Variants of the Matrix Metalloproteinase-2 but not the Matrix Metalloproteinase-9 genes significantly influence functional outcome after stroke.

BACKGROUND: Multiple lines of evidence suggest that genetic factors contribute to stroke recovery. The matrix metalloproteinases -2 (MMP-2) and -9 (MMP-9) are modulators of extracellular matrix components, with important regulatory functions in the Central Nervous System (CNS). Shortly after stroke, MMP-2 and MMP-9 have mainly damaging effects for brain tissue. However, MMPs also have a beneficial activity in angiogenesis and neurovascular remodelling during the delayed neuroinflammatory response phase, thus possibly contributing to stroke functional recovery.
METHODS: In the present study, the role of MMP-2 and MMP-9 genetic variants in stroke recovery was investigated in 546 stroke patients. Functional outcome was assessed three months after a stroke episode using the modified Rankin Scale (mRS), and patients were classified in two groups: good recovery (mRS </= 1) or poor recovery (mRS>1). Haplotype tagging single nucleotide polymorphisms (SNPs) in the MMP-2 (N = 21) and MMP-9 (N = 4) genes were genotyped and tested for association with stroke outcome, adjusting for significant non-genetic clinical variables.
RESULTS: Six SNPs in the MMP-2 gene were significantly associated with stroke outcome (0.0018<P < 0.0415), two of which survived the Bonferroni correction for multiple testing. In the subset of ischemic stroke patients, association of five of these SNPs remained positive (0.0042<P < 0.0306). No significant associations were found for the MMP-9 gene.
CONCLUSIONS: The results presented strongly indicate that MMP-2 genetic variants are an important mediator of functional outcome after stroke.

Background

While remaining one of the most common causes of death worldwide, stroke is also a leading cause of significant disability: after a first stroke event, 50-70% of stroke patients regain functional independence, but 15-30% are permanently disabled and 20% require institutional care at 3 months after onset [1]. Clinical and demographic factors can influence stroke outcome. In addition, genetic factors are likely to have an impact in stroke recovery processes and outcome: family history of stroke is associated with stroke outcome [2,3] and many animal models of stroke implicate genes that regulate angiogenesis, neuronal regeneration and proliferation, and neuroinflammation, in stroke recovery [4-7].

Several lines of evidence suggest that matrix metalloproteinases (MMPs) are fundamental players in stroke recovery. These molecules belong to a family of zinc-dependent endopeptidases that modulate extracellular matrix (ECM) components in many Central Nervous System (CNS) developmental and regenerative processes such as neurogenesis, axonal growth and regeneration, and myelin formation. The expression and activity of MMPs is tightly regulated. Most MMPs require proteolytic processing by proteases or other MMPs to become activated, and can be inhibited by tissue inhibitors of metalloproteinases (TIMPs). Dysregulated MMP activity will lead to uncontrolled degradation of ECM and basal lamina proteins, with serious harmful effects for the blood-brain barrier (BBB) integrity and neuroinflammatory or neurotoxic consequences [8,9]. Such dysregulation of MMPs is known to occur after stroke, leading to a degradation of the neurovascular matrix, disrupting cell-matrix homeostasis and weakening the BBB, and thus contributing to cell death, neurotoxicity, edema and hemorrhage [9,10]. The variation profiles of MMPs in blood after a stroke event [11,12] suggest that these molecules can eventually be used as biomarkers for brain damage and neurological outcome, while their contribution to tissue destruction renders MMPs inhibitors potentially interesting therapeutic targets for stroke.

Emerging studies, however, indicate that MMPs may also have a beneficial activity in angiogenesis and neurovascular remodelling during the delayed neuroinflammatory response phase after stroke, possibly contributing to stroke functional recovery [9]. While inhibition of MMP activity has consistently been demonstrated to be effective in reducing edema, infarct size and hemorrhagic transformation, some studies suggest the existence of a time window for these beneficial effects to take place [13,14].

