For the past 30 years, research examining predictors of successful smoking 
cessation treatment response has focused primarily on clinical variables, such 
as levels of tobacco dependence, craving, and self-efficacy. However, recent 
research has begun to determine biomarkers (such as genotype, nicotine and 
metabolite levels, and brain imaging findings) that may have utility in 
predicting smoking cessation. For genotype, genes associated with nicotinic 
acetylcholine receptors (nAChRs) and related proteins have been found to predict 
response to first-line medications (e.g. nicotine replacement therapy [NRT], 
bupropion, or varenicline) or quitting over time without a controlled treatment 
trial. For nicotine and metabolite levels, function of the cytochrome P450 2A6 
liver enzyme, which can be assessed with the nicotine metabolite ratio or via 
genotype, has been found to predict response, with slow nicotine metabolizers 
having less severe nicotine dependence and a greater likelihood of quitting with 
NRT than normal metabolizers. For brain imaging, decreased activation of brain 
regions associated with emotion regulation and increased connectivity in emotion 
regulation networks, increased responsiveness to pleasant cues, and altered 
activation with the Stroop effect have been found in smokers who quit with the 
first-line medications listed above or counseling. In addition, our group 
recently demonstrated that lower pre-treatment brain nAChR density is associated 
with a greater chance of quitting smoking with NRT or placebo. Several of these 
studies found that specific biomarkers may provide additional information for 
predicting response beyond subjective symptom or rating scale measures, thereby 
giving an initial indication that biomarkers may, in the future, be useful for 
guiding smoking cessation treatment intensity, duration, and type.