ArticleMetabolites2021
Targeted Metabolomics Analysis of Bile Acids in Patients with Idiosyncratic Drug-Induced Liver Injury.
Article in Metabolites, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed, 29 citations in OpenAlex.
- Application of metabolomics in drug-induced liver injury: A systematic review.Journal of translational internal medicine · 2026Article
- The gut-liver axis in drug-induced liver injury (DILI): Interplay between gut microbiota, intestinal barrier, and immune system.Acta pharmaceutica Sinica. B · 2026Review
- Usefulness of plasma bile acid profile as a prognostic biomarker for drug-induced liver injury.EBioMedicine · 2026Article
- [Gut microbiota-associated metabolites with drug-induced liver injury].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2026Review
- Integrated multiomics profiling predicts anti-tuberculosis drug-induced liver injury.Respiratory research · 2026Article
- Machine learning-assisted analysis of serum metabolomics for identifying biomarkers in intrinsic and idiosyncratic drug-induced liver injury.Frontiers in pharmacology · 2025Article
- Advances in drug-induced liver injury research: in vitro models, mechanisms, omics and gene modulation techniques.Cell & bioscience · 2024Review
- Salivary metabolites are promising noninvasive biomarkers of drug-induced liver injury.World journal of gastroenterology · 2024Article
- Gut-Liver Axis as a Therapeutic Target for Drug-Induced Liver Injury.Current issues in molecular biology · 2024Review
- Characterization of drug-induced liver injury associated with drug reaction with eosinophilia and systemic symptoms in two prospective DILI registries.Archives of toxicology · 2024Article
- Suppression of the gut microbiota-bile acid-FGF19 axis in patients with atrial fibrillation.Cell proliferation · 2023Article
- Study design for development of novel safety biomarkers of drug-induced liver injury by the translational safety biomarker pipeline (TransBioLine) consortium: a study protocol for a nested case-control study.Diagnostic and prognostic research · 2023Article
- Article
- Shifting perspectives in liver diseases after kidney transplantation.World journal of hepatology · 2023Review
- Advances in Idiosyncratic Drug-Induced Liver Injury Issues: New Clinical and Mechanistic Analysis Due to Roussel Uclaf Causality Assessment Method Use.International journal of molecular sciences · 2023Review
- Alteration of gastric microbiota and transcriptome in a rat with gastric intestinal metaplasia induced by deoxycholic acid.Frontiers in microbiology · 2023Article
- Bile Acid-Drug Interaction via Organic Anion-Transporting Polypeptide 4C1 Is a Potential Mechanism of Altered Pharmacokinetics of Renally Excreted Drugs.International journal of molecular sciences · 2022Article
- The Potential Role of Metabolomics in Drug-Induced Liver Injury (DILI) Assessment.Metabolites · 2022Review
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
Drug-induced liver injury (DILI) is rare but clinically important due to a high rate of mortality. However, specific biomarkers for diagnosing and predicting the severity and prognosis of DILI are lacking. Here, we used targeted metabolomics to identify and quantify specific types of bile acids that can predict the severity of DILI. A total of 161 DILI patients were enrolled in this prospective cohort study, as well as 31 health controls. A targeted metabolomics method was used to identify 24 types of bile acids. DILI patients were divided into mild, moderate, and severe groups according to disease severity. A multivariate analysis was performed to identify characteristic bile acids. Then the patients were divided into severe and non-severe groups, and logistic regression was used to identify bile acids that could predict DILI severity. Among the enrolled DILI patients, 32 were in the mild group, 90 were in the moderate group, and 39 were in the severe group. Orthogonal partial least squares-discriminant analysis (OPLS-DA) modeling clearly discriminated among the different groups. Among the four groups, glycochenodeoxycholate (GCDCA), taurochenodeoxycholate (TCDCA), deoxycholic acid (DCA), Nor Cholic acid (NorCA), glycocholic acid (GCA), and taurocholic acid (TCA) showed significant differences in concentration between at least two groups. NorCA, GCDCA, and TCDCA were all independent risk factors that differentiated severe DILI patients from the other groups. The area under the receiver operating characteristic curve (AUROC) of GCDCA, TCDCA, and NorCA was 0.856, 0.792, and 0.753, respectively. Together, these three bile acids had an AUROC of 0.895 for predicting severe DILI patients. DILI patients with different disease severities have specific bile acid metabolomics. NorCA, GCDTA, and TCDCA were independent risk factors for differentiating severe DILI patients from less-severe patients and have the potential to predict DILI severity.
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