Abstract
Purpose
Vitamin K antagonists (VKAs), such as warfarin and acenocoumarol, exert their anti-coagulant effect by inhibiting the subunit 1 of vitamin K epoxide reductase complex (VKORC1). CYP2C9 is a hepatic drug-metabolizing enzyme in the CYP450 superfamily and is the primary metabolizing enzyme of warfarin. Three single nucleotide polymorphisms, two in the CYP2C9 gene, namely CYP2C9*2 and CYP2C9*3, and one in the VKORC1 gene (c.− 1639G > A, rs9923231), have been identified to reduce VKA metabolism and enhance their anti-coagulation effect. The purpose of this study is to evaluate the prevalence of CYP2C9 and VKORC1 polymorphism in Indians receiving VKA-based anti-coagulation after valve surgery and to evaluate the usefulness of genetic information in managing VKA-based anti-coagulation.
Methods
In the current prospective observational study, 150 patients who underwent heart valve surgery and had stable INR were genotyped for VKORC1 (− 1639 G > A), CYP2C9*2, and CYP2C9*3. The VKA dosage was estimated from published algorithms and compared to the clinically stabilized dosage.
Results
Out of 150 patients, 101 (67.33%) were on warfarin and 49 (32.66%) were on acenocoumarol. Majority of the patients, the 83 in warfarin group and the 40 in acenocoumarol group, had a wild CYP2C9 diplotype. The rest had a mutant (CYP2C9*2 or CYP2C9*3) diplotype. Similarly, 67 patients in the warfarin group and 35 patients in the acenocoumarol group had wild type (G/G) of VKORC1 genotype. The rest had a mutant (G/A or A/A) VKORC1 genotype. In the warfarin group, based on the genotype, 51.5% of the patients were extensive or normal metabolizers, and 47.4% of the patients were intermediate metabolizers of VKAs. In the acenocoumarol group, 61.2% of the patients were extensive or normal metabolizers, and 38.8% of the patients were intermediate metabolizers. Individually, alleles of VKORC1 (− 1639 G > A), CYP2C9*2, and CYP2C9*3 had mean dosage reduction effect on VKA dosage, which co-related to the clinically stabilized dosages (P < 0.0001). Among the VKORC1 (− 1639 G > A) cohort, the reduction in warfarin mean weekly dosage was 13.48 mg as compared to the wild-type category (P < 0.0001) and similarly, the reduction in the mean weekly acenocoumarol dose was 6.07 mg (P < 0.03) as compared to the wild type after adjusting for age, gender, and body mass index.
Conclusion
Single nucleotide polymorphism in the CYP2C9 gene and in the VKORC1 gene is present in nearly 40% of Indian patients. VKORC1 (− 1639 G > A), CYP2C9*2, and CYP2C9*3 genotypes have significant dosage-lowering effects on VKA-based anti-coagulation therapy. The trend in estimated dosages of VKAs co-related to that of observed the clinically stabilized dosage in the cohort. The pharmacogenomic calculators used in this study tend to overestimate the VKA dosages as compared to clinical dosage due to the limitations in the algorithms and in our study. A new algorithm based on a larger dataset capturing the vast genetic variability across the Indian population and relevant clinical factors could provide better results.
Keywords: Vitamin K antagonist, CYP2C9, VKORC1
Introduction
Vitamin K antagonists (VKAs), such as warfarin and acenocoumarol, are widely used anti-coagulants after cardiac valve replacement. These have a very narrow therapeutic index and large inter-patient variability in the doses required to achieve the target anti-coagulation. Thus, accurate dosing is essential for safe anti-coagulation. Most of the known non-genetic factors, such as body size and age, fail to predict an individual’s dose requirement correctly. Therefore, considerable investigations have been performed to find out the genetic influences on VKAs’ dose requirements.
