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A Pharmacogenetics-Based Warfarin Maintenance Dosing Algorithm from Northern Chinese Patients

  • Jinxing Chen ,

    Contributed equally to this work with: Jinxing Chen, Liying Shao, Ling Gong

    Affiliation State Key Laboratory of Cardiovascular Disease, Sino-German Laboratory for Molecular Medicine, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

  • Liying Shao ,

    Contributed equally to this work with: Jinxing Chen, Liying Shao, Ling Gong

    Affiliation State Key Laboratory of Cardiovascular Disease, Sino-German Laboratory for Molecular Medicine, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

  • Ling Gong ,

    Contributed equally to this work with: Jinxing Chen, Liying Shao, Ling Gong

    Affiliations State Key Laboratory of Cardiovascular Disease, Sino-German Laboratory for Molecular Medicine, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China, Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

  • Fang Luo,

    Affiliation Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

  • Jin'e Wang,

    Affiliation State Key Laboratory of Cardiovascular Disease, Sino-German Laboratory for Molecular Medicine, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

  • Yi Shi,

    Affiliation Department of Cardiovascular Surgery, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

  • Yu Tan,

    Affiliation State Key Laboratory of Cardiovascular Disease, Sino-German Laboratory for Molecular Medicine, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

  • Qianlong Chen,

    Affiliation State Key Laboratory of Cardiovascular Disease, Sino-German Laboratory for Molecular Medicine, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

  • Yu Zhang,

    Affiliation State Key Laboratory of Cardiovascular Disease, Sino-German Laboratory for Molecular Medicine, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

  • Rutai Hui,

    Affiliations State Key Laboratory of Cardiovascular Disease, Sino-German Laboratory for Molecular Medicine, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China, Department of Cardiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

  • Yibo Wang

    yibowang@hotmail.com

    Affiliation State Key Laboratory of Cardiovascular Disease, Sino-German Laboratory for Molecular Medicine, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

Abstract

Inconsistent associations with warfarin dose were observed in genetic variants except VKORC1 haplotype and CYP2C9*3 in Chinese people, and few studies on warfarin dose algorithm was performed in a large Chinese Han population lived in Northern China. Of 787 consenting patients with heart-valve replacements who were receiving long-term warfarin maintenance therapy, 20 related Single nucleotide polymorphisms were genotyped. Only VKORC1 and CYP2C9 SNPs were observed to be significantly associated with warfarin dose. In the derivation cohort (n = 551), warfarin dose variability was influenced, in decreasing order, by VKORC1 rs7294 (27.3%), CYP2C9*3(7.0%), body surface area(4.2%), age(2.7%), target INR(1.4%), CYP4F2 rs2108622 (0.7%), amiodarone use(0.6%), diabetes mellitus(0.6%), and digoxin use(0.5%), which account for 45.1% of the warfarin dose variability. In the validation cohort (n = 236), the actual maintenance dose was significantly correlated with predicted dose (r = 0.609, P<0.001). Our algorithm could improve the personalized management of warfarin use in Northern Chinese patients.

Introduction

Warfarin has remained the mainstay of oral anticoagulant therapy for the treatment and prevention of thromboembolism. However, management of warfarin therapy is challenging because of its narrow therapeutic index and wide inter-individual variability. The correct dosing is necessary to avoid bleeding or risk of thrombotic events in case of an excessive or a too low dose. Many studies have shown that single nucleotide polymorphisms (SNPs) within CYP2C9 (cytochrome P450, family 2, subfamily C, polypeptide 9) and VKORC1 (vitamin K epoxide reductase complex, subunit 1) genes are related to warfarin dose requirement [1], [2], [3], [4]. These two genes in combination with age, gender, and body mass index have been shown to account for 30–50% of the variability in the dosage of warfarin and acenocoumarol [1], [4], [5], [6], [7], [8], [9], [10]. In addition to VKORC1 and CYP2C9 SNPs, CYP4F2 (cytochrome P450, family 4, subfamily F, polypeptide 2) rs2108622(V433M) was found to be associated with warfarin dose in 3 independent Caucasian cohorts and accounted for the difference in warfarin dose of approximately 1 mg/day between CC and TT subjects [11]. Several genome-wide association studies indicated that there seemed to be no common SNPs with large effects on warfarin maintenance dose outside of the CYP2C9, VKORC1, and CYP4F2 [12], [13], [14], [15]. A recent association study with a Chinese population also confirmed this observation [16]. Candidate gene studies have also detected associations between other SNPs and warfarin dose, such as SNPs in CYP2C19 (cytochrome P450, family 2, subfamily C, polypeptide 19) [17], GGCX (gamma-glutamyl carboxylase) [18], [19], [20], [21], EPHX1 (epoxide hydrolase 1) [21], [22], [23], [24], CALU (calumenin) [25], and MPZ (myelin protein zero) [12]. However, the associations of these SNPs with warfarin dose were inconsistent. In CYP4F2 gene, a functional haplotype block represented by rs3093105 was found to be associated with urinary 20-hydroxyeicosatetraenoic acid (20-HETE) and hypertension in Chinese individuals [26], while rs2074902 in the haplotype block and rs3093158 were found to be associated with Crohn's disease [27]. We also included some functional SNPs in related genes including F2 (coagulation factor II), F5 (coagulation factor V), F11 (coagulation factor XI), and PROCR (protein C receptor), whose proteins are involved in thrombosis. Previous studies constructed many algorithms to predict the warfarin maintenance dose in Chinese individuals [16], [28], [29], [30], [31], [32]. However, they frequently used relatively small populations (<400 subjects) except in two studies where one included 845 from Southern China [16], and the other included 641 from Central China [33]. The two algorithms included different factors and could explain the 43.6% [16] and 56.4% [33] variability in warfarin maintenance dose, respectively. The objective of the current study was to assess these genetic determinants of the warfarin maintenance dose and to construct an algorithm integrating common interference factors to predict the dose in a large population who lived in Northern China.

