Search

Background and ObjectivesWhile chronological age is one of the most influential determinants of poststroke outcomes, little is known of the impact of neuroimaging-derived biological “brain age.” We hypothesized that radiomics analyses of T2-FLAIR images texture would provide brain age estimates and that advanced brain age of patients with stroke will be associated with cardiovascular risk factors and worse functional outcomes.MethodsWe extracted radiomics from T2-FLAIR images acquired during acute stroke clinical evaluation. Brain age was determined from brain parenchyma radiomics using an ElasticNet linear regression model. Subsequently, relative brain age (RBA), which expresses brain age in comparison with chronological age-matched peers, was estimated. Finally, we built a linear regression model of RBA using clinical cardiovascular characteristics as inputs and a logistic regression model of favorable functional outcomes taking RBA as input.ResultsWe reviewed 4,163 patients from a large multisite ischemic stroke cohort (mean age = 62.8 years, 42.0% female patients). T2-FLAIR radiomics predicted chronological ages (mean absolute error = 6.9 years,r= 0.81). After adjustment for covariates, RBA was higher and therefore described older-appearing brains in patients with hypertension, diabetes mellitus, a history of smoking, and a history of a prior stroke. In multivariate analyses, age, RBA, NIHSS, and a history of prior stroke were all significantly associated with functional outcome (respective adjusted odds ratios: 0.58, 0.76, 0.48, 0.55; allp-values < 0.001). Moreover, the negative effect of RBA on outcome was especially pronounced in minor strokes.DiscussionT2-FLAIR radiomics can be used to predict brain age and derive RBA. Older-appearing brains, characterized by a higher RBA, reflect cardiovascular risk factor accumulation and are linked to worse outcomes after stroke.

Background and purpose The role of copy number variation (CNV) variation in stroke susceptibility and outcome has yet to be explored. The Copy Number Variation and Stroke (CaNVAS) Risk and Outcome study addresses this knowledge gap. Methods Over 24,500 well-phenotyped IS cases, including IS subtypes, and over 43,500 controls have been identified, all with readily available genotyping on GWAS and exome arrays, with case measures of stroke outcome. To evaluate CNV-associated stroke risk and stroke outcome it is planned to: 1) perform Risk Discovery using several analytic approaches to identify CNVs that are associated with the risk of IS and its subtypes, across the age-, sex- and ethnicity-spectrums; 2) perform Risk Replication and Extension to determine whether the identified stroke-associated CNVs replicate in other ethnically diverse datasets and use biomarker data (e.g. methylation, proteomic, RNA, miRNA, etc.) to evaluate how the identified CNVs exert their effects on stroke risk, and lastly; 3) perform outcome-based Replication and Extension analyses of recent findings demonstrating an inverse relationship between CNV burden and stroke outcome at 3 months (mRS), and then determine the key CNV drivers responsible for these associations using existing biomarker data. Results The results of an initial CNV evaluation of 50 samples from each participating dataset are presented demonstrating that the existing GWAS and exome chip data are excellent for the planned CNV analyses. Further, some samples will require additional considerations for analysis, however such samples can readily be identified, as demonstrated by a sample demonstrating clonal mosaicism. Conclusion The CaNVAS study will cost-effectively leverage the numerous advantages of using existing case-control data sets, exploring the relationships between CNV and IS and its subtypes, and outcome at 3 months, in both men and women, in those of African and European-Caucasian descent, this, across the entire adult-age spectrum.

