Chunk #28 — Results — The majority of phenotypic variability following oral nicotine administration is accounted for by identified CYP2A6 polymorphisms
Optimum multivariate regression models for both metrics include the same variables, the CYP2A6 alleles *2,*4,*12,*1D-Y351H,*1A(51A), and *9, gender and current smoking status. Fitted values derived from the optimum model predicted log(D2-cotinine: D2-nicotine) in this sample with an R2 of 0.565. Fitted values derived from the optimum additive model predicted D2-cotinine: (D2-nicotine + D2-cotinine) in this sample with an adjusted R2 of 0.644, and fitted values derived from the optimum multiplicative model predicted the metric with an adjusted R2 of 0.715. Table 5 summarizes the associations with D2-cotinine: (D2-nicotine + D2-cotinine) of each variable taken individually and as part of the optimum multiplicative model. All variables included in this model were also significant predictors of D2-cotinine: (D2-nicotine + D2-cotinine) individually according to an additive model (results not shown) with the exceptions of gender and current smoking status. Current smoking status was also not a significant predictor of D2-cotinine: (D2-nicotine + D2-cotinine) according to any additive multiple regression model (results not shown), demonstrating the relative fragility of smoking status as a predictor. The *1H allele demonstrated borderline significance (p=0.054) when included in the multiplicative regression model, and was not significant as part of an additive regression model (p=0.13).
