Platelet ADP receptor (P2RY1) polymorphism and the risk of inadequate platelet response to aspirin: Study in patients with ischemic stroke

Platelet ADP receptor (P2RY1) polymorphism and the risk of inadequate platelet response to aspirin: Study in patients with ischemic stroke | 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 Article Platelet ADP receptor (P2RY1) polymorphism and the risk of inadequate platelet response to aspirin: Study in patients with ischemic stroke Rita Christopher, Pandarisamy Sundaravadivel, Sadanandavalli Retnaswami Chandra, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3836500/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 An estimated 5–60% of patients experience inadequate antiplatelet effects after taking aspirin. We investigated the gene variants that might determine the outcome of the aspirin response in stroke patients. A total of 293 ischemic stroke patients who were taking 150 mg of aspirin for more than 7 days, were enrolled and only 230 patients with serum salicylate levels > 30 µg/mL were included. Variants in COX1 (rs1236913 C > T), COX2 (rs20417 G > C), GpIIb/IIIa (rs5981 T > C), P2RY1 (rs701265 A > G & rs1371097 C > T), PEAR1 (rs2768759 C > A), ITGB3 (rs2317676 A > G) and UGT1A6 (rs1105879 A > G & rs2070959 A > C) were genotyped. Platelet aggregation tests were carried out by light transmittance aggregometry using adenosine-di-phosphate and arachidonic acid as agonists. The ‘T’ allele of P2RY1 (rs1371097 C > T) polymorphism was significantly associated with inadequate platelet response as measured by platelet aggregometry (OR, 95%, 1.71, 1.122–2.61; p = 0.0131). Presence of a single copy of the ‘T’ allele in P2RY1 (rs1371097 C > T) increased the risk of inadequate response by 3.46 times (adjusted OR (95%), 3.46 (0.043); p = 0.043). INTRODUCTION An estimated 5–60% of patients experience inadequate antiplatelet effects or persisting platelet reactivity after taking the antiplatelet drug, aspirin, which is commonly known as aspirin non-response 1 . There is a complex link between platelet reactivity and aspirin, and the underlying causes of persistent platelet reactivity despite aspirin treatment is still unclear 2 . It is likely that variability in the genes encoding key enzymes and receptors of platelets could play a role. Previously, association between persistent platelet reactivity and polymorphisms of cyclooxygenase genes ( COX1 , COX2 ) have been inconsistently demonstrated 345 . Additionally, mutations in genes involved in the platelet aggregation pathway, including the platelet ADP receptor ( P2RY1 ), and the aspirin metabolism pathway like UDP glucuronosyltransferase 1A6 ( UGT1A6 ), have been suggested as candidates that might determine aspirin response 678910 . The purinergic receptor P2Y (P2RY1) is a G-protein-coupled receptor encoded by the P2RY1 gene located in chromosome 3. The physiological agonist for P2RY1 is adenosine-di-phosphate (ADP). Polymorphisms in the P2RY1 gene have shown an association with higher reactivity of platelets and aspirin resistance 611 . Glycoprotein IIb/IIIa (integrin αIIbβ3) is an integrin complex that serves as the receptor for fibrinogen and VWF for activation of platelets. A point mutation in the 2nd exon of the ITG3 ( GPIIIa ) gene leads to an amino acid change from leucine ( PlA1 ) to proline ( PlA2 ), which results in conformational changes in the protein as well as fibrinogen-binding regions. The PlA1/A2 polymorphism of GPIIb/IIIa has been consistently associated with antiplatelet drug resistance and an increase in cardiovascular events 78 . The platelet endothelial aggregation receptor 1 ( PEAR1 ) is highly expressed in platelets and signals during platelet-platelet contact, but is not dependent on platelet function 12 . In a previous study polymorphism in PEAR 1 (rs12041331) influenced the aggregation of platelets in a non-COX-1-dependent manner 9 . One of the important enzymes involved in aspirin bio-disposition is UDP glucuronosyltransferase family 1 member A6 (UGT1A6) 13 . Polymorphisms in UGT1A6 were found to influence in-vivo aspirin metabolism 14 . Research has been conducted to examine how candidate gene polymorphisms affect aspirin's effects on cardiovascular disease, but genetic factors influencing on-aspirin platelet reactivity in ischemic stroke patients have been relatively understudied 15 . Indians have a high prevalence of ischemic stroke 16 , and there is an urgent need for data in this area. We therefore studied candidate gene polymorphisms in COX1, COX2, GpIIb/IIIa, P2RY1, PEAR1, ITGB3 (platelet aggregation pathway), and UGT1A6 (aspirin metabolising pathway). To the best of our knowledge, this is the first large study to investigate the influence of candidate gene polymorphisms collectively from pathways of platelet aggregation and aspirin metabolism to determine the outcome response of aspirin-treated ischemic stroke patients in the Indian population. MATERIALS AND METHODS Study population A total of 293 patients diagnosed with ischemic stroke at the Neurological Services of NIMHANS, Bengaluru, India, and who were taking 150 mg of aspirin for more than 7 days for prevention of stroke recurrence, were recruited for the study. It included newly diagnosed cases as well as those on long-term treatment and regular follow-up. Patients were diagnosed based on the clinical features, and findings from CT/MRI (Computed Tomography /Magnetic Resonance Imaging) of the brain. Ischemic stroke patients had imaging evidence of acute cerebrovascular infarction. At baseline, demographic data (age, sex) and the following vascular risk factors: hypertension, diabetes mellitus, alcohol, smoking, tobacco, previous history of ischemic stroke or transient ischemic attack (TIA), family history of stroke, hypertension, and diabetes, were collected. The National Institute of Health Stroke Scale (NIHSS) score, performed by a stroke neurologist, was recorded at their admission 17 . The clinical stroke syndrome was classified according to the criteria of the TOAST (Trial of Org 10,172 in Acute Stroke Treatment) classification 18 . The following patients were excluded despite being on aspirin treatment: patients with liver or renal dysfunction, malignancy or any other terminal illnesses, patients on other anti-platelet medications, anti-inflammatory drugs and anticoagulants, patients who have undergone a surgical procedure within a month, pregnant and lactating women, patients who are unable to give written informed consent and patients who were < 18 years of age. A total of 5ml of venous blood was collected from the patients, and distributed in different tubes for platelet aggregation test, collection of serum and DNA extraction. Study design and ethical considerations This is a prospective cohort study, carried out at National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, India. Ethics approval was obtained from the Institute Ethics Committee of NIMHANS (No. NIMHANS/86th IEC/2012) and experiments were conducted in compliance with the Institutional guidelines. All study participants signed a written informed consent form. Measurement of serum salicylate levels To ascertain patient compliance, serum salicylate levels were measured in all of the participants. Spectrophotometry was used to assess salicylate levels, based on the formation of a coloured complex with ferric-mercuric reagent, as described previously 19 . Patients with serum salicylate levels less than 30 µg/mL, which was below the therapeutic range 20 , were excluded (63 patients). Platelet aggregation test to assess platelet response to aspirin Light transmittance aggregometry (LTA) is the gold standard method for qualitatively measuring persistent platelet reactivity to known platelet agonists 21 . ADP and arachidonic acid (AA) were used as agonists to assess on-aspirin platelet reactivity in this study, as proposed by Gum and colleagues, 2001 22 . Platelet aggregation tests were carried out on a Chrono-log Model 700 Aggregometer, manufactured by Chrono-log Corporation in Havertown, PA 19083 23 . Classification of patients with on-aspirin platelet reactivity assessed by LTA Patients with on-aspirin platelet reactivity were classified by combined mean demonstration of ADP and AA-induced percentage of platelet aggregation. This type of classification was proposed by Gum and co-workers 22 . Based on this classification, patients were divided into three groups namely ‘Responders’, ‘Semi-Responders’, and ‘Non-Responders’. According to this criteria, patients with platelet aggregation of > 70% with ADP and > 20% with AA were categorized as 'Non-responders,' whereas those with ≤ 70% with ADP and ≤ 20% with AA were classified as 'Responders’. Patients who met any one of the aforementioned criteria were referred to as ‘Semi-Responders’. This criterion was used in previous studies to predict the risk of severe vascular events 22 24 . List of candidate genes selected for the study A search for variants associated with on-aspirin poor response in platelet aggregation, and aspirin metabolism pathways was conducted to select the candidate genes. A total of 9 variants were selected from 7 candidate genes (Table 1 ). Table 1 Polymorphisms evaluated in candidate genes Gene rs ID Variant type Cyclooxygenase 1 (COX1) rs1236913 Missense variant Cyclooxygenase 2 (COX2) rs20417 Non-coding transcript exon variant Platelet GPIIb/IIIa receptors (GPIIb/IIIa) rs5981 Missense variant P2Y purinoceptor 1 (P2RY1) rs701265 & rs1371097 Synonymous variant and regulatory region variant Platelet endothelial aggregation receptor 1 (PEAR1) rs2768759 TF binding site variant Integrin Subunit Beta 3 (ITGB3) rs2317676 3’UTR variant UDP glycosyltransferase 1A6 (UGT1A6) rs1105879 & rs2070959 Both are missense variant TF, transcription factor; UTR, untranslated region; UDP, uridine diphosphate; GP, glycoprotein; ADP, adenosine diphosphate PCR-RFLP standardization of SNPs All the variants were genotyped by either the ‘PCR-RFLP method’ with restriction enzymes or the ‘Allelic Discrimination Assay’ using TaqMan SNP genotyping probes. PCR-RFLP primers were selected from previously published literature 25262728 . PCR amplification was performed with Takara EmeraldAmp GT PCR Master Mix (DSS Takara Bio India Private Ltd, New Delhi, India) on a Thermal Cycler (Eppendorf Master Cycler PCR, Hamburg, Germany) for COX1 (rs1236913), P2RY1 (rs701265), PEAR1 (rs2768759), COX2 (rs20417) and UGT1A6 (rs2070959 and rs1105879) variants. PCR for GPIIb/IIIa receptor mutation (rs5918) was performed by mixing individual components (dNTPs, MgCl2, Taq buffer, and primers) with Taq DNA polymerases (Fermentas Life Sciences, USA). Variants in ITG3 (rs2317676), and P2RY1 (rs1371097) were genotyped by TaqMan allelic discrimination assay (Table 2 ). Table 2 Details of PCR-RFLP conditions, and allelic discrimination assay Gene PCR-RFLP Primers PCR Tm°C Restriction digestion Fragment size (base pairs) COX1 rs1236913 Fwd-5’-GCCTCCTGG TGGAGCCTTGAATG-3’ Rev-5’-CGCGAGCAG AAAGGGGAAGCTG-3’ 68°C Digested with HpaII (5U) for 2 hours at 37°C CC:252 CT:252 + 144 + 106 TT:144 + 106 COX2 rs20417 Fwd-5′-CCGCTTCCTTTGT CCATCAG-3′ Rev-5′-GGCTGTATATCTG CTCTATATGC-3′ 57°C Digested with AciI (5U) for 2 hours at 37°C GG:306 GC:306 + 188 + 118 CC:188 + 118 GPIIb/IIIa rs5918 Fwd-5’-TGGGACTTCTCTT TGGGCTCCTGACTTAC-3’ Rev-5’-CCTTCAGCAGA TTCTCCTTCAGGTCAC-3’ 58°C Digested with Msp I (5U) for overnight at 37°C TT:82 TC:82 + 39 + 43 CC:39 + 43 P2RY1 rs701265 Fwd-5’-GCCATGTGTAA ACTGCAGAGGTTC-3’ Rev-5’-CTTGTTTGGGTT TGCTTTCACAGT-3 65°C Digested with BclI (5U) for 1 hour at 50°C AA:381 AG:381 + 276 + 105 GG:276 + 105 P2RY1 rs1371097 Allelic discrimination assay with TaqMan genotyping probes (Assay ID: C_8910679_10) - - - PEAR1 rs2768759 Fwd-5’-GGCTATTTCCT AAGGACAAAGCC-3’ Rev-5’-CCAGCCTGGACA AGAGGC-3’ 72°C Digested with AlwNI (5U) for 2 hours at 37°C CC:435 CA:435 + 275 + 161 AA:275 + 161 ITG3 rs2317676 Allelic discrimination assay with TaqMan genotyping probes (Assay ID: C_16194469_10) - - - UGT1A6 rs2070959 Fwd-5′-CCAGTGCCGTATG ACCAAGAAGAGC-3′ Rev-5′-ACCTCCAATGAA GACCATGTTGGGC-3′ 68°C Digested with NsiI (5U) for 2 hours at 37°C AA:594 AG:594 + 309 + 285 GG:309 + 285 UGT1A6 rs1105879 Fwd-5′- CCAGTGCCGTAT GACCAAGAAGAGC 3′ Rev-5′-ACCTCCAATGAA GACCATGTTGGGC 3′ 68°C Digested with Fnu4HI (5U) for 2 hours at 37°C AA:594 AC:594 + 318 + 276 CC:318 + 276 Tm°C, primer annealing temperature in degrees Celsius Statistical analysis Data were collected in specific patient proforma, and entered in an Excel sheet. Continuous variables were checked for normality distribution. Variables with non-normal distributions are shown as medians with interquartile ranges (IQR). Categorical variables and proportions were compared with the Chi-square test. A p-value of less than 0.05 was considered significant. For all of the analyses, SPSS version 24, and GraphPad Prism 8 were utilised. RESULTS Patient selection Out of 293 recruited subjects, 63 patients (21.50%) had serum salicylate below the therapeutic range (i.e., < 30 µg/mL). These patients were excluded from the study due to probable aspirin non-compliance. The study included the remaining 230 patients. Characteristics and demographic distributions of the cohort Majority of patients (78.16%) were male, with a median age of 52 years (IQR, 43–60). There were 16.95% current alcohol consumers, 6.52% current tobacco users, and 27.82% current smokers in the study sample. In terms of risk factors, 26.08% of patients had diabetes and 46.52% of patients had hypertension (Table 3 ). Table 3 Distribution of demographic features in the entire cohort Patient characteristics n = 230 The median age in years, (IQR) 52 (43–60) Male, n (%) 180 (78.26) Current smokers, n (%) 64 (27.82) Current tobacco users, n (%) 15 (6.52) Current alcohol users, n (%) 39 (16.95) Hypertension, n (%) 107 (46.52) Diabetes, n (%) 60 (26.08) Previous history of TIA, n (%) 25 (10.86) Family history of stroke, n (%) 10 (4.34) Family history of hypertension, n (%) 15 (6.52) Family history of diabetes, n (%) 7 (3.04) IQR, interquartile range; n, number of subjects On-aspirin platelet reactivity (aspirin non-response) Using the 'ADP + AA-combined' criteria to assess on-aspirin platelet reactivity by LTA, 32 (13.91%) were 'Non-Responders,' 76 (33.04%) were 'Semi-Responders,' and the remaining 122 (53.04%) were 'Responders’. Since our major goal was to identify all patients with enhanced on-aspirin platelet reactivity, either partially or completely, we grouped the 'Non-Responders' and 'Semi-Responders' into one group, and formed a new group namely, aspirin 'Inadequate-responders' (Table 4 ). Table 4 Prevalence of Responders, Non-Responders and Inadequate-Responders ADP + AA-combined Responders Non-Responders Inadequate-Responders (Semi-Res + Non-Res) Total number of subjects, n = 230 (%) 122 (53.04) 32 (13.91) 108 (46.95) n, number of patients Distribution of allele frequencies between Responders and Non-Responders The influence of the candidate gene polymorphisms in determining on-aspirin persistent platelet reactivity was evaluated by keeping ‘Responders’ at the baseline as a reference group. We did not find any significant association with allele frequencies for on-aspirin platelet reactivity between Responders and Non-Responders (Table 5 ). Table 5. Allele frequencies between Responders and Non-Responders Gene Allelic frequency Responders vs. Non-Responders Responders (n=244) Non-Responders (n=64) P value OR 95% CI COX1 (rs1236913) C T C T 194 (79.50) 50 (20.49) 51 (79.68) 13 (20.31) 0.9747 0.98 0.48 -1.91 1.01 0.52-2.07 COX2 (rs20417) C G C G 152 (62.29) 92 (37.70) 39 (60.93) 25 (39.06) 0.8421 1.05 0.59-1.86 0.94 0.53-1.66 GPIIb/IIIa (rs5918) T C T C 170 (69.67) 39 (15.98) 52 (81.25) 12 (18.75) 0.9871 1.00 0.50-2.07 0.99 0.48 -1.9 P2RY1 (rs701265) A G A G 198 (81.14) 46 (18.85) 48 (75.00) 16 (25.00) 0.2750 1.43 0.72 -2.7 0.69 0.35 -1.3 P2RY1 (rs1371097) C T C T 195 (79.91) 49 (20.08) 48 (75.00) 16 (25.00) 0.3908 1.32 0.67-2.55 0.75 0.39-1.48 PEAR1 (rs2768759) A C A C 152 (62.29) 92 (37.70) 43 (67.18) 21 (32.81) 0.4698 0.80 0.45-1.45 1.23 0.68-2.19 ITG3 (rs2317676) A G A G 213 (87.29) 31 (12.70) 53 (82.81) 11 (17.18) 0.3523 1.42 0.65-2.95 0.70 0.33-1.53 UGT1A6 (rs2070959) A G A G 135 (55.32) 109 (44.67) 33 (51.56) 31 (48.43) 0.5903 1.16 0.66-2.03 0.86 0.49-1.52 UGT1A6 (rs1105879) A C A C 131 (53.68) 113 (46.31) 38 (59.37) 26 (40.62) 0.4158 0.79 0.45-1.38 1.26 0.72-2.19 n, number of alleles in each group; χ2, chi-square value; OR 95%CI, odds ratio with 95% confidence interval; P, P-value; *P value <0.05 was considered significance Distribution of allele frequencies in Responders and Inadequate-Responders Upon allele frequency comparisons we found that ‘T’ allele of P2RY1 (rs1371097) polymorphism was significantly associated with Inadequate-Response, where the subjects in the Inadequate-Responders group showed a higher frequency of ‘T’ allele compared to that of Responders (20.08% vs. 30.09%; OR, 95%, 1.71, 1.122–2.61; p = 0.0131*) (Table 6 ). Table 6. Allele frequencies in Responders and Inadequate-Responders Allelic frequency Responders vs. Inadequate-Responders Responders (n=244) Inadequate- Responders (n=216) P value OR 95% CI COX1 rs1236913 C T C T 194 (79.50) 50 (20.49) 174 (80.55) 42 (19.44) 0.7793 0.93 (0.59-1.48) 1.06 (0.67 -1.69) COX2 rs20417 C G C G 152 (62.29) 92 (37.70) 132 (61.11) 84 (38.88) 0.7943 1.05 (0.72-1.52) 0.95 (0.65-1.37) GPIIb/IIIa rs5918 T C T C 170 (69.67) 39 (15.98) 183 (84.72) 33 (15.27) 0.3527 0.78 (0.46-1.30) 1.27 (0.76-2.14) P2RY1 rs701265 A G A G 198 (81.14) 46 (18.85) 163 (75.46) 53 (24.53) 0.1387 1.40 (0.90-2.19) 0.71 (0.45-1.10) P2RY1 rs1371097 C T C T 195 (79.91) 49 (20.08) 151 (69.90) 65 (30.09) 0.0131* 1.71 (1.12-2.61) 0.58 (0.38-0.89) PEAR1 rs2768759 A C A C 152 (62.29) 92 (37.70) 142 (65.74) 74 (34.25) 0.4425 0.86 (0.58-1.25) 1.16 (0.79-1.70) UGT1A6 rs2070959 A G A G 135 (55.32) 109 (44.6) 127 (58.79) 89 (41.20) 0.4534 0.86 (0.59-1.26) 1.15 (0.79-1.68) UGT1A6 rs1105879 A C A C 131 (53.68) 113 (46.3) 134 (62.30) 82 (37.96) 0.0706 0.70 (0.49-1.03) 1.41 (0.96-2.03) ITG3 rs2317676 A G A G 213 (87.29) 31 (12.70) 191 (88.42) 25 (11.57) 0.7112 0.89 (0.51-1.59) 1.11 (0.62-1.94) n, number of alleles in each group; χ2, chi-square value; OR 95%CI, odds ratio with 95% confidence interval; P, P value; *P value <0.05 was considered significance Odds ratio as an estimate of risk for on-aspirin platelet reactivity between Responders and Inadequate-Responders From the previous analysis, we found that the ‘T’ allele of P2RY1 (rs1371097) polymorphism was significantly associated with on-aspirin platelet reactivity in Inadequate-Responders. Hence we further created genetic models to identify the effect of this polymorphism on the odds of inadequate response as a measure of risk. With the effect of minor allele ‘T,’ the dominant model was assumed to be (CC vs CT + TT combined) and the recessive model was assumed to be (TT vs CC + CT combined). We found that the presence of a single copy of the ‘T’ allele in the P2RY1 (rs1371097) gene can increase the risk of inadequate response by 3.46 times even after adjusting for covariates like age, gender, smoking, tobacco, alcohol, hypertension, and diabetes (adjusted OR (95%), 3.46 (0.043); p = 0.043) (Table 7 ). Table 7 Estimate of risk for inadequate response P2RY1 (rs1371097) Gene Genotype Responders vs. Inadequate-Responders Responders vs. Inadequate-Responders (Model1) Crude OR (95% CI) P Adjusted OR (95% CI) P Pairwise comparison CC 1 (ref) 1 (ref) CT 1.40 (0.89–2.43) 0.234 1.40 (0.79–2.59) 0.226 TT 4.20 (1.28–13.71) 0.017* 3.76 (1.09–12.94) 0.035* Dominant Model CC vs. CT + TT 1.64 (0.97–2.79) 0.063 1.67 (0.95–2.92) 0.072 Recessive Model TT vs. CC + CT 3.68 (1.15–11.80) 0.028* 3.46 (1.04–11.50) 0.043* Model 1, adjusted for age, gender, hypertension, diabetes, smoking, tobacco, and alcohol use; P-value < 0.05 is considered as significant. DISCUSSION Aspirin is a cost-effective and widely prescribed antiplatelet agent for primary and secondary prevention of ischemic stroke, worldwide 29 . Even though aspirin is beneficial for the secondary prevention of cerebrovascular and cardiovascular disease, new thromboembolic events are common in individuals using aspirin. This might be due to the phenomenon of platelet reactivity that persists after aspirin administration, making them non-responsive to the treatment 30 . The molecular mechanism of on-aspirin platelet reactivity is complex, poorly understood, and most likely multifactorial 31 . Despite the challenges, research on the platelet response to aspirin therapy in various cardiovascular and cerebrovascular diseases is ongoing 32333435 . Our study is one such effort, in which we assessed the influence of genetic factors on on-aspirin platelet reactivity in ischemic stroke patients. Non-compliance with the treatment regime appears to be one of the more common causes of aspirin non-response [ 17 ][ 18 ]. A large percentage of people whose platelets showed no aspirin effect (57%) acknowledge not taking their prescription aspirin [ 19 ].. Hence, we thought it would be appropriate to measure the serum levels of salicylate to ensure patient compliance.. We found that 63 (21.5%) patients had serum salicylate levels below the therapeutic level. These patients were excluded from our analysis. In our study, based on the ‘ADP + AA-combined’ criteria we have found that 13.91% of ischemic stroke patients had high on-aspirin platelet reactivity categorizing them as aspirin Non-Responders and an additional 33% were Semi-Responders. We grouped the 'Non-Responders' and 'Semi-Responders', and formed a new group named aspirin 'Inadequate-Responders’ because our major goal was to identify all patients with on-aspirin platelet reactivity. Out of seven genes selected, the polymorphisms in COX1, COX2, GPIIb/IIIa, P2RY1, PEAR1 , and ITGB3 represent the genes of the platelet aggregation pathway and UGT1A6 is an aspirin metabolizing gene. We found that the ‘T’ allele of P2RY1 (rs1371097 C > T) polymorphism of platelet aggregation pathway was significantly associated with on-aspirin platelet reactivity, and increased the risk of inadequate response to aspirin by 3.46 times, after adjusting for the covariates like age, gender, smoking, tobacco, alcohol, hypertension and diabetes. In a previous study of Chinese ischemic stroke patients, the P2RY1 (rs1371097 C > T) was found to be associated with aspirin non-responsiveness with the ‘ADP + AA-combined’ classification 4 . In another study the interaction