Skip to main content
SpringerLink
Log in

Smoking may increase the usage of antidepressant: evidence from genomic perspective analysis

  • Original Paper
  • Published:
European Archives of Psychiatry and Clinical Neuroscience Aims and scope Submit manuscript

Abstract

This study uses the two-sample Mendelian randomization (TSMR) method to explore the causal relationships between smoking initiation (SMKI), never smoking (NSMK), past tobacco smoking (PTSMK), and the usage of antidepressants (ATD). Single-nucleotide polymorphisms (SNPs) with genome-wide significance (P < 5E−08) related to SMKI, NSMK, and PTSMK were selected from the genome-wide association study (GWAS) database as instrumental variables (IVs). The main method, inverse variance weighted (IVW), was utilized to investigate the causal relationship. The results demonstrated a positive causal relationship between SMKI and ATD use, where SMKI leads to an increase in ATD use. Conversely, NSMK and PTSMK showed a negative causal relationship with ATD use, meaning that NSMK and PTSMK lead to a reduction in ATD use. Additionally, sensitivity analysis showed that the results of this study were robust and reliable. Using the TSMR method and from a genetic perspective, this study found that SMKI leads to an increase in ATD use, while NSMK and PTSMK reduce ATD use.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Data availability

The dataset generated during and analyzed during the current study are available from the MR Base database (http://www.mrbase.org/).

References

  1. Richardson S, McNeill A, Brose LS (2019) Smoking and quitting behaviours by mental health conditions in Great Britain (1993–2014). Addict Behav 90:14–19

    Article  PubMed  PubMed Central  Google Scholar 

  2. Burki TK (2021) WHO releases latest report on the global tobacco epidemic. Lancet Oncol 22(9):1217

    Article  PubMed  Google Scholar 

  3. Mao Y, Zhang N, Liu J et al (2019) A systematic review of depression and anxiety in medical students in China. BMC Med Educ 19(1):1–13

    Article  CAS  Google Scholar 

  4. Shorey S, Ng ED, Wong CHJ (2022) Global prevalence of depression and elevated depressive symptoms among adolescents: a systematic review and meta-analysis. Br J Clin Psychol 61(2):287–305

    Article  PubMed  Google Scholar 

  5. Centers for Disease Control and Prevention (CDC) (2008) Smoking-attributable mortality, years of potential life lost, and productivity losses—United States, 2000–2004. MMWR Morb Mortal Wkly Rep 57(45):1226–1228

    Google Scholar 

  6. World Health Organization. Regional Office for Europe (2019) European tobacco use: trends report 2019. World Health Organization. Regional Office for Europe. https://apps.who.int/iris/handle/10665/346817

  7. Lasser K, Boyd JW, Woolhandler S et al (2000) Smoking and mental illness: a population-based prevalence study. JAMA 284(20):2606–2610

    Article  CAS  PubMed  Google Scholar 

  8. Oliveira P, Ribeiro J, Donato H et al (2017) Smoking and antidepressants pharmacokinetics: a systematic review. Ann Gen Psychiatry 16:1–8

    Article  CAS  Google Scholar 

  9. Korhonen T, Loukola A, Wedenoja J et al (2014) Role of nicotine dependence in the association between the dopamine receptor gene DRD3 and major depressive disorder. PLoS One 9(6):e98199

    Article  PubMed  PubMed Central  Google Scholar 

  10. Broms U, Wedenoja J, Largeau MR et al (2012) Analysis of detailed phenotype profiles reveals CHRNA5-CHRNA3-CHRNB4 gene cluster association with several nicotine dependence traits. Nicotine Tob Res 14(6):720–733

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Korhonen T, Koivumaa-Honkanen H, Varjonen J et al (2011) Cigarette smoking and dimensions of depressive symptoms: longitudinal analysis among Finnish male and female twins. Nicotine Tob Res 13(4):261–272

    Article  CAS  PubMed  Google Scholar 

  12. Hahad O, Beutel M, Gilan DA et al (2022) The association of smoking and smoking cessation with prevalent and incident symptoms of depression, anxiety, and sleep disturbance in the general population. J Affect Disord 313:100–109

    Article  PubMed  Google Scholar 

  13. Kagabo R, Gordon AJ, Okuyemi K (2020) Smoking cessation in inpatient psychiatry treatment facilities: a review. Addict Behav Rep 11:100255

    PubMed  PubMed Central  Google Scholar 

  14. Wills L, Ables JL, Braunscheidel KM et al (2022) Neurobiological mechanisms of nicotine reward and aversion. Pharmacol Rev 74(1):271–310

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Arias HR, Targowska-Duda KM, García-Colunga J et al (2021) Is the antidepressant activity of selective serotonin reuptake inhibitors mediated by nicotinic acetylcholine receptors? Molecules 26(8):2149

