Next Article in Journal
Testing the Protective Effects of Sulfobutylether-Βeta-Cyclodextrin (SBECD) and Sugammadex against Chlorpromazine-Induced Acute Toxicity in SH-SY5Y Cell Line and in NMRI Mice
Next Article in Special Issue
Role of IL6R Genetic Variants in Predicting Response to Tocilizumab in Patients with Rheumatoid Arthritis
Previous Article in Journal
Inorganic Nanoflowers—Synthetic Strategies and Physicochemical Properties for Biomedical Applications: A Review
Previous Article in Special Issue
Insights into the Pharmacogenetics of Tacrolimus Pharmacokinetics and Pharmacodynamics
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Pharmaceutics
Volume 14
Issue 9
10.3390/pharmaceutics14091889
pharmaceutics-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Association between Genetic Polymorphisms and Bleeding in Patients on Direct Oral Anticoagulants

by
Ha-Young Yoon
1,†,
Tae-Jin Song
2,†,
Jeong Yee
1,
Junbeom Park
3,* and
Hye-Sun Gwak
1,*
1
College of Pharmacy and Graduate School of Pharmaceutical Sciences, Ewha Womans University, 52 Ewhayeodae-gil, Seodaemun-gu, Seoul 03760, Korea
2
Department of Neurology, Ewha Womans University Seoul Hospital, Ewha Womans University College of Medicine, Seoul 07804, Korea
3
Division of Cardiology, Department of Internal Medicine, Ewha Womans University Mokdong Hospital, Ewha Womans University College of Medicine, Seoul 07985, Korea
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Pharmaceutics 2022, 14(9), 1889; https://doi.org/10.3390/pharmaceutics14091889
Submission received: 6 August 2022 / Revised: 26 August 2022 / Accepted: 3 September 2022 / Published: 7 September 2022
(This article belongs to the Special Issue Association Studies in Clinical Pharmacogenetics)
Browse Figures
Versions Notes

Abstract

:
Objectives: The purpose of our study is to investigate the effects of apolipoprotein B (APOB) and APOE gene polymorphisms on bleeding complications in patients receiving direct oral anticoagulants (DOACs). Methods: A total of 16 single nucleotide polymorphisms (SNPs) in 468 patients were genotyped. Six SNPs of ABCB1 (rs3842, rs1045642, rs2032582, rs1128503, rs3213619, and rs3747802), one SNP of CYP3A5 (rs776746), seven SNPs of APOB (rs1042034, rs2163204, rs693, rs679899, rs13306194, rs13306198, and rs1367117), and two SNPs of APOE (rs429358 and rs7412) were analyzed by a TaqMan genotyping assay. Multivariable logistic regression analysis with selected variables was performed for the construction of a risk scoring system. Two risk scoring systems were compared (demographic factors only vs. demographic factors and genetic factors). Results: In the multivariable analyses, two models were constructed; only demographic factors were included in Model I and both demographic factors and genetic factors in Model II. Rivaroxaban and anemia showed significant association with bleeding in both models. Additionally, ABCB1 rs3842 variant homozygote carriers (CC) and APOB rs13306198 variant allele carriers (AG, AA) had a higher risk of bleeding risk compared with that of wild-type allele carriers (TT, TC) and wild-type homozygote carriers (GG), respectively. Whereas the area under the receiver operating characteristic curve (AUROC) value using demographic factors only was 0.65 (95% confidence interval (CI): 0.56–0.74), the AUROC increased to 0.72 by adding genetic factors (95% CI: 0.65–0.80). The predicted bleeding risks of bleeding in patients with 0, 1, 2, 3, 4, 5, 6, 7 and 8 points from the logistic regression curve were 0.8%, 2.0%, 5.4%, 5.2%, 12.5%, 26.9%, 47.0%, 64.3% and 82.3%, respectively. Conclusions: The study results can be used for enhancing individualized treatment strategies in patients taking DOACs, helping clinicians predict the bleeding risk.

1. Introduction

Direct oral anticoagulants (DOACs) are important for the treatment of non-valvular atrial fibrillation (NVAF) and venous thromboembolism (VTE) [1]. A high inter-individual variability in drug blood levels has been observed with all DOACs, which may result in bleeding or thrombotic events. Several non-genetic factors accounting for the high variability between individuals in DOAC treatments have been identified (e.g., age, weight and renal dysfunction) [2,3]. However, differences still remain largely unexplained and genetic factors may play an important role.
All DOACs are substrates of the efflux transporter permeability-glycoprotein (P-gp, also referred to as ABCB1) [4]. Moreover, some DOACs such as apixaban, edoxaban and rivaroxaban are known to be metabolized by cytochrome P450 (CYP) 3A5 [5,6]. Several genetic polymorphisms have been reported to influence ABCB1 and CYP3A5 activity and/or expression [7,8,9,10].
Various studies have shown that apolipoproteins are involved in platelet function, the abnormal function of which leads to arterial thrombosis and bleeding complication [11,12]. In particular, apolipoprotein B (APOB)-100 component of low-density lipoprotein binds to a receptor on the platelet membrane and sensitizes platelets. Apolipoprotein E (APOE)-containing lipoproteins, such as high-density lipoproteins, mediate the inhibition of platelet function [13]. Recent publications also showed that apolipoproteins were related to hemorrhage [14,15]. However, no studies have been conducted on the effect of these gene polymorphisms on bleeding complications during DOAC treatment.
As bleeding is one of the most severe complications of DOACs, bleeding risk should be assessed during treatment [16]. However, existing scores or calculators for predicting bleeding risks have been primarily validated or evaluated using VKAs and parenteral anticoagulants [17]. Because DOACs have different metabolic pathways from VKAs, it is necessary to consider DOAC-related genetic polymorphisms rather than VKA-related genetic polymorphisms (e.g., CYP2C9*3) [18]. Therefore, bleeding risk scores for DOACs with high accuracy are necessary. The primary objective of this study was to investigate the association between genetic polymorphisms including ABCB1, CYP3A5, APOB, and APOE. The secondary objective was to construct an optimal risk scoring system for bleeding complications in patients receiving DOACs.