of P2RY1 (rs1371097) with TXA2R and GPIIIa was linked to higher platelet activation and associated with an increased risk of carotid stenosis in patients with ischemic stroke 36 . The minor allele of P2RY1 (rs1371097) was linked to TXB2 levels < 1.0 ng/mL assessed as a function of platelet reactivity while on aspirin therapy in patients with coronary artery disease 6 . Consistent with the previous findings, in our study the distribution of the ‘T’ allele of P2RY1 (rs1371097) was significantly higher in ischemic stroke patients with on-aspirin platelet reactivity, probably making them inadequately responsive to aspirin therapy. A previous study has shown that patients with coronary artery disease who were homozygous for the P2RY1 (rs701265) minor allele had a 8.5-fold increased risk of platelet aggregation in response to arachidonic acid 6 . Further, the minor allele of P2RY1 (rs701265) polymorphism was associated with increased platelet aggregation in response to ADP in healthy individuals 37 . Similarly, like the P2RY1 (rs1371097 C > T), the P2RY1 (rs701265 A > G) polymorphism was also linked to TXB2 levels < 1.0 ng/mL, assessed as a function of platelet reactivity while on aspirin therapy, in patients with coronary artery disease 6 . However, in our study we did not find any significant difference among the groups in P2RY1 (rs701265 A > G) variant. P2RY1 is a purinergic receptor that binds to the nucleotides such as ATP and ADP which are present outside the cell. Previously, haemostatic abnormalities were associated with mutations in this receptor 383940 . Mutations in the P2RY1 gene were associated with a 3-fold increase of aspirin non-responsiveness in coronary artery disease 11 . Our study shows the involvement of the P2RY1 (rs1371097 C > T) polymorphisms with the risk of inadequate-response in ischemic stroke patients. Hence it can be concluded that variations in the P2RY1 gene might have a significant role in determining the on-aspirin platelet reactivity leading to decreased responsiveness to aspirin therapy in our population. Surprisingly, candidate gene polymorphisms studied in other genes, including COX1, COX2, PEAR1, GPIIb/IIIa , and ITGB3 of platelet aggregation pathway, were not significantly associated with the phenomenon of on-aspirin platelet reactivity in our population 41 42 . Even the polymorphism in aspirin metabolizing gene UGT1A6 failed to show a significant difference between the groups in our cohort. Previously, one study has shown that genotypes in the UGT1A6 gene can modify the protective effects of aspirin 10 . In conclusion, as a step towards pharmacogenetics-based personalised medicine, our study is one of the large studies to explore the influence of genetic predispositions on platelet response to aspirin therapy in ischemic stroke. Most importantly, the phenomenon of on-aspirin platelet reactivity was found to be influenced by the polymorphisms in the P2RY1 gene. Our study adds to the complexity of the phenomenon of aspirin non response, and in the future, larger number of genetic markers for on-aspirin platelet reactivity could be identified employing high-throughput Next Generation Sequencing (NGS) techniques. Further, studying genes in alternative pathways of platelet aggregation might provide further insights about on-aspirin platelet reactivity leading to aspirin non-response. Declarations ACKNOWLEDGEMENTS We thank all patients and their family who volunteered for the research . This work was supported by Indian Council of Medical Research (ICMR), Government of India (File NO/82/18/2012/PHGEN (TF)/BMS DATED 23/02/2017), and the ICMR-Senior Research Fellowship (File NO.2019-0286/GENOMICS/BMS DATED 26/08/2019). AUTHOR CONTRIBUTION RC conceptualised the idea and designed the study. PS performed the experiments, collected the data, and analysed the results. SRC and SR recruited the patients. RC and PS wrote the manuscript. All authors approved the final manuscript DECLARATION OF CONFLICTING INTEREST The authors have no conflicts of interest to declare DATA AVAILABILITY The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. References Du G, Lin Q, Wang J. A brief review on the mechanisms of aspirin resistance. Int J Cardiol 2016; 220 : 21–26. Yang Y, Lewis JP, Hulot JS, Scott SA. The pharmacogenetic control of antiplatelet response: Candidate genes and CYP2C19. Expert Opin Drug Metab Toxicol 2015; 11 : 1599–1617. Li XL, Cao J, Fan L, Wang Q, Ye L, Cui CP et al. Genetic polymorphisms of HO-1 and COX-1 are associated with aspirin resistance defined by light transmittance aggregation in Chinese Han patients. Clin Appl Thromb 2013; 19 : 513–521. Yi X, Wang C, Zhou Q, Lin J. 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The TXA2R rs1131882, P2Y1 rs1371097 and GPIIIa rs2317676 three-loci interactions may increase the risk of carotid stenosis in patients with ischemic stroke. BMC Neurol 2019; 19 : 1–9. Hetherington SL, Singh RK, Lodwick D, Thompson JR, Goodall AH, Samani NJ. Dimorphism in the P2Y1 ADP receptor gene is associated with increased platelet activation response to ADP. Arterioscler Thromb Vasc Biol 2005; 25 : 252–257. Léon C, Hechler B, Freund M, Eckly A, Vial C, Ohlmann P et al. Defective platelet aggregation and increased resistance to thrombosis in purinergic P2Y1 receptor-null mice. J Clin Invest 1999; 104 : 1731–1737. Burnstock G. Purinergic signaling and vascular cell proliferation and death. Arterioscler Thromb Vasc Biol 2002; 22 : 364–373. Hechler B, Gachet C. Purinergic Receptors in Thrombosis and Inflammation. Arterioscler Thromb Vasc Biol 2015; 35 : 2307–2315. Yi X, Cheng W, Lin J, Zhou Q, Wang C. Interaction between COX-1 and COX-2 Variants Associated with Aspirin Resistance in Chinese Stroke Patients. J Stroke Cerebrovasc Dis 2016; 25 : 2136–2144. Kranzhofer R, Ruef J. Aspirin resistance in coronary artery disease is correlated to elevated markers for oxidative stress but not to the expression of cyclooxygenase (COX) 1/2, a novel COX-1 polymorphism or the P1A1/2 polymorphism. Platelets 2006; 17 : 163–169. Additional Declarations There is NO conflict of interest to disclose. 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. 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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-3836500","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":265975582,"identity":"245b6b03-946b-43a3-abaa-6533ac532670","order_by":0,"name":"Rita Christopher","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYDACZjApwWAAoj4wMCQQoYUZoYVxBlFaoNaAtTDzEKPFvJ3/4AfGHRbR5uxnD3+2bbPL42dvYPzwMQe3FpnDzMwSjGckcnf25KVJ57YlF0v2HGCWnLkNtxYJoF8kGNskcjccyDFjzm1jTtxwI4GNmRe/FuYfYC3n3xh/tmyrJ0oLG8SWGzkG0oxth4nSYmaRCNbyxkyy59zxxJk9B5vx+4X/4OMbH9vqgA7LMf7wo6w6sZ+9+eCHj3i0gEECjMHIBiYbCKhHAX9IUTwKRsEoGAUjBQAAIQ9MkEm8r/8AAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-0024-4748","institution":"PES University Institute of Medical Sciences \u0026 Research","correspondingAuthor":true,"prefix":"","firstName":"Rita","middleName":"","lastName":"Christopher","suffix":""},{"id":265975583,"identity":"fb5a5fdb-5e34-4c0e-afcb-f10f7400834d","order_by":1,"name":"Pandarisamy Sundaravadivel","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Pandarisamy","middleName":"","lastName":"Sundaravadivel","suffix":""},{"id":265975584,"identity":"2baa995d-ebd7-4fbb-8d84-b7c6ad3b24e4","order_by":2,"name":"Sadanandavalli Retnaswami Chandra","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Sadanandavalli","middleName":"Retnaswami","lastName":"Chandra","suffix":""},{"id":265975585,"identity":"421800f1-a57f-4a67-b80b-15eef91e37b9","order_by":3,"name":"Subasree Ramakrishnan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Subasree","middleName":"","lastName":"Ramakrishnan","suffix":""}],"badges":[],"createdAt":"2024-01-05 07:01:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3836500/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3836500/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50802543,"identity":"75fe5bae-aca4-4b3a-b29d-96ec28894016","added_by":"auto","created_at":"2024-02-07 14:09:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":655750,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3836500/v1/2cfd4b1b-2219-4cc4-9b55-d18430c6e71b.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"Platelet ADP receptor (P2RY1) polymorphism and the risk of inadequate platelet response to aspirin: Study in patients with ischemic stroke","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAn estimated 5\u0026ndash;60% of patients experience inadequate antiplatelet effects or persisting platelet reactivity after taking the antiplatelet drug, aspirin, which is commonly known as aspirin non-response \u003csup\u003e1\u003c/sup\u003e. There is a complex link between platelet reactivity and aspirin, and the underlying causes of persistent platelet reactivity despite aspirin treatment is still unclear \u003csup\u003e2\u003c/sup\u003e. It is likely that variability in the genes encoding key enzymes and receptors of platelets could play a role. Previously, association between persistent platelet reactivity and polymorphisms of cyclooxygenase genes (\u003cem\u003eCOX1\u003c/em\u003e, \u003cem\u003eCOX2\u003c/em\u003e) have been inconsistently demonstrated \u003csup\u003e345\u003c/sup\u003e. Additionally, mutations in genes involved in the platelet aggregation pathway, including the platelet ADP receptor (\u003cem\u003eP2RY1\u003c/em\u003e), and the aspirin metabolism pathway like UDP glucuronosyltransferase 1A6 (\u003cem\u003eUGT1A6\u003c/em\u003e), have been suggested as candidates that might determine aspirin response \u003csup\u003e678910\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe purinergic receptor P2Y (P2RY1) is a G-protein-coupled receptor encoded by the \u003cem\u003eP2RY1\u003c/em\u003e gene located in chromosome 3. The physiological agonist for \u003cem\u003eP2RY1\u003c/em\u003e is adenosine-di-phosphate (ADP). Polymorphisms in the \u003cem\u003eP2RY1\u003c/em\u003e gene have shown an association with higher reactivity of platelets and aspirin resistance \u003csup\u003e611\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGlycoprotein IIb/IIIa (integrin αIIbβ3) is an integrin complex that serves as the receptor for fibrinogen and VWF for activation of platelets. A point mutation in the 2nd exon of the \u003cem\u003eITG3\u003c/em\u003e (\u003cem\u003eGPIIIa\u003c/em\u003e) gene leads to an amino acid change from leucine (\u003cem\u003ePlA1\u003c/em\u003e) to proline (\u003cem\u003ePlA2\u003c/em\u003e), which results in conformational changes in the protein as well as fibrinogen-binding regions. The \u003cem\u003ePlA1/A2\u003c/em\u003e polymorphism of GPIIb/IIIa has been consistently associated with antiplatelet drug resistance and an increase in cardiovascular events \u003csup\u003e78\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe platelet endothelial aggregation receptor 1 (\u003cem\u003ePEAR1\u003c/em\u003e) is highly expressed in platelets and signals during platelet-platelet contact, but is not dependent on platelet function \u003csup\u003e12\u003c/sup\u003e. In a previous study polymorphism in \u003cem\u003ePEAR\u003c/em\u003e1 (rs12041331) influenced the aggregation of platelets in a non-COX-1-dependent manner \u003csup\u003e9\u003c/sup\u003e. One of the important enzymes involved in aspirin bio-disposition is UDP glucuronosyltransferase family 1 member A6 (UGT1A6) \u003csup\u003e13\u003c/sup\u003e. Polymorphisms in \u003cem\u003eUGT1A6\u003c/em\u003e were found to influence in-vivo aspirin metabolism \u003csup\u003e14\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eResearch has been conducted to examine how candidate gene polymorphisms affect aspirin's effects on cardiovascular disease, but genetic factors influencing on-aspirin platelet reactivity in ischemic stroke patients have been relatively understudied \u003csup\u003e15\u003c/sup\u003e. Indians have a high prevalence of ischemic stroke \u003csup\u003e16\u003c/sup\u003e, and there is an urgent need for data in this area. We therefore studied candidate gene polymorphisms in \u003cem\u003eCOX1, COX2, GpIIb/IIIa, P2RY1, PEAR1, ITGB3\u003c/em\u003e (platelet aggregation pathway), and \u003cem\u003eUGT1A6\u003c/em\u003e (aspirin metabolising pathway). To the best of our knowledge, this is the first large study to investigate the influence of candidate gene polymorphisms collectively from pathways of platelet aggregation and aspirin metabolism to determine the outcome response of aspirin-treated ischemic stroke patients in the Indian population.