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1. Scaling behaviours of deep learning and linear algorithms for the prediction of stroke severity.

Author

Bourached, A, Bonkhoff, AK, Schirmer, MD, Regenhardt, RW, Bretzner, M, Hong, S, Dalca, AV, Giese, A-K, Winzeck, S, Jern, C, Lindgren, AG, Maguire, J, Wu, O, Rhee, J, Kimchi, EY, Rost, NS, Bourached, A, Bonkhoff, AK, Schirmer, MD, Regenhardt, RW, Bretzner, M, Hong, S, Dalca, AV, Giese, A-K, Winzeck, S, Jern, C, Lindgren, AG, Maguire, J, Wu, O, Rhee, J, Kimchi, EY, and Rost, NS

Abstract

Deep learning has allowed for remarkable progress in many medical scenarios. Deep learning prediction models often require 105-107 examples. It is currently unknown whether deep learning can also enhance predictions of symptoms post-stroke in real-world samples of stroke patients that are often several magnitudes smaller. Such stroke outcome predictions however could be particularly instrumental in guiding acute clinical and rehabilitation care decisions. We here compared the capacities of classically used linear and novel deep learning algorithms in their prediction of stroke severity. Our analyses relied on a total of 1430 patients assembled from the MRI-Genetics Interface Exploration collaboration and a Massachusetts General Hospital-based study. The outcome of interest was National Institutes of Health Stroke Scale-based stroke severity in the acute phase after ischaemic stroke onset, which we predict by means of MRI-derived lesion location. We automatically derived lesion segmentations from diffusion-weighted clinical MRI scans, performed spatial normalization and included a principal component analysis step, retaining 95% of the variance of the original data. We then repeatedly separated a train, validation and test set to investigate the effects of sample size; we subsampled the train set to 100, 300 and 900 and trained the algorithms to predict the stroke severity score for each sample size with regularized linear regression and an eight-layered neural network. We selected hyperparameters on the validation set. We evaluated model performance based on the explained variance (R2) in the test set. While linear regression performed significantly better for a sample size of 100 patients, deep learning started to significantly outperform linear regression when trained on 900 patients. Average prediction performance improved by ∼20% when increasing the sample size 9× [maximum for 100 patients: 0.279 ± 0.005 (R2, 95% confidence interval), 900 patients: 0.337 ± 0.006

Published
2024

2. The relevance of rich club regions for functional outcome post-stroke is enhanced in women.

Author

Bonkhoff, AK, Schirmer, MD, Bretzner, M, Hong, S, Regenhardt, RW, Donahue, KL, Nardin, MJ, Dalca, AV, Giese, A-K, Etherton, MR, Hancock, BL, Mocking, SJT, McIntosh, EC, Attia, J, Cole, JW, Donatti, A, Griessenauer, CJ, Heitsch, L, Holmegaard, L, Jood, K, Jimenez-Conde, J, Kittner, SJ, Lemmens, R, Levi, CR, McDonough, CW, Meschia, JF, Phuah, C-L, Ropele, S, Rosand, J, Roquer, J, Rundek, T, Sacco, RL, Schmidt, R, Sharma, P, Slowik, A, Sousa, A, Stanne, TM, Strbian, D, Tatlisumak, T, Thijs, V, Vagal, A, Wasselius, J, Woo, D, Zand, R, McArdle, PF, Worrall, BB, Jern, C, Lindgren, AG, Maguire, J, Wu, O, Rost, NS, MRI-GENIE and GISCOME Investigators and the International Stroke Genetics Consortium, Bonkhoff, AK, Schirmer, MD, Bretzner, M, Hong, S, Regenhardt, RW, Donahue, KL, Nardin, MJ, Dalca, AV, Giese, A-K, Etherton, MR, Hancock, BL, Mocking, SJT, McIntosh, EC, Attia, J, Cole, JW, Donatti, A, Griessenauer, CJ, Heitsch, L, Holmegaard, L, Jood, K, Jimenez-Conde, J, Kittner, SJ, Lemmens, R, Levi, CR, McDonough, CW, Meschia, JF, Phuah, C-L, Ropele, S, Rosand, J, Roquer, J, Rundek, T, Sacco, RL, Schmidt, R, Sharma, P, Slowik, A, Sousa, A, Stanne, TM, Strbian, D, Tatlisumak, T, Thijs, V, Vagal, A, Wasselius, J, Woo, D, Zand, R, McArdle, PF, Worrall, BB, Jern, C, Lindgren, AG, Maguire, J, Wu, O, Rost, NS, and MRI-GENIE and GISCOME Investigators and the International Stroke Genetics Consortium

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.

Published
2023

3. Radiomics-Derived Brain Age Predicts Functional Outcome After Acute Ischemic Stroke.

Author

Bretzner, M, Bonkhoff, AK, Schirmer, MD, Hong, S, Dalca, A, Donahue, K, Giese, A-K, Etherton, MR, Rist, PM, Nardin, M, Regenhardt, RW, Leclerc, X, Lopes, R, Gautherot, M, Wang, C, Benavente, OR, Cole, JW, Donatti, A, Griessenauer, C, Heitsch, L, Holmegaard, L, Jood, K, Jimenez-Conde, J, Kittner, SJ, Lemmens, R, Levi, CR, McArdle, PF, McDonough, CW, Meschia, JF, Phuah, C-L, Rolfs, A, Ropele, S, Rosand, J, Roquer, J, Rundek, T, Sacco, RL, Schmidt, R, Sharma, P, Slowik, A, Sousa, A, Stanne, TM, Strbian, D, Tatlisumak, T, Thijs, V, Vagal, A, Wasselius, J, Woo, D, Wu, O, Zand, R, Worrall, BB, Maguire, J, Lindgren, AG, Jern, C, Golland, P, Kuchcinski, G, Rost, NS, Bretzner, M, Bonkhoff, AK, Schirmer, MD, Hong, S, Dalca, A, Donahue, K, Giese, A-K, Etherton, MR, Rist, PM, Nardin, M, Regenhardt, RW, Leclerc, X, Lopes, R, Gautherot, M, Wang, C, Benavente, OR, Cole, JW, Donatti, A, Griessenauer, C, Heitsch, L, Holmegaard, L, Jood, K, Jimenez-Conde, J, Kittner, SJ, Lemmens, R, Levi, CR, McArdle, PF, McDonough, CW, Meschia, JF, Phuah, C-L, Rolfs, A, Ropele, S, Rosand, J, Roquer, J, Rundek, T, Sacco, RL, Schmidt, R, Sharma, P, Slowik, A, Sousa, A, Stanne, TM, Strbian, D, Tatlisumak, T, Thijs, V, Vagal, A, Wasselius, J, Woo, D, Wu, O, Zand, R, Worrall, BB, Maguire, J, Lindgren, AG, Jern, C, Golland, P, Kuchcinski, G, and Rost, NS

Abstract

BACKGROUND AND OBJECTIVES: 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 hypothesized that radiomics analyses of T2-FLAIR images texture would provide brain age estimates and that advanced brain age of stroke patients will be associated with cardiovascular risk factors and worse functional outcomes. METHODS: We 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 to 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. RESULTS: We reviewed 4,163 patients from a large multisite ischemic stroke cohort (mean age=62.8 years, 42.0% females). 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; all p-values<0.001). Moreover, the negative effect of RBA on outcome was especially pronounced in minor strokes. DISCUSSION: T2-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.

Published
2023

4. Heterogeneous treatment effects of therapeutic-dose heparin in patients hospitalized for COVID-19

Author

Goligher, EC, Lawler, PR, Jensen, TP, Talisa, V, Berry, LR, Lorenzi, E, McVerry, BJ, Chang, C-CH, Leifer, E, Bradbury, C, Berger, J, Hunt, BJ, Castellucci, LA, Kornblith, LZ, Gordon, AC, McArthur, C, Webb, S, Hochman, J, Neal, MD, Zarychanski, R, Berry, S, Angus, DC, Aday, A, Ahuja, T, Al-Beidh, F, Annane, D, Arabi, YM, Aryal, D, Baumann Kreuziger, L, Beane, A, Berger, JS, Berry, SM, Bhimani, Z, Bihari, S, Billett, HH, Bond, L, Bonten, M, Bradbury, CA, Brooks, MM, Brunkhorst, F, Buxton, M, Buzgau, A, Carrier, M, Castelucci, LA, Chekuri, S, Chen, J-T, Cheng, AC, Chkhikvadze, T, Coiffard, B, Contreras, A, Costantini, TW, Cushman, M, De Brouwer, S, Derde, LPG, Detry, MA, Duggal, A, Džavík, V, Effron, MB, Eng, HF, Escobedo, J, Estcourt, LJ, Everett, BM, Farkough, ME, Fergusson, DA, Fitzgerald, M, Fowler, RA, Froess, JD, Fu, Z, Galanaud, J-P, Galen, BT, Gandotra, S, Girard, TD, Godoy, LD, Gong, MN, Goodman, AL, Goossens, H, Green, C, Greenstein, YY, Gross, PL, Guerrero, RM, Hamburg, N, Haniffa, R, Hanna, G, Hanna, N, Hedge, SM, Hendrickson, CM, Higgins, AM, Hindenburg, AA, Hite, RD, Hochman, JS, Hope, AA, Horowitz, JM, Horvat, CM, Houston, BL, Huang, DT, Hudock, K, Husain, M, Hyzy, RC, Iyer, V, Jacobson, JR, Jayakumar, D, Kahn, SR, Keller, NM, Khan, A, Kim, Y, Kim, KS, Kindzelski, A, King, AJ, Kirwan, B-A, Knudson, MM, Kornblith, AE, Krishnan, V, Kumar, A, Kutcher, ME, Laffan, MA, Lamontagne, F, Le Gal, G, Leeper, CM, Leifer, ES, Lewis, RJ, Lim, G, Lima, FG, Linstrum, K, Litton, E, Lopez-Sendon, J, Lopez-Sendon Moreno, JL, Lother, SA, Madrona, SG, Malhotra, S, Marcos Martin, M, Marshall, JC, Marten, N, Martinez, AS, Martinez, M, Mateos Garcia, E, Matthay, MA, Mavromichalis, S, McArthur, CJ, McAuley, DF, McDonald, EG, McGlothlin, A, McGuinness, SP, McQuilten, ZK, Middeldorp, S, Montgomery, SK, Moore, SC, Mouncey, PR, Murthy, S, Nair, GB, Nair, R, Nichol, AD, Nicolau, JC, Nunez-Garcia, B, Pandey, A, Park, JJ, Park, PK, Parke, RL, Parker, JC, Parnia, S, Paul, JD, Pompilio, M, Prekker, M, Quigley, JG, Reynolds, HR, Rosenson, RS, Rost, NS, Rowan, K, Santos, MO, Santos, FO, Santos, M, Satterwhite, L, Saunders, CT, Schreiber, J, Schutgens, REG, Seymour, CW, Shankar Hari, M, Sheehan, JP, Siegal, DM, Silva Jr., DG, Singhal, AB, Slutsky, AS, Solvason, D, Stanworth, SJ, Tritschler, T, Turgeon, AF, Turner, AM, Van Bentum-Puijk, W, Van de Veerdonk, FL, Van Diepen, S, Vazquez Grande, G, Wahid, L, Wareham, V, Webb, SA, Wells, B, Widmer, RJ, Wilson, JG, Yuriditsky, E, Zampieri, F, and Zhong, Y

Abstract

Importance Randomized clinical trials (RCTs) of therapeutic-dose heparin in patients hospitalized with COVID-19 produced conflicting results, possibly due to heterogeneity of treatment effect (HTE) across individuals. Better understanding of HTE could facilitate individualized clinical decision-making. Objective To evaluate HTE of therapeutic-dose heparin for patients hospitalized for COVID-19 and to compare approaches to assessing HTE. Design, Setting, and Participants Exploratory analysis of a multiplatform adaptive RCT of therapeutic-dose heparin vs usual care pharmacologic thromboprophylaxis in 3320 patients hospitalized for COVID-19 enrolled in North America, South America, Europe, Asia, and Australia between April 2020 and January 2021. Heterogeneity of treatment effect was assessed 3 ways: using (1) conventional subgroup analyses of baseline characteristics, (2) a multivariable outcome prediction model (risk-based approach), and (3) a multivariable causal forest model (effect-based approach). Analyses primarily used bayesian statistics, consistent with the original trial. Exposures Participants were randomized to therapeutic-dose heparin or usual care pharmacologic thromboprophylaxis. Main Outcomes and Measures Organ support–free days, assigning a value of −1 to those who died in the hospital and the number of days free of cardiovascular or respiratory organ support up to day 21 for those who survived to hospital discharge; and hospital survival. Results Baseline demographic characteristics were similar between patients randomized to therapeutic-dose heparin or usual care (median age, 60 years; 38% female; 32% known non-White race; 45% Hispanic). In the overall multiplatform RCT population, therapeutic-dose heparin was not associated with an increase in organ support–free days (median value for the posterior distribution of the OR, 1.05; 95% credible interval, 0.91-1.22). In conventional subgroup analyses, the effect of therapeutic-dose heparin on organ support–free days differed between patients requiring organ support at baseline or not (median OR, 0.85 vs 1.30; posterior probability of difference in OR, 99.8%), between females and males (median OR, 0.87 vs 1.16; posterior probability of difference in OR, 96.4%), and between patients with lower body mass index (BMI 90% for all comparisons). In risk-based analysis, patients at lowest risk of poor outcome had the highest propensity for benefit from heparin (lowest risk decile: posterior probability of OR >1, 92%) while those at highest risk were most likely to be harmed (highest risk decile: posterior probability of OR

Published
2023

5. Migraine-Associated Common Genetic Variants Confer Greater Risk of Posterior vs. Anterior Circulation Ischemic Stroke☆.

Author

Frid, P, Xu, H, Mitchell, BD, Drake, M, Wasselius, J, Gaynor, B, Ryan, K, Giese, AK, Schirmer, M, Donahue, KL, Irie, R, Bouts, MJRJ, McIntosh, EC, Mocking, SJT, Dalca, AV, Giralt-Steinhauer, E, Holmegaard, L, Jood, K, Roquer, J, Cole, JW, McArdle, PF, Broderick, JP, Jimenez-Conde, J, Jern, C, Kissela, BM, Kleindorfer, DO, Lemmens, R, Meschia, JF, Rosand, J, Rundek, T, Sacco, RL, Schmidt, R, Sharma, P, Slowik, A, Thijs, V, Woo, D, Worrall, BB, Kittner, SJ, Petersson, J, Golland, P, Wu, O, Rost, NS, Lindgren, A, Frid, P, Xu, H, Mitchell, BD, Drake, M, Wasselius, J, Gaynor, B, Ryan, K, Giese, AK, Schirmer, M, Donahue, KL, Irie, R, Bouts, MJRJ, McIntosh, EC, Mocking, SJT, Dalca, AV, Giralt-Steinhauer, E, Holmegaard, L, Jood, K, Roquer, J, Cole, JW, McArdle, PF, Broderick, JP, Jimenez-Conde, J, Jern, C, Kissela, BM, Kleindorfer, DO, Lemmens, R, Meschia, JF, Rosand, J, Rundek, T, Sacco, RL, Schmidt, R, Sharma, P, Slowik, A, Thijs, V, Woo, D, Worrall, BB, Kittner, SJ, Petersson, J, Golland, P, Wu, O, Rost, NS, and Lindgren, A

Abstract

OBJECTIVE: To examine potential genetic relationships between migraine and the two distinct phenotypes posterior circulation ischemic stroke (PCiS) and anterior circulation ischemic stroke (ACiS), we generated migraine polygenic risk scores (PRSs) and compared these between PCiS and ACiS, and separately vs. non-stroke control subjects. METHODS: Acute ischemic stroke cases were classified as PCiS or ACiS based on lesion location on diffusion-weighted MRI. Exclusion criteria were lesions in both vascular territories or uncertain territory; supratentorial PCiS with ipsilateral fetal posterior cerebral artery; and cases with atrial fibrillation. We generated migraine PRS for three migraine phenotypes (any migraine; migraine without aura; migraine with aura) using publicly available GWAS data and compared mean PRSs separately for PCiS and ACiS vs. non-stroke control subjects, and between each stroke phenotype. RESULTS: Our primary analyses included 464 PCiS and 1079 ACiS patients with genetic European ancestry. Compared to non-stroke control subjects (n=15396), PRSs of any migraine were associated with increased risk of PCiS (p=0.01-0.03) and decreased risk of ACiS (p=0.010-0.039). Migraine without aura PRSs were significantly associated with PCiS (p=0.008-0.028), but not with ACiS. When comparing PCiS vs. ACiS directly, migraine PRSs were higher in PCiS vs. ACiS for any migraine (p=0.001-0.010) and migraine without aura (p=0.032-0.048). Migraine with aura PRS did not show a differential association in our analyses. CONCLUSIONS: Our results suggest a stronger genetic overlap between unspecified migraine and migraine without aura with PCiS compared to ACiS. Possible shared mechanisms include dysregulation of cerebral vessel endothelial function.