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Husky MM, Mazure CM, Paliwal P et al (2008) Gender differences in the comorbidity of smoking behavior and major depression. Drug Alcohol Depend 93(1–2):176–179

    Article  PubMed  Google Scholar 

  17. Spring B, Pingitore R, McChargue DE (2003) Reward value of cigarette smoking for comparably heavy smoking schizophrenic, depressed, and nonpatient smokers. Am J Psychiatry 160(2):316–322

    Article  PubMed  Google Scholar 

  18. Royal College of Psychiatrists (2013) Smoking and Mental Health. Royal College of Physicians, London

    Google Scholar 

  19. Korhonen T, Ranjit A, Tuulio-Henriksson A et al (2017) Smoking status as a predictor of antidepressant medication use. J Affect Disord 207:221–227

    Article  PubMed  Google Scholar 

  20. Chéron-Launay M, Le Faou AL, Sévilla-Dedieu C et al (2011) Smoking and the consumption of antidepressants, anxiolytics and hypnotic drugs: results of a large, French epidemiological study in 2005. Addict Behav 36(7):743–748

    Article  PubMed  Google Scholar 

  21. Skrivankova VW, Richmond RC, Woolf BAR et al (2021) Strengthening the reporting of observational studies in epidemiology using mendelian randomisation (STROBE-MR): explanation and elaboration. BMJ 375:n2233

    Article  PubMed  PubMed Central  Google Scholar 

  22. Hemani G, Zheng J, Elsworth B et al (2018) The MR-Base platform supports systematic causal inference across the human phenome. Elife 7:e34408

    Article  PubMed  PubMed Central  Google Scholar 

  23. Zhu H, Sun Y, Guo S et al (2023) Causal relationship between sex hormone-binding globulin and major depression: A Mendelian randomization study. Acta Psychiatr Scand 148(5):426–436

    Article  CAS  PubMed  Google Scholar 

  24. Zhu H, Lu R, Zhou Q et al (2023) Relationship between sphingomyelin and risk of Alzheimer’s disease: a bidirectional mendelian randomization study. J Alzheimer’s Dis Rep 7(1):1289–1297

    Article  Google Scholar 

  25. Du Z, Guo S, Sun Y et al (2023) Causal relationships between dietary habits and five major mental disorders: a two-sample Mendelian randomization study. J Affect Disord 340:607–615

    Article  PubMed  Google Scholar 

  26. Ji Y, Du Z, Zheng K et al (2023) Bidirectional causal association between ischemic stroke and five mental disorders. Acta Psychiatr Scand 148(4):359–367

    Article  PubMed  Google Scholar 

  27. Sekula P, Fabiola Del Greco M, Pattaro C et al (2016) Mendelian randomization as an approach to assess causality using observational data. J Am Soc Nephrol 27(11):3253

    Article  PubMed  PubMed Central  Google Scholar 

  28. Liu M, Jiang Y, Wedow R 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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Bowden J, Del Greco MF, Minelli C et al (2016) Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic. Int J Epidemiol 45(6):1961–1974

    PubMed  PubMed Central  Google Scholar 

  30. Burgess S, Dudbridge F, Thompson SG (2016) Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods. Stat Med 35(11):1880–1906

    Article  PubMed  Google Scholar 

  31. Bowden J, Del Greco MF, Minelli C et al (2017) A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization. Stat Med 36(11):1783–1802

    Article  PubMed  PubMed Central  Google Scholar 

  32. Bowden J, Davey Smith G, Haycock PC et al (2016) Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator. Genet Epidemiol 40(4):304–314

    Article  PubMed  PubMed Central  Google Scholar 

  33. Yuan S, Carter P, Mason AM et al (2022) Genetic liability to rheumatoid arthritis in relation to coronary artery disease and stroke risk. Arthritis Rheumatol 74(10):1638–1647

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Nolte IM (2020) Metasubtract: an R-package to analytically produce leave-one-out meta-analysis GWAS summary statistics. Bioinformatics 36(16):4521–4522

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Rognli EB, Bramness JG, von Soest T (2022) Smoking in early adulthood is prospectively associated with prescriptions of antipsychotics, mood stabilizers, antidepressants and anxiolytics. Psychol Med 52(14):3241–3250

    Article  PubMed  Google Scholar 

  36. Zhao M, Ma J, Li M et al (2021) Cytochrome P450 enzymes and drug metabolism in humans. Int J Mol Sci 22(23):12808

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Bousman CA, Stevenson JM, Ramsey LB et al (2023) Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline for CYP2D6, CYP2C19, CYP2B6, SLC6A4, and HTR2A genotypes and serotonin reuptake inhibitor antidepressants. Clin Pharmacol Ther 114:51–68

