J Neurol (2013) 260:2928–2937 DOI 10.1007/s00415-013-7140-7

TECHNIQUES IN CLINICAL SCIENCE

Dynamic changes and associated factors of clopidogrel resistance in patients after cerebral infarction Bo Rong Zhou • Hong Ting Shi • Rong Wang Min Zhang • Hai Tao Guan • Zi Fan Liu • Yan Hua Deng

•

Received: 11 July 2013 / Revised: 17 September 2013 / Accepted: 28 September 2013 / Published online: 18 October 2013 Ó Springer-Verlag Berlin Heidelberg 2013

Abstract Stroke victims often exhibit clopidogrel resistance (CR). This prospective study was undertaken to observe changes that influence CR in the secondary prevention of cerebral infarction (CI). The study included 56 cases at high risk of stroke (HRS), 147 cases of CI and 68 control subjects. The CI and HRS groups were divided into CR and NCR (none clopidogrel resistance) subgroups using standard criteria. The NCR group was subdivided into DCR (dynamic CR) and CNCR (continuous NCR) groups. Platelet aggregation rate (PAR) was assessed at baseline and after 2 weeks treatment with clopidogrel 75 mg/day in the CI and HRS groups. In the NCR group, PAR was evaluated after 3 and 6 months of clopidogrel (75 mg/day) treatment. Baseline PAR was higher in the CI group than in B. R. Zhou and H. T. Shi are co-first authors. B. R. Zhou (&)  H. T. Guan  Z. F. Liu  Y. H. Deng Department of Neurology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou 510150, China e-mail: [email protected] B. R. Zhou Key Laboratory of Reproduction and Genetics of Guangdong Higher Education Institutes, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou 510150, China H. T. Shi Department of Cerebrovascular, The Fourth Affiliated Hospital of Guangzhou Medical University, Guangzhou 510150, China R. Wang Department of Clinical Laboratory, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou 510150, China M. Zhang Department of Epidemics and Health Statistics, Guangdong Pharmacy College, Guangzhou 510240, China

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the HRS or control groups (P \ 0.01). The incidence of CR was 28.6 % in the CI and 13.6 % in the HRS group (P = 0.018). Diabetes mellitus, (OR 16.627; 95 % CI 4.691–58.934) and history of TIA (OR 13.711; 95 % CI 1.667–112.784) (both P \ 0.05) were both associated with CR. Other independent risk factors included high total cholesterol, calcium antagonist or ACEI/ARB use. A total of 36 CR and 85 NCR cases completed 6 months follow-up. High total cholesterol was an independent risk factor for DCR (OR 0.415; 95 % CI 0.213–0.808; P = 0.01) which developed in 15 subjects at 6 months. PAR decreased by[10 % after 2 weeks in 71.4 % of patients with CR who subsequently changed drugs or received combination therapy. Dynamic CR may occur after CI. Many factors including DM\TIA\HCT\P2Y12 eC coexistence CYP2Y19 eA\combination drug, associate CR or DCR. Our results highlight the need for PAR monitoring. Keywords Dynamic changes  Clopidogrel resistance  Cerebral infarction  Secondary prevention  Risk factors

Introduction Platelet aggregation is the main pathogenic consequence of cerebrovascular ischemia (CVI). Clopidogrel forms the cornerstone for prevention of cardiovascular events, and its clinical effectiveness has been well established [1–3]. Chemically, clopidogrel is a thienopyridine prodrug that is metabolized by hepatic P450 enzymes to form an active drug that strongly and irreversibly inhibits adenosine diphosphate-induced platelet aggregation [2, 3]. Clopidogrel is widely used for the prevention of cardiovascular and cerebrovascular events. Clinical research has demonstrated

