Impact of COX-2 rs5275 an

d rs20417 and GPIIIa rs5918Polymorphisms on 90-Day Ischemic Stroke Functional Outcome:

A Novel Finding

Jane Maguire, BA, BNurs(Hons),*†#†† Ammarin Thakkinstian, PhD,‡xChristopher Levi, MBBS,*†k** Lisa Lincz, PhD,†{** Linda Bisset, Masters,{Jonathan Sturm, PhD,*†† Rodney Scott, PhD,#‡‡xx Scott Whyte, PhD,††

and John Attia, PhD*†‡k**

From the *School of M

University of Newcastle

†Hunter Medical Resear

Australia; ‡Centre for Cli

sity of Newcastle, Newca

Epidemiology Unit, Facul

dol University, Bangkok,

Group, Calvary Mater Ne

Australia; kDivision of M

New South Wales, Austr

of Health, University of

Australia; **Priority Res

Research, HMRI and Stro

Research Program, Unive

ment, Gosford Hospital

Gosford, New South Wal

Information-Based Medi

134

We hypothesized that polymorphisms in 5 genes related to thrombolytic and inflam-

mation pathways will independently influence occurrence, severity, and 3-month

functional outcome in patients with ischemic stroke. This was a case-control design

with ischemic stroke patients recruited from 4 public hospitals (n 5 640) and commu-

nity controls (n 5 627). Baseline clinical data were collected, and follow-up telephone

interviews were conducted with 520 patients at 90 days postevent to determine stroke

outcome using the Barthel Index (BI), Modified Rankin Scale (mRS) and Glasgow Out-

come Scale (GOS). Blood samples were collected and genotyped for polymorphisms in

platelet glycoprotein Iba (GPIba) rs224309 and rs6065, glycoprotein IIIa (GPIIIa) rs5918, tis-

sue plasminogen activator (tPA) rs63020761, plasminogen activating inhibitor (PAI-1)

rs72578597, and cyclooxygenase-2 (COX-2) rs5275 and rs20417. COX-2 polymorphism

rs5275 demonstrated a significant association with poststroke mRS, with a dominant

genetic model demonstrating the best fit (CC 1 TC) (adjusted odds ratio [aOR] 5

1.61; P 5 .026). The COX-2 rs20417 C allele showed an association with GOS (aOR 5

1.95; P 5 .012), and again a dominant genetic model demonstrated the best fit (CC 1

GC). GPIIIa rs5918 (A1A2) was associated with poststroke BI, with a dominant model

demonstrating the best fit (A1A2 1 A2A2) (aOR 5 0.56; P 5 .014). There was a signif-

icant association between stroke severity and tPA rs63020761 TTallele (aOR 5 1.96; 95%

CI 5 1.03-3.72; P 5 .040). This is the first study to demonstrate associations between

stroke functional outcome and 2 COX-2 variants (rs20417 and rs5275) and a GPIIIa

variant (rs5918). Key Words: Genetics—functional recovery—post stroke.

� 2011 by National Stroke Association

edicine and Public Health, Faculty of Health,

, Callaghan, New South Wales, Australia;

ch Institute, Newcastle, New South Wales,

nical Epidemiology and Biostatistics, Univer-

stle, New South Wales, Australia; xClinical

ty of Medicine, Ramathibodi Hospital, Mahi-

Thailand; {Hunter Haematology Research

wcastle, Edith St Waratah, New South Wales,

edicine, John Hunter Hospital, Newcastle,

alia; #School of Biomedical Sciences, Faculty

Newcastle, Callaghan, New South Wales,

earch Centre for Brain and Mental Health

ke Research Group, Brain & Mental Health

rsity of Newcastle; ††Neurosciences Depart-

, Northern Sydney Central Coast Health,

es, Australia; ‡‡Priority Research Centre for

cine, HMRI/University of Newcastle; and

xxDivision of Genetics, Hunter Area Pathology Service, John Hunter

Hospital, Lookout Road, Newcastle, New South Wales, Australia.

Received September 22, 2009; accepted October 30, 2009.

The first author’s PhD candidature was supported by an Australian

Postgraduate Award from The University of Newcastle, Australia.

Other funding was received from the Australian National Heart

Foundation and the Northern Sydney Central Coast Health Research

Committee Fund.

The authors declare no conflicts of interest.

Address correspondence to Jane Maguire, BA, BNurs(Hons), Uni-

versity of Newcastle, Neurology Unit, John Hunter Hospital, C, The

Lodge, Lookout Road, New Lambton Heights, NSW 2305, Australia.

