Meta‐analysis: analysis of mechanistic pathways in the treatment of non‐alcoholic steatohepatitis. Evidence from a Bayesian network meta‐analysis
Year: 2022
Type: article
Study designs: Systematic Review, Meta-Analysis
Abstract: BACKGROUND AND AIMS: Non-alcoholic steatohepatitis (NASH) is the most common cause of liver disease. However, there is lack of comparison of efficacy between different NASH drug classes. We conducted a network meta-analysis evaluating drug classes through comparing histological outcomes and targets of drugs. APPROACH AND RESULTS: Medline, EMBASE and CENTRAL were searched for randomised controlled trials evaluating NASH drugs in biopsy-proven NASH patients. Primary outcomes included NASH resolution without worsening of fibrosis, at least 2-point reduction in Non-alcoholic fatty liver disease Activity Score (NAS) without worsening of fibrosis and at least 1-point reduction in fibrosis. Treatments were classified into inflammation, energy, bile acid and fibrosis modulators. The analysis was conducted with Bayesian network model and surface under the cumulative ranking curve (SUCRA) analysis. Among 49 included trials, treatments modulating energy (Risk ratio (RR): 1.92, Credible intervals (Crl): 1.59-2.34) were most likely to achieve NASH resolution followed by treatments modulating fibrosis (RR 1.66, Crl: 0.65-4.50), bile acids (RR: 1.37, Crl: 0.99-1.92) and inflammation (RR: 1.00, Crl: 0.75-1.33). Energy and bile acids modulation were effective in at least 2-point NAS reduction without worsening of fibrosis (RR: 1.52, Crl 1.30-1.77; RR: 1.69, Crl 1.41-2.03) and at least 1-point reduction in fibrosis (RR: 1.26, Crl:1.05-1.49; RR: 1.54, Crl: 1.20-1.97). CONCLUSIONS: This network analysis demonstrates the relative superiority of drugs modulating energy pathways and bile acids in NASH treatment. This guides the development and selection of drugs for combination therapies.
DOI: 10.1111/apt.16808
Authors: Mark D. Muthiah, Cheng Han Ng, Jieling Xiao, Yip Han Chin, Grace Lim, Wen Hui Lim, Phoebe Tay, Darren Jun Hao Tan, Jie Ning Yong, Xin-Hui Pan, Jeffery Wei Heng Koh, Nicholas Chew, Nicholas Syn, Eunice Tan, Daniel Q. Huang, Mohammad Shadab Siddiqui, Rohit Loomba, Arun J. Sanyal, Mazen Noureddin
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4221114651