VKAs exert their anti-coagulant effect by inhibiting the subunit 1 of vitamin K epoxide reductase complex (VKORC1). CYP2C9 is a hepatic drug-metabolizing enzyme in the CYP450 superfamily and is the primary metabolizing enzyme of warfarin. Three single nucleotide polymorphisms (SNPs), two in the CYP2C9 gene, namely CYP2C9*2 (c.430C > T; p.Arg144Cys; rs1799853) and CYP2C9*3 (c.1075A > C; p.Ile359Leu; rs1057910), and one in the VKORC1 gene (c.− 1639G > A, rs9923231), have been identified to play major roles in determining the impact of warfarin therapy on coagulation. The normal or wild-type SNP of CYP2C9 is referred to as *1, and the two polymorphic versions are *2 and *3. Each person can carry any two SNPs (for example, *1/*1, *1/*2, or *2/*3). The *1 variant of CYP2C9, i.e., individual’s homozygous for the reference CYP2C9 allele (CYP2C9*1), has the “normal or extensive metabolizer (EM)” phenotype and metabolizes VKA normally. CYP2C9*2 reduces VKA metabolism by 30–40%, and CYP2C9*3 reduces VKA metabolism by 80–90%. Thus, in the presence of any one *2 or *3 variants (*1/*2 or *1/*3), the individuals shall be “intermediate metabolizers (IM),” and therapeutic effect will be enhanced requiring lower warfarin doses. The other CYP2C9 variant combinations such as *2/*2, *2/*3, and *3/*3 constitute “poor metabolizers (PM).” In the VKORC1 SNP, the common G allele is replaced by the A allele. Persons with an A allele produce less VKORC1 than those with the G allele. Hence, lower warfarin doses are needed to inhibit VKORC1 in carriers of the A allele [1, 2]. These three SNPs play key roles in determining the dose of warfarin required to produce a therapeutic anti-coagulation and reduce the risk of bleeding [3–5].
Based on the influence of these SNPs, in 2007, the US FDA modified the warfarin label, stating that CYP2C9 and VKORC1 genotypes may be useful in determining the optimal initial dose of warfarin [6]. The label was further modified in 2010 by incorporating a table (Table 1) describing recommendations for initial dosing ranges for patients with different combinations of CYP2C9 and VKORC1 genotypes. The label states that the patient’s CYP2C9 and/or VKORC1 genotypes are known, to consider these doses when selecting the initial dose of warfarin [7, 8].
Table 1.
FDA warfarin dosage recommendations for carriers of the CYP2C9 and VKORC1 variants
| VKORC1 (allele) | CYP2C9 (allele) | ||||
|---|---|---|---|---|---|
| *1/*1 | *1/*2 | *1/*3 | *2/*2 | *2/*3 | |
| G/G | 5–7 | 5–7 | 3–4 | 3–4 | 3–4 |
| G/A | 5–7 | 3–4 | 3–4 | 3–4 | 0.5–2 |
| A/A | 3–4 | 3–4 | 0.5–2 | 0.5–2 | 0.5–2 |
Ref: FDA label for warfarin and CYP2C9, VKORC1 [7]
Based on genetic and non-genetic factors, numerous studies have derived warfarin dosing algorithms to predict warfarin dose [9–13]. Two of the most commonly used algorithms have been developed by Gage and colleagues [14], and the International Warfarin Pharmacogenetics Consortium (IWPC) [15]. Using these algorithms, the desired dose of warfarin can be calculated by an online calculator available on https://www.warfarindosing.org. The 2017 Clinical Pharmacogenetics Implementation Consortium (CPIC) guideline for warfarin pharmacogenomics states that algorithms such as IWPC predicts warfarin dose better than in the FDA drug label [16]. Few algorithms have been published for acenocoumarol dosage estimation based on clinical and genetic factors. One such algorithm was developed by Rathore et al. in Indian patients using multiple regression model [17].
Though there are several studies from western and oriental countries [3–5, 18, 19], very few studies [20–25] deal with the Indian population. Several centers in the world have adopted the pharmacogenomics guided anti-coagulation, either by using an algorithm [14, 15] or using a fixed dose schedule [7]. But, we do not have extensive data in the Indian population. Although there is data on patients taking acenocoumarol from the North Indian population, there is no data on patients taking warfarin. The present study is being performed to evaluate the prevalence of genetic polymorphism in the Indian population and its effect on the dose of VKAs.
Patients and methods
Study subjects and informed consent
A total of 150 patients were recruited for this study. Patients who were either on warfarin or acenocoumarol were selected. This study was approved by the Institutional Ethics Committee. Informed consent was obtained from all individual participants included in the study.
