Abstract
Objective
Clopidogrel is widely used for preventing ischemic complications related to cardiovascular diseases. However, many patients experience clopidogrel resistance (CR). The polymorphisms of CYP2C19 have been implicated in CR, but CYP2C19 polymorphism considerably varies with both ethnic group and geographical location. This study aimed to investigate the association between CYP2C19 polymorphisms and clopidogrel resistance (CR) in patients with coronary heart disease and ischemic stroke among Han and Tibetan populations in Qinghai Province, China.
Methods
From June 2019 to January 2020, patients who were diagnosed with coronary heart disease or cerebral infarction in internal medicine of Qinghai Provincial People's Hospital and had taken dual antiplatelet drugs were included in this study. Blood was collected and routine items were completed. Whole exome sequencing was performed for CYP2C19 genetic polymorphisms of CYP2C19∗2 (rs4244285), CYP2C19∗3 (rs4986893), and CYP2C19∗17 (rs12248560).
Results
A total of 91 patients with coronary heart disease or cerebral infarction (67 Han people (65.99 ± 12.25 years old) and 24 Tibetan (63.6324 Tib years old)) including 52 cases with CR and 39 cases with non-CR were enrolled in this study. For the Han population, the differences in age, glycosylated hemoglobin, activated partial thromboplastin time (APTT), gender, aspirin resistance, and diabetes were significant between the CR and non-CR groups. For the Tibetan population, the two groups showed no significant difference in all indicators. There was no significant difference between CR and non-CR groups for all genotypes (CYP2C19 ∗2, ∗3, and ∗17) in either Han or Tibetan populations. For the Han populations, age, APTT, and aspirin resistance were significantly correlated with CR.
Conclusion
The present study indicated that CYP2C19∗2, CYP2C19∗3, and CYP2C19∗17 alleles were not correlated with CR for both Han and Tibetan populations in Qinghai Province, while age, APTT, and aspirin resistance were independent risk factors of CR in this region.
1. Introduction
Cardiac-cerebral disease is common in clinical practice and is the leading cause of long-term disability and death around the world [1]. The global challenges of cardiac-cerebral diseases present an enormous health burden. Coronary heart disease and ischemic stroke are two common cardiac-cerebral diseases, which demonstrates a high-frequency of emergency department visits to manage acute and chronic symptoms [2].
Antiplatelet therapy with aspirin and clopidogrel is frequently used for the secondary prevention of acute coronary syndrome, ischemic stroke, and other related ischemic cardiac-cerebral diseases to reduce recurrent ischemic events [3, 4]. Clopidogrel is an irreversible P2Y12 inhibitor, which is usually used for preventing ischemic complications related to cardiovascular diseases [5]. However, many patients experience relapse or bleeding [6], which is associated with increased late mortality [7, 8]. Clopidogrel resistance (CR), defined as a reduction in platelet aggregation rate by less than 10% from baseline after clopidogrel treatment [9], is considered to be critically associated with recurrent events after anti-platelet therapy.
Studies have reported that the potential mechanisms underlying the insufficient responses to clopidogrel involve several aspects, including epigenetic dysfunction (such as improper DNA methylation), clearance of active metabolites, variable absorption of precursor drugs, potential interactions between drugs, genetic polymorphisms of platelet receptors, adenosine diphosphate (ADP)-mediated variability of P2Y12 platelet receptor, and differences in signal transduction pathway of platelet [10–12]. Additionally, clopidogrel is a prodrug that should be converted into active metabolites by hepatic metabolism through cytochrome (CYP) P450 isoenzymes, including CYP1A2, CYP2C9, CYP2C19, CYP3A, and CYP2B6 [13, 14]. Among these enzymes above, CYP2C19 plays the most important role in clopidogrel transformation [6]. The polymorphisms of CYP2C19 have been demonstrated to be implicated in CR [15, 16]. A recent study demonstrated that the CYP2C19∗2 or CYP2C19∗3 alleles were significantly related to attenuated platelet response to clopidogrel and increased CR risk among Chinese patients in the Beijing district. Nevertheless, CYP2C19 polymorphism considerably varies with both ethnic group and geographical location [17, 18].
In this study, we intended to investigate the association of CYP2C19 polymorphisms with CR in patients with coronary heart disease and ischemic stroke among Han and Tibetan populations in Qinghai Province, China. The results may help to guide rational clinical drug use and reduce the incidence of cardiovascular adverse events.
2. Materials and Methods
2.1. Subjects
From June 2019 to January 2020, a total of 91 patients who were diagnosed with coronary heart disease or cerebral infarction in internal medicine of Qinghai Provincial People's Hospital and had taken dual antiplatelet drugs were included in this study. Among them, 67 patients were ethnic Han and 24 were ethnic Tibetan. This study was approved by the Ethics Committee of Qinghai Provincial People's Hospital. All participants had signed the informed consent.
2.2. Inclusion and Exclusion Criteria
The inclusion of patients with coronary heart disease was in line with the “coronary heart disease guidelines and expert consensus” in 2019, and patients with stroke or transient ischemic attacks met the “practical diagnosis and expert consensus of ischemic stroke in China” in 2020. The patients were permanent residents or long-time (10 years or more) residents in the Qinghai region. All patients received routine doses of aspirin (100 mg once daily) and clopidogrel (75 mg once daily) for 5–7 days.
The patients with one or more of the following conditions were excluded: allergic or intolerant; contraindications of antiplatelets therapy; rupture and defect of gastrointestinal mucous membrane; inflammation of endocardium or heart valve due to microbial invasion of the body; serious decline in the ability of glomeruli to expel toxins and waste; continuous growth of cancer cells; decreased ejection fraction; combined with pulmonary congestion and inadequate peripheral perfusion with contemplated surgical operation; severe liver disease and/or abnormal coagulation function; and incomplete clinical case data.
2.3. Sample Collection, DNA Extraction, and Whole Exome Sequencing
Blood was collected on an empty stomach the next morning after admission for patients who met the inclusion and exclusion criteria, and routine items such as blood routine, biochemistry, thyroid function, and saccharification were completed. Basic clinical data, such as previous history, medication history, personal history, and others, were collected.
