Genetic factors that affect warfarin dose are not routinely evaluated in the Korean population. In this study, we investigated the influence of genetic polymorphisms (GPs) on optimal warfarin dose (OWD) and derived an OWD prediction algorithm based on Korean patients with various diseases requiring anticoagulation therapy.
One hundred eight patients taking warfarin were included. We evaluated clinical characteristics, OWD, international normalized ratio (INR), VKORC1, CYP2C9, and CYP4F2 polymorphisms, as well as medication information. OWD was defined as the maintenance dose that kept a patient’s INR within the target range based on at least two consecutive laboratory measurements separated by more one 1 week.
The 108 patients (mean age: 61.5± 12.4 yr, 48% male) had a mean OWD of 3.12± 1.30 (1-9) mg/day. VKORC1 wild-type patients (AA) had a lower OWD than VKORC1 variant patients (GA). Significantly more OWD patients had the CYP2C9 wild-type genotype than CYP2C9 mutant genotypes. Among the three genotypes of CYP4F2, two carriers had a significantly higher OWD than patients who had the wildtype genotype. We derived an OWD algorithm that included VKORC1, CYP2C9, CYP4F2, body mass index (BMI), age, amiodarone use, and diuretic use.
Our algorithm was capable of explaining 41.8% of the total variation in warfarin dose in our patient cohort. Multiple GPs affect the OWD in Korean patients.
Warfarin is widely prescribed to prevent and treat thromboembolic diseases such as atrial fibrillation (AF), deep vein thrombosis (DVT), and pulmonary thromboembolic disease (PTE) [
Cytochrome P-450 2C9 (CYP2C9) and vitamin K epoxide reductase complex subunit 1 (VKORC1) genetic polymorphisms (GPs) affect warfarin pharmacodynamics [
Ethnic differences have a large influence on OWD. Asian patients have lower warfarin dose requirements than patients of other ethnicities [
One hundred eight Korean patients taking warfarin were enrolled in the this study from July 2007 to June 2018. Inclusion criteria were patients who were between 20-80 years old with a body weight above 50 kg taking warfarin. Exclusion criteria were a history of chronic liver failure, use of other anticoagulant medications, active malignancy, renal disease (creatinine >2.0 mg/dL or eGFR <45 mL/min), or life expectancy <1 year.
All patients provided written informed consent prior to participating in the study. We collected clinical data by reviewing patients’ charts and electronic medical records, from outpatient clinic visits, as well as by telephone. Data included sex, age, height, body weight, smoking, alcohol, target INR, comorbidities, concurrent medications, left ventricular ejection fraction (LVEF), and OWD. Main indications for treatment were atrial fibrillation (AF), DVT/PTE, and heart valve disease (HVD). Comorbidities were cerebral infarction (CI), congestive heart failure (CHF), hypertension, diabetes mellitus (DM), and hyperlipidemia (HLP). Concurrent medications were β-blockers, amiodarone, angiotensin receptor blockers (ARBs), angiotensin-converting enzyme inhibitors (ACEi), aspirin, clopidogrel, statins, calcium channel blockers (CCBs), diuretics, and nitrates. The concomitant medications were included when the patients were continuously taking these for at least 7 days during treatment. OWD was defined as the maintenance dose for which a patient’s INR was within the target range (INR of 2-3) on ≥ 2 consecutive laboratory measurements separated by at least 1 week [
Genomic DNA of patients was isolated from peripheral whole blood using the QIAamp Blood Mini Kit (QIAGEN, Hilden, Germany) according to standard procedures recommended by the manufacturer, and stored at -20℃ until use. CYP2C9*3 (42614A>C, rs1057910), VKORC1 (1639G>A, rs9923231), and CYP4F2 (18000G>A, rs2108622) polymorphisms were detected by polymerase chain reaction (PCR). Multiplex PCR conditions were optimized for SNaPshot reaction. PCR amplification was carried out in a total volume of 30 μL containing 100 ng of genomic DNA, 3 μL of 10X PCR buffer containing Mg2+, 250 μM of each dNTP, 0.13 μM of each primer, and 5 U/μL of rTaq DNA polymerase (TaKaRa, Shiga, Japan). Cycling was performed using the GeneAmp PCR system 9700 (Applied Biosystems, Foster City, CA, USA) and standard methods [
