Background: Sulfonylureas are widely used to treat type 2 diabetes (T2DM). Although genetic variations are associated with sulfonylurea treatment responses in T2DM patients, whether these variations can be used to predict heterogeneous treatment responses is unclear. In this study, we assessed the potential utility of combining information from multiple variants and phenotypes to predict sulfonylurea response.
Methods: Using data from the "Glibenclamide" arm (365 patients) of the Xiaoke Pill Trial that evaluated the safety and efficacy of sulfonylurea, we identified genetic variants associated with sulfonylurea treatment response, and we explored their ability to predict drug response when combined with phenotype information.
Results: The association of 780 single-nucleotide polymorphisms (using Infinium HD iSelect chip) with drug efficacy was evaluated, and four genes identified with drug metabolism (FMO2, FMO3, UGT2B15, and CYP51A1, P < 0.05) were found to be associated with changes in HbA1c. In a clinical model, the baseline values of HbA1c and disposition index (DI) were significantly associated with HbA1c and fasting plasma glucose (FPG) target achievements. Compared with clinical models, the inclusion of genetic markers significantly increased the predictive ability for both HbA1c- and FPG-based outcomes.
Conclusions: Our findings suggest that altered protein function in multiple pathways may cooperatively contribute to the increased discrimination by area under receiver operating curve for T2DM patients, and it may explain, in part, the relationship between inter-individual variability and the sulfonylurea response.