Inclusion criteria
Patients after 3 months or more of heart valve surgery, aged 18 years or more, who were receiving warfarin or acenocoumarol and had achieved steady and desired anti-coagulation status, and who consented to participate in the study. The patient was declared to have achieved steady and desired anti-coagulation status when INR values were within the target range for three consecutive visits (the interval between any two consecutive follow-up visits being more than 7 days).
Exclusion criteria
History of liver disease or serum transaminases levels 1.5-fold higher than normal
History of kidney disease, renal impairment, or serum creatinine > 1.5 mg/dL
Congestive heart failure (ejection fraction < 40%, class IV symptoms)
Thyroid dysfunction, cancer; pregnancy or breastfeeding period
Concomitant drug intake: amiodarone, rifampicin, phenytoin, carbamazepine, statins, azoles, sulpha drugs
Data collection
Through the records of the patient, the following data were collected:
Demographics: gender, age, height, weight
Type and date of surgery
History/examination/investigations suggestive of congestive heart failure and hepatic or renal dysfunction
Drugs intake: amiodarone, rifampicin, phenytoin, carbamazepine, statins, azoles, sulpha drugs
Steady-state warfarin dose, i.e., the warfarin dose that maintained the INR values within the target range for three consecutive visits (the interval between any two consecutive follow-up visits being more than 7 days)
VKORC1 and CYP2C9 genotyping analysis
Sample collection
Using aseptic technique, 4 ml of blood sample was collected in EDTA tube. All samples were anonymized and coded.
Genetic analysis
Genomic DNA (gDNA) was extracted from whole blood using the blood DNA isolation kit (Qiagen, Germany) using a standard protocol. The presence of DNA was confirmed by running DNA in 0.8% agarose gel. For mutation detection, amplicons containing the VKORC1 − 1639 and CYP2C9 *2 and *3 sites were generated by PCR primers designed by Mendelian Health Technologies Pvt. Ltd. (Pune, India) from gDNA. Quantification of PCR products was done using standard agarose gel runs. Targeted regions of VKORC1 and CYP2C9 were amplified using AMPLITAQ GOLD (N8080243) from Applied Biosystem, USA, in both forward and reverse directions. The PCR products were purified using PCR purification kit from Qiagen (Germany) and Sanger sequencing was performed as per the manufacturer’s recommended protocol in 3130XL DNA analyzer using an ABI PRISM Big Dye V2 reaction kit (Applied Biosystems, Foster City, CA, USA).
Bioinformatics analysis
The sequence results were used further downstream for bioinformatics analysis. The raw chromatograms analyzed for low-quality regions at the ends and were excluded if below Q20. Nucleotide variations were identified by comparison with the VKORC1 and CYPC29 cDNA reference sequence using NovoSNP 3.0.1 tool [26]. Wild-type and allele variations identified at VKORC1 − 1639 and CYP2C9 *2 and *3 locations were recorded for each patient. The genotypes along with clinical information were recorded in a spreadsheet.
Estimation of genotype-based warfarin or acenocoumarol dosage
The warfarin dosage was estimated using the International Warfarin Pharmacogenetics Consortium’s dosage calculator. This calculator provides a predicted starting dose for warfarin based on a pharmacogenetic algorithm developed by IWPC [15]. Age (years), height (cm), weight (kg), VKORC1 and CYP2C9 genotypes, and race were considered for warfarin dose estimation. The acenocoumarol dosage was estimated by the multiple regression formula provided by Rathore et al. [17] which also considered age, gender, height, weight, and type of surgery besides the genotypes considered in this study.
Statistical methods
The continuous variables were expressed as mean ± standard deviation (SD) and compared using t test for independent samples. Categorical variables were expressed as percentages and compared using Pearson’s chi-square test. The comparison of clinically stabilized and estimated weekly dose of each medication was performed using paired t test. Further, the comparison of these doses was also performed according to markers and their genotypes. In the warfarin group, one-way analysis of variance was used to compare the estimated and clinically stabilized dose across three types of metabolizers. The paired analysis was performed using Tukey’s post hoc test. In the acenocoumarol group, a two-group comparison was performed using t test for independent samples. In each treatment group, multivariate regression analysis was carried out to determine the effect of combination of genotypes with reference to wild-type combination adjusted for demographic parameters. Also, the linear correlation between clinically stabilized and estimated doses was obtained. All the analyses were performed using SPSS version 20.0 (IBM Corp, Armonk, NY) and the statistical significance was tested at 5% level.
