Key result
A prediction model incorporating VKORC1 c.-1639 genotype, age, and height accounted for 48.6% of the inter-individual variability in daily phenprocoumon dose requirements.
Why the study?
Do genetic and non-genetic parameters predict phenprocoumon maintenance dose and plasma concentration in patients on steady-state therapy?
Observational (n=75)
No
Do genetic and non-genetic parameters predict phenprocoumon maintenance dose and plasma concentration in patients on steady-state therapy?
A limited set of parameters including VKORC1 genotype, age, and height can predict nearly half of the inter-individual variability in phenprocoumon maintenance dose requirements.
PURPOSE: The anticoagulation response to vitamin K antagonists is characterised by high inter-individual variability. The impact of single nucleotide polymorphisms (SNPs) in several genes of enzymes involved in the vitamin K cycle on phenprocoumon dose variability and phenprocoumon plasma concentrations is still under investigation. METHODS: We assessed the influence of VKORC1 c.-1639G>A, CYP2C9*2, CYP2C9*3, CYP4F2 c.1297G>A, CALU c.*4A>G, EPHX1 c.337T>C, GGCX c.214+597G>A, F7 c.-402G>A, F7 c.-401G>T, PROC c.-228C>T and PROC c.-215G>A along with clinical and demographic parameters on steady-state phenprocoumon therapy in 75 patients. A prediction model was developed for total phenprocoumon plasma concentrations and daily phenprocoumon doses required for therapeutic anticoagulation. RESULTS: The VKORC1 c.-1639 genotype was the main predictor of the phenprocoumon daily dose (adjusted R(2) = 37.6%) and the total phenprocoumon concentration (adjusted R(2) = 38.3%). CYP2C9 affected the phenprocoumon concentration, but not the dose requirements. SNPs in the other genes of the vitamin K cycle, concomitant medication, nicotine use and alcohol consumption did not predict phenprocoumon concentrations and phenprocoumon dose requirements in a multiple linear regression model. Phenprocoumon concentrations were predicted by VKORC1 c.-1639, CYP2C9 genotype, age and BMI. The final prediction model for the daily phenprocoumon dose requirements comprised VKORC1 c.-1639 genotype, age and height accounting for 48.6% of the inter-individual variability. CONCLUSIONS: A rough prediction of phenprocoumon maintenance doses can be achieved by a limited set of parameters (VKORC1, age, height). The investigated SNPs in CYP4F2, CALU, EPHX1, GGCX, F7, and PROC did not improve the predictive value of a pharmacogenetic-based dosing equation for phenprocoumon.
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Geisen et al. (2010) conducted an observational in Stable phenprocoumon therapy (n=75). VKORC1 c.-1639 genotype, age, and height was evaluated on Inter-individual variability in daily phenprocoumon dose requirements (adjusted R2 = 48.6%). A prediction model incorporating VKORC1 c.-1639 genotype, age, and height accounted for 48.6% of the inter-individual variability in daily phenprocoumon dose requirements.
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