Evidence map›Paper›PMID 39220930›Full record

ArticleHeliyon2024

A comprehensive analysis of the health effects associated with smoking in the largest population using UK Biobank genotypic and phenotypic data.

Zixun Lin, Jiayi Xiong, Jiaqi Yang, Yuanfeng Huang, Jinchen Li, Guihu Zhao, Bin Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 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
–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

2 citing papers in PubMed.

  1. Article
  2. 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

7 authors.

Zixun LinThe Joint Institute of Smoking and Health & Bioinformatics Centre, National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
Jiayi XiongThe Joint Institute of Smoking and Health & Bioinformatics Centre, National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
Jiaqi YangThe Joint Institute of Smoking and Health & Bioinformatics Centre, National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
Yuanfeng HuangThe Joint Institute of Smoking and Health & Bioinformatics Centre, National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
Jinchen LiThe Joint Institute of Smoking and Health & Bioinformatics Centre, National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
Guihu ZhaoThe Joint Institute of Smoking and Health & Bioinformatics Centre, National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.
Bin LiThe Joint Institute of Smoking and Health & Bioinformatics Centre, National Clinical Research Centre for Geriatric Disorders, Department of Geriatrics, Xiangya Hospital, Central South University, Changsha, Hunan, 410008, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Smoking is a widespread behavior, while the relationship between smoking and various diseases remains a topic of debate. Objective: We conducted analysis to further examine the identified associations and assess potential causal relationships. Methods: We utilized seven single nucleotide polymorphisms (SNPs) known to be linked to smoking extracting genotype data from the UK Biobank, a large-scale biomedical repository encompassing comprehensive health-related and genetic information of European descent. Phenome-wide association study (PheWAS) analysis was conducted to map the association of genetically predicted smoking status with 1,549 phenotypes. The associations identified in the PheWAS were then meticulously examined through two-sample Mendelian randomization (MR) analysis, utilizing data from the UK Biobank (n = 487,365) and the Sequencing Consortium of Alcohol and Nicotine Use (GSCAN) (n = 337,334). This approach allowed us to comprehensively characterize the links between smoking and disease patterns. Results: The PheWAS analysis produced 34 phenotypes that demonstrated significant associations with smoking (P = 0.05/1460). Importantly, sickle cell anemia and type 2 diabetes exhibited the most significant SNPs (both 85.71% significant SNPs). Furthermore, the MR analyses provided compelling evidence supporting causal associations between smoking and the risk of following diseases: obstructive chronic bronchitis (IVW: Beta = 0.48, 95% confidence interval (CI) 0.36-0.61, P = 1.62×10 Conclusion: This study affirms causal relationships between smoking and obstructive chronic bronchitis, cancer of the bronchus, peripheral vascular disease, emphysema, pneumococcal pneumonia, chronic airway obstruction, type 2 diabetes, in the European population. These findings highlight the broad health impacts of smoking and support smoking cessation efforts.

Indexed as

Disease causalityMendelian randomizationPhenome-wide association studySmoking

Identifiers

PMID39220930
PMCPMC11365339

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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.