Evidence map›Paper›PMID 36381095›Full record

ArticleJournal of clinical and translational hepatology2022

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 and 10 more

Open access · diamondAbstract read
In one paragraph

Article in Journal of clinical and translational hepatology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.8field-weighted citation impact, top 29% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

20 authors at 6 institutions in 3 countries.

Jieling XiaoYong Loo Lin School of Medicine, National University of Singapore, Singapore.ORCID https://orcid.org/0000-0001-6256-5785
Cheng-Han NgYong Loo Lin School of Medicine, National University of Singapore, Singapore.ORCID https://orcid.org/0000-0002-8297-1569
Yip-Han ChinYong Loo Lin School of Medicine, National University of Singapore, Singapore.ORCID https://orcid.org/0000-0002-8417-5996
Darren Jun Hao TanYong Loo Lin School of Medicine, National University of Singapore, Singapore.ORCID https://orcid.org/0000-0001-5358-524X
Wen-Hui LimYong Loo Lin School of Medicine, National University of Singapore, Singapore.ORCID https://orcid.org/0000-0001-6446-7871
Grace LimLee Kong Chian School of Medicine, Nanyang Technological University, Singapore.
Jingxuan QuekYong Loo Lin School of Medicine, National University of Singapore, Singapore.ORCID https://orcid.org/0000-0003-3395-9907
Ansel Shao Pin TangYong Loo Lin School of Medicine, National University of Singapore, Singapore.ORCID https://orcid.org/0000-0001-8416-6857
Kai-En ChanYong Loo Lin School of Medicine, National University of Singapore, Singapore.ORCID https://orcid.org/0000-0001-9784-2623
Rou-Yi SoongYong Loo Lin School of Medicine, National University of Singapore, Singapore.
Nicholas ChewYong Loo Lin School of Medicine, National University of Singapore, Singapore.ORCID https://orcid.org/0000-0002-0640-0430
Benjamin TayDivision of Gastroenterology and Hepatology, Department of Medicine, National University Hospital, Singapore, Singapore.
Daniel Q HuangYong Loo Lin School of Medicine, National University of Singapore, Singapore.ORCID https://orcid.org/0000-0002-5165-5061
Nobuharu TamakiNAFLD Research Center, Division of Gastroenterology and Hepatology, Department of Medicine, University of California at San Diego, La Jolla, CA, USA.ORCID https://orcid.org/0000-0003-4634-6616
Roger FooYong Loo Lin School of Medicine, National University of Singapore, Singapore.ORCID https://orcid.org/0000-0002-8079-4618
Mark Y ChanYong Loo Lin School of Medicine, National University of Singapore, Singapore.ORCID https://orcid.org/0000-0003-0389-7450
Mazen NoureddinCedars-Sinai Fatty Liver Program, Division of Digestive and Liver Diseases, Department of Medicine, Comprehensive Transplant Center, Cedars-Sinai Medical Center, Los Angeles, CA, USA.ORCID https://orcid.org/0000-0003-2127-2040
Mohammad Shadab SiddiquiDivision of Gastroenterology, Hepatology and Nutrition, Department of Internal Medicine, Virginia Commonwealth University, Richmond, VA, USA.
Arun J SanyalDivision of Gastroenterology, Hepatology and Nutrition, Department of Internal Medicine, Virginia Commonwealth University, Richmond, VA, USA.ORCID https://orcid.org/0000-0001-8682-5748
Mark D MuthiahYong Loo Lin School of Medicine, National University of Singapore, Singapore.ORCID https://orcid.org/0000-0002-9724-4743
National University of Singapore · SGVirginia Commonwealth University · USCedars-Sinai Medical Center · USNanyang Technological University · SGNational University Hospital · SGUniversity of California San Diego · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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) 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.

Indexed as

DyslipidemiaLipid modulationNASH

Identifiers

PMID36381095
PMCPMC9634784
OpenAlexW4281666678

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.