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A Class Effect Network Meta-analysis of Lipid Modulation in Non-alcoholic Steatohepatitis for Dyslipidemia

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...

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Autores principales: Xiao, Jieling, Ng, Cheng-Han, Chin, Yip-Han, Tan, Darren Jun Hao, Lim, Wen-Hui, Lim, Grace, Quek, Jingxuan, Tang, Ansel Shao Pin, Chan, Kai-En, Soong, Rou-Yi, Chew, Nicholas, Tay, Benjamin, Huang, Daniel Q., Tamaki, Nobuharu, Foo, Roger, Chan, Mark Y., Noureddin, Mazen, Siddiqui, Mohammad Shadab, Sanyal, Arun J., Muthiah, Mark D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: XIA & HE Publishing Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9634784/
https://www.ncbi.nlm.nih.gov/pubmed/36381095
http://dx.doi.org/10.14218/JCTH.2022.00095
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author Xiao, Jieling
Ng, Cheng-Han
Chin, Yip-Han
Tan, Darren Jun Hao
Lim, Wen-Hui
Lim, Grace
Quek, Jingxuan
Tang, Ansel Shao Pin
Chan, Kai-En
Soong, Rou-Yi
Chew, Nicholas
Tay, Benjamin
Huang, Daniel Q.
Tamaki, Nobuharu
Foo, Roger
Chan, Mark Y.
Noureddin, Mazen
Siddiqui, Mohammad Shadab
Sanyal, Arun J.
Muthiah, Mark D.
author_facet Xiao, Jieling
Ng, Cheng-Han
Chin, Yip-Han
Tan, Darren Jun Hao
Lim, Wen-Hui
Lim, Grace
Quek, Jingxuan
Tang, Ansel Shao Pin
Chan, Kai-En
Soong, Rou-Yi
Chew, Nicholas
Tay, Benjamin
Huang, Daniel Q.
Tamaki, Nobuharu
Foo, Roger
Chan, Mark Y.
Noureddin, Mazen
Siddiqui, Mohammad Shadab
Sanyal, Arun J.
Muthiah, Mark D.
author_sort Xiao, Jieling
collection PubMed
description 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) fibrosis 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) cholesterol, 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 atherogenic dyslipidemia. Future studies are required to evaluate the cardiovascular outcomes.
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spelling pubmed-96347842022-11-14 A Class Effect Network Meta-analysis of Lipid Modulation in Non-alcoholic Steatohepatitis for Dyslipidemia Xiao, Jieling Ng, Cheng-Han Chin, Yip-Han Tan, Darren Jun Hao Lim, Wen-Hui Lim, Grace Quek, Jingxuan Tang, Ansel Shao Pin Chan, Kai-En Soong, Rou-Yi Chew, Nicholas Tay, Benjamin Huang, Daniel Q. Tamaki, Nobuharu Foo, Roger Chan, Mark Y. Noureddin, Mazen Siddiqui, Mohammad Shadab Sanyal, Arun J. Muthiah, Mark D. J Clin Transl Hepatol Original Article 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) fibrosis 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) cholesterol, 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 atherogenic dyslipidemia. Future studies are required to evaluate the cardiovascular outcomes. XIA & HE Publishing Inc. 2022-12-28 2022-05-30 /pmc/articles/PMC9634784/ /pubmed/36381095 http://dx.doi.org/10.14218/JCTH.2022.00095 Text en © 2022 Authors. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial 4.0 International License (CC BY-NC 4.0), permitting all non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Article
Xiao, Jieling
Ng, Cheng-Han
Chin, Yip-Han
Tan, Darren Jun Hao
Lim, Wen-Hui
Lim, Grace
Quek, Jingxuan
Tang, Ansel Shao Pin
Chan, Kai-En
Soong, Rou-Yi
Chew, Nicholas
Tay, Benjamin
Huang, Daniel Q.
Tamaki, Nobuharu
Foo, Roger
Chan, Mark Y.
Noureddin, Mazen
Siddiqui, Mohammad Shadab
Sanyal, Arun J.
Muthiah, Mark D.
A Class Effect Network Meta-analysis of Lipid Modulation in Non-alcoholic Steatohepatitis for Dyslipidemia
title A Class Effect Network Meta-analysis of Lipid Modulation in Non-alcoholic Steatohepatitis for Dyslipidemia
title_full A Class Effect Network Meta-analysis of Lipid Modulation in Non-alcoholic Steatohepatitis for Dyslipidemia
title_fullStr A Class Effect Network Meta-analysis of Lipid Modulation in Non-alcoholic Steatohepatitis for Dyslipidemia
title_full_unstemmed A Class Effect Network Meta-analysis of Lipid Modulation in Non-alcoholic Steatohepatitis for Dyslipidemia
title_short A Class Effect Network Meta-analysis of Lipid Modulation in Non-alcoholic Steatohepatitis for Dyslipidemia
title_sort class effect network meta-analysis of lipid modulation in non-alcoholic steatohepatitis for dyslipidemia
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9634784/
https://www.ncbi.nlm.nih.gov/pubmed/36381095
http://dx.doi.org/10.14218/JCTH.2022.00095
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