Abstract
Background: Warfarin dose requirements are highly variable, influenced by genetic (e.g., CYP2C9, VKORC1), clinical, and metabolic factors. Patients with type 2 diabetes (T2D) exhibit altered metabolic profiles that may further modulate warfarin response. This study aimed to determine whether integration of metabolomic markers with pharmacogenomic and clinical data improves warfarin dose prediction in T2D patients.Methods: We prospectively enrolled 350 T2D patients initiating warfarin therapy. Pharmacogenomic profiling included CYP2C9 and VKORC1 polymorphisms. Fasting plasma metabolomics (LC-MS/MS) quantified 89 metabolites. Multivariable regression models were built with clinical, genomic, and metabolite predictors. Prediction accuracy was assessed by coefficient of determination (R²) and root mean squared error (RMSE).Results: The clinical-genomic model explained 48% of dose variability (R²=0.48). Adding 12 selected metabolites increased R² to 0.64 (pConclusions: Metabolomic markers significantly enhance warfarin dose prediction in T2D patients beyond pharmacogenomic and clinical factors. These findings support the utility of multi-omics approaches for precision dosing.
Keywords
warfarin, type 2 diabetes, pharmacogenomics, metabolomics, CYP2C9, VKORC1, dose prediction, precision medicine