2. Methods

2.1. Study Patients and Data Collection

This study was a retrospective analysis of prospectively collected samples from June 2018 to December 2021. It was conducted at Ewha Womans University Mokdong Hospital and Ewha Womans University Seoul Hospital. This study was approved by the Institutional Review Board (IRB) of each hospital in accordance with the 1975 Helsinki Declaration and its later amendments (IRB number: 2018-04-006 and 2019-05-038, respectively). Written informed consent was obtained from all participants before enrollment.
The subjects of this study were those aged ≥ 20 years old and who received DOACs (apixaban, edoxaban, rivaroxaban or dabigatran). Patients who had thromboembolic or infarction-related events during the follow-up period experienced bleeding that was minor or unverified by health professionals while on treatment, and those who experienced bleeding after 1 year of DOAC therapy were excluded. In the case of the control group, patients treated with DOACs for less than 3 months were also excluded. The primary endpoint was major bleeding and clinically relevant non-major bleeding (CRNMB), according to the criteria of the International Society on Thrombosis and Haemostasis (ISTH) [19,20].
Data were obtained from electronic medical records. Patient demographics, including sex, age, body mass index (BMI), creatinine clearance (CrCl) estimated by the Modification of Diet in Renal Disease (MDRD) formula, aspartate transaminase (AST), alanine transferase (ALT), type and prescription dose of the DOAC used, concurrent medication, any history of myocardial infarction, stroke, transient ischemic attack, thromboembolism, or bleeding, comorbidities, smoking status and alcohol status were collected. The CHA2DS2-VASc (congestive heart failure, hypertension, age ≥ 75 years, diabetes mellitus, stroke, vascular disease, age 65–74 years and sex category; range 0–9) score, which is used for stroke risk assessment, and modified HAS-BLED (hypertension, abnormal renal or liver function, stroke, bleeding history or predisposition, elderly (age ≥ 65 years) and concomitant drug and alcohol use; range 0–8) score, which is a specific risk score designed for bleeding risk assessment, were calculated from its component variables [21,22].

2.2. Selection of Single Nucleotide Polymorphisms (SNPs) and Genotyping

To select SNPs of DOAC-associated genes, Haploreg 4.1 for minor allele frequencies, functionality, and linkage disequilibrium (LD) patterns of SNPs in Asian populations was used [23]. In this study, one missense SNP (rs2032582), two synonymous SNPs (rs1045642 and rs1128503), one 3ʹ-untranslated region (UTR) SNP (rs3842) and two 5ʹ-UTR SNPs (rs3213619 and rs3747802) were selected from ABCB1, and one splice donor SNP (rs776746) was selected from CYP3A5. Six missense SNPs (rs1042034, rs2163204, rs679899, rs13306194, rs13306198 and rs1367117) and one synonymous SNP (rs693) were selected from APOB and two missense SNPs (rs429358 and rs7412) were selected from APOE. Finally, a total of 16 SNPs were investigated.
The genomic deoxyribonucleic acid (DNA) of the patients was extracted from the blood or saliva. Genomic DNA was extracted from EDTA blood samples using the QIAamp DNA Blood Mini Kit (QIAGEN GmbH, Hilden, Germany). Otherwise, saliva samples were collected from OraGene-600 kits (DNA Genotek, Ottawa, ON, Canada) and subjected to genomic DNA extraction with PrepIT reagents (DNA Genotek, Ottawa, ON, Canada). The genotypes of 16 SNPs were analyzed by TaqMan assay.

2.3. Statistical Analysis

Unpaired t-tests were used to compare continuous variables between patients who experienced bleeding and those who did not. Chi-squared test and Fisher’s exact test were used to analyze categorical variables. A multivariable logistic regression model was used to identify independent risk factors for bleeding after adjusting for variables with p < 0.05 in univariate analysis. Variables were entered by stepwise selection when p was lower than 0.05 and were removed when p was higher than 0.1. The unadjusted odds ratio (OR) and adjusted OR with the 95% confidence interval (CI) were calculated from univariate and multivariable analyses, respectively. To test the fit of the prediction model, the Hosmer–Lemeshow goodness-of-fit test was performed. We constructed two models using demographic factors only and both demographic and genetic factors. The discrimination of the model was further evaluated by calculating the area under the receiver operating characteristic curve (AUROC).
Using the model with better accuracy, the risk scoring system was constructed by dividing the adjusted OR by the smallest adjusted OR among the variables. Then, quotients were rounded to the nearest integer to develop a risk scoring system for predicting the bleeding risk. The bleeding risk predicted by logistic regression analysis was compared with the observed risk. All analyses were based on two-tail statistics and were performed using the Statistical Package for Social Sciences version 20.0 (IBM Corp., Armonk, NY, USA). p < 0.05 was considered statistically significant.

