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Association of smoking with brain gray and white matter volume: a Mendelian randomization study

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Abstract

Background

Observational studies have found a significant association between smoking and smaller gray matter volume, but this finding was limited by the reverse causality bias and possible confounding factors. Therefore, we conducted a Mendelian randomization (MR) study to explore the causal association of smoking with brain gray and white matter volume from a genetic perspective, and to investigate the possible mediators influencing the association.

Methods

Smoking initiation (ever being a regular smoker) was used as the primary exposure from the GWAS & Sequencing Consortium of Alcohol and Nicotine use in up to 1,232,091 individuals of European descent. Their associations with brain volume were acquired from a recent genome-wide association study of brain imaging phenotypes conducted among 34,298 individuals of the UK Biobank. The random-effects inverse-variance weighted method was applied as the main analysis. Multivariable MR analysis was performed to assess the potential interference of confounding factors on causal effect.

Results

Genetic liability to smoking initiation was significantly associated with lower gray matter volume (beta, −0.100; 95% CI, −0.156 to −0.043; P=5.23×10-4) but not with white matter volume. Multivariable MR results suggested that the association with lower gray matter volume might be mediated by alcohol drinking. Regarding localized gray matter volume, genetic liability to smoking initiation was associated with lower gray matter volume in left superior temporal gyrus, anterior division and right superior temporal gyrus, posterior division.

Conclusions

This MR study supports the association between smoking and lower gray matter volume, and highlights the importance of never smoking.

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Data availability

All data generated or analyzed in this study are available in the supplementary material or associated publicly GWAS dataset.

References

  1. GBD 2019 Tobacco Collaborators (2021) Spatial, temporal, and demographic patterns in prevalence of smoking tobacco use and attributable disease burden in 204 countries and territories, 1990-2019: a systematic analysis from the Global Burden of Disease Study 2019. Lancet 397(10292):2337–2360. https://doi.org/10.1016/s0140-6736(21)01169-7

    Article  Google Scholar 

  2. Fritz HC, Wittfeld K, Schmidt CO, Domin M, Grabe HJ, Hegenscheid K et al (2014) Current smoking and reduced gray matter volume-a voxel-based morphometry study. Neuropsychopharmacology 39(11):2594–2600. https://doi.org/10.1038/npp.2014.112

    Article  PubMed  PubMed Central  Google Scholar 

  3. Mackey S, Allgaier N, Chaarani B, Spechler P, Orr C, Bunn J et al (2019) Mega-analysis of gray matter volume in substance dependence: general and substance-specific regional effects. Am J Psychiatry 176(2):119–128. https://doi.org/10.1176/appi.ajp.2018.17040415

    Article  PubMed  Google Scholar 

  4. CCox SR, Lyall DM, Ritchie SJ, Bastin ME, Harris MA, Buchanan CR et al (2019) Associations between vascular risk factors and brain MRI indices in UK Biobank. Eur Heart J 40(28):2290–2300. https://doi.org/10.1093/eurheartj/ehz100

    Article  CAS  Google Scholar 

  5. Elbejjani M, Auer R, Jacobs DR Jr, Haight T, Davatzikos C, Goff DC Jr et al (2019) Cigarette smoking and gray matter brain volumes in middle age adults: the CARDIA Brain MRI sub-study. Transl Psychiatry 9(1):78. https://doi.org/10.1038/s41398-019-0401-1

    Article  PubMed  PubMed Central  Google Scholar 

  6. Gray JC, Thompson M, Bachman C, Owens MM, Murphy M, Palmer R (2020) Associations of cigarette smoking with gray and white matter in the UK Biobank. Neuropsychopharmacology 45(7):1215–1222. https://doi.org/10.1038/s41386-020-0630-2

    Article  PubMed  PubMed Central  Google Scholar 

  7. Linli Z, Rolls ET, Zhao W, Kang J, Feng J, Guo S (2023) Smoking is associated with lower brain volume and cognitive differences: a large population analysis based on the UK Biobank. Prog Neuro-Psychopharmacol Biol Psychiatry 123:110698. https://doi.org/10.1016/j.pnpbp.2022.110698

    Article  Google Scholar 

  8. Liu M, Jiang Y, Wedow R, Li Y, Brazel DM, Chen F et al (2019) Association studies of up to 1.2 million individuals yield new insights into the genetic etiology of tobacco and alcohol use. Nat Genet 51(2):237–244. https://doi.org/10.1038/s41588-018-0307-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Daviet R, Aydogan G, Jagannathan K, Spilka N, Koellinger PD, Kranzler HR, Nave G et al (2022) Associations between alcohol consumption and gray and white matter volumes in the UK Biobank. Nat Commun 13(1):1175. https://doi.org/10.1038/s41467-022-28735-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Larsson SC, Mason AM, Bäck M, Klarin D, Damrauer SM, Michaëlsson K et al (2020) Genetic predisposition to smoking in relation to 14 cardiovascular diseases. Eur Heart J 41(35):3304–3310. https://doi.org/10.1093/eurheartj/ehaa193

