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

Jieling Xiao; Cheng Han Ng; Yip Han Chin; Darren Jun Hao Tan; Wen Hui Lim; Grace Lim; Jingxuan Quek; Ansel Shao Pin Tang; Kai En Chan; Rou Yi Soong; Nicholas Chew; Benjamin Tay; Daniel Q. Huang; Nobuharu Tamaki; Roger Foo; Mark Y. Chan; Mazen Noureddin; Mohammad Shadab Siddiqui; Arun J. Sanyal; Mark D. Muthiah

doi:10.14218/JCTH.2022.00095

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

Jieling Xiao, Cheng Han Ng, Yip Han Chin, Darren Jun Hao Tan, Wen Hui Lim, Grace Lim, Jingxuan Quek, Ansel Shao Pin Tang, Kai En Chan, Rou Yi Soong, Nicholas Chew, Benjamin Tay, Daniel Q. Huang, Nobuharu Tamaki, Roger Foo, Mark Y. Chan, Mazen Noureddin, Mohammad Shadab Siddiqui, Arun J. Sanyal, Mark D. Muthiah

Houston Methodist

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

Abstract

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.

Original language	English (US)
Pages (from-to)	1042-1049
Number of pages	8
Journal	Journal of Clinical and Translational Hepatology
Volume	10
Issue number	6
DOIs	https://doi.org/10.14218/JCTH.2022.00095
State	Published - 2022

Keywords

Dyslipidemia
Lipid modulation
NASH

ASJC Scopus subject areas

Gastroenterology

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10.14218/JCTH.2022.00095

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Xiao, J., Ng, C. H., Chin, Y. H., Tan, D. J. H., Lim, W. H., Lim, G., Quek, J., Tang, A. S. P., Chan, K. E., Soong, R. Y., Chew, N., Tay, B., Huang, D. Q., Tamaki, N., Foo, R., Chan, M. Y., Noureddin, M., Siddiqui, M. S., Sanyal, A. J., & Muthiah, M. D. (2022). A Class Effect Network Meta-analysis of Lipid Modulation in Non-alcoholic Steatohepatitis for Dyslipidemia. Journal of Clinical and Translational Hepatology, 10(6), 1042-1049. https://doi.org/10.14218/JCTH.2022.00095

Xiao, J, Ng, CH, Chin, YH, Tan, DJH, Lim, WH, Lim, G, Quek, J, Tang, ASP, Chan, KE, Soong, RY, Chew, N, Tay, B, Huang, DQ, Tamaki, N, Foo, R, Chan, MY, Noureddin, M, Siddiqui, MS, Sanyal, AJ & Muthiah, MD 2022, 'A Class Effect Network Meta-analysis of Lipid Modulation in Non-alcoholic Steatohepatitis for Dyslipidemia', Journal of Clinical and Translational Hepatology, vol. 10, no. 6, pp. 1042-1049. https://doi.org/10.14218/JCTH.2022.00095

@article{b0dcd80ed7ef4f0686968ba3e7902be4,

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

abstract = "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.",

keywords = "Dyslipidemia, Lipid modulation, NASH",

author = "Jieling Xiao and Ng, {Cheng Han} and Chin, {Yip Han} and Tan, {Darren Jun Hao} and Lim, {Wen Hui} and Grace Lim and Jingxuan Quek and Tang, {Ansel Shao Pin} and Chan, {Kai En} and Soong, {Rou Yi} and Nicholas Chew and Benjamin Tay and Huang, {Daniel Q.} and Nobuharu Tamaki and Roger Foo and Chan, {Mark Y.} and Mazen Noureddin and Siddiqui, {Mohammad Shadab} and Sanyal, {Arun J.} and Muthiah, {Mark D.}",

note = "Publisher Copyright: {\textcopyright} 2022 The Author(s).",

year = "2022",

doi = "10.14218/JCTH.2022.00095",

language = "English (US)",

volume = "10",

pages = "1042--1049",

journal = "Journal of Clinical and Translational Hepatology",

issn = "2225-0719",

publisher = "Xia & He Publishing Limited",

number = "6",

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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.

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

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