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Keywords:

  • cardiovascular disease risk;
  • factor VII;
  • genetic epidemiology;
  • hemostasis;
  • inflammation;
  • stroke

Abstract

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Addendum
  7. Acknowledgements
  8. Disclosure of Conflict of Interest
  9. References
  10. Supporting Information

Summary. Background: Thrombosis and inflammation are critical in stroke etiology, but associations of coagulation and inflammation gene variants with stroke, and particularly factor VII levels, are inconclusive. Objectives: To test the associations between 736 single-nucleotide polymorphisms (SNPs) between tagging haplotype patterns of 130 coagulation and inflammation genes, and stroke events, in the 5888 participants aged ≥ 65 years of the observational Cardiovascular Health Study cohort. Patients/Methods: With 16 years of follow-up, age-adjusted and sex-adjusted Cox models were used to estimate associations of SNPs and FVIIc levels with future stroke. Results: Eight hundred and fifteen strokes occurred in 5255 genotyped participants without baseline stroke (748 ischemic strokes; 586 among whites). Among whites, six SNPs were associated with stroke, with a nominal P-value of < 0.01: rs6046 and rs3093261 (F7); rs4918851 and rs3781387 (HABP2); and rs3138055 (NFKB1A) and rs4648004 (NFKB1). Two of these SNPs were associated with FVIIc levels (units of percentage activity): rs6046 (β = − 18.5, P = 2.38 × 10−83) and rs3093261 (β = 2.99, P = 3.93 × 10−6). After adjustment for age, sex, race, and cardiovascular risk factors, the association of FVIIc quintiles (Q) with stroke were as follows (hazard ratio; 95% confidence interval): Q1, reference; Q2, 1.4, 1.1–1.9); Q3, 1.1, 0.8–1.5); Q4, 1.5, 1.1–2.0); and Q5, 1.6, 1.2–2.2). Associations between SNPs and stroke were independent of FVIIc levels. Conclusions: Variations in FVII-related genes and FVIIc levels were associated with risk of incident ischemic stroke in this elderly cohort, suggesting a potential causal role for FVII in stroke etiology.

Stroke is a major cause of morbidity and mortality in the developed world; in the USA, one in six men and one in five women suffer a stroke in their lifetime, with stroke being responsible for 17% of all deaths [1]. Thrombosis plays a key role in ischemic stroke; after disruption of the vessel wall, thrombus is formed, and either disrupts blood flow at the site of injury or breaks off and embolizes to where the occlusion occurs [2,3]. Inflammation is also associated with ischemic stroke pathophysiology, and may relate to changes in the composition of blood or of the vessel wall [2]. Risk factors for stroke are not as well characterized as for myocardial infarction (MI), and few prospective studies have evaluated associations of hemostatic and inflammation biomarkers with stroke risk [4].

Proteins related to hemostasis and thrombosis have long been prime biomarker candidates for stroke risk, but many are difficult to measure, owing to high within-person variability and difficulty in standardizing assays. Thus, measurement of gene variants may reveal associations that cannot be determined by assessment of phenotypes.

We studied polymorphisms in genes related to hemostasis and inflammation in relation to stroke risk in the Cardiovascular Health Study (CHS) cohort, and evaluated whether the protein products of genes related to stroke (where possible) were associated with stroke and other cardiovascular disease (CVD) outcomes. We also assessed whether these protein products mediated any of the associations between the single-nucleotide polymorphisms (SNPs) and CVD. Findings may provide insights into the pathophysiology of stroke that can be exploited for risk stratification, new interventions for primary prevention, or perhaps novel treatment approaches.

Methods

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Addendum
  7. Acknowledgements
  8. Disclosure of Conflict of Interest
  9. References
  10. Supporting Information

Cohort

The CHS is a prospective, observational cohort study of risk factors for and consequences of CVD in elderly adults 65 years or older, as detailed previously [5]. Exclusion criteria at baseline include being wheelchair-bound, under active treatment for cancer, institutionalization, or inability or refusal to give informed consent. Among those approached, 9.6% were ineligible, and 57% participated [6]. The cohort originally enrolled 5201 men and women between 1989 and 1990, with a supplemental cohort of 687 African-Americans being enrolled between 1992 and 1993. Written informed consent was obtained from all participants, in accordance with Institutional Review Board guidelines from each site.