Results
The data on demographic characteristics, genotypes on three selected markers, and estimated as well as clinical stabilized doses of warfarin and acenocoumarol are shown in Table 2.
Table 2.
Summary of patient characteristics in two treatment groups
| Factors | Type of VKA | ||
|---|---|---|---|
| Warfarin (n = 101) | Acenocoumarol (n = 49) | ||
| Age in years (mean ± SD) | 35.32 ± 13.28 | 47.14 ± 15.07 | |
| Gender (%) | Male | 59 (58.41) | 26 (53.06) |
| Female | 42 (41.58) | 23 (46.94) | |
| BMI in kg/m2 (mean ± SD) | 22.38 ± 3.26 | 24.59 ± 4.56 | |
| Genetic factors | |||
| CYP2C9 diplotype | *1/*1 (wild) | 83 | 40 |
| *1/*2 | 5 | 6 | |
| *1/*3 | 11 | 2 | |
| *2/*2 | 1 | 0 | |
| *2/*3 | 1 | 0 | |
| *3/*3 | 0 | 1 | |
| VKORC1-1639 | G/G (wild) | 67 | 35 |
| G/A | 32 | 12 | |
| A/A | 2 | 2 | |
| Overall classification | |||
| EM (*1/*1) | 52 (51.48%) | 30 (61.22%) | |
| IM (*1/*2 or *1 /* 3 or VKORC1) | 46 (47.42%) | 19 (38.78%) | |
| PM (*2/*3 or *3/*3 or VKORC1 − 1689 G > A and *1/*2 or *1/*3) | 3 (3.09%) | 0 | |
| Clinically stabilized weekly dose (mg) | 32.04 ± 12.26 | 22.22 ± 9.41 | |
| Estimated weekly dose (mg) | 40.46 ± 7.91 | 25.63 ± 7.24 | |
BMI body mass index, EM extensive metabolizers, IM intermediate metabolizers, PM poor metabolizers, VKA vitamin K antagonist
Out of 150 patients, 101 (67.33%) were on warfarin and 49 (32.66%) were on acenocoumarol. Majority of the patients, 83 in the warfarin group and 40 in the acenocoumarol group, had wild CYP2C9 diplotype. The rest had a mutant CYP2C9 diplotype (Table 2). Similarly, 67 patients in the warfarin group and 35 patients in the acenocoumarol group had wild type (G/G) of VKORC1 genotype. The rest had a mutant (G/A or A/A) VKORC1 genotype. Overall frequency of EM, IM, and PM is also shown in Table 2. In the warfarin group, based on genotype, 51.5% of the patients were extensive or normal metabolizers, and 47.4% of the patients were intermediate metabolizers of VKAs. In the acenocoumarol group, 61.2% of the patients were extensive or normal metabolizers, and 38.8% of the patients were intermediate metabolizers. The mean clinically stabilized weekly dose of warfarin (32.04 ± 12.26 mg) was significantly less than the mean weekly estimated dose (40.46 ± 7.91 mg) (p < 0.0001). In the acenocoumarol group also, the mean clinically stabilized dose (22.22 ± 9.41 mg) was significantly less than the mean estimated dose (25.63 ± 7.24 mg) (0.0001).
For each anti-coagulant type, the comparison was performed between the wild type and mutants (Table 3). Both the estimated warfarin/acenocoumarol dosage and clinically stabilized dosages were significantly lower in mutant groups. Frequency of EM, IM, or PM and the estimated and clinically stabilized oral anticoagulant dose are shown in Table 4. In the warfarin group, 52 (51.5%) were EM, 46 (45.5%) were IM, and 3 (2.9%) were PM. Similarly, in the acenocoumarol group, 30 (61.2%) were EM, 19(38.8%) were IM, and 2 were PM. Both the estimated doses and the clinically stabilized doses were significantly lower in IM and PM.
Table 3.