3. Results

A total of 576 patients were selected for the study (Figure 1). Overall, 15 patients who had been treated with DOACs for <3 months, 25 patients who had a history of thromboembolic or infarction-related events, 23 patients who had reported minor bleeding that could not be verified by health professionals, 43 patients who had any bleeding at least 1 year after DOAC therapy, 1 patient with a sample that was insufficient for DNA analysis and 1 patient who withdrew informed consent were excluded. Finally, 468 patients were included in the analysis. A total of 50 patients (10.7%) experienced bleeding complications, of whom 14 patients had major bleeding and 36 patients had CRNMB. The time (mean ± standard deviation) to bleeding event was 110.04 ± 11.45 days.
Table 1 shows the demographic and clinical characteristics of the study population. The mean age of the included patients was 69.2 years, and 293 patients (62.5%) were male. Apixaban was the most prescribed DOAC, followed by edoxaban. Approximately, a third of the patients received an underdose of DOACs. Among the co-medications, the most common drug class was beta-blockers, followed by statins. Approximately 98% of the patients had atrial fibrillation, and approximately 70% of the patients had hypertension. Type of DOACs, dose of DOACs and anemia were significant factors for bleeding complications.
In grouped genotype analysis, variant homozygote carriers (CC) of ABCB1 rs3842 had a higher bleeding risk compared with that of wild-type allele carriers (20.4% vs. 9.5%, p = 0.02) (Table 2). Wild-type homozygote carriers (GG) of APOB rs693 had a higher bleeding risk compared with that of variant allele carriers (11.7% vs. 2.0%, p = 0.04) as shown in Table 3. In addition, variant allele carriers (AG, AA) of APOB rs13306198 had a greater risk of bleeding compared with that of wild-type homozygote carriers (GG) (26.0% vs. 8.9%, p < 0.01).
Multivariable logistic regression analysis was carried out using variables with p < 0.05 in addition to age and sex (Table 4). Two models were constructed; only demographic factors were included in Model I and both demographic factors and genetic factors in Model II. In Model 1, overdose, rivaroxaban and anemia were significantly associated with bleeding. In model II, patients with rivaroxaban anemia, ABCB1 rs3842 variant homozygote (CC) and APOB rs13306198 variant allele (AG, AA) had a higher risk of bleeding risk. The Hosmer–Lemeshow test for bleeding revealed a good fit for the final model in both model I and model II (χ2 = 0.78 and p = 0.94; χ2 = 0.63 and p = 0.73, respectively).
Whereas the area under the receiver operating characteristic curve (AUROC) value using demographic factors only was 0.65 (95% confidence interval (CI): 0.56–0.74), the AUROC increased to 0.72 by adding genetic factors (95% CI: 0.65–0.80) (Figure 2). Therefore, we constructed a risk scoring system using both demographic factors and genetic factors, which remained in Model II; overdose, rivaroxaban, anemia, ABCB1 rs3842, APOB rs693 and APOB rs13306198 were taken as 2, 1, 1, 1, 3 and 1 point, respectively. The total risk score ranged from 0 to 8, and there was no patient with a score of 9. The logistic regression curve obtained by mapping the scores to risk scores is shown in Figure 3, and the observed and predicted bleeding risks are presented in Table 5. The observed bleeding risks of patients receiving DOACs with 0, 1, 2, 3, 4, 5, 6, 7 and 8 points were 0.0%, 5.9%, 0.0%, 5.4%, 11.3%, 32.6%, 33.3%, 66.7% and 100.0%, respectively. The predicted bleeding risks of patients receiving DOACs with 0, 1, 2, 3, 4, 5, 6, 7 and 8 points were 0.8%, 2.0%, 5.4%, 4.2%, 12.5%, 26.9%, 47.0%, 64.3% and 82.3%, respectively.

4. Discussion

This study revealed that the rs3842 SNP of ABCB1 was associated with bleeding specifically in patients taking DOACs. The allele substitution of ABCB1 rs3842, which is a 3ʹ-UTR SNP, might affect protein expression because it disrupts or creates miRNA binding sites [24]. Carriers of the ABCB1 rs3842 variant allele (C) were found to have lower disease activity scores in a study including patients with rheumatoid arthritis who received methotrexate [25]. Variant allele carriers for ABCB1 rs3842 exhibited 26% higher efavirenz bioavailability than homozygote of wild-type allele [26]. The mechanism by which rs3842 regulates ABCB1 expression and the resulting effect on treatment response in different population warrant further investigation.
Meanwhile, in a meta-analysis, the Cmax of DOACs and AUC0-∞ of DOACs was lower among ABCB1 rs1045642 CC carriers and rs2032582 GG carriers compared to those with the TT and A/T allele [27]. In addition, the lower Cmax was observed among carriers of ABCB1 compared with those with the A/T allele. In a retrospective real-world study, ABCB1 rs1045642 was associated with a reduced risk of thromboembolic outcomes [28]. However, in our study, ABCB1 rs1128503, rs2032582 and rs1045642 were not significantly associated with bleeding, whereas rs3842 of ABCB1 was associated.
Apolipoprotein B is a key structural component of all atherogenic lipoproteins (LDL, VLDL and IDL) [29]. The APOB rs13306198 is located in the N-terminal αβ1 domain, which forms a lipid pocket necessary for VLDL and chylomicron particle assembly [30]. The effect of APOB rs13306198 on anticoagulation activity is controversial, depending on the drugs used or outcomes evaluated. APOB rs13306198 was significantly associated with an under-anticoagulation state during the first week (p = 0.011) in a study including 252 cardiac valve replacement patients treated with warfarin [31]. The findings are different from our results, possibly due to the use of different drugs.
Although APOB rs693 is a synonymous variant, it is related to the circulating concentration of LDL cholesterol. In a meta-analysis of 61 studies including 50,018 subjects, variant allele carriers had high levels of APOB, TG, TC and LDL-C and low HDL-C levels [32]. A meta-analysis including 14 case-control studies showed that APOB rs693 may be a risk factor for gallstone disease, especially in Asians [33]. The SNP rs693 in the APOB gene increased the risk of breast cancer and aortic stenosis among Chinese subjects [34,35]. Moreover, APOB rs693 was associated with the presence of plaque on carotid arteries [36]. In this study, APOB rs693 was associated with a 0.15-fold higher risk of bleeding, although statistical significance was not found in the multivariable analysis.
Similar to our research, a study showed that an increased risk of major bleeding was associated with high doses of DOACs (hazard ratio (HR) = 2.19, 95% CI: 1.07–4.46) compared with recommended doses of DOACs [37]. Moreover, the bleeding risk of patients who overdosed on DOACs was more than four times higher (OR = 4.21) in our analysis, which was two times the risk reported in the meta-analysis. Several studies have pointed out that there are some differences in oral anticoagulation treatment between Asian and non-Asian patients with AF for the following reasons: (1) The baseline risks of thromboembolism and bleeding are higher in Asians than in non-Asians; (2) Asians are more vulnerable to anticoagulation related bleeding risk [38]; and (3) Asians generally have greater use of antiplatelets [39]. Moreover, the trough edoxaban concentrations and anti-FXa activities have been found to be lower for Asians compared with non-Asians, even after accounting for the protocol mandated dose reduction [40]. Further studies are required to investigate the association between dose of DOACs and bleeding risk, especially in Asians.
In our study, rivaroxaban was the only and the least safe DOAC, which had almost three times greater bleeding risk than other DOACs. Dabigatran was the safest drug (OR = 0.35) among any anticoagulant in terms of the risk of intracranial hemorrhage (ICH) in a network meta-analysis of 17 randomized controlled trials (RCTs), whereas rivaroxaban was the least safe (OR = 0.44) [41]. Several meta-analyses also showed that rivaroxaban was associated with the highest bleeding risk. Among patients taking rivaroxaban, the risk ratio of postoperative bleeding was higher compared to with that of healthy patients, and the risk of major bleeding and CRNMB was higher compared with that of patients taking apixaban [42,43]. In another meta-analysis, compared with warfarin, apixaban and dabigatran resulted in statistically significant risk reductions in major bleeding, whereas rivaroxaban did not (HR = 0.60, 95% CI: 0.52–0.69; HR = 0.79, 95% CI: 0.70–0.90; HR = 1.03, 95% CI: 0.86–1.22, respectively) [44]. In consideration of the finding of previous studies and our study, more attention is needed when prescribing rivaroxaban.
In our study, 27.6% of the total population (129 patients) had anemia. Anemia has been identified as a strong predictor of bleeding in patients with AF taking anticoagulants [45]. In a meta-analysis of 28 studies encompassing 365,484 AF patients, anemia was associated with a 78% increase in major bleeding and a 77% increase in gastrointestinal bleeding [46]. Additionally, HEMORR2HAGEs, ATRIA, RIETE, and CHEST scores include anemia [18,47,48]. Therefore, patients with anemia need to be aware of the potential risks when they consider taking DOACs or during DOAC therapy.
Although there are several bleeding risk scoring systems for anticoagulants, most of them have been primarily validated or evaluated using VKAs and parenteral anticoagulants. No score had good diagnostic accuracy during a validation. For example, a study showed that AUROC of the HAS-BLED was 0.62 [49]. In addition, although the scoring system such as HEMORR2HAGES includes genetic factors such as CYP2C9 SNP [18], it can be only applied to warfarin, but not DOAC, due to their different metabolic pathways [47]. However, we constructed the model including genetic factors, which had better performance than the model including demographic factors only (AUROC: 0.73 vs. 0.65).
The limitations of this study are related to the retrospective design with the relatively small sample size. Our study included only Asians. In addition, a relatively large number of patients were excluded according to the exclusion criteria. However, there was no significant difference in the characteristics between the included patients and excluded patients. Further prospective large cohort studies are required to validate our findings.

