Abstract

Introduction: Warfarin is widely used and will continue to be prescribed especially in developing countries due to its low cost. Given the huge patient load requiring anticoagulation, there is a need to develop strategies to optimize warfarin therapy for ensuring safe and effective anticoagulation. In the present work, we aimed at elucidating the association of genetic and nongenetic variables with warfarin dose requirement in patients attending the cardiovascular clinic in a tertiary care center of North India. Methods: This was a prospective study conducted over 1 year. Patient demographic and clinical details were captured in customized case record forms. Genotyping was done using the polymerase chain reaction-restriction fragment length polymorphism method. Pharmacogenetic influence of CYP2C9 (rs1799853 and rs1057910) and VKORC1 (rs9923231) variant alleles was studied. The association of genetic and nongenetic factors with warfarin dose was quantified using a stepwise multivariate linear regression model. Results: Two hundred and forty patients were screened. Data from 82 eligible patients were used for quantifying the association of genetic and nongenetic factors with warfarin dose. A descriptive model based on CYP2C9*3 (rs1057910) and VKORC1 (rs9923231) variant alleles and BMI was developed. The model explains nearly half of the interindividual variation in warfarin dose requirement. Conclusion: The model explains nearly half of the interindividual variation in warfarin dose in patients with atrial fibrillation and or requiring valve replacement.

© 2021 S. Karger AG, Basel

Introduction

Warfarin represents the standard of care oral vitamin K antagonist for over 6 decades and still is widely used for prophylaxis and treatment of thromboembolic disorders across the globe [1, 2]. Recent introduction of direct oral anticoagulants (DOACs) has expanded the choice for oral anticoagulants; yet, their clinical use remains limited. In India, clinicians still prefer using warfarin (coumarin derivative) over DOACs although no estimates on market share of DOACs have been published so far [3, 4]. Not only that, warfarin will continue to be widely used in the developing world on account of low cost, ease of administration, wide market availability, and vast clinical experience in use of the drug. Furthermore, warfarin continues to be the anticoagulant of choice for many conditions where DOACs are contraindicated. These include anticoagulation in children and in those with mechanical heart valves, valvular atrial fibrillation, and renal impairment [5].

Although effective, well-known limitations to safe use of warfarin include narrow therapeutic index, large interindividual variation in drug response, and potential for numerous drug-drug and drug-food interactions [6-8]. Due to these reasons, individualization of dose is an important aspect of warfarin anticoagulation. Subtherapeutic doses put the patient at risk of thromboembolic events, whereas supratherapeutic dosing leads to high risk of bleeding complications. Finding the right dose of warfarin for an individual patient still remains a challenging task. Several studies have delineated factors that drive the interindividual variation in drug response. These include age, gender, weight, height, BMI, diet, liver function, and polymorphisms in many genes such as CYP2C9, VKORC1, CYP4F2, and GGCX [9-12].

Dosing algorithms based on these factors have been found useful for optimization of anticoagulation. Benefits of genotype-guided warfarin therapy range from shorter time to therapeutic INR and increased percentage of time in therapeutic range to reducing the occurrence of serious bleeding events [13-15]. However, these dosing algorithms have limited applicability for populations outside the ones used for development of the algorithm [16-18]. This has been attributed to a wide variation in frequency of warfarin-sensitive alleles and presence of race-specific variants influencing drug response [19]. Genotype frequencies affecting warfarin response in Indians are different from other Asian populations [20]. Furthermore, there is significant variation in frequency of warfarin-sensitive variant alleles among various regions within India [20, 21]. Most of the studies conducted in India are limited to describing the frequency of variant alleles [21-25]. Very few studies have described the association of variant alleles with warfarin dose [26-29]. Hence, in the present study, we aimed at quantifying the association of genetic and nongenetic variables with warfarin dose.