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the effectiveness of clopidogrel in preventing major adverse cardiovascular and cerebrovascular events (MACCE) in stroke victims [4, 5]. However, despite this form of antiplatelet therapy, some patients still develop recurrent cerebrovascular ischemic events [3–5]. A number of studies have clearly shown that there is marked variability in the responsiveness to clopidogrel and that poor responders are at higher risk of short and long-term MACCE [6]. Poor responders are associated with clopidogrel resistance (CR). Previous studies estimate that the overall incidence of CR varies from 16.8 to 21.0 %, and may be as high as 44 % in victims of cerebral infarction (CI) [7, 8]. The mechanisms of CR have not been fully elucidated but are likely to be multifactorial, involving genetic polymorphisms, elevated baseline platelet reactivity and drug interactions. It has been proposed that the mutations that influence responsiveness to clopidogrel may be located in genes encoding proteins responsible for clopidogrel absorption (ABCB1) and/or its biotransformation to the active form (CYP3A4, CYP3A5, CYP2C19). The platelet drug receptor gene P2Y12 and other platelet membrane receptor genes such as glycoproteins (GP) may also be involved in this process [9–14]. There is a clear need to improve the short- and longterm outcome of patients who exhibit CR. Recently published and ongoing clinical trials are being undertaken to evaluate different strategies for acute clopidogrel treatment. These include reloading clopidogrel, combining it with other antiplatelet drugs as combination therapy or switching to the routine use of prasugrel [6, 9]. Patients with primary CI or recurrent stroke have high rates of CR. Most previous research has focused on static or early stability observations of CR in MACCE. In this paper, we focused our attention on dynamic changes in CR in patients with CI, and investigated risk factors such as clinical characteristics, fasting blood glucose (FBG), blood lipids, hematology, coagulation (International Normalized Ratio; INR) and P2Y12 and/or CYP2C19 gene polymorphisms.

Subjects and methods Study design This was a prospective, open label, single-blind observation study in subjects with CI, subjects at high risk of stroke (HRS), and healthy controls (HC). Subjects with CI or HRS received clopidogrel 75 mg/day together with co-prescribed medications such as antihypertensive, antihyperlipidemic and hypoglycemic drugs. Platelet aggregation rates (PAR) were tested at baseline (pretherapy) and at 2 weeks, 3 and, 6 months after clopidogrel treatment. The

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CI group was divided into CR and NCR (none clopidogrel resistance) subgroups, at the end of the second week posttherapy, using recognized criteria. The NCR subgroup was further subdivided into DCR (dynamic CR) and CNCR (continuous NCR) subgroups according to PAR results at 3 and 6 months. NCR patients continued to receive 75 mg/ day clopidogrel for at least 3–6 months. Subjects who were found to develop CR at anytime were switched to another drug or to combination antiplatelet drug therapy (i.e. cilostazol or combined clopidogrel plus aspirin) and platelet function was tested again after 2 weeks and follow-up 1 month at least. Patients were followed up by telephone or reture visit to ensure that they were continuing to take their medications according to the primary or secondary prevention of CI guidelines published by the AHA/ASA in 2011 [15]. Subjects All subjects were recruited at the Third Affiliated Hospital of Guangzhou Medical University between Jan 2010 and Oct 2012. During this period we recruited 147 patients with various types of CI who were stable after suffering an acute cerebral infarction. A total of 59 consecutive subjects at high risk of stroke (HRS) group were recruited from the outpatient Neurological and General Medicine Department, and 68 consecutive control subjects were recruited from the physical examination center. Patients with hypertension, diabetes, hyperlipidemia and MACCE, and those not responding to platelet aggregation therapy were excluded. Clinical diagnosis of CI was made in accordance with standards established by the World Health Organization (WHO) [16], using craniocerebral computed tomography (CT) or 3.0 T magnetic resonance imaging (MRI). Classification standards for ischemic stroke from the TOAST study were used to determine the extent of aortic atherosclerotic cerebral infarction. This was defined as large artery atherosclerosis (LAA) or small artery occlusive cerebral infarction or lacunar cerebral infarction (small artery occlusion lacunar, SAA). In the HRS group [17–20], diagnosis was established in accordance with ABCD2 criteria [17]. The patients had high risk factors including hypertension or diabetes or a history of transient ischemic attack (TIA) or suspicious TIA history. All patients had total scores C3 points, indicating high risk based on assessment of age, blood pressure, clinical characteristics [including unilateral limb weakness (2 points), or words is not clear but without limbs weakness (1 point)], symptom duration and diabetes. The ABCD2 scores of HRS groups was an average of 4.6 ± 1.4. Cerebral infarction was excluded by craniocerebral CT or MRI without cerebral infarction.