OBJECTIVE:To explore genetic and lifestyle risk factors of MRI-defined brain infarcts (BI) in large population-based cohorts. METHODS:We performed meta-analyses of genome-wide association studies (GWAS) and examined associations of vascular risk factors and their genetic risk scores (GRS) with MRI-defined BI and a subset of BI, namely, small subcortical BI (SSBI), in 18 population-based cohorts (n = 20,949) from 5 ethnicities (3,726 with BI, 2,021 with SSBI). Top loci were followed up in 7 population-based cohorts (n = 6,862; 1,483 with BI, 630 with SBBI), and we tested associations with related phenotypes including ischemic stroke and pathologically defined BI. RESULTS:The mean prevalence was 17.7% for BI and 10.5% for SSBI, steeply rising after age 65. Two loci showed genome-wide significant association with BI: FBN2, p = 1.77 × 10-8; and LINC00539/ZDHHC20, p = 5.82 × 10-9. Both have been associated with blood pressure (BP)-related phenotypes, but did not replicate in the smaller follow-up sample or show associations with related phenotypes. Age- and sex-adjusted associations with BI and SSBI were observed for BP traits (p value for BI, p [BI] = 9.38 × 10-25; p [SSBI] = 5.23 × 10-14 for hypertension), smoking (p [BI] = 4.4 × 10-10; p [SSBI] = 1.2 × 10-4), diabetes (p [BI] = 1.7 × 10-8; p [SSBI] = 2.8 × 10-3), previous cardiovascular disease (p [BI] = 1.0 × 10-18; p [SSBI] = 2.3 × 10-7), stroke (p [BI] = 3.9 × 10-69; p [SSBI] = 3.2 × 10-24), and MRI-defined white matter hyperintensity burden (p [BI] = 1.43 × 10-157; p [SSBI] = 3.16 × 10-106), but not with body mass index or cholesterol. GRS of BP traits were associated with BI and SSBI (p ≤ 0.0022), without indication of directional pleiotropy. CONCLUSION:In this multiethnic GWAS meta-analysis, including over 20,000 population-based participants, we identified genetic risk loci for BI requiring validation once additional large datasets become available. High BP, including genetically determined, was the most significant modifiable, causal risk factor for BI.

Collaborators (233): McArdle PF, Wong Q, Gwinn K, Achterberg S, Algra A, Amouyel P, Arnett DK, Arsava EM, Attia J, Ay H, Bartz TM, Battey T, Benavente OR, Bevan S, Biffi A, Bis JC, Blanton SH, P J, Boncoraglio GB, Brown RD Jr, Burgess AI, Carrera C, Chapman Smith SN, Chasman DI, Chauhan G, Wei-Min Chen F, Cheng YC, Cloonan LK, Cole JW, Cotlarciuc I, Cruchaga C, Cuadrado-Godia E, Dawson J, Debette S, Delavaran H, Dell CA, Doheny KF, Dong C, Duggan DJ, Engström G, Evans MK, Pallejà XE, Faul JD, Fornage M, Frossard PM, Furie K, Gamble DM, Gieger C, Giese AK, Giralt-Steinhauer E, González HM, Goris A, Gretarsdottir S, Grewal RP, Grittner U, Gustafsson S, Han B, Hankey GJ, Heitsch L, Higgins P, Hochberg MC, Holliday E, Hopewell JC, Horenstein RB, Howard G, Ikram MA, Ilinca A, Ingelsson E, Irvin MR, Jackson RD, Jern C, Johnson JA, Jood K, Kahn MS, Kaplan R, Kappelle LJ, Kardia SL, Keene KL, Kissela BM, Kleindorfer DO, Koblar S, Labovitz D, Launer LJ, Laurie CC, Laurie CA, Lee CH, Lee JM, Lemmens R, Levi C, Leys D, Longstreth WT Jr, Maguire J, Manichaikul A, McClure LA, McDonough CW, Meisinger C, Melander O, Meschia JF, Mola-Caminal M, Mosley TH, Müller-Nurasyid M, Nalls MA, O'Connell JR, Ois Á, Papanicolaou GJ, Peddareddygari LR, Pedersén A, Pera J, Peters A, Poole D, Psaty BM, Rabionet R, Raffeld MR, Rasheed A, Redfors P, Reiner AP, Rexrode K, Ribasés M, Rich SS, Robberecht W, Rodríguez-Campello A, Rolfs A, Roquer J, Rose LM, Rosenbaum D, Rost NS, Rothwell PM, Rundek T, Ryan KA, Sacco RL, Sale MM, Saleheen D, Salomaa V, Sánchez-Mora C, Schmidt CO, Schmidt H, Schmidt R, Schürks M, Scott R, Segal HC, Seiler S, Sharma P, Shuldiner AR, Silver B, Smith JA, Bsc CS, Sparks MJ, Stanne T, Stefansson K, Stine OC, Strauch K, Sturm J, Tajuddin