Impact of Cytochrome P450 2C19 Polymorphism on Ischemic Stroke Prognosis

Impact of Cytochrome P450 2C19 Polymorphism on Ischemic Stroke Prognosis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Impact of Cytochrome P450 2C19 Polymorphism on Ischemic Stroke Prognosis Dongmei Tan, Jianfen Su, Yukuan Tang, Chen Huang, Xihua Fu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4534316/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Stroke is the leading cause of mortality and impairment in China. Genetic variations in cytochrome P450 2C19 (CYP2C19) have been associated with the occurrence and outcomes of ischemic stroke (IS). We aimed to explore the correlation between CYP2C19 polymorphism and IS morbidity and prognosis. Methods We retrospectively enrolled 157 patients with IS who were admitted to the Affiliated Panyu Centra Hospital of Guangzhou Medical University between April 2021 and March 2022 and underwent long-term antiplatelet therapy. A total of 142 inpatients without IS or coronary heart disease were enrolled as controls. Telephone follow-ups were conducted for the IS group, and the modified Rankin Scale was used to assess prognosis. We explored the association between CYP2C19 genotype and IS by using multivariate logistic regression to analyze variables influencing disease occurrence and prognosis. Results Significant differences were identified in the occurrence rates of CYP2C19*2 GG/GA and *3 GG/GA genotypes between the IS ( P = 0.014, 0.006, respectively) and control groups ( P = 0.006, 0.009, respectively). A notable disparity was observed in the allelic frequency of CYP2C19*3 G(A) ( P = 0.005). IS morbidity was considerably greater in the intermediate metabolizer (IM) group than in the extensive metabolizer group ( P < 0.001). Hyperlipidemia, diabetes, hypertension, and CYP2C19 IM were independent risk factors of IS ( P < 0.001). No variation was observed in the CYP2C19 genotype between the good and poor prognosis groups ( P = 0.893). Conclusion CYP2C19 polymorphism was related to IS morbidity but had no significant correlation with prognosis. ischemic stroke CYP2C19 polymorphism long-term antiplatelet therapy Background In recent years, stroke has emerged as the leading factor contributing to mortality and impairment among the Chinese population. Ischemic stroke (IS) is the predominant form of stroke in China, comprising approximately 69.6–70.8% of all cases [ 1 , 2 ] and having a prevalence of 1,700/100,000 (age-standardized rate, 1,256/100,000) [ 1 ]. IS is characterized by elevated mortality rates, disability incidence, recurrence frequency, and a substantial economic burden [ 3 ]. Genetic variations, or polymorphisms, significantly affect the occurrence and prognosis of IS in cytochrome P450 2C19 (CYP2C19) [ 4 ]. As a first-line antiplatelet agent, clopidogrel primarily inhibits platelet aggregation by activating CYP2C19 metabolic enzymes. Despite the significance of CYP2C19 in acute coronary syndrome, its role in IS has lacked attention among Chinese guidelines, representing a significant knowledge gap. Domestic and international studies primarily investigate CYP2C19 polymorphism in individuals diagnosed with acute coronary syndrome; moreover, genetic testing of CYP2C19 is not included in China's IS guidelines [ 5 ]. Thus, this study underscores the need for tailored medication guidance in patients with IS based on CYP2C19 genetic testing. A distinct absence of real-world research evidence to guide the individualized administration of antiplatelet therapy for patients undergoing CYP2C19 genetic testing exists. Patient data This study retrospectively analyzed the cases of patients diagnosed with IS who were admitted to the Affiliated Panyu Centra Hospital of Guangzhou Medical University between July 2020 and June 2021. This study was approved by the Independent Ethics Committee of the Affiliated Panyu Centra Hospital of Guangzhou Medical University on March 17, 2023 (PYRC-2023-068). The trial was conducted following the Declaration of Helsinki (1989), the guidelines of Good Clinical Practice, and other related guiding principles. All participants signed informed consent forms. Inclusion criteria The inclusion criteria were as follows: patients who fulfilled the diagnostic criteria for IS in the "Chinese Guidelines for the Diagnosis and Treatment of Acute Ischemic Stroke 2018" [ 5 ] and were diagnosed using magnetic resonance imaging or cranial electron computed tomography; patients who were categorized as having either aortic atherosclerosis type or arteriole occlusion type according to IS etiological classification; patients who received long-term outpatient treatment in our hospital since discharge and those who regularly used platelet drugs until final follow-up or death; patients who were administered 75 mg of clopidogrel daily, 100 mg of aspirin, or aluminum–magnesium and aspirin tablets (II) (81 mg:22 mg:11 mg) for continuous treatment; and patients who underwent CYP2C19 genetic testing. Exclusion criteria The exclusion criteria were as follows: patients with malignant tumors, severe infections, cerebral hemorrhage, cerebral infarction complicated with hemorrhage, transient ischemic attack, cardiogenic cerebral embolism, and non-atherosclerotic vascular stenosis; patients with contraindications for clopidogrel or aspirin (i.e., severe heart, liver, or kidney failure; drug allergy; active bleeding; or coagulation dysfunction); patients prescribed a long-term combination of two or more antiplatelet medications, patients with inconsistent antiplatelet medication classes; patients taking a combination of anticoagulants or certain proton pump inhibitors (i.e., omeprazole, lansoprazole, and esomeprazole); and patients who were never administered medicine in our outpatient department, had poor drug compliance, or were lost follow-up. Control group The control group comprised patients aged > 40 years who received CYP2C19 test results without any evidence of peripheral arterial disease, coronary atherosclerotic heart disease, or ischemic cerebrovascular disease after physical examination, medical history, and cranial computed tomography and/or magnetic resonance imaging. Data collection General information included follow-up time (antiplatelet drug treatment time), sex, age, smoking, drinking, hypertension, diabetes, coronary atherosclerotic heart disease, hyperlipidemia, Trial of ORG 10172 in Acute Stroke Treatment classification, antiplatelet drugs, and CYP2C19 genotype. Predictive information encompassed the modified Rankin Scale (mRS) score, along with occurrences of cardiovascular and cerebrovascular incidents. Methods Main reagents The main reagents were as follows: clopidogrel hydrogen sulfate tablets (trade name, Plavix; specification, 75 mg per tablet; manufactured by Sanofi [Hangzhou] Pharmaceutical Company Ltd.), aspirin enteric tablets (specification, 100 mg per tablet; manufactured by Bayer Healthcare Ltd.), and aluminum–magnesium and aspirin tablets (II) (trade name, ASiDe; specification, 81 mg [aspirin], 22 mg [heavy magnesium carbonate], 11 mg [aluminum glycinate] per tablet; manufactured by Shandong Zhongheihengqiao Pharmaceutical Co., Ltd.). Follow-up of patients All participants were followed up through in-person clinical visits or telephone follow-ups conducted on February 2022. The mRS score assessed the patients' living capacities after treatment. The mRS scores ranged from zero to six. A positive prognosis correlated with an mRS score of zero to two, whereas a negative prognosis was linked to an mRS score of three to six. Simultaneously, occurrences of IS recurrence, new transient ischemic attack, myocardial infarction, and death were documented. Research process According to the mRS score, patients in the IS group receiving 75 mg of clopidogrel daily were divided into those with poor and good prognoses. The general information and CYP2C19 genotype of the two groups were compared to analyze the factors that influence the prognosis of patients with IS. Individuals with CYP2C19 loss-of-function (LOF) were evaluated, and the dependent variables included mRS score (two to six) and the occurrence of adverse cardiovascular and cerebrovascular events. Univariate and/or multivariate logistic regression analysis was conducted on both general data and antiplatelet medications within the two groups. Statistical analysis All statistical analyses of the data were conducted using SPSS 25.0 software. The gene frequencies of the etiology and prognosis analyses were tested for conformity to the Hardy–Weinberg genetic balance law using the goodness of fit. If the measured data adhered to a distribution that is typically observed in statistical analysis, they were represented as the average standard deviation (x ± s), and two independent samples were used for the t-test. Otherwise, they were expressed as the medians (from the first quartile to the third quartile) (Md [P25–P75]), and the Mann–Whitney U-test was employed to compare the two groups. The enumeration data is presented as patient counts (percentages) (n [%]), and statistical analysis using either the χ 2 test or Fisher exact probability method was conducted to compare the groups. In univariate analysis, logistic regression analysis was employed to identify the independent risk factors for the morbidity and prognosis of IS; variables with influences of P < α were considered independent variables. The level of statistical significance was set at P < 0.05. Results Morbidity A total of 142 patients in the