医学
内科学
混淆
孟德尔随机化
冠状动脉疾病
心脏病学
肺活量
冲程(发动机)
疾病
肺
肺功能
遗传学
基因型
扩散能力
工程类
基因
生物
机械工程
遗传变异
作者
Daniel Higbee,Raquel Granell,Eleanor Sanderson,George Davey Smith,James Dodd
出处
期刊:The European respiratory journal
[European Respiratory Society]
日期:2021-02-11
卷期号:58 (3): 2003196-2003196
被引量:38
标识
DOI:10.1183/13993003.03196-2020
摘要
Background Observational studies suggest an association between reduced lung function and risk of coronary artery disease and ischaemic stroke, independent of shared cardiovascular risk factors such as cigarette smoking. We use the latest genetic epidemiological methods to determine whether impaired lung function is causally associated with an increased risk of cardiovascular disease. Methods and findings Mendelian randomisation uses genetic variants as instrumental variables to investigate causation. Preliminary analysis used two-sample Mendelian randomisation with lung function single nucleotide polymorphisms. To avoid collider bias, the main analysis used single nucleotide polymorphisms for lung function identified from UKBiobank in a multivariable Mendelian randomisation model conditioning for height, body mass index and smoking. Multivariable Mendelian randomisation shows strong evidence that reduced forced vital capacity (FVC) causes increased risk of coronary artery disease (OR 1.32, 95% CI 1.19–1.46 per standard deviation). Reduced forced expiratory volume in 1 s (FEV 1 ) is unlikely to cause increased risk of coronary artery disease, as evidence of its effect becomes weak after conditioning for height (OR 1.08, 95% CI 0.89–1.30). There is weak evidence that reduced lung function increases risk of ischaemic stroke. Conclusion There is strong evidence that reduced FVC is independently and causally associated with coronary artery disease. Although the mechanism remains unclear, FVC could be taken into consideration when assessing cardiovascular risk and considered a potential target for reducing cardiovascular events. FEV 1 and airflow obstruction do not appear to cause increased cardiovascular events; confounding and collider bias may explain previous findings of a causal association.
科研通智能强力驱动
Strongly Powered by AbleSci AI