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The patients in both groups were between 45 and 85 years of age and had a baseline platelet count between 150 and \500 9 109/L. None of the subjects had received clopidogrel in the previous month. None of the subjects in either group were allergic to clopidogrel and none had taken aspirin, dipyridamole, thiamethoxam chloride or other drugs that affected platelet function in the previous 2 weeks. Patients with severe renal impairment or bleeding, a history of bleeding diathesis, tumor, immune system, respiratory system diseases or atrial fibrillation were excluded from the study. Subjects who had undergone major surgery or sustained serious injury were also excluded. The protocol was approved by the Ethics Committee of the Third Hospital affiliated to Guang Zhou Medical University. All participants provided written informed consent prior to participating in the study. Platelet function Blood samples were collected using a double-syringe technique, in which the first 2–4 mL of blood was discarded to avoid spontaneous platelet activation. Platelet function was measured by light transmittance aggregometry (LTA, CHRO-N02L0G Corporation, USA). Platelet aggregation was assessed by LTA according to a standard protocol. Briefly, blood samples were drawn into Vacutainer tubes containing 0.5 mL sodium citrate (3.8 %) and processed within 60 min. Platelet-rich plasma (PRP) was obtained as a supernatant fluid after centrifuging at 120 g for 10 min. The remaining blood was further centrifuged at 1,200 g for 10 min to prepare platelet-poor plasma (PPP). Platelet rich plasma (PRP) was adjusted to platelet counts of 250,000/lL by adding PPP as needed. Platelet aggregation was assessed at 37 °C using an AggRAM aggregometer. Light transmission was adjusted to 0 % with PRP and to 100 % with PPP for each measurement. Platelet functions were measured after adding ADP (5 lmol/L, Sigma Corporation, USA), COL (2 lg/ mL) or AA (0.5 mmol/L), and curves were recorded for 10 min. Platelet aggregation rate (PAR) was measured at Agg max (peak) and 5 min later (Agg late) by laboratory personnel blinded to group assignment. Agg max is considered to reflect the activity of both P2Y1 and P2Y12 receptors, whereas Agg late is indicative of P2Y12 receptor activity. The percentage of platelet disaggregation between Agg max and Agg late was defined as: disaggregation (%) = [(Agg max - Agg late)/(Agg max)] 9 100. Platelet function was assessed by LTA in different experimental conditions in order to observe the influence of aggregate stability and consistency. Different platelet counts and concentrations of inducer, fasting and postprandial

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samples and different detection times were evaluated. This method is widely used for the determination of platelet aggregation function and is regarded as the gold standard [21, 22]. CR was defined as being present when the percent decrease in aggregation values between baseline and 14 days (or [14 days posttherapy) was B10 %. This was calculated as [23]: ([PAR at 14 day - PAR at baseline]) 9 100 B 10 %. The rate of change of platelet aggregation (DRPA), DRPA was calculated as: [(PAR at posttherapy PAR at baseline)/PAR at baseline 9 100 %]. Assessment of risk factors Demographic information including age, sex, blood pressure were recorded, together with details of patients history of coronary vascular disease (CVD), hypertension, diabetes mellitus (DM), stent implantation, transient ischemic attack (TIA) or history of ischemic stroke. Personal history such as smoking and alcohol consumption were also recorded together with details of calcium ion antagonist and angiotensin inhibitor (ACEI/ARB), statins, hypolipidemic drugs or proton pump inhibitor (PPI) drugs. Family history such as a hereditary amyloid degeneration of the blood vessels and other genetic diseases was documented. All patients underwent skull CT or MRI scans and routine blood tests (platelet count, INR, blood glucose and, lipid analysis). Coding of atrial fibrillation and carotid stenosis was undertaken following ECG or extracranial vascular imaging. Hypertension was defined as systolic blood pressure (SBP) C140 mmHg and/or diastolic blood pressure (DBP) C90 mmHg, and/or the use of antihypertensive medication. Diabetes mellitus was defined as a fasting blood glucose (FBG) level C7.0 mmol/L, or glycosylated hemoglobin (GHB) C6.0 % and/or diabetes anamnesis. The high total cholesterol (HTC) was defined as total cholesterol C5.0 mmol/L and hyperlipidemia was defined low-density lipoprotein C3.5 mmol/L or by current use of lipid-lowering therapy. Genetic analysis All subjects were genotyped for P2Y12 and CYP2Y12 receptor genes. Genomic DNA was prepared from whole blood samples using standard procedures. The primers (synthesized by Po Biotechnology Company, Da Liang, China) for the polymorphism of P2Y12 were as follows: sense primer 50 -GTTGGCATTCCTCAAAACAGGGCAT -30 and antisense primer 50 -ATGCCCTGTTTTGAGGAA TGCCAAC-30 . PCR parameters consisted of initial denaturation for 10 min at 95 °C, followed by 30 cycles of 30 s at 95 °C, 30 s at 55 °C, and 60 s at 72 °C, followed by a final