E-mail: jane.maguire@newcastle.edu.au.

1052-3057/$ - see front matter

� 2011 by National Stroke Association

doi:10.1016/j.jstrokecerebrovasdis.2009.10.011

Journal of Stroke and Cerebrovascular Diseases, Vol. 20, No. 2 (March-April), 2011: pp 134-144

GENETICS OF ISCHEMIC STROKE FUNCTIONAL OUTCOME 135

The influence of genetic variants on ischemic stroke

occurrence or poststroke functional outcome has come un-

der increased investigation. A major influence of genetic

factors alone on stroke occurrence is yet to be proven, al-

though evidence of genetic associations in ischemic stroke

risk from linkage analysis,1,2 case-control designs,3,4 and

genome-wide association studies5,6 has been published. In

contrast, studies examining the influence of genetic factors

on ischemic stroke outcome have been limited to examina-

tions of mortality, further vascular events,7 and poststroke

depression.8 Only one genetic study to date has examined

poststroke disability using the functional independence

measure, and it found no association with the ApoE4 allele.9

To the best of our knowledge, the present study is the

first genetic association study to examine the influence

of potentially relevant candidate genes on poststroke

functional outcome, as measured by well-validated stroke

scales. Candidate genes were chosen based on their bio-

logical role in stroke pathophysiology and, in some cases,

previously noted associations with stroke risk. In the

thrombolytic system, tissue plasminogen activator (tPA)

rs63020761 at sequence position 27351 C/T10,11 and plas-

minogen activating inhibitor (PAI-1) rs72578597, a 4G5 G

deletion/insertion variant in the promoter region,12

were selected. Similarly, variants in genes from the plate-

let glycoprotein complex involved in platelet adhesion

and aggregation (glycoprotein [GP] Iba rs22430913 and

rs606514 and GP IIb-IIIa rs591815) were chosen.

Inflammatory processes are also known to contribute to is-

chemic damage,16-18 and COX-2 inhibition has been shown

to attenuate brain injury in experimental models.19 Thus,

we examined the COX-2 gene variants recognized to have

functional significance. We found that rs20417 (sequence po-

sition 2765) plays a role in regulation of COX-2 transcription,

and that rs5275 (sequence position 6498) minor allele C is

associated with lower gene expression levels.20

We hypothesized that these recognized variations

in GPIba, GPIIIa, tPA, PAI-1, and COX-2 would have an

independent influence on incidence, severity, and the

3-month outcome in patients with ischemic stroke.

Methods

Participants

This study design has 2 components: a case control

genetic association study and a prospective 90-day

follow-up of functional disability in stroke cases. Cases

were patients with ischemic stroke admitted between

2003 and 2008 to any of 4 acute stroke units located in

principal referral hospitals in the Central Coast and

Hunter regions of New South Wales, Australia (n 5 640).

The inclusion criterion for cases was ischemic stroke pre-

senting to hospital, as defined by World Health Organiza-

tion clinical criteria.21,22 Exclusion criteria included age

,18 years, diagnosis of hemorrhagic stroke or transient

ischemic attack, and inability to undergo baseline brain

imaging. Computed tomography and/or magnetic

resonance imaging brain scans were obtained in all cases

to confirm ischemic stroke case status, and, as clinically

appropriate, other investigative tests, such as transesopha-

geal echocardiography, were conducted to define the

ischemic stroke mechanism.

The control sample (n 5 627), drawn from the Hunter

Community Study cohort,23 were healthy community-

dwelling men and women age 55-85, selected at random

from the New South Wales Electoral Roll between 2004

and 2007. Any control reporting a history of any type of

stroke was excluded.

Written informed consent was obtained from each par-

ticipant in accordance with University of Newcastle and

Hunter New England Area Health Ethics Committee

requirements.

Measures

Risk factor questionnaire interviews were conducted at

baseline by trained research staff using standardized

forms. Ethnicity was identified by self-report. Family his-

tory of stroke (defined as any first-degree family member

with stroke) and medications taken in the month before

admission were recorded. Stroke severity on hospital ad-

mission was assessed based on the National Institutes of

Health Stroke Severity Scale (NIHSS).24 The TOAST sys-

tem25 was used to determine the likely pathogenic mech-

anism of the cerebral ischemic event. The Oxfordshire

classification was used to categorize infarct topography.26

All clinical classifications were performed blind to case

genotype. Baseline clinical observations were recorded

at time of admission. Weight and height were measured

using calibrated scales, or by self-report if the patient

could not be measured. For the controls, similar demo-

graphic and clinical variables were collected by trained

research nurses at a research clinic.

