A Serum Metabolomic Analysis Of Primary Biliary Cirrhosis And Autoimmune Hepatitis For Diagnosis And Biomarker Discovery

BackgroundDue to variable clinical and laboratory findings, the diagnosis of autoimmune liver disease (AILD) is always a challenge. Previous studies have evaluated the diagnostic value of many kinds of diseases using metabolomics methods. The liver is the most important metabolic organ of the body and the hub of metabolism. The liver pathophysiologic changes, will lead to metabolic network changes. Metabonomics is a new platform of system biology study, which is widely and effectively applied in pharmaceutical industry and clinical diagnosis, it is to study the biological system by examining biological system’s (cell, tissue or organism) metabolites changes when perturbed by the stimulus (after the disease or environmental change). Clinically, metabonomics is mainly used in diagnosis, pathogenesis and prognosis. The commonly metabonomics study methods are nuclear magnetic resonance (NMR), gas chromatography mass spectrometry (GC/MS), liquid chromatography mass spectrometry (LC/MS), ultra-performance liquid chromatography mass spectrometry (UPLC/MS) is more efficient, rapid and sensitive compared with other methods, which is more and more used in metabonomics. Metabonomics is a robust clinical research tool.In this study, linear gradient ultra-performance liquid chromatography time of flight mass spectrometry (UPLC/TOF/MS) was used to analysis serum of patients with AIH and PBC. Obtained data analyzed by principal component analysis (PCA), partial least squares cluster analysis (PLS), Exploring early diagnostic serum biomarkers of AIH and PBC.Methods:Serum samples were collected from patients with primary biliary cirrhosis (PBC)(n=20) and autoimmune hepatitis (AIH)(n=19) as well as healthy individuals (n=25). Ultraperformance liquid chromatography-mass spectrometry (UPLC-MS) data of these blood samples were analyzed using principal component, partial least squares discrimination analysis and orthogonal partial least squares discrimination analysis. External validation was used to evaluate the predictive ability of the model for new samples.Result:The partial least squares discrimination analysis model (R2Y=0.991, Q2=0.943) was established between AIH and PBC groups and exhibited100%sensitivity and specificity. Five groups of biomarkers were identified in our study, including bile acids, free fatty acids, phosphatidylcholine, lysolecithin and sphingomyelin. In two disease groups, bile acids significantly increased compared to the healthy control group. The other biomarkers decreased in the disease groups compared to those in the healthy control group. In addition, we noted that the biomarkers were down-regulated in the AIH group compared with the PBC group.Conclusions:The biomarkers identified could reveal the pathophysiological changes in AILD and help in discriminate between AIH and PBC.The predictability of this method suggests its clinical application in the diagnosis and mechanism of AILD.