Published
2022

6. Sex-specific lesion pattern of functional outcomes after stroke

Author

Bonkhoff, AK, Bretzner, M, Hong, S, Schirmer, MD, Cohen, A, Regenhardt, RW, Donahue, KL, Nardin, MJ, Dalca, A, Giese, A-K, Etherton, MR, Hancock, BL, Mocking, SJT, McIntosh, EC, Attia, J, Benavente, OR, Bevan, S, Cole, JW, Donatti, A, Griessenauer, CJ, Heitsch, L, Holmegaard, L, Jood, K, Jimenez-Conde, J, Kittner, SJ, Lemmens, R, Levi, CR, McDonough, CW, Meschia, JF, Phuah, C-L, Rolfs, A, Ropele, S, Rosand, J, Roquer, J, Rundek, T, Sacco, RL, Schmidt, R, Sharma, P, Slowik, A, Soderholm, M, Sousa, A, Stanne, TM, Strbian, D, Tatlisumak, T, Thijs, V, Vagal, A, Wasselius, J, Woo, D, Zand, R, McArdle, PF, Worrall, BB, Jern, C, Lindgren, AG, Maguire, J, Fox, MD, Bzdok, D, Wu, O, Rost, NS, Bonkhoff, AK, Bretzner, M, Hong, S, Schirmer, MD, Cohen, A, Regenhardt, RW, Donahue, KL, Nardin, MJ, Dalca, A, Giese, A-K, Etherton, MR, Hancock, BL, Mocking, SJT, McIntosh, EC, Attia, J, Benavente, OR, Bevan, S, Cole, JW, Donatti, A, Griessenauer, CJ, Heitsch, L, Holmegaard, L, Jood, K, Jimenez-Conde, J, Kittner, SJ, Lemmens, R, Levi, CR, McDonough, CW, Meschia, JF, Phuah, C-L, Rolfs, A, Ropele, S, Rosand, J, Roquer, J, Rundek, T, Sacco, RL, Schmidt, R, Sharma, P, Slowik, A, Soderholm, M, Sousa, A, Stanne, TM, Strbian, D, Tatlisumak, T, Thijs, V, Vagal, A, Wasselius, J, Woo, D, Zand, R, McArdle, PF, Worrall, BB, Jern, C, Lindgren, AG, Maguire, J, Fox, MD, Bzdok, D, Wu, O, and Rost, NS

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 >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.

Published
2022

7. International stroke genetics consortium recommendations for studies of genetics of stroke outcome and recovery

Author

Lindgren, AG, Braun, RG, Juhl Majersik, J, Clatworthy, P, Mainali, S, Derdeyn, CP, Maguire, J, Jern, C, Rosand, J, Cole, JW, Lee, J-M, Khatri, P, Nyquist, P, Debette, S, Keat Wei, L, Rundek, T, Leifer, D, Thijs, V, Lemmens, R, Heitsch, L, Prasad, K, Jimenez Conde, J, Dichgans, M, Rost, NS, Cramer, SC, Bernhardt, J, Worrall, BB, Fernandez-Cadenas, I, Lindgren, AG, Braun, RG, Juhl Majersik, J, Clatworthy, P, Mainali, S, Derdeyn, CP, Maguire, J, Jern, C, Rosand, J, Cole, JW, Lee, J-M, Khatri, P, Nyquist, P, Debette, S, Keat Wei, L, Rundek, T, Leifer, D, Thijs, V, Lemmens, R, Heitsch, L, Prasad, K, Jimenez Conde, J, Dichgans, M, Rost, NS, Cramer, SC, Bernhardt, J, Worrall, BB, and Fernandez-Cadenas, I

Abstract

Numerous biological mechanisms contribute to outcome after stroke, including brain injury, inflammation, and repair mechanisms. Clinical genetic studies have the potential to discover biological mechanisms affecting stroke recovery in humans and identify intervention targets. Large sample sizes are needed to detect commonly occurring genetic variations related to stroke brain injury and recovery. However, this usually requires combining data from multiple studies where consistent terminology, methodology, and data collection timelines are essential. Our group of expert stroke and rehabilitation clinicians and researchers with knowledge in genetics of stroke recovery here present recommendations for harmonizing phenotype data with focus on measures suitable for multicenter genetic studies of ischemic stroke brain injury and recovery. Our recommendations have been endorsed by the International Stroke Genetics Consortium.

Published
2022

8. The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain-behavior relationships after stroke

Author

Liew, S-L, Zavaliangos-Petropulu, A, Jahanshad, N, Lang, CE, Hayward, KS, Lohse, KR, Juliano, JM, Assogna, F, Baugh, LA, Bhattacharya, AK, Bigjahan, B, Borich, MR, Boyd, LA, Brodtmann, A, Buetefisch, CM, Byblow, WD, Cassidy, JM, Conforto, AB, Craddock, RC, Dimyan, MA, Dula, AN, Ermer, E, Etherton, MR, Fercho, KA, Gregory, CM, Hadidchi, S, Holguin, JA, Hwang, DH, Jung, S, Kautz, SA, Khlif, MS, Khoshab, N, Kim, B, Kim, H, Kuceyeski, A, Lotze, M, MacIntosh, BJ, Margetis, JL, Mohamed, FB, Piras, F, Ramos-Murguialday, A, Richard, G, Roberts, P, Robertson, AD, Rondina, JM, Rost, NS, Sanossian, N, Schweighofer, N, Seo, NJ, Shiroishi, MS, Soekadar, SR, Spalletta, G, Stinear, CM, Suri, A, Tang, WKW, Thielman, GT, Vecchio, D, Villringer, A, Ward, NS, Werden, E, Westlye, LT, Winstein, C, Wittenberg, GF, Wong, KA, Yu, C, Cramer, SC, Thompson, PM, Liew, S-L, Zavaliangos-Petropulu, A, Jahanshad, N, Lang, CE, Hayward, KS, Lohse, KR, Juliano, JM, Assogna, F, Baugh, LA, Bhattacharya, AK, Bigjahan, B, Borich, MR, Boyd, LA, Brodtmann, A, Buetefisch, CM, Byblow, WD, Cassidy, JM, Conforto, AB, Craddock, RC, Dimyan, MA, Dula, AN, Ermer, E, Etherton, MR, Fercho, KA, Gregory, CM, Hadidchi, S, Holguin, JA, Hwang, DH, Jung, S, Kautz, SA, Khlif, MS, Khoshab, N, Kim, B, Kim, H, Kuceyeski, A, Lotze, M, MacIntosh, BJ, Margetis, JL, Mohamed, FB, Piras, F, Ramos-Murguialday, A, Richard, G, Roberts, P, Robertson, AD, Rondina, JM, Rost, NS, Sanossian, N, Schweighofer, N, Seo, NJ, Shiroishi, MS, Soekadar, SR, Spalletta, G, Stinear, CM, Suri, A, Tang, WKW, Thielman, GT, Vecchio, D, Villringer, A, Ward, NS, Werden, E, Westlye, LT, Winstein, C, Wittenberg, GF, Wong, KA, Yu, C, Cramer, SC, and Thompson, PM

Abstract

The goal of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well-powered meta- and mega-analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large-scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided.

Published
2022

9. Association of Stroke Lesion Pattern and White Matter Hyperintensity Burden With Stroke Severity and Outcome.

Author

Bonkhoff, AK, Hong, S, Bretzner, M, Schirmer, MD, Regenhardt, RW, Arsava, EM, Donahue, K, Nardin, M, Dalca, A, Giese, A-K, Etherton, MR, Hancock, BL, Mocking, SJT, McIntosh, E, Attia, J, Benavente, O, Cole, JW, Donatti, A, Griessenauer, C, Heitsch, L, Holmegaard, L, Jood, K, Jimenez-Conde, J, Kittner, S, Lemmens, R, Levi, C, McDonough, CW, Meschia, J, Phuah, C-L, Rolfs, A, Ropele, S, Rosand, J, Roquer, J, Rundek, T, Sacco, RL, Schmidt, R, Sharma, P, Slowik, A, Soederholm, M, Sousa, A, Stanne, TM, Strbian, D, Tatlisumak, T, Thijs, V, Vagal, A, Wasselius, J, Woo, D, Zand, R, McArdle, P, Worrall, BB, Jern, C, Lindgren, AG, Maguire, J, Golland, P, Bzdok, D, Wu, O, Rost, NS, Bonkhoff, AK, Hong, S, Bretzner, M, Schirmer, MD, Regenhardt, RW, Arsava, EM, Donahue, K, Nardin, M, Dalca, A, Giese, A-K, Etherton, MR, Hancock, BL, Mocking, SJT, McIntosh, E, Attia, J, Benavente, O, Cole, JW, Donatti, A, Griessenauer, C, Heitsch, L, Holmegaard, L, Jood, K, Jimenez-Conde, J, Kittner, S, Lemmens, R, Levi, C, McDonough, CW, Meschia, J, Phuah, C-L, Rolfs, A, Ropele, S, Rosand, J, Roquer, J, Rundek, T, Sacco, RL, Schmidt, R, Sharma, P, Slowik, A, Soederholm, M, Sousa, A, Stanne, TM, Strbian, D, Tatlisumak, T, Thijs, V, Vagal, A, Wasselius, J, Woo, D, Zand, R, McArdle, P, Worrall, BB, Jern, C, Lindgren, AG, Maguire, J, Golland, P, Bzdok, D, Wu, O, and Rost, NS

Abstract

BACKGROUND AND OBJECTIVES: To examine whether high white matter hyperintensity (WMH) burden is associated with greater stroke severity and worse functional outcomes in lesion pattern-specific ways. METHODS: MR neuroimaging and NIH Stroke Scale data at index stroke and the modified Rankin Scale (mRS) score at 3-6 months after stroke were obtained from the MRI-Genetics Interface Exploration study of patients with acute ischemic stroke (AIS). Individual WMH volume was automatically derived from fluid-attenuated inversion recovery images. Stroke lesions were automatically segmented from diffusion-weighted imaging (DWI) images, parcellated into atlas-defined brain regions and further condensed to 10 lesion patterns via machine learning-based dimensionality reduction. Stroke lesion effects on AIS severity and unfavorable outcomes (mRS score >2) were modeled within purpose-built Bayesian linear and logistic regression frameworks. Interaction effects between stroke lesions and a high vs low WMH burden were integrated via hierarchical model structures. Models were adjusted for age, age2, sex, total DWI lesion and WMH volumes, and comorbidities. Data were split into derivation and validation cohorts. RESULTS: A total of 928 patients with AIS contributed to acute stroke severity analyses (age: 64.8 [14.5] years, 40% women) and 698 patients to long-term functional outcome analyses (age: 65.9 [14.7] years, 41% women). Stroke severity was mainly explained by lesions focused on bilateral subcortical and left hemispherically pronounced cortical regions across patients with both a high and low WMH burden. Lesions centered on left-hemispheric insular, opercular, and inferior frontal regions and lesions affecting right-hemispheric temporoparietal regions had more pronounced effects on stroke severity in case of high compared with low WMH burden. Unfavorable outcomes were predominantly explained by lesions in bilateral subcortical regions. In difference to the lesion location-specific

