ArticleFrontiers in medicine2025
The serum uric acid to creatinine ratio as a diagnostic biomarker for normoalbuminuric diabetic kidney disease.
Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A meta-analysis of serum uric acid and diabetic nephropathy risk in type 2 diabetes.Frontiers in endocrinology · 2025Pooled it
- Serum uric acid to creatinine ratio as marker of early vascular damage and renal tubular injury in non-albuminuric diabetic kidney disease.Journal of endocrinological investigation · 2026Article
- Carnitine dysregulation in diabetic kidney disease: from pathogenic mechanism to precision biomarker.Journal of translational medicine · 2026Review
- Serum Uric Acid to Creatinine Ratio as a Predictor of Normoalbuminuric DKD in T2DM with Normal Renal Function.International journal of general medicine · 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
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Authors and funding
11 authors.
Funding
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Abstract
Background: To evaluate the potential of the serum uric acid to serum creatinine ratio (SUA/SCr) as a diagnostic biomarker for normoalbuminuric diabetic kidney disease (NADKD). Methods: We retrospectively analyzed demographic and biochemical data from 3,101 type 2 diabetes patients. Patients were stratified into non-diabetic kidney disease (non-DKD), albuminuric diabetic kidney disease (ADKD), and NADKD groups according to their estimated glomerular filtration rate (eGFR), urinary albumin creatinine ratio (UACR), and urinary albumin excretion rate (UAER). We employed multivariate logistic regression analyses using a stepwise forward-LR method to develop a nomogram. Both area under the curve (AUC) from receiver operating characteristic (ROC), and calibration curves were employed to assess the predictive accuracy of the nomogram. A decision curve analysis (DCA) was conducted to assess the clinical utility of the nomogram. Results: SUA/SCr, along with glycosylated hemoglobin A1c (HbA1C) and fasting plasma glucose (FPG), showed significant associations with NADKD, both pre- and post-propensity score matching (PSM). Seven variables were incorporated into the risk nomogram. The calibration plots indicated strong agreement between predicted and observed outcomes in both training and validation cohorts. The NADKD risk model demonstrated robust performance, as evidenced by the AUC from ROC analysis and DCA. Conclusion: SUA/SCr is a significant and independent predictor of NADKD risk. The developed nomograms offer valuable tools for clinical decision-making, potentially enhancing diagnostic accuracy for NADKD in type 2 diabetes patients.
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