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Metabolomic Signature as a Predictor of Liver Disease Events in Patients With HIV/HCV Coinfection
Susanna Naggie1,2, Sam Lusk1, J Will Thompson3,4
1Duke Clinical Research Institute, Duke University, Durham, North Carolina, USA.
Insights
Identifying specific metabolites can predict advanced liver disease in patients with HIV/HCV coinfection. This allows for earlier risk identification and management of end-stage liver disease (ESLD) complications.
Area of Science:
- Hepatology
- Infectious Diseases
- Metabolomics
Background:
- Advanced liver disease from Hepatitis C Virus (HCV) significantly contributes to morbidity and mortality in Human Immunodeficiency Virus (HIV)-positive individuals.
- There is a critical need for noninvasive methods to predict clinical outcomes in patients coinfected with HIV and HCV.
Purpose of the Study:
- To identify a prognostic metabolomic profile for predicting end-stage liver disease (ESLD) events in patients with HIV/HCV coinfection.
- To evaluate the accuracy of metabolite quantification in predicting ESLD development up to two years in advance.
Main Methods:
- A nested case-control study was performed involving 126 patients with HIV/HCV coinfection.
- Quantitative metabolomic assays were employed to analyze various analyte classes, including amino acids and lipid metabolites.
- Predictive modeling was conducted using demographic, clinical, and metabolomic data, with performance assessed by AUC and accuracy.
Main Results:
- A baseline model using demographic and clinical data achieved an AUC of 0.79.
- Models incorporating metabolite data (amino acids, lipid metabolites, or all combined) demonstrated high accuracy (AUC, 0.84-0.89) in predicting ESLD complications.
- The model with all combined metabolites showed a sensitivity of 0.70, specificity of 0.85, positive likelihood ratio of 4.78, and negative likelihood ratio of 0.35.
Conclusions:
- Quantification of a novel set of metabolites can facilitate earlier identification of HIV/HCV patients at high risk for ESLD.
- This metabolomic approach offers a promising strategy for proactive management and intervention in coinfected individuals.
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.
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