Ninety-Day Assessment of Functional Outcome

in Cases

Telephone follow up was conducted following dis-

charge from hospital. Poststroke disability and handicap

were assessed using the modified Rankin Scale (mRS),27

the Glasgow Outcome Scale (GOS),28 and the Barthel In-

dex (BI).29 All of these are validated and reliable outcome

measurement tools, although the reliability of mRS via

vignette ranges from substantial to near perfect.30

In cases of loss to follow-up, an alternative means of

contact was sought through general practitioners, tele-

phone directory, and last known place of residence.

Laboratory Methods

In each subject, a venous blood sample was collected,

from which DNA was extracted as described previously.31

Genotyping was performed using formerly outlined

J. MAGUIRE ET AL.136

methods for the GPIba variants Kozak sequence variation

25 C/Trs224309332 and threonine-methionine substitution

at codon 145 rs6065,33 and for GPIIIa at position 1565

rs5918.34 Previously described methods were also used for

PAI-1 4 G/5 G rs72578597 and tPA-7351 C/T rs6302076135

and the COX-2 variants in the 30 UTR 6498 T / C region

rs5275 and promoter region 2765G / C rs20417.36,37

Statistical Methods

Baseline demographic characteristics and risk factor

profiles were compared in the case and control groups

using the t test for continuous factors and the c2 test for

categorical factors. Genotype and allele frequencies were

measured in cases and controls (Tables 1 and 2). Hardy-

Weinberg equilibrium (HWE) was assessed in the control

group using Fisher’s exact test. Univariate analysis was

performed on all covariates and genes, and those with

a P value ,.20 were included in the multiple logistic

model. Multivariate adjustment was performed for ethnic-

ity, age, sex, hypertension, heart disease (including history

of myocardial infarction), high cholesterol, renal dysfunc-

tion, atrial fibrillation, diabetes, obesity, smoking, and

alcohol use. Genes were initially entered as 3-level ordered

variables, assuming an additive genetic model. If

Table 1. Baseline characteristics of is

Risk factor Stroke

Age, years, mean 6 SD 74.9 6 13.2

Males, n (%) 334 (54.3)

NIHSS score, mean 6 SD 5.96 6 6.01

Percentile, n

25th 2

50th 4

75th 8

Ethnicity, n (%)

Caucasian 610 (99.2)

Other 5 (0.8)

BMI, n (%)

$30 128 (24.5)

,30 395 (75.5)

Risk factor history, n (%)

Hypertension 475 (77.2)

High cholesterol 301 (48.9)

Heart disease 200 (32.5)

Atrial fibrillation 163 (26.5)

Diabetes 166 (27.0)

Renal dysfunctiony 203 (69.5)

Alcohol use, n (%) 232 (48.0)

Smoking history, n (%)

Current smoker 105 (17.7)

Ex-smoker 190 (32.0)

Never smoked 298 (50.3)

*Fisher’s exact test.

yRenal dysfunction: glomerular filtration rate ,60 mL/min.

a per-genotype analysis suggested another genetic model

(ie, recessive, dominant, or additive effect), then genotypes

were combined accordingly. Stepwise forward selection

was applied for final model selection. Interaction between

gene and stroke subtype was assessed as well.

Stroke functional (BI) and disease severity (NIHSS)

scores were not normally distributed, and thus poor out-

come for BI and NIHSS was categorized by cutoffs of

,9038 and .7,39 respectively. Analyses were performed

as described earlier, using Stata version 10.0 (StataCorp,

College Station, TX). A P value ,.05 was considered

statistically significant. Possible population stratification

was addressed by adjusting for ethnicity in the model.

