医学
危险系数
肺活量测定
内科学
前瞻性队列研究
肺活量
DNA甲基化
肺癌
比例危险模型
烟草烟雾
置信区间
队列
肿瘤科
作者
Christina M. Eckhardt,Haotian Wu,Diddier Prada,Pantel S. Vokonas,David Sparrow,Lifang Hou,Joel Schwartz,Andrea A. Baccarelli
标识
DOI:10.1016/j.rmed.2022.106896
摘要
The Epigenetic Smoking Status Estimator (EpiSmokEr) predicts smoking phenotypes based on DNA methylation at 121 CpG sites.Evaluate associations of EpiSmokEr-predicted versus self-reported smoking phenotypes with lung function and all-cause mortality in a cohort of older adults.The prospective Normative Aging Study collected DNA methylation measurements from 1999 to 2012 with follow-up through 2016. The R package EpiSmokEr derived predicted smoking phenotypes based on DNA methylation levels assayed by the Illumina HumanMethylation450 Beadchip. Spirometry was collected every 3-5 years. Airflow limitation was defined as forced expiratory volume in 1 s/forced vital capacity <0.7. Vital status was monitored through periodic mailings.Among 784 participants contributing 5414 person-years of follow-up, the EpiSmokEr-predicted smoking phenotypes matched the self-reported phenotypes for 228 (97%) never smokers and 22 (71%) current smokers. In contrast, EpiSmokEr classified 407 (79%) self-reported former smokers as never smokers. Nonetheless, the EpiSmokEr-predicted former smoking phenotype was more strongly associated with incident airflow limitation (hazard ratio [HR] = 3.15, 95% confidence interval [CI] = 1.50-6.59) and mortality (HR = 2.11, 95% CI = 1.56-2.85) compared to the self-reported former smoking phenotype (airflow limitation: HR = 2.21, 95% CI = 1.13-4.33; mortality: HR = 1.08, 95% CI = 0.86-1.36). Risk of airflow limitation and death did not differ among self-reported never smokers and former smokers who were classified as never smokers. The discriminative accuracy of EpiSmokEr-predicted phenotypes for incident airflow limitation and mortality was improved compared to self-reported phenotypes.The DNA methylation-based EpiSmokEr classifier may be a useful surrogate of smoking-induced lung damage and may identify former smokers most at risk of adverse smoking-related health effects.
科研通智能强力驱动
Strongly Powered by AbleSci AI