Materials and Methods

Ethics Statement

All subjects provided their written informed consent. The study protocol was conducted in accordance with the Declaration of Helsinki Principles (revised in 1983), reviewed and approved by the Ethics Committee of Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, Beijing, China.

Study design

Consenting patients with heart valve replacements were recruited, and 20 single nucleotide polymorphisms (SNPs) in related genes were examined. First, the associations of these SNPs with the warfarin maintenance dose were tested. Second, on the basis of genotypes associated with the warfarin maintenance dose, an algorithm integrating common non-genetic factors was constructed to predict the dose in the derivation cohort (70% of the whole cohort), and was assessed in the validation cohort (30% of the whole cohort).

Study population

Inpatients aged 18 and over, were consecutively recruited from patients undergoing heart valve replacement (HVR) therapy at Fuwai Hospital from April 2012 to May 2013. All patients received warfarin therapy for at least 3 months, and provided records for INR and the stable maintenance dose defined as a constant dose for at least 1 month with the INR measurements within the range of 1.6–2.5 for multiple time periods after hospitalization. All subjects had self-reported as Han nationality and having lived in Northern China.

We collected data on clinical factors which have previously been associated with warfarin dose through patient interviews and a review of medical records by a trained physician. These factors included age, gender, height, weight, current smoking habits and alcohol consumption, concomitant diseases and concurrent interacting medications. The body surface area (BSA) was calculated by height and weight using the following equation: BSA (m2) = 0.0061× height (cm) +0.0128× weight (kg) − 0.1529. The disease was defined as a diagnosis or treatment for the corresponding disease. Hypertension was diagnosed after taking a mean of 3 independent measures of blood pressure >140/90 mmHg; diabetes mellitus (DM) was diagnosed when the subject had a fasting glucose >7.0 mmol/L, or >11.1 mmol/L at 2 hours after oral glucose challenge, or both; hyperlipidemia was diagnosed with an elevation of at least one of the following: >6.22 mmol/L for total cholesterol, >2.26 mmol/L for triglycerides, or >4.14 mmol/L for LDL-cholesterol. Patients with hematological diseases, peptic ulcers, liver and kidney dysfunctions, infections, autoimmune diseases, and malignant tumors were excluded from this study.

Genotype analysis

The RelaxGene Blood DNA System DNA isolation kit (Tiangen, Beijing, China) was used for preparing genomic DNA following the recommendations of the manufacturer. The SNP rs7294 was selected to reflect the natural haplotype block for genotyping according to our previous study [34]. Genotyping was performed using the MassARRAY high-throughput DNA analysis system with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (Sequenom, Inc., San Diego, CA, USA). The primers were designed using MassARRAY Assay Design software (version 3.1). SNPs were genotyped using iPLEX Gold technology (Sequenom) followed by automated data analysis with the TYPE RT software version 4.0. Three samples were removed due to failed genotyping.

Statistical analyses

Categorical variables were reported as counts (percentages) and continuous variables were reported as medians (Q1 to Q3) where appropriate. The differences between the derivation and validation cohorts were calculated using the Wilcoxon rank-sum test or χ2-test. According to the time when they were enrolled, early enrolled patients (70%) were selected for deriving the dose algorithm for warfarin maintenance dose; the remaining later enrolled patients were selected for validating the algorithm by comparing the actual maintenance doses and predicted doses. Univariate association between the warfarin maintenance dose and each potential predictor was assessed using linear regression analysis. All SNPs were tested for deviations from Hardy-Weinberg equilibrium using the χ2-test, and for their association with the warfarin dose by Spearman correlation analysis using a co-dominant model. Categorical variables were coded as 1 if present and 0 if absent. The variables of the variant allele were coded as 0, 1, and 2 for zero copy, one copy, and two copies of the variant allele. Those potential predictors with a P-value lower than 0.20 were selected as candidate variables for the algorithm by a stepwise multiple regression method in the derivation cohort. The algorithm was validated in the validation cohort using Pearson correlation analysis. We compared the performance of the algorithm in three subsets of the entire cohort, distributed according to the warfarin dose requirement (low dose: < = 2 mg/day; intermediate dose: >2 and <4 mg/day; high dose: > = 4 mg/day). The thresholds of 2 mg and 4 mg per day cover the usual starting dose of 3 mg per day used for Chinese individuals. We evaluated the potential clinical value of our algorithm in three different dose groups by calculating the percentage of patients whose predicted warfarin dose was within 20% of the actual maintenance dose (ideal dose), at least 20% higher (overestimation) or 20% lower (underestimation) than the actual dose. A two-tailed probability value of <0.05 was considered as significant. The analyses were performed using SPSS 13.0 for Windows.