Published
2022

10. Stroke genetics informs drug discovery and risk prediction across ancestries

Author

Mishra, A, Malik, R, Hachiya, T, Jurgenson, T, Namba, S, Posner, DC, Kamanu, FK, Koido, M, Le Grand, Q, Shi, M, He, Y, Georgakis, MK, Caro, I, Krebs, K, Liaw, Y-C, Vaura, FC, Lin, K, Winsvold, BS, Srinivasasainagendra, V, Parodi, L, Bae, H-J, Chauhan, G, Chong, MR, Tomppo, L, Akinyemi, R, Roshchupkin, GV, Habib, N, Jee, YH, Thomassen, JQ, Abedi, V, Carcel-Marquez, J, Nygaard, M, Leonard, HL, Yang, C, Yonova-Doing, E, Knol, MJ, Lewis, AJ, Judy, RL, Ago, T, Amouyel, P, Armstrong, ND, Bakker, MK, Bartz, TM, Bennett, DA, Bis, JC, Bordes, C, Borte, S, Cain, A, Ridker, PM, Cho, K, Chen, Z, Cruchaga, C, Cole, JW, de Jager, PL, de Cid, R, Endres, M, Ferreira, LE, Geerlings, MI, Gasca, NC, Gudnason, V, Hata, J, He, J, Heath, AK, Ho, Y-L, Havulinna, AS, Hopewell, JC, Hyacinth, HI, Inouye, M, Jacob, MA, Jeon, CE, Jern, C, Kamouchi, M, Keene, KL, Kitazono, T, Kittner, SJ, Konuma, T, Kumar, A, Lacaze, P, Launer, LJ, Lee, K-J, Lepik, K, Li, J, Li, L, Manichaikul, A, Markus, HS, Marston, NA, Meitinger, T, Mitchell, BD, Montellano, FA, Morisaki, T, Mosley, TH, Nalls, MA, Nordestgaard, BG, O'Donnell, MJ, Okada, Y, Onland-Moret, NC, Ovbiagele, B, Peters, A, Psaty, BM, Rich, SS, Rosand, J, Sabatine, MS, Sacco, RL, Saleheen, D, Sandset, EC, Salomaa, V, Sargurupremraj, M, Sasaki, M, Satizabal, CL, Schmidt, CO, Shimizu, A, Smith, NL, Sloane, KL, Sutoh, Y, Sun, YV, Tanno, K, Tiedt, S, Tatlisumak, T, Torres-Aguila, NP, Tiwari, HK, Tregouet, D-A, Trompet, S, Tuladhar, AM, Tybjaerg-Hansen, A, van Vugt, M, Vibo, R, Verma, SS, Wiggins, KL, Wennberg, P, Woo, D, Wilson, PWF, Xu, H, Yang, Q, Yoon, K, Millwood, IY, Gieger, C, Ninomiya, T, Grabe, HJ, Jukema, JW, Rissanen, IL, Strbian, D, Kim, YJ, Chen, P-H, Mayerhofer, E, Howson, JMM, Irvin, MR, Adams, H, Wassertheil-Smoller, S, Christensen, K, Ikram, MA, Rundek, T, Worrall, BB, Lathrop, GM, Riaz, M, Simonsick, EM, Korv, J, Franca, PHC, Zand, R, Prasad, K, Frikke-Schmidt, R, de Leeuw, F-E, Liman, T, Haeusler, KG, Ruigrok, YM, Heuschmann, PU, Longstreth, WT, Jung, KJ, Bastarache, L, Pare, G, Damrauer, SM, Chasman, DI, Rotter, JI, Anderson, CD, Zwart, J-A, Niiranen, TJ, Fornage, M, Liaw, Y-P, Seshadri, S, Fernandez-Cadenas, I, Walters, RG, Ruff, CT, Owolabi, MO, Huffman, JE, Milani, L, Kamatani, Y, Dichgans, M, Debette, S, Lee, J-M, Cheng, Y-C, Meschia, JF, Chen, WM, Sale, MM, Zonderman, AB, Evans, MK, Wilson, JG, Correa, A, Traylor, M, Lewis, CM, Reiner, A, Haessler, J, Langefeld, CD, Gottesman, RF, Yaffe, K, Liu, YM, Kooperberg, C, Lange, LA, Furie, KL, Arnett, DK, Benavente, OR, Grewal, RP, Peddareddygari, LR, Hveem, K, Lindstrom, S, Wang, L, Smith, EN, Gordon, W, Vlieg, AVH, de Andrade, M, Brody, JA, Pattee, JW, Brumpton, BM, Suchon, P, Chen, M-H, Frazer, KA, Turman, C, Germain, M, MacDonald, J, Braekkan, SK, Armasu, SM, Pankratz, N, Jackson, RD, Nielsen, JB, Giulianin, F, Puurunen, MK, Ibrahim, M, Heckbert, SR, Bammler, TK, McCauley, BM, Taylor, KD, Pankow, JS, Reiner, AP, Gabrielsen, ME, Deleuze, J-F, O'Donnell, CJ, Kim, J, McKnight, B, Kraft, P, Hansen, J-B, Rosendaal, FR, Heit, JA, Tang, W, Morange, P-E, Johnson, AD, Kabrhel, C, van Dijk, EJ, Koudstaal, PJ, Luijckx, G-J, Nederkoorn, PJ, van Oostenbrugge, RJ, Visser, MC, Wermer, MJH, Kappelle, LJ, Esko, T, Metspalu, A, Magi, R, Nelis, M, Levi, CR, Maguire, J, Jimenez-Conde, J, Sharma, P, Sudlow, CLM, Rannikmae, K, Schmidt, R, Slowik, A, Pera, J, Thijs, VNS, Lindgren, AG, Ilinca, A, Melander, O, Engstrom, G, Rexrode, KM, Rothwell, PM, Stanne, TM, Johnson, JA, Danesh, J, Butterworth, AS, Heitsch, L, Boncoraglio, GB, Kubo, M, Pezzini, A, Rolfs, A, Giese, A-K, Weir, D, Ross, OA, Lemmons, R, Soderholm, M, Cushman, M, Jood, K, McDonough, CW, Bell, S, Linkohr, B, Lee, T-H, Putaala, J, Lopez, OL, Carty, CL, Jian, X, Schminke, U, Cullell, N, Delgado, P, Ibanez, L, Krupinski, J, Lioutas, V, Matsuda, K, Montaner, J, Muino, E, Roquer, J, Sarnowski, C, Sattar, N, Sibolt, G, Teumer, A, Rutten-Jacobs, L, Kanai, M, Gretarsdottir, S, Rost, NS, Yusuf, S, Almgren, P, Ay, H, Bevan, S, Brown, RD, Carrera, C, Buring, JE, Chen, W-M, Cotlarciuc, I, de Bakker, PIW, DeStefano, AL, den Hoed, M, Duan, Q, Engelter, ST, Falcone, GJ, Gustafsson, S, Hassan, A, Holliday, EG, Howard, G, Hsu, F-C, Ingelsson, E, Harris, TB, Kissela, BM, Kleindorfer, DO, Langenberg, C, Leys, D, Lin, W-Y, Lorentzen, E, Magnusson, PK, McArdle, PF, Pulit, SL, Rice, K, Sakaue, S, Sapkota, BR, Tanislav, C, Thorleifsson, G, Thorsteinsdottir, U, Tzourio, C, van Duijn, CM, Walters, M, Wareham, NJ, Amin, N, Aparicio, HJ, Attia, J, Beiser, AS, Berr, C, Bustamante, M, Caso, V, Choi, SH, Chowhan, A, Dartigues, J-F, Delavaran, H, Dorr, M, Ford, I, Gurpreet, WS, Hamsten, A, Hozawa, A, Ingelsson, M, Iwasaki, M, Kaffashian, S, Kalra, L, Kjartansson, O, Kloss, M, Labovitz, DL, Laurie, CC, Lind, L, Lindgren, CM, Makoto, H, Minegishi, N, Morris, AP, Mueller-Nurasyid, M, Norrving, B, Ogishima, S, Parati, EA, Pedersen, NL, Perola, M, Jousilahti, P, Pileggi, S, Rabionet, R, Riba-Llena, I, Ribases, M, Romero, JR, Rudd, AG, Sarin, A-P, Sarju, R, Satoh, M, Sawada, N, Sigurdsson, A, Smith, A, Stine, OC, Stott, DJ, Strauch, K, Takai, T, Tanaka, H, Touze, E, Tsugane, S, Uitterlinden, AG, Valdimarsson, EM, van der Lee, SJ, Wakai, K, Williams, SR, Wolfe, CDA, Wong, Q, Yamaji, T, Sanghera, DK, Stefansson, K, Martinez-Majander, N, Sobue, K, Soriano-Tarraga, C, Volzke, H, Akpa, O, Sarfo, FS, Akpalu, A, Obiako, R, Wahab, K, Osaigbovo, G, Owolabi, L, Komolafe, M, Jenkins, C, Arulogun, O, Ogbole, G, Adeoye, AM, Akinyemi, J, Agunloye, A, Fakunle, AG, Uvere, E, Olalere, A, Adebajo, OJ, Chen, J, Clarke, R, Collins, R, Guo, Y, Wang, C, Lv, J, Peto, R, Chen, Y, Fairhurst-Hunter, Z, Hill, M, Pozarickij, A, Schmidt, D, Stevens, B, Turnbull, I, Yu, C, Nagai, A, Murakami, Y, Shiroma, EJ, Sigurdsson, S, Ghanbari, M, Boerwinkle, E, Fongang, B, Wang, R, Ikram, MK, Volker, U, de Laat, KF, van Norden, AGW, de Kort, PL, Vermeer, SE, Brouwers, PJAM, Gons, RAR, den Heijer, T, van Dijk, GW, van Rooij, FGW, Aamodt, AH, Skogholt, AH, Willer, CJ, Heuch, I, Hagen, K, Fritsche, LG, Pedersen, LM, Ellekjaer, H, Zhou, W, Martinsen, AE, Kristoffersen, ES, Thomas, LF, Kleinschnitz, C, Frantz, S, Ungethum, K, Gallego-Fabrega, C, Lledos, M, Llucia-Carol, L, Sobrino, T, Campos, F, Castillo, J, Freijo, M, Arenillas, JF, Obach, V, Alvarez-Sabin, J, Molina, CA, Ribo, M, Munoz-Narbona, L, Lopez-Cancio, E, Millan, M, Diaz-Navarro, R, Vives-Bauza, C, Serrano-Heras, G, Segura, T, Dhar, R, Delgado-Mederos, R, Prats-Sanchez, L, Camps-Renom, P, Blay, N, Sumoy, L, Marti-Fabregas, J, Schnohr, P, Jensen, GB, Benn, M, Afzal, S, Kamstrup, PR, van Setten, J, van der Laan, SW, Vonk, JMJ, Kim, B-J, Curtze, S, Tiainen, M, Kinnunen, J, Menon, V, Sung, YJ, Saillour-Glenisson, F, Gravel, S, Mishra, A, Malik, R, Hachiya, T, Jurgenson, T, Namba, S, Posner, DC, Kamanu, FK, Koido, M, Le Grand, Q, Shi, M, He, Y, Georgakis, MK, Caro, I, Krebs, K, Liaw, Y-C, Vaura, FC, Lin, K, Winsvold, BS, Srinivasasainagendra, V, Parodi, L, Bae, H-J, Chauhan, G, Chong, MR, Tomppo, L, Akinyemi, R, Roshchupkin, GV, Habib, N, Jee, YH, Thomassen, JQ, Abedi, V, Carcel-Marquez, J, Nygaard, M, Leonard, HL, Yang, C, Yonova-Doing, E, Knol, MJ, Lewis, AJ, Judy, RL, Ago, T, Amouyel, P, Armstrong, ND, Bakker, MK, Bartz, TM, Bennett, DA, Bis, JC, Bordes, C, Borte, S, Cain, A, Ridker, PM, Cho, K, Chen, Z, Cruchaga, C, Cole, JW, de Jager, PL, de Cid, R, Endres, M, Ferreira, LE, Geerlings, MI, Gasca, NC, Gudnason, V, Hata, J, He, J, Heath, AK, Ho, Y-L, Havulinna, AS, Hopewell, JC, Hyacinth, HI, Inouye, M, Jacob, MA, Jeon, CE, Jern, C, Kamouchi, M, Keene, KL, Kitazono, T, Kittner, SJ, Konuma, T, Kumar, A, Lacaze, P, Launer, LJ, Lee, K-J, Lepik, K, Li, J, Li, L, Manichaikul, A, Markus, HS, Marston, NA, Meitinger, T, Mitchell, BD, Montellano, FA, Morisaki, T, Mosley, TH, Nalls, MA, Nordestgaard, BG, O'Donnell, MJ, Okada, Y, Onland-Moret, NC, Ovbiagele, B, Peters, A, Psaty, BM, Rich, SS, Rosand, J, Sabatine, MS, Sacco, RL, Saleheen, D, Sandset, EC, Salomaa, V, Sargurupremraj, M, Sasaki, M, Satizabal, CL, Schmidt, CO, Shimizu, A, Smith, NL, Sloane, KL, Sutoh, Y, Sun, YV, Tanno, K, Tiedt, S, Tatlisumak, T, Torres-Aguila, NP, Tiwari, HK, Tregouet, D-A, Trompet, S, Tuladhar, AM, Tybjaerg-Hansen, A, van Vugt, M, Vibo, R, Verma, SS, Wiggins, KL, Wennberg, P, Woo, D, Wilson, PWF, Xu, H, Yang, Q, Yoon, K, Millwood, IY, Gieger, C, Ninomiya, T, Grabe, HJ, Jukema, JW, Rissanen, IL, Strbian, D, Kim, YJ, Chen, P-H, Mayerhofer, E, Howson, JMM, Irvin, MR, Adams, H, Wassertheil-Smoller, S, Christensen, K, Ikram, MA, Rundek, T, Worrall, BB, Lathrop, GM, Riaz, M, Simonsick, EM, Korv, J, Franca, PHC, Zand, R, Prasad, K, Frikke-Schmidt, R, de Leeuw, F-E, Liman, T, Haeusler, KG, Ruigrok, YM, Heuschmann, PU, Longstreth, WT, Jung, KJ, Bastarache, L, Pare, G, Damrauer, SM, Chasman, DI, Rotter, JI, Anderson, CD, Zwart, J-A, Niiranen, TJ, Fornage, M, Liaw, Y-P, Seshadri, S, Fernandez-Cadenas, I, Walters, RG, Ruff, CT, Owolabi, MO, Huffman, JE, Milani, L, Kamatani, Y, Dichgans, M, Debette, S, Lee, J-M, Cheng, Y-C, Meschia, JF, Chen, WM, Sale, MM, Zonderman, AB, Evans, MK, Wilson, JG, Correa, A, Traylor, M, Lewis, CM, Reiner, A, Haessler, J, Langefeld, CD, Gottesman, RF, Yaffe, K, Liu, YM, Kooperberg, C, Lange, LA, Furie, KL, Arnett, DK, Benavente, OR, Grewal, RP, Peddareddygari, LR, Hveem, K, Lindstrom, S, Wang, L, Smith, EN, Gordon, W, Vlieg, AVH, de Andrade, M, Brody, JA, Pattee, JW, Brumpton, BM, Suchon, P, Chen, M-H, Frazer, KA, Turman, C, Germain, M, MacDonald, J, Braekkan, SK, Armasu, SM, Pankratz, N, Jackson, RD, Nielsen, JB, Giulianin, F, Puurunen, MK, Ibrahim, M, Heckbert, SR, Bammler, TK, McCauley, BM, Taylor, KD, Pankow, JS, Reiner, AP, Gabrielsen, ME, Deleuze, J-F, O'Donnell, CJ, Kim, J, McKnight, B, Kraft, P, Hansen, J-B, Rosendaal, FR, Heit, JA, Tang, W, Morange, P-E, Johnson, AD, Kabrhel, C, van Dijk, EJ, Koudstaal, PJ, Luijckx, G-J, Nederkoorn, PJ, van Oostenbrugge, RJ, Visser, MC, Wermer, MJH, Kappelle, LJ, Esko, T, Metspalu, A, Magi, R, Nelis, M, Levi, CR, Maguire, J, Jimenez-Conde, J, Sharma, P, Sudlow, CLM, Rannikmae, K, Schmidt, R, Slowik, A, Pera, J, Thijs, VNS, Lindgren, AG, Ilinca, A, Melander, O, Engstrom, G, Rexrode, KM, Rothwell, PM, Stanne, TM, Johnson, JA, Danesh, J, Butterworth, AS, Heitsch, L, Boncoraglio, GB, Kubo, M, Pezzini, A, Rolfs, A, Giese, A-K, Weir, D, Ross, OA, Lemmons, R, Soderholm, M, Cushman, M, Jood, K, McDonough, CW, Bell, S, Linkohr, B, Lee, T-H, Putaala, J, Lopez, OL, Carty, CL, Jian, X, Schminke, U, Cullell, N, Delgado, P, Ibanez, L, Krupinski, J, Lioutas, V, Matsuda, K, Montaner, J, Muino, E, Roquer, J, Sarnowski, C, Sattar, N, Sibolt, G, Teumer, A, Rutten-Jacobs, L, Kanai, M, Gretarsdottir, S, Rost, NS, Yusuf, S, Almgren, P, Ay, H, Bevan, S, Brown, RD, Carrera, C, Buring, JE, Chen, W-M, Cotlarciuc, I, de Bakker, PIW, DeStefano, AL, den Hoed, M, Duan, Q, Engelter, ST, Falcone, GJ, Gustafsson, S, Hassan, A, Holliday, EG, Howard, G, Hsu, F-C, Ingelsson, E, Harris, TB, Kissela, BM, Kleindorfer, DO, Langenberg, C, Leys, D, Lin, W-Y, Lorentzen, E, Magnusson, PK, McArdle, PF, Pulit, SL, Rice, K, Sakaue, S, Sapkota, BR, Tanislav, C, Thorleifsson, G, Thorsteinsdottir, U, Tzourio, C, van Duijn, CM, Walters, M, Wareham, NJ, Amin, N, Aparicio, HJ, Attia, J, Beiser, AS, Berr, C, Bustamante, M, Caso, V, Choi, SH, Chowhan, A, Dartigues, J-F, Delavaran, H, Dorr, M, Ford, I, Gurpreet, WS, Hamsten, A, Hozawa, A, Ingelsson, M, Iwasaki, M, Kaffashian, S, Kalra, L, Kjartansson, O, Kloss, M, Labovitz, DL, Laurie, CC, Lind, L, Lindgren, CM, Makoto, H, Minegishi, N, Morris, AP, Mueller-Nurasyid, M, Norrving, B, Ogishima, S, Parati, EA, Pedersen, NL, Perola, M, Jousilahti, P, Pileggi, S, Rabionet, R, Riba-Llena, I, Ribases, M, Romero, JR, Rudd, AG, Sarin, A-P, Sarju, R, Satoh, M, Sawada, N, Sigurdsson, A, Smith, A, Stine, OC, Stott, DJ, Strauch, K, Takai, T, Tanaka, H, Touze, E, Tsugane, S, Uitterlinden, AG, Valdimarsson, EM, van der Lee, SJ, Wakai, K, Williams, SR, Wolfe, CDA, Wong, Q, Yamaji, T, Sanghera, DK, Stefansson, K, Martinez-Majander, N, Sobue, K, Soriano-Tarraga, C, Volzke, H, Akpa, O, Sarfo, FS, Akpalu, A, Obiako, R, Wahab, K, Osaigbovo, G, Owolabi, L, Komolafe, M, Jenkins, C, Arulogun, O, Ogbole, G, Adeoye, AM, Akinyemi, J, Agunloye, A, Fakunle, AG, Uvere, E, Olalere, A, Adebajo, OJ, Chen, J, Clarke, R, Collins, R, Guo, Y, Wang, C, Lv, J, Peto, R, Chen, Y, Fairhurst-Hunter, Z, Hill, M, Pozarickij, A, Schmidt, D, Stevens, B, Turnbull, I, Yu, C, Nagai, A, Murakami, Y, Shiroma, EJ, Sigurdsson, S, Ghanbari, M, Boerwinkle, E, Fongang, B, Wang, R, Ikram, MK, Volker, U, de Laat, KF, van Norden, AGW, de Kort, PL, Vermeer, SE, Brouwers, PJAM, Gons, RAR, den Heijer, T, van Dijk, GW, van Rooij, FGW, Aamodt, AH, Skogholt, AH, Willer, CJ, Heuch, I, Hagen, K, Fritsche, LG, Pedersen, LM, Ellekjaer, H, Zhou, W, Martinsen, AE, Kristoffersen, ES, Thomas, LF, Kleinschnitz, C, Frantz, S, Ungethum, K, Gallego-Fabrega, C, Lledos, M, Llucia-Carol, L, Sobrino, T, Campos, F, Castillo, J, Freijo, M, Arenillas, JF, Obach, V, Alvarez-Sabin, J, Molina, CA, Ribo, M, Munoz-Narbona, L, Lopez-Cancio, E, Millan, M, Diaz-Navarro, R, Vives-Bauza, C, Serrano-Heras, G, Segura, T, Dhar, R, Delgado-Mederos, R, Prats-Sanchez, L, Camps-Renom, P, Blay, N, Sumoy, L, Marti-Fabregas, J, Schnohr, P, Jensen, GB, Benn, M, Afzal, S, Kamstrup, PR, van Setten, J, van der Laan, SW, Vonk, JMJ, Kim, B-J, Curtze, S, Tiainen, M, Kinnunen, J, Menon, V, Sung, YJ, Saillour-Glenisson, F, and Gravel, S

