Ay, Hakan

Stroke subtype classification, applying standard criteria to clinical data, can reduce the heterogeneity of ischemic stroke for genetics studies. To date, most of the replicated genetic loci for ischemic stroke appear subtype-specific. Increasing homogeneity of phenotype comes with a trade-off in sample size, which influences the potential for successful identification of new loci. We estimated genetic associations using the union and intersection of two widely used stroke subtyping systems to assess the influence of sample size and homogeneity on test statistics. Methods: The Stroke Genetics Network (SiGN) study used both the Causative Classification of Stroke (CCS) and the Trial of ORG10172 Acute Stroke Trial (TOAST) classification systems. The CCS generates both a causal and a phenotypic subtype. Using all available data from case-control strata from the a previously performed GWAS, we focused on three main stroke subtypes: cardioembolic (CE), large artery atherosclerosis (LAA), and small artery occlusion (SAO). We estimated genetic associations with the union of the two CCS outputs and TOAST ( eg called CE by at least one method) and the intersection ( eg called CE by all three methods). Our modelling approach included a fixed-effects meta-analysis, i.e. pooling stratum specific estimates from logistic regression models using 10 principle components to account for genetic ancestry responsible for population stratification. Results: The majority of confirmatory findings from SiGN ( PITX2 and ZFHX3 for CE and HDAC9 for LAA) were evident in both models and in both the union and intersection of the subtyping systems. Some findings ( TSPAN2 in LAA) showed up best in the intersection with stronger corresponding p-values and odds ratios whereas other findings (12q24 for SAO) appear stronger in all of the union analyses, but were also evident in intersection analyses. We identify 16 potential novel loci, 6 of which appear in numerous analyses. Conclusion: Both expanding and refining stroke subtypes may help in the identification of additional stroke genetic risk variants and should be considered as complementary to a single classification system. We are pursuing replication of the novel findings.

In the SPRINT (Systolic Blood Pressure Intervention Trial), the number of strokes did not differ significantly by treatment group. However, stroke subtypes have heterogeneous causes that could respond differently to intensive blood pressure control. SPRINT participants (N=9361) were randomized to target systolic blood pressures of <120 mm Hg (intensive treatment) compared with <140 mm Hg (standard treatment). We compared incident hemorrhage, cardiac embolism, large- and small-vessel infarctions across treatment arms. Participants randomized to the intensive arm had mean systolic blood pressures of 121.4 mm Hg in the intensive arm (N=4678) and 136.2 mm Hg in the standard arm (N=4683) at one year. Sixty-nine strokes occurred in the intensive arm and 78 in the standard arm when SPRINT was stopped. The breakdown of stroke subtypes across treatment arms included hemorrhagic (intensive treatment, n=6, standard treatment, n=7) and ischemic stroke subtypes (large artery atherosclerosis: intensive treatment n=11, standard treatment, n=13; cardiac embolism: intensive treatment n=11, standard treatment n=15; small artery occlusion: intensive treatment n=8, standard treatment n=8; other ischemic stroke: intensive treatment n=3, standard treatment n=1). Fewer strokes occurred among participants without prior cardiovascular disease in the intensive (n=43) than the standard arm (n=61), but the difference did not reach predefined statistical significance level of 0.05 ( P =0.09). The interaction between baseline cardiovascular risk factor status and treatment arm on stroke risk did not reach significance ( P =0.05). Similar numbers of stroke subtypes occurred in the intensive BP control and standard control arms of SPRINT.

Background: Current ischemic stroke (IS) subtyping systems are very labor-intensive and costly to implement in large samples. There is a need to develop phenotyping approaches for genetic and other studies that can be implemented using generally available items captured in electronic medical records. Objective: In this report, we seek to validate a simplified approach using results from a genetic association study. Specifically, the strength of association for known genetic signals in the NINDS Stroke Genetics Network study (SiGN) is compared between the established IS subtyping systems (TOAST and Causative Classification System (CCS) subtypes) and phenotyping based on a single item within the CCS. Methods: Non-mutually exclusive “Simple” IS subtypes were defined from single CCS items as follows: 1) Small vessel stroke- lacunar infarct on imaging (with no other imaging or other requirements); 2) large artery stroke - vascular imaging showing > 50% stenosis or <50% stenosis with plaque rupture or thrombosis judged to be due to atherosclerosis in clinically relevant artery; and 3) cardioembolic stroke - persistent or paroxysmal atrial fibrillation. We compared the GWAS signals using these definitions to the published SiGN GWAS results for TOAST and CCS, among those of European ancestry only. Results: The Table shows that, for loci significant in SiGN GWAS study, the “Simple” IS subtypes had similar strengths of association and p-values compared to results from TOAST and the causative and phenotypic CCS subtypes. Conclusions: “Simple” IS subtypes perform as well in GWAS analyses as more complex phenotyping systems and could potentially be derived through automated or semi-automated methods applied to electronic medical record systems. Since the ‘Simple” phenotypes were based on high quality CCS data, the current findings reflect optimal performance. Additional validation studies will be required for implementation in other settings.

Background and Purpose— NINDS (National Institute of Neurological Disorders and Stroke)-SiGN (Stroke Genetics Network) is an international consortium of ischemic stroke studies that aims to generate high-quality phenotype data to identify the genetic basis of pathogenic stroke subtypes. This analysis characterizes the etiopathogenetic basis of ischemic stroke and reliability of stroke classification in the consortium. Methods— Fifty-two trained and certified adjudicators determined both phenotypic (abnormal test findings categorized in major pathogenic groups without weighting toward the most likely cause) and causative ischemic stroke subtypes in 16 954 subjects with imaging-confirmed ischemic stroke from 12 US studies and 11 studies from 8 European countries using the web-based Causative Classification of Stroke System. Classification reliability was assessed with blinded readjudication of 1509 randomly selected cases. Results— The distribution of pathogenic categories varied by study, age, sex, and race ( P <0.001 for each). Overall, only 40% to 54% of cases with a given major ischemic stroke pathogenesis (phenotypic subtype) were classified into the same final causative category with high confidence. There was good agreement for both causative (κ 0.72; 95% confidence interval, 0.69–0.75) and phenotypic classifications (κ 0.73; 95% confidence interval, 0.70–0.75). Conclusions— This study demonstrates that pathogenic subtypes can be determined with good reliability in studies that include investigators with different expertise and background, institutions with different stroke evaluation protocols and geographic location, and patient populations with different epidemiological characteristics. The discordance between phenotypic and causative stroke subtypes highlights the fact that the presence of an abnormality in a patient with stroke does not necessarily mean that it is the cause of stroke.

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.