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

• Published in: European radiology Vol. 32; no. 1; pp. 272 - 280
• Main Authors: Medrano-Martorell, Santiago, Capellades, Jaume, Jiménez-Conde, Jordi, González-Ortiz, Sofía, Vilas-González, Marta, Rodríguez-Campello, Ana, Ois, Ángel, Cuadrado-Godia, Elisa, Avellaneda, Carla, Fernández, Isabel, Merino-Peña, Elisa, Roquer, Jaume, Martí-Fàbregas, Joan, Giralt-Steinhauer, Eva
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 2022; Springer Nature B.V
• Subjects: Basal ganglia; Body mass index; Brain stem; Cardiac arrhythmia; Cardiovascular disease; Confidence intervals; Coronary vessels; Diabetes; Diabetes mellitus; Diagnostic Radiology; Distribution patterns; Etiology; Evaluation; Ganglia; Hospitals; Hyperlipidemia; Hypertension; Imaging; Internal Medicine; Interventional Radiology; Ischemia; Magnetic resonance imaging; Medicine; Medicine & Public Health; Neuro; Neuroradiology; Patients; Radiology; Risk analysis; Risk factors; Spatial distribution; Statistical analysis; Stroke; Substantia alba; Transient ischemic attack; Ultrasound; Vein & artery diseases; Ischemic stroke; Magnetic resonance imaging; White matter; Leukoaraiosis; Risk factors
• ISSN: 0938-7994; 1432-1084
• DOI: 10.1007/s00330-021-08106-2

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.