A total of 4 ml fasting venous blood was taken and placed in an anticoagulant tube containing ethylenediamine tetraacetic acid (EDTA), and the specimen was stored at −80°C. DNA was extracted using a TianGen DNA extraction kit (TianGen Ltd, Beijing, China). The DNA concentration was determined by fluorescence quantification. Genomic DNA (l μg) was sheared by sonication, and the fragments with an average size of 150–250 bp were selected by magnetic beads. Then, the fragments were ligated to adapters and amplified by pre-PCR. PCR products were then hybridized and washed with Agilent SureSelect or BGI Hybridization and Wash kits (Agilent, CA, USA), followed by PCR amplification. The reaction conductions were 18 cycles of 98°C for 10 s, 65 C for 30 s, and 72°C for 30 s, followed by a final incubation at 72°C for 5 min, and then hold at 4°C. The primers were CYP2C19∗2: forward 5′-ATT ACAACCAGAGCTTGGCAT-3′, reverse 5′-GTTGATGTCCATCGATTCTTG-3′; CYP2C19∗3: forward 5′-CTGCAATGTGATCTGCTCCAT-3′, reverse 5′-TTCAGGGCTTGGTCAATATAG-3′; and CYP2C19∗17: forward 5′-GATGAATGTGGTATATATTCA-3′, reverse 5′-GAGAACAGGACACCTGTTGGT-3′. The library concentration was measured using the Qubit kit (Invitrogen, USA). Sequencing was performed by the combinatorial probe-anchor synthesis method [19]. CYP2C19 genetic polymorphisms of CYP2C19∗2 (rs4244285), CYP2C19∗3 (rs4986893), and CYP2C19∗17 (rs12248560) were recorded.
2.4. Statistical Analysis
Statistical analysis was performed using SPSS 22.0 (IBM, Armonk, New York, USA). Quantitative data were assessed for normality by the Shapiro–Wilk test [20]. If the data were normally distributed, they were expressed as the mean ± standard deviation, and the differences were compared by independent sample t-test. On the contrary, the data were expressed as median (interquartile range) and the differences were analyzed by Mann–Whitney Test [21]. Qualitative data were represented in the form of N (%) and the difference between groups was analyzed by chi-square test [22]. P < 0.05 was considered significant.
3. Results
3.1. Baseline Information
Finally, 91 patients (67 Han people and 24 Tibetan) meeting the inclusion and exclusion criteria were enrolled in this study. Patient demographics, and clinical and laboratory findings are shown in Table 1. Except for prothrombin time, urea nitrogen, and smoking history (P < 0.05), there was no significant difference in the other detection indexes between ethnic Han and Tibetan.
Table 1.
Baseline information for participants.
| Characteristics | Total (N = 91) | Han population (N = 67) | Tibetan population (N = 24) | P value |
|---|---|---|---|---|
| Age, years# | 65.36 ± 12.44 | 65.99 ± 12.25 | 63.63 ± 13.06 | 0.428 |
| Height (m)# | 1.68 ± 0.08 | 1.68 ± 0.09 | 1.66 ± 0.08 | 0.251 |
| Weight (Kg)# | 68.12 ± 10.75 | 68.88 ± 11.05 | 66.00 ± 9.80 | 0.262 |
| BMI (Kg/m2)# | 24.11 ± 2.71 | 24.19 ± 2.74 | 23.91 ± 2.68 | 0.663 |
| Oxygen saturation (%) | 90.0 (89.0, 93.0) | 91.0 (89.0, 93.0) | 90.0 (90.0, 92.0) | 0.816 |
| SBP (mmHg)# | 126.9 ± 24.4 | 128.7 ± 22.3 | 122.1 ± 29.4 | 0.328 |
| DBP (mmHg)# | 74.0 ± 13.5 | 73.9 ± 12.3 | 74.1 ± 16.8 | 0.960 |
| Heart rate# | 76.8 ± 15.5 | 78.5 ± 15.0 | 72.1 ± 16.2 | 0.080 |
| White blood cell | 7.20 (5.70, 11.09) | 7.05 (5.55, 10.96) | 7.88 (6.64, 11.33) | 0.471 |
| Red blood cell | 4.77 (4.26, 5.31) | 4.75 (4.40, 5.39) | 4.78 (4.03, 5.22) | 0.365 |
| Hemoglobin# | 151.1 ± 24.2 | 153.2 ± 21.5 | 145.0 ± 30.2 | 0.156 |
| Glycosylated hemoglobin | 5.81 (5.47, 6.70) | 5.78 (5.46, 6.70) | 5.95 (5.59, 6.95) | 0.418 |
| PT | 12.0 (11.5, 12.6) | 11.9 (11.4, 12.5) | 12.2 (11.9, 13.4) | 0.010 |
| APTT | 27.6 (25.4, 31.2) | 27.5 (25.4, 30.8) | 30.7 (25.9, 32.8) | 0.072 |
| ALT | 30.0 (19.0, 54.0) | 29.0 (19.0, 54.0) | 38.0 (17.0, 66.8) | 0.496 |
| AST | 38.0 (22.0, 159.0) | 34.0 (21.0, 159.0) | 44.5 (22.3, 258.5) | 0.438 |
| AST/ALT | 1.2 (0.8, 2.4) | 1.3 (0.9, 2.8) | 1.1 (0.7, 2.1) | 0.355 |
| Urea nitrogen | 6.07 (4.98, 8.10) | 5.87 (4.97, 7.58) | 7.16 (5.99, 10.68) | 0.014 |
| Creatinine | 84.0 (67.0, 94.0) | 82.0 (67.0, 93.0) | 84.5 (75.3, 114.5) | 0.242 |
| Glomerular filtration rate (mL/min × 1.73 m2)# | 78.2 ± 25.2 | 80.6 ± 24.9 | 70.9 ± 25.1 | 0.109 |
| Albumin | 37.8 (34.8, 40.6) | 38.0 (35.2, 40.7) | 36.6 (32.7, 38.3) | 0.085 |
| Total cholesterol (mmol/L)# | 4.13 ± 1.12 | 4.13 ± 1.03 | 4.02 ± 1.37 | 0.699 |
| Triglyceride | 1.42 (0.90, 2.14) | 1.59 (0.90, 2.18) | 1.32 (0.88, 1.87) | 0.418 |
| TSH | 1.60 (0.86, 2.82) | 1.62 (0.85, 2.70) | 1.30 (0.88, 2.91) | 0.571 |
|
| ||||
| Sex, n (%) | 0.475 | |||
| Male | 63 (69.2) | 45 (67.2) | 18 (75.0) | |
| Female | 28 (30.8) | 22 (32.8) | 6 (25.0) | |
|
| ||||
| Aspirin resistance, n (%) | 0.660 | |||
| No | 42 (46.2) | 30 (44.8) | 12 (50.0) | |
| Yes | 49 (53.8) | 37 (55.2) | 12 (50.0) | |
|
| ||||
| Smoking history, n (%) | 0.039 | |||
| No | 52 (57.1) | 34 (50.7) | 18 (75.0) | |
| Yes | 39 (42.9) | 33 (49.3) | 6 (25.0) | |
|
| ||||
| Coronary heart disease type, n (%) | 0.932 | |||
| STEMI | 42 (46.2) | 31 (46.3) | 11 (45.8) | |
| NSTEMI | 12 (13.2) | 8 (11.9) | 4 (16.7) | |
| SAP | 15 (16.5) | 11 (16.4) | 4 (16.7) | |
| UA | 22 (24.2) | 17 (25.4) | 5 (20.8) | |
|
| ||||
| Hypertension, n (%) | 0.291 | |||
| No | 30 (33.0) | 20 (29.9) | 10 (41.7) | |
| Yes | 61 (66.7) | 47 (70.1) | 14 (58.3) | |
|
| ||||
| Diabetes, n (%) | 0.374 | |||
| No | 45 (49.5) | 35 (52.2) | 10 (41.7) | |
| Yes | 46 (50.5) | 32 (47.8) | 14 (58.3) | |
|
| ||||
| Stroke, n (%) | 0.473 | |||
| No | 59 (64.8) | 42 (62.7) | 17 (70.8) | |
| Yes | 32 (35.2) | 25 (37.3) | 7 (29.2) | |
|
| ||||
| Hyperlipidaemia, n (%) | 0.907 | |||
| No | 54 (59.3) | 40 (59.7) | 14 (58.3) | |
| Yes | 37 (40.7) | 27 (40.3) | 10 (41.7) | |
|
| ||||
| CYP2C19∗2, n (%) | 0.120 | |||
| GG | 61 (67.0) | 45 (67.2) | 16 (66.7) | |
| GA | 24 (26.4) | 16 (23.9) | 8 (33.3) | |
| AA | 6 (6.6) | 6 (9.0) | 0 (0.0) | |
|
| ||||
| CYP2C19∗3, n (%) | 0.527 | |||
| GG | 44 (48.4) | 34 (50.7) | 10 (41.7) | |
| GA | 26 (28.6) | 17 (25.4) | 9 (37.5) | |
| AA | 21 (23.0) | 16 (23.9) | 5 (20.8) | |
|
| ||||
| CYP2C19∗17, n (%) | 0.317 | |||
| GG | 57 (62.6) | 44 (65.7) | 13 (54.2) | |
| GA | 34 (37.4) | 23 (34.3) | 11 (45.8) | |
#mean ± sd, P value, Han population vs. Tibetan population.