Results

Table 1 summarizes baseline characteristics and risk

factor profiles in the cases and controls. The differing

mean age in cases and controls (74.9 6 13.2 vs 65.9 6

7.5 years) reflects the late onset of ischemic stroke. The

proportion of males was 54.3% in the stroke group and

46.8% in controls (P 5 .008). Although ethnicity was

predominantly Caucasian in both groups, there was

a significantly greater proportion of non-Caucasians in

the control sample (99.2% vs 88.6%; P ,.001). Recent

chemic stroke cases and controls

Controls P value*

65.9 6 7.5 ,.001

280 (46.7) .008

468 (88.6) .000

60 (11.4)

225 (37.8) ,.001

370 (62.2)

341 (57.3) ,.001

235 (39.6) ,.001

84 (14.1) ,.001

53 (10.3) ,.001

81 (14.5) ,.001

89 (30.4) ,.001

251 (51.9) .023

35 (5.8) ,.001

183 (30.8) .035

377 (63.4) 1

Table 2. Genes and risk factors associated with stroke occurrence

Univariate analysis Multivariate analysis

Gene/variant OR P value 95% CI Risk factor OR P value 95% CI

tPA rs63020761 Age, years

TT 1.48 .214 0.79-2.77 .75 9.97 .000 6.87-14.46

CT 0.98 .909 0.69-1.38 #75 1

CC 1

Sex

COX-2 rs5275 Male 1.46 .022 1.06-2.03

TT 1.17 .386 0.82-1.67 Female 1

TC 1.25 .228 0.87-1.79

CC 1 Hypertension

Yes 2.46 .000 1.75-3.44

COX-2 rs20417 No 1

GG 1.26 .545 0.59-2.64

GC 1.43 .364 0.66-3.06 Smoker

CC 1 Current 5.88 .000 3.55-9.71

Ex-smoker 1.04 .794 0.74-1.49

PAI-1 rs72578597 Never 1

5G5G 0.86 .335 0.63-1.17

5G4G 0.78 .072 0.61-1.02 Heart disease

4G4G 1 Yes 1.48 .048 1.00-2.17

No 1

GPIIIa rs5918A2A2 0.48 .067 0.22-1.05 Atrial fibrillation

A1A2 0.96 .789 0.73-1.26 Yes 2.23 .000 1.46-3.40

A1A1 1 No 1

GPIba rs2243093 BMI

CC 1.24 .640 0.58-1.02 $30 0.57 .001 0.41-0.81

TC 0.77 .075 0.49-3.12 ,30 1

TT 1 Diabetes

GPIba rs6065 Yes 1.84 .002 1.25-2.72

MM 0.37 .247 0.07-1.96 No 1

TM 1.03 .843 0.74-1.44

TT 1

GENETICS OF ISCHEMIC STROKE FUNCTIONAL OUTCOME 137

publications argue convincingly that population stratifi-

cation leads to a bias of ,1%;40-42 nonetheless, to adjust

for any possible bias due to population stratification, we

adjusted for ethnicity in the multivariate model.

Traditional cerebrovascular risk factors, such as a his-

tory of hypertension, high cholesterol, heart disease, atrial

fibrillation, diabetes (insulin-dependent and non–insulin-

dependent) and renal dysfunction (calculated glomerular

filtration rate ,60), were all significantly overrepresented

in the stroke group. Smoking (current, P ,.001; past, P 5

.03) and alcohol use (P 5 .023) were more prevalent in the

stroke group.

Assessment of HWE in the control group demonstrated

that all polymorphisms except GPIIIa rs5918 (P 5 .016)

and COX-2 rs5275 (P 5 .003) complied with HWE propor-

tions. We explored this deviation from HWE and

found that the allelic frequencies for these 2 polymor-

phisms in our sample were remarkably similar to those

reported in previous studies (GPIIIa, 14%-27%;43,44

COX-2, 33%20).

Stroke Occurrence and Severity

We found no evidence of a genetic association with stroke

occurrence, although there was some suggestion of a signal

for both the GPIba rs2243093 TC genotype (P 5 .08) and

GPIIIa A2A2 rs5918 (P 5 .07) (Table 2). A significant associ-

ation was found between the tPA rs63020761 TT allele and

stroke severity (odds ratio [OR] 5 2.08; 95% confidence

interval [CI] 5 1.16-3.74; P 5 .014) that persisted after

adjustment for covariates (OR 5 1.96; 95% CI 5 1.03-3.72;

P 5 .040) (Table 3).