Genotyping

Genotyping was supported through the Thrombosis Genetics, Myocardial Infarction and Stroke in Older Adults ancillary study [7]. We selected for analysis 736 SNPs of minor allele frequency ≥ 5% from 130 autosomal candidate hemostasis-related and inflammation-related genes (Table S1). SNPs were located between 5 kb upstream of the transcription start site and 3 kb downstream of the transcription end site of the 130 genes, and were selected with the LDselect algorithm [7,8]. Detailed genotyping methods have been published elsewhere [7]. Genotyping was attempted on the 5759 (of 5888) individuals who provided informed consent for genetic studies.

Outcomes and follow-up

Participants were contacted biannually, alternating between telephone interviews and clinic examinations through 1999; telephone interviews continue to the present time. All deaths were reviewed and classified by a committee, using information from death certificates, autopsy and coroners’ forms, hospital records, and interviews with physicians and next of kin [6,9]. Incident stroke and MI events were determined by a committee on the basis of medical record review, in-person examinations, laboratory and imaging data, telephone interviews (when in-person examinations were not possible), or reports from proxies of participants, using established protocols [5,9]. Periodic searches of the Health Care Financing Administration Medicare utilization files were compiled to identify events missed by other methods. The cerebrovascular adjudication committee, a physician review panel that included neurologists, internists, and neuroradiologists, adjudicated all suspected stroke events, and classified strokes as ischemic, hemorrhagic, or unknown, using all available information [10]. Adjudication of outcomes was complete through June 2005 for this analysis.

Laboratory methods

Phlebotomy was performed on the morning of enrollment after an 8–12-h fast [11]. Factor VII coagulant activity (FVIIc) was measured with the use of FVII-deficient plasma and human placenta-derived thromboplastin (Thromborel S; Behring Diagnostics, Marburg, Germany) on the Coag-A-Mate X2 (Organon-Teknika, Durham, NC, USA). The sample was citrated plasma (32 g L−1) processed at room temperature; coefficients of variation (CVs) were between 5.89% and 6.16%. Units are expressed as percentage of normal pool. Cholesterol and creatinine were measured with standard methods, with CVs of 2.52% and 3.58%, respectively. High-sensitivity C-reactive protein (CRP) was measured with a validated in-house ELISA [12].

Definitions

Race was defined by participant self-report from a list (white, black, American Indian/Alaskan native, Asian/Pacific Islander, or other). Baseline stroke and MI were identified by participant self-report, and confirmed by medical record review, with standardized criteria [13,14]. Diabetes was defined as a fasting glucose level > 126 mg dL−1 or treatment with insulin or oral hypoglycemic medications. Hypertension was defined as a blood pressure > 140/90 mmHg on enrollment, or the use of antihypertensive medications with a physician report of hypertension. Estimated glomerular filtration rate was calculated from the four-variable Modification in Diet in Renal Disease equation; an estimated glomerular filtration rate < 60 mL min−1 per 1.73 m2 on enrollment was defined as chronic kidney disease (CKD) [15].

Statistical methods

Analyses were performed with StataSE version 8.2 (Stata Corp LP, College Station, TX, USA). In initial models, associations of SNPs (coded additively as 0/1/2 copies of minor allele) with incident stroke were assessed with age-adjusted and sex-adjusted Cox proportional hazard models to account for residual confounding. For the purpose of SNP discovery, we considered a result to be statistically significant if P < 0.01. Subsequent analyses were driven by the findings from these initial analyses; after the initial SNP identification, we used more stringent corrected P-values for significance threshold holding, based on the number of significance tests. In later models, we stratified by stroke type: ischemic or hemorrhagic. We requested a look-up from the Cohorts for Heart and Aging Research in Genetic Epidemiology (CHARGE) Consortium for any SNPs that we found to be significant and proxy SNPs with r2 > 0.9. The methodologies used in the CHARGE Consortium are described in detail elsewhere [16,17].

Owing to different haplotype frequencies, all models using genetic data were run in the non-minority cohort only. As four of the six SNPs associated with stroke were in or near the FVII gene (F7) or the FVII-activating protease gene (HABP2), we assessed the association between these four SNPs and FVIIc levels by linear regression (P for significance < 0.01). As warfarin affects FVIIc levels, baseline users of warfarin were excluded from analyses including FVIIc.

