Woo, Daniel

Background: Intracerebral hemorrhage (ICH) is the stroke subtype with the worst prognosis and has no established acute treatment. ICH is classified as lobar and non-lobar based on the location of the hemorrhage, with genetic variation contributing to risk of both subtypes. We report the first genome wide association study (GWAS) of this condition. Methods: The discovery phase included GWAS data (Illumina 610 or Affymetrix 6.0) from 6 ICH studies that enrolled subjects of European ancestry in the US and Europe. Cases were classified as lobar or non-lobar ICH based on head CT. ICH-free controls were selected from ambulatory clinics or by random digit dialing. Standard quality controls filters were applied and principal component analysis was used to account for population structure. Unobserved genotypes were inferred by imputation to 1000 genomes references panels. Association testing was carried out using logistic regression, assuming additive effects, and including age, gender and principal components in the model. Meta-analyses were completed using the inverse normal method, weighting by sample size. Replication of signals with p<1x10E-6 was pursued in an independent multi-ethnic study utilizing direct genotyping. Results: The discovery phase included 1545 cases (664 lobar and 881 non-lobar) and 1481 controls, and tested 5,358,103 SNPs. Two loci were identified: for lobar ICH, an intergenic region located on chromosome 12q21.1 (rs11179580: minor allele frequency [MAF] 0.22, OR 1.54, 95% CI 1.33 - 1.84; p = 6.7x10E-8); and for non-lobar ICH, a region on chromosome 1q22 encompassing the PMF1 and SLC25A44 genes (rs2984613: MAF 0.31, OR 1.45, 95% CI 1.2 - 1.64; p = 8.9x10E-9). Replication included 1256 cases (329 whites, 506 blacks and 421 Hispanics) and 760 controls (237 whites, 346 blacks and 177 Hispanics) and confirmed both loci: 12q21.1 (p = 0.037, meta-analysis p = 2.3x10E-8) and 1q22 (p = 3.0x10E-4, meta-analysis p = 2.2x10E-11). Conclusions: Two new susceptibility loci for ICH were identified, both showing location-specificity. These results confirm the presence of biological heterogeneity across ICH subtypes and highlight that different genetic risk factors may contribute to the small vessel diseases that underlie each ICH subtype.

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.

Abstract Stroke represents a considerable burden of disease for both men and women. However, a growing body of literature suggests clinically relevant sex differences in the underlying causes, presentations and outcomes of acute ischaemic stroke. In a recent study, we reported sex divergences in lesion topographies: specific to women, acute stroke severity was linked to lesions in the left-hemispheric posterior circulation. We here determined whether these sex-specific brain manifestations also affect long-term outcomes. We relied on 822 acute ischaemic patients [age: 64.7 (15.0) years, 39% women] originating from the multi-centre MRI-GENIE study to model unfavourable outcomes (modified Rankin Scale &gt;2) based on acute neuroimaging data in a Bayesian hierarchical framework. Lesions encompassing bilateral subcortical nuclei and left-lateralized regions in proximity to the insula explained outcomes across men and women (area under the curve = 0.81). A pattern of left-hemispheric posterior circulation brain regions, combining left hippocampus, precuneus, fusiform and lingual gyrus, occipital pole and latero-occipital cortex, showed a substantially higher relevance in explaining functional outcomes in women compared to men [mean difference of Bayesian posterior distributions (men – women) = −0.295 (90% highest posterior density interval = −0.556 to −0.068)]. Once validated in prospective studies, our findings may motivate a sex-specific approach to clinical stroke management and hold the promise of enhancing outcomes on a population level.

Abstract While chronological age is one of the most influential determinants of post-stroke outcomes, little is known of the impact of neuroimaging-derived biological brain age. We here first examine whether radiomics analysis of the texture of brain T2-FLAIR MRI images can be used to predict brain age in stroke patients. We then assess the clinical determinants of accelerated brain aging and, finally, its impact on post-stroke functional outcomes. Leveraging a multisite cohort of 4,163 ischemic stroke patients, we show that older-appearing patients have more hypertension, diabetes mellitus, prior strokes, and smoking history and are more likely to develop worse post-stroke outcomes than their younger-appearing counterparts. Our results strengthen the importance of preventive medicine for maintaining brain health in stroke patients as they age and suggest a novel methodology to capture previously undescribed prognostic information available on commonly acquired MRI sequences during routine stroke care.