5. Conclusions

In conclusion, our study constructed bleeding risk scoring systems using both demographic factors and genetic factors that affected bleeding complications during DOAC therapy. After validation of the results, the constructed models can be used to predict bleeding risks in patients taking DOAC, identify the high-risk patients in advance, and provide dose adjustments or close monitoring. By applying these results in clinical practice, it would be expected to provide patients with more effective and safer anticoagulation therapy in terms of personalized medicine.

Author Contributions

Conceptualization, H.-Y.Y., T.-J.S., J.P. and H.-S.G.; Data curation, J.Y.; Formal analysis, H.-Y.Y. and J.Y.; Funding acquisition, H.-S.G.; Investigation, J.P. and H.-S.G.; Methodology, T.-J.S. and J.P.; Supervision, J.P. and H.-S.G.; Validation, H.-S.G.; Writing—original draft, H.-Y.Y. and T.-J.S.; Writing—review & editing, J.P. and H.-S.G. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (grant number: NRF-2020R1A2C1008120).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of Ewha Womans University Mokdong Hospital and Ewha Womans University Seoul Hospital (IRB number: 2018-04-006 and 2019-05-038, respectively).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Deutsch, D.; Boustière, C.; Ferrari, E.; Albaladejo, P.; Morange, P.-E.; Benamouzig, R. Direct oral anticoagulants and digestive bleeding: Therapeutic management and preventive measures. Ther. Adv. Gastroenterol. 2017, 10, 495–505. [Google Scholar] [CrossRef] [PubMed]
  2. Gong, I.Y.; Kim, R.B. Importance of Pharmacokinetic Profile and Variability as Determinants of Dose and Response to Dabigatran, Rivaroxaban, and Apixaban. Can. J. Cardiol. 2013, 29 (Suppl. 7), S24–S33. [Google Scholar] [CrossRef] [PubMed]
  3. Chan, N.; Sager, P.T.; Lawrence, J.; Ortel, T.; Reilly, P.; Berkowitz, S.; Kubitza, D.; Eikelboom, J.; Florian, J.; Stockbridge, N.; et al. Is there a role for pharmacokinetic/pharmacodynamic-guided dosing for novel oral anticoagulants? Am. Heart J. 2018, 199, 59–67. [Google Scholar] [CrossRef]
  4. Li, A.; Li, M.K.; Crowther, M.; Vazquez, S.R. Drug-drug interactions with direct oral anticoagulants associated with adverse events in the real world: A systematic review. Thromb. Res. 2020, 194, 240–245. [Google Scholar] [CrossRef] [PubMed]
  5. Wanat, M.A. Novel Oral Anticoagulants: A Review of New Agents. Postgrad. Med. 2013, 125, 103–114. [Google Scholar] [CrossRef] [PubMed]
  6. Raymond, J.; Imbert, L.; Cousin, T.; Duflot, T.; Varin, R.; Wils, J.; Lamoureux, F. Pharmacogenetics of Direct Oral Anticoagulants: A Systematic Review. J. Pers. Med. 2021, 11, 37. [Google Scholar] [CrossRef] [PubMed]
  7. Sennesael, A.-L.; Panin, N.; Vancraeynest, C.; Pochet, L.; Spinewine, A.; Haufroid, V.; Elens, L. Effect of ABCB1 genetic polymorphisms on the transport of rivaroxaban in HEK293 recombinant cell lines. Sci. Rep. 2018, 8, 10514. [Google Scholar] [CrossRef] [PubMed]
  8. Lorenzini, K.I.; Daali, Y.; Fontana, P.; Desmeules, J.; Samer, C. Rivaroxaban-Induced Hemorrhage Associated with ABCB1 Genetic Defect. Front. Pharmacol. 2016, 7, 494. [Google Scholar] [CrossRef]
  9. Dimatteo, C.; D’Andrea, G.; Vecchione, G.; Paoletti, O.; Tiscia, G.L.; Santacroce, R.; Correale, M.; Brunetti, N.; Grandone, E.; Testa, S.; et al. ABCB1 SNP rs4148738 modulation of apixaban interindividual variability. Thromb. Res. 2016, 145, 24–26. [Google Scholar] [CrossRef]
  10. Ueshima, S.; Hira, D.; Fujii, R.; Kimura, Y.; Tomitsuka, C.; Yamane, T.; Tabuchi, Y.; Ozawa, T.; Itoh, H.; Horie, M.; et al. Impact of ABCB1, ABCG2, and CYP3A5 polymorphisms on plasma trough concentrations of apixaban in Japanese patients with atrial fibrillation. Pharm. Genom. 2017, 27, 329–336. [Google Scholar] [CrossRef]
  11. Mehta, A.; Shapiro, M.D. Apolipoproteins in vascular biology and atherosclerotic disease. Nat. Rev. Cardiol. 2022, 19, 168–179. [Google Scholar] [CrossRef]
  12. Yee, J.; Kim, W.; Chang, B.C.; Chung, J.E.; Lee, K.E.; Gwak, H.S. APOB gene polymorphisms may affect the risk of minor or minimal bleeding complications in patients on warfarin maintaining therapeutic INR. Eur. J. Hum. Genet. 2019, 27, 1542–1549. [Google Scholar] [CrossRef]