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extension for 5 min at 72 °C. The PCR product was digested with TaiI (Promega, USA) for 1.5 h at 65 °C. Digestion occurred in the presence of the C allele, yielding 2 fragments of 372 and 161 base pairs (bp), and a single band of 533 bp observed in the case of the T allele. The primers for the polymorphism of CYP2C19 were as follows: sense primer 50 -CAGAGCTTGGCATATTGTATC30 , antisense primer 50 -TAGTAAACACAAAACTAGTCA ATG-30 PCR parameters consisted of initial denaturation for 10 min at 95 °C, followed by 35 cycles of 30 s at 95 °C, 30 s at 55 °C, and 60 s at 72 °C, followed by a final extension for 5 min at 72 °C. The PCR product was digested with SmaI (Promega, USA) for 3 h at 37 °C.

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Digestion occurred in the presence of the C allele, yielding two fragments of 210 and 113 base pairs (bp), and a single band of 323 bp was observed in the case of the T allele. Statistical analysis Statistical the analysis was performed using the SPSS version 16.0 for Windows (SPSS Inc., USA). Continuous variables, presented as means and standard deviations (mean ± SD), were tested using Student’s t test. Categorical variables were presented as frequencies and were compared using the Pearson chi-square test. Deviations from the Hardy–Weinberg equilibrium for the SNP was tested by the chi-square test among all the

Fig. 1 Study design

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cases) in CI group, compared with 13.6 % (8 cases) in the HRS group (P = 0.018).

genotyped patients. Differences in genotype and allele frequency distributions were also tested using the chisquare test, and associations were analyzed using additive, dominant and recessive multivariate regression models. Multifactorial logistic analyses analyzed the relationship between the CR and risk factors such as clinical demographics, FBG, blood lipids, INR and P2Y12 and/or CYP2C19 gene polymorphisms. Meanwhile, the relationship between the DCR and the factors which related with CR were analyzed in single-factor analysis. Values of P \ 0.05 were considered statistically significant.

Analysis of risk factors for CR Table 2 compares the clinical characteristics of patients with CI according to the presence or absence of CR. The results for CYP2Y19 eA and P2Y12 eC coexistence and CYP2Y19 eA are shown in (Figs. 2, 3). In CR group of CI, the Numerical or percentage of the blood total cholesterol, FBG and GHB, CYP2Y19 eA, P2Y12 eC coexistence CYP2Y19 eA, taking calcium channel blockers, ACEI/ARB and proton pump inhibitors, diabetes, history of TIA or stents were separately more than that of NCR group (P \ 0.05; Table 2). Multiple logistic regression analysis indicated that diabetes, high cholesterol, a history of TIA and long-term use of calcium ion antagonist, ACEIs or ARBs were independent risk factors for CR in this population. As shown in Table 3, the strongest associations between CR were identified for diabetes mellitus (OR 16.627; 95 % CI 4.691–58.934; P = 0.0001) and a history of TIA (OR 13.711; 95 % CI 1.667–112.78; P = 0.015). The incidence of P2Y12 eC mutation was similar in the CR group and NCR subgroups, suggesting that P2Y12 eC was not related to CR (P [ 0.05).

