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

被引:16
作者
Mo, Chen [1 ]
Wang, Jingtao [2 ]
Ye, Zhenyao [1 ]
Ke, Hongjie [3 ]
Liu, Song [4 ]
Hatch, Kathryn [1 ]
Gao, Si [1 ]
Magidson, Jessica [5 ]
Chen, Chixiang [6 ]
Mitchell, Braxton D. D. [7 ]
Kochunov, Peter [1 ]
Hong, L. Elliot [1 ]
Ma, Tianzhou [8 ]
Chen, Shuo [1 ,6 ]
机构
[1] Univ Maryland, Maryland Psychiat Res Ctr, Dept Psychiat, Sch Med, Baltimore, MD 21201 USA
[2] Shandong Univ, Qilu Hosp, Dept Hematol, Jinan, Shandong, Peoples R China
[3] Univ Maryland, Dept Math, College Pk, MD USA
[4] Qilu Univ Technol, Shandong Acad Sci, Sch Comp Sci & Technol, Jinan, Shandong, Peoples R China
[5] Univ Maryland, Dept Psychol, College Pk, MD USA
[6] Univ Maryland, Dept Epidemiol & Publ Hlth, Div Biostat & Bioinformat, Sch Med, Baltimore, MD USA
[7] Univ Maryland, Dept Med, Sch Med, Baltimore, MD USA
[8] Univ Maryland, Sch Publ Hlth, Dept Epidemiol & Biostat, College Pk, MD 20742 USA
基金
美国国家卫生研究院;
关键词
Brain aging; causal inference; cigarette per day; Mendelian randomization; smoking behaviors; smoking status; white matter fractional anisotropy; CIGARETTE-SMOKING; GENE-CLUSTER; AGE; NICOTINE; RISK; SCHIZOPHRENIA; ATROPHY; HISTORY;
D O I
10.1111/add.16088
中图分类号
R194 [卫生标准、卫生检查、医药管理];
学科分类号
摘要
Background and AimsTobacco 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. DesignMendelian 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. SettingUnited Kingdom. ParticipantsThe 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]. MeasurementsGenetic 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. FindingsThe 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 x 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 x 10(-3); GSCAN: 95% CI = 0.02, 0.31; P-value = 0.03). The sensitivity analyses showed consistent results. ConclusionsThere 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.
引用
收藏
页码:739 / 749
页数:11
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