ArticleMetabolism open2025
Lipidomics reveals potential biomarkers and pathophysiological insights in the progression of diabetic kidney disease.
Article in Metabolism open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Urotensin II Receptor Deletion Mitigates Kidney Injury and Renal Lipid Remodeling in Mice Fed a Long-Term High-Fat Diet.FASEB bioAdvances · 2026Article
- MASLD: Lipotoxicity and Imaging Parallels from Liver Steatosis to Kidney Injury.Life (Basel, Switzerland) · 2025Review
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12 authors.
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Abstract
Background: Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease, affecting over 30 % of diabetes mellitus (DM) patients. Early detection of DKD in DM patients can enable timely preventive therapies, and potentially delay disease progression. Since the kidney relies on fatty acid oxidation for energy, dysregulated lipid metabolism has been implicated in proximal tubular cell damage and DKD pathogenesis. This study aimed to identify lipid alterations during DKD development and potential biomarkers differentiating DKD from DM. Methods: lipidomics analysis was performed on serum collected from 55 patients with DM, 21 with early DKD stage and 32 with advanced DKD, and 22 healthy subjects. Associations between lipids and DKD risk were evaluated by logistic regression. Results: Lipid profiling revealed elevated levels of certain lysophosphatidylethanolamines (LPEs), phosphatidylethanolamines (PEs), ceramides (Cers), and diacylglycerols (DAGs) in the DM-DKD transition, while most LPEs, lysophosphatidylcholines (LPCs), along with several monoacylglycerol (MAG) and triacylglycerols (TAGs), increased further from DKD-E to DKD-A. Logistic regression indicated positive associations between LPCs, LPEs, PEs, and DAGs with DKD risk, with most LPEs correlating significantly with urinary albumin-to-creatinine ratio (UACR) and inversely with estimated glomerular filtration rate (eGFR). A machine-learning-derived biomarker panel, Lipid9, consisting of LPC(18:2), LPC(20:5), LPE (16:0), LPE (18:0), LPE (18:1), LPE (24:0), PE (34:1), PE (34:2), and PE (36:2), accurately distinguished DKD (AUC: 0.78, 95 % CI 0.68-0.86) from DM. Incorporating two clinical indexes, serum creatinine and blood urea nitrogen, the Lipid9-SCB model further improved DKD detection (AUC: 0.83, 95 % CI 0.75-0.90) from DM, and was notably more sensitive for identifying DKD-E (AUC: 0.79, 95 % CI 0.67-0.91). Conclusion: This study deciphers the lipid signature in DKD progression, and suggests the Lipid9-SCB panel as a promising tool for early DKD detection in DM patients.
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