Categorical variables are presented as percentages, while continuous variables are presented as means±standard deviations. The independent
As shown in
Ninety-seven patients (89.8%) were homozygous for the wild-type A allele of VKORC1, 11 patients (10.2%) were heterozygous for the wild-type A allele, and no patients were homozygous for the variant G allele. Ninety-seven patients (89.8%) were homozygous for CYP2C9*1 and 11 patients (10.2%) were heterozygous for CYP2C9*3; no *3/*3 genotypes were observed. CYP4F2 allele frequencies were 68.1% for the G allele and 31.9% for the A allele (
We first used single linear recursive analysis, including OWD as the dependent variable and gender, age, BMI, habits, concomitant diseases, combined medications, LVEF, and the three genotypes as potential factors to determine the correlation between these factors and OWD. We developed an OWD prediction algorithm by including factors with a
The equation we derived is
OWD (mg/day)= 4.165+1.500×VKORC1 (GA)-1.115×CYP2C9 (*1/*3)-0.672× CYP4F2 (GG) -0.791× CYP4F2 (GA)-0.993× Age (60-70)-0.810 × Age ( > 70)+0.351 * BMI (18.5-23)+0.656 × BMI (>23)-0.400×Amiodarone-0.382×Diuretics.
Coding was as follows: VKORC1 GA=1 if the VKORC1 genotype was GA, otherwise 0; CYP2C9 *1/*3= 1 if the CYP2C9 genotype was *1/*3, otherwise 0; CYP4F2 GG= 1 if the CYP4F2 genotype was GG, otherwise 0; CYP4F2 GA= 1 if the CYP4F2 genotype was GA, otherwise 0; age in decades= 1 for 60-70 years, otherwise 0; age in decades= 1 for > 70 years, otherwise 0; BMI value=1 for: 18.5-23 kg/m², otherwise 0; BMI value= 1 for: 23 kg/m², otherwise 0; amiodarone status= 1 if patient taking amiodarone, otherwise 0; diuretics status= 1 if patient taking diuretics, otherwise 0.
The main contribution of this study was to derive an algorithm to predict OWD in Korean patients with various diseases requiring anticoagulation therapy. This algorithm was able to explain 41.8% of variation in the warfarin dose among the Korean patients enrolled in this study. We also established a definite link between multiple GPs and OWD. We demonstrated that the required dose of warfarin in Korean patients with the wild-type VKORC1 genotype (AA) was lower than that required in patients with the variant (GA) genotype (2.95 vs. 4.63 mg/day). CYP2C9*1/*1-carriers had a significantly higher OWD than CYP2C9*1/*3-carriers (3.30 vs. 1.65 mg/day). Although some researchers observed no significant associations between CYP4F2 polymorphisms and warfarin dose requirement in Korean and Indian patient cohorts [
It is well known that ethnicity affects OWD because of genetic variation among different ethnic populations. For example, African–Americans require a higher warfarin dose than other ethnic groups [
In a previous study that derived an algorithm to predict warfarin maintenance dose in Korean patients with AF, variables included were age, body surface area (BSA), statin status, and genetic factors (VKORC1 and CYP2C9). However, the CYP4F2 genotype was not included [
One clinical trial found that a genotype-guided dosing strategy did not result in better outcomes than clinically-guided dosing [
Our study had several limitations. It was conducted based on data collected exclusively from Koreans, and it was a single-center study. Our sample size was also comparatively small. Our goal in the future is to collect more patient-related information to validate the effectiveness of our algorithm and determine other factors that may affect OWD in Korean patients (such as polymorphisms in the gamma-glutamyl carboxylase gene).
In conclusion, our algorithm was able to explain 41.8% of warfarin dose differences in Korean patients with various diseases requiring anticoagulation therapy. VKORC1, CYP2C9, and CYP4F2 GPs all affected OWD in Korean patients. Although CYP4F2 polymorphisms only appear to have a mild influence on OWD, including this gene in an algorithm can improve the ability of the algorithm to accurately predict OWD.