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy population\u003c/h2\u003e \u003cp\u003eA total of 293 patients diagnosed with ischemic stroke at the Neurological Services of NIMHANS, Bengaluru, India, and who were taking 150 mg of aspirin for more than 7 days for prevention of stroke recurrence, were recruited for the study. It included newly diagnosed cases as well as those on long-term treatment and regular follow-up. Patients were diagnosed based on the clinical features, and findings from CT/MRI (Computed Tomography /Magnetic Resonance Imaging) of the brain. Ischemic stroke patients had imaging evidence of acute cerebrovascular infarction. At baseline, demographic data (age, sex) and the following vascular risk factors: hypertension, diabetes mellitus, alcohol, smoking, tobacco, previous history of ischemic stroke or transient ischemic attack (TIA), family history of stroke, hypertension, and diabetes, were collected. The National Institute of Health Stroke Scale (NIHSS) score, performed by a stroke neurologist, was recorded at their admission \u003csup\u003e17\u003c/sup\u003e. The clinical stroke syndrome was classified according to the criteria of the TOAST (Trial of Org 10,172 in Acute Stroke Treatment) classification \u003csup\u003e18\u003c/sup\u003e. The following patients were excluded despite being on aspirin treatment: patients with liver or renal dysfunction, malignancy or any other terminal illnesses, patients on other anti-platelet medications, anti-inflammatory drugs and anticoagulants, patients who have undergone a surgical procedure within a month, pregnant and lactating women, patients who are unable to give written informed consent and patients who were \u0026lt;\u0026thinsp;18 years of age. A total of 5ml of venous blood was collected from the patients, and distributed in different tubes for platelet aggregation test, collection of serum and DNA extraction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy design and ethical considerations\u003c/h2\u003e \u003cp\u003eThis is a prospective cohort study, carried out at National Institute of Mental Health and Neuro Sciences (NIMHANS), Bengaluru, India. Ethics approval was obtained from the Institute Ethics Committee of NIMHANS (No. NIMHANS/86th IEC/2012) and experiments were conducted in compliance with the Institutional guidelines. All study participants signed a written informed consent form.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMeasurement of serum salicylate levels\u003c/h2\u003e \u003cp\u003eTo ascertain patient compliance, serum salicylate levels were measured in all of the participants. Spectrophotometry was used to assess salicylate levels, based on the formation of a coloured complex with ferric-mercuric reagent, as described previously\u003csup\u003e19\u003c/sup\u003e. Patients with serum salicylate levels less than 30 \u0026micro;g/mL, which was below the therapeutic range \u003csup\u003e20\u003c/sup\u003e, were excluded (63 patients).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003ePlatelet aggregation test to assess platelet response to aspirin\u003c/h2\u003e \u003cp\u003eLight transmittance aggregometry (LTA) is the gold standard method for qualitatively measuring persistent platelet reactivity to known platelet agonists \u003csup\u003e21\u003c/sup\u003e. ADP and arachidonic acid (AA) were used as agonists to assess on-aspirin platelet reactivity in this study, as proposed by Gum and colleagues, 2001 \u003csup\u003e22\u003c/sup\u003e. Platelet aggregation tests were carried out on a Chrono-log Model 700 Aggregometer, manufactured by Chrono-log Corporation in Havertown, PA 19083\u003csup\u003e23\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eClassification of patients with on-aspirin platelet reactivity assessed by LTA\u003c/h2\u003e \u003cp\u003ePatients with on-aspirin platelet reactivity were classified by combined mean demonstration of ADP and AA-induced percentage of platelet aggregation. This type of classification was proposed by Gum and co-workers\u003csup\u003e22\u003c/sup\u003e. Based on this classification, patients were divided into three groups namely \u0026lsquo;Responders\u0026rsquo;, \u0026lsquo;Semi-Responders\u0026rsquo;, and \u0026lsquo;Non-Responders\u0026rsquo;. According to this criteria, patients with platelet aggregation of \u0026gt;\u0026thinsp;70% with ADP and \u0026gt;\u0026thinsp;20% with AA were categorized as 'Non-responders,' whereas those with \u0026le;\u0026thinsp;70% with ADP and \u0026le;\u0026thinsp;20% with AA were classified as 'Responders\u0026rsquo;. Patients who met any one of the aforementioned criteria were referred to as \u0026lsquo;Semi-Responders\u0026rsquo;. This criterion was used in previous studies to predict the risk of severe vascular events \u003csup\u003e22 24\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eList of candidate genes selected for the study\u003c/h2\u003e \u003cp\u003eA search for variants associated with on-aspirin poor response in platelet aggregation, and aspirin metabolism pathways was conducted to select the candidate genes. A total of 9 variants were selected from 7 candidate genes (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\u003ePolymorphisms evaluated in candidate genes\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVariant type\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCyclooxygenase 1 \u003cem\u003e(COX1)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers1236913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMissense variant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCyclooxygenase 2 \u003cem\u003e(COX2)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers20417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-coding transcript exon variant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet \u003cem\u003eGPIIb/IIIa\u003c/em\u003e receptors \u003cem\u003e(GPIIb/IIIa)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers5981\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMissense variant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eP2Y purinoceptor 1 \u003cem\u003e(P2RY1)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers701265 \u0026amp; rs1371097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSynonymous variant and regulatory region variant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet endothelial aggregation receptor 1 \u003cem\u003e(PEAR1)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers2768759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTF binding site variant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntegrin Subunit Beta 3 \u003cem\u003e(ITGB3)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers2317676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u0026rsquo;UTR variant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUDP glycosyltransferase 1A6 \u003cem\u003e(UGT1A6)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ers1105879 \u0026amp; rs2070959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBoth are missense variant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eTF, transcription factor; UTR, untranslated region; UDP, uridine diphosphate; GP, glycoprotein; ADP, adenosine diphosphate\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePCR-RFLP standardization of SNPs\u003c/h2\u003e \u003cp\u003eAll the variants were genotyped by either the \u0026lsquo;PCR-RFLP method\u0026rsquo; with restriction enzymes or the \u0026lsquo;Allelic Discrimination Assay\u0026rsquo; using TaqMan SNP genotyping probes. PCR-RFLP primers were selected from previously published literature \u003csup\u003e25262728\u003c/sup\u003e. PCR amplification was performed with Takara EmeraldAmp GT PCR Master Mix (DSS Takara Bio India Private Ltd, New Delhi, India) on a Thermal Cycler (Eppendorf Master Cycler PCR, Hamburg, Germany) for \u003cem\u003eCOX1\u003c/em\u003e (rs1236913), \u003cem\u003eP2RY1\u003c/em\u003e (rs701265), \u003cem\u003ePEAR1\u003c/em\u003e (rs2768759), \u003cem\u003eCOX2\u003c/em\u003e (rs20417) and \u003cem\u003eUGT1A6\u003c/em\u003e (rs2070959 and rs1105879) variants. PCR for \u003cem\u003eGPIIb/IIIa\u003c/em\u003e receptor mutation (rs5918) was performed by mixing individual components (dNTPs, MgCl2, Taq buffer, and primers) with Taq DNA polymerases (Fermentas Life Sciences, USA). Variants in \u003cem\u003eITG3\u003c/em\u003e (rs2317676), and \u003cem\u003eP2RY1\u003c/em\u003e (rs1371097) were genotyped by TaqMan allelic discrimination assay (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\u003eDetails of PCR-RFLP conditions, and allelic discrimination assay\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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePCR-RFLP Primers\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePCR\u003c/p\u003e \u003cp\u003eTm\u0026deg;C\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRestriction digestion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFragment size\u003c/p\u003e \u003cp\u003e(base pairs)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCOX1\u003c/em\u003e\u003c/p\u003e \u003cp\u003ers1236913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFwd-5\u0026rsquo;-GCCTCCTGG\u003c/p\u003e \u003cp\u003eTGGAGCCTTGAATG-3\u0026rsquo;\u003c/p\u003e \u003cp\u003eRev-5\u0026rsquo;-CGCGAGCAG\u003c/p\u003e \u003cp\u003eAAAGGGGAAGCTG-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDigested with HpaII (5U) for 2 hours at 37\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCC:252\u003c/p\u003e \u003cp\u003eCT:252\u0026thinsp;+\u0026thinsp;144\u0026thinsp;+\u0026thinsp;106\u003c/p\u003e \u003cp\u003eTT:144\u0026thinsp;+\u0026thinsp;106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCOX2\u003c/em\u003e\u003c/p\u003e \u003cp\u003ers20417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFwd-5\u0026prime;-CCGCTTCCTTTGT\u003c/p\u003e \u003cp\u003eCCATCAG-3\u0026prime;\u003c/p\u003e \u003cp\u003eRev-5\u0026prime;-GGCTGTATATCTG\u003c/p\u003e \u003cp\u003eCTCTATATGC-3\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e57\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDigested with AciI (5U) for 2 hours at 37\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGG:306\u003c/p\u003e \u003cp\u003eGC:306\u0026thinsp;+\u0026thinsp;188\u0026thinsp;+\u0026thinsp;118\u003c/p\u003e \u003cp\u003eCC:188\u0026thinsp;+\u0026thinsp;118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGPIIb/IIIa\u003c/em\u003e\u003c/p\u003e \u003cp\u003ers5918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFwd-5\u0026rsquo;-TGGGACTTCTCTT TGGGCTCCTGACTTAC-3\u0026rsquo;\u003c/p\u003e \u003cp\u003eRev-5\u0026rsquo;-CCTTCAGCAGA\u003c/p\u003e \u003cp\u003eTTCTCCTTCAGGTCAC-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDigested with Msp I (5U) for overnight at 37\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTT:82\u003c/p\u003e \u003cp\u003eTC:82\u0026thinsp;+\u0026thinsp;39\u0026thinsp;+\u0026thinsp;43\u003c/p\u003e \u003cp\u003eCC:39\u0026thinsp;+\u0026thinsp;43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP2RY1\u003c/em\u003e rs701265\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFwd-5\u0026rsquo;-GCCATGTGTAA\u003c/p\u003e \u003cp\u003eACTGCAGAGGTTC-3\u0026rsquo;\u003c/p\u003e \u003cp\u003eRev-5\u0026rsquo;-CTTGTTTGGGTT\u003c/p\u003e \u003cp\u003eTGCTTTCACAGT-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDigested with BclI (5U) for 1 hour at 50\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAA:381\u003c/p\u003e \u003cp\u003eAG:381\u0026thinsp;+\u0026thinsp;276\u0026thinsp;+\u0026thinsp;105\u003c/p\u003e \u003cp\u003eGG:276\u0026thinsp;+\u0026thinsp;105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP2RY1\u003c/em\u003e\u003c/p\u003e \u003cp\u003ers1371097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAllelic discrimination assay with TaqMan genotyping probes\u003c/p\u003e \u003cp\u003e(Assay ID: C_8910679_10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePEAR1\u003c/em\u003e\u003c/p\u003e \u003cp\u003ers2768759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFwd-5\u0026rsquo;-GGCTATTTCCT\u003c/p\u003e \u003cp\u003eAAGGACAAAGCC-3\u0026rsquo;\u003c/p\u003e \u003cp\u003eRev-5\u0026rsquo;-CCAGCCTGGACA\u003c/p\u003e \u003cp\u003eAGAGGC-3\u0026rsquo;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDigested with AlwNI (5U) for 2 hours at 37\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCC:435\u003c/p\u003e \u003cp\u003eCA:435\u0026thinsp;+\u0026thinsp;275\u0026thinsp;+\u0026thinsp;161\u003c/p\u003e \u003cp\u003eAA:275\u0026thinsp;+\u0026thinsp;161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eITG3\u003c/em\u003e\u003c/p\u003e \u003cp\u003ers2317676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAllelic discrimination assay with TaqMan genotyping probes\u003c/p\u003e \u003cp\u003e(Assay ID: C_16194469_10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eUGT1A6\u003c/em\u003e\u003c/p\u003e \u003cp\u003ers2070959\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFwd-5\u0026prime;-CCAGTGCCGTATG\u003c/p\u003e \u003cp\u003eACCAAGAAGAGC-3\u0026prime;\u003c/p\u003e \u003cp\u003eRev-5\u0026prime;-ACCTCCAATGAA\u003c/p\u003e \u003cp\u003eGACCATGTTGGGC-3\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDigested with NsiI (5U) for 2 hours at 37\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAA:594\u003c/p\u003e \u003cp\u003eAG:594\u0026thinsp;+\u0026thinsp;309\u0026thinsp;+\u0026thinsp;285\u003c/p\u003e \u003cp\u003eGG:309\u0026thinsp;+\u0026thinsp;285\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eUGT1A6\u003c/em\u003e\u003c/p\u003e \u003cp\u003ers1105879\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFwd-5\u0026prime;- CCAGTGCCGTAT\u003c/p\u003e \u003cp\u003eGACCAAGAAGAGC 3\u0026prime;\u003c/p\u003e \u003cp\u003eRev-5\u0026prime;-ACCTCCAATGAA\u003c/p\u003e \u003cp\u003eGACCATGTTGGGC 3\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDigested with Fnu4HI (5U) for 2 hours at 37\u0026deg;C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAA:594\u003c/p\u003e \u003cp\u003eAC:594\u0026thinsp;+\u0026thinsp;318\u0026thinsp;+\u0026thinsp;276\u003c/p\u003e \u003cp\u003eCC:318\u0026thinsp;+\u0026thinsp;276\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eTm\u0026deg;C, primer annealing temperature in degrees Celsius\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData were collected in specific patient proforma, and entered in an Excel sheet. Continuous variables were checked for normality distribution. Variables with non-normal distributions are shown as medians with interquartile ranges (IQR). Categorical variables and proportions were compared with the Chi-square test. A p-value of less than 0.05 was considered significant. For all of the analyses, SPSS version 24, and GraphPad Prism 8 were utilised.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec12\"\u003e\n \u003ch2\u003ePatient selection\u003c/h2\u003e\n \u003cp\u003eOut of 293 recruited subjects, 63 patients (21.50%) had serum salicylate below the therapeutic range (i.e., \u0026lt;\u0026thinsp;30 \u0026micro;g/mL). These patients were excluded from the study due to probable aspirin non-compliance. The study included the remaining 230 patients.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\"\u003e\n \u003ch2\u003eCharacteristics and demographic distributions of the cohort\u003c/h2\u003e\n \u003cp\u003eMajority of patients (78.16%) were male, with a median age of 52 years (IQR, 43\u0026ndash;60). There were 16.95% current alcohol consumers, 6.52% current tobacco users, and 27.82% current smokers in the study sample. In terms of risk factors, 26.08% of patients had diabetes and 46.52% of patients had hypertension (Table\u0026nbsp;\u003cspan\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDistribution of demographic features in the entire cohort\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePatient characteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;230\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe median age in years, (IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e52 (43\u0026ndash;60)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e180 (78.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent smokers, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64 (27.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent tobacco users, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (6.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCurrent alcohol users, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39 (16.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107 (46.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60 (26.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrevious history of TIA, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (10.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFamily history of stroke, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (4.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFamily history of hypertension, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (6.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFamily history of diabetes, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (3.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\"\u003eIQR, interquartile range; n, number of subjects\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\"\u003e\n \u003ch2\u003eOn-aspirin platelet reactivity (aspirin non-response)\u003c/h2\u003e\n \u003cp\u003eUsing the \u0026apos;ADP\u0026thinsp;+\u0026thinsp;AA-combined\u0026apos; criteria to assess on-aspirin platelet reactivity by LTA, 32 (13.91%) were \u0026apos;Non-Responders,\u0026apos; 76 (33.04%) were \u0026apos;Semi-Responders,\u0026apos; and the remaining 122 (53.04%) were \u0026apos;Responders\u0026rsquo;. Since our major goal was to identify all patients with enhanced on-aspirin platelet reactivity, either partially or completely, we grouped the \u0026apos;Non-Responders\u0026apos; and \u0026apos;Semi-Responders\u0026apos; into one group, and formed a new group namely, aspirin \u0026apos;Inadequate-responders\u0026apos; (Table\u0026nbsp;\u003cspan\u003e4\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003ePrevalence of Responders, Non-Responders and Inadequate-Responders\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eADP\u0026thinsp;+\u0026thinsp;AA-combined\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResponders\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNon-Responders\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eInadequate-Responders\u003c/p\u003e\n \u003cp\u003e(Semi-Res\u0026thinsp;+\u0026thinsp;Non-Res)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal number of subjects,\u003c/p\u003e\n \u003cp\u003en\u0026thinsp;=\u0026thinsp;230 (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e122 (53.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32 (13.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e108 (46.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003en, number of patients\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\"\u003e\n \u003ch2\u003eDistribution of allele frequencies between Responders and Non-Responders\u003c/h2\u003e\n \u003cp\u003eThe influence of the candidate gene polymorphisms in determining on-aspirin persistent platelet reactivity was evaluated by keeping \u0026lsquo;Responders\u0026rsquo; at the baseline as a reference group. We did not find any significant association with allele frequencies for on-aspirin platelet reactivity between Responders and Non-Responders (Table\u0026nbsp;\u003cspan\u003e5\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv\u003e\n \u003cp\u003e\u003cstrong\u003eTable \u0026nbsp;5. Allele frequencies between Responders and Non-Responders\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.141414141414142%\" rowspan=\"3\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"58.58585858585859%\" colspan=\"4\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eAllelic frequency\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.272727272727273%\" colspan=\"3\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eResponders\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003evs.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eNon-Responders\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"23\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50.87719298245614%\" colspan=\"2\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eResponders\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=244)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"49.12280701754386%\" colspan=\"2\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-Responders\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=64)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"32\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.22222222222222%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"44.44444444444444%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"4\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" rowspan=\"3\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eCOX1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(rs1236913)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.122448979591836%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e194 (79.