Introduction: Low rates of enrollment into stroke clinical trials are an ongoing challenge for the stroke research community. We sought to retrospectively describe the association between medical eligibility and recruitment rates for two acute clinical trials. Methods: Within a population of 1.3 million, we screened local hospital admissions in 2010 using ICD-9 discharge codes 430-436, and performed chart review. Within the region are 15 adult acute care hospitals, all of which are covered by the same stroke team for clinical care and trial enrollment. Medical eligibility, the number of eligible patients/total number of patients, was calculated for the IMS-III trial and the CLEAR-ER trials using their inclusion/exclusion criteria. The number of patients enrolled in the trial per month of open enrollment was determined for each hospital. Statistical analysis included paired signed rank test and regression. Results: In 2010, 2035 ischemic strokes (IS) were hospitalized in the region. Regional medical eligibility was 1.7% for IMS III and 4.5% for CLEARER (p=0.001), overall regional hospital enrollment/month was 0.81 for IMS III and 1.84 for CLEAR-ER (p=0.001). Eligibility rates per hospital ranged from 0-9.5%, and recruitment rates per hospital/mo ranged from 0-0.38. Hospital-level eligibility and recruitment rates were significantly correlated (see figure for both trials combined, adjusted r2 = 0.57, p<0.001.) Discussion: Within our population, medical eligibility explained 57% of the variability in hospital-level enrollment rates for two acute IS trials. The use of population-based epidemiology in trial planning may identify optimal trial enrollment criteria, which must be weighed against proper patient selection. Many other factors impact enrollment rates, such as competing trials, which were not considered here. Further evaluation of our population-based eligibility rates in sites outside our region and with additional clinical trials is needed.

Background: Considerable disagreement exists among experts regarding the appropriate extent of diagnostic testing after ischemic stroke (IS). Evidence-based data guiding stroke evaluation for individual patients according to the underlying phenotype are unknown. We sought to determine the yield of cardiac and arterial evaluations for identifying an alternative major stroke etiology in patients with a given phenotype in the NINDS Stroke Genetics Network (SiGN). Methods: SiGN is a consortium of IS studies at 22 US and European sites aiming to identify stroke susceptibility genes. Adjudicators certified in Causative Classification of Stroke (CCS) system determined major etiologic stroke phenotypes based on chart review. All diagnostic data entered into the CCS software were stored in a confidential database. We determined the probability of identifying an alternative major abnormality based on cardiac (history, EKG, echocardiography) and arterial [intracranial (IC), extracranial (EC)] evaluations in 3 stroke phenotypes: lacunar infarct (LI), large artery atherosclerosis (LAA), and cardiac embolism (CE). Proportions of alternative major etiologies were calculated in patients with complete evaluations in each phenotype. Results: The analyses included 15720 patients. Among these, 2299 had phenotypic subtype of LI (1660 with cardiac and 1691 with arterial evaluations); 4228 had CE (2451 with arterial evaluation); and 2613 had LAA (2047 with cardiac evaluation). Cardiac evaluation revealed a major cardiac source of embolism in 12.3% of patients with LI and 20.3% with LAA. Echocardiography demonstrated an additional major structural cardiac source not seen by history/examination/EKG in 1.9% and 1.6% in LI and LAA respectively. Arterial evaluation led to the identification of ≥50%ipsilateral stenosis in 13.5% (IC: 8.9%, EC: 5.8%) of patients with LI and 17% (IC: 8.1%, EC: 11.3%) of those with CE. In LI patients with complete cardiac and vascular evaluations, 24.1% had an alternative major stroke etiology. Conclusions: A considerable proportion of IS patients with LI, LAA, and CE harbor an alternative major etiology. In the future, these results can be used to generate evidence-based and cost-effective evaluation and management strategies.

Objectives: Identification of gene variants of stroke subtypes is important for the development of tailored ischemic stroke therapies among various ethnic groups. Valid and reliable determination of ischemic stroke subtype is essential for achieving this goal and to standardize a classification scheme across multi-center studies and different populations. Causative Classification System for Ischemic Stroke (CCS) is a novel computerized subclassification tool developed to improve reliability and accuracy of classifying stroke types. The CCS algorithm relies on both phenotypic and causative stroke variables. A Hispanic subset of the SiGN, an important and distinct target population with greater risk of certain stroke subtypes, was evaluated with Trial of Org 10172 in Acute Stroke Treatment (TOAST) and CCS and the agreement between the two classification systems was analyzed. Methods: Over 6000 subjects at 15 sites across US and Europe were enrolled, with TOAST and CCS locally adjudicated. Blood collection and central data quality control (10% central readjudication) were performed on all participants. A subset of Hispanics was analyzed for the purpose of this study and the agreement between the TOAST and CCS were assessed by kappa statistic. Findings: Hispanics (n=595, 10.9%) compared to non-Hispanics (n=5457) were more likely to be younger (63.7 vs. 64.0), male (55% vs. 46%) and have fewer of the traditional stroke risk factors HTN (54% vs. 64%), Afib (11% vs. 14%), DM(23% vs. 25%), CAD(16% vs. 20%) and smoking(19% vs. 22%). While the TOAST showed no differences between stroke subtypes for Hispanic vs. non-Hispanics, in CCS, Hispanics were classified with more of large vessel (22% vs. 20%), cardioembolic (37% vs. 30%) and small vessel strokes (13% vs. 9%) and fewer with undetermined etiology (28% vs. 40%) as compared to non-Hispanics. TOAST and CCS offered moderate correlation across all stroke types in Hispanics: kappa of 0.66 for large artery atherosclerosis, 0.58 for cardioembolic, and 0.58 for small artery occlusion. Conclusion: CCS offers a more sensitive and accurate system for subphenotyping of strokes in Hispanics who tended to have relatively fewer risk factors and unclassified strokes. Further studies correlating the two classification systems and their relation to genotyping data are warranted.

