TY - JOUR
T1 - A Class Effect Network Meta-analysis of Lipid Modulation in Non-alcoholic Steatohepatitis for Dyslipidemia
AU - Xiao, Jieling
AU - Ng, Cheng Han
AU - Chin, Yip Han
AU - Tan, Darren Jun Hao
AU - Lim, Wen Hui
AU - Lim, Grace
AU - Quek, Jingxuan
AU - Tang, Ansel Shao Pin
AU - Chan, Kai En
AU - Soong, Rou Yi
AU - Chew, Nicholas
AU - Tay, Benjamin
AU - Huang, Daniel Q.
AU - Tamaki, Nobuharu
AU - Foo, Roger
AU - Chan, Mark Y.
AU - Noureddin, Mazen
AU - Siddiqui, Mohammad Shadab
AU - Sanyal, Arun J.
AU - Muthiah, Mark D.
N1 - Funding Information:
AJS is President of Sanyal Biotechnology and has stock options in Genfit, Akarna, Tiziana, Indalo, Durect, and Galmed. He has served as a consultant to Astra Zeneca, Nitto Den-ko, Enyo, Ardelyx, Conatus, Nimbus, Amarin, Salix, Tobira, Takeda, Jannsen, Gilead, Terns, Birdrock, Merck, Valeant, Boehringer-Ingelheim, Lilly, Hemoshear, Zafgen, Novartis, Novo Nordisk, Pfizer, Exhalenz, and Genfit. He has been an unpaid consultant to Intercept, Echosens, Immuron, Galec-tin, Fractyl, Syntlogic, Affimune, Chemomab, Zydus, Nordic Bioscience, Albireo, Prosciento, Surrozen, and Bristol My-ers Squibb. His institution has received grant support from Gilead, Salix, Tobira, Bristol Myers, Shire, Intercept, Merck, Astra Zeneca, Malinckrodt, Cumberland, and Novartis. He receives royalties from Elsevier and UptoDate. MN has been on the advisory board for 89BIO, Gilead, Intercept, Pfizer, Novo Nordisk, Blade, EchoSens, Fractyl, Terns, Siemens, and Roche diagnostic; MN has received research support from Allergan, BMS, Gilead, Galmed, Galectin, Genfit, Conatus, Enanta, Madrigal, Novartis, Pfizer, Shire, Viking, and Zydus; MN is a minor shareholder or has stocks in Anaetos, Rivus Pharma, and Viking. The other authors have no conflict of interests related to this publication.
Publisher Copyright:
© 2022 The Author(s).
PY - 2022
Y1 - 2022
N2 - Background and Aims: Pharmaceutical therapy for NASH is associated with lipid modulation, but the consensus on drug treatment is limited and lacks comparative analysis of effectiveness. A network meta-analysis was conducted to compare NASH drug classes in lipid modulation. Methods: Online databases were searched for randomized controlled trails (RCTs) evaluating NASH treatments in biopsy-proven NASH patients. Treatments were classified into four groups: (1) inflammation, (2) energy, (3) bile acids, and (4) fibro-sis based on the mechanism of action. A Bayesian network analysis was conducted with outcome measured by mean difference (MD) with credible intervals (Crl) and surface under the cumulative ranking curve (SUCRA). Results: Forty-four RCTs were included in the analysis. Bile acid modulating treatments (MD: 0.05, Crl: 0.03–0.07) were the best treatment for improvement in high-density lipid (HDL) cho-lesterol, followed by treatments modulating energy (MD: 0.03, Crl: 0.02–0.04) and fibrosis (MD: 0.01, Crl: −0.12 to 0.14) compared with placebo. The top three treatments for reduction in triglycerides were treatments modulating energy (MD: −0.46, Crl: −0.49 to −0.43), bile acids (MD: −0.22, Crl: −0.35 to −0.09), and inflammation (MD: −0.08, Crl: −0.13 to −0.03) compared with placebo. SUCRA found treatment modulating fibrosis (MD: −1.27, Crl: −1.76 to −0.79) was the best treatment for reduction in low-density lipid (LDL) cholesterol followed by treatment modulating inflammation (MD: −1.03, Crl: −1.09 to −0.97) and energy (MD: −0.37, Crl: −0.39 to −0.34) compared with placebo, but LDL cholesterol was worsened by treatments modulating bile acids. Conclusions: Network analysis comparing the class effects of dyslipidemia modulation in NASH found that treatment targets can include optimization of athero-genic dyslipidemia. Future studies are required to evaluate the cardiovascular outcomes.
AB - Background and Aims: Pharmaceutical therapy for NASH is associated with lipid modulation, but the consensus on drug treatment is limited and lacks comparative analysis of effectiveness. A network meta-analysis was conducted to compare NASH drug classes in lipid modulation. Methods: Online databases were searched for randomized controlled trails (RCTs) evaluating NASH treatments in biopsy-proven NASH patients. Treatments were classified into four groups: (1) inflammation, (2) energy, (3) bile acids, and (4) fibro-sis based on the mechanism of action. A Bayesian network analysis was conducted with outcome measured by mean difference (MD) with credible intervals (Crl) and surface under the cumulative ranking curve (SUCRA). Results: Forty-four RCTs were included in the analysis. Bile acid modulating treatments (MD: 0.05, Crl: 0.03–0.07) were the best treatment for improvement in high-density lipid (HDL) cho-lesterol, followed by treatments modulating energy (MD: 0.03, Crl: 0.02–0.04) and fibrosis (MD: 0.01, Crl: −0.12 to 0.14) compared with placebo. The top three treatments for reduction in triglycerides were treatments modulating energy (MD: −0.46, Crl: −0.49 to −0.43), bile acids (MD: −0.22, Crl: −0.35 to −0.09), and inflammation (MD: −0.08, Crl: −0.13 to −0.03) compared with placebo. SUCRA found treatment modulating fibrosis (MD: −1.27, Crl: −1.76 to −0.79) was the best treatment for reduction in low-density lipid (LDL) cholesterol followed by treatment modulating inflammation (MD: −1.03, Crl: −1.09 to −0.97) and energy (MD: −0.37, Crl: −0.39 to −0.34) compared with placebo, but LDL cholesterol was worsened by treatments modulating bile acids. Conclusions: Network analysis comparing the class effects of dyslipidemia modulation in NASH found that treatment targets can include optimization of athero-genic dyslipidemia. Future studies are required to evaluate the cardiovascular outcomes.
KW - Dyslipidemia
KW - Lipid modulation
KW - NASH
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U2 - 10.14218/JCTH.2022.00095
DO - 10.14218/JCTH.2022.00095
M3 - Article
AN - SCOPUS:85136482550
SN - 2225-0719
VL - 10
SP - 1042
EP - 1049
JO - Journal of Clinical and Translational Hepatology
JF - Journal of Clinical and Translational Hepatology
IS - 6
ER -