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.857142857142858%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e50 (20.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e51 (79.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e13 (20.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e0.9747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.48 -1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n 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width=\"10.714285714285714%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.8421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.59-1.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"17.857142857142858%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"bottom\"\u003e\n 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width=\"14.285714285714286%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.122448979591836%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e170 (69.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.857142857142858%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e39 (15.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e52 (81.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e12 (18.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e0.9871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.50-2.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.66666666666667%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.48 -1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"5\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" rowspan=\"3\" valign=\"bottom\"\u003e\n 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valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e198 (81.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.857142857142858%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e46 (18.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e48 (75.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e16 (25.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e0.2750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.72 -2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.66666666666667%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.35 -1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"5\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" rowspan=\"3\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP2RY1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(rs1371097)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"bottom\"\u003e\n 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\u003cp\u003e0.39-1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"5\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" rowspan=\"3\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePEAR1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(rs2768759)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.122448979591836%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e152 (62.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.857142857142858%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e92 (37.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e43 (67.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e21 (32.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e0.4698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.45-1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.66666666666667%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.68-2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"5\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" rowspan=\"3\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eITG3\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(rs2317676)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.122448979591836%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e213 (87.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.857142857142858%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e31 (12.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e53 (82.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e11 (17.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e0.3523\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.65-2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.66666666666667%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.33-1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"5\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" rowspan=\"3\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eUGT1A6\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(rs2070959)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.122448979591836%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e135 (55.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.857142857142858%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e109 (44.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e33 (51.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e31 (48.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e0.5903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.66-2.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.66666666666667%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.49-1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"5\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" rowspan=\"3\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eUGT1A6\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e(rs1105879)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.306122448979592%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.122448979591836%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16.666666666666668%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e131 (53.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.857142857142858%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e113 (46.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e38 (59.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.666666666666668%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e26 (40.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.714285714285714%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e0.4158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.45-1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"21\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"33.333333333333336%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"66.66666666666667%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.72-2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"0%\" height=\"5\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003en, number of alleles in each group;\u0026nbsp;\u0026chi;2,\u0026nbsp;chi-square value; OR 95%CI, odds ratio with 95% confidence interval; P, P-value; *P value \u0026lt;0.05 was considered significance\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\"\u003e\n \u003ch2\u003eDistribution of allele frequencies in Responders and Inadequate-Responders\u003c/h2\u003e\n \u003cp\u003eUpon allele frequency comparisons we found that \u0026lsquo;T\u0026rsquo; allele of \u003cem\u003eP2RY1\u003c/em\u003e (rs1371097) polymorphism was significantly associated with Inadequate-Response, where the subjects in the Inadequate-Responders group showed a higher frequency of \u0026lsquo;T\u0026rsquo; allele compared to that of Responders (20.08% vs. 30.09%; OR, 95%, 1.71, 1.122\u0026ndash;2.61; p\u0026thinsp;=\u0026thinsp;0.0131*) (Table\u0026nbsp;\u003cspan\u003e6\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable \u0026nbsp;6. Allele frequencies in Responders and Inadequate-Responders\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"57.142857142857146%\" colspan=\"4\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eAllelic frequency\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.612244897959183%\" colspan=\"3\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eResponders vs.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eInadequate-Responders\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eResponders\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(n=244)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\" colspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eInadequate-\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eResponders\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;(n=216)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.122448979591836%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.26530612244898%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.371134020618557%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eCOX1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ers1236913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.185567010309279%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.647058823529413%\" valign=\"bottom\"\u003e\n \u003cp\u003e194 (79.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.117647058823529%\" valign=\"bottom\"\u003e\n \u003cp\u003e50 (20.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.470588235294116%\" valign=\"bottom\"\u003e\n \u003cp\u003e174 (80.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.470588235294116%\" valign=\"bottom\"\u003e\n \u003cp\u003e42 (19.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.7793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.0588235294117645%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.294117647058824%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.59-1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.185567010309279%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.67 -1.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.371134020618557%\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eCOX2\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ers20417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.185567010309279%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.647058823529413%\" valign=\"bottom\"\u003e\n \u003cp\u003e152 (62.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.117647058823529%\" valign=\"bottom\"\u003e\n \u003cp\u003e92 (37.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.470588235294116%\" valign=\"bottom\"\u003e\n \u003cp\u003e132 (61.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.470588235294116%\" valign=\"bottom\"\u003e\n \u003cp\u003e84 (38.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.7943\u003c/p\u003e\n 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width=\"13.402061855670103%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.76-2.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.371134020618557%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP2RY1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ers701265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.185567010309279%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.647058823529413%\" valign=\"bottom\"\u003e\n \u003cp\u003e198 (81.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.117647058823529%\" valign=\"bottom\"\u003e\n \u003cp\u003e46 (18.