  13. Riddell, D.R.; Graham, A.; Owen, J.S. Apolipoprotein E inhibits platelet aggregation through the L-arginine: Nitric oxide pathway. Implications for vascular disease. J. Biol. Chem. 1997, 272, 89–95. [Google Scholar] [CrossRef]
  14. Sudlow, C.; Martínez González, N.A.; Kim, J.; Clark, C. Does apolipoprotein E genotype influence the risk of ischemic stroke, intracerebral hemorrhage, or subarachnoid hemorrhage? Systematic review and meta-analyses of 31 studies among 5961 cases and 17,965 controls. Stroke 2006, 37, 364–370. [Google Scholar] [CrossRef]
  15. Svensson, E.H.; Abul-Kasim, K.; Engström, G.; Söderholm, M. Risk factors for intracerebral haemorrhage—Results from a prospective population-based study. Eur. Stroke J. 2020, 5, 278–285. [Google Scholar] [CrossRef]
  16. Gao, X.; Cai, X.; Yang, Y.; Zhou, Y.; Zhu, W. Diagnostic Accuracy of the HAS-BLED Bleeding Score in VKA- or DOAC-Treated Patients With Atrial Fibrillation: A Systematic Review and Meta-Analysis. Front. Cardiovasc. Med. 2021, 8, 757087. [Google Scholar] [CrossRef]
  17. Tchen, S.; Ryba, N.; Patel, V.; Cavanaugh, J.; Sullivan, J.B. Validation of bleeding risk prediction scores for patients with major bleeding on direct oral anticoagulants. Ann. Pharmacother. 2020, 54, 1175–1184. [Google Scholar] [CrossRef]
  18. Apostolakis, S.; Lane, D.A.; Guo, Y.; Buller, H.; Lip, G.Y. Performance of the HEMORR2HAGES, ATRIA, and HAS-BLED bleeding risk-prediction scores in patients with atrial fibrillation undergoing anticoagulation: The AMADEUS (evaluating the use of SR34006 compared to warfarin or acenocoumarol in patients with atrial fibrillation) Study. J. Am. Coll. Cardiol. 2012, 60, 861–867. [Google Scholar]
  19. Schulman, S.; Kearon, C.; Subcommittee on Control of Anticoagulation of the Scientific and Standardization Committee of the International Society on Thrombosis and Haemostasis. Definition of major bleeding in clinical investigations of antihemostatic medicinal products in non-surgical patients. J. Thromb. Haemost. 2005, 3, 692–694. [Google Scholar] [CrossRef]
  20. Kaatz, S.; Ahmad, D.; Spyropoulos, A.C.; Schulman, S. Subcommittee on Control of Anticoagulation. Definition of clinically relevant non-major bleeding in studies of anticoagulants in atrial fibrillation and venous thromboembolic disease in non-surgical patients: Communication from the SSC of the ISTH. J. Thromb. Haemost. 2015, 13, 2119–2126. [Google Scholar] [CrossRef]
  21. Lip, G.Y.; Nieuwlaat, R.; Pisters, R.; Lane, D.A.; Crijns, H. Refining clinical risk stratification for predicting stroke and thromboembolism in atrial fibrillation using a novel risk factor-based approach: The euro heart survey on atrial fibrillation. Chest 2010, 137, 263–272. [Google Scholar] [CrossRef] [PubMed]
  22. Pisters, R.; Lane, D.A.; Nieuwlaat, R.; de Vos, C.B.; Crijns, H.J.; Lip, G.Y. A novel user-friendly score (HAS-BLED) to assess 1-year risk of major bleeding in patients with atrial fibrillation: The Euro Heart Survey. Chest 2010, 138, 1093–1100. [Google Scholar] [CrossRef] [PubMed]
  23. Ward, L.D.; Kellis, M. HaploReg v4: Systematic mining of putative causal variants, cell types, regulators and target genes for human complex traits and disease. Nucleic Acids Res. 2016, 44, D877–D881. [Google Scholar] [CrossRef] [PubMed]
  24. Gow, J.M.; Hodges, L.M.; Chinn, L.W.; Kroetz, D.L. Substrate-dependent effects of human ABCB1 coding polymorphisms. J. Pharmacol. Exp. Ther. 2008, 325, 435–442. [Google Scholar] [CrossRef] [PubMed]
  25. Cen, H.; Wen, Q.-W.; Zhang, H.-Q.; Yu, H.; Zeng, Z.; Jin, T.; Wang, T.-H.; Qin, W.; Huang, H.; Wu, X.-D. Associations Between Genetic Polymorphisms Within Transporter Genes and Clinical Response to Methotrexate in Chinese Rheumatoid Arthritis Patients: A Pilot Study. Pharm. Pers. Med. 2022, 15, 327–339. [Google Scholar] [CrossRef] [PubMed]
  26. Mukonzo, J.K.; Röshammar, D.; Waako, P.; Andersson, M.; Fukasawa, T.; Milani, L.; Svensson, J.O.; Ogwal-Okeng, J.; Gustafsson, L.L.; Aklillu, E. A novel polymorphism in ABCB1 gene, CYP2B6*6 and sex predict single-dose efavirenz population pharmacokinetics in Ugandans. Br. J. Clin. Pharmacol. 2009, 68, 690–699. [Google Scholar] [CrossRef] [PubMed]
