医学
危险系数
比例危险模型
置信区间
慢性阻塞性肺病
遗传变异
遗传模型
人口
内科学
环境卫生
基因型
遗传学
生物
基因
作者
Peidong Zhang,Xiru Zhang,Ao Zhang,Zhihao Li,Dan Liu,Shouxin Zhang,Chen Mao
出处
期刊:The European respiratory journal
[European Respiratory Society]
日期:2021-06-25
卷期号:59 (2): 2101320-2101320
被引量:20
标识
DOI:10.1183/13993003.01320-2021
摘要
Background Genetic factors and smoking contribute to chronic obstructive pulmonary disease (COPD), but whether a combined polygenic risk score (PRS) is associated with incident COPD and whether it has a synergistic effect on smoking remains unclear. We aimed to investigate the association of the PRS with COPD and explore whether smoking behaviours could modify such association. Methods Multivariable Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals for the association of the PRS and smoking with COPD. Results The study included 439 255 participants (mean age 56.5 years; 53.9% female), with a median follow-up of 9.0 years. PRS lasso containing 2.5 million variants showed better discrimination and a stronger association for incident COPD than PRS 279 containing 279 genome-wide significance variants. Compared with low genetic risk, the HRs of medium and high genetic risk were 1.39 (95% CI 1.31–1.48) and 2.40 (95% CI 2.24–2.56), respectively. The HR of high genetic risk and current smoking was 11.62 (95% CI 10.31–13.10) times that of low genetic risk and never smoking. There were significant interactions between PRS lasso and smoking status for incident COPD (p interaction <0.001). From low genetic risk to high genetic risk, the HRs of current smoking increased from 4.32 (95% CI 3.69–5.06) to 6.89 (95% CI 6.21–7.64) and the population-attributable risks of smoking increased from 42.7% to 61.1%. Conclusions The PRS constructed from millions of variants below genome-wide significance showed significant associations with incident COPD. Participants with a high genetic risk may be more susceptible to developing COPD when exposed to smoking.
科研通智能强力驱动
Strongly Powered by AbleSci AI