Metabolomic Signature as a Predictor of Liver Disease Events in Patients With HIV/HCV Coinfection. (5th June 2020)

• Record Type: Journal Article
• Authors: Naggie, Susanna; Lusk, Sam; Thompson, J Will; Mock, Meredith; Moylan, Cynthia; Lucas, Joseph E; Dubois, Laura; St John-Williams, Lisa; Moseley, M Arthur; Patel, Keyur
• Is Part Of: Journal of infectious diseases. Volume 222:Number 12(2020)
• Page Start: 2012
• Page End: 2020
• Publication Date: 2020-06-05
• Subjects: biomarker -- end-stage liver disease -- metabolomic -- hepatic decompensation -- accuracy; Communicable diseases -- Periodicals; Diseases -- Causes and theories of causation -- Periodicals; Medicine -- Periodicals; Communicable Diseases -- Periodicals; Electronic journals; 616.9
• DOI: 10.1093/infdis/jiaa316
• Languages: English
• ISSNs: 0022-1899

Abstract

Abstract: Background: Advanced 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. Methods: We 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. Results: The 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. Conclusions: We 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.