McClure, Leslie A.

Background: Recruitment and phenotyping constitute major costs in stroke genetics research. Clinical trial datasets that include biological samples provide an opportunity to leverage richly phenotyped cases for genomic research. However, the heterogeneity of stroke and methodological differences across studies present challenges. We developed and validated a systematic method of mapping data abstracted from a multicenter randomized clinical trial to the inputs of the CCS stroke subtyping system for the SiGN study. Methods: The SiGN Phenotype Committee and the Secondary Prevention of Small Subcortical Strokes (SPS3) research team systematically reviewed SPS3 case report forms (CRF) to identify key elements required to generate CCS subtyping, and drafted and refined mapping rules using a derivation set of 30 charts. The resultant algorithm was compared to manual entry of clinical information into the CCS in a test set of 30 randomly selected charts. This identified problems due to multiple versions of CRFs (up to 7 versions) and prompted revision of the mapping rules. We assessed the revised algorithm in an independent test set. Results: None of the subtype classifications agreed in the initial testing prompting revision to allow capture information from different versions of the CRFs. The revised mapping algorithm applied to the same test set performed well; 29/30 agreed - 20 small artery occlusion (SAO) evident, 4 SAO possible, 2 SAO probable and 3 supra-aortic large artery atherosclerosis evident). The one chart that disagreed was classified as SAO evident by manual entry but as undetermined by the data mapping rules. In the 2nd independent test set, 30/30 agreed: 22 SAO evident, 4 SAO possible, 3 supra-aortic large artery atherosclerosis evident and 1 undetermined unknown - incomplete evaluation. Conclusions: We created mapping rules using CRFs from a clinical trial to allow reliable subtype classification using the CCS. Issues related to multiple iterations of CRFs presented challenges. Highly phenotyped stroke cases from clinical trials represent a cost-effective opportunity for genomic research.

Background: Levels of the pro-inflammatory cytokines interleukin (IL)-6 and IL-8 and the anti-inflammatory cytokine IL-10 are altered in acute stroke patients compared to controls. However, results for IL-10 are inconsistent (higher in stroke patients compared to controls in some studies and decreased in others) and very few studies have examined associations of these biomarkers with risk of incident stroke. Methods: We examined associations of baseline levels of IL-6, IL-8 and IL-10 with stroke risk factors and risk of incident stroke in 1,572 white and black men and women from the REGARDS Study; an observational cohort of 30,239 adults followed for 5 years. Among those without prebaseline stroke, stroke cases (n=592, 53% men, 59% white, mean age 70 years) were compared to a cohort random sample (n=980, 44% men, 58% white, mean age 65 years). We used Cox proportional hazards models to examine associations of biomarkers with incident stroke. Hazard ratios (HR; 95% confidence intervals) for highest compared to lowest quartile for each biomarker are presented. Results: Baseline IL-6 was significantly higher in incident stroke cases compared to the cohort sample (p<0.001) while IL-8 and IL-10 levels did not vary significantly (p>0.05 for both). Adjusting for age, sex and race, IL-6 was higher in blacks compared to whites and higher in current smokers compared to never/former smokers (all p≤0.01). IL-6 was inversely associated with physical activity, alcohol use and education (all p≤0.01). IL-8 and IL-10 did not vary significantly with the risk factors examined. Adjusting for age, sex and race, the HR for risk of incident stroke in those in the highest compared to lowest quartile of IL-6 was 2.4 (1.6-3.4). HRs for IL-8 and IL-10 were 1.5 (1.0-2.1) and 1.4 (1.0-1.9), respectively. Adjusting for Framingham stroke risk factors and history of coronary heart disease, only IL-6 remained significantly associated with stroke risk (HR 2.0; 1.3-3.2). HRs for IL-8 and IL-10 were 1.1 (0.7-1.6) and 1.2 (0.8-1.8), respectively. IL-6 remained significantly associated with stroke risk when C-reactive protein was added to the model (HR 1.7; 1.1-2.7). Associations did not differ by race. Conclusions: In this population-based sample of US black and white adults, IL-6, but not IL-8 or IL-10, was associated with stroke risk factors and risk of incident stroke. Further study is needed on the clinical utility of IL-6 measurement in stroke risk assessment.

Background and Purpose— Meta-analyses of extant genome-wide data illustrate the need to focus on subtypes of ischemic stroke for gene discovery. The National Institute of Neurological Disorders and Stroke SiGN (Stroke Genetics Network) contributes substantially to meta-analyses that focus on specific subtypes of stroke. Methods— The National Institute of Neurological Disorders and Stroke SiGN includes ischemic stroke cases from 24 genetic research centers: 13 from the United States and 11 from Europe. Investigators harmonize ischemic stroke phenotyping using the Web-based causative classification of stroke system, with data entered by trained and certified adjudicators at participating genetic research centers. Through the Center for Inherited Diseases Research, the Network plans to genotype 10 296 carefully phenotyped stroke cases using genome-wide single nucleotide polymorphism arrays and adds to these another 4253 previously genotyped cases, for a total of 14 549 cases. To maximize power for subtype analyses, the study allocates genotyping resources almost exclusively to cases. Publicly available studies provide most of the control genotypes. Center for Inherited Diseases Research–generated genotypes and corresponding phenotypes will be shared with the scientific community through the US National Center for Biotechnology Information database of Genotypes and Phenotypes, and brain MRI studies will be centrally archived. Conclusions— The Stroke Genetics Network, with its emphasis on careful and standardized phenotyping of ischemic stroke and stroke subtypes, provides an unprecedented opportunity to uncover genetic determinants of ischemic stroke.

Background and Purpose— We compared the associations of self-reported atrial fibrillation (AF) and ECG-detected AF with incident stroke in the Risk of Stroke in the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study. Methods— In this analysis, 27 109 participants aged 45 years or older without previous stroke were included. Stroke cases were identified and adjudicated during an average of 4.4 years of follow-up. Cox proportional hazards analysis was used to calculate hazard ratios (HR) of self-reported AF, ECG-detected AF, and AF detected by either method with incident stroke. We also examined the predictive ability of the Framingham Stroke Risk Score (FSRS) when the component AF was defined by different methods. Results— After adjustment for components of the FSRS, self-reported AF, ECG-detected AF, and AF by either method were predictive of incident stroke (HR, 1.41; 95% CI, 1.05–1.88; HR, 1.90; 95% CI, 1.10–3.27; HR, 1.53; 95% CI, 1.16–2.01, respectively). When self-report, ECG, or either method, separately, were considered as the method of AF ascertainment in the FSRS, the HR per 1% increase in the FSRS were identical across AF ascertainment methods (HR, 1.04; 95% CI, 1.03–1.04; HR, 1.04; 95% CI, 1.04–1.05; HR, 1.04; 95% CI, 1.03–1.04; respectively). Conclusions— Self-reported AF is a strong predictor of stroke that can be used interchangeably or in combination with ECG-detected AF in stroke risk prediction models.