ArticleJournal of nanobiotechnology2022
Lipidomic identification of urinary extracellular vesicles for non-alcoholic steatohepatitis diagnosis.
Article in Journal of nanobiotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed, 51 citations in OpenAlex.
- Impact of Isolation Techniques on the Content of Small Extracellular Vesicles.Journal of extracellular vesicles · 2026Article
- Article
- Urinary extracellular vesicles levels of n-3 and n-6 polyunsaturated fatty acids in children with steroid-sensitive nephrotic syndrome: a pilot study.BMC nephrology · 2026Article
- Applications of Artificial Intelligence and Smart Devices in Metabolic Dysfunction-associated Steatotic Liver Disease.Journal of clinical and translational hepatology · 2026Article
- Extracellular Vesicles: Orchestrators of Intrahepatic and Systemic Crosstalk in Metabolic Dysfunction-Associated Steatotic Liver Disease.Pharmaceutics · 2026Review
- Integrating mass spectrometry-based multi-omic signatures of extracellular vesicles: from discovery to clinical translation.Clinical & translational immunology · 2026Review
- Extracellular vesicle-mediated immune-metabolic crosstalk: research advances in inflammatory regulation and the pathogenesis of metabolic diseases.Frontiers in immunology · 2026Review
- Exosomes as Emerging Nanocarriers for Targeted Cancer Therapy.International journal of nanomedicine · 2026Review
- Human extracellular vesicle blueprints.Nature cell biology · 2025Article
- Assessing the ecological quality of Rheum tanguticum based on machine learning models and cultivation verification.NPJ science of food · 2025Article
- Metabolic and hepatic effects of semaglutide and empagliflozin on metabolic dysfunction-associated steatotic liver disease mice.World journal of hepatology · 2025Article
- Extracellular vesicles in metabolic dysfunction-associated steatotic liver disease: From intercellular signaling to clinical translation.World journal of hepatology · 2025Review
- Multiomics analysis to evaluate the enrichment of extracellular vesicles from human plasma.Journal of lipid research · 2025Article
- A deep, quantitative lipid atlas of extracellular vesicles across multiple cell lines.bioRxiv : the preprint server for biology · 2025Article
- Emerging Biomarker Potential of Extracellular Vesicle-Enclosed MicroRNAs for Liver Fibrosis Detection.Cells · 2025Review
- Review
- Review
- Unveiling urinary extracellular vesicle mRNA signature for early diagnosis and prognosis of bladder cancer.Theranostics · 2025Article
- The hepatocellular model of fatty liver disease: from current imaging diagnostics to innovative proteomics technologies.Frontiers in medicine · 2025Review
- Extracellular Vesicles in Acute Kidney Injury: Mechanisms, Biomarkers, and Therapeutic Potential.International journal of nanomedicine · 2025Review
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Authors and funding
16 authors at 4 institutions in 2 countries.
Funding
Abstract
BACKGROUND AND
aimsNon-alcoholic fatty liver disease (NAFLD) is a usual chronic liver disease and lacks non-invasive biomarkers for the clinical diagnosis and prognosis. Extracellular vesicles (EVs), a group of heterogeneous small membrane-bound vesicles, carry proteins and nucleic acids as promising biomarkers for clinical applications, but it has not been well explored on their lipid compositions related to NAFLD studies. Here, we investigate the lipid molecular function of urinary EVs and their potential as biomarkers for non-alcoholic steatohepatitis (NASH) detection.
methodsThis work includes 43 patients with non-alcoholic fatty liver (NAFL) and 40 patients with NASH. The EVs of urine were isolated and purified using the EXODUS method. The EV lipidomics was performed by LC-MS/MS. We then systematically compare the EV lipidomic profiles of NAFL and NASH patients and reveal the lipid signatures of NASH with the assistance of machine learning.
resultsBy lipidomic profiling of urinary EVs, we identify 422 lipids mainly including sterol lipids, fatty acyl lipids, glycerides, glycerophospholipids, and sphingolipids. Via the machine learning and random forest modeling, we obtain a biomarker panel composed of 4 lipid molecules including FFA (18:0), LPC (22:6/0:0), FFA (18:1), and PI (16:0/18:1), that can distinguish NASH with an AUC of 92.3%. These lipid molecules are closely associated with the occurrence and development of NASH.
conclusionThe lack of non-invasive means for diagnosing NASH causes increasing morbidity. We investigate the NAFLD biomarkers from the insights of urinary EVs, and systematically compare the EV lipidomic profiles of NAFL and NASH, which holds the promise to expand the current knowledge of disease pathogenesis and evaluate their role as non-invasive biomarkers for NASH diagnosis and progression.
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