Abstract

Previous genome-wide association studies (GWASs) of stroke - the second leading cause of death worldwide - were conducted predominantly in populations of European ancestry1,2. Here, in cross-ancestry GWAS meta-analyses of 110,182 patients who have had a stroke (five ancestries, 33% non-European) and 1,503,898 control individuals, we identify association signals for stroke and its subtypes at 89 (61 new) independent loci: 60 in primary inverse-variance-weighted analyses and 29 in secondary meta-regression and multitrait analyses. On the basis of internal cross-ancestry validation and an independent follow-up in 89,084 additional cases of stroke (30% non-European) and 1,013,843 control individuals, 87% of the primary stroke risk loci and 60% of the secondary stroke risk loci were replicated (P < 0.05). Effect sizes were highly correlated across ancestries. Cross-ancestry fine-mapping, in silico mutagenesis analysis3, and transcriptome-wide and proteome-wide association analyses revealed putative causal genes (such as SH3PXD2A and FURIN) and variants (such as at GRK5 and NOS3). Using a three-pronged approach4, we provide genetic evidence for putative drug effects, highlighting F11, KLKB1, PROC, GP1BA, LAMC2 and VCAM1 as possible targets, with drugs already under investigation for stroke for F11 and PROC. A polygenic score integrating cross-ancestry and ancestry-specific stroke GWASs with vascular-risk factor GWASs (integrative polygenic scores) strongly predicted ischaemic stroke in populations of European, East Asian and African ancestry5. Stroke genetic risk scores were predictive of ischaemic stroke independent of clinical risk factors in 52,600 clinical-trial participants with cardiometabolic disease. Our results provide insights to inform biology, reveal potential drug targets and derive genetic risk prediction tools across ancestries.

Published
2022

11. Deep profiling of multiple ischemic lesions in a large, multi-center cohort: Frequency, spatial distribution, and associations to clinical characteristics

Author

Bonkhoff, AK, Ullberg, T, Bretzner, M, Hong, S, Schirmer, MD, Regenhardt, RW, Donahue, KL, Nardin, MJ, Dalca, A, Giese, A-K, Etherton, MR, Hancock, BL, Mocking, SJT, McIntosh, EC, Attia, J, Cole, JW, Donatti, A, Griessenauer, CJ, Heitsch, L, Holmegaard, L, Jood, K, Jimenez-Conde, J, Kittner, SJ, Lemmens, R, Levi, CR, McDonough, CW, Meschia, JF, Phuah, C-L, Ropele, S, Rosand, J, Roquer, J, Rundek, T, Sacco, RL, Schmidt, R, Sharma, P, Slowik, A, Sousa, A, Stanne, TM, Strbian, D, Tatlisumak, T, Thijs, V, Vagal, A, Woo, D, Zand, R, McArdle, PF, Worrall, BB, Jern, C, Lindgren, AG, Maguire, J, Wu, O, Frid, P, Rost, NS, Wasselius, J, Bonkhoff, AK, Ullberg, T, Bretzner, M, Hong, S, Schirmer, MD, Regenhardt, RW, Donahue, KL, Nardin, MJ, Dalca, A, Giese, A-K, Etherton, MR, Hancock, BL, Mocking, SJT, McIntosh, EC, Attia, J, Cole, JW, Donatti, A, Griessenauer, CJ, Heitsch, L, Holmegaard, L, Jood, K, Jimenez-Conde, J, Kittner, SJ, Lemmens, R, Levi, CR, McDonough, CW, Meschia, JF, Phuah, C-L, Ropele, S, Rosand, J, Roquer, J, Rundek, T, Sacco, RL, Schmidt, R, Sharma, P, Slowik, A, Sousa, A, Stanne, TM, Strbian, D, Tatlisumak, T, Thijs, V, Vagal, A, Woo, D, Zand, R, McArdle, PF, Worrall, BB, Jern, C, Lindgren, AG, Maguire, J, Wu, O, Frid, P, Rost, NS, and Wasselius, J

Abstract

BACKGROUND PURPOSE: A substantial number of patients with acute ischemic stroke (AIS) experience multiple acute lesions (MAL). We here aimed to scrutinize MAL in a large radiologically deep-phenotyped cohort. MATERIALS AND METHODS: Analyses relied upon imaging and clinical data from the international MRI-GENIE study. Imaging data comprised both Fluid-attenuated inversion recovery (FLAIR) for white matter hyperintensity (WMH) burden estimation and diffusion-weighted imaging (DWI) sequences for the assessment of acute stroke lesions. The initial step featured the systematic evaluation of occurrences of MAL within one and several vascular supply territories. Associations between MAL and important imaging and clinical characteristics were subsequently determined. The interaction effect between single and multiple lesion status and lesion volume was estimated by means of Bayesian hierarchical regression modeling for both stroke severity and functional outcome. RESULTS: We analyzed 2,466 patients (age = 63.4 ± 14.8, 39% women), 49.7% of which presented with a single lesion. Another 37.4% experienced MAL in a single vascular territory, while 12.9% featured lesions in multiple vascular territories. Within most territories, MAL occurred as frequently as single lesions (ratio ∼1:1). Only the brainstem region comprised fewer patients with MAL (ratio 1:4). Patients with MAL presented with a significantly higher lesion volume and acute NIHSS (7.7 vs. 1.7 ml and 4 vs. 3, p FDR < 0.001). In contrast, patients with a single lesion were characterized by a significantly higher WMH burden (6.1 vs. 5.3 ml, p FDR = 0.048). Functional outcome did not differ significantly between patients with single versus multiple lesions. Bayesian analyses suggested that the association between lesion volume and stroke severity between single and multiple lesions was the same in case of anterior circulation stroke. In case of posterior circulation stroke, lesion volume was linked to a higher NIHSS only

Published
2022

12. EXPRESS: International Stroke Genetics Consortium Recommendations for Studies of Genetics of Stroke Outcome and Recovery

Author

Lindgren, A, Braun, R, Majersik, JJ, Clatworthy, P, Mainali, S, Derdeyn, CP, Maguire, JM, Jern, C, Rosand, J, Cole, JW, Lee, J-M, Khatri, P, Nyquist, PA, Debette, SP, Keat Wei, L, Rundek, T, Leifer, D, Thijs, V, Lemmens, R, Heitsch, L, Prasad, K, Jimenez-Conde, J, Dichgans, M, Rost, NS, Cramer, SC, Bernhardt, J, Worrall, BB, and Cadenas, I

Subjects
Neurology & Neurosurgery, cardiovascular diseases, 1103 Clinical Sciences, 1109 Neurosciences
Abstract

Numerous biological mechanisms contribute to outcome after stroke, including brain injury, inflammation, and repair mechanisms. Clinical genetic studies have the potential to discover biological mechanisms affecting stroke recovery in humans and identify intervention targets. Large sample sizes are needed to detect commonly occurring genetic variations related to stroke brain injury and recovery. However, this usually requires combining data from multiple studies where consistent terminology, methodology, and data collection timelines are essential. Our group of expert stroke and rehabilitation clinicians and researchers with knowledge in genetics of stroke recovery here present recommendations for harmonizing phenotype data with focus on measures suitable for multicenter genetic studies of ischemic stroke brain injury and recovery. Our recommendations have been endorsed by the International Stroke Genetics Consortium.

Published
2021

13. MRI Radiomic Signature of White Matter Hyperintensities Is Associated With Clinical Phenotypes

Author

Bretzner, M, Bonkhoff, AK, Schirmer, MD, Hong, S, Dalca, A, Donahue, KL, Giese, A-K, Etherton, MR, Rist, PM, Nardin, M, Marinescu, R, Wang, C, Regenhardt, RW, Leclerc, X, Lopes, R, Benavente, OR, Cole, JW, Donatti, A, Griessenauer, CJ, Heitsch, L, Holmegaard, L, Jood, K, Jimenez-Conde, J, Kittner, SJ, Lemmens, R, Levi, CR, McArdle, PF, McDonough, CW, Meschia, JF, Phuah, C-L, Rolfs, A, Ropele, S, Rosand, J, Roquer, J, Rundek, T, Sacco, RL, Schmidt, R, Sharma, P, Slowik, A, Sousa, A, Stanne, TM, Strbian, D, Tatlisumak, T, Thijs, V, Vagal, A, Wasselius, J, Woo, D, Wu, O, Zand, R, Worrall, BB, Maguire, JM, Lindgren, A, Jern, C, Golland, P, Kuchcinski, G, Rost, NS, Bretzner, M, Bonkhoff, AK, Schirmer, MD, Hong, S, Dalca, A, Donahue, KL, Giese, A-K, Etherton, MR, Rist, PM, Nardin, M, Marinescu, R, Wang, C, Regenhardt, RW, Leclerc, X, Lopes, R, Benavente, OR, Cole, JW, Donatti, A, Griessenauer, CJ, Heitsch, L, Holmegaard, L, Jood, K, Jimenez-Conde, J, Kittner, SJ, Lemmens, R, Levi, CR, McArdle, PF, McDonough, CW, Meschia, JF, Phuah, C-L, Rolfs, A, Ropele, S, Rosand, J, Roquer, J, Rundek, T, Sacco, RL, Schmidt, R, Sharma, P, Slowik, A, Sousa, A, Stanne, TM, Strbian, D, Tatlisumak, T, Thijs, V, Vagal, A, Wasselius, J, Woo, D, Wu, O, Zand, R, Worrall, BB, Maguire, JM, Lindgren, A, Jern, C, Golland, P, Kuchcinski, G, and Rost, NS

Abstract

OBJECTIVE: Neuroimaging measurements of brain structural integrity are thought to be surrogates for brain health, but precise assessments require dedicated advanced image acquisitions. By means of quantitatively describing conventional images, radiomic analyses hold potential for evaluating brain health. We sought to: (1) evaluate radiomics to assess brain structural integrity by predicting white matter hyperintensities burdens (WMH) and (2) uncover associations between predictive radiomic features and clinical phenotypes. METHODS: We analyzed a multi-site cohort of 4,163 acute ischemic strokes (AIS) patients with T2-FLAIR MR images with total brain and WMH segmentations. Radiomic features were extracted from normal-appearing brain tissue (brain mask-WMH mask). Radiomics-based prediction of personalized WMH burden was done using ElasticNet linear regression. We built a radiomic signature of WMH with stable selected features predictive of WMH burden and then related this signature to clinical variables using canonical correlation analysis (CCA). RESULTS: Radiomic features were predictive of WMH burden (R 2 = 0.855 ± 0.011). Seven pairs of canonical variates (CV) significantly correlated the radiomics signature of WMH and clinical traits with respective canonical correlations of 0.81, 0.65, 0.42, 0.24, 0.20, 0.15, and 0.15 (FDR-corrected p-values CV 1 - 6 < 0.001, p-value CV 7 = 0.012). The clinical CV1 was mainly influenced by age, CV2 by sex, CV3 by history of smoking and diabetes, CV4 by hypertension, CV5 by atrial fibrillation (AF) and diabetes, CV6 by coronary artery disease (CAD), and CV7 by CAD and diabetes. CONCLUSION: Radiomics extracted from T2-FLAIR images of AIS patients capture microstructural damage of the cerebral parenchyma and correlate with clinical phenotypes, suggesting different radiographical textural abnormalities per cardiovascular risk profile. Further research could evaluate radiomics to predict the progression of WMH and for the follow-up of

Published
2021

14. Outcome after acute ischemic stroke is linked to sex-specific lesion patterns

Author

Bonkhoff, AK, Schirmer, MD, Bretzner, M, Hong, S, Regenhardt, RW, Brudfors, M, Donahue, KL, Nardin, MJ, Dalca, A, Giese, A-K, Etherton, MR, Hancock, BL, Mocking, SJT, McIntosh, EC, Attia, J, Benavente, OR, Bevan, S, Cole, JW, Donatti, A, Griessenauer, CJ, Heitsch, L, Holmegaard, L, Jood, K, Jimenez-Conde, J, Kittner, SJ, Lemmens, R, Levi, CR, McDonough, CW, Meschia, JF, Phuah, C-L, Rolfs, A, Ropele, S, Rosand, J, Roquer, J, Rundek, T, Sacco, RL, Schmidt, R, Sharma, P, Slowik, A, Soderholm, M, Sousa, A, Stanne, TM, Strbian, D, Tatlisumak, T, Thijs, V, Vagal, A, Wasselius, J, Woo, D, Zand, R, McArdle, PF, Worrall, BB, Jern, C, Lindgren, AG, Maguire, J, Bzdok, D, Wu, O, Rost, NS, Bonkhoff, AK, Schirmer, MD, Bretzner, M, Hong, S, Regenhardt, RW, Brudfors, M, Donahue, KL, Nardin, MJ, Dalca, A, Giese, A-K, Etherton, MR, Hancock, BL, Mocking, SJT, McIntosh, EC, Attia, J, Benavente, OR, Bevan, S, Cole, JW, Donatti, A, Griessenauer, CJ, Heitsch, L, Holmegaard, L, Jood, K, Jimenez-Conde, J, Kittner, SJ, Lemmens, R, Levi, CR, McDonough, CW, Meschia, JF, Phuah, C-L, Rolfs, A, Ropele, S, Rosand, J, Roquer, J, Rundek, T, Sacco, RL, Schmidt, R, Sharma, P, Slowik, A, Soderholm, M, Sousa, A, Stanne, TM, Strbian, D, Tatlisumak, T, Thijs, V, Vagal, A, Wasselius, J, Woo, D, Zand, R, McArdle, PF, Worrall, BB, Jern, C, Lindgren, AG, Maguire, J, Bzdok, D, Wu, O, and Rost, NS

Abstract

Acute ischemic stroke affects men and women differently. In particular, women are often reported to experience higher acute stroke severity than men. We derived a low-dimensional representation of anatomical stroke lesions and designed a Bayesian hierarchical modeling framework tailored to estimate possible sex differences in lesion patterns linked to acute stroke severity (National Institute of Health Stroke Scale). This framework was developed in 555 patients (38% female). Findings were validated in an independent cohort (n = 503, 41% female). Here, we show brain lesions in regions subserving motor and language functions help explain stroke severity in both men and women, however more widespread lesion patterns are relevant in female patients. Higher stroke severity in women, but not men, is associated with left hemisphere lesions in the vicinity of the posterior circulation. Our results suggest there are sex-specific functional cerebral asymmetries that may be important for future investigations of sex-stratified approaches to management of acute ischemic stroke.

