ArticleWorld journal of diabetes2024
Clinical study of different prediction models in predicting diabetic nephropathy in patients with type 2 diabetes mellitus.
Article in World journal of diabetes, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it, 12 citations in OpenAlex.
- Risk factors for diabetic kidney disease in type 2 diabetes mellitus in Asia: a meta-analysis.Frontiers in endocrinology · 2026Pooled it
- Machine learning-based risk predictive models for diabetic kidney disease in type 2 diabetes mellitus patients: a systematic review and meta-analysis.Frontiers in endocrinology · 2025Pooled it
- Ferroptosis-related proteins ALOX15 and HMOX1 are upregulated and associated with inflammation in patients with diabetic nephropathy: a prospective cross-sectional study.International urology and nephrology · 2026Article
- Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics.Briefings in bioinformatics · 2026Article
- Development and Validation of a Nomogram for Predicting Chronic Kidney Disease in Older Patients with Type 2 Diabetes Mellitus and Cardiovascular Disease.Journal of multidisciplinary healthcare · 2026Article
- Association of serum methylglyoxal with endothelial dysfunction in patients with type 2 diabetes.Frontiers in pharmacology · 2026Article
- Hypertensive Nephrosclerosis: Pathological Changes and Overlap with Diabetic Nephropathy.Cureus · 2025Review
- Depletion of intrinsic renal macrophages with moderate-to-high expression ofTranslational andrology and urology · 2024Article
- Exploring the NRF2/HO-1 and NF-κB Pathways: Spirulina Nanoparticles as a Novel Approach to Combat Diabetic Nephropathy.ACS omega · 2024Article
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4 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundAmong older adults, type 2 diabetes mellitus (T2DM) is widely recognized as one of the most prevalent diseases. Diabetic nephropathy (DN) is a frequent complication of DM, mainly characterized by renal microvascular damage. Early detection, aggressive prevention, and cure of DN are key to improving prognosis. Establishing a diagnostic and predictive model for DN is crucial in auxiliary diagnosis.
aimTo investigate the factors that impact T2DM complicated with DN and utilize this information to develop a predictive model.
methodsThe clinical data of 210 patients diagnosed with T2DM and admitted to the First People's Hospital of Wenling between August 2019 and August 2022 were retrospectively analyzed. According to whether the patients had DN, they were divided into the DN group (complicated with DN) and the non-DN group (without DN). Multivariate logistic regression analysis was used to explore factors affecting DN in patients with T2DM. The data were randomly split into a training set (
resultsAmong the 210 patients with T2DM, 74 (35.34%) had DN. The validation dataset showed that the accuracies of the nomogram, decision tree, and random forest models in predicting DN in patients with T2DM were 0.746, 0.714, and 0.730, respectively. The sensitivities were 0.710, 0.710, and 0.806, respectively; the specificities were 0.844, 0.875, and 0.844, respectively; the area under the receiver operating characteristic curve (AUC) of the patients were 0.811, 0.735, and 0.850, respectively. The Delong test results revealed that the AUC values of the decision tree model were lower than those of the random forest and nomogram models (
conclusionAmong the three prediction models, random forest performs best and can help identify patients with T2DM at high risk of DN.
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