Results

Characteristics of enrolled patients

A total of 800 patients were initially enrolled. Of these, 10 patients were excluded because they did not achieve a stable anticoagulation in the follow-up, 3 patients were excluded because a low call rate was in their DNA genotyping. The patients' characteristics showed that 70% patients (n = 551) were enrolled in the derivation cohort and 30% patients (n = 236) were in the validation cohort (Table 1). Usually, most HVR patients with heart failure were treated with digoxin for 3–6 months after hospitalization. Digoxin therapy would be stopped if their heart function recovered. The patients in the derivation cohort were enrolled earlier, thus the ratio of patients using digoxin was much lower in the derivation cohort than that in the validation cohort.

Genetic determinants affecting the warfarin maintenance dose

Due to a nature haplotype block in the VKORC1 gene in the Chinese population, the rs7294 SNP reflecting the haplotype was selected for genotyping. Table 2 shows the relationships between the candidate SNPs and the warfarin maintenance dose in the derivation cohort. VKORC1 rs7294, CYP2C9*3 rs1057910, and CYP2C9 rs4917639 were significantly associated with the warfarin maintenance dose. In related genes, such as CYP4F2, GGCX, CALU, CYP2C19, EPHX1, F2, F5, F11, and PROCR, we could not confirm previous associations of these SNPs with warfarin dose in our Chinese population. However, CYP4F2 rs2108622, EPHX1 rs4653436, and F2 rs3136516 showed an insignificant correlation with P<0.20.

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Table 2. Association of candidate SNPs with warfarin maintenance dose in the derivation cohort.

https://doi.org/10.1371/journal.pone.0105250.t002

We further evaluated the effect of rs2108622 on warfarin dose in patients grouped by VKORC1 rs7294 due to the significant effect of the VKORC1 haplotype on warfarin dose. In patients with rs7294 wild-type genotype GG (n = 649), the results showed a significant difference (P for ANOVA was 0.030) and correlation (P for Spearman was 0.005). In patients with the rs7294 heterozygotes genotype (n = 135), the results also showed a significant difference (P for ANOVA was 0.031) and correlation (P for Spearman was 0.027) (Figure 1).

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Figure 1. The effect of CYP4F2 rs2108622 on warfarin maintenance dose grouped by VKORC1 rs7294 genotypes (mg/day).

Each box indicates the 25th to 75th percentile values (interquartile range); the black lines represent the median daily warfarin maintenance dose value, the maximum length of the whisker is 1.5 times the interquartile range. In detail below are the specific statistical significances for each group. VKORC1 rs7294 GG: PANOVA = 0.030; PSpearman = 0.005. VKORC1 rs7294 AG: PANOVA = 0.031, PSpearman = 0.027.

https://doi.org/10.1371/journal.pone.0105250.g001

Multiple regression model for warfarin maintenance dose

In the derivation cohort, we first analyzed the correlation between non-genetic factors and the warfarin maintenance dose. The factors with a linear regression P-value <0.20 including age, gender, BSA, target INR, amiodarone, digoxin, and DM are shown in Table 3. Adding 6 genetic factors (VKORC1 rs7294, CYP2C9*3 rs1057910, CYP2C9 rs4917639, CYP4F2 rs2108622, EPHX1 rs4653436, and F2 rs3136516) with P-value less than 0.20, a total of 13 factors were carried in the stepwise regression analyses of the derivation cohort (n = 551). Finally, 9 factors included in the regression model were listed in Table 4. In this model, the VKORC1 and CYP2C9 genetic factors contributed most (27.3% and 7%, respectively) to the inter-individual variability in warfarin dose. Age and BSA, of the non-genetic factors, contribute most (4.2% and 2.7%, respectively) to the inter-individual variability. To derive a patient's dose using our model, a clinician would complete the following algorithm using the patient's clinical and genetic characteristics:

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Table 3. Non-genetic factors influencing the warfarin maintenance dose.

https://doi.org/10.1371/journal.pone.0105250.t003

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Table 4. Final model produced by stepwise regression analysis.

https://doi.org/10.1371/journal.pone.0105250.t004

Warfarin maintenance dose (mg/day)  = 0.135+1.781×rs7294 - 1.214×rs1057910+1.288×BSA - 0.019×age+0.708×(target INR) +0.159×rs2108622+0.373×DM - 0.581×Amiodarone - 0.252×Digoxin

In total, the algorithm could explain 45.1% of the variability in warfarin dose.

