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

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 were 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. KEYWORDS 1. White Matter 2. Magnetic Resonance Imaging 3. Leukoaraiosis 4. Risk Factors 5. Ischemic Stroke KEY POINTS 1. An observational study of WMH in a large IS cohort was assessed by a modified visual evaluation. 2. Different CVRF profiles were significantly related to specific WMH spatial distribution patterns. 3. Distinct WMH anatomical patterns could be related to different pathophysiological mechanisms.

Periventricular rather than deep white matter hyperintensities mediate effects of hypertension on cognitive performance in the population-based 1000BRAINS study

“…Associations were significant both for PVH and DWMH with slightly stronger associations for PVH than DWMH like in our analysis [35]. Also, in a cohort of 618 patients with ischemic stroke or transient ischemic attack, hypertension identified in medical records was significantly associated with higher PVH and DWMH in univariable but only with PVH in multivariable analyses adjusted for age and diabetes [7]. Similarly, self-reported hypertension showed a stronger association with PVH than DWMH in 567 patients from a Chinese neurology department examined because of dizziness and headaches [16].…”

“…Although SVD is regarded as a common cause of cognitive impairment and dementia, its pathophysiology has not fully been elucidated yet [5,6]. