Evidence map›Paper›PMID 38895120›Full record

ArticleFrontiers in immunology2024

Exploring gut microbiota's role in rheumatic valve disease: insights from a Mendelian randomization study and mediation analysis.

Xiwei Chen, Guangwen Hu, Dong Ning, Daxin Wang

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Article in Frontiers in immunology, 2024. 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
–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

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3 · Its place in the literature

Who cites it

3 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

4 authors.

Xiwei Chen *The Hospital Affiliated to Medical School of Yangzhou University (Taizhou People's Hospital), Taizhou, Jiangsu, China.
Guangwen Hu *The Hospital Affiliated to Medical School of Yangzhou University (Taizhou People's Hospital), Taizhou, Jiangsu, China.
Dong NingDepartment of Physiology, Human Biology Building, School of Medicine, National University of Ireland (NUI), Galway, Ireland.
Daxin WangThe Hospital Affiliated to Medical School of Yangzhou University (Taizhou People's Hospital), Taizhou, Jiangsu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Investigating the relationship between gut microbiota and Rheumatic Valve Disease (RVD) is crucial for understanding the disease's etiology and developing effective interventions. Our study adopts a novel approach to examine the potential causal connections between these factors. Methods: Utilizing a two-sample Mendelian Randomization (MR) framework, we incorporated a multi-variable MR (MVMR) strategy to assess the mediatory mechanisms involved. This approach involved analyzing data from the MiBioGen consortium for gut microbiota and the FinnGen for RVD, among other sources. Instrumental variables (IVs) were carefully selected based on rigorous MR principles, and statistical analysis was conducted using bidirectional two-sample MR, such as inverse variance-weighted (IVW), weighted median, MR-Egger regression and MR Steiger Test methods. The MR-PRESSO strategy was employed for outlier detection, and MVMR was used to untangle the complex relationships between multiple microbiota and RVD. Results: Our analysis highlighted several gut microbiota classes and families with potential protective effects against RVD, including Conclusion: This study underscores the intricate and potentially causal relationship between gut microbiota and RVD, mediated through a range of immune and hormonal factors. The use of MVMR in our methodological approach provides a more comprehensive understanding of these interactions, highlighting the gut microbiota's potential as therapeutic targets in RVD management. Our findings pave the way for further research to explore these complex relationships and develop targeted interventions for RVD.

Indexed as

Gastrointestinal MicrobiomeMendelian Randomization AnalysisRheumatic Heart DiseaseHumansMediation Analysisestradiolgut microbiotaimmune cellmediation analysisMendelian randomizationrheumatic valve disease

Identifiers

PMID38895120
PMCPMC11183100

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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.