Ninety-Day Functional Outcome

Out of the 640 stroke patients, 520 were assessed 90 days

after the acute event. GP1ba rs6065 and rs2243093, tPA

rs63020761, and PAI-1 rs72578597 demonstrated no associ-

ation with 90-day outcome scores for mRS, BI or GOS in the

univariate analysis and thus were not included in the

multivariate logistic regression model. In univariate anal-

ysis, association of COX-2 rs5275 T/C was significant for

Table 3. Associations between genes and covariates by stroke severity

Univariate analysis Multivariate analysis

Gene/variant OR P value 95% CI Gene/variant OR P value 95% CI

tPA rs63020761 tPA rs63020761

TT 2.08 .014 1.16-3.74 TT 1.96 .040 0.82-1.93

CT 1.19 .389 0.80-1.77 CT 1.26 .287 1.03-3.72

CC 1 CC 1

COX-2 rs5275 Covariate

CC 1.44 .209 0.82-2.53 Premorbid mRS score

TC 0.73 .137 0.49-1.10 .2 3.3 .001 1.67-6.73

TT 1 #2 1

COX-2 rs20417 Smoker

CC 0.57 .475 0.12-2.67 Current 0.74 .278 0.43-1.28

GC 0.97 .892 0.64-1.48 Ex-smoker 0.54 .008 0.34-0.85

GG 1 Never 1

PAI-1 rs72578597 Arthritis

5G5G 0.82 .394 0.79-1.83 Yes 0.54 .005 0.35-0.83

5G4G 1.20 .472 0.48-1.40 No 1

4G4G 1

GPIIIa rs5918A2A2 1.58 .537 0.71-1.71

A1A2 1.10 .670 0.37-6.70

A1A1 1

GPIba rs2243093CC 1.76 .384 0.49-6.37

TC 1.16 .543 0.72-1.85

TT 1

GPIba rs6065MM 0 0 0

TM 0.89 .698 0.51-1.57

TT 1

J. MAGUIRE ET AL.138

the 90-day mRS score but not for GOS or BI score (OR 5

1.54; P 5.038) (Table 4); this effect persisted after multivar-

iate logistic modeling, using both an allelic model (T/C;

OR 5 1.88; P 5 .022) and a dominant genetic model

(CC 1 TC; OR 5 1.61, P 5 .026). COX-2 rs20417 GC was

not associated with mRS or BI score, but the C allele was

highly associated with GOS score (unadjusted OR 5

1.95, P 5 .012; adjusted OR [aOR] 5 2.18, P 5 .015) (Table

4). We found an association between GPIIIa rs5918 and BI

score that followed a dominant model (A1A2 1 A2A2 vs

A1A1: unadjusted OR 5 0.57, P 5 .013; aOR 5 0.56,

P 5 .014), but this association was not significant for mRS

and GOS scores (Table 5). Table 6 reports genotype

frequencies for controls and for cases, by stroke subtype.

Table 7 reports case genotype frequencies by favourable

outcome scores.

Discussion

Three variants from 2 of the 5 genes that we examined,

COX-2 and GPIIIa, demonstrated a significant and novel

association with functional status at 90 days. Having 1

or 2 copies of the A2 allele for GPIIIa rs5918 polymor-

phism improved the likelihood of having a poor BI score,

whereas having the C allele for COX-2 rs5275 and rs20417

decreased the likelihood of having a poor functional

outcome at 90 days.

We detected a trend of association between stroke sever-

ity and the tPA gene. Carriers of the rs63020761 T allele ex-

perienced more severe stroke. Previous studies of the tPA

27351 C/T polymorphism demonstrated an increased

risk of myocardial infarction and stroke with the TT geno-

type;10,45 however, we found no association between tPA

alleles and either stroke occurrence or outcome, most

likely due to lack of statistical power.

We did not replicate any of the previously reported

significant associations with stroke occurrence. Our

previous meta-analyses provided evidence of associations

between stroke occurrence and platelet GP1ba gene vari-

ants rs2243093 and rs606546 as well as the PAI-1 4 G/4 G

polymorphism;35 however, both our results and results

reported by other groups12,47 confirm that these associa-

tions were highly heterogeneous in nature, with ORs of

.1 and ,1. We speculate that this heterogeneity might be

due to different patterns of linkage disequilibrium across

the studies, a situation termed genetic ‘‘flip-flop.’’48,49 In

Table 4. Associations between genes and 90-day favorable outcome, univariate analysis