    Article  PubMed  PubMed Central  Google Scholar 

  11. Park S, Lee S, Kim Y, Cho S, Kim K, Kim YC et al (2021) Causal effects of atrial fibrillation on brain white and gray matter volume: a Mendelian randomization study. BMC Med 19(1):274. https://doi.org/10.1186/s12916-021-02152-9

    Article  PubMed  PubMed Central  Google Scholar 

  12. Sutherland MT, Riedel MC, Flannery JS, Yanes JA, Fox PT, Stein EA et al (2016) Chronic cigarette smoking is linked with structural alterations in brain regions showing acute nicotinic drug-induced functional modulations. Behav Brain Funct 12(1):16. https://doi.org/10.1186/s12993-016-0100-5

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Lawlor DA, Harbord RM, Sterne JA, Timpson N, Davey Smith G (2008) Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med 27(8):1133–1163. https://doi.org/10.1002/sim.3034

    Article  PubMed  Google Scholar 

  14. Myers TA, Chanock SJ, Machiela MJ (2020) LDlinkR: An R package for rapidly calculating linkage disequilibrium statistics in diverse populations. Front Genet 11:157. https://doi.org/10.3389/fgene.2020.00157

    Article  PubMed  PubMed Central  Google Scholar 

  15. Wootton RE, Richmond RC, Stuijfzand BG, Lawn RB, Sallis HM, Taylor GMJ et al (2020) Evidence for causal effects of lifetime smoking on risk for depression and schizophrenia: a Mendelian randomisation study. Psychol Med 50(14):2435–2443. https://doi.org/10.1017/s0033291719002678

    Article  PubMed  Google Scholar 

  16. Burgess S, Thompson SG (2011) Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol 40(3):755–764. https://doi.org/10.1093/ije/dyr036

    Article  PubMed  Google Scholar 

  17. Smith SM, Douaud G, Chen W, Hanayik T, Alfaro-Almagro F, Sharp K et al (2021) An expanded set of genome-wide association studies of brain imaging phenotypes in UK Biobank. Nat Neurosci 24(5):737–745. https://doi.org/10.1038/s41593-021-00826-4

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Skrivankova VW, Richmond RC, Woolf BAR, Davies NM, Swanson SA, VanderWeele TJ et al (2021) Strengthening the reporting of observational studies in epidemiology using mendelian randomisation (STROBE-MR): explanation and elaboration. BMJ 375:n2233. https://doi.org/10.1136/bmj.n2233

    Article  PubMed  PubMed Central  Google Scholar 

  19. Burgess S, Butterworth A, Thompson SG (2013) Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol 37(7):658–665. https://doi.org/10.1002/gepi.21758

    Article  PubMed  PubMed Central  Google Scholar 

  20. Bowden J, Davey Smith G, Burgess S (2015) Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol 44(2):512–525. https://doi.org/10.1093/ije/dyv080

    Article  PubMed  PubMed Central  Google Scholar 

  21. Zhao Q, Wang J, Hemani G, Bowden J, Small DS (2020) Statistical inference in two-sample summary-data Mendelian randomization using robust adjusted profile score. Ann Stat 48(3):1742–1769. https://doi.org/10.1214/19-AOS1866

    Article  Google Scholar 

  22. Verbanck M, Chen CY, Neale B, Do R (2018) Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet 50(5):693–698. https://doi.org/10.1038/s41588-018-0099-7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Bowden J, Davey Smith G, Haycock PC, Burgess S (2016) Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol 40(4):304–314. https://doi.org/10.1002/gepi.21965

    Article  PubMed  PubMed Central  Google Scholar 

  24. Greco MF, Minelli C, Sheehan NA, Thompson JR (2015) Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome. Stat Med 34(21):2926–2940. https://doi.org/10.1002/sim.6522

    Article  Google Scholar 

  25. Burgess S, Thompson SG (2015) Multivariable Mendelian randomization: the use of pleiotropic genetic variants to estimate causal effects. Am J Epidemiol 181(4):251–260. https://doi.org/10.1093/aje/kwu283

    Article  PubMed  PubMed Central  Google Scholar 

  26. Locke AE, Kahali B, Berndt SI, Justice AE, Pers TH, Day FR et al (2015) Genetic studies of body mass index yield new insights for obesity biology. Nature 518(7538):197–206. https://doi.org/10.1038/nature14177

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Lee JJ, Wedow R, Okbay A, Kong E, Maghzian O, Zacher M et al (2018) Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat Genet 50(8):1112–1121. https://doi.org/10.1038/s41588-018-0147-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Nielsen JB, Thorolfsdottir RB, Fritsche LG, Zhou W, Skov MW, Graham SE et al (2018) Biobank-driven genomic discovery yields new insight into atrial fibrillation biology. Nat Genet 50(9):1234–1239. https://doi.org/10.1038/s41588-018-0171-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Malik R, Chauhan G, Traylor M, Sargurupremraj M, Okada Y, Mishra A et al (2018) Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes. Nat Genet 50(4):524–537. https://doi.org/10.1038/s41588-018-0058-3

