Evidence map›Paper›PMID 39156599›Full record

ArticleThe World Allergy Organization journal2024

Genetic prediction of asthma increases multiple sepsis risks: A Mendelian randomization study.

Jihang Luo, Puyu Liu, Yawen Luo

Abstract read
In one paragraph

Article in The World Allergy Organization journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. The Causal Effects of Inflammation and Coagulation-Related Biomarkers on Sepsis-Related Complications: A Mendelian Randomization Study.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis
    Article
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

3 authors.

Jihang LuoDepartment of Infectious Diseases, Affiliated Hospital of Zunyi Medical University, Zunyi, China.
Puyu LiuDepartment of Pathology, Affiliated Hospital of Zunyi Medical University, Zunyi, China.
Yawen LuoDepartment of Infectious Diseases, Affiliated Hospital of Zunyi Medical University, Zunyi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Observational epidemiological studies have indicated a potential association between asthma and sepsis, although the causal relationship between these 2 conditions remains uncertain. To further investigate this relationship, the present study utilized Mendelian randomization (MR) analysis approach to explore the potential links between asthma and various types of sepsis. Methods: In a large-scale genome-wide association study, single nucleotide polymorphisms (SNPs) associated with asthma were selected as instrumental variables. Three methods, including inverse-variance weighted (IVW), MR-Egger regression, and weighted median were used to assess the causal relationship between asthma and sepsis. The odds ratio (OR) and 95% confidence interval (CI) were used as the evaluation metrics for causal relationships, and sensitivity analysis was conducted to assess pleiotropy and instrument validity. Finally, a reverse MR analysis was conducted to investigate whether there is a causal relationship between sepsis and asthma. Results: We found a positive association between asthma and an increased risk of sepsis ( Conclusion: Our study demonstrates a causal relationship between asthma and different types of sepsis. These findings suggest the importance of healthcare providers paying attention to the potential risk of sepsis in asthma patients and implementing appropriate preventive and intervention measures in a timely manner.

Indexed as

AsthmaMendelian randomization (MR)SepsisSepticaemiaStreptococcal

Identifiers

PMID39156599
PMCPMC11327466

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Registered trials

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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.