mRS GOS BI

Gene/variant OR P value 95% CI OR P value 95% CI OR P value 95% CI

tPA rs63020761TT 1.22 .534 0.65-2.32 0.85 .642 0.43-1.68 0.98 .967 0.52-1.87

CT 1.14 .504 0.77-1.69 1.07 .752 0.69-1.67 1.09 .662 0.73-1.65

CC 1 1 1

COX-2 rs5275CC 1.09 .778 0.61-1.95 0.93 .812 0.48-1.75 1.24 .477 0.69-2.23

TC 1.54 .038 1.02-2.31 1.43 .128 0.90-2.27 1.53 .171 0.83-2.81

TT 1 1 1

COX-2 rs20417CC 3.87 .204 0.48-31.3 0 0 0 3.12 .277 0.40-24.33

GC 1.35 .178 0.87-2.07 1.81 .025 1.08-3.06 0.93 .772 0.60-1.45

GG 1 1 1

PAI-1 rs725785975G5G 1.21 .484 0.71-2.04 1.16 .630 0.64-2.08 0.89 .677 0.64-1.56

5G4G 1.00 .995 0.65-1.53 1.03 .915 0.64-1.65 1.00 .988 0.64-1.56

4G4G 1 1 1

GPIIIa rs5918A2A2 0.67 .589 0.16-2.85 0.83 .470 0.51-1.37 0.52 .355 0.13-2.06

A1A2 0.72 .138 0.46-1.11 0.43 .256 0.10-1.84 0.57 .013 0.37-0.89

A1A1 1 1 1

GPIba rs2243093CC 0.45 .260 0.11-1.81 0.43 .260 0.10-1.85 1.15 .855 0.24-5.43

TC 1.16 .557 0.71-1.89 0.84 .509 0.49-1.42 0.89 .656 0.54-1.46

TT 1 1 1

GPIba rs6065MM 0 0 0 0 0 0 0 0 0

TM 1.04 .880 0.61-1.78 0.91 .744 0.50-1.63 0.68 .168 0.40-1.17

TT 1 1 1

GENETICS OF ISCHEMIC STROKE FUNCTIONAL OUTCOME 139

this case-control analysis, no association between stroke

outcome and any of these 3 variants was seen.

The COX-2 polymorphisms demonstrated no evidence

of association with stroke occurrence. Published evidence

regarding the COX-2 rs20417 variant and ischemic stroke

risk is limited to 3 studies to date and is inconsistent.

Colaizzo et al50 found an association between the

rs20417 variant and cerebrovascular ischemia, but

Cipollone et al51 found this same variant to be associated

with a decreased risk of both myocardial infarction and

stroke. Both of these studies were conducted in Italian

populations. In the large prospective Atherosclerosis

Risk in Communities Study, rs20417 was found to in-

crease the risk of incident stroke in African-Americans

but not in other ethnic groups, including Caucasians.52

We found a significant association between the COX-2

gene and stroke outcome; carriers of either the rs5275 or

rs20417 variant had relatively lower disability and stroke

handicap scores. This finding has biological plausibility.

In general, COX-2 is known predominantly for its media-

tory function in the inflammatory pathway, through

its role in the conversion of arachidonic acid to

prostaglandin H2 and the production of macrophage

MMP-2 and MMP-9, both of which are thought to contrib-

ute to atherosclerotic plaque rupture. COX-2 also is gener-

ally considered a proinflammatory enzyme,16 although is

also known to aid in the cessation of the inflammatory

process.53

In brain ischemia, COX-2 inhibition has been shown to

attenuate brain injury and contribute to an imbalance in

prostanoid synthesis.19 Cipollone et al51 argued that a shift

in this balance between lipocalin-type prostaglandin D2

dependent synthase (anti-inflammatory) and type-1

inducible prostaglandin E2 dependent (inflammatory)

synthase could contribute to increased activation of the

inflammatory mechanisms in rupture-prone plaques. In

rat and mouse models, COX-2 has been shown to play

an active role in inflammation-related advancement of

cerebral ischemic impairment,54 and in the human brain,

evidence suggests increased expression of COX-2 con-

fined to the area of damage following ischemic stroke.18

Although there are no published in vivo studies of

COX-2 gene expression levels and their associated poly-

morphisms, there is one functional human study.20 This

Table 5. Associations between risk factors and 90-day favorable outcome, multivariate analysis*

mRS GOS BI

Risk factor OR P value 95% CI OR P value 95% CI OR P value 95% CI

Age

,70 2.73 .002 1.43-5.20 3.96 ,.001 1.83-8.56 1.61 .096 0.92-2.82

$70-#80 1.49 .097 0.93-2.40 1.62 .073 0.95-2.75 1.51 .087 0.94-2.40

.80 1 1 1

Sex

Male 1.52 .048 1.00-2.29

Female 1

Pre-mRS score

.2 0.25 ,.001 0.12-0.52 0.18 ,.001 0.08-0.38 0.24 ,.001 0.12-0.47

#2 1 1 1

Heart disease

Yes 0.50 .001 0.34-0.74

No 1

Atrial fibrillation

Yes 0.47 ,.001 0.32-0.70 0.45 .002 0.27-0.75

No 1 1

Diabetes

Yes 0.64 .035 0.38-0.96 0.44 .002 0.26-0.73

No 1 1

Renal dysfunction

Yes 1.64 .033 1.04-2.58 1.73 .026 1.07-2.79 1.92 .003 1.24-2.95

No 1 1 1

*Adjusted result shown only for covariates that were significant.

