ArticleMetabolomics : Official journal of the Metabolomic Society2025
Comprehensive and quantitative urinary metabolomic profiling for improved characterization of diabetic nephropathy.
Article in Metabolomics : Official journal of the Metabolomic Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- The Need for Omics Studies in Chronic Kidney Disease of Unknown Etiology (CKDu): A Narrative Review and Perspective.International journal of molecular sciences · 2026Review
- Metabolomics for Preclinical Detection of Diabetic Kidney Disease: A Comprehensive Review.International journal of molecular sciences · 2026Review
- Metabolomics in Traditional Chinese Medicine for Diabetes Mellitus: Mechanistic Insights, Biomarker Discovery, and Clinical Application Prospects.International journal of analytical chemistry · 2026Review
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11 authors.
Funding
Abstract
introductionDiabetic nephropathy (DN) is a major cause of chronic kidney disease and end-stage renal failure worldwide. The current diagnostic marker, albuminuria, lacks specificity and often detects renal damage only at advanced stages.
objectivesThis study aimed to characterize urinary metabolic alterations associated with DN and explore metabolite panels with diagnostic potential.
methodsA targeted urinary metabolomics analysis was performed using the validated TMIC Urine MEGA Assay, quantifying 268 metabolites in 60 participants (20 controls, 20 type 2 diabetes mellitus [DM-2], and 20 DN patients). Data were analyzed by Partial Least Squares Discriminant Analysis (PLS-DA) for visualization, and penalized regression algorithms [Least Absolute Shrinkage and Selection Operator (LASSO) and Elastic Net (EN) with a Genetic Algorithm (GA)] followed by logistic regression (LR) modeling to identify potential discriminative variables.
resultsDN patients showed marked alterations in metabolites related to oxidative stress, mitochondrial dysfunction, and inflammation. Twenty-four of 86 quantified uremic toxins differed significantly between DN and comparison groups. The LASSO-derived model identified β-alanine, kynurenine, glucose and argininic acid as key discriminants (AUC = 0.905, 10-fold CV), while inclusion of GFR and additional metabolites (2-hydroxybutyric acid, shikimic acid) improved performance (AUC = 0.96).
conclusionsQuantitative urinary metabolomics revealed metabolic perturbations reflective of DN pathophysiology and identified candidate metabolite panels with potential for non-invasive disease characterization. These findings, though preliminary, provide a foundation for validation in larger, longitudinal cohorts and for integrating urinary metabolomics into precision diagnostics for diabetic kidney disease.
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