Photo courtesy of NIGMS
Investigators have proposed the use of race-stratified algorithms to help clinicians better calculate the appropriate warfarin dose for a patient.
The team’s study, published in Blood, showed that clinical and genetic factors affecting warfarin dose requirements vary by race.
“As the outcomes of disease can vary by race, so can response to medications,” said Nita Limdi, PhD, PharmD, of the University of Alabama at Birmingham.
“Therefore, warfarin dosing equations that combine race groups for analysis (race-adjusted analysis) assume that the effect of variables—such as age and genetics—are the same across race groups, which may compromise dose prediction among patients of both races.”
To better understand how genetics and clinical factors influence warfarin dosing across race groups, Dr Limdi and her colleagues analyzed 1357 patients—595 African American and 762 European American—treated with warfarin.
The team calculated and compared dose recommendations according to both race-adjusted dosing models and race-stratified dosing models. They found that race-stratified analysis improved dose prediction in both racial groups, as compared to race- adjusted analysis.
Race-stratified analysis showed that European Americans with the CYP2C9*2 variant required less warfarin than European Americans with wild-type CYP2C9. But the same was not true for African Americans.
And although all participants who carried VKORC1 required lower doses, regardless of race, the proportional dose reduction was greater among European Americans.
The investigators therefore concluded that the influence of genetic and clinical factors on warfarin dose differs by race. So race-stratified algorithms, rather than race-adjusted algorithms, should be used to guide warfarin dosing.
“Our findings highlight the need for adequate racial representation in warfarin dosing studies to improve our understanding of how the factors that influence warfarin dose differ according to race,” Dr Limdi said. “This is the first step to developing race-specific algorithms to personalize therapy.”