Comparison of anti-coagulant dosages between wild-type and aggregated mutant populations
| Drug | Method | CYP2C9 | P value | VKORC1 − 1639 | P value | ||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| Wild type | Mutant | Wild type | Mutant | ||||||||
| n | Mean ± SD | n | Mean ± SD | n | Mean ± SD | n | Mean ± SD | ||||
| Warfarin dose (mg) | Estimated weekly | 83 | 42.22 ± 6.70 | 18 | 33.76 ± 8.03 | < 0.0001 | 67 | 43.43 ± 6.95 | 34 | 34.97 ± 6.59 | < 0.0001 |
| Clinically stabilized | 83 | 33.76 ± 8.03 | 18 |
25.12 ± 10.95 |
0.0065 | 67 | 35.76 ± 11.31 | 34 | 24.71 ± 9.90 | < 0.0001 | |
| Acenocoumarol dose (mg) | Estimated weekly | 40 | 26.78 ± 7.16 | 9 | 20.61 ± 5.38 | 0.0192 | 35 | 27.50 ± 6.36 | 14 | 21.00 ± 7.39 | 0.0034 |
| Clinically stabilized | 40 | 23.54 ± 9.80 | 9 | 16.33 ± 3.91 | 0.0012 | 35 | 23.80 ± 9.63 | 14 | 18.25 ± 7.79 | 0.0615 | |
Table 4.
Estimated and clinically stabilized doses corresponding to overall classification
| Drug | Overall classification | P value | |||
|---|---|---|---|---|---|
| EM | IM | PM | |||
| Warfarin dose (mg) | n (%) | 52 (51.48) | 46 (47.42) | 3 (2.9) | |
| Estimated [mean ± SD] | 45.92 ± 5.41a | 36.30 ± 3.97b | 17.00 ± 5.00c | < 0.0001* | |
| Clinically stabilized [mean ± SD] | 38.79 ± 10.75a | 26.40 ± 9.35b | 10.50 ± 3.50c | < 0.0001* | |
| Acenocoumarol dose (mg) | n (%) | 30 (61.22) | 19 (38.78) | 0 | |
| Estimated [mean ± SD] | 28.12 ± 6.6 | 21.74 ± 6.56 | 0 ± 0 | 0.002† | |
| Clinically stabilized [mean ± SD] | 24.62 ± 10.13 | 18.42 ± 6.79 | 0 ± 0 | 0.0137† | |
*Obtained using one-way ANOVA. Values with different superscripts (a,b,c) indicates significant difference in means obtained using Tukey’s post hoc test; †obtained using independent sample t test. EM extensive metabolizers, IM intermediate metabolizers, PM poor metabolizers
The effect of mutation on dose requirement compared to wild-type combination of genotypes was studied after adjusting with demographic parameters like age, gender, and BMI using multiple linear regression (Table 5). The mean dose required for mutated cases with respect to wild types was determined independently for warfarin and acenocoumarol. For warfarin, the coefficient − 13.48 (SE 2.33) corresponding to the genotype suggests that the mean weekly dose was smaller in mutated category by 13.48 mg as compared to wild type category (p < 0.0001) after adjusting for age, gender, and BMI. Similarly, for acenocoumarol, the mean weekly dose in mutated category was smaller by 6.07 mg as compared to wild type (p = 0.0316) after adjusting for age, gender, and BMI. Further, in males, the mean acenocoumarol dose requirement was significantly higher (11.51 mg) as compared to females (p < 0.0001). For the unit increase in BMI, the mean acenocoumarol daily dose increased by 1.06 mg (p = 0.0004).
Table 5.