For all patients, according to the definition of CR, there were 52 cases with CR and 39 cases with non-CR. The differences between CR and non-CR groups were significant for age, glycosylated hemoglobin (HBALC), activated partial thromboplastin time (APTT), gender, aspirin resistance, diabetes, and coronary heart disease classification (P < 0.05), and the other indicators were not significantly different between the two groups (Table 2).
Table 2.
Comparison of differences in baseline information between CR and non-CR for all participants.
| Characteristics | Non-CR (N = 39) | CR (N = 52) | P value |
|---|---|---|---|
| Age, years# | 59.46 ± 11.01 | 69.79 ± 11.67 | <0.001 |
| Height (m)# | 1.70 ± 0.08 | 1.66 ± 0.09 | 0.062 |
| Weight (Kg)# | 68.56 ± 9.64 | 67.79 ± 11.60 | 0.736 |
| BMI (Kg/m2)# | 23.71 ± 2.43 | 24.41 ± 2.89 | 0.225 |
| Oxygen saturation (%) | 91.0 (89.0, 93.0) | 90.0 (90.0, 92.8) | 0.827 |
| SBP (mmHg)# | 126.3 ± 27.2 | 127.4 ± 22.3 | 0.837 |
| DBP (mmHg)# | 74.9 ± 14.9 | 73.2 ± 12.5 | 0.553 |
| Heart rate# | 76.5 ± 14.8 | 77.1 ± 16.2 | 0.854 |
| White blood cell | 7.32 (5.55, 10.06) | 7.17 (5.71, 11.33) | 0.721 |
| Red blood cell | 4.89 (4.40, 5.54) | 4.70 (4.25, 5.22) | 0.342 |
| Hemoglobin# | 153.2 ± 28.7 | 149.5 ± 20.3 | 0.500 |
| Glycosylated hemoglobin | 5.63 (5.38, 6.19) | 6.06 (5.52, 7.17) | 0.005 |
| PT | 12.0 (11.7, 12.7) | 11.9 (11.4, 12.5) | 0.271 |
| APTT | 28.7 (26.7, 32.3) | 26.9 (24.3, 31.0) | 0.008 |
| ALT | 37.0 (19.0, 82.0) | 28.5 (19.3, 42.0) | 0.183 |
| AST | 48.0 (22.0, 221.0) | 33.5 (21.3, 109.5) | 0.195 |
| AST/ALT | 1.3 (0.9, 3.4) | 1.2 (0.8, 2.3) | 0.431 |
| Urea nitrogen | 5.91 (4.97, 10.04) | 6.28 (5.25, 7.99) | 0.745 |
| Creatinine | 83.0 (67.0, 96.0) | 84.0 (67.3, 92.8) | 0.591 |
| Glomerular filtration rate (mL/min × 1.73 m2)# | 80.7 ± 28.3 | 76.0 ± 22.7 | 0.389 |
| Albumin | 38.2 (34.7, 41.1) | 37.8 (34.9, 39.7) | 0.510 |
| Total cholesterol (mmol/L)# | 4.00 ± 1.22 | 4.17 ± 1.05 | 0.478 |
| Triglyceride | 1.32 (0.90, 1.97) | 1.59 (0.90, 2.38) | 0.432 |
| TSH | 1.19 (0.54, 2.68) | 1.68 (1.20, 2.89) | 0.156 |
|
| |||
| Sex, n (%) | 0.022 | ||
| Male | 32 (82.1) | 31 (59.6) | |
| Female | 7 (17.9) | 21 (40.4) | |
|
| |||
| Aspirin resistance, n (%) | <0.001 | ||
| No | 28 (71.8) | 14 (26.9) | |
| Yes | 11 (28.2) | 38 (73.1) | |
|
| |||
| Smoking history, n (%) | 0.328 | ||
| No | 20 (51.3) | 32 (61.5) | |
| Yes | 19 (48.7) | 20 (38.5) | |
|
| |||
| Coronary heart disease type, n (%) | 0.041 | ||
| STEMI | 19 (48.7) | 23 (44.2) | |
| NSTEMI | 8 (20.5) | 4 (7.7) | |
| SAP | 2 (5.1) | 13 (25.0) | |
| UA | 10 (25.6) | 12 (23.1) | |
|
| |||
| Hypertension, n (%) | 0.157 | ||
| No | 16 (41.0) | 14 (26.9) | |
| Yes | 23 (59.0) | 38 (73.1) | |
|
| |||
| Diabetes, n (%) | 0.004 | ||
| No | 26 (66.7) | 19 (36.5) | |
| Yes | 13 (33.3) | 33 (63.5) | |
|
| |||
| Stroke, n (%) | 0.899 | ||
| No | 25 (64.1) | 34 (65.4) | |
| Yes | 14 (35.9) | 18 (34.6) | |
|
| |||
| Hyperlipidaemia, n (%) | 0.355 | ||
| No | 21 (53.8) | 33 (63.5) | |
| Yes | 18 (46.2) | 19 (36.5) | |
|
| |||
| CYP2C19∗2, n (%) | 0.412 | ||
| GG | 24 (61.5) | 37 (71.2) | |
| GA | 11 (28.2) | 13 (25.0) | |
| AA | 4 (10.3) | 2 (3.8) | |
|
| |||
| CYP2C19∗2, n (%) | 0.334 | ||
| GG | 24 (61.5) | 37 (71.2) | |
| GA + AA | 15 (38.5) | 15 (28.8) | |
|
| |||
| CYP2C19∗3, n (%) | 0.387 | ||
| GG | 22 (56.4) | 22 (42.3) | |
| GA | 10 (25.6) | 16 (30.8) | |
| AA | 7 (17.9) | 14 (26.9) | |
|
| |||
| CYP2C19∗3, n (%) | 0.183 | ||
| GG | 22 (56.4) | 22 (42.3) | |
| GA + AA | 17 (43.6) | 30 (57.7) | |
|
| |||
| CYP2C19∗17, n (%) | 0.532 | ||
| GG | 23 (59.0) | 34 (65.4) | |
| GA | 16 (41.0) | 18 (34.6) | |
For the Han population, the differences in age, HBALC, APTT, gender, aspirin resistance, and diabetes were statistically significant between CR and non-CR groups (P < 0.05), and the differences in the other indicators were not significant (Table 3). For the Tibetan population, the two groups showed no significant difference in all indicators (Table 4).