Background: The Stroke Genetics Network (SiGN) funded by the NINDS aims to identify genetic risk factors in ischemic stroke using whole-genome association studies (GWAS). High quality phenotyping is crucial to successful application of GWAS. As a heterogenous disorder, stroke poses specific challenges. The Trial of Org 10172 in Acute Stroke Treatment (TOAST) classification is a broadly used, but its validity is challenged especially when performed by multiple investigators with differing interpretations of the system. The Causative Classification System for Ischemic Stroke (CCS) system is a new, web-based, and computerized algorithm that integrates clinical, diagnostic, and etiologic stroke characteristics in an evidence-based manner ( ccs.mgh.harvard.edu ) to generate subtypes. Methods: In planning the SiGN proposal, a sample of 20 coded charts were collected from a subset of participating studies to assess feasibility of central adjudication and comparability to study-specific TOAST. Two central adjudicators reviewed all records and generated TOAST and CCS subtypes. These were compared to study-specific TOAST subtype and the CCS phenotype generated for SiGN by local trained adjudicators. CCS data is now available for 7134 included cases using both a 5 and a 7 category system as defined in the table . Results: All 4 phenotypes were available for 115 ischemic stroke cases from 6 studies in SiGN. Basic demographics were 54% women, 63% white, and median age between 65-74. Table 1 provides the agreement between the various subtypes. Table 2 describes the types of disagreement. Conclusions: Central adjudication with only two adjudicators and curated medical records yielded more consistent subtyping independent of phenotyping system. The agreement for TOAST was higher than published rates by independent groups (∼0.50). In contrast, the agreement for CCS was lower than previously published (0.85-0.95). Site adjudicators' familiarity with TOAST and inexperience with CCS may contribute. Although CCS is an automated algorithm and has a number of user friendly features, our findings suggest that formal training and certification process before starting to use CCS may be worthwhile to achieve optimal benefit from the system.

Background: Identification of clinical and imaging characteristics of ischemic stroke that can predict the underlying etiology is critical for accurate stroke subtyping. We sought to investigate the relationship between stroke characteristics and stroke etiology in 7,591 patients in the NINDS Stroke Genetics Network Study. Methods: We determined the probability of having a major etiology in the presence of certain stroke characteristics that were previously linked to specific stroke mechanisms. We excluded patients with multiple competing major etiologies. All patients had brain imaging, intra- and extra-cranial vascular imaging, and complete cardiac assessment. We defined major etiologies according to the Causative Classification of Stroke System Criteria without reference to the stroke characteristics under study. Results: History of 1 ipsilateral ischemic events within the last month, the presence of internal watershed infarcts or multiple, ipsilateral, unilateral, and temporally separate infarcts were associated with large artery atherosclerosis (Table). Multiple acute infarcts in different cerebral circulations were more common in patients with major cardiac source of embolism as well as in those with uncommon/cryptogenic causes. Stereotypic lacunar TIAs within the last week and lacunar syndrome were associated with lacunar infarction Discussion: Clinical and imaging stroke characteristics appear to predict stroke etiologies. This information may find utility in developing algorithms for a guided stroke evaluation as well as help identify the most likely causative etiology in the presence of multiple competing etiologies.

Introduction: White matter hyperintensity volume (WMHv) is an important and highly heritable cerebrovascular phenotype; however, manual or semi-automated approaches to clinically acquired MRI analysis hinder large-scale studies in acute ischemic stroke (AIS). In this work, we develop a high-throughput, fully automated WMHv analysis pipeline for clinical fluid-attenuated inversion recovery (FLAIR) images to facilitate rapid genetic discovery in AIS. Methods: Automated WMHv extraction from multiple subjects relies on significant pre-processing of medical scans, including co-registration of the images. To reduce the effects of anisotropic voxel sizes, each FLAIR image is upsampled using bi-cubic interpolation. Brain extraction is performed using RObust Brain EXtraction (ROBEX). Images are then registered to an in-house FLAIR template using Advanced Normalization Tools (ANTs). The spatial covariation of WMH is learned through principal component analysis (PCA) of manual outlines from 100 subjects. Areas of leukoaraiosis are identified and separated from other lesions using the PCA modes. Volumes are then computed using non-interpolated slices for each subject. Standard deviation (SD) in WMHv (9 subjects; 6 raters each) is calculated as a measure of variability. Good agreement between automated and manual outlines is assessed in 358 subjects (automated WMHv within 3SD of manual WMHv). Results: As part of the MRI - Gen etics I nterface E xploration (MRI-GENIE) study, WMHv were calculated on a set of 2703 FLAIR images of patients from 12 independent AIS cohorts (sites). Results are shown in Figure 1. Comparing manual and automated WMHv shows that 88% of the automated WMHv fall within 3 SD from the manual WMHv, suggesting good agreement. Conclusion: WMHv segmentation using a fully-automated pipeline for analysis of clinical MRIs is both feasible and accurate. Ongoing analysis of the extracted WMHv is expected to advance current knowledge of risks and outcomes in AIS.