Continuing our focus on F7 (whose gene product, FVIIc, is measured in the CHS), we then assessed the association of quintiles of FVIIc levels and allele frequencies of the four F7 and HABP2 SNPs with CVD risk factors. P-values for trend were calculated with t-tests, χ2-tests, or Wilcoxon rank-sum tests, as appropriate. We used staged Cox proportional hazard models to evaluate the association between FVIIc levels and incident ischemic stroke, hemorrhagic stroke, and MI, including covariates based on prior established CVD risk factors and the cross-sectional associations of FVIIc with covariates. Model A adjusted for demographic variables: age (continuous), sex, and race (white vs. minority). Model B, in addition to the covariates in Model A, adjusted for cardiovascular risk factors: smoking (current vs. never or former), diabetes (yes/no), hypertension (yes/no), systolic blood pressure (continuous), baseline CVD (stroke in MI models; MI in stroke models), HDL and LDL cholesterol (continuous), body mass index (BMI) (continuous), and prebaseline cancer (yes/no). A final model (Model C) added novel CVD risk factors to Model B: CRP (natural log transformed) and CKD (yes/no).

In a final analysis to assess for mediation, we evaluated the association of FVIIc levels and F7 SNPs for stroke together in the same Cox proportional hazard models, using the covariates from Model B above.

Results

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Addendum
  7. Acknowledgements
  8. Disclosure of Conflict of Interest
  9. References
  10. Supporting Information

Through June 2005 (up to 15 years of follow-up), the 5759 participants without baseline stroke from the original and minority cohorts experienced 815 incident strokes: 748 were ischemic and 106 were hemorrhagic (the overlap resulted from incident strokes of different types). Through the follow-up period, the 4382 CHS white participants without baseline stroke experienced 713 incident strokes: 586 ischemic and 79 hemorrhagic. Through June 2005, there were 736 incident MIs in the entire cohort.

Of the 736 SNPs examined, six were associated with stroke, with nominal P-values < 0.01 (Table 1). One SNP (rs6046) was located within the coding region and another (rs3093261) within the 3′-flanking region of F7 (between F7 and the FX gene) on chromosome 13. Two SNPs (rs4918841 and rs3781387) were located within introns of HABP2 on chromosome 10. Two SNPs were located near inflammation-related genes: rs3138055 within the 3′-flanking region of I-kappa-B-alpha (NFKBIA) on chromosome 14, and rs4648004 within the intron of NF-kappa-B p50 subunit (NFKB1) on chromosome 4. Tables S2 and S3 present the findings from the SNP look-up in the CHARGE Consortium. Within the CHARGE Consortium, none of the six SNPs that we identified was replicated for all-cause stroke or ischemic stroke; however rs3138055 (or any proxy for this SNP) was not assessed in the CHARGE Consortium, and rs4648004 was measured through a proxy SNP only (rs3960787, P = 0.02 for ischemic stroke).

Table 1.   Coagulation and inflammation gene single-nucleotide polymorphisms (SNPs) associated with risk of incident stroke in Cardiovascular Health Study whites
SNP no.Gene symbolGene nameChromosomeAllelesLocationNominal P-value*
  1. *Seven hundred and thirty-six SNPs were evaluated; the significance threshold was set at P < 0.01 in age-adjusted and sex-adjusted Cox proportional hazard models.

rs4918851HABP2Factor VII-activating protease10A/CIntron0.0007
rs3138055NFKBIAI-kappa-B-alpha14C/T3′-Flanking0.0047
rs3781387HABP2Factor VII-activating protease10G/AIntron0.0051
rs3093261F7Factor VII13T/C3′-Flanking0.0051
rs6046F7Factor VII13A/GCoding0.0054
rs4648004NFKB1NF-kappa-B p50 subunit4G/AIntron0.0092

Table 2 presents age-adjusted and sex-adjusted hazard ratios (HRs) for associations of the six SNPs with ischemic and hemorrhagic stroke among white participants. With a corrected P-value of 0.004 (α = 0.05/12; six SNPs × two outcomes), three of the six SNPs were associated with ischemic stroke: both of the F7 SNPs (rs6046, HR 0.73, P = 0.002; rs3093261, HR 1.21, P = 0.002) and one of the HABP2 SNPs (rs4918841, HR 0.75, P = 0.0002). None of the six SNPs was associated with hemorrhagic stroke or MI (in an exploratory analysis; all P-values > 0.004; Table 2).

