Evidence map›Paper›PMID 37539057›Full record

SynthesisFrontiers in immunology2023

Assessing causal relationships between gut microbiota and asthma: evidence from two sample Mendelian randomization analysis.

Rong Li, Qi Guo, Jian Zhao, Wenhui Kang, Ruoyu Lu, Zichong Long, Lili Huang, Yiting Chen, Anda Zhao, Jinhong Wu and 2 more

Open access · goldAbstract readMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Frontiers in immunology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 4 pooled it
6.3field-weighted citation impact, top 3% 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

28 citing papers in PubMed, 4 syntheses or guidelines pooled it, 41 citations in OpenAlex.

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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

12 authors at 3 institutions in 1 country.

Rong LiMinistry of Education-Shanghai Key Laboratory of Children's Environmental Health, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Qi GuoSchool Health Department, Shanghai Center for Disease Control and Prevention, Shanghai, China.
Jian ZhaoMinistry of Education-Shanghai Key Laboratory of Children's Environmental Health, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Wenhui KangMinistry of Education-Shanghai Key Laboratory of Children's Environmental Health, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Ruoyu LuMinistry of Education-Shanghai Key Laboratory of Children's Environmental Health, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Zichong LongMinistry of Education-Shanghai Key Laboratory of Children's Environmental Health, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Lili HuangMinistry of Education-Shanghai Key Laboratory of Children's Environmental Health, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yiting ChenMinistry of Education-Shanghai Key Laboratory of Children's Environmental Health, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Anda ZhaoShanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Jinhong WuShanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Yong YinShanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Shenghui LiMinistry of Education-Shanghai Key Laboratory of Children's Environmental Health, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Shanghai Jiao Tong University · CNShanghai Children's Medical Center · CNShanghai Municipal Center For Disease Control Prevention · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Accumulating evidence has suggested that gut microbiota dysbiosis is commonly observed in asthmatics. However, it remains unclear whether dysbiosis is a cause or consequence of asthma. We aimed to examine the genetic causal relationships of gut microbiota with asthma and its three phenotypes, including adult-onset asthma, childhood-onset asthma, and moderate-severe asthma. Methods: To elucidate the causality of gut microbiota with asthma, we applied two sample Mendelian randomization (MR) based on the largest publicly available genome-wide association study (GWAS) summary statistics. Inverse variance weighting meta-analysis (IVW) was used to obtain the main estimates; and Weighted median, MR-Egger, Robust Adjusted Profile Score (MR-RAPS), Maximum likelihood method (ML), and MR pleiotropy residual sum and outlier (MR-PRESSO) methods were applied in sensitivity analyses. Finally, a reverse MR analysis was performed to evaluate the possibility of reverse causation. Results: In the absence of heterogeneity and horizontal pleiotropy, the IVW method revealed that genetically predicted Conclusion: This study suggested that microbial genera were causally associated with asthma as well as its three phenotypes. The findings deepened our understanding of the role of gut microbiota in the pathology of asthma, which emphasizes the potential of opening up a new vista for the prevention and diagnosis of asthma.

Indexed as

AsthmaGastrointestinal MicrobiomeDysbiosisGenome-Wide Association StudyHumansMendelian Randomization Analysisasthmacausalitychildhood-onset asthmagut microbiotaMendelian randomization

Identifiers

PMID37539057
PMCPMC10394653
OpenAlexW4384823554

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.