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.470588235294116%\" valign=\"bottom\"\u003e\n \u003cp\u003e163 (75.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.470588235294116%\" valign=\"bottom\"\u003e\n \u003cp\u003e53 (24.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.1387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.0588235294117645%\" valign=\"bottom\"\u003e\n 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width=\"6.185567010309279%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.647058823529413%\" valign=\"bottom\"\u003e\n \u003cp\u003e195 (79.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.117647058823529%\" valign=\"bottom\"\u003e\n \u003cp\u003e49 (20.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.470588235294116%\" valign=\"bottom\"\u003e\n \u003cp\u003e151 (69.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.470588235294116%\" valign=\"bottom\"\u003e\n \u003cp\u003e65 (30.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0131*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.0588235294117645%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.294117647058824%\" valign=\"bottom\"\u003e\n \u003cp\u003e(1.12-2.61)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.185567010309279%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.38-0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.371134020618557%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ePEAR1\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ers2768759\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.185567010309279%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.647058823529413%\" valign=\"bottom\"\u003e\n \u003cp\u003e152 (62.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.117647058823529%\" valign=\"bottom\"\u003e\n \u003cp\u003e92 (37.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.470588235294116%\" valign=\"bottom\"\u003e\n \u003cp\u003e142 (65.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.470588235294116%\" valign=\"bottom\"\u003e\n \u003cp\u003e74 (34.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.4425\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.0588235294117645%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.294117647058824%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.58-1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.185567010309279%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.79-1.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.371134020618557%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eUGT1A6\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ers2070959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.185567010309279%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.647058823529413%\" valign=\"bottom\"\u003e\n \u003cp\u003e135 (55.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.117647058823529%\" valign=\"bottom\"\u003e\n \u003cp\u003e109 (44.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.470588235294116%\" valign=\"bottom\"\u003e\n \u003cp\u003e127 (58.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.470588235294116%\" valign=\"bottom\"\u003e\n \u003cp\u003e89 (41.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.4534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.0588235294117645%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.294117647058824%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.59-1.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.185567010309279%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.79-1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.371134020618557%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eUGT1A6\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ers1105879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.185567010309279%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.647058823529413%\" valign=\"bottom\"\u003e\n \u003cp\u003e131 (53.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.117647058823529%\" valign=\"bottom\"\u003e\n \u003cp\u003e113 (46.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.470588235294116%\" valign=\"bottom\"\u003e\n \u003cp\u003e134 (62.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.470588235294116%\" valign=\"bottom\"\u003e\n \u003cp\u003e82 (37.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.0706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.0588235294117645%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.294117647058824%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.49-1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.185567010309279%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.96-2.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.371134020618557%\" rowspan=\"2\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eITG3\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ers2317676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.185567010309279%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.647058823529413%\" valign=\"bottom\"\u003e\n \u003cp\u003e213 (87.29)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.117647058823529%\" valign=\"bottom\"\u003e\n \u003cp\u003e31 (12.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.470588235294116%\" valign=\"bottom\"\u003e\n \u003cp\u003e191 (88.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.470588235294116%\" valign=\"bottom\"\u003e\n \u003cp\u003e25 (11.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.941176470588236%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.7112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.0588235294117645%\" valign=\"bottom\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.294117647058824%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.51-1.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.463917525773196%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"14.43298969072165%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\" valign=\"bottom\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.185567010309279%\" valign=\"bottom\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\" valign=\"bottom\"\u003e\n \u003cp\u003e(0.62-1.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003en, number of alleles in each group; \u0026chi;2, chi-square value; OR 95%CI, odds ratio with 95% confidence interval; P, P value; *P value \u0026lt;0.05 was considered significance\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\"\u003e\n \u003ch2\u003eOdds ratio as an estimate of risk for on-aspirin platelet reactivity between Responders and Inadequate-Responders\u003c/h2\u003e\n \u003cp\u003eFrom the previous analysis, we found that the \u0026lsquo;T\u0026rsquo; allele of \u003cem\u003eP2RY1\u003c/em\u003e (rs1371097) polymorphism was significantly associated with on-aspirin platelet reactivity in Inadequate-Responders. Hence we further created genetic models to identify the effect of this polymorphism on the odds of inadequate response as a measure of risk. With the effect of minor allele \u0026lsquo;T,\u0026rsquo; the dominant model was assumed to be (CC vs CT\u0026thinsp;+\u0026thinsp;TT combined) and the recessive model was assumed to be (TT vs CC\u0026thinsp;+\u0026thinsp;CT combined). We found that the presence of a single copy of the \u0026lsquo;T\u0026rsquo; allele in the \u003cem\u003eP2RY1\u003c/em\u003e (rs1371097) gene can increase the risk of inadequate response by 3.46 times even after adjusting for covariates like age, gender, smoking, tobacco, alcohol, hypertension, and diabetes (adjusted OR (95%), 3.46 (0.043); p\u0026thinsp;=\u0026thinsp;0.043) (Table\u0026nbsp;\u003cspan\u003e7\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 7\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eEstimate of risk for inadequate response\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"6\"\u003e\n \u003cp\u003e\u003cem\u003eP2RY1\u003c/em\u003e (rs1371097)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGene\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eGenotype\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eResponders vs.\u003c/p\u003e\n \u003cp\u003eInadequate-Responders\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eResponders vs.\u003c/p\u003e\n \u003cp\u003eInadequate-Responders (Model1)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCrude\u003c/p\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdjusted\u003c/p\u003e\n \u003cp\u003eOR (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePairwise\u003c/p\u003e\n \u003cp\u003ecomparison\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 (ref)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40 (0.89\u0026ndash;2.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.40 (0.79\u0026ndash;2.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.226\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.20 (1.28\u0026ndash;13.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.017*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.76 (1.09\u0026ndash;12.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.035*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDominant\u003c/p\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003cp\u003evs.\u003c/p\u003e\n \u003cp\u003eCT\u0026thinsp;+\u0026thinsp;TT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.64 (0.97\u0026ndash;2.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.67 (0.95\u0026ndash;2.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.072\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRecessive\u003c/p\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTT\u003c/p\u003e\n \u003cp\u003evs.\u003c/p\u003e\n \u003cp\u003eCC\u0026thinsp;+\u0026thinsp;CT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.68 (1.15\u0026ndash;11.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.028*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.46 (1.04\u0026ndash;11.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.043*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eModel 1, adjusted for age, gender, hypertension, diabetes, smoking, tobacco, and alcohol use; P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 is considered as significant.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eAspirin is a cost-effective and widely prescribed antiplatelet agent for primary and secondary prevention of ischemic stroke, worldwide \u003csup\u003e29\u003c/sup\u003e. Even though aspirin is beneficial for the secondary prevention of cerebrovascular and cardiovascular disease, new thromboembolic events are common in individuals using aspirin. This might be due to the phenomenon of platelet reactivity that persists after aspirin administration, making them non-responsive to the treatment \u003csup\u003e30\u003c/sup\u003e. The molecular mechanism of on-aspirin platelet reactivity is complex, poorly understood, and most likely multifactorial \u003csup\u003e31\u003c/sup\u003e. Despite the challenges, research on the platelet response to aspirin therapy in various cardiovascular and cerebrovascular diseases is ongoing \u003csup\u003e32333435\u003c/sup\u003e. Our study is one such effort, in which we assessed the influence of genetic factors on on-aspirin platelet reactivity in ischemic stroke patients.