  27. Xie, Q.; Xiang, Q.; Mu, G.; Ma, L.; Chen, S.; Zhou, S.; Hu, K.; Zhang, Z.; Cui, Y.; Jiang, J. Effect of ABCB1 Genotypes on the Pharmacokinetics and Clinical Outcomes of New Oral Anticoagulants: A Systematic Review and Meta-analysis. Curr. Pharm. Des. 2018, 24, 3558–3565. [Google Scholar] [CrossRef]
  28. Lähteenmäki, J.; Vuorinen, A.; Pajula, J.; Harno, K.; Lehto, M.; Niemi, M.; van Gils, M. Pharmacogenetics of Bleeding and Thromboembolic Events in Direct Oral Anticoagulant Users. Clin. Pharmacol. Ther. 2021, 110, 768–776. [Google Scholar] [CrossRef]
  29. Behbodikhah, J.; Ahmed, S.; Elyasi, A.; Kasselman, L.J.; De Leon, J.; Glass, A.D.; Reiss, A.B. Apolipoprotein B and Cardiovascular Disease: Biomarker and Potential Therapeutic Target. Metabolites 2021, 11, 690. [Google Scholar] [CrossRef]
  30. Zhou, Y.; Mägi, R.; Milani, L.; Lauschke, V.M. Global genetic diversity of human apolipoproteins and effects on cardiovascular disease risk. J. Lipid Res. 2018, 59, 1987–2000. [Google Scholar] [CrossRef]
  31. Li, D.; Luo, Z.-Y.; Chen, Y.; Zhu, H.; Song, G.-B.; Zhou, X.-M.; Yan, H.; Zhou, H.-H.; Zhang, W.; Li, X. LRP1 and APOA1 Polymorphisms: Impact on Warfarin International Normalized Ratio-Related Phenotypes. J. Cardiovasc. Pharmacol. 2020, 76, 71–76. [Google Scholar] [CrossRef]
  32. Niu, C.; Luo, Z.; Yu, L.; Yang, Y.; Chen, Y.; Luo, X.; Lai, F.; Song, Y. Associations of the APOB rs693 and rs17240441 polymorphisms with plasma APOB and lipid levels: A meta-analysis. Lipids Health Dis. 2017, 16, 166. [Google Scholar] [CrossRef]
  33. Zhu, H.; Yu, L.; Feng, L. Association of apolipoprotein B XbaI (rs693) polymorphism and gallstone disease risk based on a comprehensive analysis. Genes Environ. 2021, 43, 17. [Google Scholar] [CrossRef]
  34. Liu, X.; Wang, Y.; Qu, H.; Hou, M.; Cao, W.; Ma, Z.; Wang, H. Associations of polymorphisms of rs693 and rs1042031 in apolipoprotein B gene with risk of breast cancer in Chinese. Jpn. J. Clin. Oncol. 2013, 43, 362–368. [Google Scholar] [CrossRef]
  35. Wang, Y.-T.; Li, Y.; Ma, Y.-T.; Yang, Y.-N.; Ma, X.; Li, X.-M.; Liu, F.; Chen, B.-D. Association between apolipoprotein B genetic polymorphism and the risk of calcific aortic stenosis in Chinese subjects, in Xinjiang, China. Lipids Health Dis. 2018, 17, 40. [Google Scholar] [CrossRef]
  36. Starčević, J.N.; Letonja, M..; Pražnikar, Z.J.; Makuc, J.; Vujkovac, A.C.; Petrovič, D. Polymorphisms XbaI (rs693) and EcoRI (rs1042031) of the ApoB gene are associated with carotid plaques but not with carotid intima-media thickness in patients with diabetes mellitus type 2. Vasa 2014, 43, 171–180. [Google Scholar] [CrossRef]
  37. Yao, X.; Shah, N.D.; Sangaralingham, L.R.; Gersh, B.J.; Noseworthy, P.A. Non-Vitamin K Antagonist Oral Anticoagulant Dosing in Patients With Atrial Fibrillation and Renal Dysfunction. J. Am. Coll. Cardiol. 2017, 69, 2779–2790. [Google Scholar] [CrossRef]
  38. Gaikwad, T.; Ghosh, K.; Shetty, S. VKORC1 and CYP2C9 genotype distribution in Asian countries. Thromb. Res. 2014, 134, 537–544. [Google Scholar] [CrossRef]
  39. Liu, X.; Huang, M.; Ye, C.; Zeng, J.; Zeng, C.; Ma, J. The role of non-vitamin K antagonist oral anticoagulants in Asian patients with atrial fibrillation: A PRISMA-compliant article. Medicine 2020, 99, e21025.28. [Google Scholar] [CrossRef]
  40. Chao, T.-F.; Chen, S.-A.; Ruff, C.T.; Hamershock, R.A.; Mercuri, M.F.; Antman, E.M.; Braunwald, E.; Giugliano, R.P. Clinical outcomes, edoxaban concentration, and anti-factor Xa activity of Asian patients with atrial fibrillation compared with non-Asians in the ENGAGE AF-TIMI 48 trial. Eur. Heart J. 2019, 40, 1518–1527. [Google Scholar] [CrossRef]
  41. Wolfe, Z.; Nasir, F.; Subramanian, C.R.; Lash, B.; Khan, S.U. A systematic review and Bayesian network meta-analysis of risk of intracranial hemorrhage with direct oral anticoagulants. J. Thromb. Haemost. 2018, 16, 1296–1306. [Google Scholar] [CrossRef] [PubMed]
  42. Bensi, C.; Belli, S.; Paradiso, D.; Lomurno, G. Postoperative bleeding risk of direct oral anticoagulants after oral surgery procedures: A systematic review and meta-analysis. Int. J. Oral Maxillofac. Surg. 2018, 47, 923–932. [Google Scholar] [CrossRef] [PubMed]
  43. Aryal, M.R.; Gosain, R.; Donato, A.; Yu, H.; Katel, A.; Bhandari, Y.; Dhital, R.; Kouides, P.A. Systematic review and meta-analysis of the efficacy and safety of apixaban compared to rivaroxaban in acute VTE in the real world. Blood Adv. 2019, 3, 2381–2387. [Google Scholar] [CrossRef] [PubMed]