Table 2.   Age-adjusted and sex-adjusted hazard ratios (HRs) of single-nucleotide polymorphism (SNPs) for incident stroke among Cardiovascular Health Study whites by stroke type
Gene symbolSNP no.Ischemic stroke (n = 586)Hemorrhagic stroke (n = 79)Myocardial infarction (n = 669)
HR** (95% CI)P-value*HR** (95% CI)P-value*HR** (95% CI)P-value*
  1. CI, confidence interval. *P < 0.004 defined as significant (α = 0.05/12). **Per each additional copy of the minor allele.

HABP2rs49188510.75 (0.64–0.87)0.00021.08 (0.73–1.60)0.691.01 (0.88–1.15)0.94
F7rs60460.73 (0.60–0.89)0.0020.74 (0.44–1.25)0.261.01 (0.85–1.19)0.94
F7rs30932611.21 (1.07–1.36)0.0020.88 (0.64–1.22)0.460.99 (0.88–1.11)0.88
HPBP2rs37813870.77 (0.63–0.93)0.0060.91 (0.55–1.51)0.711.01 (0.86–1.19)0.91
NFKB1Ars31380550.83 (0.72–0.95)0.0070.82 (0.57–1.18)0.291.00 (0.89–1.14)0.95
NFKB2rs46480041.14 (1.01–1.28)0.031.40 (1.02–1.92)0.040.95 (0.85–1.07)0.40

Table 3 shows that the two F7 SNPs (rs6046 and rs3093261) were associated with FVIIc levels in CHS whites; each copy of the minor allele of rs6046 was associated with an 18.5% point lower FVII level (P = 2.38 × 10−83), and each copy of the minor allele of rs3093261 was associated with a 2.99% point higher FVIIc level (P = 3.93 × 10−6). HABP2 SNPs were not associated with FVIIc levels. The two F7 SNPs (rs6046 and rs3093261) were associated with FVIIc quintiles (both P < 0.001).

Table 3.   Unadjusted correlation of stroke-associated single-nucleotide polymorphisms (SNPs) and baseline factor VIIc level among Cardiovascular Health Study whites (N = 4286)
GeneSNP no.β (%)SER2P-value*
  1. SE, standard error. *P < 0.01 defined as significant (α = 0.05/4).

F7rs6046− 18.5< 0.010.0842.38 × 10−83
F7rs30932612.990.640.0053.93 × 10−6
HABP2rs37813872.060.950.0010.031
HABP2rs49188510.9120.770.00030.24

Given the association of the F7 SNPs with stroke, we assessed the association of FVIIc levels with CVD risk factors (Table 4). FVIIc was lower with greater age, in males than in females, and in blacks than whites. FVIIc was higher with hypertension, higher HDL and LDL cholesterol, greater BMI, presence of CKD, and higher CRP. FVIIc was not associated with smoking status or diabetes.

Table 4.   Baseline associations of factor VIIc quintiles with cardiovascular risk factors and stroke-associated single-nucleotide polymorphisms
 FVIIc quintilesP-value (trend)
Q1Q2Q3Q4Q5
  1. BMI, body mass index; BP, blood pressure; CRP, C-reactive protein; SD, standard deviation. *Estimated glomerular filtration rate < 60 mL min−1 per 1.73 m2.

N11481121114211701178 
FVII range (%)41–99100–113114–126127–144145–346 
Number of strokes136173145172189 
Average follow-up (years)10.511.211.611.511.7 
Age (years ± SD)73.3 ± 5.873.1 ± 5.672.9 ± 5.672.8 ± 5.572.0 ± 5.2< 0.001
Male, n (%)759 (66)622 (55)475 (42)362 (31)225 (19)< 0.001
Black, n (%)283 (25)213 (19)143 (13)137 (12)99 (8)< 0.001
Current smoking, n (%)136 (12)145 (13)135 (12)147 (13)125 (11)0.33
Diabetes, n (%)347 (30)305 (27)327 (29)350 (30)375 (32)0.19
Hypertension, n (%)475 (41)474 (42)463 (41)551 (47)578 (49)< 0.001
Systolic BP (mmHg ± SD)135 ± 22136 ± 22136 ± 21137 ± 23139 ± 22< 0.001
HDL cholesterol (mg dL−1 ± SD)51 ± 1453 ± 1554 ± 1556 ± 1657 ± 18< 0.001
LDL cholesterol (mg dL−1 ± SD)118 ± 33124 ± 31132 ± 34136 ± 36140 ± 39< 0.001
BMI (kg m−2 ± SD)26.1 ± 4.526.4 ± 4.726.5 ± 4.726.8 ± 4.727.5 ± 4.8< 0.001
Chronic kidney disease*, n (%)63 (5.7)55 (5.1)58 (5.3)68 (6.0)99 (8.7)< 0.001
CRP (mg L−1 ± SD)3.6 ± 6.93.1 ± 6.63.5 ± 6.43.5 ± 5.04.3 ± 5.40.001
rs3093261 (minor allele frequency)0.290.350.380.380.41< 0.001
rs6046 (minor allele frequency)0.250.150.100.080.05< 0.001

