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

• Published in: Addiction (Abingdon, England) Vol. 118; no. 4; pp. 739 - 749
• Main Authors: Mo, Chen, Wang, Jingtao, Ye, Zhenyao, Ke, Hongjie, Liu, Song, Hatch, Kathryn, Gao, Si, Magidson, Jessica, Chen, Chixiang, Mitchell, Braxton D., Kochunov, Peter, Hong, L. Elliot, Ma, Tianzhou, Chen, Shuo
• Format: Journal Article
• Language: English
• Published: England Blackwell Publishing Ltd 01.04.2023
• Subjects: Age differences; Aging; Analysis; Biobanks; Biological Specimen Banks; Brain; Brain aging; causal inference; cigarette per day; Cigarettes; Cognitive ability; Cognitive functioning; Female; Genetic diversity; Humans; Male; Mendelian randomization; Mendelian Randomization Analysis - methods; Middle Aged; Neuroimaging; Risk factors; Sensitivity analysis; Smoking; Smoking - epidemiology; Smoking - genetics; smoking behaviors; smoking status; Substantia alba; Tobacco; Tobacco smoking; Tobacco Smoking - genetics; United Kingdom - epidemiology; Variants; White Matter - diagnostic imaging; white matter fractional anisotropy; United Kingdom; United Kingdom > UK; Brain aging; smoking behaviors; cigarette per day; smoking status; white matter fractional anisotropy; Mendelian randomization; causal inference
• ISSN: 0965-2140; 1360-0443; 1360-0443
• DOI: 10.1111/add.16088

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. Findings The estimated BAG had a mean of 0.10 (95% CI = 0.06, 0.14) years. The MR analysis showed evidence of positive causal effect of smoking behaviors on BAG: the effect of smoking is 0.21 (in years, 95% CI = 6.5 × 10−3, 0.41; P‐value = 0.04), and the effect of CPD is 0.16 year/cigarette (UKB: 95% CI = 0.06, 0.26; P‐value = 1.3 × 10−3; GSCAN: 95% CI = 0.02, 0.31; P‐value = 0.03). The sensitivity analyses showed consistent results. Conclusions There appears to be a significant causal effect of smoking on the brain age gap, which suggests that smoking prevention can be an effective intervention for accelerated brain aging and the age‐related decline in cognitive function.