Santiago Medrano-Martorell1,2, Jaume Capellades3, Jordi Jiménez-Conde4, Sofía González-Ortiz5,3, Marta Vilas-González3, Ana Rodríguez-Campello4, Ángel Ois4, Elisa Cuadrado-Godia4, Carla Avellaneda4, Isabel Fernández4, Elisa Merino-Peña6, Jaume Roquer4, Joan Martí-Fàbregas7, Eva Giralt-Steinhauer4. 1. Department of Neuroradiology, Hospital Clínic i Provincial, Villarroel, 170, Barcelona, Spain. santimedra@gmail.com. 2. Department of Neuroradiology, Hospital del Mar, Department of Medicine, Universitat Autònoma de Barcelona (UAB), Barcelona, Spain. santimedra@gmail.com. 3. Department of Neuroradiology, Hospital del Mar, Department of Medicine, Universitat Autònoma de Barcelona (UAB), Barcelona, Spain. 4. Department of Neurology, Hospital del Mar; Neurovascular Research Group, IMIM (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autònoma de Barcelona (UAB)/DCEXS-Universitat Pompeu Fabra (UPF), Barcelona, Spain. 5. Department of Neuroradiology, Hospital Clínic i Provincial, Villarroel, 170, Barcelona, Spain. 6. Department of Neuroradiology, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona (UAB), Barcelona, Spain. 7. Department of Neurology, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona (UAB), Barcelona, Spain.
Abstract
OBJECTIVES: The spectrum of distribution of white matter hyperintensities (WMH) may reflect different functional, histopathological, and etiological features. We examined the relationships between cerebrovascular risk factors (CVRF) and different patterns of WMH in MRI using a qualitative visual scale in ischemic stroke (IS) patients. METHODS: We assembled clinical data and imaging findings from patients of two independent cohorts with recent IS. MRI scans were evaluated using a modified visual scale from Fazekas, Wahlund, and Van Swieten. WMH distributions were analyzed separately in periventricular (PV-WMH) and deep (D-WMH) white matter, basal ganglia (BG-WMH), and brainstem (B-WMH). Presence of confluence of PV-WMH and D-WMH and anterior-versus-posterior WMH predominance were also evaluated. Statistical analysis was performed with SPSS software. RESULTS: We included 618 patients, with a mean age of 72 years (standard deviation [SD] 11 years). The most frequent WMH pattern was D-WMH (73%). In a multivariable analysis, hypertension was associated with PV-WMH (odds ratio [OR] 1.79, 95% confidence interval [CI] 1.29-2.50, p = 0.001) and BG-WMH (OR 2.13, 95% CI 1.19-3.83, p = 0.012). Diabetes mellitus was significantly related to PV-WMH (OR 1.69, 95% CI 1.24-2.30, p = 0.001), D-WMH (OR 1.46, 95% CI 1.07-1.49, p = 0.017), and confluence patterns of D-WMH and PV-WMH (OR 1.62, 95% CI 1.07-2.47, p = 0.024). Hyperlipidemia was found to be independently related to brainstem distribution (OR 1.70, 95% CI 1.08-2.69, p = 0.022). CONCLUSIONS: Different CVRF profiles were significantly related to specific WMH spatial distribution patterns in a large IS cohort. KEY POINTS: • An observational study of WMH in a large IS cohort was assessed by a modified visual evaluation. • Different CVRF profiles were significantly related to specific WMH spatial distribution patterns. • Distinct WMH anatomical patterns could be related to different pathophysiological mechanisms.
OBJECTIVES: The spectrum of distribution of white matter hyperintensities (WMH) may reflect different functional, histopathological, and etiological features. We examined the relationships between cerebrovascular risk factors (CVRF) and different patterns of WMH in MRI using a qualitative visual scale in ischemic stroke (IS) patients. METHODS: We assembled clinical data and imaging findings from patients of two independent cohorts with recent IS. MRI scans were evaluated using a modified visual scale from Fazekas, Wahlund, and Van Swieten. WMH distributions were analyzed separately in periventricular (PV-WMH) and deep (D-WMH) white matter, basal ganglia (BG-WMH), and brainstem (B-WMH). Presence of confluence of PV-WMH and D-WMH and anterior-versus-posterior WMH predominance were also evaluated. Statistical analysis was performed with SPSS software. RESULTS: We included 618 patients, with a mean age of 72 years (standard deviation [SD] 11 years). The most frequent WMH pattern was D-WMH (73%). In a multivariable analysis, hypertension was associated with PV-WMH (odds ratio [OR] 1.79, 95% confidence interval [CI] 1.29-2.50, p = 0.001) and BG-WMH (OR 2.13, 95% CI 1.19-3.83, p = 0.012). Diabetes mellitus was significantly related to PV-WMH (OR 1.69, 95% CI 1.24-2.30, p = 0.001), D-WMH (OR 1.46, 95% CI 1.07-1.49, p = 0.017), and confluence patterns of D-WMH and PV-WMH (OR 1.62, 95% CI 1.07-2.47, p = 0.024). Hyperlipidemia was found to be independently related to brainstem distribution (OR 1.70, 95% CI 1.08-2.69, p = 0.022). CONCLUSIONS: Different CVRF profiles were significantly related to specific WMH spatial distribution patterns in a large IS cohort. KEY POINTS: • An observational study of WMH in a large IS cohort was assessed by a modified visual evaluation. • Different CVRF profiles were significantly related to specific WMH spatial distribution patterns. • Distinct WMH anatomical patterns could be related to different pathophysiological mechanisms.
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