Efficacy of the algorithm for predicting the warfarin maintenance dose

We calculated the predicted dose of warfarin with the algorithm in the validation cohort (n = 236). The efficacy of the novel algorithm was assessed by Pearson coefficient analysis. A moderately strong correlation between predicted and actual warfarin dose was observed (Pearson r = 0.609, P-value<0.001).

Furthermore, we evaluated the accuracy of our algorithm in subgroups distributed according to the warfarin dose range in the whole cohort (Table 5). In the intermediate dose group, the accuracy of the dose prediction was much higher than in the other two groups. In the intermediate dose group (70.1% in the whole cohort), low dose group (13.1% in the whole cohort), and the high dose group (16.8% in the whole cohort), 66.8%, 19.4%, and 43.9% of the dose predictions fell within 20% of the actual dose (ideal dose), respectively. Most of the predictions (71.8%) were overestimated in the low dose group; however, most of the predictions (56.1%) were underestimated in high dose group.

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Table 5. Percentage of patients in the whole cohort with an ideal, underestimated or overestimated dose of warfarin estimated with algorithms derived in Chinese.

https://doi.org/10.1371/journal.pone.0105250.t005

Comparing our algorithm with the two algorithms derived from southern and central Chinese populations

We further compared our algorithm with the other two algorithms derived from southern Chinese [16] and central Chinese [33] populations. The two algorithms are Square root of Warfarin maintenance dose (mg/day)  = 1.68143 – 0.0029*age +0.30784*BSA – 0.2633*(VKORC1 g.3588G>A) – 0.19114*(CYP2C9*3) +0.14735*(CYP4F2 c.1297G>A) – 0.1797*amiodarone – 0.4138*fluconazole – 0.1888*diltiazem and Square root of Warfarin maintenance dose (mg/day)  = 2.140-0.370*(VKORC1-1639 G>A) – 0.332* (CYP2C9*3) +0.324 *BSA -0.004*age-0.231*(number of increasing INR drugs) +0.105*(smoking habit) -0.135*(preoperative stroke history) – 0.108* (hypertension), respectively.

Our algorithm could explain 45.1% variability of warfarin dose. This level of accountability compares favorably with southern algorithm and central algorithm, which were 43.6% and 56.4%, respectively. Table 5 shows the percentage of patients in the entire cohort with an ideal, underestimated or overestimated dose of warfarin estimated with the three algorithms. Our algorithm and the southern algorithm showed better performances for the intermediate-dose group, our algorithm and central algorithm showed better in high-dose group, whereas the other two algorithms showed improved performances in the low dose group. In the whole cohort, our algorithm was the most accurate following Pearson correlation analysis; the coefficients were 0.648, 0.573, and 0.569, for our algorithm, the southern algorithm, and the central algorithm, respectively. (All P-values <0.001)

Discussion

Our study is the first to develop an algorithm for predicting warfarin maintenance from a relatively large population in the northern area of China. Our algorithm could explain the 45.1% variability of warfarin dose observed in northern Chinese individuals. Common genetic variants could explain the 35% variability of warfarin maintenance dose, and common non-genetic factors could explain 10% variability.

Genetic factors contribute most of variability of warfarin dose

Compared with previous reports regarding the VKORC1 haplotype and CYP2C9 *3, we also observed that they made the most contributions to warfarin dose. Three variants, rs1057910 (*3), rs4917639, and rs9332127 in the CYP2C9 gene have been reported to be associated with warfarin dose [12], [22], [24]. Among these variants, rs9332127 was not detected in Caucasian individuals but was reported to affect warfarin dose in Chinese people [22], [24], [35]; however, no significant association was observed in Indonesian populations [36]. In our study and previous studies [12], [37], univariate analyses showed that rs4917639 was significantly associated with warfarin dose. However, in stepwise multiple regression studies, only rs1057910 was retained in our final algorithm. In a study of Chinese individuals by Liu et al, no association of rs4917639 with warfarin was observed [37]. We speculated that rs1057910 noticeably affected warfarin dose and the association between rs4917639 or rs9332127 and warfarin dose was derived from the linkage disequilibrium. We calculated the D′ and r2 between rs1057910 with the other two variants, which were D′ = 1, r2 = 0.52 between rs1057910 and rs4917639, and D′ = 0.09, r2 = 0.01 between rs1057910 and rs9332127, respectively. However, the D′ and r2 were 0.97 and 0.44 between rs4917639 and rs9332127, respectively. Therefore, the association of rs4917639 or rs9332127 with warfarin dose is reflective of the moderate LD between the two SNPs and CYP2C9*3 rs1057910. In our study, we observed that P-values of the correlation between rs1057910, rs4917639, and rs9332127 and warfarin dose were 6.3×10−8, 1.5×10−5, and 0.374 in univariate analyses, respectively.

