Journal of Neuroimmune Pharmacology

, Volume 13, Issue 4, pp 430–437 | Cite as

Most Current Smokers Desire Genetic Susceptibility Testing and Genetically-Efficacious Medication

  • Ami Chiu
  • Sarah Hartz
  • Nina Smock
  • Jingling Chen
  • Amaan Qazi
  • Jeffrey Onyeador
  • Alex T. Ramsey
  • Laura J. Bierut
  • Li-Shiun Chen


The clinical translation of genetic research on nicotine dependence and treatment response requires acceptance of genetic testing by smokers. This study determines (1) which current smokers are receptive to genetic susceptibility testing for nicotine dependence and (2) to what potential extent smokers motivated to quit desire to take smoking cessation medication when hypothetical genetic results predict their pharmacogenetic medication response. Current smokers from a genetic nicotine dependence study (n = 1306) and an ongoing smoking cessation trial (n = 209) were surveyed on their hypothetical interest in seeing genetic testing results related to risk of nicotine dependence. Most current smokers (84.8%) reported high interest in receiving genetic testing results. Factors associated with high interest included age ≥ 40 years, having a college degree, and a positive medical history (≥1 medical condition). In the ongoing smoking cessation trial, current smokers motivated to quit (n = 474) were surveyed on their desire to take smoking cessation medication given hypothetical below or above average pharmacogenetic responses to the medication. When the hypothetical medication response changed from below to above average, significantly more smokers reported a desire to take medication (from 61.0% to 97.5%, p < .0001). These preliminary findings suggest that genetic testing for personalized smoking cessation treatment is well-received by smokers and that a positive hypothetical pharmacogenetic response increases desire to take smoking cessation medication among current smokers motivated to quit.

Graphical abstract


Interest in genetic testing Smoking cessation Genetic predisposition testing Pharmacogenomic testing Precision medicine 



Data reported in this publication were supported by the National Cancer Institute under Award Number P01CA089392 and by the National Institute on Drug Abuse under Award Number R01DA038076 (LSC). Dr. Hartz is supported by National Institutes of Health (NIH) grants R21AA024888, R21DA044744, and UL1TR002345. Dr. Ramsey is supported by NIH grant K12DA041449 and a grant from the Foundation for Barnes-Jewish Hospital. Dr. Bierut is supported by NIH grants R01DA036583, UL1TR002345, and P30CA091842. This content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Compliance with Ethical Standards

Ethical Approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Conflict of Interest

LJB is listed as an inventor on Issued U.S. Patent 8,080,371 “Markers for Addiction” covering the use of certain SNPs in determining the diagnosis, prognosis, and treatment of addiction, and served as a consultant for the pharmaceutical company Pfizer in 2008. The remaining authors declare no conflict of interest.

Supplementary material

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  1. Babb S, Malarcher A, Schauer G et al (2017) Quitting smoking among adults - United States, 2000-2015. MMWR Morb Mortal Wkly Rep 65:1457–1464. CrossRefPubMedGoogle Scholar
  2. Caponnetto P, Polosa R (2008) Common predictors of smoking cessation in clinical practice. Respir Med 102:1182–1192. CrossRefPubMedGoogle Scholar
  3. Chen L-S, Baker TB, Grucza R et al (2012) Dissection of the phenotypic and genotypic associations with nicotinic dependence. Nicotine Tob Res 14:425–433. CrossRefPubMedGoogle Scholar
  4. Chen LS, Horton A, Bierut L (2018) Pathways to precision medicine in smoking cessation treatments. Neurosci Lett 669:83–92. CrossRefPubMedGoogle Scholar
  5. de Viron S, Van der Heyden J, Ambrosino E et al (2012) Impact of genetic notification on smoking cessation: systematic review and pooled-analysis. PLoS One 7:e40230. CrossRefPubMedPubMedCentralGoogle Scholar
  6. Giordimaina AM, Sheldon JP, Petty EM (2014) Anticipated motivation for genetic testing among smokers, nonsmokers, and former smokers: An exploratory qualitative study of decision making. Public Health Genomics 17:228–239. CrossRefPubMedGoogle Scholar
  7. Hartz SM, Olfson E, Culverhouse R et al (2015) Return of individual genetic results in a high-risk sample: enthusiasm and positive behavioral change. Genet Med 17:374–379. CrossRefPubMedGoogle Scholar
  8. Hartz SM, Quan T, Ibiebele A et al (2016) The significant impact of education, poverty, and race on Internet-based research participant engagement. Genet Med 19:240–243. CrossRefPubMedPubMedCentralGoogle Scholar
  9. Heatherton TF, Kozlowski LT, Frecker RC, Fagerstrom KO (1991) The Fagerstrom test for nicotine dependence: a revision of the Fagerstrom Tolerance Questionnaire. Br J Addict 86:1119–1127. CrossRefPubMedGoogle Scholar
  10. Lipkus IM, Schwartz-Bloom R, Kelley MJ, Pan W (2015) A preliminary exploration of college smokers’ reactions to nicotine dependence genetic susceptibility feedback. Nicotine Tob Res 17:337–343. CrossRefPubMedGoogle Scholar
  11. Olfson E, Hartz S, Carere DA et al (2016) Implications of personal genomic testing for health behaviors: the case of smoking. Nicotine Tob Res 18:2273–2277. CrossRefPubMedPubMedCentralGoogle Scholar
  12. Raupach T, Brown J, Herbec A et al (2014) A systematic review of studies assessing the association between adherence to smoking cessation medication and treatment success. Addiction 109:35–43. CrossRefPubMedGoogle Scholar
  13. Schuit E, Panagiotou OA, Munafò MR et al (2017) Pharmacotherapy for smoking cessation: effects by subgroup defined by genetically informed biomarkers. Cochrane Database Syst Rev 9:CD011823. CrossRefPubMedGoogle Scholar
  14. Smerecnik C, Grispen JEJ, Quaak M (2012) Effectiveness of testing for genetic susceptibility to smoking-related diseases on smoking cessation outcomes: a systematic review and meta-analysis. Tob Control 21:347–354. CrossRefPubMedGoogle Scholar
  15. Thorgeirsson TE, Gudbjartsson DF, Surakka I et al (2010) Sequence variants at CHRNB3–CHRNA6 and CYP2A6 affect smoking behavior. Nat Genet 42:448–453. CrossRefPubMedPubMedCentralGoogle Scholar
  16. U.S. Department of Health and Human Services (2014) The health consequences of smoking—50 years of progress. A report of the Surgeon General, AtlantaGoogle Scholar
  17. Vangeli E, Stapleton J, Smit ES et al (2011) Predictors of attempts to stop smoking and their success in adult general population samples: A systematic review. Addiction 106:2110–2121. CrossRefPubMedGoogle Scholar
  18. Wells QS, Freiberg MS, Greevy RA et al (2017) Nicotine metabolism-informed care for smoking cessation: a pilot precision RCT. Nicotine Tob Res:1–8.
  19. West R, Raw M, McNeill A et al (2015) Health-care interventions to promote and assist tobacco cessation: A review of efficacy, effectiveness and affordability for use in national guideline development. Addiction 110:1388–1403. CrossRefPubMedPubMedCentralGoogle Scholar
  20. World Health Organization (2012) WHO Global Report: Mortality attributable of tobacco. World Health Organization, GenevaGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of PsychiatryWashington University School of MedicineSt. LouisUSA

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