2 diabetes mellitus.
resultsPredictors included systolic blood pressure, diastolic blood pressure, fasting blood glucose, glycosylated hemoglobin A1c, total triglycerides, serum creatinine, blood urea nitrogen and body mass index. The model displayed medium predictive power with a C-index of 0.744 and an area under curve of 0.744. Internal verification of C-index reached 0.737. The decision curve analysis showed the risk threshold was 20%. The value of net reclassification improvement and integrated discrimination improvement were 0.131, 0.05, and that the nomogram could be applied in clinical practice.
conclusionDiabetic nephropathy incidence risk nomogram incorporating 8 features is useful to predict diabetic nephropathy incidence risk in type 2 diabetes mellitus patients.
methodsQuestionnaires, physical examinations and biochemical tests were performed on 3489 T2DM patients in six communities in Shanghai. LASSO regression was used to optimize feature selection by running cyclic coordinate descent. Logistic regression analysis was applied to build a prediction model incorporating the selected features. The C-index, calibration plot, curve analysis, forest plot, net reclassification improvement, integrated discrimination improvement and internal validation were used to validate the discrimination, calibration and clinical usefulness of the model.
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