The enzyme encoded by the CYP4F2 gene catalyzes the conversion of vitamin K to hydroxyl vitamin K and acts as a counterpart to VKORC1 in limiting the accumulation of vitamin K in hepatocytes [38]. A genome-wide association in Europeans revealed an association of the warfarin dose requirement with rs2108622 in CYP4F2, but only after adjusting for CYP2C9 and VKORC1 [13]. In the present study, the P-value of the correlation was 0.152 in univariate analysis, however, the P-value was 0.01 in the final multiple regression model. We further evaluated the effect of rs2108622 on warfarin dose in patients grouped by VKORC1 rs7294 due to the significant effect of the VKORC1 haplotype on warfarin dose and confirmed that the association between CYP4F2 rs2108622 might be masked by VKORC1 variants.

CYP4F2 is clearly involved in warfarin metabolism. In addition to rs2108622 Val/Met, the rs3093105 Trp/Gly is a non-synonymous substitution and might be functional [26] but we did not observe its association with warfarin dose. In related genes such as GGCX, CALU, CYP2C19, EPHX1, F2, F5, F11, and PROCR, we could not confirm the previous associations of these SNPs with warfarin dose in our Chinese population.

Some non-genetic factors should be included in the algorithm

The non-genetic factors, age, BSA and target INR value, contribute most to the variability of warfarin dose, which is consistent with previous studies [28], [29], [30], [31], [32], [33], [39], [40], [41], [42], [43]. In common concurrent medications and comorbidities, we found that DM status and treatment with amiodarone and digoxin should also be included in the algorithm. Usually amiodarone use was often found in algorithms constructed in previous studies. Diabetes is known to be associated with hypercoagulable states [44], [45], [46], [47], [48], [49]. In three previous studies, diabetes was included in the final regression model, but this resulted in an inconsistent effect of diabetes on warfarin dose. The Hillman [50] and Lenzini groups [39] found that diabetes was negatively associated with warfarin dose, however, the Garcia group [51] observed that diabetes was positively associated with warfarin dose. Our results showed that patients with diabetes had a relatively higher warfarin dose (3.36±1.52 mg/day vs. 3.07±1.25 mg/day, P = 0.091). In the final regression model, diabetes showed a significant association (P = 0.018).

Digoxin, amiodarone and warfarin are known to interact with each other [52], and this interaction could increase the concentration of these drugs. However, digoxin has not been previously entered into the algorithm for predicting the warfarin maintenance dose. In our study, patients using digoxin had a significant lower warfarin maintenance dose than patients who were not using digoxin. The mean doses were 2.92 mg/day and 3.18 mg/day, respectively, and the P-value for ANOVA was 0.007. In final algorithm, digoxin use also showed a significant association with dose (P = 0.025). The contribution of amiodarone to warfarin dose is more than digoxin. If many patients had other drugs increasing INR in addition to digoxin in one study, the contribution of digoxin might be masked by amiodarone or other drugs in increasing INR. In our derivation cohort of 89 patients with digoxin use, only 5 patients used amiodarone at the same time. Thus, we could determine the contribution of digoxin to warfarin dose.

In many previous studies, gender was used in the final regression models [53], [54]. However, gender was removed from our model, although this factor was significantly associated with warfarin dose in univariate analysis. We found that gender was removed from the model when the BSA was included. Our results showed that the BSA was larger in men than in women (1.82±0.17 m2 vs. 1.59±0.14 m2, P<0.001). Although men had a higher warfarin dose than women (3.18±1.25 mg/day vs. 2.98±1.29 mg/day, P = 0.032) this observation might be due to the difference of BSA.

The major limitation in this study is that all subjects were recruited from a single center, and significant differences in genetic background and environmental factors were previously described in Northern China compared with Southern China and Central China. Therefore, the study sample may not be representative of the entire Chinese population. Our study population lived in Northern China, and 45.1% of the variation in warfarin maintenance dose could be accounted for by genetic variants in VKORC1, CYP2C9, and CYP4F2, and nongenetic factors including age, BSA, target INR, DM status, and use of amiodarone digoxin. Furthermore, our algorithm demonstrated the best predictor in northern Chinese people compared with the other two algorithms derived from southern Chinese and central Chinese populations. Thus, our results could be useful in the clinical practice for warfarin treatment of Chinese people who live in Northern China.

Acknowledgments

We are indebted to the participants in the study and their families for their outstanding commitment and cooperation.

Author Contributions

Conceived and designed the experiments: YW RH. Performed the experiments: JC LS LG. Analyzed the data: JC LS LG. Contributed reagents/materials/analysis tools: FL JW YS YT QC YZ. Wrote the paper: YW.