In the present study we tested the impact of genetic variants in MMP-2 and MMP-9 in stroke recovery, in a population sample of 546 patients evaluated for stroke outcome at three months after the stroke event.

Methods

Participants in the present study were recruited in the context of a wider research project to evaluate stroke risk factors in a Portuguese population sample, which enrolled first-ever stroke patients under 65 years of age through Neurology and Internal Medicine Departments of several hospitals in Portugal. Stroke was defined as a focal neurological deficit of sudden or rapid onset lasting more than 24 hours, and classified into ischemic or intracerebral hemorrhage based on brain imaging (computed tomography and/or magnetic resonance imaging). The diagnosis of stroke was confirmed by a neurologist. Demographic characteristics (age and gender), information on previous vascular risk factors and comorbid conditions (diabetes mellitus, hypertension, cardiac disease, dyslipidemia, obesity), life-style risk factors (smoking status, alcohol consumption, physical inactivity and others), and detailed clinical data during hospitalization, including neurological symptoms, complications and interventions, were collected for the majority of patients. Occurrence of aphasia, neglect, paresis, gaze paresis, dysphagia, permanent consciousness disturbance, urinary incontinence and medical and neurological complications were clinical parameters indicative of stroke severity. Stroke outcome at discharge and at three months was assessed, by direct interview, using the modified Rankin Scale (mRS).

For the present study, 568 patients with relevant clinical data and a DNA sample were available. Eight patients had a second stroke event after enrolment, affecting patient recovery, and were thus excluded. Of the remaining 560, 14 did not return after discharge for the three months evaluation, and therefore only 546 patients were included in the analysis. Patients were classified in two groups, according to their mRS at three months: patients with mRS ≤ 1 were assigned to the "good recovery" group and patients with mRS>1 were assigned to the "poor recovery" group (handicapped patients). 276 individuals were included in the good recovery group (63.0% males and 37.0% females) and 270 in the poor recovery group (64.4% males, 35.6% females). The poor recovery group included 12 patients who died before the three months evaluation (seven of them before hospital discharge, and five others after discharge). Genetic power calculations were performed using the CaTS software [15].

The study was approved by the Ethics Committee of Instituto Nacional de Saúde Dr. Ricardo Jorge and other hospitals involved, subjects gave informed consent and procedures followed were in accordance with institutional guidelines.

Single nucleotide polymorphisms (SNPs) within the MMP-2 and MMP-9 genes and up to 5 kb of the flanking regions were selected using the Haploview software (v4.0) [16], based on their tagging potential (HapMap Release 21/phase II July 2006). 4 SNPs in MMP-9 and 20 SNPs in MMP-2 were genotyped using the Sequenom iPLEX assays with allele detection by mass spectroscopy, using Sequenom MassARRAY technology (Sequenom, San Diego, USA) and following the manufacturer's protocol. Primer sequences were designed using Sequenom's MassARRAY Assay Design 3.0 software. 1 SNP in MMP-2 was genotyped using TaqMan® Pre-Designed SNP Genotyping Assays, in an ABI PRISM 7900HT Sequence Detector System (Applied Biosystems, Foster City, USA). Extensive quality control was performed using eight HapMap individuals, duplicated samples within and across genotyping plates, Mendelian segregation in three pedigrees and no-template samples. Call rates <90% and deviation from Hardy-Weinberg equilibrium led to SNP exclusion from the analysis. 2 SNPs in MMP-9 failed quality control and were substituted. In total, 21 MMP-2 SNPs and 4 MMP-9 SNPs were analysed.

