{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,24]],"date-time":"2026-10-24T00:49:53Z","timestamp":1792802993156,"version":"4.5.0"},"reference-count":48,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>Diabetes mellitus (DM) is a prevalent chronic disease, with diabetic nephropathy (DN) being a significant complication. Early detection of DN is critical for effective management. Current diagnostic methods, such as urinary albumin-to-creatinine ratio (uACR) and estimated glomerular filtration rate (eGFR), have limitations. Metabolomics offers a promising alternative by identifying specific metabolic signatures associated with DM and DN. This study aimed to identify potential metabolic biomarkers of DN using metabolomics.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A total of 100 participants were recruited, including 20 healthy controls and 80 DM patients, who were classified into three groups based on uACR: normoalbuminuria (DM), microalbuminuria (DN-1), and macroalbuminuria (DN-2). Metabolomic profiles were analyzed using ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS\/MS).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Results showed 74, 86, and 107 differentially expressed metabolites in the DM, DN-1, and DN-2 groups, respectively, compared to healthy controls. Compared to the DM group, DN-1 had 70 differential metabolites (55 upregulated, 15 downregulated), and DN-2 had 91 (81 upregulated, 10 downregulated). Between DN-1 and DN-2, 71 differential metabolites were identified (57 upregulated, 14 downregulated). Key metabolites such as lactate, L-ornithine, L-tryptophan, L-alanine, adenine, and cholecalciferol emerged as potential biomarkers and therapeutic targets. Venn diagram analysis identified 36 common differential metabolites across all groups. KEGG enrichment analysis highlighted significant involvement of amino acid biosynthesis and arginine and proline metabolism pathways in DN.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>In conclusion, this study provides valuable insights into potential metabolic markers and mechanisms for early identification and prediction of DN progression, which may aid in developing more accurate diagnostic tools and targeted therapies for DN.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fendo.2025.1581691","type":"journal-article","created":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T04:13:58Z","timestamp":1756700038000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Identification of potential biomarkers for diabetic nephropathy via UPLC-MS\/MS-based 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