Background/Aims Cholestatic liver disease (CLD) is a pathological condition characterized by impaired bile formation, secretion, and excretion. However, the key pathophysiological mechanisms of CLD remain elusive, and therapeutic efficacy is unsatisfactory.
Methods We administered berberine (BBR) or dihydroberberine (dhBBR) in bile duct ligation-, ANIT-, and mdr2-/- CLD mouse models to evaluate the anti-CLD effect. We conducted fecal microbiota transplantation to determine the role of gut microbiota in BBR’s effect. We conducted a randomized, controlled clinical trial to evaluate the effects of BBR in patients with CLD.
Results Oral BBR alleviates cholestatic liver injury in multiple mouse models. Gut microbes can transform BBR into dhBBR, which suppresses 5-hydroxytryptamine (5-HT) production in gut enterochromaffin cells by antagonizing tryptophan hydroxylase 1 (TPH1) activity and downregulating Tph1 transcription. This further ameliorates CLD by interrupting the 5-HT/5-HTR axis. A clinical study validated that BBR improved blood biochemical indicators in patients with CLD and decreased 5-HT levels.
Conclusions BBR is transformed by gut microbiota to ameliorate CLD via inhibiting 5-HT, suggesting potential novel strategies for further clinical use.
Citations
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Background/Aims A large percentage of patients undergoing esophagogastroduodenoscopy (EGD) screening do not have esophageal varices (EV) or have only small EV. We evaluated a large, international, multicenter cohort to develop a novel score, termed FIB-4plus, by combining the fibrosis-4 (FIB-4) score, liver stiffness measurement (LSM), and spleen stiffness measurement (SSM) to identify high-risk EV (HRV) in compensated cirrhosis.
Methods This international cohort study involved patients with compensated cirrhosis from 17 Chinese hospitals and one Croatian institution (NCT04546360). Two-dimensional shear wave elastography-derived LSM and SSM values, and components of the FIB-4 score (i.e., age, aspartate aminotransferase, alanine aminotransferase, and platelet count [PLT]) were combined using machine learning algorithms (logistic regression [LR] and extreme gradient boosting [XGBoost]) to develop the LR-FIB-4plus and XGBoost-FIB-4plus models, respectively. Shapley Additive exPlanations method was used to interpret the model predictions.
Results We analyzed data from 502 patients with compensated cirrhosis who underwent EGD screening. The XGBoost-FIB-4plus score demonstrated superior predictive performance for HRV, with an area under the receiver operating characteristic curve (AUROC) of 0.927 (95% confidence interval [CI] 0.897–0.957) in the training cohort (n=268), and 0.919 (95% CI 0.843–0.995) and 0.902 (95% CI 0.820–0.984) in the first (n=118) and second (n=82) external validation cohorts, respectively. Additionally, the XGBoost-FIB-4plus score exhibited high AUROC values for predicting EV across all cohorts. The FIB-4plus score outperformed the individual parameters (LSM, SSM, PLT, and FIB-4).
Conclusions The FIB-4plus score effectively predicted EV and HRV in patients with compensated cirrhosis, providing clinicians with a valuable tool for optimizing patient management and outcomes.
Citations
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The evolution of non-invasive strategies in cirrhosis management—from screening to precision monitoring: Editorial on “Fibrosis-4plus score: a novel machine learning-based tool for screening high-risk varices in compensated cirrhosis (CHESS2004): an inter Haiyu Wang, Jinjun Chen Clinical and Molecular Hepatology.2026; 32(1): 403. CrossRef
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