SM, Talbert RL, Tatlisumak T, Thijs V, Thorleifsson G, Thorsteindottir U, Trompet S, Valant V, Waldenberger M, Walters M, Wang L, P J, Wang XQ, Wassertheil-Smoller S, Weir DR, Wiggins KL, Williams SR, Wloch-Kopec D, Woodfield R, Wu O, Xu H, Zonderman AB, de Bakker PIW, Kittner SJ, Bevan S, Hopewell JC, Holliday EG, Zhao W, Abrantes P, Amouyel P, Attia JR, Battey TW, Berger K, Boncoraglio GB, Chauhan G, Cheng YC, Chen WM, Clarke R, Cotlarciuc I, Debette S, Ferro JM, Gamble DM, Ilinca A, Kittner SJ, Lemmens R, Levi CR, Lichtner P, Liu J, Meschia JF, Oliveira SA, Pera J, Reiner AP, Rothwell PM, Sharma P, Tatlisumak T, Thijs V, Vicente AM, Saleheen D, Thorsteinsdottir U, DeStefano AL, Gretarsdottir S, Donnelly P, Barroso I, Blackwell JM, Bramon E, Brown MA, Casas JP, Corvin A, Deloukas P, Duncanson A, Jankowski J, Markus HS, Mathew CG, Palmer CN, Plomin R, Rautanen A, Sawcer SJ, Trembath RC, Viswanathan AC, Wood NW, Spencer CC. Vicente AM - Departamento de Promoção da Saúde e Prevenção de Doenças Não Transmissíveis. Objective: To determine whether common variants in familial cerebral small vessel disease (SVD) genes confer risk of sporadic cerebral SVD. Methods: We meta-analyzed genotype data from individuals of European ancestry to determine associations of common single nucleotide polymorphisms (SNPs) in 6 familial cerebral SVD genes (COL4A1, COL4A2, NOTCH3, HTRA1, TREX1, and CECR1) with intracerebral hemorrhage (ICH) (deep, lobar, all; 1,878 cases, 2,830 controls) and ischemic stroke (IS) (lacunar, cardioembolic, large vessel disease, all; 19,569 cases, 37,853 controls). We applied data quality filters and set statistical significance thresholds accounting for linkage disequilibrium and multiple testing. Results: A locus in COL4A2 was associated (significance threshold p < 3.5 × 10−4) with both lacunar IS (lead SNP rs9515201: odds ratio [OR] 1.17, 95% confidence interval [CI] 1.11–1.24, p = 6.62 × 10−8) and deep ICH (lead SNP rs4771674: OR 1.28, 95% CI 1.13–1.44, p = 5.76 × 10−5). A SNP in HTRA1 was associated (significance threshold p < 5.5 × 10−4) with lacunar IS (rs79043147: OR 1.23, 95% CI 1.10–1.37, p = 1.90 × 10−4) and less robustly with deep ICH. There was no clear evidence for association of common variants in either COL4A2 or HTRA1 with non-SVD strokes or in any of the other genes with any stroke phenotype. Conclusions: These results provide evidence of shared genetic determinants and suggest common pathophysiologic mechanisms of distinct ischemic and hemorrhagic cerebral SVD stroke phenotypes, offering new insights into the causal mechanisms of cerebral SVD. This work was funded in part by NIH grants U01 NS069208 and P30 DK072488 (SiGN). M.D. and R.M. were supported by grants from the Deutsche Forschungsgemeinschaft (CRC 1123 [B3] and Munich Cluster for Systems Neurology [SyNergy]), the German Federal Ministry of Education and Research (BMBF, e:Med programme e:AtheroSysMed), the FP7/2007–2103 European Union project CVgenes@target (grant agreement Health-F2-2013-601456), the European Union Horizon 2020 projects SVDs@target (grant agreement 66688) and CoSTREAM (grant agreement 667375), the Fondation Leducq (Transatlantic Network of Excellence on the Pathogenesis of Small Vessel Disease of the Brain), the Vascular Dementia Research Foundation, and the Jackstaedt Foundation. G.P. and M.C. were supported by the Canadian Stroke Network, Canadian Institutes of Health Research, and Heart & Stroke Foundation. N.S.R. acknowledges the National Institute of Neurologic Disorders and Stroke K23NS064052, R01NS086905, and R01NS082285. info:eu-repo/semantics/publishedVersion

