Evaluating the causal effect of tobacco smoking on white matter brain aging: a two‐sample Mendelian randomization analysis in UK Biobank

作者：Chen Mo, Jingtao Wang, Zhenyao Ye, Hongjie Ke, Song Liu, Kathryn S. Hatch, Si Gao, Jessica F. Magidson, Chixiang Chen, Braxton D. Mitchell, Peter Kochunov, L. Elliot Hong, Tianzhou Ma, Shuo Chen · 发表于：Addiction · 年份：2022 · DOI：10.1111/add.16088 · 被引用次数：30 · 研究领域：Dementia and Cognitive Impairment Research、Genetic Associations and Epidemiology、Advanced Neuroimaging Techniques and Applications

BACKGROUND AND AIMS: Tobacco smoking is a risk factor for impaired brain function, but its causal effect on white matter brain aging remains unclear. This study aimed to measure the causal effect of tobacco smoking on white matter brain aging. DESIGN: Mendelian randomization (MR) analysis using two non-overlapping data sets (with and without neuroimaging data) from UK Biobank (UKB). The group exposed to smoking and control group consisted of current smokers and never smokers, respectively. Our main method was generalized weighted linear regression with other methods also included as sensitivity analysis. SETTING: United Kingdom. PARTICIPANTS: The study cohort included 23 624 subjects [10 665 males and 12 959 females with a mean age of 54.18 years, 95% confidence interval (CI) = 54.08, 54.28]. MEASUREMENTS: Genetic variants were selected as instrumental variables under the MR analysis assumptions: (1) associated with the exposure; (2) influenced outcome only via exposure; and (3) not associated with confounders. The exposure smoking status (current versus never smokers) was measured by questionnaires at the initial visit (2006-10). The other exposure, cigarettes per day (CPD), measured the average number of cigarettes smoked per day for current tobacco users over the life-time. The outcome was the 'brain age gap' (BAG), the difference between predicted brain age and chronological age, computed by training machine learning model on a non-overlapping set of never smokers. FINDIN...