Published
2021

15. Genetic Predisposition to Mosaic Chromosomal Loss Is Associated With Functional Outcome After Ischemic Stroke.

Author

Johansson, M, Pedersen, A, Cole, JW, Lagging, C, Lindgren, A, Maguire, JM, Rost, NS, Söderholm, M, Worrall, BB, Stanne, TM, Jern, C, Johansson, M, Pedersen, A, Cole, JW, Lagging, C, Lindgren, A, Maguire, JM, Rost, NS, Söderholm, M, Worrall, BB, Stanne, TM, and Jern, C

Abstract

Background and Objectives: To test the hypothesis that a predisposition to acquired genetic alterations is associated with ischemic stroke outcome by investigating the association between a polygenic risk score (PRS) for mosaic loss of chromosome Y (mLOY) and outcome in a large international data set. Methods: We used data from the genome-wide association study performed within the Genetics of Ischemic Stroke Functional Outcome network, which included 6,165 patients (3,497 men and 2,668 women) with acute ischemic stroke of mainly European ancestry. We assessed a weighted PRS for mLOY and examined possible associations with the modified Rankin Scale (mRS) score 3 months poststroke in logistic regression models. We investigated the whole study sample as well as men and women separately. Results: Increasing PRS for mLOY was associated with poor functional outcome (mRS score >2) with an odds ratio (OR) of 1.11 (95% confidence interval [CI] 1.03-1.19) per 1 SD increase in the PRS after adjustment for age, sex, ancestry, stroke severity (NIH Stroke Scale), smoking, and diabetes mellitus. In sex-stratified analyses, we found a statistically significant association in women (adjusted OR 1.20, 95% CI 1.08-1.33). In men, the association was in the same direction (adjusted OR 1.04, 95% CI 0.95-1.14), and we observed no significant genotype-sex interaction. Discussion: In this exploratory study, we found associations between genetic variants predisposing to mLOY and stroke outcome. The significant association in women suggests underlying mechanisms related to genomic instability that operate in both sexes. These findings need replication and mechanistic exploration.

Published
2021

16. Detailed phenotyping of posterior vs. anterior circulation ischemic stroke: a multi-center MRI study

Author

Frid, P, Drake, M, Giese, AK, Wasselius, J, Schirmer, MD, Donahue, KL, Cloonan, L, Irie, R, Bouts, MJRJ, McIntosh, EC, Mocking, SJT, Dalca, AV, Sridharan, R, Xu, H, Giralt-Steinhauer, E, Holmegaard, L, Jood, K, Roquer, J, Cole, JW, McArdle, PF, Broderick, JP, Jimenez-Conde, J, Jern, C, Kissela, BM, Kleindorfer, DO, Lemmens, R, Meschia, JF, Rundek, T, Sacco, RL, Schmidt, R, Sharma, P, Slowik, A, Thijs, V, Woo, D, Worrall, BB, Kittner, SJ, Mitchell, BD, Petersson, J, Rosand, J, Golland, P, Wu, O, Rost, NS, Lindgren, A, Frid, P, Drake, M, Giese, AK, Wasselius, J, Schirmer, MD, Donahue, KL, Cloonan, L, Irie, R, Bouts, MJRJ, McIntosh, EC, Mocking, SJT, Dalca, AV, Sridharan, R, Xu, H, Giralt-Steinhauer, E, Holmegaard, L, Jood, K, Roquer, J, Cole, JW, McArdle, PF, Broderick, JP, Jimenez-Conde, J, Jern, C, Kissela, BM, Kleindorfer, DO, Lemmens, R, Meschia, JF, Rundek, T, Sacco, RL, Schmidt, R, Sharma, P, Slowik, A, Thijs, V, Woo, D, Worrall, BB, Kittner, SJ, Mitchell, BD, Petersson, J, Rosand, J, Golland, P, Wu, O, Rost, NS, and Lindgren, A

Abstract

OBJECTIVE: Posterior circulation ischemic stroke (PCiS) constitutes 20-30% of ischemic stroke cases. Detailed information about differences between PCiS and anterior circulation ischemic stroke (ACiS) remains scarce. Such information might guide clinical decision making and prevention strategies. We studied risk factors and ischemic stroke subtypes in PCiS vs. ACiS and lesion location on magnetic resonance imaging (MRI) in PCiS. METHODS: Out of 3,301 MRIs from 12 sites in the National Institute of Neurological Disorders and Stroke (NINDS) Stroke Genetics Network (SiGN), we included 2,381 cases with acute DWI lesions. The definition of ACiS or PCiS was based on lesion location. We compared the groups using Chi-squared and logistic regression. RESULTS: PCiS occurred in 718 (30%) patients and ACiS in 1663 (70%). Diabetes and male sex were more common in PCiS vs. ACiS (diabetes 27% vs. 23%, p < 0.05; male sex 68% vs. 58%, p < 0.001). Both were independently associated with PCiS (diabetes, OR = 1.29; 95% CI 1.04-1.61; male sex, OR = 1.46; 95% CI 1.21-1.78). ACiS more commonly had large artery atherosclerosis (25% vs. 20%, p < 0.01) and cardioembolic mechanisms (17% vs. 11%, p < 0.001) compared to PCiS. Small artery occlusion was more common in PCiS vs. ACiS (20% vs. 14%, p < 0.001). Small artery occlusion accounted for 47% of solitary brainstem infarctions. CONCLUSION: Ischemic stroke subtypes differ between the two phenotypes. Diabetes and male sex have a stronger association with PCiS than ACiS. Definitive MRI-based PCiS diagnosis aids etiological investigation and contributes additional insights into specific risk factors and mechanisms of injury in PCiS.

Published
2020

17. Association of common genetic variants with brain microbleeds: A genome-wide association study

Author

Knol, Maria, Lu, Difei, Traylor, M, Adams, Hieab, Romero, JR, Smith, AV, Fornage, M, Hofer, E, Liu, Jun, Hostettler, IC, Luciano, M, Trompet, S, Giese, A K, Hilal, S, van den Akker, EB, Vojinovic, Dina, Li, S, Sigurdsson, S, van der Lee, Sven, Jack, CR, Wilson, D, Yilmaz, Pinar, Satizabal, CL, Liewald, DCM, van der Grond, J, Chen, C, Saba, Y, van der Lugt, Aad, Bastin, ME, Windham, BG, Cheng, CY (Ching-Yu), Pirpamer, L, Kantarci, K, Himali, JJ, Yang, Q, Morris, Z, Beiser, AS, Tozer, DJ, Vernooij, Meike, Amin, Najaf, Beekman, M, Koh, JY, Stott, DJM, Houlden, H, Schmidt, R, Gottesman, RF, MacKinnon, AD, DeCarli, C, Gudnason, V, Deary, IJ, Duijn, Cornelia, Slagboom, PE, Wong, TY, Rost, NS, Jukema, JW, Mosley, TH, Jr, Werring, DJ, Schmidt, H, Wardlaw, JM, Ikram, Arfan, Seshadri, S, Launer, LJ, Markus, HS, Knol, Maria, Lu, Difei, Traylor, M, Adams, Hieab, Romero, JR, Smith, AV, Fornage, M, Hofer, E, Liu, Jun, Hostettler, IC, Luciano, M, Trompet, S, Giese, A K, Hilal, S, van den Akker, EB, Vojinovic, Dina, Li, S, Sigurdsson, S, van der Lee, Sven, Jack, CR, Wilson, D, Yilmaz, Pinar, Satizabal, CL, Liewald, DCM, van der Grond, J, Chen, C, Saba, Y, van der Lugt, Aad, Bastin, ME, Windham, BG, Cheng, CY (Ching-Yu), Pirpamer, L, Kantarci, K, Himali, JJ, Yang, Q, Morris, Z, Beiser, AS, Tozer, DJ, Vernooij, Meike, Amin, Najaf, Beekman, M, Koh, JY, Stott, DJM, Houlden, H, Schmidt, R, Gottesman, RF, MacKinnon, AD, DeCarli, C, Gudnason, V, Deary, IJ, Duijn, Cornelia, Slagboom, PE, Wong, TY, Rost, NS, Jukema, JW, Mosley, TH, Jr, Werring, DJ, Schmidt, H, Wardlaw, JM, Ikram, Arfan, Seshadri, S, Launer, LJ, and Markus, HS

Published
2020

18. Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes (vol 50, pg 524, 2018)

Author

Malik, R, Chauhan, G, Traylor, M, Sargurupremraj, M, Okada, Y, Mishra, A, Rutten-Jacobs, L, Giese, A-K, Van der Laan, SW, Gretarsdottir, S, Anderson, CD, Chong, M, Adams, HHH, Ago, T, Almgren, P, Amouyel, P, Ay, H, Bartz, TM, Benavente, OR, Bevan, S, Boncoraglio, GB, Brown, RD, Butterworth, AS, Carrera, C, Carty, CL, Chasman, DI, Chen, W-M, Cole, JW, Correa, A, Cotlarciuc, I, Cruchaga, C, Danesh, J, De Bakker, PIW, DeStefano, AL, Den Hoed, M, Duan, Q, Engelter, ST, Falcone, GJ, Gottesman, RF, Grewal, RP, Gudnason, V, Gustafsson, S, Haessler, J, Harris, TB, Hassan, A, Havulinna, AS, Heckbert, SR, Holliday, EG, Howard, G, Hsu, F-C, Hyacinth, IH, Ikram, MA, Ingelsson, E, Irvin, MR, Jian, X, Jimenez-Conde, J, Johnson, JA, Jukema, JW, Kanai, M, Keene, KL, Kissela, BM, Kleindorfer, DO, Kooperberg, C, Kubo, M, Lange, LA, Langefeld, CD, Langenberg, C, Launer, LJ, Lee, J-M, Lemmens, R, Leys, D, Lewis, CM, Lin, W-Y, Lindgren, AG, Lorentzen, E, Magnusson, PK, Maguire, J, Manichaikul, A, McArdle, PF, Meschia, JF, Mitchell, BD, Mosley, TH, Nalls, MA, Ninomiya, T, O'Donnell, MJ, Psaty, BM, Pulit, SL, Rannikmae, K, Reiner, AP, Rexrode, KM, Rice, K, Rich, SS, Ridker, PM, Rost, NS, Rothwell, PM, Rotter, JI, Rundek, T, Sacco, RL, Sakaue, S, Sale, MM, Salomaa, V, Sapkota, BR, Schmidt, R, Schmidt, CO, Schminke, U, Sharma, P, Slowik, A, Sudlow, CLM, Tanislav, C, Tatlisumak, T, Taylor, KD, Thijs, VNS, Thorleifsson, G, Thorsteinsdottir, U, Tiedt, S, Trompet, S, Tzourio, C, Van Duijn, CM, Walters, M, Wareham, NJ, Wassertheil-Smoller, S, Wilson, JG, Wiggins, KL, Yang, Q, Yusuf, S, Bis, JC, Pastinen, T, Ruusalepp, A, Schadt, EE, Koplev, S, Bjorkegren, JLM, Codoni, V, Civelek, M, Smith, NL, Tregouet, DA, Christophersen, IE, Roselli, C, Lubitz, SA, Ellinor, PT, Tai, ES, Kooner, JS, Kato, N, He, J, Van der Harst, P, Elliott, P, Chambers, JC, Takeuchi, F, Johnson, AD, Sanghera, DK, Melander, O, Jern, C, Strbian, D, Fernandez-Cadenas, I, Longstreth, WT, Rolfs, A, Hata, J, Woo, D, Rosand, J, Pare, G, Hopewell, JC, Saleheen, D, Stefansson, K, Worrall, BB, Kittner, SJ, Seshadri, S, Fornage, M, Markus, HS, Howson, JMM, Kamatani, Y, Debette, S, and Dichgans, M

Subjects
Genetics & Heredity, International Genomics of Blood Pressure (iGEN-BP) Consortium, COMPASS Consortium, MEGASTROKE Consortium, Science & Technology, UK Young Lacunar DNA Study, AFGen Consortium, INVENT Consortium, STARNET, 06 Biological Sciences, METASTROKE Consortium, EPIC-CVD Consortium, BioBank Japan Cooperative Hospital Group, International Stroke Genetics Consortium (ISGC), NINDS Stroke Genetics Network (SiGN), Neurology Working Group of the CHARGE Consortium, EPIC-InterAct Consortium, Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium, Life Sciences & Biomedicine, 11 Medical and Health Sciences, Developmental Biology
Published
2019

19. Genetically determined risk of depression and functional outcome after ischemic stroke

Author

Gill, D, James, NE, Monori, G, Lorentzen, E, Fernandez-Cadenas, I, Lemmens, R, Thijs, V, Rost, NS, Scott, R, Hankey, GJ, Lindgren, A, Jern, C, Maguire, JM, and International Stroke Genetics Consortium and the GISCOME Network

Subjects
Science & Technology, Neurology & Neurosurgery, Clinical Neurology, morbidity, 1103 Clinical Sciences, stroke, Peripheral Vascular Disease, depression, International Stroke Genetics Consortium and the GISCOME Network, Cardiovascular System & Cardiology, Neurosciences & Neurology, 1109 Neurosciences, Life Sciences & Biomedicine, 1102 Cardiorespiratory Medicine and Haematology
Abstract

Background and Purpose- Psychosocial factors can have implications for ischemic stroke risk and recovery. This study investigated the effect of genetically determined risk of depression on these outcomes using the Mendelian randomization (MR) framework. Methods- Genetic instruments for risk of depression were identified in a discovery genome-wide association study of 246 363 cases and 561 190 controls and further replicated in a separate population of 474 574 cases and 1 032 579 controls. Corresponding genetic association estimates for risk of ischemic stroke were taken from 60 341 cases and 454 450 controls, with those for functional outcome 3 months after ischemic stroke taken from an analysis of 6021 patients. Following statistical power calculation, inverse-variance weighted MR was performed to pool estimates across different instruments. The Cochran Q heterogeneity test, weighted median MR, and MR pleiotropy residual sum and outlier were used to explore possible bias relating to inclusion of pleiotropic variants. Results- There was no MR evidence for an effect of genetically determined risk of depression on ischemic stroke risk. Although suffering low statistical power, the main inverse-variance weighted MR analysis was suggestive of a detrimental effect of genetically determined risk of depression on functional outcome after ischemic stroke (odds ratio of poor outcome [modified Rankin Scale, ≥3] per 1-SD increase in genetically determined risk of depression, 1.81; 95% CI, 0.98-3.35; P=0.06). There was no evidence of heterogeneity between MR estimates produced by different instruments (Q P=0.26). Comparable MR estimates were obtained with weighted median MR (odds ratio, 2.57; 95% CI, 1.05-6.25; P=0.04) and MR pleiotropy residual sum and outlier (odds ratio, 1.81; 95% CI, 0.95-3.46; P=0.08). Conclusions- We found no MR evidence of genetically determined risk of depression affecting ischemic stroke risk but did find consistent MR evidence suggestive of a possible effect on functional outcome after ischemic stroke. Given the widespread prevalence of depression-related morbidity, these findings could have implications for prognostication and personalized rehabilitation after stroke.