Multiple regression of clinically stabilized weekly dose of Warfarin and Acenocoumarol on demographic factors and genotype
| Parameters | Warfarin | Acenocoumarol | ||
|---|---|---|---|---|
| Coefficients | P value | Coefficients | P value | |
| B (SE) | B (SE) | |||
| (Constant) | 29.32 (8.56) | 0.001 | 4.22 (6.47) | 0.519 |
| Age in years | − 0.16 (0.09) | 0.0736 | − 0.22 (0.07) | 0.0055 |
| Gender (M) | − 1.14 (2.33) | 0.6249 | 11.51 (2.10) | < 0.0001 |
| Body mass index | 0.75 (0.40) | 0.0642 | 1.06 (0.27) | 0.0004 |
| Genotype (mutated) | − 13.48 (2.33) | < 0.0001 | − 6.07 (2.70) | 0.0316 |
Adjusted R2: warfarin = 0.340; acenocoumarol = 0.622
The correlation between estimated and clinically stabilized doses was obtained for warfarin and acenocoumarol and is shown in Fig. 1. For warfarin, the coefficient was 0.594 indicating a positive relationship between the two dose estimates, which was statistically significant (p < 0.0001). Also, for acenocoumarol, the correlation was 0.863 indicating a strong positive correlation between the two doses, which was statistically significant (p < 0.0001).
Fig. 1.
Scatter plot showing correlation between estimated and clinically stabilized doses of warfarin and acenocoumarol
Discussion
VKAs and inter-patient variability
VKAs are one of the most widely prescribed anti-coagulants. Both warfarin and acenocoumarol demonstrate a narrow therapeutic index and large inter-patient variability in the dose required to achieve target anti-coagulation. Until individual’s therapeutic dose is known, patients on VKAs are at high risk for serious adverse health events, such as bleeding, especially during the drug initiation. Multiple factors contribute to the inter-individual variability. Approximately 20% of the variability is explained by age, presence of comorbidities (e.g., diabetes, cancer, concomitant use of some medications etc.), and other characteristics such as age, gender, and BMI. About 35–40% of VKA dose variability is attributed to polymorphisms in CYP2C9, VKORC1, and CYP4F2 genes [1–5]. Indian studies have confirmed the role of CYP2C9 and VKORC1 in VKA dose variation [20–25]. Majority of these studies are based on the South Indian population and overall VKA pharmacogenomic data is limited in the Indian population. Although there is data on patients taking acenocoumarol from North Indian population, there is no data on patients taking warfarin.
Selection of genotyping method
Our study used Sanger sequencing for genotyping because it is considered as GOLD standard platform for identifying and validating mutations. Although polymerase chain reaction (PCR), array hybridization chip, and amplified fragment length polymorphism (AFLP) techniques are available, Sanger sequencing provides highest reliable data and is easier to use. Also, data analysis and interpretation are highly standardized with Sanger sequencing. AFLP is prone to errors. Allele-specific PCR or probe-based assay requires much standardization.
Distribution of genotypes
In this study, the distribution of variant alleles of CYP2C9 (diplotypes) and VKORC1 was analyzed in our population. Heterozygous CYP2C9 diplotypes frequencies of *1/*2 and *1/*3 were 7.3% and 8.67% respectively, while homozygous CYP2C9 *2/*2 and *3/*3 along with *2/*3 cases were negligible (1 case each). VKORC1 heterozygous G/A and homozygous G/G mutants’ frequencies are 29.3% and 2.6%. The genotype frequencies of CYP2C9 and haplotype frequency of VKORC1 − 1639 are compared across some major Indian studies in Table 6. We observe that the *1/*2 genotype frequency of CYP2C9 and VKORC1 − 1639 are agreeable to the other North Indian population studies reported. As compared to the South Indian population, the mutant population is more prevalent in North India. Compared to global average frequencies of CYP2C9 diplotypes and VKORC1 G/A available from the CPIC guideline [16], our population shows lower frequencies of the mutant population.
Table 6.
