Evidence map›Paper›PMID 39372201›Full record

ArticleFrontiers in pharmacology2024

Metabolomics and network pharmacology reveal partial insights into the hypolipidemic mechanisms of ferulic acid in a dyslipidemia mouse model.

Zhihao Zeng, Guanlin Xiao, Yanchang Liu, Minshan Wu, Xingqin Wei, Canhui Xie, Guangying Wu, Dezheng Jia, Yangxue Li, Sumei Li and 1 more

Abstract read
In one paragraph

Article in Frontiers in pharmacology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
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

4 citing papers in PubMed.

  1. Identification of Hypolipidemic Constituents fromPharmaceuticals (Basel, Switzerland) · 2026
    Article
  2. Review
  3. Article
  4. 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

11 authors.

Zhihao Zeng *School of the Fifth Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou, China.
Guanlin Xiao *Guangdong Provincial Engineering Technology Research Institute of Traditional Chinese Medicine/Guangdong Provincial Key Laboratory of Research and Development in Traditional Chinese Medicine, Guangzhou, China.
Yanchang LiuSchool of the Fifth Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou, China.
Minshan WuSchool of the Fifth Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou, China.
Xingqin WeiSchool of the Fifth Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou, China.
Canhui XieSchool of the Fifth Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou, China.
Guangying WuSchool of the Fifth Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou, China.
Dezheng JiaSchool of the Fifth Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou, China.
Yangxue LiGuangdong Provincial Engineering Technology Research Institute of Traditional Chinese Medicine/Guangdong Provincial Key Laboratory of Research and Development in Traditional Chinese Medicine, Guangzhou, China.
Sumei LiGuangdong Provincial Engineering Technology Research Institute of Traditional Chinese Medicine/Guangdong Provincial Key Laboratory of Research and Development in Traditional Chinese Medicine, Guangzhou, China.
Xiaoli BiSchool of the Fifth Clinical Medicine, Guangzhou University of Chinese Medicine, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Hyperlipidemia is a condition characterized by abnormal levels of lipids and lipoproteins in the plasma, posing significant health risks. Ferulic acid (FA) is an organic acid with therapeutic properties for diabetes and hyperlipidemia. Methods: To explore biomarkers for FA treatment of hyperlipidemia and elucidate the mechanisms of lipid-lowering-related changes in metabolic pathways by metabolomics and network pharmacology. Initially, a hyperlipidemic mouse model induced by triton WR-1339 was established to evaluate the therapeutic effects of FA. Subsequently, serum metabolomics was utilized to identify differential metabolites, and metabolic pathway analysis was performed using MetaboAnalyst 6.0. Thirdly, network pharmacology was employed to identify potential targets of FA for hyperlipidemia. Finally, the compound-target-metabolite (C-T-M) network obtained core targets and validated them with molecular docking. Results: Biochemical analysis and histological examination showed that FA had lipid-lowering effects on hyperlipidemic mice. It identified 31 potential biomarkers for FA against hyperlipidemia by metabolomics involving lipid and amino acid metabolism. Lipid and atherosclerosis signaling pathways were identified as the key signaling pathways of FA against hyperlipidemia by KEGG analysis. Conjoint analysis showed that FA against hyperlipidemia was associated with 18 core targets and six biomarkers. Molecular docking results showed that FA has a high binding affinity to these core targets. Discussion: Through the synergy of network pharmacology and metabolomics, this study provides insights into how FA regulates endogenous metabolites, underscoring its promise as a treatment for hyperlipidemia.

Indexed as

conjoint analysisferulic acidhyperlipidemiametabolomicsnetwork pharmacology

Identifiers

PMID39372201
PMCPMC11453126

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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.