Patients and Methods

A prospective study was conducted over a period of 1 year in the Department of Pharmacology in collaboration with Cardiology and Cardiothoracic Vascular Surgery (CTVS) Outpatient Departments of our tertiary care hospital. The study was initiated after getting approval from the Institute Ethics Committee. Consecutive patients were screened for potential eligibility in the study after obtaining written informed consent.

Adult patients of either sex suffering from atrial fibrillation and or those requiring heart valve repair/replacement surgeries on stable anticoagulation with warfarin were included. Patients with transient atrial fibrillation; with underlying hepatic (defined as elevation in alanine/aspartate transaminase >2.5 times the upper limit of normal) and or severe renal insufficiency (defined as creatinine clearance of <30 mL/min); patients with known contraindications to anticoagulant therapy; pregnant and nursing women; and those who were unwilling to participate were excluded. Eligible patients were recruited. The study was registered in the Clinical Trial Registry of India (CTRI/2018/06/014614).

Following enrollment, patient demographic and clinical details such as age, sex, height, weight, smoking and alcohol status, indication, warfarin dose, and use of concomitant cardiac medications were captured. Stable anticoagulation was defined as having 2 INR readings in the therapeutic range (2.0–3.0) over a period of 3 months taken at least 1 week apart with no adjustment of warfarin dose. The warfarin dose in the period of stable anticoagulation was considered as stable dose for the patient. Adverse events such as bleeding and thromboembolic events or hospitalization as a result of these events were also recorded.

For genotype evaluation with regard to presence of variant alleles in CYP2C9 (CYP2C9*2; rs1799853, CYP2C9*3; rs1057910) and VKORC1 (−1639G>A; rs9923231) genes, the PCR-RFLP method was used [30]. Single venous blood sample (6 mL) was obtained from the patients. Blood was collected in an EDTA vial and was stored in a deep freezer at −40°C until further processing. DNA was extracted using the commercial genomic DNA extraction kit (QIAGEN amp DNA mini kit). Polymerase chain reaction (PCR) was done to amplify CYP2C9 and VKORC1 regions of the genome. This was done from 100 ng genomic DNA in a total reaction volume of 25 μL. Reaction mixture consisted of 2.5 μL of 10× buffer, 1.5 μL of 25 mM of MgCl2, 1 μL of 10 mM dNTP mix, 1 µL of 10 mM of forward and reverse primers of the CYP2C9 gene, and 1 μL of 1U/μL of Taq DNA polymerase. In case of VKORC1 gene amplification, optimal primer concentration was 0.3 μL of 10 mM of forward and reverse primers each. Nuclease-free water was added to make the final reaction volume to 25 μL. Primers and PCR conditions for gene amplification are presented in Table 1. Amplification of candidate genes was confirmed by electrophoresis of PCR products on 2% agarose gel at 120 V for 60 min stained with 0.5 μg/mL ethidium bromide in Tris borate EDTA buffer pH 8.0 and visualization using a gel documentation system. The amplicon sizes for CYP2C9*2, CYP2C9*3, and VKORC1 were 690 bp, 166 bp, and 636 base pairs, respectively. PCR products were kept for overnight incubation at 37°C in an appropriate reaction mixture for restriction digestion. Digestion mixture comprised 10 µL of the PCR product with 10 µL of respective restriction endonuclease in appropriate buffer. The restriction enzymes used for digestion and restriction fragments so generated with their interpretation are presented in Table 2. Representative gel pictures are shown in Figure 1a–c.

Table 1.

Forward and reverse primers and PCR protocol

Table 2.

