Evidence map›Paper›PMID 38784809›Full record

ArticleFrontiers in microbiology2024

Unveiling the microbiota-metabolite-myocardium axis: a novel perspective on cardiovascular health.

Zhenhua Guo, Yangfang Zhong, Le Zhou, Peier Xu, Naijing Gao, Jinyue Lu, Xueyun Yan, Huaming Cao

Abstract read
In one paragraph

Article in Frontiers in microbiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

8 authors.

Zhenhua Guo *Department of Cardiology, Shibei Hospital, Shanghai, China.
Yangfang Zhong *Shanghai Jing'an District Pengpu Town Second Community Health Service Center, Shanghai, China.
Le ZhouDepartment of Cardiology, Shibei Hospital, Shanghai, China.
Peier XuDepartment of Cardiology, Shibei Hospital, Shanghai, China.
Naijing GaoDepartment of Cardiology, Shibei Hospital, Shanghai, China.
Jinyue LuDepartment of Cardiology, Shibei Hospital, Shanghai, China.
Xueyun YanDepartment of Cardiology, Shibei Hospital, Shanghai, China.
Huaming CaoDepartment of Cardiology, Shibei Hospital, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Cardiovascular diseases, including myocardial infarction, remain a leading cause of death globally. Emerging evidence suggests the gut microbiota plays a crucial role in cardiovascular health. This study aims to explore the impact of gut microbiota on myocardial infarction using a mouse model. Methods: The research utilizes a multi-omics approach, including 16S rDNA sequencing and LC-MS-based metabolomics to analyze fecal and serum samples from mice modeled to mimic myocardial infarction. This methodology allows for a comprehensive analysis of microbial populations and their metabolic output. Results: The findings reveal a significant reduction in gut microbiota α-diversity in mice with induced myocardial infarction compared to healthy controls. Notably, there is an increase in populations of Fusobacteria and Clostridia. Metabolomic analysis indicates disruptions in amino acid and energy metabolism, suggesting a metabolic dysregulation linked to myocardial health. Discussion: The study proposes a novel microbiota-metabolite-myocardium axis, where specific microbial metabolites may directly affect heart health. This connection points to the gut microbiota as a potential player in the pathogenesis of myocardial infarction and may open new therapeutic avenues targeting the gut microbiome to combat cardiovascular diseases.

Indexed as

16S rDNALC-MSmetabolomicsmicrobiota-metabolite-myocardium axismyocardial infarction

Identifiers

PMID38784809
PMCPMC11112089

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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.