Evaluating the causal effect of tobacco smoking on white matter brain aging: a two‐sample Mendelian randomization analysis in UK Biobank
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 no...
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| Published in | Addiction (Abingdon, England) Vol. 118; no. 4; pp. 739 - 749 |
|---|---|
| Main Authors | , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
England
Blackwell Publishing Ltd
01.04.2023
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| Subjects | |
| Online Access | Get full text |
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| Abstract | 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. |
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| AbstractList | 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. 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 × 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.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. 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.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.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.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.United Kingdom.SETTINGUnited Kingdom.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].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].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.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.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.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 × 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.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.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. 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. 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. United Kingdom. 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]. 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. 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 , 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 ; GSCAN: 95% CI = 0.02, 0.31; P-value = 0.03). The sensitivity analyses showed consistent results. 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. |
| Author | Ye, Zhenyao Mo, Chen Liu, Song Hong, L. Elliot Hatch, Kathryn Gao, Si Mitchell, Braxton D. Ke, Hongjie Kochunov, Peter Chen, Shuo Chen, Chixiang Wang, Jingtao Ma, Tianzhou Magidson, Jessica |
| AuthorAffiliation | 5 Department of Psychology, University of Maryland, College Park, MD, USA 1 Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA 7 Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA 3 Department of Mathematics, University of Maryland, College Park, MD, USA 2 Department of Hematology, Qilu Hospital of Shandong University, Jinan, Shandong, China 8 Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD, USA 6 Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD, USA 4 School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China |
| AuthorAffiliation_xml | – name: 1 Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA – name: 2 Department of Hematology, Qilu Hospital of Shandong University, Jinan, Shandong, China – name: 5 Department of Psychology, University of Maryland, College Park, MD, USA – name: 8 Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD, USA – name: 3 Department of Mathematics, University of Maryland, College Park, MD, USA – name: 6 Division of Biostatistics and Bioinformatics, Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD, USA – name: 7 Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA – name: 4 School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong, China |
| Author_xml | – sequence: 1 givenname: Chen orcidid: 0000-0003-1299-2302 surname: Mo fullname: Mo, Chen organization: University of Maryland School of Medicine – sequence: 2 givenname: Jingtao surname: Wang fullname: Wang, Jingtao organization: Qilu Hospital of Shandong University – sequence: 3 givenname: Zhenyao surname: Ye fullname: Ye, Zhenyao organization: University of Maryland School of Medicine – sequence: 4 givenname: Hongjie surname: Ke fullname: Ke, Hongjie organization: University of Maryland – sequence: 5 givenname: Song surname: Liu fullname: Liu, Song organization: Qilu University of Technology (Shandong Academy of Sciences) – sequence: 6 givenname: Kathryn surname: Hatch fullname: Hatch, Kathryn organization: University of Maryland School of Medicine – sequence: 7 givenname: Si orcidid: 0000-0002-4473-1142 surname: Gao fullname: Gao, Si organization: University of Maryland School of Medicine – sequence: 8 givenname: Jessica surname: Magidson fullname: Magidson, Jessica organization: University of Maryland – sequence: 9 givenname: Chixiang surname: Chen fullname: Chen, Chixiang organization: University of Maryland School of Medicine – sequence: 10 givenname: Braxton D. surname: Mitchell fullname: Mitchell, Braxton D. organization: University of Maryland School of Medicine – sequence: 11 givenname: Peter surname: Kochunov fullname: Kochunov, Peter organization: University of Maryland School of Medicine – sequence: 12 givenname: L. Elliot surname: Hong fullname: Hong, L. Elliot organization: University of Maryland School of Medicine – sequence: 13 givenname: Tianzhou surname: Ma fullname: Ma, Tianzhou email: tma0929@umd.edu organization: University of Maryland – sequence: 14 givenname: Shuo surname: Chen fullname: Chen, Shuo email: shuochen@som.umaryland.edu organization: University of Maryland School of Medicine |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/36401354$$D View this record in MEDLINE/PubMed |
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| Keywords | Brain aging smoking behaviors cigarette per day smoking status white matter fractional anisotropy Mendelian randomization causal inference |
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| License | 2022 Society for the Study of Addiction. |
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| MergedId | FETCHMERGED-LOGICAL-c4448-c09d40233c48e5d33d3b947e81023b3719b3c5da654eb19fdb2d5d9419871ae13 |
| Notes | Funding information University of Maryland MPower Brain Health and Human Performance seed grant; National Institute on Drug Abuse, Grant/Award Number: 1DP1DA04896801; University of Maryland; National Institutes of Health, Grant/Award Numbers: EB008281, EB008432 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 AUTHOR CONTRIBUTIONS Chen Mo: Data curation; formal analysis; methodology; visualization; writing-original draft; writing-review and editing. Jingtao Wang: Formal analysis; methodology; writing-original draft; writing-review and editing. Zhenyao Ye: Data curation; methodology; visualization; writing-original draft; writing-review and editing. Hongjie Ke: Methodology; writing-original draft; writing-review and editing. Song Liu: Data curation; writing-review and editing. Kathryn Hatch: Data curation; writing-review and editing. Si Gao: Data curation; writing-review and editing. Jessica Magidson: Writing-review and editing. Chixiang Chen: Writing-review and editing. Braxton D. Mitchell: Writing-review and editing. Peter Kochunov: Writing-review and editing. L. Elliot Hong: Writing-review and editing. Tianzhou Ma: Conceptualization; methodology; supervision; writing-review and editing. Shuo Chen: Conceptualization; funding acquisition; methodology; supervision; writing-review and editing. |
| ORCID | 0000-0003-1299-2302 0000-0002-4473-1142 |
| OpenAccessLink | https://www.ncbi.nlm.nih.gov/pmc/articles/10443605 |
| PMID | 36401354 |
| PQID | 2781374181 |
| PQPubID | 37458 |
| PageCount | 11 |
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| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | April 2023 |
| PublicationDateYYYYMMDD | 2023-04-01 |
| PublicationDate_xml | – month: 04 year: 2023 text: April 2023 |
| PublicationDecade | 2020 |
| PublicationPlace | England |
| PublicationPlace_xml | – name: England – name: Abingdon |
| PublicationTitle | Addiction (Abingdon, England) |
| PublicationTitleAlternate | Addiction |
| PublicationYear | 2023 |
| Publisher | Blackwell Publishing Ltd |
| Publisher_xml | – name: Blackwell Publishing Ltd |
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| Snippet | 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... 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... 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... |
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| SubjectTerms | 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 |
| Title | Evaluating the causal effect of tobacco smoking on white matter brain aging: a two‐sample Mendelian randomization analysis in UK Biobank |
| URI | https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fadd.16088 https://www.ncbi.nlm.nih.gov/pubmed/36401354 https://www.proquest.com/docview/2781374181 https://www.proquest.com/docview/2738192327 https://pubmed.ncbi.nlm.nih.gov/PMC10443605 |
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