Restriction enzymes and digested product sizes (in base pairs)

Fig. 1.

a 2.5% agarose gel pattern of CYP2C9*2 (C430T). *Lanes 1 to 3 show the homozygous mutant TT genotype (690 bp), lanes 4 and 5 show the wild homozygous (CC) genotype (521 bp, 169 bp), and lanes 6 and 7 show the heterozygous CT genotype (690 bp, 521 bp, and 169 bp). b 2.5% agarose gel pattern of CYP2C9*3 (A1075C). *Lanes 1, 3, and 6 show the wild-type AA genotype (166 bp), lanes 2, 4, and 5 show the homozygous mutant CC genotype (136 bp and 30 bp), and lane 7 shows the heterozygous mutant AC genotype (166 bp and 136 bp). Note that the 30-base pair product is not visible. c 2.5% agarose gel pattern of VKORC1 −1639G>A. *Lanes 1 and 6 show the homozygous mutant genotype AA (522 bp, 114 bp, and 50 bp), lanes 2 and 4 show the wild homozygous (GG) genotype (472 bp, 114 bp, and 50 bp), lanes 3 and 5 show heterozygous AG genotype, and lane 7 shows the undigested (U) VKORC1gene PCR product (636 bp). Note that the 50-base pair product is not visible.

Statistical Analysis

A sample size of 80 gives a probability of >99% for detecting all alleles in the 3 genes under study, that is, VKORC1, CYP2C9*2, and CYP2C9*3 assuming 80–90% frequency of the wild type and about 5–10% frequency of the other variants [31]. Descriptive statistics were used for continuous (mean ± SD, or median [range]) variables. Categorical data were presented as frequencies or percentages. The Kolmogorov-Smirnov test was used for assessing the normality of data. Log transformation was done to normalize the distribution of the warfarin dose. Comparison of mean stable warfarin dose across different genotypes was done using 1-way analysis of variance followed by the post hoc Scheffe test. Stepwise multiple linear regression analysis was undertaken to model the relationship of warfarin dose per week with the clinical and pharmacogenetic variables. p value <0.05 was considered statistically significant. All analyses were done using SPSS version 22.

Results

A total of 240 patients were screened over a period of 1 year with consequent enrollment of 89 eligible patients. The analysis dataset includes data from 82 patients who completed the study and had no missing data on any of the study variables (shown in Fig. 2). The majority (86.5%) of the participants received warfarin for atrial fibrillation. Other demographic and clinical details including genotype frequencies with 95% confidence intervals are presented in Table 3. Mean (±SD) stable warfarin dose was 28.82 (±12.59) mg/week with an interquartile range of 14 mg/week. Stable warfarin dose in the study cohort ranged from 5 mg/week to 77 mg/week. This represents over 15-fold variation in warfarin dose requirement among patients. Further CYP2C9 haplotypes were constructed based on combined individual CYP2C9*2 and CYP2C9*3 genotypes. The frequency distribution of study subjects based on CYP2C9 haplotypes and VKORC1 genotype is summarized in Table 4.

Table 3.

Demographic, clinical, and genotype characteristics of the study cohort

Table 4.

Frequency distribution of study participants with regard to CYP2C9 haplotypes and VKORC1 genotype

Fig. 2.

Flow of participants during the study period.

Comparison of Warfarin Dose Response across Different Genotypes

Comparison of mean (±SD) stable warfarin doses across CYP2C9*2 genotypes revealed a dose requirement of 29.18 ± 12.76 mg/week in individuals with the wild genotype, followed by 27.13 ± 11.35 mg/week in heterozygous and 16 mg/week in 1 patient with homozygous mutant genotype. Although there was trend to decreased requirement of warfarin with increasing number of variant CYP2C9*2 alleles, the difference did not reach statistical significance (p = 0.543). Similarly, across CYP2C9*3 genotypes, we observed the highest dose requirement of 30.9 ± 12.30 mg/week in the wild genotype followed by 24.08 ± 9.25 mg/week in heterozygous and the lowest dose requirement of 9.50 ± 3.69 mg/week in homozygous mutants (p = 0.001). To study the effect of CYP2C9*2 and CYP2C9*3 allelic variation in combination, we compared warfarin dose requirements in individuals with different haplotypes of the CYP2C9 gene (Fig. 3). The difference was found to be statistically significant (p = 0.002).

Fig. 3.

Distribution of weekly warfarin dose across CYP2C9 haplotypes.