Published
2019

20. White matter hyperintensity quantification in large-scale clinical acute ischemic stroke cohorts - The MRI-GENIE study

Author

Schirmer, MD, Dalca, A, Sridharan, R, Giese, A-K, Donahue, KL, Nardin, MJ, Mocking, SJT, McIntosh, EC, Frid, P, Wasselius, J, Cole, JW, Holmegaard, L, Jern, C, Jimenez-Conde, J, Lemmens, R, Lindgren, AG, Meschia, JF, Roquer, J, Rundek, T, Sacco, RL, Schmidt, R, Sharma, P, Slowik, A, Thijs, V, Woo, D, Vagal, A, Xu, H, Kittner, SJ, McArdle, PF, Mitchell, BD, Rosand, J, Worrall, BB, Wu, O, Golland, P, Rost, NS, Schirmer, MD, Dalca, A, Sridharan, R, Giese, A-K, Donahue, KL, Nardin, MJ, Mocking, SJT, McIntosh, EC, Frid, P, Wasselius, J, Cole, JW, Holmegaard, L, Jern, C, Jimenez-Conde, J, Lemmens, R, Lindgren, AG, Meschia, JF, Roquer, J, Rundek, T, Sacco, RL, Schmidt, R, Sharma, P, Slowik, A, Thijs, V, Woo, D, Vagal, A, Xu, H, Kittner, SJ, McArdle, PF, Mitchell, BD, Rosand, J, Worrall, BB, Wu, O, Golland, P, and Rost, NS

Abstract

White matter hyperintensity (WMH) burden is a critically important cerebrovascular phenotype linked to prediction of diagnosis and prognosis of diseases, such as acute ischemic stroke (AIS). However, current approaches to its quantification on clinical MRI often rely on time intensive manual delineation of the disease on T2 fluid attenuated inverse recovery (FLAIR), which hinders high-throughput analyses such as genetic discovery. In this work, we present a fully automated pipeline for quantification of WMH in clinical large-scale studies of AIS. The pipeline incorporates automated brain extraction, intensity normalization and WMH segmentation using spatial priors. We first propose a brain extraction algorithm based on a fully convolutional deep learning architecture, specifically designed for clinical FLAIR images. We demonstrate that our method for brain extraction outperforms two commonly used and publicly available methods on clinical quality images in a set of 144 subject scans across 12 acquisition centers, based on dice coefficient (median 0.95; inter-quartile range 0.94-0.95; p < 0.01) and Pearson correlation of total brain volume (r = 0.90). Subsequently, we apply it to the large-scale clinical multi-site MRI-GENIE study (N = 2783) and identify a decrease in total brain volume of -2.4 cc/year. Additionally, we show that the resulting total brain volumes can successfully be used for quality control of image preprocessing. Finally, we obtain WMH volumes by building on an existing automatic WMH segmentation algorithm that delineates and distinguishes between different cerebrovascular pathologies. The learning method mimics expert knowledge of the spatial distribution of the WMH burden using a convolutional auto-encoder. This enables successful computation of WMH volumes of 2533 clinical AIS patients. We utilize these results to demonstrate the increase of WMH burden with age (0.950 cc/year) and show that single site estimates can be biased by the number of sub

Published
2019

22. Genome-wide association meta-analysis of functional outcome after ischemic stroke

Author

Soderholm, M, Pedersen, A, Lorentzen, E, Stanne, TM, Bevan, S, Olsson, M, Cole, JW, Fernandez-Cadenas, I, Hankey, GJ, Jimenez-Conde, J, Jood, K, Lee, J-M, Lemmens, R, Levi, C, Mitchell, BD, Norrving, B, Rannikmaee, K, Rost, NS, Rosand, J, Rothwell, PM, Scott, R, Strbian, D, Sturm, JW, Sudlow, C, Traylor, M, Thijs, V, Tatlisumak, T, Woo, D, Worrall, BB, Maguire, JM, Lindgren, A, Jern, C, Soderholm, M, Pedersen, A, Lorentzen, E, Stanne, TM, Bevan, S, Olsson, M, Cole, JW, Fernandez-Cadenas, I, Hankey, GJ, Jimenez-Conde, J, Jood, K, Lee, J-M, Lemmens, R, Levi, C, Mitchell, BD, Norrving, B, Rannikmaee, K, Rost, NS, Rosand, J, Rothwell, PM, Scott, R, Strbian, D, Sturm, JW, Sudlow, C, Traylor, M, Thijs, V, Tatlisumak, T, Woo, D, Worrall, BB, Maguire, JM, Lindgren, A, and Jern, C

Abstract

OBJECTIVE: To discover common genetic variants associated with poststroke outcomes using a genome-wide association (GWA) study. METHODS: The study comprised 6,165 patients with ischemic stroke from 12 studies in Europe, the United States, and Australia included in the GISCOME (Genetics of Ischaemic Stroke Functional Outcome) network. The primary outcome was modified Rankin Scale score after 60 to 190 days, evaluated as 2 dichotomous variables (0-2 vs 3-6 and 0-1 vs 2-6) and subsequently as an ordinal variable. GWA analyses were performed in each study independently and results were meta-analyzed. Analyses were adjusted for age, sex, stroke severity (baseline NIH Stroke Scale score), and ancestry. The significance level was p < 5 × 10-8. RESULTS: We identified one genetic variant associated with functional outcome with genome-wide significance (modified Rankin Scale scores 0-2 vs 3-6, p = 5.3 × 10-9). This intronic variant (rs1842681) in the LOC105372028 gene is a previously reported trans-expression quantitative trait locus for PPP1R21, which encodes a regulatory subunit of protein phosphatase 1. This ubiquitous phosphatase is implicated in brain functions such as brain plasticity. Several variants detected in this study demonstrated suggestive association with outcome (p < 10-5), some of which are within or near genes with experimental evidence of influence on ischemic stroke volume and/or brain recovery (e.g., NTN4, TEK, and PTCH1). CONCLUSIONS: In this large GWA study on functional outcome after ischemic stroke, we report one significant variant and several variants with suggestive association to outcome 3 months after stroke onset with plausible mechanistic links to poststroke recovery. Future replication studies and exploration of potential functional mechanisms for identified genetic variants are warranted.

Published
2019

23. Genetic Imbalance Is Associated With Functional Outcome After Ischemic Stroke

Author

Pfeiffer, D, Chen, B, Schlicht, K, Ginsbach, P, Abboud, S, Bersano, A, Bevan, S, Brandt, T, Caso, V, Debette, S, Erhart, P, Freitag-Wolf, S, Giacalone, G, Grau, AJ, Hayani, E, Jern, C, Jiménez-Conde, J, Kloss, M, Krawczak, M, Lee, JM, Lemmens, R, Leys, D, Lichy, C, Maguire, JM, Martin, JJ, Metso, AJ, Metso, TM, Mitchell, BD, Pezzini, A, Rosand, J, Rost, NS, Stenman, M, Tatlisumak, T, Thijs, V, Touzé, E, Traenka, C, Werner, I, Woo, D, Del Zotto, E, Engelter, ST, Kittner, SJ, Cole, JW, Grond-Ginsbach, C, Lyrer, PA, Lindgren, A, Pfeiffer, D, Chen, B, Schlicht, K, Ginsbach, P, Abboud, S, Bersano, A, Bevan, S, Brandt, T, Caso, V, Debette, S, Erhart, P, Freitag-Wolf, S, Giacalone, G, Grau, AJ, Hayani, E, Jern, C, Jiménez-Conde, J, Kloss, M, Krawczak, M, Lee, JM, Lemmens, R, Leys, D, Lichy, C, Maguire, JM, Martin, JJ, Metso, AJ, Metso, TM, Mitchell, BD, Pezzini, A, Rosand, J, Rost, NS, Stenman, M, Tatlisumak, T, Thijs, V, Touzé, E, Traenka, C, Werner, I, Woo, D, Del Zotto, E, Engelter, ST, Kittner, SJ, Cole, JW, Grond-Ginsbach, C, Lyrer, PA, and Lindgren, A

Abstract

Background and Purpose- We sought to explore the effect of genetic imbalance on functional outcome after ischemic stroke (IS). Methods- Copy number variation was identified in high-density single-nucleotide polymorphism microarray data of IS patients from the CADISP (Cervical Artery Dissection and Ischemic Stroke Patients) and SiGN (Stroke Genetics Network)/GISCOME (Genetics of Ischaemic Stroke Functional Outcome) networks. Genetic imbalance, defined as total number of protein-coding genes affected by copy number variations in an individual, was compared between patients with favorable (modified Rankin Scale score of 0-2) and unfavorable (modified Rankin Scale score of ≥3) outcome after 3 months. Subgroup analyses were confined to patients with imbalance affecting ohnologs-a class of dose-sensitive genes, or to those with imbalance not affecting ohnologs. The association of imbalance with outcome was analyzed by logistic regression analysis, adjusted for age, sex, stroke subtype, stroke severity, and ancestry. Results- The study sample comprised 816 CADISP patients (age 44.2±10.3 years) and 2498 SiGN/GISCOME patients (age 67.7±14.2 years). Outcome was unfavorable in 122 CADISP and 889 SiGN/GISCOME patients. Multivariate logistic regression analysis revealed that increased genetic imbalance was associated with less favorable outcome in both samples (CADISP: P=0.0007; odds ratio=0.89; 95% CI, 0.82-0.95 and SiGN/GISCOME: P=0.0036; odds ratio=0.94; 95% CI, 0.91-0.98). The association was independent of age, sex, stroke severity on admission, stroke subtype, and ancestry. On subgroup analysis, imbalance affecting ohnologs was associated with outcome (CADISP: odds ratio=0.88; 95% CI, 0.80-0.95 and SiGN/GISCOME: odds ratio=0.93; 95% CI, 0.89-0.98) whereas imbalance without ohnologs lacked such an association. Conclusions- Increased genetic imbalance was associated with poorer functional outcome after IS in both study populations. Subgroup analysis revealed that this associ

Published
2019

24. Serum magnesium and calcium levels in relation to ischemic stroke: Mendelian randomization study

Author

Larsson, SC, Traylor, M, Burgess, S, Boncoraglio, GB, Jern, C, Michaëlsson, K, Markus, HS, Malik, R, Chauhan, G, Sargurupremraj, M, Okada, Y, Mishra, A, Rutten-Jacobs, L, Giese, AK, Van Der Laan, SW, Gretarsdottir, S, Anderson, CD, Chong, M, Adams, HHH, Ago, T, Almgren, P, Amouyel, P, Ay, H, Bartz, RM, Benavente, OR, Bevan, S, Brown, RD, Butterworth, AS, Carrera, C, Carty, CL, Chasman, DI, Chen, WM, Cole, JW, Correa, A, Cotlarciuc, I, Cruchaga, C, Danesh, J, De Bakker, PIW, Destefano, AL, Hoed, MD, Duan, Q, Engelter, ST, Falcone, GJ, Gottesman, RF, Grewal, RP, Gudnason, V, Gustafsson, S, Haessler, J, Harris, TB, Hassan, A, Havulinna, AS, Heckbert, SR, Holliday, EG, Howard, G, Hsu, FC, Hyacinth, HI, Ikram, MA, Ingelsson, E, Irvin, MR, Jian, X, Jiménez-Conde, J, Johnson, JA, Jukema, JW, Kanai, M, Keene, KL, Kissela, BM, Kleindorfer, DO, Kooperberg, C, Kubo, M, Lange, LA, Langefeld, CD, Langenberg, C, Launer, LJ, Lee, JM, Lemmens, R, Leys, D, Lewis, CM, Lin, WY, Lindgren, AG, Lorentzen, E, Magnusson, PK, Maguire, J, Manichaikul, A, McArdle, PF, Meschia, JF, Mitchell, BD, Mosley, TH, Nalls, MA, Ninomiya, T, O'Donnell, MJ, Psaty, BM, Pulit, SL, Rannikmäe, K, Reiner, AP, Rexrode, KM, Rice, K, Rich, SS, Ridker, PM, Rost, NS, Rothwell, PM, Larsson, SC, Traylor, M, Burgess, S, Boncoraglio, GB, Jern, C, Michaëlsson, K, Markus, HS, Malik, R, Chauhan, G, Sargurupremraj, M, Okada, Y, Mishra, A, Rutten-Jacobs, L, Giese, AK, Van Der Laan, SW, Gretarsdottir, S, Anderson, CD, Chong, M, Adams, HHH, Ago, T, Almgren, P, Amouyel, P, Ay, H, Bartz, RM, Benavente, OR, Bevan, S, Brown, RD, Butterworth, AS, Carrera, C, Carty, CL, Chasman, DI, Chen, WM, Cole, JW, Correa, A, Cotlarciuc, I, Cruchaga, C, Danesh, J, De Bakker, PIW, Destefano, AL, Hoed, MD, Duan, Q, Engelter, ST, Falcone, GJ, Gottesman, RF, Grewal, RP, Gudnason, V, Gustafsson, S, Haessler, J, Harris, TB, Hassan, A, Havulinna, AS, Heckbert, SR, Holliday, EG, Howard, G, Hsu, FC, Hyacinth, HI, Ikram, MA, Ingelsson, E, Irvin, MR, Jian, X, Jiménez-Conde, J, Johnson, JA, Jukema, JW, Kanai, M, Keene, KL, Kissela, BM, Kleindorfer, DO, Kooperberg, C, Kubo, M, Lange, LA, Langefeld, CD, Langenberg, C, Launer, LJ, Lee, JM, Lemmens, R, Leys, D, Lewis, CM, Lin, WY, Lindgren, AG, Lorentzen, E, Magnusson, PK, Maguire, J, Manichaikul, A, McArdle, PF, Meschia, JF, Mitchell, BD, Mosley, TH, Nalls, MA, Ninomiya, T, O'Donnell, MJ, Psaty, BM, Pulit, SL, Rannikmäe, K, Reiner, AP, Rexrode, KM, Rice, K, Rich, SS, Ridker, PM, Rost, NS, and Rothwell, PM

Abstract

© 2019 American Academy of Neurology. ObjectiveTo determine whether serum magnesium and calcium concentrations are causally associated with ischemic stroke or any of its subtypes using the mendelian randomization approach.MethodsAnalyses were conducted using summary statistics data for 13 single-nucleotide polymorphisms robustly associated with serum magnesium (n = 6) or serum calcium (n = 7) concentrations. The corresponding data for ischemic stroke were obtained from the MEGASTROKE consortium (34,217 cases and 404,630 noncases).ResultsIn standard mendelian randomization analysis, the odds ratios for each 0.1 mmol/L (about 1 SD) increase in genetically predicted serum magnesium concentrations were 0.78 (95% confidence interval [CI] 0.69-0.89; p = 1.3 × 10-4) for all ischemic stroke, 0.63 (95% CI 0.50-0.80; p = 1.6 × 10-4) for cardioembolic stroke, and 0.60 (95% CI 0.44-0.82; p = 0.001) for large artery stroke; there was no association with small vessel stroke (odds ratio 0.90, 95% CI 0.67-1.20; p = 0.46). Only the association with cardioembolic stroke was robust in sensitivity analyses. There was no association of genetically predicted serum calcium concentrations with all ischemic stroke (per 0.5 mg/dL [about 1 SD] increase in serum calcium: odds ratio 1.03, 95% CI 0.88-1.21) or with any subtype.ConclusionsThis study found that genetically higher serum magnesium concentrations are associated with a reduced risk of cardioembolic stroke but found no significant association of genetically higher serum calcium concentrations with any ischemic stroke subtype.

