Observational studyFrontiers in endocrinology2022
New Diagnostic Model for the Differentiation of Diabetic Nephropathy From Non-Diabetic Nephropathy in Chinese Patients.
Observational study in Frontiers in endocrinology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03865914 (Correlation Study Between Clinical Phenotype and Pathology of Type 2 Diabetic Nephropathy), which is not on this map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Correlation Study Between Clinical Phenotype and Pathology of Type 2 Diabetic Nephropathy
Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it, 44 citations in OpenAlex.
- 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
- A Novel Risk Score Model for the Differential Diagnosis of Type 2 Diabetic Nephropathy: A Multicenter Study.Journal of diabetes research · 2023Trial
- Merging artificial intelligence and retinal vascular geometric parameters: A novel tool for diagnosis and prognosis prediction of diabetic nephropathy.Chinese medical journal · 2026Article
- Ultrasound radiomics-based machine learning models for differentiating diabetic kidney disease from non-diabetic kidney disease in type 2 diabetes.BMC nephrology · 2026Article
- Applications of Machine Learning for Early Diagnosis and Prognosis of Chronic Kidney Disease: Current Evidence.Diagnostics (Basel, Switzerland) · 2026Review
- Feasibility of Integrating Urinary Proteomics and Machine Learning for Diagnosing Diabetic Nephropathy.Journal of proteome research · 2026Article
- Artificial Intelligence in Nephrology-State of the Art on Theoretical Background, Molecular Applications, and Clinical Interpretation.International journal of molecular sciences · 2026Review
- Diabetes and its complications: molecular mechanisms, prevention and treatment.Signal transduction and targeted therapy · 2026Review
- Serum microRNA-126 Levels Are Associated With Diabetic Nephropathy in Patients With Type 2 Diabetes Mellitus.International journal of endocrinology · 2026Article
- Development and validation of a multivariable prediction model for non-invasive discrimination between diabetic and non-diabetic kidney disease in type 2 diabetes: a clinical nomogram.Frontiers in endocrinology · 2026Article
- A non-invasive predictive model for identifying non-diabetic kidney disease in type 2 diabetes mellitus: development and multicenter validation.American journal of clinical and experimental urology · 2026Article
- Analysis of potential biomarkers for diabetic kidney disease and non-diabetic kidney disease based on urinary metabolomics analysis.BMC nephrology · 2025Article
- Histopathology of Diabetic Nephropathy: Beyond Glomerular Basement Membrane Thickening.Cureus · 2025Review
- Implementing novel complete blood count-derived inflammatory indices in the diabetic kidney diseases diagnostic models.Journal of diabetes and metabolic disorders · 2025Article
- A multimodal transformer system for noninvasive diabetic nephropathy diagnosis via retinal imaging.NPJ digital medicine · 2025Article
- Development and validation of a predictive nomogram for differentiating diabetic nephropathy from non-diabetic nephropathy in patients with T2DM: a multicenter study.Frontiers in nutrition · 2025Article
- Study on Predictive Models for Differential Diagnosis of Diabetic Kidney Disease and Non-Diabetic Kidney Disease Based on Clinical and Biochemical Indicators.International journal of medical sciences · 2025Article
- Article
- Correlation between retinal vascular geometric parameters and pathologically diagnosed type 2 diabetic nephropathy.Clinical kidney journal · 2024Article
- CYSTATIN C-A Monitoring Perspective of Chronic Kidney Disease in Patients with Diabetes.International journal of molecular sciences · 2024Review
Corrections and comments
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Authors and funding
13 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The disease pathology for diabetes mellitus patients with chronic kidney disease (CKD) may be diabetic nephropathy (DN), non-diabetic renal disease (NDRD), or DN combined with NDRD. Considering that the prognosis and treatment of DN and NDRD differ, their differential diagnosis is of significance. Renal pathological biopsy is the gold standard for diagnosing DN and NDRD. However, it is invasive and cannot be implemented in many patients due to contraindications. This article constructed a new noninvasive evaluation model for differentiating DN and NDRD. Methods: We retrospectively screened 1,030 patients with type 2 diabetes who has undergone kidney biopsy from January 2005 to March 2017 in a single center. Variables were ranked according to importance, and the machine learning methods (random forest, RF, and support vector machine, SVM) were then used to construct the model. The final model was validated with an external group (338 patients, April 2017-April 2019). Results: In total, 929 patients were assigned. Ten variables were selected for model development. The areas under the receiver operating characteristic curves (AUCROCs) for the RF and SVM methods were 0.953 and 0.947, respectively. Additionally, 329 patients were analyzed for external validation. The AUCROCs for the external validation of the RF and SVM methods were 0.920 and 0.911, respectively. Conclusion: We successfully constructed a predictive model for DN and NDRD using machine learning methods, which were better than our regression methods. Clinical Trial Registration: ClinicalTrial.gov, NCT03865914.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.