Principal Investigator: Elliot Stein
Funding Mechanism: National Institutes of Health-Intramural
ID number: 252400
Award Date: 10/1/2012
Institution: National Institute on Drug Abuse (NIDA)
Emerging evidence suggests a biological basis for nicotine addiction; however, no consensus biomarkers that can objectively measure nicotine addiction severity, follow the trajectory of developing addition, or predict outcome for tobacco users exist. The goal of this project is to develop a quantitative, genetically-informed, brain-based nicotine addiction biomarker that predicts smoking and smokeless tobacco adverse health outcomes, quantifies addiction severity and reflects the addiction potential of alternative tobacco products. Study aims are: (1) to determine whether support vector machine (SVM)/network pattern classifiers identify ex-smokers as “current smokers” or “never smokers,” and whether SVM/network classification can separate non-treatment seeking smokers from treatment seekers; (2) to determine whether switching cigarette smokers to e-cigarettes yields a unique biomarker pattern that distinguishes them from smokers, ex-smokers and nonsmokers; (3) to determine whether changes in biomarkers predict successful tobacco use cessation; and (4) to determine the time course of biomarker changes as a function of abstinence duration. Subjects will include treatment-seeking smokers and three comparison groups: ex-smokers, non-treatment-seeking smokers, and healthy controls aged 18-55. The biomarker will be created using neuroimaging, genotyping and epigenetic data with multivariate feature selection techniques. Researchers will first engage subjects in a 12-week monitored cessation phase (with e-cigarettes substituting for tobacco cigarettes), which will allow an assessment of addiction severity based on withdrawal symptoms; anatomical and functional measures will be taken before a quit attempt and post-quit at days 2 and 7 and again at 3, 6 and 12 months. Analysis of neuroanatomical, functional activation and resting data, informed by genetic markers, will classify cigarette smokers by addiction severity, differentiate traditional smokers from alternative tobacco users, and describe brain circuits that follow dependence severity and predict outcome success or failure.