References

  1. 1. Sconce EA, Khan TI, Wynne HA, Avery P, Monkhouse L, et al. (2005) The impact of CYP2C9 and VKORC1 genetic polymorphism and patient characteristics upon warfarin dose requirements: proposal for a new dosing regimen. Blood 106: 2329–2333.
  2. 2. Rieder MJ, Reiner AP, Gage BF, Nickerson DA, Eby CS, et al. (2005) Effect of VKORC1 haplotypes on transcriptional regulation and warfarin dose. N Engl J Med 352: 2285–2293.
  3. 3. Borgiani P, Ciccacci C, Forte V, Romano S, Federici G, et al. (2007) Allelic variants in the CYP2C9 and VKORC1 loci and interindividual variability in the anticoagulant dose effect of warfarin in Italians. Pharmacogenomics 8: 1545–1550.
  4. 4. Carlquist JF, Horne BD, Muhlestein JB, Lappe DL, Whiting BM, et al. (2006) Genotypes of the cytochrome p450 isoform, CYP2C9, and the vitamin K epoxide reductase complex subunit 1 conjointly determine stable warfarin dose: a prospective study. J Thromb Thrombolysis 22: 191–197.
  5. 5. Teichert M, van Schaik RH, Hofman A, Uitterlinden AG, de Smet PA, et al. (2009) Genotypes associated with reduced activity of VKORC1 and CYP2C9 and their modification of acenocoumarol anticoagulation during the initial treatment period. Clin Pharmacol Ther 85: 379–386.
  6. 6. D'Andrea G, D'Ambrosio RL, Di Perna P, Chetta M, Santacroce R, et al. (2005) A polymorphism in the VKORC1 gene is associated with an interindividual variability in the dose-anticoagulant effect of warfarin. Blood 105: 645–649.
  7. 7. Geisen C, Watzka M, Sittinger K, Steffens M, Daugela L, et al. (2005) VKORC1 haplotypes and their impact on the inter-individual and inter-ethnical variability of oral anticoagulation. Thromb Haemost 94: 773–779.
  8. 8. Aquilante CL, Langaee TY, Lopez LM, Yarandi HN, Tromberg JS, et al. (2006) Influence of coagulation factor, vitamin K epoxide reductase complex subunit 1, and cytochrome P450 2C9 gene polymorphisms on warfarin dose requirements. Clin Pharmacol Ther 79: 291–302.
  9. 9. Oldenburg J, Bevans CG, Fregin A, Geisen C, Muller-Reible C, et al. (2007) Current pharmacogenetic developments in oral anticoagulation therapy: the influence of variant VKORC1 and CYP2C9 alleles. Thromb Haemost 98: 570–578.
  10. 10. Wadelius M, Chen LY, Eriksson N, Bumpstead S, Ghori J, et al. (2007) Association of warfarin dose with genes involved in its action and metabolism. Hum Genet 121: 23–34.
  11. 11. Caldwell MD, Awad T, Johnson JA, Gage BF, Falkowski M, et al. (2008) CYP4F2 genetic variant alters required warfarin dose. Blood 111: 4106–4112.
  12. 12. Cooper GM, Johnson JA, Langaee TY, Feng H, Stanaway IB, et al. (2008) A genome-wide scan for common genetic variants with a large influence on warfarin maintenance dose. Blood 112: 1022–1027.
  13. 13. Takeuchi F, McGinnis R, Bourgeois S, Barnes C, Eriksson N, et al. (2009) A genome-wide association study confirms VKORC1, CYP2C9, and CYP4F2 as principal genetic determinants of warfarin dose. PLoS Genet 5: e1000433.
  14. 14. Teichert M, Eijgelsheim M, Rivadeneira F, Uitterlinden AG, van Schaik RH, et al. (2009) A genome-wide association study of acenocoumarol maintenance dosage. Hum Mol Genet 18: 3758–3768.
  15. 15. Cha PC, Mushiroda T, Takahashi A, Kubo M, Minami S, et al. (2010) Genome-wide association study identifies genetic determinants of warfarin responsiveness for Japanese. Hum Mol Genet 19: 4735–4744.
  16. 16. Zhong SL, Yu XY, Liu Y, Xu D, Mai LP, et al. (2012) Integrating interacting drugs and genetic variations to improve the predictability of warfarin maintenance dose in Chinese patients. Pharmacogenet Genomics 22: 176–182.
  17. 17. Rusdiana T, Araki T, Nakamura T, Subarnas A, Yamamoto K (2013) Responsiveness to low-dose warfarin associated with genetic variants of VKORC1, CYP2C9, CYP2C19, and CYP4F2 in an Indonesian population. Eur J Clin Pharmacol 69: 395–405.