  44. Lobraico-Fernandez, J.; Baksh, S.; Nemec, E. Elderly Bleeding Risk of Direct Oral Anticoagulants in Nonvalvular Atrial Fibrillation: A Systematic Review and Meta-Analysis of Cohort Studies. Drugs R D 2019, 19, 235–245. [Google Scholar] [CrossRef]
  45. Westenbrink, B.D.; Alings, M.; Granger, C.B.; Alexander, J.H.; Lopes, R.D.; Hylek, E.M.; Thomas, L.; Wojdyla, D.M.; Hanna, M.; Keltai, M.; et al. Anemia is associated with bleeding and mortality, but not stroke, in patients with atrial fibrillation: Insights from the Apixaban for Reduction in Stroke and Other Thromboembolic Events in Atrial Fibrillation (ARISTOTLE) trial. Am. Heart J. 2017, 185, 140–149. [Google Scholar] [CrossRef]
  46. Tu, S.J.; Hanna-Rivero, N.; Elliott, A.D.; Clarke, N.; Huang, S.; Pitman, B.M.; Gallagher, C.; Linz, D.; Mahajan, R.; Lau, D.H.; et al. Associations of anemia with stroke, bleeding, and mortality in atrial fibrillation: A systematic review and meta-analysis. J. Cardiovasc. Electrophysiol. 2021, 32, 686–694. [Google Scholar] [CrossRef]
  47. Whirl-Carrillo, M.; McDonagh, E.M.; Hebert, J.M.; Gong, L.; Sangkuhl, K.; Thorn, C.F.; Altman, R.B.; Klein, T.E. Pharmacogenomics knowledge for personalized medicine. Clin. Pharmacol. Ther. 2012, 92, 414–417. [Google Scholar] [CrossRef]
  48. Ruíz-Giménez, N.; Suárez, C.; González, R.; Nieto, J.A.; Todolí, J.A.; Samperiz, Á.L.; Monreal, M.; The RIETE Investigators Predictive variables for major bleeding events in patients presenting with documented acute venous thromboembolism. Findings from the RIETE Registry. Thromb. Haemost. 2008, 100, 26–31. [Google Scholar] [CrossRef]
  49. Yoshida, R.; Ishii, H.; Morishima, I.; Tanaka, A.; Morita, Y.; Takagi, K.; Yoshioka, N.; Hirayama, K.; Iwakawa, N.; Tashiro, H.; et al. Performance of HAS-BLED, ORBIT, PRECISE-DAPT, and PARIS risk score for predicting long-term bleeding events in patients taking an oral anticoagulant undergoing percutaneous coronary intervention. J. Cardiol. 2019, 73, 479–487. [Google Scholar] [CrossRef]
Pharmaceutics 14 01889 g001 550
Figure 1. Patient flowchart.
Figure 1. Patient flowchart.
Pharmaceutics 14 01889 g001
Pharmaceutics 14 01889 g002 550
Figure 2. The receiver operating characteristic (ROC) curve for bleeding using demographic factors only and using both demographic factors and genetic factors.
Figure 2. The receiver operating characteristic (ROC) curve for bleeding using demographic factors only and using both demographic factors and genetic factors.
Pharmaceutics 14 01889 g002
Pharmaceutics 14 01889 g003 550
Figure 3. The logistic regression curve of the probability of bleeding versus the proposed scoring scale.
Figure 3. The logistic regression curve of the probability of bleeding versus the proposed scoring scale.
Pharmaceutics 14 01889 g003
Table
Table 1. Baseline characteristics of patients included in this study.
Table 1. Baseline characteristics of patients included in this study.
CharacteristicBleeding
(n = 50)		No Bleeding
(n = 418)		p
Sex 0.93
Female19 (38.0)156 (37.3)
Male31 (62.0)262 (62.7)
Age (years)69.32 ± 9.2869.12 ± 0.500.90
<6516 (32.0)125 (29.9)0.76
≥6534 (68.0)293 (70.1)
BMI (kg/m2)25.48 ± 3.9424.97 ± 3.380.34
<2522 (45.8)204 (51.1)0.49
≥2526 (54.2)195 (48.9)
Creatinine clearance (mL/min)68.04 ± 25.3669.08 ± 24.700.78
<305 (10.4)18 (4.5)0.09
≥3043 (89.6)384 (95.5)
AST (IU/L) 0.92
<4042 (87.5)344 (88.0)
≥406 (12.5)7 (12.0)
ALT (IU/L) 0.39
<4043 (89.6)322 (84.9)
≥405 (7.8)59 (15.1)
Types of DOACs 0.04
Apixaban13 (26.0)168 (40.2)
Edoxaban18 (36.0)141 (33.7)
Rivaroxaban16 (32.0)58 (13.9)
Dabigatran3 (6.0)51 (12.2)
Prescription dose a 0.01
Underdose18 (36.0)133 (31.8)
Standard dose28 (56.0)278 (66.5)
Overdose4 (8.0)7 (1.7)
Co-medications
Antiplatelets3 (6.0)51 (12.2)0.20
ACEI or ARBs19 (38.0)186 (44.5)0.38
Beta-blockers38 (76.0)295 (70.6)0.42
Calcium channel blockers13 (26.0)116 (27.8)0.79
Diuretics11 (22.0)109 (26.1)0.53
Statins28 (56.0)245 (58.6)0.72
CYP inducers0 (0.0)1 (0.2)1.00
CYP inhibitors7 (14.0)58 (13.9)0.99
Previous myocardial infarction4 (8.0)36 (8.6)1.00
Previous stroke/TIA/thromboembolism28 (56.0)179 (42.8)0.08
Previous bleeding events4 (8.0)18 (4.3)0.28
Comorbidities
Atrial fibrillation49 (98.0)398 (98.5)0.78
