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June 23, 2008
Success Quitting Smoking Based Partly on Genetics
Smokers' genetic make-up influences their chances for successful quitting. According to a new study, it may also help determine which treatment is most likely to help them quit.
National smoking rates have been in decline. Among the many reasons are the rising cost of cigarettes and the ever-decreasing social acceptance of smoking. Yet some people's attempts to quit are a constant and frustrating battle. Several cessation aids are available to smokers, including various nicotine replacement therapies like nicotine-containing patches, gum or nasal spray, and new prescription medications like bupropion (Zyban). Each method seems to help some people but not others; the reasons for this are complex and elusive.
A research team led by Dr. George Uhl of NIH's National Institute on Drug Abuse (NIDA) searched across the genome, using an approach called genome-wide association study, for genetic variations among people trying to quit smoking. Their goal was to pinpoint genetic differences between those who successfully quit and those who didn't. The study included both people using nicotine replacement therapy and those taking Zyban. NIH's National Cancer Institute (NCI), the Pennsylvania Department of Health and GlaxoSmithKline, Inc provided additional funding.
The researchers reported on June 6, 2008, in Archives of General Psychiatry the identification of a set of genetic variations that could be used to predict whether a smoker has a better chance of quitting using nicotine replacement therapies or Zyban.
Of the genes identified in the study, many were found to influence brain function. Some are thought to play roles in addiction and memory formation. Further studies will be needed, however, to determine their specific contributions to both nicotine addiction and successful quitting.
“These findings suggest that we may be able to improve the success rate for smoking cessation by using the results of simple DNA tests,” Dr. Uhl said. One day, genetic testing might help doctors prescribe the treatment that is most likely to work for each individual patient.