Evidence map›Paper›PMID 39697203›Full record

ArticleFrontiers in medicine2024

Exploring the relationship between gut microbiota and breast diseases using Mendelian randomization analysis.

Xin Wang, Haoyu Gao, Yiyao Zeng, Jie Chen

Abstract read
In one paragraph

Article in Frontiers in medicine, 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

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.

  1. Review
  2. Article
  3. Mammary Gland Microbiota in Benign Breast Diseases.International journal of molecular sciences · 2025
    Review
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.

Xin Wang *Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China.
Haoyu Gao *Division of Cardiovascular Surgery, Department of General Surgery, West China Hospital, Sichuan University, Chengdu, China.
Yiyao ZengDepartment of Cardiology, Dushu Lake Hospital Affiliated to Soochow University, Medical Center of Soochow University, Suzhou Dushu Lake Hospital, Suzhou, Jiangsu, China.
Jie ChenDepartment of General Surgery, West China Hospital, Sichuan University, Chengdu, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Growing evidence suggests a relationship between gut microbiota composition and breast diseases, although the precise nature of this association remains uncertain. To investigate the causal relationship between gut microbiota and breast diseases, we utilized two-way Mendelian randomization (MR) analysis. Methods: Four common diseases were included as outcomes: breast cancer, breast cysts, inflammatory disorders of the breast, and infections of the breast associated with childbirth, along with their subtypes. Genetic data on gut microbiota were extracted from genome-wide association studies (GWAS). The primary approach used to investigate the association between these genetic factors and gut microbiota was the inverse-variance-weighted (IVW) method with random-effects types. Sensitivity analyses, such as Cochran's Q test, the MR-Egger intercept test, and leave-one-out analysis, were conducted to ensure the stability and reliability of the MR findings. Results: We discovered plausible causal links between 20 microbial categories and the breast diseases, with a significance level of Conclusion: Our Mendelian randomization analysis confirmed a causal relationship between gut microbiota and breast diseases. Early stool tests may be a viable method for screening diseases to identify people at higher risk of breast diseases.

Indexed as

breast cancerbreast diseasesgut microbiotaMendelian randomizationsingle nucleotide polymorphisms (SNPs)

Identifiers

PMID39697203
PMCPMC11654425

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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.