Table 5 presents associations between FVIIc quintiles and ischemic stroke, hemorrhagic stroke and MI in the three models described in Methods. Quintile 1 served as the reference group. The top two quintiles of FVIIc were associated with ischemic stroke in all models, with a 36% increased risk for both quintiles 4 and 5 vs. quintile 1 in Model C. There was no significant association in any model between FVIIc quintiles and hemorrhagic stroke or MI. Results were similar when estimated glomerular filtration rate was coded as a continuous variable.

Table 5.   Adjusted hazard ratios for factor VIIc quintiles and cardiovascular outcomes
OutcomeModelFVIIc quintileP-value (trend)
Q1Q2Q3Q4Q5
  1. Model A: adjusted for age, sex, and race. Model B: adjusted for age, sex, race, ever smoker, diabetes, hypertension, baseline cardiovascular disease (CVD), prebaseline cancer, systolic blood pressure, HDL cholesterol, LDL cholesterol, and body mass index (BMI). Model C: adjusted for age, sex, race, ever smoker, diabetes, hypertension, baseline CVD, prebaseline cancer, systolic blood pressure, HDL cholesterol, LDL cholesterol, BMI, C-reactive protein, and chronic kidney disease.

Ischemic stroke (n = 748)AReference1.32 (1.02–1.71)1.11 (0.84–1.45)1.40 (1.07–1.81)1.60 (1.22–2.08)0.001
BReference1.31 (1.01–1.69)1.05 (0.80–1.38)1.32 (1.01–1.73)1.46 (1.11–1.92)0.016
CReference1.29 (1.00–1.66)1.06 (0.81–1.38)1.36 (1.04–1.77)1.36 (1.03–1.79)0.03
Hemorrhagic stroke (n = 106)AReference1.43 (0.77–2.67)0.81 (0.40–1.65)0.81 (0.40–1.67)1.03 (0.51–2.08)0.51
BReference1.50 (0.81–2.79)0.80 (0.39–1.66)0.91 (0.44–1.90)1.06 (0.51–2.22)0.61
CReference1.38 (0.74–2.59)0.65 (0.30–1.40)0.66 (0.30–1.45)0.72 (0.32–1.59)0.11
Myocardial infarction (n = 736)AReference0.95 (0.75–1.18)1.01 (0.80–1.27)1.07 (0.85–1.35)1.12 (0.88–1.42)0.22
BReference0.89 (0.71–1.11)0.95 (0.75–1.20)0.98 (0.77–1.25)0.98 (0.78–1.27)0.81
CReference0.86 (0.68–1.08)0.94 (0.75–1.19)0.95 (0.74–1.22)0.96 (0.74–1.25)0.95

In the traditional CVD risk factor model (Model B), we assessed the mediation by the F7 SNPs (rs6046 and rs3093261) on the association between FVIIc quintiles and our primary outcome, stroke, by adding both FVIIc quintiles and the SNPs in the same proportional hazard model (Table 6). The fourth (HR 1.46; 95% confidence interval [CI] 1.08–1.98) and fifth (HR 1.58; 95% CI 1.15–2.16) quintiles of FVIIc were associated with stroke when no SNPs were added into the model. When rs6046 (the SNP with the largest association with FVIIc level) was added to the model, the HRs for stroke for the fourth (HR 1.28, 95% CI 0.93–1.76) and fifth (HR 1.34; 95% CI 0.96–1.87) quintiles of FVIIc were lower. With singular adjustment for rs3093261 alone, only minimal changes in the FVIIc HRs were observed. When both SNPs were added to the model, FVIIc quintiles were no longer associated with stroke risk (Table 6). The associations of rs6046 (HR 0.71; 95% CI 0.58–0.87) and rs3093261 (HR 1.17; 95% CI 1.04–1.32) changed minimally when FVIIc quintiles were added into the model (Model B): for rs6046, HR 0.75 (95% CI 0.60–0.93); and for rs3093261, HR 1.15 (95% CI 1.02–1.30).