Published
2019

25. Genetically Determined Risk of Depression and Functional Outcome after Ischemic Stroke: Mendelian Randomization Study

Author

Gill, D, James, NE, Monori, G, Lorentzen, E, Fernandez-Cadenas, I, Lemmens, R, Thijs, V, Rost, NS, Scott, R, Hankey, GJ, Lindgren, A, Jern, C, Maguire, JM, Gill, D, James, NE, Monori, G, Lorentzen, E, Fernandez-Cadenas, I, Lemmens, R, Thijs, V, Rost, NS, Scott, R, Hankey, GJ, Lindgren, A, Jern, C, and Maguire, JM

Abstract

© 2019 American Heart Association, Inc. Background and Purpose-Psychosocial factors can have implications for ischemic stroke risk and recovery. This study investigated the effect of genetically determined risk of depression on these outcomes using the Mendelian randomization (MR) framework. Methods-Genetic instruments for risk of depression were identified in a discovery genome-wide association study of 246 363 cases and 561 190 controls and further replicated in a separate population of 474 574 cases and 1 032 579 controls. Corresponding genetic association estimates for risk of ischemic stroke were taken from 60 341 cases and 454 450 controls, with those for functional outcome 3 months after ischemic stroke taken from an analysis of 6021 patients. Following statistical power calculation, inverse-variance weighted MR was performed to pool estimates across different instruments. The Cochran Q heterogeneity test, weighted median MR, and MR pleiotropy residual sum and outlier were used to explore possible bias relating to inclusion of pleiotropic variants. Results-There was no MR evidence for an effect of genetically determined risk of depression on ischemic stroke risk. Although suffering low statistical power, the main inverse-variance weighted MR analysis was suggestive of a detrimental effect of genetically determined risk of depression on functional outcome after ischemic stroke (odds ratio of poor outcome [modified Rankin Scale, ≥3] per 1-SD increase in genetically determined risk of depression, 1.81; 95% CI, 0.98-3.35; P=0.06). There was no evidence of heterogeneity between MR estimates produced by different instruments (Q P=0.26). Comparable MR estimates were obtained with weighted median MR (odds ratio, 2.57; 95% CI, 1.05-6.25; P=0.04) and MR pleiotropy residual sum and outlier (odds ratio, 1.81; 95% CI, 0.95-3.46; P=0.08). Conclusions-We found no MR evidence of genetically determined risk of depression affecting ischemic stroke risk but did find consistent

Published
2019

26. Serum magnesium and calcium levels in relation to ischemic stroke: Mendelian randomization study

Author

Larsson, SC, Traylor, M, Burgess, S, Boncoraglio, GB, Jern, C, Michaëlsson, K, Markus, HS, Malik, R, Chauhan, G, Sargurupremraj, M, Okada, Y, Mishra, A, Rutten-Jacobs, L, Giese, AK, Van Der Laan, SW, Gretarsdottir, S, Anderson, CD, Chong, M, Adams, HHH, Ago, T, Almgren, P, Amouyel, P, Ay, H, Bartz, RM, Benavente, OR, Bevan, S, Brown, RD, Butterworth, AS, Carrera, C, Carty, CL, Chasman, DI, Chen, WM, Cole, JW, Correa, A, Cotlarciuc, I, Cruchaga, C, Danesh, J, De Bakker, PIW, Destefano, AL, Hoed, MD, Duan, Q, Engelter, ST, Falcone, GJ, Gottesman, RF, Grewal, RP, Gudnason, V, Gustafsson, S, Haessler, J, Harris, TB, Hassan, A, Havulinna, AS, Heckbert, SR, Holliday, EG, Howard, G, Hsu, FC, Hyacinth, HI, Ikram, MA, Ingelsson, E, Irvin, MR, Jian, X, Jiménez-Conde, J, Johnson, JA, Jukema, JW, Kanai, M, Keene, KL, Kissela, BM, Kleindorfer, DO, Kooperberg, C, Kubo, M, Lange, LA, Langefeld, CD, Langenberg, C, Launer, LJ, Lee, JM, Lemmens, R, Leys, D, Lewis, CM, Lin, WY, Lindgren, AG, Lorentzen, E, Magnusson, PK, Maguire, J, Manichaikul, A, McArdle, PF, Meschia, JF, Mitchell, BD, Mosley, TH, Nalls, MA, Ninomiya, T, O'Donnell, MJ, Psaty, BM, Pulit, SL, Rannikmäe, K, Reiner, AP, Rexrode, KM, Rice, K, Rich, SS, Ridker, PM, Rost, NS, and Rothwell, PM

Subjects
Stroke, Neurology & Neurosurgery, Intracranial Embolism, Humans, Calcium, Magnesium, Mendelian Randomization Analysis, Polymorphism, Single Nucleotide, Brain Ischemia
Abstract

© 2019 American Academy of Neurology. ObjectiveTo determine whether serum magnesium and calcium concentrations are causally associated with ischemic stroke or any of its subtypes using the mendelian randomization approach.MethodsAnalyses were conducted using summary statistics data for 13 single-nucleotide polymorphisms robustly associated with serum magnesium (n = 6) or serum calcium (n = 7) concentrations. The corresponding data for ischemic stroke were obtained from the MEGASTROKE consortium (34,217 cases and 404,630 noncases).ResultsIn standard mendelian randomization analysis, the odds ratios for each 0.1 mmol/L (about 1 SD) increase in genetically predicted serum magnesium concentrations were 0.78 (95% confidence interval [CI] 0.69-0.89; p = 1.3 × 10-4) for all ischemic stroke, 0.63 (95% CI 0.50-0.80; p = 1.6 × 10-4) for cardioembolic stroke, and 0.60 (95% CI 0.44-0.82; p = 0.001) for large artery stroke; there was no association with small vessel stroke (odds ratio 0.90, 95% CI 0.67-1.20; p = 0.46). Only the association with cardioembolic stroke was robust in sensitivity analyses. There was no association of genetically predicted serum calcium concentrations with all ischemic stroke (per 0.5 mg/dL [about 1 SD] increase in serum calcium: odds ratio 1.03, 95% CI 0.88-1.21) or with any subtype.ConclusionsThis study found that genetically higher serum magnesium concentrations are associated with a reduced risk of cardioembolic stroke but found no significant association of genetically higher serum calcium concentrations with any ischemic stroke subtype.

Published
2018

27. Publisher correction: Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes(Nature Genetics, (2018) 50, 4, (524-537), 10.1038/s41588-018-0058-3)

Author

Malik, R, Chauhan, G, Traylor, M, Sargurupremraj, M, Okada, Y, Mishra, A, Rutten-Jacobs, L, Giese, AK, van der Laan, SW, Gretarsdottir, S, Anderson, CD, Chong, M, Adams, HHH, Ago, T, Almgren, P, Amouyel, P, Ay, H, Bartz, TM, Benavente, OR, Bevan, S, Boncoraglio, GB, Brown, RD, Butterworth, AS, Carrera, C, Carty, CL, Chasman, DI, Chen, WM, Cole, JW, Correa, A, Cotlarciuc, I, Cruchaga, C, Danesh, J, de Bakker, PIW, Destefano, AL, Den Hoed, M, Duan, Q, Engelter, ST, Falcone, GJ, Gottesman, RF, Grewal, RP, Gudnason, V, Gustafsson, S, Haessler, J, Harris, TB, Hassan, A, Havulinna, AS, Heckbert, SR, Holliday, EG, Howard, G, Hsu, FC, Hyacinth, HI, Arfan Ikram, M, Ingelsson, E, Irvin, MR, Jian, X, Jiménez-Conde, J, Johnson, JA, Wouter Jukema, J, Kanai, M, Keene, KL, Kissela, BM, Kleindorfer, DO, Kooperberg, C, Kubo, M, Lange, LA, Langefeld, CD, Langenberg, C, Launer, LJ, Lee, JM, Lemmens, R, Leys, D, Lewis, CM, Lin, WY, Lindgren, AG, Lorentzen, E, Magnusson, PK, Maguire, J, Manichaikul, A, McArdle, PF, Meschia, JF, Mitchell, BD, Mosley, TH, Nalls, MA, Ninomiya, T, O’donnell, MJ, Psaty, BM, Pulit, SL, Rannikmäe, K, Reiner, AP, Rexrode, KM, Rice, K, Rich, SS, Ridker, PM, Rost, NS, Rothwell, PM, Rotter, JI, Rundek, T, Sacco, RL, Sakaue, S, and Sale, MM

Subjects
Developmental Biology
Abstract

© 2019, Nature Publishing Group. All rights reserved. In the HTML version of this article initially published, the author groups ‘AFGen Consortium’, ‘Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium’, ‘International Genomics of Blood Pressure (iGEN-BP) Consortium’, ‘INVENT Consortium’, ‘STARNET’, ‘BioBank Japan Cooperative Hospital Group’, ‘COMPASS Consortium’, ‘EPIC-CVD Consortium’, ‘EPIC-InterAct Consortium’, ‘International Stroke Genetics Consortium (ISGC)’, ‘METASTROKE Consortium’, ‘Neurology Working Group of the CHARGE Consortium’, ‘NINDS Stroke Genetics Network (SiGN)’, ‘UK Young Lacunar DNA Study’ and ‘MEGASTROKE Consortium’ appeared at the end of the author list but should have appeared earlier in the list. In addition, the author group ‘MEGASTROKE Consortium’ was duplicated, and its members were not displayed in the ‘Author information’ section. The errors have been corrected in the HTML version of the article.

Published
2018

28. Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes

Author

Malik, R, Chauhan, G, Traylor, M, Sargurupremraj, M, Okada, Y, Mishra, A, Rutten-Jacobs, L, Giese, AK, Van Der Laan, SW, Gretarsdottir, S, Anderson, CD, Chong, M, Adams, HHH, Ago, T, Almgren, P, Amouyel, P, Ay, H, Bartz, TM, Benavente, OR, Bevan, S, Boncoraglio, GB, Brown, RD, Butterworth, AS, Carrera, C, Carty, CL, Chasman, DI, Chen, WM, Cole, JW, Correa, A, Cotlarciuc, I, Cruchaga, C, Danesh, J, De Bakker, PIW, Destefano, AL, Den Hoed, M, Duan, Q, Engelter, ST, Falcone, GJ, Gottesman, RF, Grewal, RP, Gudnason, V, Gustafsson, S, Haessler, J, Harris, TB, Hassan, A, Havulinna, AS, Heckbert, SR, Holliday, EG, Howard, G, Hsu, FC, Hyacinth, HI, Ikram, MA, Ingelsson, E, Irvin, MR, Jian, X, Jiménez-Conde, J, Johnson, JA, Jukema, JW, Kanai, M, Keene, KL, Kissela, BM, Kleindorfer, DO, Kooperberg, C, Kubo, M, Lange, LA, Langefeld, CD, Langenberg, C, Launer, LJ, Lee, JM, Lemmens, R, Leys, D, Lewis, CM, Lin, WY, Lindgren, AG, Lorentzen, E, Magnusson, PK, Maguire, J, Manichaikul, A, McArdle, PF, Meschia, JF, Mitchell, BD, Mosley, TH, Nalls, MA, Ninomiya, T, O'Donnell, MJ, Psaty, BM, Pulit, SL, Rannikmäe, K, Reiner, AP, Rexrode, KM, Rice, K, Rich, SS, Ridker, PM, Rost, NS, Rothwell, PM, Rotter, JI, Rundek, T, Sacco, RL, Sakaue, S, Sale, MM, Malik, R, Chauhan, G, Traylor, M, Sargurupremraj, M, Okada, Y, Mishra, A, Rutten-Jacobs, L, Giese, AK, Van Der Laan, SW, Gretarsdottir, S, Anderson, CD, Chong, M, Adams, HHH, Ago, T, Almgren, P, Amouyel, P, Ay, H, Bartz, TM, Benavente, OR, Bevan, S, Boncoraglio, GB, Brown, RD, Butterworth, AS, Carrera, C, Carty, CL, Chasman, DI, Chen, WM, Cole, JW, Correa, A, Cotlarciuc, I, Cruchaga, C, Danesh, J, De Bakker, PIW, Destefano, AL, Den Hoed, M, Duan, Q, Engelter, ST, Falcone, GJ, Gottesman, RF, Grewal, RP, Gudnason, V, Gustafsson, S, Haessler, J, Harris, TB, Hassan, A, Havulinna, AS, Heckbert, SR, Holliday, EG, Howard, G, Hsu, FC, Hyacinth, HI, Ikram, MA, Ingelsson, E, Irvin, MR, Jian, X, Jiménez-Conde, J, Johnson, JA, Jukema, JW, Kanai, M, Keene, KL, Kissela, BM, Kleindorfer, DO, Kooperberg, C, Kubo, M, Lange, LA, Langefeld, CD, Langenberg, C, Launer, LJ, Lee, JM, Lemmens, R, Leys, D, Lewis, CM, Lin, WY, Lindgren, AG, Lorentzen, E, Magnusson, PK, Maguire, J, Manichaikul, A, McArdle, PF, Meschia, JF, Mitchell, BD, Mosley, TH, Nalls, MA, Ninomiya, T, O'Donnell, MJ, Psaty, BM, Pulit, SL, Rannikmäe, K, Reiner, AP, Rexrode, KM, Rice, K, Rich, SS, Ridker, PM, Rost, NS, Rothwell, PM, Rotter, JI, Rundek, T, Sacco, RL, Sakaue, S, and Sale, MM

Abstract

© 2018 The Author(s). Stroke has multiple etiologies, but the underlying genes and pathways are largely unknown. We conducted a multiancestry genome-wide-association meta-analysis in 521,612 individuals (67,162 cases and 454,450 controls) and discovered 22 new stroke risk loci, bringing the total to 32. We further found shared genetic variation with related vascular traits, including blood pressure, cardiac traits, and venous thromboembolism, at individual loci (n = 18), and using genetic risk scores and linkage-disequilibrium-score regression. Several loci exhibited distinct association and pleiotropy patterns for etiological stroke subtypes. Eleven new susceptibility loci indicate mechanisms not previously implicated in stroke pathophysiology, with prioritization of risk variants and genes accomplished through bioinformatics analyses using extensive functional datasets. Stroke risk loci were significantly enriched in drug targets for antithrombotic therapy.