Table 3.
Comparison of differences in baseline information between CR and non-CR for Han populations.
| Characteristics | Non-CR (N = 27) | CR (N = 40) | P value |
|---|---|---|---|
| Age, years# | 59.33 ± 10.48 | 70.48 ± 11.38 | <0.001 |
| Height (m)# | 1.70 ± 0.08 | 1.67 ± 0.09 | 0.115 |
| Weight (Kg)# | 69.04 ± 9.93 | 68.78 ± 11.86 | 0.925 |
| BMI (Kg/m2)# | 23.60 ± 2.13 | 24.59 ± 3.04 | 0.151 |
| Oxygen saturation (%) | 91.0 (89.0, 93.0) | 90.5 (89.3, 93.0) | 0.892 |
| SBP (mmHg)# | 129.6 ± 28.1 | 128.1 ± 17.7 | 0.809 |
| DBP (mmHg)# | 75.0 ± 14.1 | 73.2 ± 11.0 | 0.549 |
| Heart rate# | 77.7 ± 14.2 | 79.1 ± 15.7 | 0.699 |
| White blood cell | 7.17 (5.55, 10.53) | 6.93 (5.48, 11.06) | 0.898 |
| Red blood cell | 5.10 (4.55, 5.55) | 4.60 (4.25, 5.26) | 0.120 |
| Hemoglobin# | 158.5 ± 22.1 | 149.7 ± 20.6 | 0.098 |
| Glycosylated hemoglobin | 5.57 (5.38, 5.97) | 6.09 (5.49, 7.24) | 0.009 |
| PT | 11.9 (11.6, 12.7) | 11.7 (11.2, 12.5) | 0.169 |
| APTT | 27.9 (26.7, 30.9) | 26.5 (24.5, 30.0) | 0.031 |
| ALT | 33.0 (20.0, 70.0) | 27.0 (18.3, 39.5) | 0.125 |
| AST | 60.0 (22.0, 209.0) | 31.0 (20.0, 121.5) | 0.179 |
| AST/ALT | 1.4 (1.0, 3.4) | 1.2 (0.8, 2.4) | 0.315 |
| Urea nitrogen | 5.41 (4.86, 7.54) | 6.11 (5.25, 7.63) | 0.201 |
| Creatinine | 78.0 (67.0, 90.0) | 84.5 (67.0, 93.0) | 0.908 |
| Glomerular filtration rate (mL/min × 1.73 m2)# | 87.4 ± 28.3 | 75.9 ± 21.5 | 0.064 |
| Albumin | 38.6 (36.1, 41.4) | 37.8 (35.2, 40.0) | 0.457 |
| Total cholesterol (mmol/L)# | 4.07 ± 1.02 | 4.16 ± 1.04 | 0.731 |
| Triglyceride | 1.29 (0.90, 1.97) | 1.77 (0.90, 2.51) | 0.315 |
| TSH | 1.79 (0.66, 2.65) | 1.62 (1.15, 2.81) | 0.498 |
|
| |||
| Sex, n (%) | 0.010 | ||
| Male | 23 (85.2) | 22 (55.0) | |
| Female | 4 (14.8) | 18 (45.0) | |
|
| |||
| Aspirin resistance, n (%) | <0.001 | ||
| No | 20 (74.1) | 10 (25.0) | |
| Yes | 7 (25.9) | 30 (75.0) | |
|
| |||
| Smoking history, n (%) | 0.065 | ||
| No | 10 (37.0) | 24 (60.0) | |
| Yes | 17 (63.0) | 16 (40.0) | |
|
| |||
| Coronary heart disease type, n (%) | 0.206 | ||
| STEMI | 14 (51.9) | 17 (42.5) | |
| NSTEMI | 5 (18.5) | 3 (7.5) | |
| SAP | 2 (7.4) | 9 (22.5) | |
| UA | 6 (22.2) | 11 (27.5) | |
|
| |||
| Hypertension, n (%) | 0.110 | ||
| No | 11 (40.7) | 9 (22.5) | |
| Yes | 16 (59.3) | 31 (77.5) | |
|
| |||
| Diabetes, n (%) | 0.003 | ||
| No | 20 (74.1) | 15 (37.5) | |
| Yes | 7 (25.9) | 25 (62.5) | |
|
| |||
| Stroke, n (%) | 0.969 | ||
| No | 17 (63.0) | 25 (62.5) | |
| Yes | 10 (37.0) | 15 (37.5) | |
|
| |||
| Hyperlipidaemia, n (%) | 0.570 | ||
| No | 15 (55.6) | 25 (62.5) | |
| Yes | 12 (44.4) | 15 (37.5) | |
|
| |||
| CYP2C19∗2, n (%) | 0.333 | ||
| GG | 16 (59.3) | 29 (72.5) | |
| GA | 7 (25.9) | 9 (22.5) | |
| AA | 4 (14.8) | 2 (5.0) | |
|
| |||
| CYP2C19∗2, n (%) | 0.258 | ||
| GG | 16 (59.3) | 29 (72.5) | |
| GA + AA | 11 (40.7) | 11 (27.5) | |
|
| |||
| CYP2C19∗3, n (%) | 0.216 | ||
| GG | 17 (63.0) | 17 (42.5) | |
| GA | 6 (22.2) | 11 (27.5) | |
| AA | 4 (14.8) | 12 (30.0) | |
|
| |||
| CYP2C19∗3, n (%) | 0.100 | ||
| GG | 17 (63.0) | 17 (42.5) | |
| GA + AA | 10 (37.0) | 23 (57.5) | |
|
| |||
| CYP2C19∗17, n (%) | 0.888 | ||
| GG | 18 (66.7) | 26 (65.0) | |
| GA | 9 (33.3) | 14 (35.0) | |
Table 4.
