ArticleFrontiers in molecular biosciences2025
Serum metabolic profiling of patients with diabetic kidney disease based on gas chromatography-mass spectrometry.
Article in Frontiers in molecular biosciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Exosomes in diabetic kidney disease: pathogenesis, biomarker discovery, and emerging therapeutics-a comprehensive systematic review.Renal failure · 2026Pooled it
- Plasma Metabolomics Reveals a Shared Metabolomic Profile in Experimental and Human Chronic Kidney Disease.Toxins · 2026Article
- Fecal short-chain fatty acids and serum metabolites: the impact of COVID-19 infection on dialysis patients.Frontiers in nutrition · 2026Article
- Analysis of potential biomarkers for diabetic kidney disease and non-diabetic kidney disease based on urinary metabolomics analysis.BMC nephrology · 2025Article
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Given the increasing incidence rate of diabetic kidney disease (DKD), there is an urgent need for methods to diagnose and treat DKD in clinics. Methods: Serum samples were collected from 56 DKD patients and 32 healthy controls (HCs) at the First Affiliated Hospital of Ningbo University, and the metabolic profiles were obtained through untargeted metabolomics using gas chromatography mass spectrometry. The data were then analyzed using principal components analysis, orthogonal partial least-squares discriminant analysis, Pearson correlation analysis, and receiver operating characteristic (ROC) curve. Results: It was found that the serum metabolic profiles of the DKD patients were significantly different from those of the HCs. A total of 68 potential differential metabolites were identified that were involved in arginine biosynthesis, ascorbate and aldarate metabolism, and galactose metabolism, among others; a total of 31 differential metabolites were also identified between early-stage (EDG) and late-stage (LDG) DKD patients. Additionally, 30 significant metabolic differences were observed among the EDG, LDG, and HC groups. Based on Pearson correlation analysis between the abundances of the differential metabolites and clinical markers (estimated glomerular filtration rate, blood urea nitrogen, serum creatinine, and urinary albumin/creatinine ratio) and area under the ROC curve (AUROC) analysis, the AUROC values of myoinositol and gluconic acid were found to be 0.992 and 0.991, respectively, which can be used to distinguish DKD patients from HCs. Discussion: These results indicate that myoinositol and gluconic acid could possibly be used as biomarkers of DKD.
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