Significant variation was also noted for warfarin dose requirement across different genotypes for the VKORC1 gene (p = 0.02). Mean (±SD) weekly warfarin dose requirement was 31 ± 13.03 mg/week followed by 23.52 ± 9.76 and 18.50 ± 3.53 mg/week in individuals with wild, heterozygous, and homozygous mutant genotypes, respectively. Combined effect of warfarin-sensitive variant alleles in both VKORC1 and CYP2C9 genes on warfarin dose is shown in Figure 4. Adverse event of minor bleeding occurred in 2 patients during the study period.

Fig. 4.

Distribution of weekly warfarin dose in the study cohort based on genotypes for both CYP2C9 and VKORC1*. *Codes refer to the following combination of CYP2C9 haplotypes and VKORC1 genotype: 0 → (*1/*1 CYP2C9 haplotype + GG genotype of VKORC1); 1→ (*1/*1 CYP2C9 haplotype + AG genotype of VKORC1); 2 → (*1/*1 CYP2C9 haplotype + AA genotype of VKORC1); 3→ (*1/*2 CYP2C9 haplotype + GG genotype of VKORC1); 4→ (*1/*2 CYP2C9 haplotype + AG genotype of VKORC1); 5→ (*1/*2 CYP2C9 haplotype + AA genotype of VKORC1); 6→ (*1/*3 CYP2C9 haplotype + GG genotype of VKORC1); 7→ (*1/*3 CYP2C9 haplotype + AG genotype of VKORC1); 8→ (*2/*2 CYP2C9 haplotype + AG genotype of VKORC1); 9→ (*3/*3 CYP2C9 haplotype + GG genotype of VKORC1); 10→ (*3/*3 CYP2C9 haplotype + AG genotype of VKORC1).

Modeling the Warfarin Dose Requirement

Stepwise multiple linear regression analysis was undertaken to model and quantify the association of genetic and nongenetic factors with weekly warfarin dose requirement. To avoid overfitting of data, 5 pertinent candidate variables were considered, that is, age, gender, BMI, CYP2C9*3, and VKORC1 (−1639G>A) variant alleles. The multivariate regression model identified CYP2C9*3, VKORC1, and BMI as significant determinants of warfarin dose. The model explains 47.5% of interindividual variability in warfarin dose (Table 5). Based on the model (Table 6), the weekly warfarin dose requirement can be represented as

Table 5.

Stepwise multiple linear regression model

Table 6.

Regression model used to model warfarin dose requirement

Log warfarin dose (mg/week) = 1.682 – 0.247­(CYP2C9*3) – 0.152 (VKORC1) + 0.010 x BMI.

Discussion

In the present work, we aimed at quantifying the association of genetic and nongenetic factors with the warfarin dose requirements in cardiac outpatients attending North Indian tertiary care hospital. Genotype frequencies for both CYP2C9 and VKORC1 genes reported here are in agreement with previous reports [20-22].

We found over 15-fold variation in warfarin dose requirement among the studied cohort. Individuals carrying warfarin-sensitive variant alleles for CYP2C9*3 and VKORC1 required significantly lower warfarin doses compared to individuals with the wild genotype. We observed a trend of decreasing warfarin dose in patients carrying CYP2C9*2 variant alleles as well, but the difference was not statistically significant. The presence of variant alleles in both CYP2C9 and VKORC1 genes showed additive effect on decreasing warfarin doses. For instance, 1 patient with the *3/*3 haplotype of CYP2C9 with the AG genotype for VKORC1 required the lowest dose of 5 mg/week among the studied cohort. In contrast, an individual with only the *3/*3 haplotype with the wild genotype for the VKORC1 gene had a higher dose requirement of 11 mg/week. Similarly, presence of the AG genotype along with the *1/*2 haplotype decreased warfarin dose requirements from 30.83 ± 10.7 mg/week to 16 mg/week compared to *1/*2 haplotype individuals possessing wild-type VKORC1alleles. Two patients with minor bleeding also had variant alleles in both CYP2C9 and VKORC1 genes. Studies have pointed to more chances of out of range INR and more bleeding events in individuals with variant alleles [32, 33].