control group met the inclusion criteria and comprised 87 men and 55 women aged 46–91 (69 [58–76]) years. The IS group contained 157 patients, including 83 men and 72 women aged 38–89 (69 [60–77]) years, who were followed up for 3–19 (13 [ 10 – 16 ]) months. No statistically significant disparity was observed in sex (χ 2 = 2.145, P = 0.143) or age (Z = -0.803, P = 0.422) between the two comparable groups (Table 1 ). Table 1 Comparison of the general data (n [%]) Group Control group (n = 142) Cerebral infarction group(n = 157) χ 2 / t / Z P Men, n (%) 87 (61.3%) 83 (52.9%) 2.145 0.143 Women, n (%) 55 (38.7%) 74 (47.1%) Age, y [Md (P25–P75)] 69 (58–76) 69 (60–77) -0.803 0.422 Smoke, n (%) 34 (23.9%) 28 (17.8%) 1.693 0.193 Drinking, n (%) 19 (13.4%) 16 (10.2%) 0.734 0.392 Hypertension, n (%) 64 (45.1%) 129 (82.2%) 44.836 0.000 Diabetes, n (%) 23 (16.2%) 55 (35.0%) 13.718 0.000 Hyperlipidemia, n (%) 38 (26.8%) 85 (54.1%) 23.083 0.000 UM, n (%) 3 (2.1%) 1 (0.6%) 11.150 0.001 EM, n (%) 51 (35.9%) 28 (17.8%) IM, n (%) 61 (43.0%) 96 (61.1%) PM, n (%) 27 (19.0%) 32 (20.4%) UM: Ultra-Rapid Metabolizer, EM: Extensive Metabolizer, IM: Intermediate, Metabolizer, PM: Poor Metabolizer Hardy–Weinberg equilibrium The distributions of the three genotypes, namely, CYP2C19*2, *3, and *17, in the control (χ 2 = 2.098, 0.004, and 0.002; P = 0.350, 0.947, and 1.000, all respectively) and IS groups (χ 2 = 4.201, 0.079, and 0.004; P = 0.122, 1.000, and 1.000, all respectively) were consistent with the Hardy–Weinberg equilibrium, which is representative of the population (Table 2 ). Table 2 Distribution of the CYP2C19 genes in the control and IS groups (n [%]) Group Control group (n = 142) Cerebral infarction group (n = 157) χ 2 P CYP2C19*2 GG 58 (40.8%) 43 (27.4%) 6.036 0.014 GA 59 (41.5%) 90 (57.3%) 7.423 0.006 AA 25 (17.6%) 24 (15.3%) 0.293 0.589 G 175 (61.6%) 176 (56.1%) 1.907 0.167 A 109 (38.4%) 138 (43.9%) CYP2C19*3 GG 136 (95.8%) 136 (86.6%) 7.600 0.006 GA 6 (4.2%) 20 (12.7%) 6.807 0.009 AA 0 1 (0.6%) - 1.000* G 278 (97.9%) 292 (93.0%) 8.002 0.005 A 6 (2.1%) 22 (7.0%) CYP2C19*17 CC 138 (97.2%) 156 (99.4%) 1.033 0.309 CT 4 (2.8%) 1 (0.6%) TT 0 0 - - C 280 (98.6%) 313 (99.7%) 1.024 0.311 T 4 (1.4%) 1 (0.3%) Note: * Fisher is the exact probability method . Correlation between CYP2C19 polymorphism and IS Compared with that of the control group, the allelic frequency of CYP2C19*3A notably increased among individuals with IS. Additionally, the GG gene frequencies of CYP2C19*2 and *3 were found to be significantly lower in the IS group, whereas the GA gene frequencies were higher (χ 2 = 8.002, P = 0.005; χ 2 = 6.036 and 7.600, P = 0.014 and 0.006; χ 2 = 7.423 and 6.827, P = 0.006 and 0.009, all respectively), as shown in Table 2 . Single-factor analysis of influencing morbidity The prevalences of hypertension, diabetes, and hyperlipidemia were markedly elevated in the IS group compared with those in the control group ( P 0.05) among the four categories of CYP2C19 metabolizers: extensive metabolizer (EM), intermediate metabolizer (IM), poor metabolizer (PM), and ultra-rapid metabolizer. Likewise, the incidence of IS was consistent across the groups (χ 2 = 11.150, P = 0.001). Compared with the EM group individually, the IM group had a significantly higher morbidity of IS (χ 2 = 13.925, P < 0.001), as summarized in Table 1 . Logistic regression analysis of multiple factors influencing disease morbidity The dependent variable in this study was the incidence of IS, whereas the independent variables were hypertension, diabetes, hyperlipidemia, and CYP2C19 IM based on their statistical significance in the univariate analysis. These factors were found to be independent risk factors for IS, according to Table 3 . Table 3 Multivariate logistic regression analysis of IS β SE Waldχ 2 P OR 95% CI Hypertension 1.659 0.293 31.985 0.000 5.255 2.957–9.338 Diabetes 1.181 0.331 12.695 0.000 3.257 1.701–6.237 Hyperlipidemia 1.284 0.285 20.265 0.000 3.610 2.064–6.314 CYP2C19 UM 1.0 (Reference groups) CYP2C19 EM 0.114 1.256 0.008 0.928 0.892 0.076–10.468 CYP2C19 IM 1.196 0.332 12.963 0.000 3.306 1.724–6.339 CYP2C19 PM 0.559 0.405 1.908 0.167 1.749 0.791–3.866 β: Beta coefficient, SE: Standard Error, Wald χ2: Wald Chi-Square, P : P -value, OR: Odds Ratio, 95% CI: 95% Confidence Interval, UM: Ultra-Rapid Metabolizer, EM: Extensive Metabolizer, IM: Intermediate Metabolizer, PM: Poor Metabolizer Prognosis In the IS group, 51 patients who were followed up for 4–19 (average [13.43 ± 3.540]) months had a good prognosis after treatment with clopidogrel, including 21 men and 30 women aged 43–87 (69 [60–78]) years. Additionally, 30 patients with poor prognosis, including 18 men and 12 women aged 57–89 (76 [70–85]) years, were followed up for 4–19 months (average [12.23 ± 3.481]) months. Follow-up between the two groups (t = 1.480, P = 0.143) showed no statistically significant difference. The comprehensive analyses can be found in Table 4 . Table 4 Comparison of general data between the good prognosis group and poor prognosis group (n [%]) Group Good prognosis group (n = 51) Poor prognosis group (n = 30) χ 2 / t / Z P Follow-up time (x ± s) 13.43 ± 3.540 12.23 ± 3.481 1.480 0.143 Age, y [Md (P25–P75)] 69 (60–78) 76 (70–85) -3.000 0.003 Men, n (%) 21 (41.2%) 18 (60.0%) 2.681 0.102 Women, n (%) 30 (58.8%) 12 (40.0%) Smoking, n (%) 9 (17.6%) 4 (13.3%) 0.039 0.844 Drinking, n (%) 4 (7.8%) 5 (16.7%) 0.730 0.393 Hypertension, n (%) 40 (78.4%) 27 (90.0%) 1.768 0.184 Diabetes, n (%) 19 (37.3%) 13 (43.3%) 0.292 0.589 Coronary heart disease, n (%) 10 (19.6%) 8 (26.7%) 0.545 0.461 Hyperlipidemia, n (%) 24 (47.1%) 14 (46.7%) 0.001 0.973 Arteriole occlusion, n (%) 39 (76.5%) 21 (70.0%) 0.412 0.521 Atherosclerosis of the aorta, n (%) 12 (23.5%) 9 (30.0%) EM, n (%) 17 (33.3%) 11 (36.7%) 0.201 0.904 IM, n (%) 23 (45.1%) 12 (40.0%) PM, n (%) 11 (21.6%) 7 (23.3%) EM: Extensive Metabolizer, IM: Intermediate Metabolizer, PM: Poor Metabolizer Hardy–Weinberg equilibrium The distribution of the CYP2C19*2, *3, and *17 genotypes was evaluated using a goodness-of-fit test. The results indicated no significant deviation between the observed patients and the expected frequencies (χ 2 = 1.202 and 5.252; P = 0.548 and 0.102), suggesting that the sample accurately represented the population. Univariate analysis of influencing prognosis With the exception of age (Z = -3.000, P = 0.003), no significant variations were observed in the overall data (Table 4 ). Personalized medicine According to the genotype, 128 patients carrying CYP2C19 LOF *2 and *3, namely IM and PM, were grouped according to the antiplatelet medication used. Among them, 75 patients (58.6%) belonged to the aspirin group, with 44 (58.7%) being men aged 38–88 years (average age, 65.20 ± 10.624 years). These individuals were maintained under observation for a period ranging from 3 to 19 months, with a median follow-up of 12 months (range, 9–16 months). Furthermore, patients (8.0%) in this group had a history of coronary heart disease. The clopidogrel group comprised 53 patients (41.4%), of whom 27 patients (50.9%) were men aged 49–89 years (average age, 72.30 ± 10.721 years) who were followed up for 4–19 (14 [ 10 – 16 ]) months, and 13 of the patients (24.5%) had coronary heart disease. The incidence of coronary heart disease was found to be significantly elevated in the clopidogrel group, compared with that in the control group ( P = 0.000 and 0.010, respectively). Analysis of the factors influencing mRS score (two to six) in patients with CYP2C19 LOF In this study, patients were categorized into two groups based on age: ≤65 years (n = 53) and > 65 years (n = 75). Analysis revealed no significant disparity in the CYP2C19 genotype between these two groups ( P = 0.914) when considering the mRS score (2–6) as the dependent variable. The incidence of mRS scores (2–6 points) for the different antiplatelet medicines were comparable. First, the general data were analyzed using single-factor logistic regression analysis. The findings revealed a noteworthy association between age and the utilization of antiplatelet medication in both cohorts ( P = 0.021 and 0.007). A multifactorial logistic regression analysis was conducted, considering both age and the utilization of antiplatelet agents. The findings indicated that patients with CYP2C19 LOF who were administered clopidogrel had a notably reduced incidence of mRS scores (zero to one) compared with those given aspirin ( P = 0.018). Additionally, the occurrence of mRS scores (zero to one) was significantly lower in individuals aged > 65 years than in those aged ≤ 65 years ( P = 0.026), as outlined in Table 5 . Table 5 Logistic regression analysis of mRS score (two to six) of different antiplatelet drugs Related factors Univariate analysis Multiplicity analysis OR 95% CI P OR 95% CI P CYP2C19 EM 1.0 (Reference groups) CYP2C19 IM 1.048 0.450–2.436 0.914 / / / Follow-up time 1.060 0.962–1.168 0.238 / / / Age, y ≤ 65 1.0 (Reference groups) 1.0 (Reference groups) Age, y > 65 2.586 1.154–5.793 0.021 2.548 1.116–5.815 0.026 Female 1.0 (Reference groups) Male 1.572 0.742–3.331 0.237 / / / No smoking 1.0 (Reference groups) Smoking 1.067 0.413–2.756 0.894 / / / No drinking 1.0 (Reference groups) Drinking 2.595 0.744–9.048 0.135 / / / No hypertension 1.0 (Reference groups) Hypertension 1.900 0.650–5.557 0.241 / / / No Diabetes 1.0 (Reference groups) Diabetes 1.634 0.763–3.499 0.206 / / / No coronary heart disease 1.0 (Reference groups) Coronary heart disease 0.898 0.316–2.555 0.840 / / / No hyperlipidemia 1.0 (Reference groups) Hyperlipidemia 0.701 0.336–1.465 0.345 / / / Arteriole occlusion 1.0 (Reference groups) Atherosclerosis of the aorta 1.180 0.515–2.702 0.696 / / / Aspirin 1.0 (Reference groups) 1.0 (Reference groups) Clopidogrel 2.827 1.327–6.022 0.007 2.552 1.177–5.532 0.018 OR: Odds Ratio, 95% CI: 95% Confidence Interval Discussion Here, the genotype of CYP2C19 was detected, and the carriers of CYP2C19*2(A), *3(A), and *17(T) in the IS group accounted for 43.9%, 7.0%, and 0.3%, respectively. The observed mutation frequency of CYP2C19*2 was found to be higher than the previously reported rates, whereas the mutation frequency of CYP2C19*17 was comparatively lower than that documented in the existing literature. This study confirmed the diversity of CYP2C19 allele distribution in different regions. The findings indicated that IS was associated with diabetes, hypertension, hyperlipidemia, and CYP2C19 IM as separate risk factors. However, no statistically significant difference was observed in the frequencies of the CYP2C19 AA genotype and CYP2C19*2 A and G alleles between the IS and control groups. CYP2C19 PM was related to the occurrence of IS, and the others were consistent with the existing literature. Our results indicate that CYP2C19 polymorphism is closely associated with the occurrence of IS. Clopidogrel resistance (CR) is an independent risk factor for ischemic vascular events [ 6 ]. CR accounts for 4–30% of patients treated with conventional doses of clopidogrel and 28% of patients with IS [ 7 – 9 ], and the risk of recurrent thrombotic events is up to 40% in patients with CR [ 10 ]. Patients carrying CYP2C19*2 and/or *3 show decreased clopidogrel efficacy owing to decreased enzyme activity. The risk of poor prognosis and ischemic events in patients with IS treated with clopidogrel was significantly higher in patients with the CYP2C19 deficiency genotype (primarily CYP2C19*2) than in non-carriers [ 11 – 13 ]. This research revealed negligible disparities in the prognosis of patients categorized as CYP2C19 EM, IM, or PM. The findings contradict previous results mentioned in the literature, possibly due to the study's retrospective nature and small sample size. CYP2C19 polymorphism is the primary cause of clopidogrel hyperresponsiveness, but it can only explain approximately 12% of the CR phenomenon [ 14 ]. Aside from CYP2C19 polymorphism, non-genetic factors significantly correlate with CR. Therefore, only a comprehensive combination of clinical manifestations, CYP2C19 genotype, and platelet aggregation rate can provide patients with more accurate individualized antiplatelet therapies and improve their prognosis [ 15 ]. This study revealed that IS was associated with independent risk factors such as hypertension, diabetes, and hyperlipidemia. The risk of IS increased by 4.255 times for hypertension, 2.257 times for diabetes, and 2.610 times for hyperlipidemia. In our cohort, 94.9% of patients were treated with statins. Aging plays a crucial role in amplifying the cumulative impact of cardiovascular disease risk and risk factors for stroke, leading to a notable association with an elevated likelihood of IS incidence and an unfavorable prognosis. This study confirms that age independently contributes to the prognostic assessment of patients with IS, aligning with existing literature. The frequency of mRS score (two to six points) among patients administered a standard dosage of clopidogrel was notably greater than that of those receiving aspirin or aluminum–magnesium and aspirin tablets (II). Individualized medicine for patients with IS based on the CYP2C19 genotype can significantly improve their prognosis and life expectancy. However, this study had limitations due to its retrospective design and single-center setting with a limited number of samples. Additionally, disparities were identified in the baseline characteristics between the groups receiving aspirin and clopidogrel, which introduces an inherent bias in the clinical selection of these two antiplatelet medicines. Individualized medicine is a popular research topic, an essential part of precision medicine, and an inevitable trend in medical development. On January 16, 2022, the Clinical Pharmacogenetics Implementation Consortium released clinical guidelines for the CYP2C19 gene and clopidogrel therapy after 2011 and 2013 [ 16 ]. The new version of the guidelines; which added the CYP2C19*9, *12, and *14 loci; emphasizes that CYP2C19 polymorphism remains the most important genetic factor affecting clopidogrel. Based on the CYP2C19 phenotype, individualized dosing recommendations were provided for patients with IS treated with clopidogrel. The prevalence of individualized medical approaches based on genotype has increased following the discovery of the CYP2C19 gene. Given the variations in the prevalence of the CYP2C19 gene across diverse ethnicities and populations, formulating antiplatelet therapy guidelines suitable for Chinese people can provide more accurate individualized medication guidance for patients with IS and maximize their benefits. Conclusions This study revealed that CYP2C19 polymorphism is related to morbidity in patients with IS, but its variation is not significantly correlated with prognosis. Compared with clopidogrel, aspirin can greatly enhance the prognosis of individuals with IS who possess the CYP2C19 LOF gene variant. Future research with expanded sample size and increased data on acute stroke treatment options should be conducted to evaluate the impact of thrombolytic therapy. Abbreviations IS ischemic stroke CYP2C19 cytochrome P450 2C19 mRS modified Rankin Scale LOF loss-of-function EM extensive metabolizer IM intermediate metabolizer PM poor metabolizer CR clopidogrel resistance Declarations Ethics approval and consent to participate This study was approved by the Independent Ethics Committee of the Affiliated Panyu Centra Hospital of Guangzhou Medical University on March 17, 2023 (PYRC-2023-068). All participants signed informed consent forms. Consent for publication Not applicable. Availability of data and materials The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the Affiliated Panyu Centra Hospital of Guangzhou Medical University [grant number 2021Y001] and the Guangzhou Municipal Science and Technology Bureau [grant numbers 202103000002, 201904010065]. The sponsors had no role in the design, data collection, data analysis, data interpretation, or writing of the report Author contributions DT was mainly responsible for writing the paper, reviewing and analyzing data, and drawing conclusions based on data results. JS was responsible for the overall guidance of the writing of the paper, indicating the direction of research, correcting the analysis of process data, and revising the conclusions and opinions. YT was responsible for processing data with statistical software and formulating data comparison tables. CH was responsible for the statistics and collection of data required for the test and provided the pre-research and induction of cases. XF was responsible for consulting the relevant references of the thesis, providing evidence and support for the argument, and comparing the similarities and differences between this study and the research carried out in the references. Acknowledgments Not applicable. Data availability statement The data are available from the corresponding author on reasonable request. References Wang W, Jiang B, Sun H, Ru X, Sun D, Wang L, et al. Prevalence, incidence, and mortality of stroke in China: results from a nationwide population-based survey of 480 687 adults. Circulation. 2017;135:759–71. Wang D, Liu J, Liu M, Lu C, Brainin M, Zhang J. 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Lancet. 2010;376:1320–8. Mega JL, Close SL, Wiviott SD, Shen L, Hockett RD, Brandt JT, et al. Cytochrome P450 genetic polymorphisms and the response to prasugrel: Relationship to pharmacokinetic, pharmacodynamic, and clinical outcomes. Circulation. 2009;119:2553–60. Pan Y, Chen W, Xu Y, Yi X, Han Y, Yang Q, et al. Genetic polymorphisms and clopidogrel efficacy for acute ischemic stroke or transient ischemic attack: a systematic review and meta-analysis. Circulation. 2017;135:21–33. Yang J, Zhou JS, Zhao YX, Yang ZH, Zhao HD, Zhang YD, et al. ABCB1 hypomethylation is associated with decreased antiplatelet effects of clopidogrel in Chinese ischemic stroke patients. Pharmazie. 2015;70:97–102. Zhang X, Wang Y. Status quo and countermeasure of clopidogrel resistance predicted by gene testing. Chin J Med Genet. 2019;36:649–53. Lee CR, Luzum JA, Sangkuhl K, Gammal RS, Sabatine MS, Stein CM, et al. Clinical pharmacogenetics implementation consortium guideline for CYP2C19 genotype and clopidogrel therapy: 2022 update. Clin Pharmacol Ther. 2022;112:959–67. