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Biomedical sciences publications May 1, 2014

Nicotine Dependence as a Moderator of Genetic Influences on Smoking Cessation Treatment Outcome

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Leventhal, A. M., Lee, W., Bergen, A. W., Swan, G. E., Tyndale, R. F., Lerman, C., & Conti, D. V. (2014). Nicotine dependence as a moderator of genetic influences on smoking cessation treatment outcome. Drug and Alcohol Dependence, 138, 109-117. doi: 10.1016/j.drugalcdep.2014.02.016

Abstract

Genetic influences on smoking cessation treatment outcome may be affected by pretreatment patient characteristics. Nicotine dependence is arguably the most salient clinical factor in smoking cessation. Methods: In this secondary analysis of clinical trial data (N = 793), we examined nicotine dependence severity as a moderator of the effects of 1198 single nucleotide polymorphisms (SNPs) in 53 biologically-relevant gene regions on smoking cessation outcomes. P-values were adjusted to account for multiple correlated SNPs within a gene region; corrected system-wide significance was 5 × 10−4. Results: SNP × nicotine dependence interactions reached region-wide significance for several SNPs in the Dopamine Beta Hydroxylase (DBH) locus (0.0005 < Adjusted-P < 0.05), including rs1541333, which reached system-wide significance for predicting end of treatment (EOT) abstinence (Adjusted-P = 0.0004). A haplotype including 6 DBH SNPs predicted abstinence at EOT (OR = 1.7, P = 0.001) and 6-month follow-up (OR = 1.6, P = 0.008) in those with high nicotine dependence (n = 526) but not in those with low dependence (n = 227). The DBH signal observed here may be distinct from a previously reported genome-wide significant signal for former smoking status and from the principal haplotype associated with plasma dopamine beta-hydroxylase activity. A haplotype within the Chromosome 15 Nicotinic Acetylcholine Receptor gene region predicted abstinence at EOT in those with high (OR = 2.0, P = 0.0004) but not low (P = 0.6) dependence in post hoc analyses. Conclusions: Considering pre-treatment nicotine dependence level may optimize the prediction of genetic influences on cessation outcomes. If replicated, results like these may inform prognosticative genomic screening panels designed to identify smokers at high risk of relapse when coupled with severe nicotine dependence.

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