Comparison of differences in baseline information between CR and non-CR for Tibetan populations.
| Characteristics | Non-CR (N = 12) | CR (N = 12) | P value |
|---|---|---|---|
| Age, years# | 59.75 ± 12.63 | 67.50 ± 12.82 | 0.150 |
| Height (m)# | 1.68 ± 0.08 | 1.64 ± 0.08 | 0.227 |
| Weight (Kg)# | 67.50 ± 9.26 | 64.50 ± 10.49 | 0.466 |
| BMI (Kg/m2)# | 23.97 ± 3.11 | 23.84 ± 2.31 | 0.911 |
| Oxygen saturation (%) | 90.5 (89.0, 92.8) | 90.0 (90.0, 92.0) | 0.630 |
| SBP (mmHg)# | 119.1 ± 24.6 | 125.2 ± 34.4 | 0.623 |
| DBP (mmHg)# | 74.8 ± 17.3 | 73.4 ± 17.1 | 0.851 |
| Heart rate# | 73.8 ± 16.4 | 70.3 ± 16.5 | 0.607 |
| White blood cell | 7.35 (5.36, 9.55) | 9.08 (6.86, 15.20) | 0.266 |
| Red blood cell | 4.55 (3.78, 5.49) | 4.79 (4.19, 5.19) | 0.731 |
| Hemoglobin# | 141.1 ± 38.3 | 149.0 ± 20.1 | 0.533 |
| Glycosylated hemoglobin | 5.69 (5.26, 7.04) | 6.01 (5.81, 6.91) | 0.291 |
| PT | 12.1 (11.9, 13.3) | 12.3 (11.9, 13.5) | 0.799 |
| APTT | 31.8 (27.2, 35.0) | 29.4 (23.6, 32.0) | 0.114 |
| ALT | 45.0 (12.0, 134.8) | 35.5 (20.3, 51.8) | 0.887 |
| AST | 43.0 (20.0, 771.8) | 44.5 (23.3, 106.0) | 0.843 |
| AST/ALT | 1.0 (0.7, 4.7) | 1.1 (0.6, 2.1) | 0.887 |
| Urea nitrogen | 8.56 (5.99, 13.66) | 7.16 (5.13, 8.80) | 0.443 |
| Creatinine | 93.5 (76.3, 127.5) | 82.5 (72.8, 88.5) | 0.319 |
| Glomerular filtration rate (mL/min × 1.73 m2)# | 65.5 ± 22.6 | 76.4 ± 27.3 | 0.297 |
| Albumin | 37.7 (32.6, 41.0) | 36.2 (33.2, 38.3) | 0.833 |
| Total cholesterol (mmol/L)# | 3.84 ± 1.62 | 4.20 ± 1.10 | 0.528 |
| Triglyceride | 1.38 (0.92, 1.95) | 1.16 (0.89, 2.09) | 0.799 |
| TSH | 1.12 (0.36, 2.94) | 2.04 (1.26, 2.90) | 0.211 |
|
| |||
| Sex, n (%) | 1.000 | ||
| Male | 9 (75.0) | 9 (75.0) | |
| Female | 3 (25.0) | 3 (25.0) | |
|
| |||
| Aspirin resistance, n (%) | 0.102 | ||
| No | 8 (66.7) | 4 (33.3) | |
| Yes | 4 (33.3) | 8 (66.7) | |
|
| |||
| Smoking history, n (%) | 0.342 | ||
| No | 10 (83.3) | 8 (66.7) | |
| Yes | 2 (16.7) | 4 (33.3) | |
|
| |||
| Coronary heart disease type, n (%) | 0.618 | ||
| STEMI | 5 (41.7) | 6 (50.0) | |
| NSTEMI | 3 (25.0) | 1 (8.3) | |
| SAP | 0 (0.0) | 4 (33.3) | |
| UA | 4 (33.3) | 1 (8.3) | |
|
| |||
| Hypertension, n (%) | 1.000 | ||
| No | 5 (41.7) | 5 (41.7) | |
| Yes | 7 (58.3) | 7 (58.3) | |
|
| |||
| Diabetes, n (%) | 0.408 | ||
| No | 6 (50.0) | 4 (33.3) | |
| Yes | 6 (50.0) | 8 (66.7) | |
|
| |||
| Stroke, n (%) | 0.653 | ||
| No | 8 (66.7) | 9 (75.0) | |
| Yes | 4 (33.3) | 3 (25.0) | |
|
| |||
| Hyperlipidaemia, n (%) | 0.408 | ||
| No | 6 (50.0) | 8 (66.7) | |
| Yes | 6 (50.0) | 4 (33.3) | |
|
| |||
| CYP2C19∗2, n (%) | 1.000 | ||
| GG | 8 (66.7) | 8 (66.7) | |
| GA | 4 (33.3) | 4 (33.3) | |
|
| |||
| CYP2C19∗3, n (%) | 0.855 | ||
| GG | 5 (41.7) | 5 (41.7) | |
| GA | 4 (33.3) | 5 (41.7) | |
| AA | 3 (25.0) | 2 (16.7) | |
|
| |||
| CYP2C19∗3, n (%) | 1.000 | ||
| GG | 5 (41.7) | 5 (41.7) | |
| GA + AA | 7 (58.3) | 7 (58.3) | |
|
| |||
| CYP2C19∗17, n (%) | 0.219 | ||
| GG | 5 (41.7) | 8 (66.7) | |
| GA | 7 (58.3) | 4 (33.3) | |
3.2. Comparative Analysis of CYP2C19 ∗2 and ∗3 Loci in Different Ethnic Groups
The genotypes of the CYP2C19∗2 locus in all participants included GG (67.0%), GA (26.4%), and AA (6.6%), respectively. The genotypes of the CYP2C19∗3 locus were GG (48.4%), GA (26.6%), and AA (23.0%), respectively. The genotypes of the CYP2C19∗17 locus were GG and GA, accounting for 65.1% and 34.9%, respectively. For the Han population, the genotypes of the CYP2C19∗2 locus were GG (67.2%), GA (23.9%), and AA (9.0%), respectively. The genotypes of CYP2C19∗3 locus were GG (50.7%), GA (25.4%), and AA (23.9%), respectively. The genotypes of the CYP2C19∗17 locus were GG and GA, accounting for 65.7% and 34.3%, respectively. For the Tibetan population, there were 66.7% GG and 33.3% GA for the genotypes of CYP2C19∗2, respectively. The genotypes of the CYP2C19∗3 locus were GG (41.7%), GA (37.5%), and AA (20.8%), respectively. For the genotypes of the CYP2C19∗17 locus, GG accounted for 54.2 and GA accounted for 45.8% (Table 1). There was no significant difference between ethnic Han and Tibetan.