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4534316","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":321402938,"identity":"32f894f6-44ec-4295-a53a-91d7bf7920e5","order_by":0,"name":"Dongmei Tan","email":"","orcid":"","institution":"Panyu District Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Dongmei","middleName":"","lastName":"Tan","suffix":""},{"id":321402939,"identity":"c9b2dd39-787e-434f-b834-92076ed4c2d7","order_by":1,"name":"Jianfen Su","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYHACNgjF3tj48ANpWngONxtLkKZFIr1NgIcY9fIzco89+LiDwZ5f8mEbgwSDnZxuAwEtBjfy0g1nnmFInDk7se1BAUOysdkBQlokcsykedsYEgxuJ7YbSDAcSNxGSIv8DKCWv20M9gY3D7ZJ8BCjheEGUAtjGwPjhhuMRGoxOPMuTbK3DeiXnkRgIBsQ4Rf59txjEj+BDuNnP/7w4YcKOzmCWoBRCCL+wywlqByuZRSMglEwCkYBHgAAkgA+Bl+LnwoAAAAASUVORK5CYII=","orcid":"","institution":"Panyu District Central Hospital","correspondingAuthor":true,"prefix":"","firstName":"Jianfen","middleName":"","lastName":"Su","suffix":""},{"id":321402940,"identity":"8f4973bf-0f01-4d80-bb40-2312147869d1","order_by":2,"name":"Yukuan Tang","email":"","orcid":"","institution":"Panyu District Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yukuan","middleName":"","lastName":"Tang","suffix":""},{"id":321402941,"identity":"643fe320-bc00-4317-afe3-35fa29a82813","order_by":3,"name":"Chen Huang","email":"","orcid":"","institution":"Panyu District Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chen","middleName":"","lastName":"Huang","suffix":""},{"id":321402942,"identity":"d8e937f6-392a-41e7-9f70-4b920f7ea36b","order_by":4,"name":"Xihua Fu","email":"","orcid":"","institution":"Panyu District Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xihua","middleName":"","lastName":"Fu","suffix":""}],"badges":[],"createdAt":"2024-06-05 13:06:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4534316/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4534316/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68151727,"identity":"df18c3a2-a0fb-40c5-8ebd-e929b5d8a826","added_by":"auto","created_at":"2024-11-04 07:09:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":704175,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4534316/v1/bd754456-e3f6-441e-80ac-72ad11e759df.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of Cytochrome P450 2C19 Polymorphism on Ischemic Stroke Prognosis","fulltext":[{"header":"Background","content":"\u003cp\u003eIn recent years, stroke has emerged as the leading factor contributing to mortality and impairment among the Chinese population. Ischemic stroke (IS) is the predominant form of stroke in China, comprising approximately 69.6\u0026ndash;70.8% of all cases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] and having a prevalence of 1,700/100,000 (age-standardized rate, 1,256/100,000) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. IS is characterized by elevated mortality rates, disability incidence, recurrence frequency, and a substantial economic burden [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Genetic variations, or polymorphisms, significantly affect the occurrence and prognosis of IS in cytochrome P450 2C19 (CYP2C19) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. As a first-line antiplatelet agent, clopidogrel primarily inhibits platelet aggregation by activating CYP2C19 metabolic enzymes. Despite the significance of CYP2C19 in acute coronary syndrome, its role in IS has lacked attention among Chinese guidelines, representing a significant knowledge gap. Domestic and international studies primarily investigate CYP2C19 polymorphism in individuals diagnosed with acute coronary syndrome; moreover, genetic testing of CYP2C19 is not included in China's IS guidelines [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Thus, this study underscores the need for tailored medication guidance in patients with IS based on CYP2C19 genetic testing. A distinct absence of real-world research evidence to guide the individualized administration of antiplatelet therapy for patients undergoing CYP2C19 genetic testing exists.\u003c/p\u003e\n\u003ch3\u003ePatient data\u003c/h3\u003e\n\u003cp\u003eThis study retrospectively analyzed the cases of patients diagnosed with IS who were admitted to the Affiliated Panyu Centra Hospital of Guangzhou Medical University between July 2020 and June 2021.\u003c/p\u003e \u003cp\u003e This study was approved by the Independent Ethics Committee of the Affiliated Panyu Centra Hospital of Guangzhou Medical University on March 17, 2023 (PYRC-2023-068). The trial was conducted following the Declaration of Helsinki (1989), the guidelines of Good Clinical Practice, and other related guiding principles. All participants signed informed consent forms.\u003c/p\u003e \u003cp\u003e \u003cem\u003eInclusion criteria\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe inclusion criteria were as follows: patients who fulfilled the diagnostic criteria for IS in the \"Chinese Guidelines for the Diagnosis and Treatment of Acute Ischemic Stroke 2018\" [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and were diagnosed using magnetic resonance imaging or cranial electron computed tomography; patients who were categorized as having either aortic atherosclerosis type or arteriole occlusion type according to IS etiological classification; patients who received long-term outpatient treatment in our hospital since discharge and those who regularly used platelet drugs until final follow-up or death; patients who were administered 75 mg of clopidogrel daily, 100 mg of aspirin, or aluminum\u0026ndash;magnesium and aspirin tablets (II) (81 mg:22 mg:11 mg) for continuous treatment; and patients who underwent CYP2C19 genetic testing.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eExclusion criteria\u003c/h2\u003e \u003cp\u003eThe exclusion criteria were as follows: patients with malignant tumors, severe infections, cerebral hemorrhage, cerebral infarction complicated with hemorrhage, transient ischemic attack, cardiogenic cerebral embolism, and non-atherosclerotic vascular stenosis; patients with contraindications for clopidogrel or aspirin (i.e., severe heart, liver, or kidney failure; drug allergy; active bleeding; or coagulation dysfunction); patients prescribed a long-term combination of two or more antiplatelet medications, patients with inconsistent antiplatelet medication classes; patients taking a combination of anticoagulants or certain proton pump inhibitors (i.e., omeprazole, lansoprazole, and esomeprazole); and patients who were never administered medicine in our outpatient department, had poor drug compliance, or were lost follow-up.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eControl group\u003c/h2\u003e \u003cp\u003eThe control group comprised patients aged\u0026thinsp;\u0026gt;\u0026thinsp;40 years who received CYP2C19 test results without any evidence of peripheral arterial disease, coronary atherosclerotic heart disease, or ischemic cerebrovascular disease after physical examination, medical history, and cranial computed tomography and/or magnetic resonance imaging.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData collection\u003c/h2\u003e \u003cp\u003eGeneral information included follow-up time (antiplatelet drug treatment time), sex, age, smoking, drinking, hypertension, diabetes, coronary atherosclerotic heart disease, hyperlipidemia, Trial of ORG 10172 in Acute Stroke Treatment classification, antiplatelet drugs, and CYP2C19 genotype. Predictive information encompassed the modified Rankin Scale (mRS) score, along with occurrences of cardiovascular and cerebrovascular incidents.\u003c/p\u003e \u003c/div\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eMain reagents\u003c/h2\u003e \u003cp\u003eThe main reagents were as follows: clopidogrel hydrogen sulfate tablets (trade name, Plavix; specification, 75 mg per tablet; manufactured by Sanofi [Hangzhou] Pharmaceutical Company Ltd.), aspirin enteric tablets (specification, 100 mg per tablet; manufactured by Bayer Healthcare Ltd.), and aluminum\u0026ndash;magnesium and aspirin tablets (II) (trade name, ASiDe; specification, 81 mg [aspirin], 22 mg [heavy magnesium carbonate], 11 mg [aluminum glycinate] per tablet; manufactured by Shandong Zhongheihengqiao Pharmaceutical Co., Ltd.).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFollow-up of patients\u003c/h2\u003e \u003cp\u003eAll participants were followed up through in-person clinical visits or telephone follow-ups conducted on February 2022. The mRS score assessed the patients' living capacities after treatment. The mRS scores ranged from zero to six. A positive prognosis correlated with an mRS score of zero to two, whereas a negative prognosis was linked to an mRS score of three to six. Simultaneously, occurrences of IS recurrence, new transient ischemic attack, myocardial infarction, and death were documented.