J. MAGUIRE ET AL.140

research demonstrated that both COX-2 rs5275 and

rs20417 had a significant relationship with COX-2 gene

expression. COX-2 expression levels were lower in car-

riers of the rs20417 C allele, whereas rs5275 appeared to

exert its effect through decreased mRNA stability.20

Based on the foregoing evidence, it may be speculated

that inflammation due to damage to the cerebral matter is

decreased, and thus functional recovery is potentially

enhanced, in carriers of the Cox-2 rs20417 and rs5275

variants. In the rodent model, expression of COX-2 and

mRNA was found to be up-regulated following cerebral

ischemia;55,56 thus, it would seem biologically plausible

that the presence of a variant such as rs5275 might affect

COX-2 expression and in turn reduce the degree of

inflammatory response, limiting damage to the cerebral

tissue and thus influencing poststroke outcome. This

hypothesis has yet to be tested, however.

We failed to demonstrate an association between GPIIIa

and stroke occurrence in our case-control analysis, but

did find an association with stroke outcome using the

BI. GPIIIa is a fundamental receptor site for fibrinogen,

and evidence suggests that its participation in platelet

aggregation increases thrombus formation through either

alterations in ligand binding or changes to postreceptor

inside-out signaling.57 The rs5918 T-to-C transition at nu-

cleotide 1565 results in a proline-to-leucine substitution at

amino acid 33, leading to multiple conformational

changes within the GPIIIa receptor.57 Current data are

conflicting as to whether this conformational change in-

creases or decreases binding to immobilized fibrinogen,

thereby affecting fibrin clot retraction.58 It is plausible

that the GPIIIa rs5918 C allele might play a role in stroke

etiology, given that this allele has been linked to an

increased risk of stroke.59

Our study has several limitations. We were able to deter-

mine a genetic association in patients who carried a COX-2

variant when outcome was measured by the mRS or GOS;

however, despite the fact that mRS and GOS scores were

highly correlated with BI score, we found no correspond-

ing association with BI. One possible explanation for this

is that the BI was originally designed to measure only mus-

culoskeletal causes of deficits in self-care,29 and although

this index has been shown to be a valid and reliable

measure of disability in stroke patients30 and to be highly

correlated with the mRS and GOS, it fails to measure other

aspects of disability that may contribute to stroke outcome,

such as aphasias, which severely affect the ability to

communicate, and cognition, which affects the ability to

manage one’s own affairs.

Follow-up did not occur in cases recruited at study

commencement, because this was not part of the study

protocol initially. These patients had similar risk factors

and stroke severity profiles as those patients who were

followed up. Adding follow-up data for these patients

might have increased the strength of our findings, be-

cause then we would have had a larger sample size.

Table 6. Genotype frequency for stroke TOAST subtypes and healthy community controls

Healthy

controls, n (%) Large artery, n (%) Cardioembolism, n (%) Small vessel, n (%) Determined, n (%) Undetermined, n (%)

tPA rs63020761CC 84 (45.7) 56 (20.6) 87 (31.9) 55 (20.1) 3 (1.1) 72 (26.3)

CT 86 (46.7) 70 (25.9) 76 (28.0) 52 (19.3) 8 (2.9) 65 (23.9)

TT 14 (7.6) 18 (27.3) 16 (24.2) 13 (19.7) 1 (1.5) 18 (27.3)

COX-2 rs5275CC 80 (14.1) 21 (28.8) 19 (26.0) 15 (20.5) 2 (2.7) 16 (21.9)

TC 221 (38.9) 61 (24.6) 65 (26.2) 53 (21.4) 3 (1.2) 66 (26.6)

TT 267 (47.0) 59 (20.99) 93 (33.1) 51 (18.1) 7 (2.5) 71 (25.3)

COX-2 rs20417CC 16 (2.7) 4 (33.3) 3 (25) 3 (25) 0 2 (16.7)

GC 139 (23.2) 40 (25.5) 44 (28.0) 37 (25.5) 2 (1.3) 34 (21.7)

