Evidence map›Paper›PMID 38654203›Full record

ArticleBMC infectious diseases2024

Gut microbial features may influence antiviral IgG levels after vaccination against viral respiratory infectious diseases: the evidence from two-sample bidirectional mendelian randomization.

Junlan Tu, Yidi Wang, Xiangyu Ye, Yifan Wang, Yixin Zou, Linna Jia, Sheng Yang, Rongbin Yu, Wei Liu, Peng Huang

Open access · goldAbstract read
In one paragraph

Article in BMC infectious diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.5field-weighted citation impact, top 38% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. 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

10 authors at 2 institutions in 1 country.

Junlan Tu *Department of Epidemiology, Center for Global Health, School of Public Health, National Vaccine Innovation Platform, Nanjing Medical University, 211166, Nanjing, China.
Yidi Wang *Department of Epidemiology, Center for Global Health, School of Public Health, National Vaccine Innovation Platform, Nanjing Medical University, 211166, Nanjing, China.
Xiangyu YeDepartment of Epidemiology, Center for Global Health, School of Public Health, National Vaccine Innovation Platform, Nanjing Medical University, 211166, Nanjing, China.
Yifan WangDepartment of Infectious Disease, Jurong Hospital Affiliated to Jiangsu University, Jurong, China.
Yixin ZouDepartment of Epidemiology, Center for Global Health, School of Public Health, National Vaccine Innovation Platform, Nanjing Medical University, 211166, Nanjing, China.
Linna JiaDepartment of Epidemiology, Center for Global Health, School of Public Health, National Vaccine Innovation Platform, Nanjing Medical University, 211166, Nanjing, China.
Sheng YangDepartment of Biostatistics, Center for Global Health, School of Public Health, National Vaccine Innovation Platform, Nanjing Medical University, 211166, Nanjing, China.
Rongbin YuDepartment of Epidemiology, Center for Global Health, School of Public Health, National Vaccine Innovation Platform, Nanjing Medical University, 211166, Nanjing, China. rongbinyu@njmu.edu.cn.
Wei LiuDepartment of Epidemiology, Center for Global Health, School of Public Health, National Vaccine Innovation Platform, Nanjing Medical University, 211166, Nanjing, China. liuwei@bmi.ac.cn.
Peng HuangDepartment of Epidemiology, Center for Global Health, School of Public Health, National Vaccine Innovation Platform, Nanjing Medical University, 211166, Nanjing, China. huangpeng@njmu.edu.cn.
Nanjing Medical University · CNJiangsu University · CN

Funding

Natural Science Foundation of China 82173585Natural Science Foundation of China 82273741Natural Science Foundation of Jiangsu Higher Education Institutions of China 21KJB330005Natural Science Foundation of Jiangsu Higher Education Institutions of China 22KJB330007
6 · The paper itself

Abstract

backgroundVaccination is effective in preventing viral respiratory infectious diseases through protective antibodies and the gut microbiome has been proven to regulate human immunity. This study explores the causal correlations between gut microbial features and serum-specific antiviral immunoglobulin G (IgG) levels.

methodsWe conduct a two-sample bidirectional Mendelian randomization (MR) analysis using genome-wide association study (GWAS) summary data to explore the causal relationships between 412 gut microbial features and four antiviral IgG (for influenza A, measles, rubella, and mumps) levels. To make the results more reliable, we used four robust methods and performed comprehensive sensitivity analyses.

resultsThe MR analyses revealed 26, 13, 20, and 18 causal associations of the gut microbial features influencing four IgG levels separately. ​Interestingly, ten microbial features, like genus Collinsella, species Bifidobacterium longum, and the biosynthesis of L-alanine have shown the capacity to regulate multiple IgG levels with consistent direction (rise or fall). The ​reverse MR analysis suggested several potential causal associations of IgG levels affecting microbial features.

conclusionsThe human immune response against viral respiratory infectious diseases could be modulated by changing the abundance of gut microbes, which provided new approaches for the intervention of viral respiratory infections.

Indexed as

Gastrointestinal MicrobiomeImmunoglobulin GMendelian Randomization AnalysisRespiratory Tract InfectionsAntibodies, ViralGenome-Wide Association StudyHumansVaccinationVirus DiseasesAntibodies, ViralImmunoglobulin GGut microbial featuresImmunityImmunoglobulin GRespiratory infectious diseaseTwo-sample mendelian randomization analysis

Identifiers

PMID38654203
PMCPMC11036767
OpenAlexW4395048026

What OpenQuestion holds

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LicenceCC BY
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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.