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eResearch process\u003c/h2\u003e \u003cp\u003eAccording to the mRS score, patients in the IS group receiving 75 mg of clopidogrel daily were divided into those with poor and good prognoses. The general information and CYP2C19 genotype of the two groups were compared to analyze the factors that influence the prognosis of patients with IS. Individuals with CYP2C19 loss-of-function (LOF) were evaluated, and the dependent variables included mRS score (two to six) and the occurrence of adverse cardiovascular and cerebrovascular events. Univariate and/or multivariate logistic regression analysis was conducted on both general data and antiplatelet medications within the two groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll statistical analyses of the data were conducted using SPSS 25.0 software. The gene frequencies of the etiology and prognosis analyses were tested for conformity to the Hardy\u0026ndash;Weinberg genetic balance law using the goodness of fit. If the measured data adhered to a distribution that is typically observed in statistical analysis, they were represented as the average standard deviation (x\u0026thinsp;\u003cb\u003e\u0026plusmn;\u003c/b\u003e\u0026thinsp;s), and two independent samples were used for the t-test. Otherwise, they were expressed as the medians (from the first quartile to the third quartile) (Md [P25\u0026ndash;P75]), and the Mann\u0026ndash;Whitney U-test was employed to compare the two groups. The enumeration data is presented as patient counts (percentages) (n [%]), and statistical analysis using either the χ\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e test or Fisher exact probability method was conducted to compare the groups. In univariate analysis, logistic regression analysis was employed to identify the independent risk factors for the morbidity and prognosis of IS; variables with influences of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;α were considered independent variables. The level of statistical significance was set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cem\u003eMorbidity\u003c/em\u003e \u003c/p\u003e \u003cp\u003eA total of 142 patients in the control group met the inclusion criteria and comprised 87 men and 55 women aged 46\u0026ndash;91 (69 [58\u0026ndash;76]) years. The IS group contained 157 patients, including 83 men and 72 women aged 38\u0026ndash;89 (69 [60\u0026ndash;77]) years, who were followed up for 3\u0026ndash;19 (13 [\u003cspan additionalcitationids=\"CR11 CR12 CR13 CR14 CR15\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]) months. No statistically significant disparity was observed in sex (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;2.145, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.143) or age (Z = -0.803, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.422) between the two comparable groups (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of the general data (n [%])\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;142)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCerebral infarction group(n\u0026thinsp;=\u0026thinsp;157)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e/ t / Z\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87 (61.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83 (52.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55 (38.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74 (47.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, y [Md (P25\u0026ndash;P75)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69 (58\u0026ndash;76)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69 (60\u0026ndash;77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.803\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.422\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoke, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e34 (23.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (17.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.193\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (13.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (10.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.392\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64 (45.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e129 (82.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (16.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55 (35.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.718\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (26.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85 (54.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUM, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e11.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEM, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51 (35.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28 (17.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIM, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61 (43.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e96 (61.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (19.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32 (20.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csub\u003eUM: Ultra-Rapid Metabolizer, EM: Extensive Metabolizer, IM: Intermediate, Metabolizer, PM: Poor Metabolizer\u003c/sub\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHardy\u0026ndash;Weinberg equilibrium\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eThe distributions of the three genotypes, namely, CYP2C19*2, *3, and *17, in the control (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;2.098, 0.004, and 0.002; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.350, 0.947, and 1.000, all respectively) and IS groups (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;4.201, 0.079, and 0.004; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.122, 1.000, and 1.000, all respectively) were consistent with the Hardy\u0026ndash;Weinberg equilibrium, which is representative of the population (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistribution of the CYP2C19 genes in the control and IS groups (n [%])\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;142)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCerebral infarction group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;157)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCYP2C19*2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58 (40.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43 (27.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (41.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e90 (57.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (17.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24 (15.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.589\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e175 (61.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e176 (56.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e109 (38.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e138 (43.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCYP2C19*3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e136 (95.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e136 (86.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20 (12.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e278 (97.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e292 (93.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e8.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (2.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22 (7.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCYP2C19*17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e138 (97.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e156 (99.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.309\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e280 (98.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e313 (99.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.311\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csub\u003eNote: * Fisher is the exact probability method\u003c/sub\u003e.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCorrelation between CYP2C19 polymorphism and IS\u003c/em\u003e\u003c/p\u003e \n \u003cp\u003eCompared with that of the control group, the allelic frequency of CYP2C19*3A notably increased among individuals with IS. Additionally, the GG gene frequencies of CYP2C19*2 and *3 were found to be significantly lower in the IS group, whereas the GA gene frequencies were higher (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;8.002, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005; χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;6.036 and 7.600, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014 and 0.006; χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;7.423 and 6.827, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006 and 0.009, all respectively), as shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cem\u003eSingle-factor analysis of influencing morbidity\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe prevalences of hypertension, diabetes, and hyperlipidemia were markedly elevated in the IS group compared with those in the control group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No notable disparities were identified in sex, age, smoking rates, or drinking rates (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) among the four categories of CYP2C19 metabolizers: extensive metabolizer (EM), intermediate metabolizer (IM), poor metabolizer (PM), and ultra-rapid metabolizer. Likewise, the incidence of IS was consistent across the groups (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;11.150, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001). Compared with the EM group individually, the IM group had a significantly higher morbidity of IS (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;13.925, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cem\u003eLogistic regression analysis of multiple factors influencing disease morbidity\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThe dependent variable in this study was the incidence of IS, whereas the independent variables were hypertension, diabetes, hyperlipidemia, and CYP2C19 IM based on their statistical significance in the univariate analysis. These factors were found to be independent risk factors for IS, according to Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate logistic regression analysis of IS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWaldχ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.957\u0026ndash;9.338\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.701\u0026ndash;6.237\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.284\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.610\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.064\u0026ndash;6.314\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCYP2C19 