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Biomarkers to optimize the treatment of nicotine dependence.

ABSTRACT: The application of genomic medicine to the treatment of nicotine dependence holds great promise for revitalizing the steady decline in smoking rates witnessed in the USA over the past several decades. This paper examines the current knowledge base concerning the use of biomarkers to guide the selection of nicotine dependence treatments. First, we review the neurobiology of nicotine dependence and present evidence that supports its heritability. We then discuss the various studies of pharmacokinetic and pharmacodynamic genes related to therapeutic response. Current evidence suggests that biomarkers of genetic variability in both nicotine metabolism, referred to as the nicotine metabolite ratio, and dopamine genotypes may be useful for guiding treatment selection for nicotine dependence. Barriers to the translation of this research to clinical practice are discussed, as are directions for future research.

SUBMITTER: Schnoll RA

PROVIDER: S-EPMC3285251 | biostudies-literature | 2011 Dec

REPOSITORIES: biostudies-literature

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Biomarkers to optimize the treatment of nicotine dependence.

Schnoll Robert A RA Leone Frank T FT

Biomarkers in medicine 20111201 6

The application of genomic medicine to the treatment of nicotine dependence holds great promise for revitalizing the steady decline in smoking rates witnessed in the USA over the past several decades. This paper examines the current knowledge base concerning the use of biomarkers to guide the selection of nicotine dependence treatments. First, we review the neurobiology of nicotine dependence and present evidence that supports its heritability. We then discuss the various studies of pharmacokine ...[more]

PMID: 22103610

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Project description:IntroductionThe nicotine metabolite ratio and nicotine equivalents are measures of metabolism rate and intake. Genome-wide prediction of these nicotine biomarkers in multiethnic samples will enable tobacco-related biomarker, behavioral, and exposure research in studies without measured biomarkers.Aims and methodsWe screened genetic variants genome-wide using marginal scans and applied statistical learning algorithms on top-ranked genetic variants, age, ethnicity and sex, and, in additional modeling, cigarettes per day (CPD), (in additional modeling) to build prediction models for the urinary nicotine metabolite ratio (uNMR) and creatinine-standardized total nicotine equivalents (TNE) in 2239 current cigarette smokers in five ethnic groups. We predicted these nicotine biomarkers using model ensembles and evaluated external validity using dependence measures in 1864 treatment-seeking smokers in two ethnic groups.ResultsThe genomic regions with the most selected and included variants for measured biomarkers were chr19q13.2 (uNMR, without and with CPD) and chr15q25.1 and chr10q25.3 (TNE, without and with CPD). We observed ensemble correlations between measured and predicted biomarker values for the uNMR and TNE without (with CPD) of 0.67 (0.68) and 0.65 (0.72) in the training sample. We observed inconsistency in penalized regression models of TNE (with CPD) with fewer variants at chr15q25.1 selected and included. In treatment-seeking smokers, predicted uNMR (without CPD) was significantly associated with CPD and predicted TNE (without CPD) with CPD, time-to-first-cigarette, and Fagerström total score.ConclusionsNicotine metabolites, genome-wide data, and statistical learning approaches developed novel robust predictive models for urinary nicotine biomarkers in multiple ethnic groups. Predicted biomarker associations helped define genetically influenced components of nicotine dependence.ImplicationsWe demonstrate development of robust models and multiethnic prediction of the uNMR and TNE using statistical and machine learning approaches. Variants included in trained models for nicotine biomarkers include top-ranked variants in multiethnic genome-wide studies of smoking behavior, nicotine metabolites, and related disease. Association of the two predicted nicotine biomarkers with Fagerström Test for Nicotine Dependence items supports models of nicotine biomarkers as predictors of physical dependence and nicotine exposure. Predicted nicotine biomarkers may facilitate tobacco-related disease and treatment research in samples with genomic data and limited nicotine metabolite or tobacco exposure data.

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Biomarkers to optimize the treatment of nicotine dependence.

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Biomarkers to optimize the treatment of nicotine dependence.

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