This research was supported by Dong-A Unversity Research Fund.
Dr. MH Kim received a research grant from Bayer Co. None of the other authors have conflicts of interest to report.
Genetic polymorphisms influencing warfarin dose.
Baseline characteristics of the study population
Total (n = 108) | Numbers of patients (%) |
---|---|
Female | 56 (52) |
Age (yr) | |
< 60 | 46 (42) |
60-70 | 58 (54) |
> 70 | 4 (4) |
BMI (kg/m²) | |
< 18.5 | 5 (5) |
18.5-23 | 34 (31) |
> 23 | 69 (64) |
Smoking | 11 (10) |
Alcohol | 12 (11) |
LVEF (%) | |
≥ 50 | 78 (72) |
< 50 | 25 (22) |
Main indications for treatment | |
AF | 83 (77) |
PTE/DVT | 14 (13) |
Heart valve disease | 36 (33) |
Comorbidities | |
Cerebral infarction | 20 (19) |
Congestive heart failure | 34 (31) |
Hypertension | 36 (33) |
Diabetic mellitus | 15 (14) |
Hyperlipidemia | 6 (6) |
Medications | |
β-blockers | 13 (12) |
Amiodarone | 32 (30) |
ARBs | 20 (19) |
ACEi | 10 (9) |
Aspirin | 16 (15) |
Clopidogrel | 17 (16) |
Statins | 23 (21) |
CCBs | 20 (19) |
Diuretics | 74 (69) |
Nitrates | 7 (6) |
Values are presented as numbers (%).
BMI, body mass index; LVEF, left ventricular ejection fraction; AF, atrial fibrillation; PTE, pulmonary thromboembolic disease; DVT, deep vein thrombosis; ARBs, angiotensin receptor blockers; ACEi, angiotensin-converting enzyme inhibitors; CCBs, calcium channel blockers.
Daily stable warfarin dose of the study patients
Total (n = 108) | Warfarin dose (mg/day) | |
---|---|---|
Sex | ||
Female | 3.1 ± 1.5 | 0.703 |
Male | 3.2 ± 1.1 | |
Age (yr) | ||
< 60 | 3.6 ± 1.6 | 0.001 |
60-70 | 2.8 ± 0.9 | |
> 70 | 2.7 ± 0.6 | |
BMI (kg/m²) | ||
< 18.5 | 2.4 ± 1.1 | 0.058 |
18.5-23 | 2.8 ± 1.0 | |
> 23 | 3.3 ± 1.4 | |
Smoking | ||
Yes | 3.1 ± 1.1 | 0.971 |
No | 3.1 ± 1.3 | |
Alcohol | ||
Yes | 3.2 ± 1.2 | 0.887 |
No | 3.1 ± 1.3 | |
LVEF (%) | ||
≥ 50 | 3.2 ± 1.4 | 0.059 |
< 50 | 2.7 ± 1.0 | |
Main indications for treatment | ||
AF | ||
Present | 2.9 ± 1.2 | 0.006 |
Absent | 3.7 ± 1.4 | |
PTE/DVT | ||
Present | 3.3 ± 1.2 | 0.529 |
Absent | 3.1 ± 1.3 | |
Heart valve disease | ||
Present | 3.5 ± 1.3 | 0.060 |
Absent | 3.0 ± 1.3 | |
Comorbidities | ||
Cerebral infarction | ||
Present | 2.9 ± 1.1 | 0.314 |
Absent | 3.2 ± 1.3 | |
Congestive heart failure | ||
Present | 2.7 ± 1.1 | 0.319 |
Absent | 3.3 ± 1.3 | |
Hypertension | ||
Present | 3.1 ± 0.9 | 0.926 |
Absent | 3.1 ± 1.5 | |
Diabetic mellitus | ||
Present | 3.1 ± 1.8 | 0.917 |
Absent | 3.1 ± 1.2 | |
Hyperlipidemia | ||
Present | 3.5 ± 1.2 | 0.413 |
Absent | 3.1 ± 1.3 | |
Medications | ||
β-blockers | ||
Yes | 3.0 ± 1.1 | 0.777 |
No | 3.1 ± 1.3 | |
Amiodarone | ||
Yes | 2.6 ± 1.1 | 0.012 |
No | 3.3 ± 1.3 | |
ARBs | ||
Yes | 3.3 ± 1.1 | 0.548 |
No | 3.1 ± 1.3 | |
ACEi | ||
Yes | 3.0 ± 1.2 | 0.769 |
No | 3.1 ± 1.3 | |
Aspirin | ||
Yes | 3.1 ± 1.1 | 0.983 |
No | 3.1 ± 1.3 | |
Clopidogrel | ||
Yes | 3.0 ± 1.2 | 0.729 |
No | 3.1 ± 1.3 | |
Statins | ||
Yes | 3.0 ± 1.2 | 0.617 |
No | 3.1 ± 1.3 | |
CCBs | ||
Yes | 3.0 ± 1.2 | 0.735 |
No | 3.1 ± 1.3 | |
Diuretics | ||
Yes | 2.9 ± 1.2 | 0.026 |
No | 3.5 ± 1.4 | |
Nitrates | ||
Yes | 3.9 ± 0.9 | 0.103 |
No | 3.1 ± 1.3 |
Values are presented as means ± standard deviations.
