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DMET Genes, Nicotine Metabolism and Prospective Abstinence

This study (DA033813; PI: Andrew W Bergen; PMID:26132489) includes samples from two laboratory studies of nicotine metabolism. The Pharmacokinetics of Nicotine Metabolism in Twins study (PKTWIN; PI: Gary E Swan; PMID: 15527659) was based on recruitment from a twin registry (PMID: 23084148). The Integrated Research Project on Tobacco Use and Dependence (IRP; PI: Gary E Swan; PMID: 14578134) was based on recruitment from a pedigree-based longitudinal study of risk factors for substance use, the Smoking in Families study (SMOFAM; DA03706; PI: Hy Hops). These two laboratory studies (PKTWIN and IRP/SMOFAM) served as the Stage I dataset to interrogate Drug Metabolizing Enzyme and Transporter genes with a targeted SNP array for association with the Nicotine Metabolite Ratio (NMR, ratio of trans-3'-hydroxycotinine and cotinine), an established biomarker of nicotine metabolism. In addition to the laboratory studies, samples from eight RCTs (PMID: 23249876) with the NMR and smoking-related measures used to test SNPs identified in Stage I (PMID: 26132489). In a third stage, a lung cancer meta-analysis database (PMID: 24880342) was used to assess association of SNPs identified in Stage II with lung cancer.

The objectives of the study were to identify novel genes and SNPs contributing to nicotine metabolism (Stage I), and to validate PK SNPs associated with the NMR from individuals participating in a clinical laboratory protocol with the NMR obtained from treatment-seeking smokers, and then to investigate association with prospective smoking cessation (Stage II). This study built upon existing studies of nicotine metabolism and randomized trials of smoking cessation therapies. Enhanced knowledge of the genes influencing nicotine metabolism and prospective abstinence may help personalize smoking cessation treatment and risk assessment for smoking-related diseases.

For Stage I, both subject [fixed-dose NMR, covariates (age, BMI, ethnicity, sex, smoking status, and hormone use), and pedigree relationships] and sample (common DMET SNP genotype, genotyping quality control) data are available in this accession. The analysis protocol, quality control summaries, summary genotype, summary phenotype, and analysis results are available for Stage I, II and III samples (PMID: 26132489). Extensive discussion of the prior CYP2A6 association literature with the NMR, abstinence, smoking heaviness and lung cancer risk is available (PMID: 26132489).

The NMR has previously been associated with CYP2A6 activity, response to smoking cessation treatments, and cigarette consumption. We searched for drug metabolizing enzyme and transporter (DMET) gene variation associated with the NMR and prospective abstinence in 2,946 participants of laboratory studies of nicotine metabolism and of clinical trials of smoking cessation therapies. Stage I was a meta-analysis of the association of 507 common single nucleotide polymorphisms (SNPs) at 173 DMET genes with the NMR in 449 participants of two laboratory studies. Nominally significant associations were identified in ten genes after adjustment for intragenic SNPs; CYP2A6 and two CYP2A6 SNPs attained experiment-wide significance adjusted for correlated SNPs (CYP2A6 PACT=4.1E-7, rs4803381 PACT=4.5E-5, rs1137115, PACT=1.2E-3). Stage II was mega-regression analyses of 10 DMET SNPs with pretreatment NMR and prospective abstinence in up to 2,497 participants from eight trials. rs4803381 and rs1137115 SNPs were associated with pretreatment NMR at genome-wide significance. In post-hoc analyses of CYP2A6 SNPs, we observed nominally significant association with: abstinence in one pharmacotherapy arm; cigarette consumption among all trial participants; and lung cancer in four case:control studies. CYP2A6 minor alleles were associated with reduced NMR, CPD, and lung cancer risk. We confirmed the major role that CYP2A6 plays in nicotine metabolism, and made novel findings with respect to genome-wide significance and associations with CPD, abstinence and lung cancer risk. Additional multivariate analyses with patient variables and genetic modeling will improve prediction of nicotine metabolism, disease risk and smoking cessation treatment prognosis.