Hypertension34 (68.0)282 (67.5)0.94
Diabetes mellitus14 (28.0)119 (28.5)0.95
Heart failure5 (10.0)78 (18.7)0.13
Anemia22 (44.0)107 (25.6)0.01
Smoking6 (12.0)57 (13.6)0.75
Alcohol17 (34.0)134 (32.1)0.78
CHA2DS2-VASc risk of stroke3.62 ± 1.693.47 ± 1.760.57
Modified HAS-BLED2.00 ± 0.951.94 ± 1.040.70
ACEIs: angiotensin converting enzyme inhibitors; ALT: alanine transferase; AST: aspartate transaminase; ARBs: angiotensin II receptor blockers; BMI: body mass index; CYP: cytochrome P450 family; DOACs: direct oral anticoagulants; TIA: transient ischemic attack. a Standard dose was defined according to the FDA-approved labeling.
Table
Table 2. Effects of ABCB1 and CYP3A5 grouped genotypes on bleeding complication.
Table 2. Effects of ABCB1 and CYP3A5 grouped genotypes on bleeding complication.
dbSNP rsIDGrouped GenotypeBleeding
(n = 50)		No Bleeding
(n = 418)		p
ABCB1
rs3842 (T>C)TT, CT40 (80.0)379 (90.7)0.02
CC10 (20.0)39 (9.3)
rs1045642 (A>G)AA7 (14.0)42 (10.1)0.39
AG, GG43 (86.0)375 (89.9)
rs2032582 (A>C)AA, AC37 (79.0)331 (79.6)0.36
CC13 (26.0)85 (20.4)
rs1128503 (A>G)AA, AG40 (80.0)339 (81.1)0.85
GG10 (20.0)79 (18.9)
rs3213619 (A>G)AA, AG50 (100.0)413 (99.5)1.00
GG0 (0.0)2 (0.5)
rs3747802 (A>G)AA43 (86.0)368 (88.2)0.64
AG, GG7 (14.0)49 (11.8)
CYP3A5
rs776746 (C>T)CC, CT46 (92.0)396 (95.7)0.28
TT4 (8.0)18 (4.3)
Table
Table 3. Effects of APOB and APOE grouped genotypes on bleeding complication.
Table 3. Effects of APOB and APOE grouped genotypes on bleeding complication.
dbSNP rsIDGrouped GenotypeBleeding
(n = 50)		No Bleeding
(n = 418)		p
APOB
rs1042034 (C>T)CC34 (68.0)233 (55.9)0.10
CT, TT16 (32.0)184 (44.1)
rs2163204 (T>G)TT, GT50 (100.0)413 (99.0)0.49
GG0 (0.0)4 (1.0)
rs693 (G>A)GG49 (98.0)369 (88.3)0.04
AG, AA1 (2.0)49 (11.7)
rs679899 (G>A)GG, AG12 (24.0)119 (28.5)0.50
AA38 (76.0)298 (71.5)
rs13306194 (G>A)GG, AG49 (98.0)413 (98.8)0.49
AA1 (2.0)5 (1.2)
rs13306198 (G>A)GG37 (74.0)381 (91.1)<0.01
AG, AA13 (26.0)37 (8.9)
rs1367117 (G>A)GG42 (84.0)324 (77.5)0.29
AG, AA8 (16.0)94 (22.5)
APOE
rs429358 (T>C)TT43 (86.0)329 (79.5)0.27
CT, CC7 (14.0)85 (20.5)
rs7412 (C>T)CC42 (89.4)345 (85.2)0.44
CT, TT5 (10.6)60 (14.8)
Table
Table 4. Univariate and multivariable regression analyses to identify predictors for bleeding.
Table 4. Univariate and multivariable regression analyses to identify predictors for bleeding.
PredictorsUnadjusted OR (95% CIs)Model IModel II
Adjusted OR (95% CI)Adjusted OR (95% CI)
Female1.03 (0.56–1.88)
Age (≥65)0.91 (0.48–1.70)
Overdose5.11 (1.44–18.10)4.02 (1.04–15.46) *4.21 (1.00–17.73)
Rivaroxaban2.92 (1.52–5.63)2.70 (1.37–5.33) *2.59 (1.26–5.30) *
Anemia2.28 (1.25–4.16)2.33 (1.26–4.31) **2.61 (1.37–4.96) **
ABCB1 rs3842 CC2.43 (1.13–5.23) 2.44 (1.07–5.58) *
APOB rs693 GG6.49 (0.88–47.62) 6.85 (0.90–52.63)
APOB rs13306198 AG, AA3.62 (1.77–7.41) 3.00 (1.39–6.47) *
CI: confidence interval; OR: odds ratio. Model I included variables of sex, age, overdose, rivaroxaban and anemia. Model II included variables of sex, age, overdose, rivaroxaban, anemia, ABCB1 rs3842, APOB rs693 and APOB rs13306198. * p < 0.05; ** p < 0.01.
Table
Table 5. The observed and predicted bleeding risk (%) by risk score.
Table 5. The observed and predicted bleeding risk (%) by risk score.
ScoreBleedingTotalObserved Bleeding Risk (%)Predicted Bleeding Risk (%)
00250.000.79
11175.882.02
2070.005.41
3112035.425.18
41816011.2512.51
5144332.5626.90
63933.3347.00
72366.6764.27
811100.0082.25
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Yoon, H.-Y.; Song, T.-J.; Yee, J.; Park, J.; Gwak, H.-S. Association between Genetic Polymorphisms and Bleeding in Patients on Direct Oral Anticoagulants. Pharmaceutics 2022, 14, 1889. https://doi.org/10.3390/pharmaceutics14091889

AMA Style

Yoon H-Y, Song T-J, Yee J, Park J, Gwak H-S. Association between Genetic Polymorphisms and Bleeding in Patients on Direct Oral Anticoagulants. Pharmaceutics. 2022; 14(9):1889. https://doi.org/10.3390/pharmaceutics14091889

Chicago/Turabian Style

Yoon, Ha-Young, Tae-Jin Song, Jeong Yee, Junbeom Park, and Hye-Sun Gwak. 2022. "Association between Genetic Polymorphisms and Bleeding in Patients on Direct Oral Anticoagulants" Pharmaceutics 14, no. 9: 1889. https://doi.org/10.3390/pharmaceutics14091889

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop