Lin Zixun, Xiong Jiayi, Yang Jiaqi, Huang Yuanfeng, Li Jinchen, Zhao Guihu, Li Bin
The 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.
Xiangya School of Medicine, Central South University, Changsha, Hunan, 410013, China.
Heliyon. 2024 Aug 2;10(16):e35649. doi: 10.1016/j.heliyon.2024.e35649. eCollection 2024 Aug 30.
Smoking is a widespread behavior, while the relationship between smoking and various diseases remains a topic of debate.
We conducted analysis to further examine the identified associations and assess potential causal relationships.
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.
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), cancer of the bronchus (IVW: Beta = 0.92, 95% CI 0.68-1.17, P = 2.02×10, peripheral vascular disease (IVW: Beta = 1.09, 95% CI 0.71-1.46, P = 1.63×10), emphysema (IVW: Beta = 1.63, 95% CI 0.90-2.36, P = 1.29×10), pneumococcal pneumonia (IVW: Beta = 0.30, 95% CI 0.11-0.49, P = 1.60×10), chronic airway obstruction (IVW: Beta = 0.83, 95% CI 0.30-1.36, P = 2.00×10) and type 2 diabetes (IVW: Beta = 0.53, 95% CI 0.16-0.90, P = 5.08×10).
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.
吸烟是一种广泛存在的行为,而吸烟与各种疾病之间的关系仍是一个有争议的话题。
我们进行分析以进一步研究已确定的关联,并评估潜在的因果关系。
我们利用7个已知与吸烟相关的单核苷酸多态性(SNP),从英国生物银行提取基因型数据,该生物银行是一个大规模生物医学数据库,包含欧洲血统人群全面的健康相关信息和遗传信息。进行全表型组关联研究(PheWAS)分析,以绘制基因预测的吸烟状态与1549种表型之间的关联图谱。然后,利用来自英国生物银行(n = 487365)和酒精与尼古丁使用测序联盟(GSCAN)(n = 337334)的数据,通过两样本孟德尔随机化(MR)分析对PheWAS中确定的关联进行细致研究。这种方法使我们能够全面描述吸烟与疾病模式之间的联系。
PheWAS分析产生了34种与吸烟有显著关联的表型(P = 0.05/1460)。重要的是,镰状细胞贫血和2型糖尿病表现出最显著的SNP(两者均为85.71%的显著SNP)。此外,MR分析提供了有力证据,支持吸烟与以下疾病风险之间的因果关联:阻塞性慢性支气管炎(逆方差加权法:β = 0.48,95%置信区间(CI)0.36 - 0.61,P = 1.62×10)、支气管癌(逆方差加权法:β = 0.92,95% CI 0.68 - 1.17,P = 2.02×10)、外周血管疾病(逆方差加权法:β = 1.09,95% CI 0.71 - 1.46,P = 1.63×10)、肺气肿(逆方差加权法:β = 1.63,95% CI 0.90 - 2.36,P = 1.29×10)、肺炎球菌肺炎(逆方差加权法:β = 0.30,95% CI 0.11 - 0.49,P = 1.60×10)、慢性气道阻塞(逆方差加权法:β = 0.83,95% CI 0.30 - 1.36,P = 2.00×10)和2型糖尿病(逆方差加权法:β = 0.53,95% CI 0.16 - 0.90,P = 5.08×10)。
本研究证实了在欧洲人群中吸烟与阻塞性慢性支气管炎、支气管癌、外周血管疾病、肺气肿、肺炎球菌肺炎、慢性气道阻塞、2型糖尿病之间的因果关系。这些发现突出了吸烟对健康的广泛影响,并支持戒烟努力。