GG 445 (74.2) 98 (22.5) 128 (29.4) 80 (18.4) 10 (2.3) 119 (27.5)

PAI-1 rs725785974G4G 169 (28.2) 48 (24.5) 63 (31.6) 27 (13.8) 4 (2.0) 56 (28.0)

5G4G 302 (50.3) 61 (21.9) 83 (29.8) 63 (22.6) 7 (2.5) 65 (23.3)

5G5G 129 (21.5) 35 (25.9) 35 (25.9) 30 (22.2) 1 (0.7) 34 (25.2)

GPIIIa rs5918A1A1 452 (75.3) 111 (23.7) 131 (27.9) 96 (20.5) 11 (2.4) 119 (25.4)

A1A2 129 (21.5) 30 (22.9) 44 (33.6) 24 (18.3) 1 (0.8) 32 (24.4)

A2A2 19 (3.2) 3 (30.0) 2 (20.0) 1 (10.0) 0 4 (40.0)

GPIba rs6065MM 5 (0.8) 0 0 0 1 (50.0) 1 (50.0)

TM 76 (12.7) 21 (26.9) 15 (19.2) 18 (23.0) 2 (2.6) 22 (28.2)

TT 519 (86.5) 123 (23.4) 163 (30.9) 102 (19.4) 9 (1.7) 129 (24.5)

GPIba rs2243093CC 8 (1.3) 2 (20.0) 4 (40.0) 2 (20.0) 0 2 (20.0)

TC 131 (21.8) 22 (20.6) 33 (30.8) 20 (18.7) 0 32 (29.9)

TT 461 (76.8) 120 (24.6) 141 (28.9) 97 (19.9) 12 (2.5) 118 (24.2)

GE

NE

TIC

SO

FIS

CH

EM

ICS

TR

OK

EF

UN

CT

ION

AL

OU

TC

OM

E1

41

Table 7. Case genotype frequencies by 90-day favorable stroke outcome score

Gene Variant Genotype GOS score .2, n (%) mRS score .2, n (%) BI score .90, n (%)

COX-2 rs5275 CC 16 (14.7) 21 (13.4) 19 (16.8)

TC 38 (34.9) 55 (35.0) 47 (32.7)

TT 55 (50.5) 81 (51.6) 63 (50.5)

COX-2 rs20417 CC 0 1 (0.6) 1 (0.8)

GC 21 (18.9) 37 (23.3) 35 (26.9)

GG 90 (81.1) 121 (76.1) 94 (72.3)

GPIIIa rs5918 A1/A1 83 (73.5) 116 (72.5) 90 (68.7)

A1/A2 27 (23.9) 41 (25.6) 38 (29.0)

A2/A2 3 (2.7) 3 (1.9) 3 (2.3)

GPIba rs6065 TT 0 0 0

TM 17 (15.1) 22 (13.8) 22 (16.8)

MM 96 (84.9) 138 (86.3) 109 (83.2)

GPIba rs2243093 TT 3 (2.7) 4 (2.5) 2 (1.5)

TC 23 (20.4) 27 (16.8) 25 (19.0)

CC 87 (76.9) 130 (80.8) 104 (79.4)

PAI-1 rs72578597 4G4G 36 (31.9) 51 (31.7) 42 (32.1)

5G4G 54 (47.8) 78 (48.5) 59 (45.0)

5G5G 23 (20.3) 32 (19.9) 30 (22.9)

tPA rs63020761 CC 51 (45.1) 76 (47.2) 60 (45.8)

CT 48 (42.5) 69 (42.9) 56 (42.8)

TT 14 (12.4) 16 (9.9) 15 (11.4)

J. MAGUIRE ET AL.142

In summary, we were unable to find an association

between stroke occurrence and the 7 genetic variants

studied. However, we did find significant associations

between stroke functional outcome at 90 days and 2

COX-2 variants (rs20417 and rs5275) and a GPIIIa variant

(rs5918), with the COX-2 variants contributing to a more

favorable stroke outcome and the GPIIIa variant contrib-

uting to an unfavorable outcome as measured by 3 well-

validated disability and handicap tools. Given the novel

nature of our results and the number of comparisons

made, these results should be considered hypothesis-

generating. More evidence is needed to determine the

robustness of the findings. This could be obtained

through independent replication in another population,

or, alternatively, an in silico model could be generated

to test the hypothesis that these genetic variations are

indeed associated with poststroke outcome.

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