Table 6.   Impact of factor VII gene single-nucleotide polymorphisms on association of FVIIc with stroke in whites
Model*HR (95% CI) for stroke by FVIIc quintile
Q1Q2Q3Q4Q5
  1. CI, confidence interval; HR, hazard ratio. *Adjusted for age, sex, race, ever smoker, diabetes, hypertension, baseline cardiovascular disease, prebaseline cancer, systolic blood pressure, HDL cholesterol, LDL cholesterol, and body mass index.

Model B aloneReference1.42 (1.06–1.91)1.12 (0.82–1.53)1.46 (1.08–1.98)1.58 (1.15–2.16)
Model B + rs6046Reference1.31 (0.97–1.77)1.00 (0.72–1.37)1.28 (0.93–1.76)1.34 (0.96–1.87)
Model B + rs3093261Reference1.39 (1.03–1.87)1.09 (0.79–1.48)1.41 (1.04–1.92)1.52 (1.11–2.08)
Model B + rs6046 + rs3093261Reference1.23 (0.97–1.56)0.92 (0.71–1.18)1.15 (0.89–1.48)1.21 (0.92–1.57)

Discussion

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Addendum
  7. Acknowledgements
  8. Disclosure of Conflict of Interest
  9. References
  10. Supporting Information

Of 736 SNPs from 130 candidate inflammation and hemostasis genes, six were associated with stroke among whites in this elderly cohort: two SNPs were located within or near F7 (rs6046 and rs3093261); two were within introns of HABP2 (rs4918841 and rs3781387); and two were near inflammation-related genes (rs3138055, NFKBIA; and rs4648004, NFKB1). The two F7 SNPs were associated with FVIIc levels (rs6046 and rs3093261). FVIIc levels were also significantly associated with ischemic stroke, but not with MI or hemorrhagic stroke. Addition of both F7 SNPs into a model for total stroke attenuated the association of FVIIc with stroke, but FVIIc did not attenuate the association of the two F7 SNPs with stroke.

These data extend those from prior studies on the genetics of stroke risk. In a recent genome-wide association study from the CHARGE Consortium of 2 194 468 SNPs, only two SNPs on chromosome 12, near NINJ2, were associated with stroke, with genome-wide significance [16]. NINJ2 encodes an adhesion molecule expressed in glia. The CHARGE Consortium meta-analysis included 19 602 Caucasian individuals and 1544 incident stroke cases (including data from the CHS). Although the CHS was part of the CHARGE Consortium, its dataset used a shorter follow-up period than in this analysis, and only included 459 incident stroke events from the CHS. None of the associations in the current study of stroke was close to having genome-wide significance; the lowest P-value was 0.02, for rs3960787 (a proxy for rs4648004, NFKB1), with all other P-values being > 0.10. Furthermore, in our protocol, we did not assess any SNPs from NINJ2, as we concentrated on 736 SNPs from 130 inflammation-related and hemostasis-related genes. Not surprisingly, the genome-wide association study did not detect any of the associations that we observed, as our lowest P-value was 7 × 10−4 for rs4918851, which was well above the threshold selected for the genome-wide statistical significance in the CHARGE Consortium (P < 5 × 10−8).

Other studies of the SNPs and genes that we identified here have reported mixed results. F7 encodes FVII, an essential component of hemostasis at sites of vessel injury. The rs6046 SNP has long been known to affect FVII levels, and has been studied in relation to risk of stroke, MI and venous thrombosis in case–control studies [18–21]. Although the effects of rs6046 on FVIIc levels are well documented, the reason for the lower FVIIc levels is unclear. The minor haplotype of rs6046 results in a guanine to adenine substitution in codon 353 of the F7 gene, resulting in glutamine to arginine substitution [22]. Whether this SNP in and of itself reduces FVIIc levels or is a marker for a hypofunctioning phenotype requires further investigation [23]. In the Framingham Heart Study, using a stepwise selection algorithm to select SNPs associated with FVII levels, rs6046 fell out of the model with inclusion of rs1755685 (within the 5′-flanking/promoter region of F7). These SNPs are in linkage disequilibrium (r2 = 0.79), and the stepwise selection algorithm makes no assumptions on function, but only determines which variable produces a better model fit for analysis [19]. In a recent meta-analysis of 1537 cases of ischemic stroke and 3133 controls, the rs6046 SNP showed no overall association with stroke (odds ratio 0.9; 95% CI 0.4–1.9), although some of the individual studies did show associations [20]. In terms of MI, in the Framingham Heart Study, with only 155 CVD events (the majority of which were MIs), rs6046 was associated with FVII levels, but no F7 SNPs were associated with the combined CVD endpoint [19]. In another case–control study, rs6046 was associated with MI in patients with established coronary artery disease [18]. In a recent analysis of the Women’s Genome Health Study, rs6046 was associated with a lower risk of idiopathic venous thrombosis in women [21]. Differences among studies may reflect heterogeneity of the phenotypes of stroke, MI, and venous thrombosis. In our elderly population, we hypothesize that stroke may represent a less heterogeneous phenotype than in younger populations. SNP rs3093261 is in the 3′-flanking region of F7, between F7 and the FX gene, with an r2 = 0.137 between rs6046 and rs3093261 [24]. When both rs6046 and rs3093261 were in the same model for stroke risk, rs3093261 was not significantly associated with stroke (P = 0.13), and so this association may represent a weak linkage with rs6046 or another SNP. We are not aware of any other data for rs3093261, FVII levels, and CVD risk.