  18. 18. Cha PC, Mushiroda T, Takahashi A, Saito S, Shimomura H, et al. (2007) High-resolution SNP and haplotype maps of the human gamma-glutamyl carboxylase gene (GGCX) and association study between polymorphisms in GGCX and the warfarin maintenance dose requirement of the Japanese population. J Hum Genet 52: 856–864.
  19. 19. Chan SL, Goh BC, Chia KS, Chuah B, Wong A, et al. (2011) Effects of CYP4F2 and GGCX genetic variants on maintenance warfarin dose in a multi-ethnic Asian population. Thromb Haemost 105: 1100–1102.
  20. 20. Huang SW, Xiang DK, Huang L, Chen BL, An BQ, et al. (2011) Influence of GGCX genotype on warfarin dose requirements in Chinese patients. Thromb Res 127: 131–134.
  21. 21. Luxembourg B, Schneider K, Sittinger K, Toennes SW, Seifried E, et al. (2011) Impact of pharmacokinetic (CYP2C9) and pharmacodynamic (VKORC1, F7, GGCX, CALU, EPHX1) gene variants on the initiation and maintenance phases of phenprocoumon therapy. Thromb Haemost 105: 169–180.
  22. 22. Wang TL, Li HL, Tjong WY, Chen QS, Wu GS, et al. (2008) Genetic factors contribute to patient-specific warfarin dose for Han Chinese. Clin Chim Acta 396: 76–79.
  23. 23. Pautas E, Moreau C, Gouin-Thibault I, Golmard JL, Mahe I, et al. (2010) Genetic factors (VKORC1, CYP2C9, EPHX1, and CYP4F2) are predictor variables for warfarin response in very elderly, frail inpatients. Clin Pharmacol Ther 87: 57–64.
  24. 24. Gu Q, Kong Y, Schneede J, Xiao YB, Chen L, et al. (2010) VKORC1-1639G>A, CYP2C9, EPHX1691A>G genotype, body weight, and age are important predictors for warfarin maintenance doses in patients with mechanical heart valve prostheses in southwest China. Eur J Clin Pharmacol 66: 1217–1227.
  25. 25. Voora D, Koboldt DC, King CR, Lenzini PA, Eby CS, et al. (2010) A polymorphism in the VKORC1 regulator calumenin predicts higher warfarin dose requirements in African Americans. Clin Pharmacol Ther 87: 445–451.
  26. 26. Liu H, Zhao Y, Nie D, Shi J, Fu L, et al. (2008) Association of a functional cytochrome P450 4F2 haplotype with urinary 20-HETE and hypertension. J Am Soc Nephrol 19: 714–721.
  27. 27. Costea I, Mack DR, Israel D, Morgan K, Krupoves A, et al. (2010) Genes involved in the metabolism of poly-unsaturated fatty-acids (PUFA) and risk for Crohn's disease in children & young adults. PLoS One 5: e15672.
  28. 28. Xu Q, Xu B, Zhang Y, Yang J, Gao L, et al. (2012) Estimation of the warfarin dose with a pharmacogenetic refinement algorithm in Chinese patients mainly under low-intensity warfarin anticoagulation. Thromb Haemost 108: 1132–1140.
  29. 29. You JH, Wong RS, Waye MM, Mu Y, Lim CK, et al. (2011) Warfarin dosing algorithm using clinical, demographic and pharmacogenetic data from Chinese patients. J Thromb Thrombolysis 31: 113–118.
  30. 30. Zhang W, Zhang WJ, Zhu J, Kong FC, Li YY, et al. (2012) Genetic polymorphisms are associated with variations in warfarin maintenance dose in Han Chinese patients with venous thromboembolism. Pharmacogenomics 13: 309–321.
  31. 31. Huang SW, Chen HS, Wang XQ, Huang L, Xu DL, et al. (2009) Validation of VKORC1 and CYP2C9 genotypes on interindividual warfarin maintenance dose: a prospective study in Chinese patients. Pharmacogenet Genomics 19: 226–234.
  32. 32. Wei M, Ye F, Xie D, Zhu Y, Zhu J, et al. (2012) A new algorithm to predict warfarin dose from polymorphisms of CYP4F2, CYP2C9 and VKORC1 and clinical variables: derivation in Han Chinese patients with non valvular atrial fibrillation. Thromb Haemost 107: 1083–1091.
  33. 33. Tan SL, Li Z, Song GB, Liu LM, Zhang W, et al. (2012) Development and comparison of a new personalized warfarin stable dose prediction algorithm in Chinese patients undergoing heart valve replacement. Pharmazie 67: 930–937.
  34. 34. Wang Y, Zhang W, Zhang Y, Yang Y, Sun L, et al. (2006) VKORC1 haplotypes are associated with arterial vascular diseases (stroke, coronary heart disease, and aortic dissection). Circulation 113: 1615–1621.
  35. 35. Chern HD, Ueng TH, Fu YP, Cheng CW (2006) CYP2C9 polymorphism and warfarin sensitivity in Taiwan Chinese. Clin Chim Acta 367: 108–113.