Prevalence of single nucleotide polymorphism in various populations
| Study | Population | N | CYP2C9 *1/*2 (%) | CYP2C9 *1/*3 (%) | VKORC1 − 1639 G/A (%) | VKORC1 − 1639 G/G (%) |
|---|---|---|---|---|---|---|
| Present study | North India | 150 | 7.3 | 8.67 | 29.3 | 2.6 |
| Nahar et al. [25] | North India | 209 | 9.6 | 20 | 34.4 | 1.4 |
| Rathore et al. [17] | North India | 225 | 9.3 | 16.9 | 29.3 | 4 |
| Krishna Kuma et al. [20] | South India | 240 | 6.7 | 13.8 | 18.8 | 1.2 |
| Pavani et al. [24] | South India | 240 | 4.25 | 3.25 | 11.9 | NA |
| CPIC guideline supplementary data* [16] | Caucasian (European + North American) | – | 20.17 | 11.33 | 41.24 | NA |
| Middle Eastern | – | 20.32 | 14.32 | 46.52 | NA | |
| East Asian | – | 0.12 | 6.5 | 88.16 | NA |
NA not available
*Average frequencies based on the reported frequencies in one or multiple studies
Genotype-based algorithms and Indian population
A number of algorithms, based on both genetic and non-genetic factors, have been published for estimating therapeutically stable warfarin starting dosages. Based on the comparison and reviews [9, 10, 13–15] of various algorithms, two algorithms the IWPC [15] and Gage et al. [14] are found to perform better than others. These algorithms are generated from European and American-African datasets. Very few regression models are available, specifically for acenocoumarol dosage prediction. We have used the IWPC [15] and Rathore [17] regression models for warfarin and acenocoumarol dose prediction respectively. In our study, compared to clinical dosage, the estimate dosage from respective algorithms shows a significant difference. The flexibility of the IWPC algorithm to adapt in any local population is challenged by ethnicity, lifestyle factors such as diet, BMI, and distribution of polymorphisms [10, 16–18, 24]. The Rathore [17] acenocoumarol model, though works well, is limited by a very low number of patient data and is specific to the local North Indian population. Another warfarin algorithm by Pavani et al. [24] has been shown to work well in the South Indian population but was not used in this study.
Difference in clinical stabilized dose and calculated dose on the basis of genotype
The estimated VKA dosage from algorithms correlated with clinically stabilized dose (Fig. 1). The scatter plot captures a positive trend in the variation of VKA dosages across the study population which attained the therapeutic INR range. Although the estimated dosage predicted by the two algorithms correlates well to the clinical dosage, the estimated dosages are significantly higher for both VKAs. The IWPC algorithm is for warfarin dosage estimation and is primarily based on the Caucasian population along with African-Americans. It considers age, weight, height, race, CYP2C9*2, CYP2C9*3, and VKORC1 genotypes, along with co-medications such as amiodarone and CYP2C9 inducers. We have applied this algorithm directly to the Indian population where height, weight, ethnicity, diet, and lifestyle differ vastly from that of Caucasians or Afro-Americans. The overestimation of warfarin dosage by the IPWC algorithm was also found in a comparative study [9] where the IPWC algorithm was compared to four other prediction algorithms based on East Asian population. The study concluded that the impact of various factors should be considered before selecting the appropriate model for the region-specific population. In another review, the IWPC algorithm was less accurate in African-American population as compared to Caucasians [18]. Besides population-specific polymorphisms, factors like co-medications, alcohol, and smoking could influence the VKA dosages. There might be unknown genetic markers specific to Indian ethnicities which may influence the variance of VKA dosage. The acenocoumarol dosage prediction algorithm [17], based on the Indian population, has performed more satisfactorily when compared to the clinically stabilized dosage. However, it has its own limitations. This algorithm is based on only 125 cases and may not be adequate for capturing the diverse genetic pool of Indian cases.
A comparison study [10] of 13 published warfarin genotype algorithms including 1940 subjects, demonstrated that, overall, all algorithms had similar performance (mean absolute error 10.3 mg/week and mean percentage within 20–41.4%). However, the algorithms derived from racially mixed population tended to perform better than race-specific algorithms. Hence, an appropriate and optimal solution to this issue of over- or underprediction is to have a prediction model based on all ethnic groups of India with a large sample size along with the clinical factors.
Association between genotype and VKA requirements
There was a significant association between the mutant population and VKA dosage reduction as compared to the wild types for all genotypes of CYP2C9 and VKORC1. Together, the genetic factors contributed around 40–46% dosage reduction. Pavani et al. [24] and Krishna Kumar et al. [20] could explain up to 46% of the warfarin dosage variation in their study population based on genetic factors alone. Both the studies also covered additional genetic markers in VKORC1, CYP4F2, and GGCX as compared to our study. Rathore et al. [17, 22] reported VKORC1 G > A as most significantly associated to acenocoumarol dosage reduction and could explain 37% of dosage reduction based on VKORC1, CYP4F2, gender, and weight parameters. In a recent large European study, acenocoumarol and phenprocoumon algorithms were found to explain 59.4% and 49.0% variations in dose requirements respectively [27]. Common variants in CYP2C9 and VKORC1 account for up to 18% and 30% respectively of the variance in stable warfarin dose among the patients of European ancestry [4, 14].