3.3. Comparative Analysis of Different Locus between CR and Non-CR Groups
For all patients, the genotypes of CYP2C19∗2, CYP2C19∗3, and CYP2C19∗17 for CR and non-CR groups are shown in Table 2. The genotypes of CYP2C19∗2, CYP2C19∗3, and CYP2C19∗17 for the Han population in two groups are shown in Table 3 and for the Tibetan population in two groups are shown in Table 4. There was no significant difference between CR and non-CR groups for all genotypes in either Han or Tibetan populations.
3.4. Logistic Analysis of Risk Factors for CR
Variables with significant differences in baseline information were included for univariate and multivariate logistic regression analyses to explore the significant related factors of CR. As shown in Table 5, for all patients, age was a significant risk factor for CR, with an odds ratio (OR) (95% confidence interval (CI)) = 1.08 (1.02, 1.13), P=0.005. The older the patients, the higher the risk of CR. APTT was a significant negative correlation factor for CR (OR (95% CI) = 0.81 (0.69, 0.95), P=0.011), and the risk of CR decreased with the increase of APTT. Aspirin resistance was a significant positive correlation factor for CR (OR (95% CI) = 6.47 (2.02, 20.67), P=0.002). Patients with aspirin resistance were at a significantly increased risk of developing CR. There was no significant association between coronary heart disease type and CR. For the Han populations, age, APTT, and aspirin resistance were significantly correlated with CR (P < 0.05) (Table 6).
Table 5.
Univariate and multivariate logistics regression analyses for CR in all participants.
| Variables | Univariate analysis | Multivariate analysis | ||
|---|---|---|---|---|
| OR (95% CI) | P value | OR (95% CI) | P value | |
| Age | 1.08 (1.04, 1.12) | <0.001 | 1.08 (1.02, 1.13) | 0.005 |
| Glycosylated hemoglobin | 2.00 (1.19, 3.39) | 0.010 | Excluded | |
| APTT | 0.86 (0.78, 0.96) | 0.006 | 0.81 (0.69, 0.95) | 0.011 |
| Sex | Excluded | |||
| Male | Reference | |||
| Female | 3.10 (1.15, 8.32) | 0.025 | ||
| Aspirin resistance | ||||
| No | Reference | Reference | ||
| Yes | 6.91 (2.73, 17.48) | <0.001 | 6.47 (2.02, 20.67) | 0.002 |
| Diabetes | Excluded | |||
| No | Reference | |||
| Yes | 3.47 (1.45, 8.32) | 0.005 | ||
| Coronary heart disease type, n (%) | 0.073 | 0.053 | ||
| STEMI | Reference | Reference | ||
| NSTEMI | 0.41 (0.11, 1.59) | 0.198 | 0.19 (0.03, 1.12) | 0.066 |
| SAP | 5.37 (1.08, 26.81) | 0.040 | 4.43 (0.57, 34.57) | 0.156 |
| UA | 0.99 (0.35, 2.79) | 0.987 | 0.39 (0.10, 1.55) | 0.181 |
Table 6.
Univariate and multivariate logistics regression analyses for CR in Han populations.
| Variables | Univariate analysis | Multivariate analysis | ||
|---|---|---|---|---|
| OR (95% CI) | P value | OR (95% CI) | P value | |
| Age | 1.09 (1.04, 1.15) | 0.001 | 1.09 (1.03, 1.15) | 0.003 |
| Glycosylated hemoglobin | 2.37 (1.16, 4.87) | 0.019 | Excluded | |
| APTT | 0.86 (0.76, 0.98) | 0.027 | 0.82 (0.67, 1.00) | 0.048 |
|
| ||||
| Sex | Excluded | |||
| Male | Reference | |||
| Female | 4.71 (1.37, 16.11) | 0.014 | ||
|
| ||||
| Aspirin resistance | ||||
| No | Reference | Reference | ||
| Yes | 8.57 (2.80, 26.25) | <0.001 | 7.92 (2.14, 29.36) | 0.002 |
|
| ||||
| Diabetes | Excluded | |||
| No | Reference | |||
| Yes | 4.76 (1.63, 13.92) | 0.004 | ||
4. Discussion
Clopidogrel combined with aspirin is usually recommended for preventing ischemic events in patients with cardiovascular [23]. Despite the standard treatment, there are still a lot of adverse cardiovascular events, and CR is considered to be the main reason [24]. In this study, we investigated the association between ∗2, ∗3, and ∗17 allelic variants of the CYP2C19 gene and CR in patients with coronary heart disease and ischemic stroke among Han and Tibetan populations. The results showed that three alleles were not statistically correlated with CR, while age, APTT, and aspirin resistance were significantly correlated with CR.
Presently, the mechanisms underlying CR have not been fully elucidated. The CYP2C19 genotype is the most important determinant of the pharmacodynamic and pharmacokinetic responses to clopidogrel [25]. It has been reported that CYP2C19∗2 and CYP2C19∗3, the main mutant alleles, are the most common genotypes in Asian populations [26]. CYP2C19∗2 or CYP2C19∗3 allelic variants increase the risk of CR [27]. CYP2C19∗17 allele is correlated with an increased risk of bleeding [28]. However, the present study showed that CYP2C19∗2, CYP2C19∗3, and CYP2C19∗17 alleles were not significantly different between Han or Tibetan populations as well as between CR and non-CR groups, which suggested that the three alleles were not statistically correlated with CR in this study.
Our result was in accordance with a recent study that investigated the association between CYP2C19∗2, CYP2C19∗3, and CYP2C19∗17 variants of the CYP2C19 gene and CR in patients with acute coronary syndromes in Morocco, and demonstrated that none of the three alleles showed a statistical correlation with CR. Different from the results of our study, that study identified a synergic effect among the three alleles on CR [29]. In fact, the correlation between polymorphisms of CYP2C19 and platelet responsiveness to clopidogrel has been widely recognized among patients with acute coronary syndrome and percutaneous coronary intervention, but the association with other indications, such as arterial fibrillation and stable angina, is negative [30, 31]. The inconsistent results may be due to the magnitude of the influence of CYP2C19 on the effectiveness of clopidogrel and may be consistent with the influence of this molecule on specific clinical indications [32, 33].
In this study, APTT, age, and aspirin resistance were significantly correlated with CR. The APTT is a widely available test used to screen for hypercoagulable states in bleeding disorders [34]. Shortened APTT is an independent risk factor for ischemic stroke [35], but its role in CR has not been reported to our knowledge. Age was a positively correlated factor of CR, which was inconsistent with previous studies. Prabhakaran et al. [36] have reported that being older than 55 years contributed to a low response to clopidogrel loading. It has been reported that patients with aspirin resistance have increased platelet reactivity [37]. High on-treatment platelet reactivity has become the most important factor inhibiting the antiplatelet effect of clopidogrel, resulting in the ineffectiveness of this agent [38]. Clopidogrel's high on-treatment platelet reactivity could negatively influence the clinical course of a stroke and increase the risk of recurrent vascular events [39]. Therefore, platelet function testing is necessary for stroke individuals, especially those predisposed to CR.
