From the Department of Neurology (A.-K.G., M.D.S., K.L.D., M.N., J.R., O.W., N.S.R.), Massachusetts General Hospital, Harvard Medical School, Boston; Program in Medical and Population Genetics (A.K.-G, J.R.), Broad Institute of MIT and Harvard; Computer Science and Artificial Intelligence Lab (M.D.S., A.V.D., R. Sridharan, P.G.), Massachusetts Institute of Technology, Cambridge; Department of Population Health Sciences (M.D.S.), German Centre for Neurodegenerative Diseases, Bonn, Germany; Athinoula A. Martinos Center for Biomedical Imaging (A.V.D., R.I., E.C.M., S.J.T.M., J.R., O.W.), Department of Radiology, Massachusetts General Hospital, Charlestown; Division of Endocrinology, Diabetes and Nutrition (H.X., P.F.M., B.D.M.), Department of Medicine, University of Maryland School of Medicine; Department of Neurology (J.W.C., S.J.K.), University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore; Department of Neurology (E.G.-S., J.J.-C.), Neurovascular Research Group, IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Spain; Institute of Biomedicine (C.J.), Sahlgrenska Academy at University of Gothenburg, Sweden; Department of Neurology and Rehabilitation Medicine (D.O.K., D.W.), University of Cincinnati College of Medicine, OH; KU Leuven-University of Leuven (R.L.), Department of Neurosciences, Experimental Neurology; VIB (R.L.), Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Department of Neurology, Belgium; Department of Clinical Sciences Lund (J.W., A.L.), Neurology, Lund University; Department of Neurology and Rehabilitation Medicine (A.L.), Neurology, Skåne University Hospital, Lund, Sweden; Department of Neurology (T.R., R.L.S.), Miller School of Medicine, University of Miami, The Evelyn F. McKnight Brain Institute, FL; Department of Neurology (R. Schmidt), Clinical Division of Neurogeriatrics, Medical University Graz, Austria; Institute of Cardiovascular Research (P.S.), Royal Holloway University of London, Egham, UK; Ashford and St Peter's Hospital (P.S.), UK; Department of Neurology (A.S.), Jagiellonian University Medical College, Krakow, Poland; Stroke Division (V.T.), Florey Institute of Neuroscience and Mental Health, University of Melbourne Heidelberg; Department of Neurology (V.T.), Austin Health, Heidelberg, Victoria, Australia; Departments of Neurology and Public Health Sciences (B.B.W.), University of Virginia, Charlottesville; Center for Genomic Medicine (J.R.), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health (J.R.), Boston, MA; and Department of Neurology (J.F.M.), Mayo Clinic, Jacksonville, FL.
Neurology. 2020 Jul 7;95(1):e79-e88. doi: 10.1212/WNL.0000000000009728. Epub 2020 Jun 3.
To examine etiologic stroke subtypes and vascular risk factor profiles and their association with white matter hyperintensity (WMH) burden in patients hospitalized for acute ischemic stroke (AIS).
For the MRI Genetics Interface Exploration (MRI-GENIE) study, we systematically assembled brain imaging and phenotypic data for 3,301 patients with AIS. All cases underwent standardized web tool-based stroke subtyping with the Causative Classification of Ischemic Stroke (CCS). WMH volume (WMHv) was measured on T2 brain MRI scans of 2,529 patients with a fully automated deep-learning trained algorithm. Univariable and multivariable linear mixed-effects modeling was carried out to investigate the relationship of vascular risk factors with WMHv and CCS subtypes.
Patients with AIS with large artery atherosclerosis, major cardioembolic stroke, small artery occlusion (SAO), other, and undetermined causes of AIS differed significantly in their vascular risk factor profile (all < 0.001). Median WMHv in all patients with AIS was 5.86 cm (interquartile range 2.18-14.61 cm) and differed significantly across CCS subtypes ( < 0.0001). In multivariable analysis, age, hypertension, prior stroke, smoking (all < 0.001), and diabetes mellitus ( = 0.041) were independent predictors of WMHv. When adjusted for confounders, patients with SAO had significantly higher WMHv compared to those with all other stroke subtypes ( < 0.001).
In this international multicenter, hospital-based cohort of patients with AIS, we demonstrate that vascular risk factor profiles and extent of WMH burden differ by CCS subtype, with the highest lesion burden detected in patients with SAO. These findings further support the small vessel hypothesis of WMH lesions detected on brain MRI of patients with ischemic stroke.
研究病因性卒中亚型和血管风险因素谱及其与急性缺血性卒中（AIS）住院患者脑白质高信号（WMH）负担的关系。
为了进行 MRI 遗传学接口探索（MRI-GENIE）研究，我们系统地收集了 3301 例 AIS 患者的脑影像学和表型数据。所有病例均采用基于标准化网络工具的致病因分类（CCS）进行卒中亚型分类。在 2529 例患者的 T2 脑 MRI 扫描中，采用完全自动化的深度学习训练算法测量 WMH 体积（WMHv）。采用单变量和多变量线性混合效应模型研究血管风险因素与 WMHv 和 CCS 亚型的关系。
大动脉粥样硬化、大血管心源性栓塞性卒中、小动脉闭塞（SAO）、其他和未确定病因的 AIS 患者在血管风险因素谱方面存在显著差异（均<0.001）。所有 AIS 患者的中位 WMHv 为 5.86cm（四分位距 2.18-14.61cm），且在 CCS 亚型间存在显著差异（<0.0001）。多变量分析显示，年龄、高血压、既往卒中、吸烟（均<0.001）和糖尿病（=0.041）是 WMHv 的独立预测因素。在调整混杂因素后，SAO 患者的 WMHv 明显高于其他卒中亚型（<0.001）。
在这项国际多中心、基于医院的 AIS 患者队列研究中，我们证明血管风险因素谱和 WMH 负担程度因 CCS 亚型而异，SAO 患者的病变负担最高。这些发现进一步支持了脑 MRI 上检测到的缺血性卒中患者 WMH 病变的小血管假说。