Genotype classification of EM/IM/PM
The trend of VKA dosage reduction between EM, IMs, and PMs was observed in both clinically stabilized dosage and algorithmically estimated dosages. The warfarin dosage reduction for IMs was about 12% and for PMs was 90% as compared to the EMs. Similarly, the dosage reduction between EMs and IMs for the acenocoumarol group was 36%. There was no PM among the acenocoumarol cases. This study confirms that CYP2C9 *2 and *3 have a moderate effect on the VKA dosage and VKORC1 − 1639 G > A majorly contributes towards VKA sensitivity. Almost all literature [1–24] confirms this finding.
Effect of other clinical parameters
In the acenocoumarol group of our study, males required an 11.5 mg/week higher dose as compared to the females. Several studies [8, 10, 21], but not all, report that sex is a determinant of warfarin dose, women requiring lower doses than men. Pavani et al. [24] have also reported that males require a higher dose of warfarin than females (P = 0.0001). A retrospective chart review of atrial fibrillation or venous thromboembolism patients attending the anti-coagulation clinic [28] observed that though women required a lower dose than men, the difference was not statistically significant.
Usefulness and future perspective
Controlling INR is a major issue in patients at initiation of VKA therapy. When anti-coagulation level is less than the desired level, patients are more likely to experience adverse thrombotic or embolic events. Excess anti-coagulation imposes increased risk of major bleeding. Pharmacogenetic algorithms use clinical as well as known genetic factors such as CYP2C9 and VKORC1 to predict optimal dosage for patients to attain and maintain therapeutic INR range. If clinicians know the genetic profile of patients, they can optimize the VKA dosage faster, leading to better TTR (time in therapeutic range) and lower rates of adverse events [29].
Future studies should consider all clinical and genetic cofactors influencing of VKA such as drug interactions, other genes not considered in the study. Cases of high resistance to VKA therapy can be included. A new, robust VKA dosage prediction model with large sample size specific to Indian ethnicity and an algorithm using machine learning and advanced statistics can improve the estimation statics.
Limitations
There are few limitations in our study. We did not include other concomitant factors such as smoking status, alcohol status, and vitamin K diet in our study. We have considered only 150 subjects, that too from North India. Hence, we cannot generalize our results for the entire Indian population.
Conclusion
Single nucleotide polymorphism in the CYP2C9 gene and in the VKORC1 gene is present in nearly 40% Indian patients. VKORC1 (− 1639 G > A), CYP2C9*2, and CYP2C9*3 genotypes have significant dosage-lowering effects on VKA-based anti-coagulation therapy. The trend in estimated dosages of VKAs co-related to that of observed clinically stabilized dosage in the cohort. Although the two algorithms used in this study capture the genetic variation in the dosages, these tend to overestimate the dosages. In order to adapt the algorithms to clinical settings further improvements are necessary.
Acknowledgments
We acknowledge contributions of Dr. Dhananjay Raje and Ms. Moumita Chakraborty from MDS Bioanalytics, Pune, for conducting the statistical analysis. Blood samples for genotyping were analyzed free of cost at Mendelian Health Technologies Pvt. Ltd., Pune, India.
Compliance with ethical standards
Conflict of interest
Mathew A B, Hote MP, Talwar S, Rajashekhar P, and Choudhary SK have no conflict of interest. One of the authors (Parhar A) is employed with a commercial clinical genomic services organization (Mendelian Health Technologies, Pune, India) and was involved in the design, analysis, and discussion of findings. This study was a collaborative project between All India Institute of Medical Sciences, New Delhi, India, and Mendelian Health Technologies, Pune, India. No financial relationship exists between any organizations that might have interest in the submitted work.
Ethical approval for research involving human participants and/or animals
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Ethical approval was obtained from institutional ethics committee.
This article does not contain any studies with animals performed by any of the authors.
Informed consent
Informed consent was obtained from all individual participants included in the study.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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