Rannikmäe, Kristiina

Background: Causative Classification of Stroke (CCS) system is a novel, web-based, and fully-computerized algorithm that integrates clinical, diagnostic, and etiologic stroke characteristics in an evidence-based manner (available at http://ccs.mgh.harvard.edu ) and provides in a given patient both phenotypic subtypes where abnormal evaluation findings are simply organized in major etiologic categories and causative subtypes where abnormal evaluation findings are filtered through a decision making process to identify the most likely cause of stroke. In this study, we sought to identify the concordance between phenotypic and causative CCS subtypes in the NINDS Stroke Genetics Network (SiGN). Methods: A total of 24 adjudicators from 15 US and European sites certified in CCS retrospectively determined CCS subtypes through chart review in 7134 patients. The CCS software provided causative subtypes in 3 confidence levels as “evident”, “probable” or “possible” based on the weight of causal evidence. We estimated how often CCS classified a given major abnormal evaluation finding as the causative stroke mechanism. Results: CCS assigned 55% of patients with the phenotype of moderate-to-severe atherosclerotic stenosis into the causative category of “evident large artery atherosclerosis”. Likewise, 50% of patients with a major cardiac source (such as atrial fibrillation) were classified into “evident cardio-aortic embolism”, and 74% of those with imaging evidence of a typical lacunar infarct into “evident small artery occlusion”. In 20% to 30% of patients, phenotypic and causative categories were the same but the causative subtype was assigned with a lower level of confidence. Six to 20% of patients with a given phenotypic feature were classified into a different causative category. Failure to investigate for other subtypes in the presence of a positive test finding and the presence of multiple competing etiologies were key contributors to the observed disconcordance between phenotypic and causative subtypes. Conclusions: Our findings show that the presence of a major abnormality in stroke evaluation does not necessarily mean that it is always the cause. The ability of CCS system to discriminate between phenotypic and causative characteristics of stroke etiology provides the opportunity to refine stroke phenotypes for use in research studies.

Introduction: Genetic studies of early-onset disease have been an effective strategy to identify novel pathways and drug targets generalizable also to later-onset disease. Few studies have investigated the sex-specific genetic associations with early-onset ischemic stroke even though several features of ischemic stroke differ between males and females. We hypothesized that stratifying the GWAS by sex would reveal novel stroke loci. Methods: We performed a transethnic ischemic stroke GWAS of 3,056 female cases and 4,462 male cases < 60 years-old and 16,192 and 16,048 sex-matched controls, respectively, from the Early Onset Stroke Genetics Consortium. Results: We identified a significant association in women with a locus in close proximity to TMX1 , a transmembrane platelet protein that inhibits platelet function. Additionally, we identified 2 other suggestive (P < 5 x 10 -6 ) loci in females (see Table), i.e., at APOH , which encodes beta2-glycoprotein I, an established GWAS locus for lipoprotein(a), and LRFN2 which has been previously reported to associate with obesity-related measures and type II diabetes. We observed suggestive evidence for association in males with MMP3/MMP12 , a known stroke susceptibility locus. Conclusions: Despite a very modest sample size, sex-specific analyses identified suggestive associations at biologically important novel loci in females and a known stroke locus in males. Further studies of sex-specific associations in both early- and later-onset ischemic stroke are needed.

Introduction: In observational studies, higher plasma high-density lipoprotein cholesterol (HDL-C) has been associated with increased risk of spontaneous intracerebral hemorrhage (ICH). Common DNA sequence variants within the cholesteryl ester transfer protein ( CETP ) gene decrease CETP protein activity and increase plasma HDL-C; as such, medicines that inhibit CETP and raise HDL-C are in clinical development to combat coronary artery disease. Hypothesis: Common CETP DNA sequence variants associated with higher HDL-C also increase risk for ICH. Methods: We performed a two-stage case-control genetic association study in Caucasians. The discovery phase utilized data on 12 independent loci within CETP (+/- 50 kilobases) from 3 genome-wide association studies of ICH. Replication involved direct genotyping in 5 additional studies. We also constructed a genetic risk score with 7 independent CETP variants and tested it for association with HDL-C and ICH risk. We used principal component analysis to account for population structure and a Bonferroni-adjusted p<0.004 (12 tests) to declare statistical significance. Results: The discovery phase included 1149 ICH cases (43% lobar hemorrhages) and 1238 controls. Twelve variants were nominally associated (p<0.05) with ICH, with the strongest association at the rs173539 locus (Figure 1: OR 1.25, 95%CI 1.11-1.41; p=6.0x10 -4 ) and no heterogeneity across studies (I 2 =0%). This association was replicated in 1625 cases (43% lobar hemorrhages) and 1845 controls (OR 1.12, 95%CI 1.02-1.24; p=0.03). A genetic score of independent CETP variants known to increase HDL-C by ~2.85 mg/dL was strongly associated with ICH risk (OR 1.86, 95%CI 1.44-2.40; p=1.4x10 -6 ). Conclusion: Genetic variants in CETP associated with increased HDL-C raise the risk of ICH. Given ongoing therapeutic development in CETP inhibition and other HDL-raising strategies, further exploration of potential adverse cerebrovascular outcomes is warranted.

ObjectiveIn observational epidemiologic studies, higher plasma high‐density lipoprotein cholesterol (HDL‐C) has been associated with increased risk of intracerebral hemorrhage (ICH). DNA sequence variants that decrease cholesteryl ester transfer protein (CETP) gene activity increase plasma HDL‐C; as such, medicines that inhibit CETP and raise HDL‐C are in clinical development. Here, we test the hypothesis that CETP DNA sequence variants associated with higher HDL‐C also increase risk for ICH.MethodsWe performed 2 candidate‐gene analyses of CETP. First, we tested individual CETP variants in a discovery cohort of 1,149 ICH cases and 1,238 controls from 3 studies, followed by replication in 1,625 cases and 1,845 controls from 5 studies. Second, we constructed a genetic risk score comprised of 7 independent variants at the CETP locus and tested this score for association with HDL‐C as well as ICH risk.ResultsTwelve variants within CETP demonstrated nominal association with ICH, with the strongest association at the rs173539 locus (odds ratio [OR] = 1.25, standard error [SE] = 0.06, p = 6.0 × 10−4) with no heterogeneity across studies (I2 = 0%). This association was replicated in patients of European ancestry (p = 0.03). A genetic score of CETP variants found to increase HDL‐C by ∼2.85mg/dl in the Global Lipids Genetics Consortium was strongly associated with ICH risk (OR = 1.86, SE = 0.13, p = 1.39 × 10−6).InterpretationGenetic variants in CETP associated with increased HDL‐C raise the risk of ICH. Given ongoing therapeutic development in CETP inhibition and other HDL‐raising strategies, further exploration of potential adverse cerebrovascular outcomes may be warranted. Ann Neurol 2016;80:730–740

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.