The association of FVII with stroke has been controversial. Theoretically, FVII is a strong hemostasis candidate protein for vascular disease risk [25]. In primary hemostasis, FVII plays a key role in vascular wall injury, and exogenous administration of activated FVII leads to thrombotic complications, including stroke, MI, and venous thrombosis [26–28]. Epidemiologic studies, including in the CHS, have shown mixed results for the association between FVIIc and CVD risk [2,29,30]. The first study to suggest an association was the Northwick Park Study, which showed an association between fatal CVD and FVII levels [30]. Prior analyses in the CHS have had fewer stroke events or combined stroke and transient ischemic attack as an endpoint [2,31]. FVII levels in the Atherosclerosis Risk in Communities cohort (a younger cohort) were not associated with stroke risk for 268 ischemic strokes [29]. Our analysis revealed a modest association between FVII and stroke, and many other studies would be underpowered according to the association that we observed, and have often used a combined CVD endpoint (which may attenuate associations, owing to the lack of association of FVIIc with MI seen here). Although arterial diseases in diverse vascular beds share many common risk factors, emerging evidence suggests that each has a unique risk factor profile. Furthermore, the quality of the literature examining novel risk factors and biomarkers for stroke is sparse as compared with the equivalent literature for MI [4]. Our current analysis represents the largest prospective study that we are aware of relating FVII levels and cardiovascular risk in an elderly population. Further basic science studies are needed to determine the reasons for differing risk factors in diverse vascular beds.

HABP2 encodes a heterodimeric serine protease that has a variety of effects on coagulation and inflammation genes, including cleaving fibrinogen and activating pro-urokinase and FVII [32]. HABP2 has been a target of study for CVD risk, with the Marburg I and II polymorphisms being associated with stroke, MI, and venous thrombosis [32–34]. Marburg I and II SNPs were not evaluated here, as their gene frequencies were < 0.05, but we found that two SNPs within introns of HABP2 were associated with stroke risk. Neither of these SNPs has specifically been linked with stroke risk previously, and they are not in linkage disequilibrium [24]. Several hypotheses link variants of HABP2 with vascular diseases: one espouses a decoupling of activation of the fibrinolytic system (pro-urokinase to urokinase) with activation of FVII and fibrinogen, and another postulates increased smooth muscle cell proliferation [32]. Neither of the HABP2 SNPs studied here was associated with FVII levels after adjustment for multiple testing. The protein product of HABP2 can also be measured in blood, but was unavailable for this study, so we could not assess the correlation between HABP2 SNPs, FVII-activating protease levels, and stroke risk [35].

We observed nominal associations between stroke and two SNPs near inflammation-related genes: rs4648004 (NFKB1, p50 subunit) and rs3138055 (NFKBIA). Nuclear factor kappaB is a complex multimeric transcription factor regulating hundreds of inflammation and cellular apoptotic proteins, and is upregulated in brain ischemia models [36]. NFKBIA encodes a component of the inhibitor of kappaB kinase [36]. Animal models suggest that these proteins play a key role in cerebral ischemia, and specific inhibitors have been investigated in stroke models, with mixed results [36].