UM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003e1.0 (Reference groups)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCYP2C19 EM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.076\u0026ndash;10.468\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCYP2C19 IM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.724\u0026ndash;6.339\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCYP2C19 PM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.749\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.791\u0026ndash;3.866\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003csub\u003eβ: Beta coefficient, SE: Standard Error, Wald χ2: Wald Chi-Square, \u003cem\u003eP\u003c/em\u003e: \u003cem\u003eP\u003c/em\u003e-value, OR: Odds Ratio, 95% CI: 95% Confidence Interval, UM: Ultra-Rapid Metabolizer, EM: Extensive Metabolizer, IM: Intermediate Metabolizer, PM: Poor Metabolizer\u003c/sub\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \n\u003cp\u003e\u003cem\u003ePrognosis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eIn the IS group, 51 patients who were followed up for 4\u0026ndash;19 (average [13.43\u0026thinsp;\u0026plusmn;\u0026thinsp;3.540]) months had a good prognosis after treatment with clopidogrel, including 21 men and 30 women aged 43\u0026ndash;87 (69 [60\u0026ndash;78]) years. Additionally, 30 patients with poor prognosis, including 18 men and 12 women aged 57\u0026ndash;89 (76 [70\u0026ndash;85]) years, were followed up for 4\u0026ndash;19 months (average [12.23\u0026thinsp;\u0026plusmn;\u0026thinsp;3.481]) months. Follow-up between the two groups (t\u0026thinsp;=\u0026thinsp;1.480, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u003cb\u003e=\u003c/b\u003e\u0026thinsp;0.143) showed no statistically significant difference. The comprehensive analyses can be found in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of general data between the good prognosis group and poor prognosis group (n [%])\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGood prognosis group (n\u0026thinsp;=\u0026thinsp;51)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePoor prognosis group\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eχ\u003csup\u003e2\u003c/sup\u003e/ t / Z\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow-up time (x\u0026thinsp;\u0026plusmn;\u0026thinsp;s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.43\u0026thinsp;\u0026plusmn;\u0026thinsp;3.540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.23\u0026thinsp;\u0026plusmn;\u0026thinsp;3.481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, y [Md (P25\u0026ndash;P75)]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e69 (60\u0026ndash;78)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76 (70\u0026ndash;85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMen, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21 (41.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18 (60.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e2.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.102\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWomen, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (58.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (40.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (17.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (13.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.844\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (7.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (16.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.393\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (78.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27 (90.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.184\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19 (37.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13 (43.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.589\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary heart disease,\u003c/p\u003e \u003cp\u003en (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (19.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (26.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.461\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (47.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14 (46.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArteriole occlusion, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (76.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (70.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.521\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtherosclerosis of the aorta, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (23.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9 (30.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEM, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (33.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (36.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIM, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (45.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (40.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (21.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (23.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csub\u003eEM: Extensive Metabolizer, IM: Intermediate Metabolizer, PM: Poor Metabolizer\u003c/sub\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003cp\u003e\u003cem\u003eHardy\u0026ndash;Weinberg equilibrium\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003eThe distribution of the CYP2C19*2, *3, and *17 genotypes was evaluated using a goodness-of-fit test. The results indicated no significant deviation between the observed patients and the expected frequencies (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;1.202 and 5.252; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.548 and 0.102), suggesting that the sample accurately represented the population.\u003c/p\u003e \u003cp\u003e \u003cem\u003eUnivariate analysis of influencing prognosis\u003c/em\u003e \u003c/p\u003e \u003cp\u003eWith the exception of age (Z = -3.000, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), no significant variations were observed in the overall data (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003ePersonalized medicine\u003c/em\u003e \u003c/p\u003e \u003cp\u003eAccording to the genotype, 128 patients carrying CYP2C19 LOF *2 and *3, namely IM and PM, were grouped according to the antiplatelet medication used. Among them, 75 patients (58.6%) belonged to the aspirin group, with 44 (58.7%) being men aged 38\u0026ndash;88 years (average age, 65.20\u0026thinsp;\u0026plusmn;\u0026thinsp;10.624 years). These individuals were maintained under observation for a period ranging from 3 to 19 months, with a median follow-up of 12 months (range, 9\u0026ndash;16 months). Furthermore, patients (8.0%) in this group had a history of coronary heart disease. The clopidogrel group comprised 53 patients (41.4%), of whom 27 patients (50.9%) were men aged 49\u0026ndash;89 years (average age, 72.30\u0026thinsp;\u0026plusmn;\u0026thinsp;10.721 years) who were followed up for 4\u0026ndash;19 (14 [\u003cspan additionalcitationids=\"CR11 CR12 CR13 CR14 CR15\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]) months, and 13 of the patients (24.5%) had coronary heart disease. The incidence of coronary heart disease was found to be significantly elevated in the clopidogrel group, compared with that in the control group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.000 and 0.010, respectively).\u003c/p\u003e \u003cp\u003e \u003cem\u003eAnalysis of the factors influencing mRS score (two to six) in patients with CYP2C19 LOF\u003c/em\u003e \u003c/p\u003e \u003cp\u003eIn this study, patients were categorized into two groups based on age: \u0026le;65 years (n\u0026thinsp;=\u0026thinsp;53) and \u0026gt;\u0026thinsp;65 years (n\u0026thinsp;=\u0026thinsp;75). Analysis revealed no significant disparity in the CYP2C19 genotype between these two groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.914) when considering the mRS score (2\u0026ndash;6) as the dependent variable. The incidence of mRS scores (2\u0026ndash;6 points) for the different antiplatelet medicines were comparable. First, the general data were analyzed using single-factor logistic regression analysis. The findings revealed a noteworthy association between age and the utilization of antiplatelet medication in both cohorts (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021 and 0.007). A multifactorial logistic regression analysis was conducted, considering both age and the utilization of antiplatelet agents. The findings indicated that patients with CYP2C19 LOF who were administered clopidogrel had a notably reduced incidence of mRS scores (zero to one) compared with those given aspirin (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018). Additionally, the occurrence of mRS scores (zero to one) was significantly lower in individuals aged\u0026thinsp;\u0026gt;\u0026thinsp;65 years than in those aged\u0026thinsp;\u0026le;\u0026thinsp;65 years (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026), as outlined in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLogistic regression analysis of mRS score (two to six) of different antiplatelet drugs\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRelated factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eMultiplicity analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eOR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCYP2C19 EM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c10\" namest=\"c2\"\u003e \u003cp\u003e1.0 (Reference groups)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCYP2C19 IM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.450\u0026ndash;2.