ArticleThe Journal of infectious diseases2020
Metabolomic Signature as a Predictor of Liver Disease Events in Patients With HIV/HCV Coinfection.
Article in The Journal of infectious diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Lipidomics of HIV/HCV-related liver decompensation: association between plasma lipid depletion and immune dysregulation.Frontiers in immunology · 2026Article
- Current and Investigational Biomarkers of Liver Disease in HIV-HBV Co-infection.Current HIV/AIDS reports · 2025Review
- Plasma Lipid Metabolites Differentiate Metabolic From Viral Chronic Liver Disease.The Journal of infectious diseases · 2025Article
- Higher levels of plasmatic saturated fatty acid were significantly associated with liver fibrosis in HIV mono-infection: A case-control study.Metabolism open · 2024Article
- A Cross-Platform Metabolomics Comparison Identifies Serum Metabolite Signatures of Liver Fibrosis Progression in Chronic Hepatitis C Patients.Frontiers in molecular biosciences · 2021Article
- Prediction of Liver Weight Recovery by an Integrated Metabolomics and Machine Learning Approach After 2/3 Partial Hepatectomy.Frontiers in pharmacology · 2021Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
backgroundAdvanced liver disease due to hepatitis C virus (HCV) is a leading cause of human immunodeficiency virus (HIV)-related morbidity and mortality. There remains a need to develop noninvasive predictors of clinical outcomes in persons with HIV/HCV coinfection.
methodsWe conducted a nested case-control study in 126 patients with HIV/HCV and utilized multiple quantitative metabolomic assays to identify a prognostic profile that predicts end-stage liver disease (ESLD) events including ascites, hepatic encephalopathy, hepatocellular carcinoma, esophageal variceal bleed, and spontaneous bacterial peritonitis. Each analyte class was included in predictive modeling, and area under the receiver operator characteristic curves (AUC) and accuracy were determined.
resultsThe baseline model including demographic and clinical data had an AUC of 0.79. Three models (baseline plus amino acids, lipid metabolites, or all combined metabolites) had very good accuracy (AUC, 0.84-0.89) in differentiating patients at risk of developing an ESLD complication up to 2 years in advance. The all combined metabolites model had sensitivity 0.70, specificity 0.85, positive likelihood ratio 4.78, and negative likelihood ratio 0.35.
conclusionsWe report that quantification of a novel set of metabolites may allow earlier identification of patients with HIV/HCV who have the greatest risk of developing ESLD clinical events.
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Registered trials
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