The stepwise multivariate regression model identified CYP2C9*3, VKORC1 −1639G>A, and BMI as significant determinants of warfarin dose. Together, these factors explain nearly half of the interindividual variation in warfarin dose requirement. The model showed genetic factors to be more important sources of variability in warfarin dose response compared to clinical factors. These findings are in conformity with previous research reports [10-12, 27]. CYP2C9*3 polymorphism alone accounted for 29% of variation in dose response and can be seen as large compared to other studies. In contrast to other studies, which identified CYP2C9*2 as another important factor for varying warfarin doses, we did not find the association of the CYP2C9*2 gene with warfarin dose as significant, although trend to decreasing warfarin dose was observed [27, 29, 34]. Compared to other studies, the effect of BMI in our model was modest (β = 0.010). One of the plausible reasons for this could be the absence of morbidly obese patients in our study cohort. A recent study (n = 831) showed that mean weekly warfarin dose increases by a factor of 0.69 for a unit increase in BMI. The large impact of BMI on warfarin doses in this study was likely due to inclusion of patients over a wide BMI range of 13.4–63.1 kg/m2. They included about 49 morbidly obese (BMI >40 kg/m2) patients in the analysis. Moreover, they studied the effect of BMI on warfarin dose in isolation [35]. The impact of smoking and alcohol on warfarin dose requirement is unclear from the present study due to very limited representation of smokers (n = 1) and occasional alcoholics (n = 7).

Strengths of the study: the study quantifies the association of genetic and nongenetic factors with warfarin dose in a homogenous cohort of cardiac patients on warfarin. This may be of significance in view of a recent randomized trial showing greater benefit of genotype-guided warfarin dosing in patients of atrial fibrillation compared to patients with pulmonary embolism and deep vein thrombosis [36].

Limitations of the study: limitations of our work include small sample size and studying a limited number of genetic polymorphisms. However, we accounted for the most important polymorphisms reported to affect warfarin pharmacokinetics and pharmacodynamics across the literature.

Acknowledgments

The authors would like to thank all the patients who volunteered for the study. They also extend their gratitude to Ms. Jasbir Kaur; lab technician, and Ms. Anu Aggarwal, PhD student, Department of Hematology, for contribution in method development.

Statement of Ethics

The study was conducted ethically in accordance with the principles of the Declaration of Helsinki. All the study subjects have given their written informed consent. The study protocol was approved by the Institute Ethics Committee (Intramural) at PGIMER, Chandigarh, Vide Reference No. NK/2608/DM/3107.

Conflict of Interest Statement

The authors have no conflicts of interest to declare.

Funding Sources

The work was done as a DM (Clinical Pharmacology) thesis and partly supported by departmental funding.

Author Contributions

Dr. Nusrat Shafiq and Dr. Samir Malhotra conceptualized the study, monitored conduct and analysis, and critically reviewed the manuscript. Dr. Navjot Kaur and Dr. Avaneesh Pandey contributed to data collection, genotyping, data analysis, compilation of results, and drafting the manuscript. Dr. Reena Das and Dr. Jasmina Ahluwalia designed genotype experiments and contributed to genotyping data analysis and reviewing the manuscript. Dr. Ankur Gupta and Dr. Harkant Singh contributed to enrolling eligible patients, data analysis, and critical review of the manuscript.

Data Availability Statement

All data generated or analyzed during this study are included in this article. Further enquiries can be directed to the corresponding author.

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Nusrat Shafiq, nusrat.shafiq.pgi@gmail.com

Article / Publication Details

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Received: August 29, 2020
Accepted: August 30, 2021
Published online: October 21, 2021

Number of Print Pages: 9
Number of Figures: 4
Number of Tables: 6

ISSN: 1662-4246 (Print)
eISSN: 1662-8063 (Online)

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