Objectives: Autoimmune hepatitis (AIH) can progress into severe outcomes, i.e., decompensated cirrhosis, from remarkable and persistent inflammation in the liver. Considering the energy-expending nature of inflammation, we tried to define the metabolomics signatures of AIH to uncover the underlying mechanisms of cirrhosis development and its metabolic biomarkers.Methods: Untargeted metabolomics analysis was performed on sera samples from 79 AIH patients at the stages (phenotypes) of non-cirrhosis (n = 27), compensated cirrhosis (n = 22), and decompensated cirrhosis (n = 30). Pattern recognition was used to find unique metabolite fingerprints of cirrhosis with or without decompensation.Results: Out of the 294 annotated metabolites identified, 2 metabolic fingerprints were found associated with the development of cirrhosis (independent of the decompensated state, 42 metabolites) and the evolution of decompensated cirrhosis (out of 47 metabolites), respectively. The cirrhosis-associated fingerprints (eigenmetabolite) showed better capability to differentiate cirrhosis from non-cirrhosis patients than the aminotransferase-to-platelet ratio index. From the metabolic fingerprints, we found two pairs of metabolites (Mesobilirubinogen/6-Hydroxynicotinic acid and LysoPA(8:0/0:0)/7alpha-Hydroxycholesterol) calculated as ratio of intensities, which revealed robust abilities to identify cirrhosis or predict decompensated patients, respectively. These phenotype-related fingerprint metabolites featured fundamental energy supply disturbance along with the development of AIH cirrhosis and progression to decompensation, which was characterized as increased lipolysis, enhanced proteolysis, and increased glycolysis.Conclusions: Remodeling of metabolism to meet the liver inflammation-related energy supply is one of the key signatures of AIH in the development of cirrhosis and decompensation. Therefore, drug regulation metabolism has great potential in the treatment of AIH.
Exploring the metabolic biomarkers and pathway changes in crucian under carbonate alkalinity exposure using high-throughput metabolomics analysis based on UPLC-ESI-QTOF-MS