\u003c/p\u003e \u003cp\u003eNon-compliance with the treatment regime appears to be one of the more common causes of aspirin non-response [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e][\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. A large percentage of people whose platelets showed no aspirin effect (57%) acknowledge not taking their prescription aspirin [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].. Hence, we thought it would be appropriate to measure the serum levels of salicylate to ensure patient compliance.. We found that 63 (21.5%) patients had serum salicylate levels below the therapeutic level. These patients were excluded from our analysis.\u003c/p\u003e \u003cp\u003eIn our study, based on the \u0026lsquo;ADP\u0026thinsp;+\u0026thinsp;AA-combined\u0026rsquo; criteria we have found that 13.91% of ischemic stroke patients had high on-aspirin platelet reactivity categorizing them as aspirin Non-Responders and an additional 33% were Semi-Responders. We grouped the 'Non-Responders' and 'Semi-Responders', and formed a new group named aspirin 'Inadequate-Responders\u0026rsquo; because our major goal was to identify all patients with on-aspirin platelet reactivity.\u003c/p\u003e \u003cp\u003eOut of seven genes selected, the polymorphisms in \u003cem\u003eCOX1, COX2, GPIIb/IIIa, P2RY1, PEAR1\u003c/em\u003e, and \u003cem\u003eITGB3\u003c/em\u003e represent the genes of the platelet aggregation pathway and \u003cem\u003eUGT1A6\u003c/em\u003e is an aspirin metabolizing gene. We found that the \u0026lsquo;T\u0026rsquo; allele of \u003cem\u003eP2RY1\u003c/em\u003e (rs1371097 C\u0026thinsp;\u0026gt;\u0026thinsp;T) polymorphism of platelet aggregation pathway was significantly associated with on-aspirin platelet reactivity, and increased the risk of inadequate response to aspirin by 3.46 times, after adjusting for the covariates like age, gender, smoking, tobacco, alcohol, hypertension and diabetes. In a previous study of Chinese ischemic stroke patients, the \u003cem\u003eP2RY1\u003c/em\u003e (rs1371097 C\u0026thinsp;\u0026gt;\u0026thinsp;T) was found to be associated with aspirin non-responsiveness with the \u0026lsquo;ADP\u0026thinsp;+\u0026thinsp;AA-combined\u0026rsquo; classification \u003csup\u003e4\u003c/sup\u003e. In another study the interaction of \u003cem\u003eP2RY1\u003c/em\u003e (rs1371097) with \u003cem\u003eTXA2R\u003c/em\u003e and \u003cem\u003eGPIIIa\u003c/em\u003e was linked to higher platelet activation and associated with an increased risk of carotid stenosis in patients with ischemic stroke \u003csup\u003e36\u003c/sup\u003e. The minor allele of \u003cem\u003eP2RY1\u003c/em\u003e (rs1371097) was linked to TXB2 levels\u0026thinsp;\u0026lt;\u0026thinsp;1.0 ng/mL assessed as a function of platelet reactivity while on aspirin therapy in patients with coronary artery disease \u003csup\u003e6\u003c/sup\u003e. Consistent with the previous findings, in our study the distribution of the \u0026lsquo;T\u0026rsquo; allele of \u003cem\u003eP2RY1\u003c/em\u003e (rs1371097) was significantly higher in ischemic stroke patients with on-aspirin platelet reactivity, probably making them inadequately responsive to aspirin therapy.\u003c/p\u003e \u003cp\u003eA previous study has shown that patients with coronary artery disease who were homozygous for the \u003cem\u003eP2RY1\u003c/em\u003e (rs701265) minor allele had a 8.5-fold increased risk of platelet aggregation in response to arachidonic acid \u003csup\u003e6\u003c/sup\u003e. Further, the minor allele of \u003cem\u003eP2RY1\u003c/em\u003e (rs701265) polymorphism was associated with increased platelet aggregation in response to ADP in healthy individuals \u003csup\u003e37\u003c/sup\u003e. Similarly, like the \u003cem\u003eP2RY1\u003c/em\u003e (rs1371097 C\u0026thinsp;\u0026gt;\u0026thinsp;T), the \u003cem\u003eP2RY1\u003c/em\u003e (rs701265 A\u0026thinsp;\u0026gt;\u0026thinsp;G) polymorphism was also linked to TXB2 levels\u0026thinsp;\u0026lt;\u0026thinsp;1.0 ng/mL, assessed as a function of platelet reactivity while on aspirin therapy, in patients with coronary artery disease \u003csup\u003e6\u003c/sup\u003e. However, in our study we did not find any significant difference among the groups in \u003cem\u003eP2RY1\u003c/em\u003e (rs701265 A\u0026thinsp;\u0026gt;\u0026thinsp;G) variant.\u003c/p\u003e \u003cp\u003e \u003cem\u003eP2RY1\u003c/em\u003e is a purinergic receptor that binds to the nucleotides such as ATP and ADP which are present outside the cell. Previously, haemostatic abnormalities were associated with mutations in this receptor \u003csup\u003e383940\u003c/sup\u003e. Mutations in the \u003cem\u003eP2RY1\u003c/em\u003e gene were associated with a 3-fold increase of aspirin non-responsiveness in coronary artery disease \u003csup\u003e11\u003c/sup\u003e. Our study shows the involvement of the \u003cem\u003eP2RY1\u003c/em\u003e (rs1371097 C\u0026thinsp;\u0026gt;\u0026thinsp;T) polymorphisms with the risk of inadequate-response in ischemic stroke patients. Hence it can be concluded that variations in the \u003cem\u003eP2RY1\u003c/em\u003e gene might have a significant role in determining the on-aspirin platelet reactivity leading to decreased responsiveness to aspirin therapy in our population.\u003c/p\u003e \u003cp\u003eSurprisingly, candidate gene polymorphisms studied in other genes, including \u003cem\u003eCOX1, COX2, PEAR1, GPIIb/IIIa\u003c/em\u003e, and \u003cem\u003eITGB3\u003c/em\u003e of platelet aggregation pathway, were not significantly associated with the phenomenon of on-aspirin platelet reactivity in our population \u003csup\u003e41 42\u003c/sup\u003e. Even the polymorphism in aspirin metabolizing gene \u003cem\u003eUGT1A6\u003c/em\u003e failed to show a significant difference between the groups in our cohort. Previously, one study has shown that genotypes in the \u003cem\u003eUGT1A6\u003c/em\u003e gene can modify the protective effects of aspirin \u003csup\u003e10\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn conclusion, as a step towards pharmacogenetics-based personalised medicine, our study is one of the large studies to explore the influence of genetic predispositions on platelet response to aspirin therapy in ischemic stroke. Most importantly, the phenomenon of on-aspirin platelet reactivity was found to be influenced by the polymorphisms in the \u003cem\u003eP2RY1\u003c/em\u003e gene. Our study adds to the complexity of the phenomenon of aspirin non response, and in the future, larger number of genetic markers for on-aspirin platelet reactivity could be identified employing high-throughput Next Generation Sequencing (NGS) techniques. Further, studying genes in alternative pathways of platelet aggregation might provide further insights about on-aspirin platelet reactivity leading to aspirin non-response.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank all patients and their family who volunteered for the research\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003eThis work was supported by Indian Council of Medical Research (ICMR), Government of India (File NO/82/18/2012/PHGEN (TF)/BMS DATED 23/02/2017), and the ICMR-Senior Research Fellowship (File NO.2019-0286/GENOMICS/BMS DATED 26/08/2019).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHOR CONTRIBUTION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRC conceptualised the idea and designed the study. PS performed the experiments, collected the data, and analysed the results. SRC and SR recruited the patients. RC and PS wrote the manuscript. All authors approved the final manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDECLARATION OF CONFLICTING INTEREST\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDATA AVAILABILITY\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDu G, Lin Q, Wang J. A brief review on the mechanisms of aspirin resistance. \u003cem\u003eInt J Cardiol\u003c/em\u003e 2016; \u003cstrong\u003e220\u003c/strong\u003e: 21\u0026ndash;26.\u003c/li\u003e\n\u003cli\u003eYang Y, Lewis JP, Hulot JS, Scott SA. 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Aspirin resistance in coronary artery disease is correlated to elevated markers for oxidative stress but not to the expression of cyclooxygenase (COX) 1/2, a novel COX-1 polymorphism or the P1A1/2 polymorphism. \u003cem\u003ePlatelets\u003c/em\u003e 2006; \u003cstrong\u003e17\u003c/strong\u003e: 163\u0026ndash;169.\u003c/li\u003e\n\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":"","lastPublishedDoi":"10.21203/rs.3.rs-3836500/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3836500/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAn estimated 5\u0026ndash;60% of patients experience inadequate antiplatelet effects after taking aspirin. We investigated the gene variants that might determine the outcome of the aspirin response in stroke patients. A total of 293 ischemic stroke patients who were taking 150 mg of aspirin for more than 7 days, were enrolled and only 230 patients with serum salicylate levels\u0026thinsp;\u0026gt;\u0026thinsp;30 \u0026micro;g/mL were included. Variants in \u003cem\u003eCOX1\u003c/em\u003e (rs1236913 C\u0026thinsp;\u0026gt;\u0026thinsp;T), \u003cem\u003eCOX2\u003c/em\u003e (rs20417 G\u0026thinsp;\u0026gt;\u0026thinsp;C), \u003cem\u003eGpIIb/IIIa\u003c/em\u003e (rs5981 T\u0026thinsp;\u0026gt;\u0026thinsp;C), \u003cem\u003eP2RY1\u003c/em\u003e (rs701265 A\u0026thinsp;\u0026gt;\u0026thinsp;G \u0026amp; rs1371097 C\u0026thinsp;\u0026gt;\u0026thinsp;T), \u003cem\u003ePEAR1\u003c/em\u003e (rs2768759 C\u0026thinsp;\u0026gt;\u0026thinsp;A), \u003cem\u003eITGB3\u003c/em\u003e (rs2317676 A\u0026thinsp;\u0026gt;\u0026thinsp;G) and \u003cem\u003eUGT1A6\u003c/em\u003e (rs1105879 A\u0026thinsp;\u0026gt;\u0026thinsp;G \u0026amp; rs2070959 A\u0026thinsp;\u0026gt;\u0026thinsp;C) were genotyped. Platelet aggregation tests were carried out by light transmittance aggregometry using adenosine-di-phosphate and arachidonic acid as agonists. The \u0026lsquo;T\u0026rsquo; allele of \u003cem\u003eP2RY1\u003c/em\u003e (rs1371097 C\u0026thinsp;\u0026gt;\u0026thinsp;T) polymorphism was significantly associated with inadequate platelet response as measured by platelet aggregometry (OR, 95%, 1.71, 1.122\u0026ndash;2.61; p\u0026thinsp;=\u0026thinsp;0.0131). Presence of a single copy of the \u0026lsquo;T\u0026rsquo; allele in \u003cem\u003eP2RY1\u003c/em\u003e (rs1371097 C\u0026thinsp;\u0026gt;\u0026thinsp;T) increased the risk of inadequate response by 3.46 times (adjusted OR (95%), 3.46 (0.043); p\u0026thinsp;=\u0026thinsp;0.043).\u003c/p\u003e","manuscriptTitle":"Platelet ADP receptor (P2RY1) polymorphism and the risk of inadequate platelet response to aspirin: Study in patients with ischemic stroke","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-10 09:38:52","doi":"10.21203/rs.3.rs-3836500/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":"c7e31f0a-f73f-4dea-b9fd-774dfb98357a","owner":[],"postedDate":"January 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-07T14:01:37+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-10 09:38:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3836500","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3836500","identity":"rs-3836500","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}