Results Patient demographics and comparison CR incidence Three of the 59 patients in the HRS group failed to complete the study; one case each due to acute cardiovascular events, gastrointestinal bleeding and lost to follow-up. The remaining 56 cases were observed for 2 weeks (see Fig. 1). There was no significant difference between the three groups with respect to general demographic information (P [ 0.05). Of the 68 healthy controls 42 (61.7 %) were male and the mean age was 69.1 ± 9.1 years, of the 56 cases in HRS group, 33 (58.9 %) were male and the mean age was 70.3 ± 8.7 years, of the 147 cases in CI group 86 (58.5 %) were male and the mean age was 69.0 ± 9.7 years. The HRS group and CI groups were comparable with respect to the incidence of co-existing hypertension (55.9 versus 58.7 %, respectively), diabetes mellitus (22.0 versus 23.1 %) and hyperlipidemia (69.4 % versus 67.8 %) (all P [ 0.05). Prior to taking clopidogrel, the platelet aggregation rate (PAR) was significantly higher in the CI group than in the HRS or control groups (P \ 0.01; Table 1). There was no significant difference in PAR between HRS and control groups. In the CI and HRS group there was a significant decline in PAR from baseline to 2 weeks posttherapy (Table 1). At week 2, the incidence of CR was 28.6 % (42

Incidence of DCR and comparison of PAR at different time periods Among the original 147 subjects with CI, 121 cases completed the 6 month follow-up; 26 patients (17.7 %) were excluded because of inadequate laboratory monitoring; six with CR and 20 with NCR. Dynamic observation was continued in 36 cases with CR and in 85 cases with NCR. No cases of DCR were observed at month 3, but 15 cases occurred at month 6. Thus the total number of CR cases was 57 (6 cases lost to follow-up, 36 cases undergoing dynamic monitoring and 15 cases of DCR. The total CR rate was 38.8 % (57/147). PAR in the DCR group was higher after 6 months treatment, than at the 2-week evaluation, and was higher than in the CNCR group at both assessment times

Table 1 Comparison of PAR changes in CI and HRS and CN after therapy second week Group

Cases (n)

Pretherapy PAR

Posttherapy PAR –

HC

68

50.5 ± 12.4

HRS

56

56.7 ± 11.3b,

147

65.2 ± 13.5b

CI

c

HC healthy controls, HRS high risk stroke, CI cerebral infarction a b c

P \ 0.01, compared with pretherapy/posttherapy P \ 0.05, compared with HC group/HRS group or CI group P \ 0.01, compared with CI group/HRS group

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31.4 ± 15.3a, 40.6 ± 17.5a

c

DRPA (%)

CR incident n (%)

–

–

26.3 ± 13.0

8 (13.6 %)c

24.6 ± 16.9

42 (28.6 %)

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Table 2 Clinical characteristics and risk factors of comparison between CR and NCR in CI patients Group

CR group (n = 42)

NCR group (n = 105)

Chi-square t value

P value

Age, years

69.7 ± 10.4

69.6 ± 9.0

0.074

0.941

Male [n (%)]

22 (52.4)

64 (61.0)

0.908

0.341

Smoke [n (%)]

7 (16.67)

18 (17.1)

0.005

0.945

Drink [n (%)]

10 (23.81)

14 (13.3)

2.410

0.121

Total cholesterol (mmol/L)

5.23 ± 1.07

4.60 ± 1.11

3.121*

0.002

Triglycerides (mmol/L)

1.59 ± 0.65

1.52 ± 0.86

0.505

0.614

HDL (mmol/L)

1.46 ± 0.37

1.40 ± 0.53

0.645

0.520

LDL (mmol/L)

2.98 ± 0.95

2.80 ± 0.95

1.030

0.305

Fasting blood glucose (mmol/L)

6.65 ± 2.19

5.43 ± 1.15

3.442*

0.001

Glycosylated hemoglobin (%)

6.40 ± 1.42

5.48 ± 1.09

3.780*

0.000

Uric acid (lmol/L) International normalized ratio

393.23 ± 132.49 0.89 ± 0.74

381.68 ± 98.35 0.91 ± 0.65

0.511 1.66

0.611 0.10

Platelet count, 9109/L

233.76 ± 65.52

227.96 ± 62.25

0.503

0.616

P2Y12 eC, n (%)

20 (27.8)

29 (17.1)

3.61

0.058

CYP2Y19 eA, n (%)

14 (19.4)

16 (9.4)

4.69*

0.027

P2Y12 eC coexist CYP2Y19 eA, n (%)

7 (19.4)

4 (4.7)

5.81*

0.016

Hypertension, n (%)

35 (83.3)

86 (81.9)

0.042

0.838

Diabetes mellitus, n (%)

23 (54.7)

12 (11.4)

31.054*

0.000

Coronary vascular disease, n (%)

