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Published on: May 10, 2022
Metabolomics Reveals Metabolic Characteristics of Functional Cure in Chronic Hepatitis B Treated With Entecavir
Junling Wang1, Xianghong Kong2, Huiping Xu3
1Department of Hepatology, Heze Mudan People's Hospital, Heze, Shandong, China.
Background:
Entecavir (ETV) combined with pegylated interferon alpha (PEG-IFNα) improves chronic hepatitis B (CHB) functional cure rates, but therapeutic heterogeneity and underlying metabolic mechanisms remain unclear. This study used untargeted metabolomics to identify metabolic signatures, mechanisms, and predictive biomarkers of functional cure with ETV-PEG-IFNα.
Methods:
Thirty-eight CHB patients were grouped into ETV monotherapy (Group E, n = 12) and ETV-PEG-IFNα combination therapy (Group Z, n = 26); Group Z was subdivided into cured (Group A, n = 13) and noncured (Group B, n = 13). Serum metabolomic profiling, multivariate statistics, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis identified differential metabolites. A random forest model was built using key metabolites.
Results:
Three hundred eighty-eight metabolites were identified. Four differential metabolites distinguished Group A and B (upregulated guanidinoacetic acid, uracil 5-carboxylate; downregulated L-methionine S-oxide, oleamide), enriching amino acid metabolism pathways. Nine differential metabolites between Group E and Z implicated amino acid, immune, and fatty acid pathways. The random forest model based on the four Group A/B metabolites showed 88.5% cross-validation accuracy (AUC = 0.920), with L-methionine S-oxide and oleamide as key predictors.
Conclusions:
This study reveals metabolic rewiring in CHB functional cure via ETV-PEG-IFNα therapy, involving energy metabolism, oxidative stress, and immunomodulation, based on which we propose a tentative metabolism-immunity synergy model to guide future research. Key metabolites, especially L-methionine S-oxide and oleamide, show exploratory predictive potential for functional cure that warrants further validation in independent cohorts.
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