Serum Metabolomic Profiling in Acute Alcoholic Hepatitis Identifies Multiple Dysregulated Pathways

Abstract

Background and ObjectivesWhile animal studies have implicated derangements of global energy homeostasis in the pathogenesis of acute alcoholic hepatitis (AAH), the relevance of these findings to the development of human AAH remains unclear. Using global, unbiased serum metabolomics analysis, we sought to characterize alterations in metabolic pathways associated with severe AAH and identify potential biomarkers for disease prognosis.MethodsThis prospective, case-control study design included 25 patients with severe AAH and 25 ambulatory patients with alcoholic cirrhosis. Serum samples were collected within 24 hours of the index clinical encounter. Global, unbiased metabolomics profiling was performed. Patients were followed for 180 days after enrollment to determine survival.ResultsLevels of 234 biochemicals were altered in subjects with severe AAH. Random-forest analysis, principal component analysis, and integrated hierarchical clustering methods demonstrated that metabolomics profiles separated the two cohorts with 100% accuracy. Severe AAH was associated with enhanced triglyceride lipolysis, impaired mitochondrial fatty acid beta oxidation, and upregulated omega oxidation. Low levels of multiple lysolipids and related metabolites suggested decreased plasma membrane remodeling in severe AAH. While most measured bile acids were increased in severe AAH, low deoxycholate and glycodeoxycholate levels indicated intestinal dysbiosis. Several changes in substrate utilization for energy homeostasis were identified in severe AAH, including increased glucose consumption by the pentose phosphate pathway, altered tricarboxylic acid (TCA) cycle activity, and enhanced peptide catabolism. Finally, altered levels of small molecules related to glutathione metabolism and antioxidant vitamin depletion were observed in patients with severe AAH. Univariable logistic regression revealed 15 metabolites associated with 180-day survival in severe AAH.ConclusionSevere AAH is characterized by a distinct metabolic phenotype spanning multiple pathways. Metabolomics profiling revealed a panel of biomarkers for disease prognosis, and future studies are planned to validate these findings in larger cohorts of patients with severe AAH.

Metabolomics studies identify novel diagnostic and prognostic indicators in patients with alcoholic hepatitis

“…In more recent years physicians have been exploring the potential of metabolite profiling in providing diagnostic and prognostic information for many diseases, such as AH. For example, Rachakonda et al [5] demonstrated via metabolomics profiling that specific biomarkers could be used to determine disease prognosis in patients with severe AH. Thus, the potential of utilizing biomarkers in diagnosis of liver disease, assessing liver disease severity, and determining long-term survival in patients with AH is worth investigating; further exploration is warranted as there is limited information on this subject.…”

Plasma Amino Acid Profile in Patients with Aortic Dissection

“…The plasma levels of citrate, the important intermediate in TCA cycle, were down-regulated in both AD patients (acute AD and chronic AD) (Fig. 6), indicating the dys-regulation of TCA cycle and impairment on mitochondrial function in both acute AD and chronic AD patients181920.…”

Dynamic Data-Driven Modeling for Ex Vivo Data Analysis: Insights into Liver Transplantation and Pathobiology

“…In a metabolomic study of NAFLD in both mice and humans, PCA was utilized to suggest a series of biomarkers associated with disease progression (e.g., creatinine and eicosanoid metabolites [46]. PCA was one of a suite of data-driven modeling tools used in another clinical metabolomic study of acute alcoholic hepatitis to distinguish biomarkers in patients vs. controls [47]. In a more recent study, Zhang et al examined circulating microRNAs in small cohorts of chronic hepatitis B and NASH patients vs. healthy controls, and found that PCA (but not the raw data) could distinguish patient sub-groups from healthy controls [46].…”