21 (50.0)

42 (40.0)

1.225

0.268

History of TIA, n (%)

34 (81.0)

28 (26.7)

36.251*

0.000

Stent, n (%)

11 (26.2)

4 (3.8)

16.400*

0.000

Statins, n (%)

28 (66.7)

75 (71.4)

0.324

0.569

Beta blockers, n (%)

17 (40.5)

42 (40.0)

0.003

0.958

ACEI/ARB, n (%)

28 (66.7)

45 (42.9)

6.803*

0.009

Calcium antagonists, n (%)

35 (83.3)

57 (54.3)

10.810*

0.001

Proton pump inhibitors, n (%)

20 (47.6)

15 (14.3)

18.375*

0.000

LAA, n (%) SAA, n (%)

26 (61.9) 16 (38.1)

67 (63.8) 38 (36.2)

0.047 0.047

0.829 0.829

HDL high density lipoprotein cholesterol, LDL low density lipoprotein cholesterol, ACEI/ARB angiotensin inhibitors, LAA large artery atherosclerosis, SAA small artery occlusion lacunar * Measurement data using t test, counting data with v2 test, P \ 0.05

Fig. 3 CYP2Y19 gene polymorphism map Fig. 2 P2Y12 gene polymorphism map

(P \ 0.01; Table 4). The DRPA at 6 months was also significantly higher in the CNCR group than in the DCR group (P \ 0.01; Table 4).

Among the 51 cases of CR and DCR that were followed up, 23 patients did not switch drug treatment, eight cases of which developed MACCE at the request of a family member (8/23, 34.8 %). The other 28 patients were

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Table 3 Multivariable logistic regression analysis of risk factors for CR (n = 147) Independent variable

Regression coefficient

SE

P value

OR

95 % CI

Diabetes mellitus

2.618

1.075

0.015

13.711

1.667–112.784

High total cholesterol

1.039

0.299

0.001

2.828

History of TIA

2.811

0.646

0.000

16.627

4.691–58.934

ACEI/ARB

1.577

0.585

0.007

4.841

1.539–15.231

Calcium antagonists

1.422

0.699

0.042

4.147

1.053–16.332

Table 4 DCR incidence and comparison of PAR in different time periods in cerebral infarction groups (n = 121) Group

CR group (n = 36)

NCR group (n = 85) DCR

N (%)

CNCR

1.574–5.080

the proton pump inhibitors were all significantly higher in the DCR subgroup of patients with CI group, than in the CNCR subgroup (P \ 0.05; Table 5). Multiple factor logistic correlation analysis identified high total cholesterol as the only independent risk factor, associated with DCR, with an OR of 0.415 (95 % CI 0.213–0.808; P = 0.01). The results are shown in Table 6.

15 (17.7 %)

70 (82.4 %)

66.06 ± 10.73

67.34 ± 11.59

65.26 ± 14.58

Posttherapy 2 weeks 60.89 ± 10.99

40.85 ± 9.29a

30.71 ± 12.13a

63.49 ± 9.41b

30.67 ± 12.15c

Discussion

26.49 ± 9.75

34.55 ± 13.53

Previous studies indicate that many factors contribute to clopidogrel resistance (CR) [9, 13, 14, 16], including genetic predisposition, inadequate dosage, smoking, age, sex and drug compliance issues. Single-factor analysis of our data showed that total cholesterol, co-existence of CYP2Y19 eA, and P2Y12 eC, CYP2Y19 eA, calcium channel blockers, ACEI/ARB, proton pump inhibitors, diabetes, history of TIA or stents are all risk factors for CR. These results were consistent with previous findings and support the fact that the multiple factors contribute to CR [9]. Multivariable-factors analysis identified diabetes mellitus, HTC, history of TIA and long-term use of calcium channel blockers and ACEI/ARB as independent risk factors for CR. Among these, the highest statistical associations were seen for diabetes mellitus and history of TIA which were, respectively associated with a 16.6- and 13.7-fold increase in risk of CR. Prior to treatment, PAR in was higher in the CI group than in the HRS or control groups and the incidence of CR was markedly higher in the CI group than in the HRS group. Both factors predispose patients to the risk of TIA, indicate that CI is associated with CR. Diabetes mellitus, but not FBG per se, was identified as an independent risk factor for CR, consistent with the findings of previous studies [24, 25]. One explanation might lie in the fact that upregulation of P2Y12 activity may increase platelet activity, and enhance aggregation ability due to DM-induced insulin resistance. Apart from TIA and diabetes, other independent risk factors for CR were mainly associated with the long-term use of co-prescribed drugs. Because our study included patients receiving various drug combinations no