  36. 36. Suriapranata IM, Tjong WY, Wang T, Utama A, Raharjo SB, et al. (2011) Genetic factors associated with patient-specific warfarin dose in ethnic Indonesians. BMC Med Genet 12: 80.
  37. 37. Liu Y, Zhong SL, Tan HH, Yang M, Fei HW, et al. (2011) Impact of CYP2C9 and VKORC1 polymorphism on warfarin response during initiation of therapy. Zhonghua Xin Xue Guan Bing Za Zhi 39: 929–935.
  38. 38. McDonald MG, Rieder MJ, Nakano M, Hsia CK, Rettie AE (2009) CYP4F2 is a vitamin K1 oxidase: An explanation for altered warfarin dose in carriers of the V433M variant. Mol Pharmacol 75: 1337–1346.
  39. 39. Lenzini P, Wadelius M, Kimmel S, Anderson JL, Jorgensen AL, et al. (2010) Integration of genetic, clinical, and INR data to refine warfarin dosing. Clin Pharmacol Ther 87: 572–578.
  40. 40. Choi JR, Kim JO, Kang DR, Yoon SA, Shin JY, et al. (2011) Proposal of pharmacogenetics-based warfarin dosing algorithm in Korean patients. J Hum Genet 56: 290–295.
  41. 41. Liang R, Li L, Li C, Gao Y, Liu W, et al. (2012) Impact of CYP2C9*3, VKORC1-1639, CYP4F2rs2108622 genetic polymorphism and clinical factors on warfarin maintenance dose in Han-Chinese patients. J Thromb Thrombolysis 34: 120–125.
  42. 42. Ramirez AH, Shi Y, Schildcrout JS, Delaney JT, Xu H, et al. (2012) Predicting warfarin dosage in European-Americans and African-Americans using DNA samples linked to an electronic health record. Pharmacogenomics 13: 407–418.
  43. 43. Wang M, Lang X, Cui S, Fei K, Zou L, et al. (2012) Clinical application of pharmacogenetic-based warfarin-dosing algorithm in patients of Han nationality after rheumatic valve replacement: a randomized and controlled trial. Int J Med Sci 9: 472–479.
  44. 44. Imperatore G, Cadwell BL, Geiss L, Saadinne JB, Williams DE, et al. (2004) Thirty-year trends in cardiovascular risk factor levels among US adults with diabetes: National Health and Nutrition Examination Surveys, 1971–2000. Am J Epidemiol 160: 531–539.
  45. 45. Aso Y, Matsumoto S, Fujiwara Y, Tayama K, Inukai T, et al. (2002) Impaired fibrinolytic compensation for hypercoagulability in obese patients with type 2 diabetes: association with increased plasminogen activator inhibitor-1. Metabolism 51: 471–476.
  46. 46. Knobl P, Schernthaner G, Schnack C, Pietschmann P, Griesmacher A, et al. (1993) Thrombogenic factors are related to urinary albumin excretion rate in type 1 (insulin-dependent) and type 2 (non-insulin-dependent) diabetic patients. Diabetologia 36: 1045–1050.
  47. 47. Vinik AI, Erbas T, Park TS, Nolan R, Pittenger GL (2001) Platelet dysfunction in type 2 diabetes. Diabetes Care 24: 1476–1485.
  48. 48. Yazbek N, Bapat A, Kleiman N (2003) Platelet abnormalities in diabetes mellitus. Coron Artery Dis 14: 365–371.
  49. 49. Kamali F, Edwards C, Butler TJ, Wynne HA (2000) The influence of (R)- and (S)-warfarin, vitamin K and vitamin K epoxide upon warfarin anticoagulation. Thromb Haemost 84: 39–42.
  50. 50. Hillman MA, Wilke RA, Caldwell MD, Berg RL, Glurich I, et al. (2004) Relative impact of covariates in prescribing warfarin according to CYP2C9 genotype. Pharmacogenetics 14: 539–547.
  51. 51. Garcia D, Regan S, Crowther M, Hughes RA, Hylek EM (2005) Warfarin maintenance dosing patterns in clinical practice: implications for safer anticoagulation in the elderly population. Chest 127: 2049–2056.
  52. 52. Trujillo TC, Nolan PE (2000) Antiarrhythmic agents: drug interactions of clinical significance. Drug Saf 23: 509–532.
  53. 53. Mazzaccara C, Conti V, Liguori R, Simeon V, Toriello M, et al. (2013) Warfarin anticoagulant therapy: a Southern Italy pharmacogenetics-based dosing model. PLoS One 8: e71505.
  54. 54. Gong IY, Schwarz UI, Crown N, Dresser GK, Lazo-Langner A, et al. (2011) Clinical and genetic determinants of warfarin pharmacokinetics and pharmacodynamics during treatment initiation. PLoS One 6: e27808.