There were several limitations in the present study. First, there was a lack of sequence analysis that could provide more robust information on the investigated CYP2C19 polymorphisms. Second, the study only comprised Chinese patients, while multicentric investigation might have been more informative in terms of data robustness. Third, there was a lack of functional correlation between examined gene polymorphisms and enzyme activity in patients. At last, no control group represented by healthy individuals was included in the analysis. Furthermore, studies including larger sample sizes and control groups may help to better understand the phenomenon of heterogeneity in clopidogrel response.
5. Conclusion
In conclusion, the present study indicated that CYP2C19∗2, CYP2C19∗3, and CYP2C19∗17 alleles were not correlated with CR for both Han and Tibetan populations in Qinghai Province, while age, APTT, and aspirin resistance were independent risk factors of CR in this region. Our results may provide useful data for precision medicine based on individual gene sequencing results.
Acknowledgments
This work was supported by the Natural Science Foundation of Qinghai Province Science and Technology Department (grant no. 2018-ZJ-904), Qinghai Provincial Health Commission Fund Project (grant no. 2020-wjzdx-36), and The Special Funds for Medical and Health Work in Longhua District (grant no. YL20180201).
Contributor Information
Rong Chang, Email: changrong6617@163.com.
Jinchun Wu, Email: wujinchun117@sina.com.
Data Availability
The data used to support the findings of this study are available from the corresponding author upon request.
Ethical Approval
This study was approved by the Ethics Committee of Qinghai Provincial People's Hospital.
Conflicts of Interest
The authors declare that they have no conflicts of interest.
Authors' Contributions
RC and JW were responsible for conception and design of the research, YY, WZ, and XZ were responsible for acquisition of data, YY and WZ were responsible for analysis and interpretation of data, RC and JW were responsible for statistical analysis, RC and JW were responsible for obtaining funding, RC and JW were responsible for drafting the manuscript, and YL was responsible for revision of the manuscript for important intellectual content. All authors have read and approved the final manuscript.
References
- 1.Tong X., Yang Q., Ritchey M. D., et al. Peer reviewed: the burden of cerebrovascular disease in the United States. Preventing Chronic Disease . 2019;16 doi: 10.5888/pcd16.180411.180411 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Baptiste D.-L., Turkson-Ocran R.-A., Han H.-R., Dennison Himmelfarb C., Commodore-Mensah Y. Social determinants of emergency department visits among persons diagnosed with coronary heart disease and stroke. Ethnicity & Disease . 2021;31(1):41–46. doi: 10.18865/ed.31.1.41. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Liu J., Hu X., Wang Y., Guan X., Chen J., Liu H. The safety and effectiveness of early anti-platelet therapy after alteplase for acute ischemic stroke: a meta-analysis. Journal of Clinical Neuroscience . 2021;91:176–182. doi: 10.1016/j.jocn.2021.07.002. [DOI] [PubMed] [Google Scholar]
- 4.Hackam D. G., Spence J. D. Antiplatelet therapy in ischemic stroke and transient ischemic attack: an overview of major trials and meta-analyses. Stroke . 2019;50(3):773–778. doi: 10.1161/strokeaha.118.023954. [DOI] [PubMed] [Google Scholar]
- 5.Elmariah S., Doros G., Benavente O. R., et al. Impact of clopidogrel therapy on mortality and cancer in patients with cardiovascular and cerebrovascular disease: a patient-level meta-analysis. Circulation: Cardiovascular Interventions . 2018;11(1) doi: 10.1161/circinterventions.117.005795.e005795 [DOI] [PubMed] [Google Scholar]
- 6.Lee J.-B., Lee K.-A., Lee K.-Y. Cytochrome P450 2C19 polymorphism is associated with reduced clopidogrel response in cerebrovascular disease. Yonsei Medical Journal . 2011;52(5):734–738. doi: 10.3349/ymj.2011.52.5.734. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Eikelboom J. W., Mehta S. R., Anand S. S., Xie C., Fox K. A., Yusuf S. Adverse impact of bleeding on prognosis in patients with acute coronary syndromes. Circulation . 2006;114(8):774–782. doi: 10.1161/circulationaha.106.612812. [DOI] [PubMed] [Google Scholar]
- 8.Armstrong P. W., Fu Y., Chang W.-C., et al. Acute coronary syndromes in the GUSTO-IIb trial: prognostic insights and impact of recurrent ischemia. Circulation . 1998;98(18):1860–1868. doi: 10.1161/01.cir.98.18.1860. [DOI] [PubMed] [Google Scholar]
- 9.Wang L., Wang X., Chen F. Clopidogrel resistance is associated with long-term thrombotic events in patients implanted with drug-eluting stents. Drugs in R&D . 2010;10(4):219–224. doi: 10.2165/11539580-000000000-00000. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Ohlmann P., Eckly A., Freund M., Cazenave J. P., Offermanns S., Gachet C. ADP induces partial platelet aggregation without shape change and potentiates collagen-induced aggregation in the absence of Gαq. Blood . 2000;96(6):2134–2139. doi: 10.1182/blood.v96.6.2134.h8002134_2134_2139. [DOI] [PubMed] [Google Scholar]
- 11.Nguyen T. A., Diodati J. G., Pharand C. Resistance to clopidogrel: a review of the evidence. Journal of the American College of Cardiology . 2005;45(8):1157–1164. doi: 10.1016/j.jacc.2005.01.034. [DOI] [PubMed] [Google Scholar]
- 12.Yang J., Yu Q., Xu Z., et al. Clopidogrel resistance is associated with DNA methylation of genes from whole blood of humans. Frontiers in Genetics . 2020;11 doi: 10.3389/fgene.2020.583215.583215 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Cresci S., Depta J. P., Lenzini P. A., et al. Cytochrome p450 gene variants, race, and mortality among clopidogrel-treated patients after acute myocardial infarction. Circulation: Cardiovascular Genetics . 2014;7(3):277–286. doi: 10.1161/circgenetics.113.000303. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Rotondo J. C., Giari L., Guerranti C., et al. Environmental doses of perfluorooctanoic acid change the expression of genes in target tissues of common carp. Environmental Toxicology and Chemistry . 2018;37(3):942–948. doi: 10.1002/etc.4029. [DOI] [PubMed] [Google Scholar]