Pretherapy

6 months

–

DRPA (%) 2 weeks 6 months

5.17 ± 2.61 –

3.85 ± 3.42

c

34.59 ± 14.55

DRPA: rate change of platelet aggregation (%) CR clopidogrel resistance, DCR dynamic CR, CNCR continuous nonCR a P \ 0.01 compared with pretherapy b

P \ 0.01 compared with 2 weeks posttherapy

c

P \ 0.01 compared with DCR group at 6 months

switched to cilostazol or received clopidogrel in combination with antiplatelet drugs for treatment of CR or DCR. In 20 of the 28 cases (71.4 %), PAR decreased by more than 10 %, 2 weeks after changing treatment. Eight of these patients received low molecular weight heparin. There was no significant increased in the incidence of bleeding, and only one of the 28 cases (3.6 %) had a recurrence of MACCE. By contrast, there were fives cases of MACCE among the 70 patients in the CNCR group (7.1 %). However, as shown in Fig. 1, a high number of patients (29 cases) were lost to follow-up and could not be evaluated according to the study design (Fig. 1). Risk factor analysis for dynamic CR in patients with cerebral infarction The Table 5 showed the relationship between the DCR and the factors which related with CR in single-factor analyse. The frequency of diabetes, high total cholesterol, CYP2Y19 eA, P2Y12 eC and CYP2Y19 eA co-existence, history of TIA, long-term use of calcium antagonists and

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Table 5 Risk factors distinguishing between DCR and CNCR (n = 121) Group

DCR (n = 15)

CNCR (n = 70)

Chi square t value 4.384*

P value

Total cholesterol (mmol/L)

5.80 ± 0.99

4.55 ± 1.01

Triglycerides (mmol/L)

1.57 ± 0.96

1.72 ± 1.44

-0.387

0.700

0.000

HDL (mmol/L)

1.57 ± 0.52

1.49 ± 0.56

0.442

0.660

LDL (mmol/L)

2.65 ± 1.14

2.59 ± 0.88

0.238

0.812

UA (lmol/L)

371.67 ± 101.81

368.82 ± 97.61

0.102

2.848

CYP2Y19 eA [n (%)]

10 (66.67)

6 (8.57)*

27.28*

0.000

P2Y12 eC coexist CYP2Y19 eA [n (%)]

3 (20.00)

1 (1.43)

9.501*

DM [n (%)]

6 (40.00)

5 (7.14)

9.100*

0.003

TIA

5 (33.33)

5 (7.14)

5.304*

0.021

ACEI/ARB

5 (33.33)

27 (38.57)

0.144

0.704

Calcium antagonists [n (%)] PPI [n (%)]

9 (60.00) 7 (46.67)

18 (25.71) 8 (11.43)

5.211* 8.269*

0.022 0.004

0.002

Measurement data using t test, counting data with v2 test P probability, t kind of significance test, n number, UA uric acid, DM diabetes mellitus, TIA transient ischemic attack, PPI proton pump inhibitors * P \ 0.05

Table 6 Multivariable logistic regression analysis risk factors of DCR Independent variable Diabetes mellitus High total cholesterol History of TIA

Regression coefficients 21.010

SE

P value

OR

95 % CI

4.019

1.000

1.332

0.000–0.000

a

-0.880

0.340

0.010

0.415

0.213–0.808

-21.015

4.019

1.000

0.000

0.000–0.000

Calcium antagonists

0.725

0.927

0.434

2.065

0.336–12.703

PPI use

0.528

1.504

0.727

1.696

0.087–32.953

a

HTC (high total cholesterol) was an independent risk factor of DCR, OR 0.415, 95 % CI 0.213–0.808, P = 0.010

