Woo, Daniel

Abstract This study aimed to investigate the influence of stroke lesions in predefined highly interconnected (rich‐club) brain regions on functional outcome post‐stroke, determine their spatial specificity and explore the effects of biological sex on their relevance. We analyzed MRI data recorded at index stroke and ~3‐months modified Rankin Scale (mRS) data from patients with acute ischemic stroke enrolled in the multisite MRI‐GENIE study. Spatially normalized structural stroke lesions were parcellated into 108 atlas‐defined bilateral (sub)cortical brain regions. Unfavorable outcome (mRS > 2) was modeled in a Bayesian logistic regression framework. Effects of individual brain regions were captured as two compound effects for (i) six bilateral rich club and (ii) all further non‐rich club regions. In spatial specificity analyses, we randomized the split into “rich club” and “non‐rich club” regions and compared the effect of the actual rich club regions to the distribution of effects from 1000 combinations of six random regions. In sex‐specific analyses, we introduced an additional hierarchical level in our model structure to compare male and female‐specific rich club effects. A total of 822 patients (age: 64.7[15.0], 39% women) were analyzed. Rich club regions had substantial relevance in explaining unfavorable functional outcome (mean of posterior distribution: 0.08, area under the curve: 0.8). In particular, the rich club‐combination had a higher relevance than 98.4% of random constellations. Rich club regions were substantially more important in explaining long‐term outcome in women than in men. All in all, lesions in rich club regions were associated with increased odds of unfavorable outcome. These effects were spatially specific and more pronounced in women.

Background: Epsilon (e) variants in the Apolipoprotein E (APOE) gene are well-established risk factors for spontaneous intracerebral hemorrhage (s-ICH). We tested the hypothesis that APOE-e variants are also associated with warfarin-related ICH (w-ICH) and examined interactions between APOE and warfarin. Methods: Prospective multicenter 2-stage (discovery and replication) study. ICH was classified as lobar and non-lobar based on admission head CT. In the discovery stage, w-ICH cases were matched with warfarin-exposed controls (w-controls). In replication, w-ICH cases were matched with non-warfarin controls (nw-controls). APOE was directly genotyped. Association testing was performed using multivariable logistic regression. Gene-environment interaction between APOE and warfarin was formally tested using a case only approach (combining s-ICH and w-ICH cases). Results: The discovery stage included 316 w-ICHs (43% lobar and 57% non-lobar) and 355 w-controls. Results are presented in the Table: APOE-e2 was associated with lobar and non-lobar w-ICH, and APOE-e4 was associated with lobar but not with non-lobar w-ICH. In case-only analysis, 885 s-ICHs were combined with w-ICHs (total n=1201) and no evidence of interaction between APOE and warfarin was found (all p>0.20). The replication included 63 w-ICHs (44% lobar and 56% non-lobar) and 990 nw-controls. The distribution of APOE variants was similar in w-controls (discovery) and nw-controls (replication) for both e2 (p=0.81) and e4 (p=0.88). In replication, APOE-e2 and e4 were associated with lobar but not with non-lobar w-ICH. Conclusions: APOE-e variants constitute strong risk factors for lobar w-ICH. An effect may also exist between APOE-e2 and non-lobar hemorrhages, but given the lack of replication larger sample sizes are required to properly assess this association. APOE exerts its effect independently of warfarin, although power limitations render this absence of interaction preliminary.

Objectives: Genetic and environmental influences contribute to differences in ischemic stroke phenotype across race-ethnic groups. The Causative Classification System (CSS) is an ischemic stroke phenotype characterization tool developed for etiological and causative classification. The CCS phenotypes across ethnic groups were analyzed and compared. Methods: The NINDS S troke G enetics N etwork (SiGN) is a large consortium aimed at identifying subtype-specific ischemic stroke susceptibility genes. Over 12,000 subjects at 22 sites across US and Europe were enrolled, with CSS locally adjudicated. Central CCS quality control was performed on all participants. Analysis was performed on 7,674 patients with complete workups. Findings: Significant ethnic differences were found in stroke etiology across ethnic groups (p <0.0001) with large artery atherosclerosis present most commonly in Asians, cardioembolic sources among Caucasian Europeans, and lacunar strokes among African Americans. African Americans were the youngest ethnic cohort analyzed (see Table 1). Conclusion: Significant differences in stroke subtypes are present across ethnic groups. Future genetic analyses may contribute to our understanding of these differences.

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.