Despite our study having a well-characterized cohort with a large number of validated strokes, a major limitation was the small number of non-whites; our results may not generalize to other races or ethnicities. Furthermore, we were limited by the need to balance between the number of SNPs and genes assessed and the potential for false-positive results. For SNP discovery, we used P < 0.01; this is greater than in the most conservative method (Bonferroni correction), which would require P < 6.7 × 10−5. The application of a Bonferroni threshold, which does not factor in the correlation between the test statistics for the SNPs that are in linkage disequilibrium, is strictly agnostic regarding biological information about the genes being studied. We note that three of the genes (F7, HABP2, and NFKB1) that contain, or are near, SNPs that were nominally associated with stroke have strong circumstantial evidence relating them to CVD. We also note that there were strong internal consistencies between the genotypes of the F7 SNPs and the phenotypes of the F7 gene product and stroke; rs6046 was associated with lower FVIIc levels and a lower risk of ischemic stroke, and rs3093261 was associated with higher FVIIc levels and a higher risk of ischemic stroke. When both F7 SNPs were added into a model with FVIIc levels, the association of FVIIc with stroke was partly mediated by the genotypes. The genotypes probably better reflect FVIIc levels over time than the one-time determination of FVIIc levels.

In summary, six SNPs within four hemostasis-related and inflammation-related genes (F7, HABP2, NFKBIA, and NFKB1) were nominally associated with stroke risk. Two F7 SNPs that were associated with stroke were also associated with FVIIc levels, which in turn were associated with ischemic stroke risk. The F7 SNPs mediated the association of FVIIc levels with stroke. The consistency and the rigorous methods used to acquire these data provide strong supporting evidence that, in this elderly population, FVII may play an etiologic role in stroke. Future work needs to confirm these findings and further elucidate the role of inflammation and hemostasis in stroke risk.

Addendum

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Addendum
  7. Acknowledgements
  8. Disclosure of Conflict of Interest
  9. References
  10. Supporting Information

N. A. Zakai: drafting of the manuscript. All coauthors helped to design and interpret the analyses, and provided critical revision of the manuscript for scientific content. A. P. Reiner: securing of grant funding; A. P. Reiner and L. Lange: performance of the analyses.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Addendum
  7. Acknowledgements
  8. Disclosure of Conflict of Interest
  9. References
  10. Supporting Information

A full list of principal CHS investigators and institutions can be found at http://www.chs-nhlbi.org/pi.htm.

The authors acknowledge the role of the Neurology Working Group of the CHARGE Consortium in providing data for independent verification of the genetic association findings in this article. In addition to the CHS, CHARGE Consortium members include the National Heart, Lung, and Blood Institute Atherosclerosis Risk in Communities Study, National Institute on Aging Iceland Age, Gene/Environment Susceptibility Study, Framingham Heart Study, and Netherlands Rotterdam Study. The research reported in this article was supported by the National Institute on Aging (AG-023629). The CHS was supported by contract numbers N01-HC-85079 to N01-HC-85086, N01-HC-35129, N01 HC-15103, N01 HC-55222, and N01-HC-75150, N01-HC-45133, grant number U01 HL080295 from the National Heart, Lung, and Blood Institute, and grants R01 HL-071862 and R01 HL59367, with an additional contribution from the National Institute of Neurological Disorders and Stroke. Additional support was provided through R01 AG-15928, R01 AG-20098 and AG-027058 from the National Institute on Aging, R01 HL-075366 from the National Heart, Lung and Blood Institute, and the University of Pittsburgh Claude. D. Pepper Older Americans Independence Center (P30-AG-024827).

Disclosure of Conflict of Interest

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Addendum
  7. Acknowledgements
  8. Disclosure of Conflict of Interest
  9. References
  10. Supporting Information

The authors declare that they have no conflict of interest.

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  5. Discussion
  6. Addendum
  7. Acknowledgements
  8. Disclosure of Conflict of Interest
  9. References
  10. Supporting Information
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Supporting Information

  1. Top of page
  2. Abstract
  3. Methods
  4. Results
  5. Discussion
  6. Addendum
  7. Acknowledgements
  8. Disclosure of Conflict of Interest
  9. References
  10. Supporting Information

Table S1. Inflammation and hemostasis related genes and tagging single nucleotide polymorphisms.

Table S2. Association of single nucleotide polymorphism with all-cause stroke outcome in cohorts for heart and aging research in genomic epidemiology.

Table S3. Association of single nucleotide polymorphism with ischemic stroke outcome in cohorts for heart and aging research in genomic epidemiology.

FilenameFormatSizeDescription
JTH_4149_sm_TableS1.doc84KSupporting info item
JTH_4149_sm_TableS2andS3.doc103KSupporting info item

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