conclusions can be drawn concerning the risk contribution of individual drug classes, dosage, or duration of treatment. Further evaluations of the factors affecting propensity to CR should, as far as possible, avoid combined drug use. In previous studies in patients with cerebral infarction or CVD, the incidence of CR resistance was in the range 28–44 %, the lower end of which is comparable to the incidence in our evaluation (28.6 %) [8, 26, 27]. In our study, there were 85 confirmed cases of NCR at week 2, among whom 15 cases (17.7 %) developed into DCR after continuous treatment for 6 months. Thus the occurrence of CR is to a certain extent time-dependent. The overall incidence of high platelet activity over the 6 month period was 38.8 % in the CI group. These results show the clinical importance of monitoring platelet function throughout the course of treatment. Single-factor analysis, indicated that DCR-related factors were essentially the same as those identified for CR, namely HTC, CYP2Y19 eA and P2Y12 eC coexistence, CYP2Y19 eA, calcium channel blockers, PPIs, DM and history of TIA. However, among these, HTC was the only independent risk factor identified by multivariate logistic

analysis. Recent studies show that atherosclerosis progression is more apparent in patients with CR, and antiplatelet drugs have been shown to slow and even improve arterial sclerosis to a greater extent than that achieved with statin drugs [28–30]. However, the wide range of possible factors and their interaction with each other makes the exact cause of DCR difficult to identify. The risk of acute coronary syndrome (ACS) was found to be 89 higher in patients with CR in patients with than in those without CR [31]. It has also been shown that the incidence of silent embolic cerebral infarction is increased in patients resistant to antiplatelet therapy [32]. The incidence of silent embolic infarction was 50 % in patients resistant to two antiplatelet agents, 22 % in those resistance to one antiplatelet agent and 4 % in those with no resistance. Data from meta-analyses and conclusions from the 2011 AHA/ASA guideline [15] indicate that combined use of antiplatelet drugs is more effective than monotherapy in reducing thrombosis during acute stroke. Our study revealed, that antiplatelet replacement therapy (with cilostazol or combination aspirin therapy) resulted in a

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[10 % decrease in PAR in 71.4 % patients with CR, at the expense of a 3.6 % incidence of incidence of MACCE recurrence. This incidence of MACCE was lower than in the cases of not switching drug (34.8 %) and CNCR group (7.1 %). The incidence of bleeding events did not increase. As a relatively high proportion of patients were unable to complete switching drugs after CR occurence according to the protocol, our results should be used for reference purposes only. Recent clinical studies on anti-platelet resistance, a combination of drugs show that it is possible to reduce the incidence of CR and MACCE without causing an obvious increase the bleeding rate in the short term [33, 34], support our results. Therefore, regular monitoring of platelet aggregation should become an integral part of strategies for the secondary prevention of CI, in addition to taking steps to prevention and treat diabetes, hyperlipidemia, hypertension and atherosclerosis. This will facilitate early detection of CR allowing timely adjustment of anti-platelet drugs or other secondary prevention strategies. It has been reported that the P2Y12 gene H2 allele is associated with a decreased platelet response to clopidogrel [7]. However, not all researchers have been able to confirm this relationship between P2Y12 gene polymorphism and CR [8, 35]. Interestingly, CYP2C19*3 single nucleotide polymorphism has been identified as an independent risk factor for CR [36]. It has also been reported that coexistence of the C allele of the T744C polymorphism of P2Y12 and the A allele of the G681A polymorphism of CYP2C19 are associated with persisting platelet activity during clopidogrel treatment in patients with ACS [35, 37]. The results of our study support these findings, indicating that CYP2Y19 eA had a significant impact on the development CR or DCR. Our study is the first to evaluate the coexistence of gene polymorphism on the dynamic development of CR. The results show that coexistence of P2Y12 eC and CYP2Y19 eA develops over time and is associated with the development of CR. Although these genes were excluded as independent risk factors in multivariate analysis, there was a univariate association between these genetic mutations and the incidence of CR and DCR. The exact reason for this is unclear, but it may be related to the coexistence of allele C and A inhibiting the sustained response of platelets to clopidogrel. Although CR or DCR is a close correlation with the CI, an interventional adequate study improving antiplatelet resistance should be carried out in a multicenter study in the future. Acknowledgments This work was supported by the Science Technology Planning Project [(2008)146, NO 0.23] of Guangdong Province, China.

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J Neurol (2013) 260:2928–2937 Conflicts of interest of interest.

The authors declare that they have no conflict

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