    Article  CAS  PubMed  Google Scholar 

  38. Protti M, Mandrioli R, Marasca C et al (2020) New-generation, non-SSRI antidepressants: drug–drug interactions and therapeutic drug monitoring. Part 2: NaSSAs, NRIs, SNDRIs, MASSAs, NDRIs, and others. Med Res Rev 40(5):1794–1832

    Article  CAS  PubMed  Google Scholar 

  39. Austin-Zimmerman I, Wronska M, Wang B et al (2021) The influence of CYP2D6 and CYP2C19 genetic variation on diabetes mellitus risk in people taking antidepressants and antipsychotics. Genes 12(11):1758

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Hiemke C, Bergemann N, Clement HW et al (2018) Consensus guidelines for therapeutic drug monitoring in neuropsychopharmacology: update 2017. Pharmacopsychiatry 51(01/02):9–62

    Article  CAS  PubMed  Google Scholar 

  41. Al-Arifi MN, Maayah ZH, AlShamrani AA et al (2012) Impact of cigarette smoke exposure on the expression of cardiac hypertrophic genes, cytochrome P450 enzymes, and oxidative stress markers in rats. J Toxicol Sci 37(5):1083–1090

    Article  CAS  PubMed  Google Scholar 

  42. Scherf-Clavel M, Samanski L, Hommers LG et al (2019) Analysis of smoking behavior on the pharmacokinetics of antidepressants and antipsychotics: evidence for the role of alternative pathways apart from CYP1A2. Int Clin Psychopharmacol 34(2):93–100

    Article  PubMed  Google Scholar 

  43. Desai HD, Seabolt J, Jann MW (2001) Smoking in patients receiving psychotropic medications: a pharmacokinetic perspective. CNS Drugs 15:469–494

    Article  CAS  PubMed  Google Scholar 

  44. Augustin M, Schoretsanitis G, Hiemke C et al (2018) Differences in duloxetine dosing strategies in smoking and nonsmoking patients: therapeutic drug monitoring uncovers the impact on drug metabolism. J Clin Psychiatry 79(5):17m12086

    Article  PubMed  Google Scholar 

  45. Zevin S, Benowitz NL (1999) Drug interactions with tobacco smoking: an update. Clin Pharmacokinet 36:425–438

    Article  CAS  PubMed  Google Scholar 

  46. Wium-Andersen MK, Ørsted DD, Nielsen SF et al (2013) Elevated C-reactive protein levels, psychological distress, and depression in 73 131 individuals. JAMA Psychiat 70(2):176–184

    Article  CAS  Google Scholar 

  47. Barbosa ML, de Meneses AAPM, de Aguiar RPS et al (2020) Oxidative stress, antioxidant defense and depressive disorders: a systematic review of biochemical and molecular markers. Neurol Psychiatry Brain Res 36:65–72

    Article  Google Scholar 

Download references

Funding

The work is supported by the National Natural Science Foundation of China (82104244), Wuxi Municipal Science and Technology Bureau (K20231039 and K20231049), Top Talent Support Program for young and middle-aged people of Wuxi Health Committee (HB2023088), Scientific Research Program of Wuxi Health Commission (Q202101 and ZH202110), Wuxi Taihu Talent Project (WXTTP2021), Medical Key Discipline Program of Wuxi Health Commission (FZXK2021012).

Author information

Author notes

  1. Yucai Qu and Zhiqiang Du have contributed equally to this work and are co-first authors.

Authors and Affiliations

  1. Mental Health Center of Jiangnan University, Wuxi Central Rehabilitation Hospital, Wuxi, 214151, Jiangsu, China

    Yucai Qu, Zhiqiang Du, Yuan Shen, Qin Zhou, Zhenhe Zhou, Ying Jiang & Haohao Zhu

Authors
  1. Yucai Qu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhiqiang Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuan Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Qin Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhenhe Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Ying Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Haohao Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Ying Jiang, Zhiqiang Du, Haohao Zhu conceived the study; Ying Jiang, Haohao Zhu, Qin Zhou, Yucai Qu and Yuan Shen collected the report; Zhiqiang Du, Zhenhe Zhou and Haohao Zhu wrote the manuscript and edited the manuscript. All authors have approved publishment of the manuscript.

Corresponding authors

Correspondence to Zhenhe Zhou, Ying Jiang or Haohao Zhu.

Ethics declarations

Conflict of interest

The authors declare no conflicts of interest.

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Qu, Y., Du, Z., Shen, Y. et al. Smoking may increase the usage of antidepressant: evidence from genomic perspective analysis. Eur Arch Psychiatry Clin Neurosci (2024). https://doi.org/10.1007/s00406-024-01802-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s00406-024-01802-2

Keywords

Search

Navigation