BMI, body mass index; LVEF, left ventricular ejection fraction; AF, atrial fibrillation; PTE, pulmonary thromboembolic disease; DVT, deep vein thrombosis; ARBs, angiotensin receptor blockers; ACEi, angiotensin-converting enzyme inhibitors; CCBs, calcium channel blockers.
Genotype frequencies of VKORC1, CYP2C9, and CYP4F2
Gene | SNP | Allele | Patients, No. (%) | Genotype | Patients, No. (%) |
---|---|---|---|---|---|
VKORC1 | 1639G > A | A | 205 (94.9) | AA | 97 (89.8) |
(rs9923231) | G | 11 (5.1) | GA | 11 (10.2) | |
CYP2C9 | 42614A > C | *1 | 205 (94.9) | *1/*1 | 97 (89.8) |
(rs1057910) | *3 | 11 (5.1) | *1/*3 | 11 (10.2) | |
CYP4F2 | 18000 G > A | G | 147 (68.1) | GG | 47 (43.5) |
(rs2108622) | A | 69 (31.9) | GA | 53 (49.1) | |
AA | 8 (7.4) |
Values are presented as numbers (%).
SNP, single nucleotide polymorphism.
Univariate factors affecting warfarin dose
Variables | Univariate |
|||||||
---|---|---|---|---|---|---|---|---|
B | SE | Β | R2 | Adjusted R2 | F-value | |||
Age (ref = < 60 yr) | ||||||||
60-70 | -1.064 | 0.268 | -0.381 | 0.000 | 0.151 | 0.135 | 9.329 | 0.048 |
> 70 | -0.817 | 0.273 | -0.288 | 0.003 | ||||
BMI (ref = < 18.5 kg/m²) | ||||||||
18.5-23 | 0.822 | 0.672 | 0.297 | 0.224 | 0.061 | 0.044 | 3.439 | 0.036 |
> 23 | 1.330 | 0.655 | 0.493 | 0.045 | ||||
Gender (ref = female) | 0.096 | 0.252 | 0.037 | 0.703 | 0.001 | -0.008 | 0.146 | 0.703 |
Smoking | -0.015 | 0.416 | -0.004 | 0.971 | 0.000 | -0.009 | 0.001 | 0.971 |
Alcohol | 0.057 | 0.401 | 0.014 | 0.887 | 0.000 | -0.009 | 0.020 | 0.887 |
VKORC1 (ref = AA) | 1.465 | 0.391 | 0.342 | < 0.001 | 0.117 | 0.109 | 14.027 | < 0.001 |
CYP2C9 (ref = *1/*1) | -1.344 | 0.395 | -0.314 | 0.001 | 0.098 | 0.090 | 11.557 | 0.001 |
CYP4F2 (ref = AA) | ||||||||
GG | -1.220 | 0.488 | -0.467 | 0.014 | 0.056 | 0.038 | 3.120 | 0.000 |
GA | -1.038 | 0.484 | -0.400 | 0.034 | ||||
Main indications for treatment | ||||||||
PTE/DVT | 0.236 | 0.374 | 0.061 | 0.529 | 0.004 | -0.006 | 0.398 | 0.529 |
Heart valvular disease | 0.511 | 0.263 | 0.186 | 0.055 | 0.035 | 0.025 | 3.760 | 0.055 |
Comorbidities | ||||||||
Cerebral infarction | -0.326 | 0.323 | -0.098 | 0.314 | 0.010 | 0.000 | 1.024 | 0.314 |
