Risk factors analysis according to regional distribution of white matter hyperintensities in a stroke cohort

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 sca...

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Bibliographic Details
Authors: Medrano-Martorell, Santiago, Capellades Font, Jaume, Jiménez Conde, Jordi, González-Ortiz, Sofía, Vilas-González, Marta, Rodríguez-Campello, Ana, Ois Santiago, Angel Javier, Cuadrado-Godia, Elisa, Avellaneda Gómez, Carla, Fernández, Isabel, Merino-Peña, Elisa, Roquer, Jaume, Martí-Fàbregas, Joan, Giralt-Steinhauer, Eva
Format: article
Status:Versión aceptada para publicación
Publication Date:2022
Country:España
Institution:Universitat Pompeu Fabra
Repository:Repositorio Digital de la UPF
OAI Identifier:oai:repositori.upf.edu:10230/52805
Online Access:http://hdl.handle.net/10230/52805
http://dx.doi.org/10.1007/s00330-021-08106-2
Access Level:Open access
Keyword:Ischemic stroke
Leukoaraiosis
Magnetic resonance imaging
Risk factors
White matter
Description
Summary: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.

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