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow-up time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.962\u0026ndash;1.168\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, y\u0026thinsp;\u0026le;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e1.0 (Reference groups)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e1.0 (Reference groups)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, y\u0026thinsp;\u0026gt;\u0026thinsp;65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.154\u0026ndash;5.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e2.548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.116\u0026ndash;5.815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c10\" namest=\"c2\"\u003e \u003cp\u003e1.0 (Reference groups)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.572\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.742\u0026ndash;3.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo smoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c10\" namest=\"c2\"\u003e \u003cp\u003e1.0 (Reference groups)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.413\u0026ndash;2.756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo drinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c10\" namest=\"c2\"\u003e \u003cp\u003e1.0 (Reference groups)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.744\u0026ndash;9.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo hypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c10\" namest=\"c2\"\u003e \u003cp\u003e1.0 (Reference groups)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.650\u0026ndash;5.557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo Diabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c10\" namest=\"c2\"\u003e \u003cp\u003e1.0 (Reference groups)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.763\u0026ndash;3.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo coronary heart disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c10\" namest=\"c2\"\u003e \u003cp\u003e1.0 (Reference groups)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary heart disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.316\u0026ndash;2.555\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo hyperlipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c10\" namest=\"c2\"\u003e \u003cp\u003e1.0 (Reference groups)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.336\u0026ndash;1.465\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArteriole occlusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c10\" namest=\"c2\"\u003e \u003cp\u003e1.0 (Reference groups)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAtherosclerosis of the aorta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.515\u0026ndash;2.702\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspirin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003e1.0 (Reference groups)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e1.0 (Reference groups)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eClopidogrel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.827\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.327\u0026ndash;6.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e2.552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.177\u0026ndash;5.532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003csub\u003eOR: Odds Ratio, 95% CI: 95% Confidence Interval\u003c/sub\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e "},{"header":"Discussion","content":"\u003cp\u003eHere, the genotype of CYP2C19 was detected, and the carriers of CYP2C19*2(A), *3(A), and *17(T) in the IS group accounted for 43.9%, 7.0%, and 0.3%, respectively. The observed mutation frequency of CYP2C19*2 was found to be higher than the previously reported rates, whereas the mutation frequency of CYP2C19*17 was comparatively lower than that documented in the existing literature. This study confirmed the diversity of CYP2C19 allele distribution in different regions. The findings indicated that IS was associated with diabetes, hypertension, hyperlipidemia, and CYP2C19 IM as separate risk factors. However, no statistically significant difference was observed in the frequencies of the CYP2C19 AA genotype and CYP2C19*2 A and G alleles between the IS and control groups. CYP2C19 PM was related to the occurrence of IS, and the others were consistent with the existing literature.\u003c/p\u003e \u003cp\u003eOur results indicate that CYP2C19 polymorphism is closely associated with the occurrence of IS. Clopidogrel resistance (CR) is an independent risk factor for ischemic vascular events [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. CR accounts for 4\u0026ndash;30% of patients treated with conventional doses of clopidogrel and 28% of patients with IS [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], and the risk of recurrent thrombotic events is up to 40% in patients with CR [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePatients carrying CYP2C19*2 and/or *3 show decreased clopidogrel efficacy owing to decreased enzyme activity. The risk of poor prognosis and ischemic events in patients with IS treated with clopidogrel was significantly higher in patients with the CYP2C19 deficiency genotype (primarily CYP2C19*2) than in non-carriers [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis research revealed negligible disparities in the prognosis of patients categorized as CYP2C19 EM, IM, or PM. The findings contradict previous results mentioned in the literature, possibly due to the study's retrospective nature and small sample size.\u003c/p\u003e \u003cp\u003eCYP2C19 polymorphism is the primary cause of clopidogrel hyperresponsiveness, but it can only explain approximately 12% of the CR phenomenon [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Aside from CYP2C19 polymorphism, non-genetic factors significantly correlate with CR. Therefore, only a comprehensive combination of clinical manifestations, CYP2C19 genotype, and platelet aggregation rate can provide patients with more accurate individualized antiplatelet therapies and improve their prognosis [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study revealed that IS was associated with independent risk factors such as hypertension, diabetes, and hyperlipidemia. The risk of IS increased by 4.255 times for hypertension, 2.257 times for diabetes, and 2.610 times for hyperlipidemia. In our cohort, 94.9% of patients were treated with statins. Aging plays a crucial role in amplifying the cumulative impact of cardiovascular disease risk and risk factors for stroke, leading to a notable association with an elevated likelihood of IS incidence and an unfavorable prognosis. This study confirms that age independently contributes to the prognostic assessment of patients with IS, aligning with existing literature.\u003c/p\u003e \u003cp\u003eThe frequency of mRS score (two to six points) among patients administered a standard dosage of clopidogrel was notably greater than that of those receiving aspirin or aluminum\u0026ndash;magnesium and aspirin tablets (II). Individualized medicine for patients with IS based on the CYP2C19 genotype can significantly improve their prognosis and life expectancy. However, this study had limitations due to its retrospective design and single-center setting with a limited number of samples. Additionally, disparities were identified in the baseline characteristics between the groups receiving aspirin and clopidogrel, which introduces an inherent bias in the clinical selection of these two antiplatelet medicines.\u003c/p\u003e \u003cp\u003eIndividualized medicine is a popular research topic, an essential part of precision medicine, and an inevitable trend in medical development. On January 16, 2022, the Clinical Pharmacogenetics Implementation Consortium released clinical guidelines for the CYP2C19 gene and clopidogrel therapy after 2011 and 2013 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The new version of the guidelines; which added the CYP2C19*9, *12, and *14 loci; emphasizes that CYP2C19 polymorphism remains the most important genetic factor affecting clopidogrel. Based on the CYP2C19 phenotype, individualized dosing recommendations were provided for patients with IS treated with clopidogrel. The prevalence of individualized medical approaches based on genotype has increased following the discovery of the CYP2C19 gene. Given the variations in the prevalence of the CYP2C19 gene across diverse ethnicities and populations, formulating antiplatelet therapy guidelines suitable for Chinese people can provide more accurate individualized medication guidance for patients with IS and maximize their benefits.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study revealed that CYP2C19 polymorphism is related to morbidity in patients with IS, but its variation is not significantly correlated with prognosis. Compared with clopidogrel, aspirin can greatly enhance the prognosis of individuals with IS who possess the CYP2C19 LOF gene variant. Future research with expanded sample size and increased data on acute stroke treatment options should be conducted to evaluate the impact of thrombolytic therapy.