Congestive heart failure | -0.588 | 0.265 | -0.211 | 0.290 | 0.011 | 0.001 | 4.920 | 0.290 |
Hypertension | 0.025 | 0.267 | 0.009 | 0.926 | 0.000 | -0.009 | 0.009 | 0.926 |
Diabetic mellitus | -0.038 | 0.364 | -0.010 | 0.917 | 0.000 | -0.009 | 0.011 | 0.917 |
Hyperlipidemia | 0.451 | 0.548 | 0.080 | 0.413 | 0.006 | -0.003 | 0.677 | 0.413 |
Medications | ||||||||
Amiodarone | -0.687 | 0.268 | -0.242 | 0.012 | 0.059 | 0.050 | 6.587 | 0.012 |
Diuretics | -0.599 | 0.265 | -0.215 | 0.026 | 0.046 | 0.037 | 5.121 | 0.026 |
β-blockers | -0.110 | 0.387 | -0.028 | 0.777 | 0.001 | -0.009 | 0.081 | 0.777 |
ARBs | 0.195 | 0.324 | 0.058 | 0.548 | 0.003 | -0.006 | 0.364 | 0.548 |
ACEi | -0.128 | 0.434 | -0.029 | 0.769 | 0.001 | -0.009 | 0.087 | 0.769 |
Aspirin | -0.008 | 0.354 | -0.002 | 0.983 | 0.000 | -0.009 | 0.000 | -0.983 |
Clopidogrel | -0.120 | 0.346 | -0.034 | 0.729 | 0.001 | -0.008 | 0.121 | 0.729 |
Statins | -0.154 | 0.307 | 0.049 | 0.617 | 0.002 | -0.007 | 0.252 | 0.617 |
CCBs | -0.110 | 0.324 | -0.033 | 0.735 | 0.001 | -0.008 | 0.116 | 0.735 |
Nitrates | 0.831 | 0.505 | 0.158 | 0.103 | 0.025 | 0.016 | 2.706 | 0.103 |
Values are presented as numbers.
BMI, body mass index; PTE, pulmonary thromboembolic disease; DVT, deep vein thrombosis; ARBs, angiotensin receptor blockers; ACEi, angiotensin-converting enzyme inhibitors; CCBs, calcium channel blockers.
Multivariate factors affect warfarin dose
Variables | Multivariate |
|||
---|---|---|---|---|
B | SE | β | ||
Interceptor | 4.165 | 0.694 | 0.000 | |
Age (ref = < 60 yr) | ||||
60-70 | -0.993 | 0.226 | -0.356 | 0.000 |
> 70 | -0.810 | 0.228 | -0.285 | 0.001 |
BMI (ref = < 18.5 kg/m²) | ||||
18.5-23 | 0.351 | 0.549 | 0.127 | 0.524 |
> 23 | 0.656 | 0.540 | 0.243 | 0.227 |
Diuretics | -0.382 | 0.215 | -0.135 | 0.079 |
Amiodarone | -0.400 | 0.216 | -0.143 | 0.067 |
VKORC1 (ref = AA) | 1.500 | 0.326 | 0.350 | 0.000 |
CYP2C9 (ref = *1/*1) | -1.115 | 0.326 | -0.260 | 0.001 |
CYP4F2 (ref = AA) | ||||
GG | -0.672 | 0.391 | -0.257 | 0.089 |
GA | -0.791 | 0.384 | -0.305 | 0.042 |
R2 | 0.472 | |||
Adjusted R2 | 0.418 | |||
F-value | 8.673 | |||
< 0.001 | ||||
Durbin-Watson | 2.001 |
Values are presented as numbers.
BMI, body mass index.