OBJECTIVE: To explore genetic and lifestyle risk factors of MRI-defined brain infarcts (BI) in large population-based cohorts. METHODS: We performed meta-analyses of genome-wide association studies (GWAS) and examined associations of vascular risk factors and their genetic risk scores (GRS) with MRI-defined BI and a subset of BI, namely, small subcortical BI (SSBI), in 18 population-based cohorts (n = 20,949) from 5 ethnicities (3,726 with BI, 2,021 with SSBI). Top loci were followed up in 7 population-based cohorts (n = 6,862; 1,483 with BI, 630 with SBBI), and we tested associations with related phenotypes including ischemic stroke and pathologically defined BI. RESULTS: The mean prevalence was 17.7% for BI and 10.5% for SSBI, steeply rising after age 65. Two loci showed genome-wide significant association with BI: FBN2, p = 1.77 × 10-8; and LINC00539/ZDHHC20, p = 5.82 × 10-9. Both have been associated with blood pressure (BP)-related phenotypes, but did not replicate in the smaller follow-up sample or show associations with related phenotypes. Age- and sex-adjusted associations with BI and SSBI were observed for BP traits (p value for BI, p [BI] = 9.38 × 10-25; p [SSBI] = 5.23 × 10-14 for hypertension), smoking (p [BI] = 4.4 × 10-10; p [SSBI] = 1.2 × 10-4), diabetes (p [BI] = 1.7 × 10-8; p [SSBI] = 2.8 × 10-3), previous cardiovascular disease (p [BI] = 1.0 × 10-18; p [SSBI] = 2.3 × 10-7), stroke (p [BI] = 3.9 × 10-69; p [SSBI] = 3.2 × 10-24), and MRI-defined white matter hyperintensity burden (p [BI] = 1.43 × 10-157; p [SSBI] = 3.16 × 10-106), but not with body mass index or cholesterol. GRS of BP traits were associated with BI and SSBI (p ≤ 0.0022), without indication of directional pleiotropy. CONCLUSION: In this multiethnic GWAS meta-analysis, including over 20,000 population-based participants, we identified genetic risk loci for BI requiring validation once additional large datasets become available. High BP, including genetically determined, was the most significant modifiable, causal risk factor for BI.

BACKGROUND: Genome-wide association studies have identified multiple loci associated with stroke. However, the specific stroke subtypes affected, and whether loci influence both ischemic and hemorrhagic stroke, remains unknown. For loci associated with stroke, we aimed to infer the combination of stroke subtypes likely to be affected, and in doing so assess the extent to which such loci have homogeneous effects across stroke subtypes. METHODS: We performed Bayesian multinomial regression in 16 664 stroke cases and 32 792 controls of European ancestry to determine the most likely combination of stroke subtypes affected for loci with published genome-wide stroke associations, using model selection. Cases were subtyped under 2 commonly used stroke classification systems, TOAST (Trial of Org 10172 Acute Stroke Treatment) and causative classification of stroke. All individuals had genotypes imputed to the Haplotype Reference Consortium 1.1 Panel. RESULTS: Sixteen loci were considered for analysis. Seven loci influenced both hemorrhagic and ischemic stroke, 3 of which influenced ischemic and hemorrhagic subtypes under both TOAST and causative classification of stroke. Under causative classification of stroke, 4 loci influenced both small vessel stroke and intracerebral hemorrhage. An EDNRA locus demonstrated opposing effects on ischemic and hemorrhagic stroke. No loci were predicted to influence all stroke subtypes in the same direction, and only one locus (12q24) was predicted to influence all ischemic stroke subtypes. CONCLUSIONS: Heterogeneity in the influence of stroke-associated loci on stroke subtypes is pervasive, reflecting differing causal pathways. However, overlap exists between hemorrhagic and ischemic stroke, which may reflect shared pathobiology predisposing to small vessel arteriopathy. Stroke is a complex, heterogeneous disorder requiring tailored analytic strategies to decipher genetic mechanisms.