Submassive hepatic necrosis (SMHN, defined as necrosis of 15–90% of the entire liver on explant) is a likely characteristic pathological feature of ACLF in patients with hepatitis B cirrhosis. We aimed to comprehensively explore microbiome and bile acids patterns across enterhepatic circulation and build well-performing machine learning models to predict SMHN status.
Based on the presence or absence of SMHN, 17 patients with HBV-related end-stage liver disease who received liver transplantation were eligible for inclusion. Serum, portal venous blood, and stool samples were collected for comparing differences of BA spectra and gut microbiome and their interactions. We adopted the random forest algorithm with recursive feature elimination (RF-RFE) to predict SMHN status.
By comparing total BA spectrum between SMHN (−) and SMHN (+) patients, significant changes were detected only in fecal (
Our study demonstrated the changes and interactions of intestinal microbiome and BAs during enterohepatic circulation in ACLF patients with SMHN. In addition, we identified a combinatorial marker panel as non-invasive biomarkers to distinguish the SMHN status with high AUC.