↓ Skip to main content

A multi-factorial analysis of response to warfarin in a UK prospective cohort

Overview of attention for article published in Genome Medicine, January 2016
Altmetric Badge

About this Attention Score

  • Good Attention Score compared to outputs of the same age (74th percentile)

Mentioned by

twitter
9 X users

Citations

dimensions_citation
45 Dimensions

Readers on

mendeley
74 Mendeley
citeulike
2 CiteULike
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
A multi-factorial analysis of response to warfarin in a UK prospective cohort
Published in
Genome Medicine, January 2016
DOI 10.1186/s13073-015-0255-y
Pubmed ID
Authors

Stephane Bourgeois, Andrea Jorgensen, Eunice J. Zhang, Anita Hanson, Matthew S. Gillman, Suzannah Bumpstead, Cheng Hock Toh, Paula Williamson, Ann K. Daly, Farhad Kamali, Panos Deloukas, Munir Pirmohamed

Abstract

Warfarin is the most widely used oral anticoagulant worldwide, but it has a narrow therapeutic index which necessitates constant monitoring of anticoagulation response. Previous genome-wide studies have focused on identifying factors explaining variance in stable dose, but have not explored the initial patient response to warfarin, and a wider range of clinical and biochemical factors affecting both initial and stable dosing with warfarin. A prospective cohort of 711 patients starting warfarin was followed up for 6 months with analyses focusing on both non-genetic and genetic factors. The outcome measures used were mean weekly warfarin dose (MWD), stable mean weekly dose (SMWD) and international normalised ratio (INR) > 4 during the first week. Samples were genotyped on the Illumina Human610-Quad chip. Statistical analyses were performed using Plink and R. VKORC1 and CYP2C9 were the major genetic determinants of warfarin MWD and SMWD, with CYP4F2 having a smaller effect. Age, height, weight, cigarette smoking and interacting medications accounted for less than 20 % of the variance. Our multifactorial analysis explained 57.89 % and 56.97 % of the variation for MWD and SMWD, respectively. Genotypes for VKORC1 and CYP2C9*3, age, height and weight, as well as other clinical factors such as alcohol consumption, loading dose and concomitant drugs were important for the initial INR response to warfarin. In a small subset of patients for whom data were available, levels of the coagulation factors VII and IX (highly correlated) also played a role. Our multifactorial analysis in a prospectively recruited cohort has shown that multiple factors, genetic and clinical, are important in determining the response to warfarin. VKORC1 and CYP2C9 genetic polymorphisms are the most important determinants of warfarin dosing, and it is highly unlikely that other common variants of clinical importance influencing warfarin dosage will be found. Both VKORC1 and CYP2C9*3 are important determinants of the initial INR response to warfarin. Other novel variants, which did not reach genome-wide significance, were identified for the different outcome measures, but need replication.

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 74 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 1%
Unknown 73 99%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 12 16%
Student > Master 10 14%
Researcher 9 12%
Other 6 8%
Student > Ph. D. Student 6 8%
Other 18 24%
Unknown 13 18%
Readers by discipline Count As %
Medicine and Dentistry 21 28%
Pharmacology, Toxicology and Pharmaceutical Science 9 12%
Biochemistry, Genetics and Molecular Biology 9 12%
Agricultural and Biological Sciences 6 8%
Nursing and Health Professions 4 5%
Other 8 11%
Unknown 17 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 20 January 2016.
All research outputs
#6,323,754
of 23,506,079 outputs
Outputs from Genome Medicine
#1,048
of 1,466 outputs
Outputs of similar age
#99,332
of 396,973 outputs
Outputs of similar age from Genome Medicine
#31
of 42 outputs
Altmetric has tracked 23,506,079 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 1,466 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 25.9. This one is in the 28th percentile – i.e., 28% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 396,973 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 74% of its contemporaries.
We're also able to compare this research output to 42 others from the same source and published within six weeks on either side of this one. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.