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Linli Z, Feng J, Zhao W, Guo S (2022) Associations between smoking and accelerated brain ageing. Prog Neuro-Psychopharmacol Biol Psychiatry 113:110471. https://doi.org/10.1016/j.pnpbp.2021.110471

    Article  Google Scholar 

  31. Swan GE, Lessov-Schlaggar CN (2007) The effects of tobacco smoke and nicotine on cognition and the brain. Neuropsychol Rev 17(3):259–273. https://doi.org/10.1007/s11065-007-9035-9

    Article  PubMed  Google Scholar 

  32. Rabinowitz JA, Campos AI, Ong JS, García-Marín LM, Alcauter S, Mitchell BL et al (2022) Shared genetic etiology between cortical brain morphology and tobacco, alcohol, and cannabis use. Cereb Cortex 32(4):796–807. https://doi.org/10.1093/cercor/bhab243

    Article  PubMed  Google Scholar 

  33. Weng JC, Chuang YC, Zheng LB, Lee MS, Ho MC (2022) Assessment of brain connectome alterations in male chronic smokers using structural and generalized q-sampling MRI. Brain Imaging Behav 16(4):1761–1775. https://doi.org/10.1007/s11682-022-00647-4

    Article  PubMed  Google Scholar 

  34. Liu H, Guan L, Nie Y, Li Q, Xue J, Yang Y et al (2022) Brain magnetic resonance imaging features of nicotine-dependent individuals and its correlation with polymorphisms of dopamine d receptor gene. Contrast Media Mol Imaging 2022:2296776. https://doi.org/10.1155/2022/2296776

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Logtenberg E, Overbeek MF, Pasman JA, Abdellaoui A, Luijten M, van Holst RJ et al (2022) Investigating the causal nature of the relationship of subcortical brain volume with smoking and alcohol use. Br J Psychiatry 221(1):377–385. https://doi.org/10.1192/bjp.2021.81

    Article  PubMed  Google Scholar 

  36. Topiwala A, Ebmeier KP, Maullin-Sapey T, Nichols TE (2022) Alcohol consumption and MRI markers of brain structure and function: cohort study of 25,378 UK Biobank participants. Neuroimage Clin 35:103066. https://doi.org/10.1016/j.nicl.2022.103066

    Article  PubMed  PubMed Central  Google Scholar 

  37. Carmody TP, Brischetto CS, Matarazzo JD, O'Donnell RP, Connor WE (1985) Co-occurrent use of cigarettes, alcohol, and coffee in healthy, community-living men and women. Health Psychol 4(4):323–335. https://doi.org/10.1037//0278-6133.4.4.323

    Article  CAS  PubMed  Google Scholar 

  38. Debette S, Markus HS (2010) The clinical importance of white matter hyperintensities on brain magnetic resonance imaging: systematic review and meta-analysis. BMJ 341:c3666. https://doi.org/10.1136/bmj.c3666

    Article  PubMed  PubMed Central  Google Scholar 

  39. Taylor-Bateman V, Gill D, Georgakis M, Malik R, Munroe P, Traylor M et al (2022) Cardiovascular risk factors and MRI markers of cerebral small vessel disease: a Mendelian randomization study. Neurology 98(4). https://doi.org/10.1212/WNL.0000000000013120

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Acknowledgements

We thank all the participants and staff of the GWAS dataset we used.

Author information

Author notes

  1. Wenjuan Lin and Lisheng Zhu contributed equally to this study.

Authors and Affiliations

  1. Department of Cardiology, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang, China

    Wenjuan Lin & Yunlong Lu

  2. Cardiovascular Key Lab of Zhejiang Province, Department of Cardiology, The Second Affiliated Hospital, Zhejiang University School of Medicine, Zhejiang University, Hangzhou, China

    Lisheng Zhu

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  1. Wenjuan Lin
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Contributions

Wenjuan Lin and Yunlong Lu contributed to the concept and design of the work. Wenjuan Lin and Lisheng Zhu contributed to the data processing, integrative analyses, writing manuscript, and generating figures and tables. Yunlong Lu critically revised the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Yunlong Lu.

Ethics declarations

Ethics approval and consent to participate

Our study is based on publicly available data sources, and no identifiable patient data were collected. Ethical approval and informed consent were available in the original genomic studies.

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Not applicable.

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The authors declare no competing interests.

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Tables S1–S6 (PDF 1254 kb)

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Lin, W., Zhu, L. & Lu, Y. Association of smoking with brain gray and white matter volume: a Mendelian randomization study. Neurol Sci 44, 4049–4055 (2023). https://doi.org/10.1007/s10072-023-06854-1

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