Despite these successes, attempts to individualize therapies using genetics have been limited by inconsistent results. Existing evidence suggests that smoking cessation prediction is influenced by not just genetics, but also by patient characteristics (gender, age of onset, nicotine dependence, and race/ethnicity) and treatment protocol (clinician interaction; type, number and length of counseling sessions; and pharmacotherapy). Biomarkers, specifically the nicotine metabolite ratio (NMR, the ratio of 3’-trans-hydroxycotinine/cotinine), have also been shown to influence pharmacological therapies success [42, 43, 45]. Advancing the understanding of complex relationships among multiple genetic and environmental factors, smoking behavior, nicotine dependence, and treatment outcomes requires large sample sizes. Current clinical studies are challenged by relatively small sample sizes in treatment arms; thus, pooling data across clinical trials and observational studies is essential, but complicated by heterogeneity in trial study designs, genotyping technologies, and genetic marker content.
