ArticleInternational urology and nephrology2024
Establishment and validation of a diagnostic model for diabetic nephropathy in type 2 diabetes mellitus.
Article in International urology and nephrology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
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2 citing papers in PubMed, 1 synthesis or guideline pooled it, 2 citations in OpenAlex.
- Association between serum lipid levels and the risk of diabetic nephropathy: a meta-analysis.Frontiers in endocrinology · 2026Pooled it
- Merging artificial intelligence and retinal vascular geometric parameters: A novel tool for diagnosis and prognosis prediction of diabetic nephropathy.Chinese medical journal · 2026Article
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThere are few studies on the establishment of diagnostic models for diabetic nephropathy (DN) in in type 2 diabetes mellitus (T2DM) patients based on biomarkers. This study was to establish a model for diagnosing DN in T2DM.
methodsIn this cross-sectional study, data were collected from the Second Hospital of Shijiazhuang between August 2018 to March 2021. Totally, 359 eligible participants were included. Clinical characteristics and laboratory data were collected. LASSO regression analysis was used to screen out diagnostic factors, and the selected factors were input into the decision tree for fivefold cross validation; then a diagnostic model was established. The performances of the diagnosis model were evaluated by the area under the receiver operator characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy. The diagnostic performance of the model was also validated through risk stratifications.
resultsTotally, 199 patients (55.43%) were diagnosed with DN. Age, diastolic blood pressure (DBP), fasting blood glucose, insulin treatment, mean corpuscular hemoglobin concentration (MCHC), platelet distribution width (PDW), uric acid (UA), serum creatinine (SCR), fibrinogen (FIB), international normalized ratio (INR), and low-density lipoprotein cholesterol (LDL-C) were the diagnostic factors for DN in T2DM. The diagnostic model presented good performances, with the sensitivity, specificity, PPV, NPV, AUC, and accuracy being 0.849, 0.969, 0.971, 0.838, 0.965, and 0.903, respectively. The diagnostic model based on the stratifications also showed excellent diagnostic performance for diagnosing DN in T2DM patients.
conclusionOur diagnostic model with simple and accessible factors provides a noninvasive method for the diagnosis of DN.
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