Published
2018

29. COL4A2 is associated with lacunar ischemic stroke and deep ICH: Meta-analyses among 21,500 cases and 40,600 controls

Author

Rannikmäe, K, Sivakumaran, V, Millar, H, Malik, R, Anderson, CD, Chong, M, Dave, T, Falcone, GJ, Fernandez-Cadenas, I, Jimenez-Conde, J, Lindgren, A, Montaner, J, O'Donnell, M, Paré, G, Radmanesh, F, Rost, NS, Slowik, A, Söderholm, M, Traylor, M, Pulit, SL, Seshadri, S, Worrall, BB, Woo, D, Markus, HS, Mitchell, BD, Dichgans, M, Rosand, J, Sudlow, CLM, Stroke Genetics Network (SiGN), METASTROKE Collaboration, and International Stroke Genetics Consortium (ISGC), and Maguire, J

Subjects
Collagen Type IV, Europe, Neurology & Neurosurgery, Stroke, Lacunar, Humans, Polymorphism, Single Nucleotide, 1103 Clinical Sciences, 1109 Neurosciences, 1702 Cognitive Sciences, Genetic Association Studies, Cerebral Hemorrhage
Abstract

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.

Published
2017

30. Design and rationale for examining neuroimaging genetics in ischemic stroke The MRI-GENIE study

Author

Giese, A-K, Schirmer, MD, Donahue, KL, Cloonan, L, Irie, R, Winzeck, S, Bouts, MJRJ, McIntosh, EC, Mocking, SJ, Dalca, AV, Sridharan, R, Xu, H, Frid, P, Giralt-Steinhauer, E, Holmegaard, L, Roquer, J, Wasselius, J, Cole, JW, McArdle, PF, Broderick, JP, Jimenez-Conde, J, Jern, C, Kissela, BM, Kleindorfer, DO, Lemmens, R, Lindgren, A, Meschia, JF, Rundek, T, Sacco, RL, Schmidt, R, Sharma, P, Slowik, A, Thijs, V, Woo, D, Worrall, BB, Kittner, SJ, Mitchell, BD, Rosand, J, Golland, P, Wu, O, Rost, NS, Giese, A-K, Schirmer, MD, Donahue, KL, Cloonan, L, Irie, R, Winzeck, S, Bouts, MJRJ, McIntosh, EC, Mocking, SJ, Dalca, AV, Sridharan, R, Xu, H, Frid, P, Giralt-Steinhauer, E, Holmegaard, L, Roquer, J, Wasselius, J, Cole, JW, McArdle, PF, Broderick, JP, Jimenez-Conde, J, Jern, C, Kissela, BM, Kleindorfer, DO, Lemmens, R, Lindgren, A, Meschia, JF, Rundek, T, Sacco, RL, Schmidt, R, Sharma, P, Slowik, A, Thijs, V, Woo, D, Worrall, BB, Kittner, SJ, Mitchell, BD, Rosand, J, Golland, P, Wu, O, and Rost, NS

Abstract

OBJECTIVE: To describe the design and rationale for the genetic analysis of acute and chronic cerebrovascular neuroimaging phenotypes detected on clinical MRI in patients with acute ischemic stroke (AIS) within the scope of the MRI-GENetics Interface Exploration (MRI-GENIE) study. METHODS: MRI-GENIE capitalizes on the existing infrastructure of the Stroke Genetics Network (SiGN). In total, 12 international SiGN sites contributed MRIs of 3,301 patients with AIS. Detailed clinical phenotyping with the web-based Causative Classification of Stroke (CCS) system and genome-wide genotyping data were available for all participants. Neuroimaging analyses include the manual and automated assessments of established MRI markers. A high-throughput MRI analysis pipeline for the automated assessment of cerebrovascular lesions on clinical scans will be developed in a subset of scans for both acute and chronic lesions, validated against gold standard, and applied to all available scans. The extracted neuroimaging phenotypes will improve characterization of acute and chronic cerebrovascular lesions in ischemic stroke, including CCS subtypes, and their effect on functional outcomes after stroke. Moreover, genetic testing will uncover variants associated with acute and chronic MRI manifestations of cerebrovascular disease. CONCLUSIONS: The MRI-GENIE study aims to develop, validate, and distribute the MRI analysis platform for scans acquired as part of clinical care for patients with AIS, which will lead to (1) novel genetic discoveries in ischemic stroke, (2) strategies for personalized stroke risk assessment, and (3) personalized stroke outcome assessment.

Published
2017

31. Genetic Variation at 16q24.2 Is Associated With Small Vessel Stroke

Author

Traylor, M, Malik, R, Nalls, MA, Cotlarciuc, I, Radmanesh, F, Thorleifsson, G, Hanscombe, KB, Langefeld, C, Saleheen, D, Rost, NS, Yet, I, Spector, TD, Bell, JT, Hannon, E, Mill, J, Chauhan, G, Debette, S, Bis, JC, Longstreth, WT, Ikram, MA, Launer, LJ, Seshadri, S, Hamilton-Bruce, MA, Jimenez-Conde, J, Cole, JW, Schmidt, R, Slowik, A, Lemmens, R, Lindgren, A, Melander, O, Grewal, RP, Sacco, RL, Rundek, T, Rexrode, K, Arnett, DK, Johnson, JA, Benavente, OR, Wasssertheil-Smoller, S, Lee, J-M, Pulit, SL, Wong, Q, Rich, SS, de Bakker, PIW, McArdle, PF, Woo, D, Anderson, CD, Xu, H, Heitsch, L, Fornage, M, Jern, C, Stefansson, K, Thorsteinsdottir, U, Gretarsdottir, S, Lewis, CM, Sharma, P, Sudlow, CLM, Rothwell, PM, Boncoraglio, GB, Thijs, V, Levi, C, Meschia, JF, Rosand, J, Kittner, SJ, Mitchell, BD, Dichgans, M, Worrall, BB, Markus, HS, Traylor, M, Malik, R, Nalls, MA, Cotlarciuc, I, Radmanesh, F, Thorleifsson, G, Hanscombe, KB, Langefeld, C, Saleheen, D, Rost, NS, Yet, I, Spector, TD, Bell, JT, Hannon, E, Mill, J, Chauhan, G, Debette, S, Bis, JC, Longstreth, WT, Ikram, MA, Launer, LJ, Seshadri, S, Hamilton-Bruce, MA, Jimenez-Conde, J, Cole, JW, Schmidt, R, Slowik, A, Lemmens, R, Lindgren, A, Melander, O, Grewal, RP, Sacco, RL, Rundek, T, Rexrode, K, Arnett, DK, Johnson, JA, Benavente, OR, Wasssertheil-Smoller, S, Lee, J-M, Pulit, SL, Wong, Q, Rich, SS, de Bakker, PIW, McArdle, PF, Woo, D, Anderson, CD, Xu, H, Heitsch, L, Fornage, M, Jern, C, Stefansson, K, Thorsteinsdottir, U, Gretarsdottir, S, Lewis, CM, Sharma, P, Sudlow, CLM, Rothwell, PM, Boncoraglio, GB, Thijs, V, Levi, C, Meschia, JF, Rosand, J, Kittner, SJ, Mitchell, BD, Dichgans, M, Worrall, BB, and Markus, HS

Abstract

OBJECTIVE: Genome-wide association studies (GWAS) have been successful at identifying associations with stroke and stroke subtypes, but have not yet identified any associations solely with small vessel stroke (SVS). SVS comprises one quarter of all ischemic stroke and is a major manifestation of cerebral small vessel disease, the primary cause of vascular cognitive impairment. Studies across neurological traits have shown that younger-onset cases have an increased genetic burden. We leveraged this increased genetic burden by performing an age-at-onset informed GWAS meta-analysis, including a large younger-onset SVS population, to identify novel associations with stroke. METHODS: We used a three-stage age-at-onset informed GWAS to identify novel genetic variants associated with stroke. On identifying a novel locus associated with SVS, we assessed its influence on other small vessel disease phenotypes, as well as on messenger RNA (mRNA) expression of nearby genes, and on DNA methylation of nearby CpG sites in whole blood and in the fetal brain. RESULTS: We identified an association with SVS in 4,203 cases and 50,728 controls on chromosome 16q24.2 (odds ratio [OR; 95% confidence interval {CI}] = 1.16 [1.10-1.22]; p = 3.2 × 10-9 ). The lead single-nucleotide polymorphism (rs12445022) was also associated with cerebral white matter hyperintensities (OR [95% CI] = 1.10 [1.05-1.16]; p = 5.3 × 10-5 ; N = 3,670), but not intracerebral hemorrhage (OR [95% CI] = 0.97 [0.84-1.12]; p = 0.71; 1,545 cases, 1,481 controls). rs12445022 is associated with mRNA expression of ZCCHC14 in arterial tissues (p = 9.4 × 10-7 ) and DNA methylation at probe cg16596957 in whole blood (p = 5.3 × 10-6 ). INTERPRETATION: 16q24.2 is associated with SVS. Associations of the locus with expression of ZCCHC14 and DNA methylation suggest the locus acts through changes to regulatory elements. Ann Neurol 2017;81:383-394.

Published
2017

34. Association of MTHFR C677T Genotype with Ischemic Stroke Is Confined to Cerebral Small Vessel Disease Subtype

Author

Rutten-Jacobs, LCA, Traylor, M, Adib-Samii, P, Thijs, V, Sudlow, C, Rothwell, PM, Boncoraglio, G, Dichgans, M, Meschia, J, Maguire, J, Levi, C, Rost, NS, Rosand, J, Hassan, A, Bevan, S, Markus, HS, Rutten-Jacobs, LCA, Traylor, M, Adib-Samii, P, Thijs, V, Sudlow, C, Rothwell, PM, Boncoraglio, G, Dichgans, M, Meschia, J, Maguire, J, Levi, C, Rost, NS, Rosand, J, Hassan, A, Bevan, S, and Markus, HS

Abstract

© 2016 American Heart Association, Inc. Elevated plasma homocysteine levels are associated with stroke. However, this might be a reflection of bias or confounding because trials have failed to demonstrate an effect from homocysteine lowering in stroke patients, although a possible benefit has been suggested in lacunar stroke. Genetic studies could potentially overcome these issues because genetic variants are inherited randomly and are fixed at conception. Therefore, we tested the homocysteine levels-associated genetic variant MTHFR C677T for association with magnetic resonance imaging-confirmed lacunar stroke and compared this with associations with large artery and cardioembolic stroke subtypes. Methods-We included 1359 magnetic resonance imaging-confirmed lacunar stroke cases, 1824 large artery stroke cases, 1970 cardioembolic stroke cases, and 14 448 controls, all of European ancestry. Furthermore, we studied 3670 ischemic stroke patients in whom white matter hyperintensities volume was measured. We tested MTHFR C677T for association with stroke subtypes and white matter hyperintensities volume. Because of the established association of homocysteine with hypertension, we additionally stratified for hypertension status. Results-MTHFR C677T was associated with lacunar stroke (P=0.0003) and white matter hyperintensity volume (P=0.04), but not with the other stroke subtypes. Stratifying the lacunar stroke cases for hypertension status confirmed this association in hypertensive individuals (P=0.0002), but not in normotensive individuals (P=0.30). Conclusions-MTHFR C677T was associated with magnetic resonance imaging-confirmed lacunar stroke, but not large artery or cardioembolic stroke. The association may act through increased susceptibility to, or interaction with, high blood pressure. This heterogeneity of association might explain the lack of effect of lowering homocysteine in secondary prevention trials which included all strokes.

Published
2016

35. Common NOTCH3 Variants and Cerebral Small-Vessel Disease

Author

Rutten-Jacobs, LCA, Traylor, M, Adib-Samii, P, Thijs, V, Sudlow, C, Rothwell, PM, Boncoraglio, G, Dichgans, M, Bevan, S, Meschia, J, Levi, C, Rost, NS, Rosand, J, Hassan, A, Markus, HS, Rutten-Jacobs, LCA, Traylor, M, Adib-Samii, P, Thijs, V, Sudlow, C, Rothwell, PM, Boncoraglio, G, Dichgans, M, Bevan, S, Meschia, J, Levi, C, Rost, NS, Rosand, J, Hassan, A, and Markus, HS

Abstract

BACKGROUND AND PURPOSE: The most common monogenic cause of cerebral small-vessel disease is cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy, caused by NOTCH3 gene mutations. It has been hypothesized that more common variants in NOTCH3 may also contribute to the risk of sporadic small-vessel disease. Previously, 4 common variants (rs10404382, rs1043994, rs10423702, and rs1043997) were found to be associated with the presence of white matter hyperintensity in hypertensive community-dwelling elderly. METHODS: We investigated the association of common single nucleotide polymorphisms (SNPs) in NOTCH3 in 1350 patients with MRI-confirmed lacunar stroke and 7397 controls, by meta-analysis of genome-wide association study data sets. In addition, we investigated the association of common SNPs in NOTCH3 with MRI white matter hyperintensity volumes in 3670 white patients with ischemic stroke. In each analysis, we considered all SNPs within the NOTCH3 gene, and within 50-kb upstream and downstream of the coding region. A total of 381 SNPs from the 1000 genome population with a mean allele frequency>0.01 were included in the analysis. A significance level of P<0.0015 was used, adjusted for the effective number of independent SNPs in the region using the Galwey method. RESULTS: We found no association of any common variants in NOTCH3 (including rs10404382, rs1043994, rs10423702, and rs1043997) with lacunar stroke or white matter hyperintensity volume. We repeated our analysis stratified for hypertension but again found no association. CONCLUSIONS: Our study does not support a role for common NOTCH3 variation in the risk of sporadic small-vessel disease.

Published
2015

36. Recommendations from the international stroke genetics consortium, part 1: Standardized phenotypic data collection

Author

Majersik, JJ, Cole, JW, Golledge, J, Rost, NS, Chan, YFY, Gurol, ME, Lindgren, AG, Woo, D, Fernandez-Cadenas, I, Chen, DT, Thijs, V, Worrall, BB, Kamal, A, Bentley, P, Wardlaw, JM, Ruigrok, YM, Battey, TWK, Schmidt, R, Montaner, J, Giese, AK, Roquer, J, Jiménez-Conde, J, Lee, C, Ay, H, Martin, JJ, Rosand, J, Maguire, J, Majersik, JJ, Cole, JW, Golledge, J, Rost, NS, Chan, YFY, Gurol, ME, Lindgren, AG, Woo, D, Fernandez-Cadenas, I, Chen, DT, Thijs, V, Worrall, BB, Kamal, A, Bentley, P, Wardlaw, JM, Ruigrok, YM, Battey, TWK, Schmidt, R, Montaner, J, Giese, AK, Roquer, J, Jiménez-Conde, J, Lee, C, Ay, H, Martin, JJ, Rosand, J, and Maguire, J

Published
2015

41. Early plasma biomarkers of ischemic penumbra in acute stroke

Author

Lorenzano, S, Rost, Ns, Brouwers, Hb, Caceres, Aj, Siket, Mj, Pontes Neto OM, Li, H, Green, Re, Thankachan, T, Dipietro, Aj, Thornell, Bj, Wu, O, Hartdegen, S, Som, At, Pham, Ld, Kelly, Pj, Harris, Gj, Lo, Eh, Feske, Sk, and Furie, Kl

Published
2012

45. Impact of acute ischemic stroke treatment in patients >80 years of age: the specialized program of translational research in acute stroke (SPOTRIAS) consortium experience.

Author

Willey JZ, Ortega-Gutierrez S, Petersen N, Khatri P, Ford AL, Rost NS, Ali LK, Gonzales NR, Merino JG, Meyer BC, Marshall RS, Willey, Joshua Z, Ortega-Gutierrez, Santiago, Petersen, Nils, Khatri, Pooja, Ford, Andria L, Rost, Natalia S, Ali, Latisha K, Gonzales, Nichole R, and Merino, Jose G

Published
2012
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