ArticleFrontiers in immunology2026
Urinary extracellular vesicle metabolomic profiling reveals a distinct molecular signature for the non-invasive diagnosis of lupus nephritis.
Article in Frontiers in immunology, 2026. 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.
- Urinary Extracellular Vesicles Biomarkers in CKD: Clinical Laboratory Translation.Diagnostics (Basel, Switzerland) · 2026Review
- Plasma exosomal fibroblast activation protein: a novel biomarker links fatty acid metabolic dysregulation to systemic lupus erythematosus.Arthritis research & therapy · 2026Article
- Systemic lupus erythematosus-accelerated atherosclerosis: mechanistic insights and clinical implications.Frontiers in immunology · 2026Review
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7 authors.
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Abstract
Background: Lupus nephritis (LN) is a severe complication of systemic lupus erythematosus (SLE), underscoring an urgent need for non-invasive diagnostic biomarkers. Objective: This study aimed to define the metabolomic signature of urinary extracellular vesicles (uEVs) in LN and to identify novel biomarkers for precision diagnosis. Methods: uEVs were isolated from urine samples of 29 SLE patients with LN, 22 SLE patients without renal involvement, and 20 healthy controls (HCs) using a standardized precipitation-based protocol. uEVs were rigorously characterized in accordance with the Minimal Information for Studies of Extracellular Vesicles (MISEV) guidelines, including transmission electron microscopy, nanoparticle tracking analysis, and the assessment of canonical EV markers. Comprehensive untargeted metabolomic profiling of uEVs was subsequently performed using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Results: A total of 284 differential metabolites were identified between LN patients and the SLE group, including 230 upregulated and 54 downregulated metabolites. Machine learning-based feature prioritization using a random forest algorithm identified a panel of ten candidate metabolites. Notably, three metabolites-Glucosylsphingosine (Lyso-Gb1), phosphatidylethanolamine N-methylated (PE-NMe), and PC(20:5/TXB2)-demonstrated excellent discriminatory performance for differentiating LN from non-renal SLE, with areas under the receiver operating characteristic curve (AUCs) of 0.912, 0.906, and 0.897, respectively. Conclusion: We identified a distinct uEV metabolic signature in LN and developed a robust, non-invasive biomarker panel. This strategy holds significant promise for the early detection and personalized management of LN, offering a compelling alternative to invasive renal biopsy.
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