The effect of discrete and continuous non-genetic variables on stroke outcome at three months was determined using the Pearson χ2 test and Mann-Whitney test, respectively. These included age, gender, stroke risk factors as well as data on clinical variables collected during hospitalization (like occurrence of paresis, aphasia and medical complications). Variables with a P < 0.25 in univariate analysis or of particular clinical relevance were included in a logistic regression model using forward selection [17] and were maintained in the model if they were associated at a P ≤ 0.05 level with stroke outcome. Logistic regression analyses were then used to determine the effect of each genetic variable on stroke outcome after adjustment for those significant non-genetic variables. Odds ratio (OR) and 95% confidence intervals (95% CI) were computed for the log-additive model. Univariate and logistic regression analyses were performed using MASS and SNPassoc packages of the R software [18] (v2.6.0). The Gabriel et al. (2002) [19] default method of the Haploview software [16] (v4.0) was used to determine haplotype blocks in the MMP-2 and MMP-9 genes. Since recovery processes may be regulated differently in ischemic and hemorrhagic stroke patients, we performed the same analyses in the subset of ischemic stroke patients. The small number of hemorrhagic stroke patients (N = 105) precluded the independent analysis of this subset.

Significant associations in individual SNP analysis were corrected for multiple testing using the Bonferroni method. The alternative SNPSpD approach, based on the spectral decomposition (SpD) of matrices of pairwise linkage disequilibrium (LD) between SNPs was also applied [20]. Since some of the 21 SNPs genotyped in the MMP-2 gene are in LD with each other in our sample, we used the SNPSpD approach to estimate the effective number of independent SNPs in our sample for multiple testing corrections.

Results

Clinical and demographic characteristics of the population sample are presented in Table 1. Univariate analysis showed that type of stroke and six clinical features indicative of stroke severity - occurrence of aphasia, urinary incontinence, paresis, consciousness disturbance, medical and neurological complications during hospitalization - were significant predictors of poor outcome. Sex ratio, age, and stroke risk factors were similar between the poor and good recovery groups, and approximately the same proportion of patients was being treated for hypertension in either group (34.0% and 34.6% in the good and poor recovery groups, respectively). Assuming an additive genetic model and disease allele frequency of 30%, our sample was 82% powered to detect a genotype relative risk of 1.5 with a type I error of 5%.

Table 1

Demographic and clinical characteristics of stroke patients.

Characteristic	Good Recovery (mRS ≤ 1)	Poor Recovery (mRS>1)	P*
Age and Gender
Age, mean ± SD (yrs)	50.8 ± 9	52.5 ± 8.5	0.028
Gender (male), n/N (%)	174/276 (63.0)	174/270 (64.4)	0.734
Past History, n/N (%)
Hypertension	159/241 (66.0)	143/240 (59.6)	0.147
Diabetes	36/259 (13.9)	47/246 (19.1)	0.115
Cardiac Disease	37/264 (14.0)	43/257 (16.7)	0.390
Sroke type, n/N (%)
Ischemic stroke	238/276 (86.2)	193/270 (71.5)	-
Hemorrhagic stroke	33/276 (12.0)	72/270 (26.7)	-
Unknow type of stroke	5/276 (1.8)	5/270 (1.9)	-
Stroke Features, n/N (%)
Aphasia	53/258 (20.5)	98/250 (39.2)	4.23 × 10-6
Neglect	11/266 (4.1)	19/240 (7.9)	0.072
Dysphagia	15/270 (5.6)	25/251 (10.0)	0.059
Urinary Incontinence	5/272 (1.8)	15/251 (6.0)	0.014
Paresis	203/273 (74.4)	244/269 (90.7)	5.59 × 10-7
Consciousness disturbance	21/275 (7.6)	59/265 (22.3)	1.72 × 10-6
Medical complications	18/265 (6.8)	82/254 (32.3)	1.83 × 10-13
Neurologic complications	14/274 (5.1)	39/267 (14.6)	2.03 × 10-4

*Mann-Whitney test or χ2 test.

SD - standard deviation, yrs - years.

Of 21 MMP-2 SNPs, six were associated with stroke outcome at three months under a log-additive model (0.0018