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eischemic stroke\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCYP2C19\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ecytochrome P450 2C19\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003emRS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003emodified Rankin Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLOF\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eloss-of-function\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eextensive metabolizer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eintermediate metabolizer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003epoor metabolizer\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eclopidogrel resistance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Independent Ethics Committee of the Affiliated Panyu Centra Hospital of Guangzhou Medical University on March 17, 2023 (PYRC-2023-068). All participants signed informed consent forms.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Affiliated Panyu Centra Hospital of Guangzhou Medical University [grant number 2021Y001] and the Guangzhou Municipal Science and Technology Bureau [grant numbers 202103000002, 201904010065]. The sponsors had no role in the design, data collection, data analysis, data interpretation, or writing of the report\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDT was mainly responsible for writing the paper, reviewing and analyzing data, and drawing conclusions based on data results.\u003c/p\u003e\n\u003cp\u003eJS was responsible for the overall guidance of the writing of the paper, indicating the direction of research, correcting the analysis of process data, and revising the conclusions and opinions.\u003c/p\u003e\n\u003cp\u003eYT was responsible for processing data with statistical software and formulating data comparison tables.\u003c/p\u003e\n\u003cp\u003eCH was responsible for the statistics and collection of data required for the test and provided the pre-research and induction of cases.\u003c/p\u003e\n\u003cp\u003eXF was responsible for consulting the relevant references of the thesis, providing evidence and support for the argument, and comparing the similarities and differences between this study and the research carried out in the references.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWang W, Jiang B, Sun H, Ru X, Sun D, Wang L, et al. Prevalence, incidence, and mortality of stroke in China: results from a nationwide population-based survey of 480 687 adults. Circulation. 2017;135:759\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang D, Liu J, Liu M, Lu C, Brainin M, Zhang J. Patterns of stroke between university hospitals and nonuniversity hospitals in mainland China: prospective multicenter hospital-based registry study. World Neurosurg. 2017;98:258\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang L, Peng B, Zhang H, Wang Y, Liu M, Shan C, et al. Brief report on stroke prevention and treatment in China, 2020. Chin J Cerebrovasc Dis. 2022;19:136\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJia DM, Chen ZB, Zhang MJ, Yang WJ, Jin JL, Xia YQ, et al. CYP2C19 polymorphisms and antiplatelet effects of clopidogrel in acute ischemic stroke in China. Stroke. 2013;44:1717\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCerebrovascular Group of Chinese Medical Association Branch of Neurology, Chinese Medical Association. Chinese guidelines for diagnosis and treatment of acute ischemic stroke 2018. Chin J Neurol. 2018;51:666\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYi X, Lin J, Zhou Q, Wu L, Cheng W, Wang C. Clopidogrel resistance increases rate of recurrent stroke and other vascular events in Chinese population. J Stroke Cerebrovasc Dis. 2016;25:1222\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoukarbel GV, Bhatt DL. Antiplatelet therapy and proton pump inhibition: Clinician update. Circulation. 2012;125:375\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMijajlovic MD, Shulga O, Bloch S, Covickovic-Sternic N, Aleksic V, Bornstein NM. Clinical consequences of aspirin and clopidogrel resistance: an overview. Acta Neurol Scand. 2013;128:213\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFong J, Cheng-Ching E, Hussain MS, Katzan I, Gupta R. Predictors of biochemical aspirin and clopidogrel resistance in patients with ischemic stroke. J Stroke Cerebrovasc Dis. 2011;20:227\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatetzky S, Shenkman B, Guetta V, Shechter M, Beinart R, Goldenberg I, et al. Clopidogrel resistance is associated with increased risk of recurrent atherothrombotic events in patients with acute myocardial infarction. Circulation. 2004;109:3171\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWallentin L, James S, Storey RF, Armstrong M, Barratt BJ, Horrow J, et al. Effect of CYP2C19 and ABCB1 single nucleotide polymorphisms on outcomes of treatment with ticagrelor versus clopidogrel for acute coronary syndromes: a genetic substudy of the PLATO trial. Lancet. 2010;376:1320\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMega JL, Close SL, Wiviott SD, Shen L, Hockett RD, Brandt JT, et al. Cytochrome P450 genetic polymorphisms and the response to prasugrel: Relationship to pharmacokinetic, pharmacodynamic, and clinical outcomes. Circulation. 2009;119:2553\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePan Y, Chen W, Xu Y, Yi X, Han Y, Yang Q, et al. Genetic polymorphisms and clopidogrel efficacy for acute ischemic stroke or transient ischemic attack: a systematic review and meta-analysis. Circulation. 2017;135:21\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang J, Zhou JS, Zhao YX, Yang ZH, Zhao HD, Zhang YD, et al. ABCB1 hypomethylation is associated with decreased antiplatelet effects of clopidogrel in Chinese ischemic stroke patients. Pharmazie. 2015;70:97\u0026ndash;102.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang X, Wang Y. Status quo and countermeasure of clopidogrel resistance predicted by gene testing. Chin J Med Genet. 2019;36:649\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee CR, Luzum JA, Sangkuhl K, Gammal RS, Sabatine MS, Stein CM, et al. Clinical pharmacogenetics implementation consortium guideline for CYP2C19 genotype and clopidogrel therapy: 2022 update. Clin Pharmacol Ther. 2022;112:959\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"ischemic stroke, CYP2C19 polymorphism, long-term antiplatelet therapy","lastPublishedDoi":"10.21203/rs.3.rs-4534316/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4534316/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eStroke is the leading cause of mortality and impairment in China. Genetic variations in cytochrome P450 2C19 (CYP2C19) have been associated with the occurrence and outcomes of ischemic stroke (IS). We aimed to explore the correlation between CYP2C19 polymorphism and IS morbidity and prognosis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe retrospectively enrolled 157 patients with IS who were admitted to the Affiliated Panyu Centra Hospital of Guangzhou Medical University between April 2021 and March 2022 and underwent long-term antiplatelet therapy. A total of 142 inpatients without IS or coronary heart disease were enrolled as controls. Telephone follow-ups were conducted for the IS group, and the modified Rankin Scale was used to assess prognosis. We explored the association between CYP2C19 genotype and IS by using multivariate logistic regression to analyze variables influencing disease occurrence and prognosis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSignificant differences were identified in the occurrence rates of CYP2C19*2 GG/GA and *3 GG/GA genotypes between the IS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.014, 0.006, respectively) and control groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006, 0.009, respectively). A notable disparity was observed in the allelic frequency of CYP2C19*3 G(A) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005). IS morbidity was considerably greater in the intermediate metabolizer (IM) group than in the extensive metabolizer group (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Hyperlipidemia, diabetes, hypertension, and CYP2C19 IM were independent risk factors of IS (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No variation was observed in the CYP2C19 genotype between the good and poor prognosis groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.893).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eCYP2C19 polymorphism was related to IS morbidity but had no significant correlation with prognosis.\u003c/p\u003e","manuscriptTitle":"Impact of Cytochrome P450 2C19 Polymorphism on Ischemic Stroke Prognosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-02 15:47:20","doi":"10.21203/rs.3.rs-4534316/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2ca85f10-3333-4ad8-a0e2-b4ff4dcdab47","owner":[],"postedDate":"July 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-04T07:08:57+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-02 15:47:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4534316","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4534316","identity":"rs-4534316","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}