- 15.Su Q., Li J., Tang Z., et al. Association of CYP2C19 polymorphism with clopidogrel resistance in patients with acute coronary syndrome in China. Medical Science Monitor . 2019;25:7138–7148. doi: 10.12659/msm.915971. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Mega J. L., Close S. L., Wiviott S. D., et al. Cytochrome p-450 polymorphisms and response to clopidogrel. New England Journal of Medicine . 2009;360(4):354–362. doi: 10.1056/nejmoa0809171. [DOI] [PubMed] [Google Scholar]
- 17.Zhong Z., Hou J., Li B., et al. Analysis of CYP2C19 genetic polymorphism in a large ethnic Hakka population in southern China. Medical Science Monitor . 2017;23:6186–6192. doi: 10.12659/msm.905337. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Shuldiner A. R., O’Connell J. R., Bliden K. P., et al. Association of cytochrome P450 2C19 genotype with the antiplatelet effect and clinical efficacy of clopidogrel therapy. JAMA . 2009;302(8):849–857. doi: 10.1001/jama.2009.1232. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Fehlmann T., Reinheimer S., Geng C., et al. cPAS-based sequencing on the BGISEQ-500 to explore small non-coding RNAs. Clinical Epigenetics . 2016;8(1):123–211. doi: 10.1186/s13148-016-0287-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Hanusz Z., Tarasinska J., Zielinski W. Shapiro–wilk test with known mean. REVSTAT-Statistical Journal . 2016;14(1):89–100. [Google Scholar]
- 21.Hart A. Mann-Whitney test is not just a test of medians: differences in spread can be important. BMJ . 2001;323(7309):391–393. doi: 10.1136/bmj.323.7309.391. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Tallarida R. J., Murray R. B. Chi-square Test. Manual of Pharmacologic Calculations . Berlin, Germany: Springer; 1987. pp. 140–142. [Google Scholar]
- 23.Thomson R. M., Anderson D. C. Aspirin and clopidogrel for prevention of ischemic stroke. Current Neurology and Neuroscience Reports . 2013;13(2):327–329. doi: 10.1007/s11910-012-0327-y. [DOI] [PubMed] [Google Scholar]
- 24.Joob B., Wiwanitkit V. CYP2C19∗2 polymorphism and clopidogrel resistance. Archivos de cardiologia de Mexico (English ed. Internet) . 2021;90(4) doi: 10.24875/acme.m20000160.20000396 [DOI] [PubMed] [Google Scholar]
- 25.Pereira N. L., Rihal C. S., So D. Y. F., et al. Clopidogrel pharmacogenetics. Circulation: Cardiovascular Interventions . 2019;12(4) doi: 10.1161/circinterventions.119.007811.007811 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Xie H. G., Zou J. J., Hu Z. Y., Zhang J. J., Ye F., Chen S. L. Individual variability in the disposition of and response to clopidogrel: pharmacogenomics and beyond. Pharmacology & Therapeutics . 2011;129(3):267–289. doi: 10.1016/j.pharmthera.2010.10.001. [DOI] [PubMed] [Google Scholar]
- 27.Zhuo Z. L., Xian H. P., Long Y., et al. Association between CYP2C19 and ABCB1 polymorphisms and clopidogrel resistance in clopidogrel-treated Chinese patients. The Anatolian Journal of Cardiology . 2018;19(2):123–129. doi: 10.14744/anatoljcardiol.2017.8097. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Sibbing D., Koch W., Gebhard D., et al. Cytochrome 2C19∗17 allelic variant, platelet aggregation, bleeding events, and stent thrombosis in clopidogrel-treated patients with coronary stent placement. Circulation . 2010;121(4):512–518. doi: 10.1161/circulationaha.109.885194. [DOI] [PubMed] [Google Scholar]
- 29.Hassani Idrissi H., Hmimech W., Khorb N. E., Akoudad H., Habbal R., Nadifi S. A synergic effect between CYP2C19∗2, CYP2C19∗3 loss-of-function and CYP2C19∗17 gain-of-function alleles is associated with Clopidogrel resistance among moroccan acute coronary syndromes patients. BMC Research Notes . 2018;11(1):46–3132. doi: 10.1186/s13104-018-3132-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Bhatt D. L., Paré G., Eikelboom J. W., et al. The relationship between CYP2C19 polymorphisms and ischaemic and bleeding outcomes in stable outpatients: the CHARISMA genetics study. European Heart Journal . 2012;33(17):2143–2150. doi: 10.1093/eurheartj/ehs059. [DOI] [PubMed] [Google Scholar]
- 31.Johnson J. A., Roden D. M., Lesko L. J., Ashley E., Klein T. E., Shuldiner A. R. Clopidogrel: a case for indication‐specific pharmacogenetics. Clinical Pharmacology & Therapeutics . 2012;91(5):774–776. doi: 10.1038/clpt.2012.21. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Roden D. M. Cardiovascular pharmacogenomics: the future of cardiovascular therapeutics? Canadian Journal of Cardiology . 2013;29(1):58–66. doi: 10.1016/j.cjca.2012.07.845. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Paré G., Mehta S. R., Yusuf S., et al. Effects of CYP2C19 genotype on outcomes of clopidogrel treatment. New England Journal of Medicine . 2010;363(18):1704–1714. doi: 10.1056/nejmoa1008410. [DOI] [PubMed] [Google Scholar]
- 34.Abdullah W. Z. Shortened activated partial thromboplastin time (APTT): a simple but important marker of hypercoagulable state during acute coronary event. Coronary Artery Disease–New Insights and Novel Approaches . 2012;157 [Google Scholar]
- 35.Lin C. H., Kuo Y. W., Kuo C. Y., et al. Shortened activated partial thromboplastin time is associated with acute ischemic stroke, stroke severity, and neurological worsening. Journal of Stroke and Cerebrovascular Diseases . 2015;24(10):2270–2276. doi: 10.1016/j.jstrokecerebrovasdis.2015.06.008. [DOI] [PubMed] [Google Scholar]
- 36.Prabhakaran S., Wells K., Lee V., Flaherty C., Lopes D. Prevalence and risk factors for aspirin and clopidogrel resistance in cerebrovascular stenting. American Journal of Neuroradiology . 2008;29(2):281–285. doi: 10.3174/ajnr.a0818. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Eikelboom J. W., Hirsh J., Weitz J. I., Johnston M., Yi Q., Yusuf S. Aspirin-resistant thromboxane biosynthesis and the risk of myocardial infarction, stroke, or cardiovascular death in patients at high risk for cardiovascular events. Circulation . 2002;105(14):1650–1655. doi: 10.1161/01.cir.0000013777.21160.07. [DOI] [PubMed] [Google Scholar]
- 38.Fiolaki A., Katsanos A. H., Kyritsis A. P., et al. High on treatment platelet reactivity to aspirin and clopidogrel in ischemic stroke: a systematic review and meta-analysis. Journal of the Neurological Sciences . 2017;376:112–116. doi: 10.1016/j.jns.2017.03.010. [DOI] [PubMed] [Google Scholar]
- 39.Wiśniewski A., Filipska K. The phenomenon of clopidogrel high on-treatment platelet reactivity in ischemic stroke subjects: a comprehensive review. International Journal of Molecular Sciences . 2020;21(17):p. 6408. doi: 10.3390/ijms21176408. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data used to support the findings of this study are available from the corresponding author upon request.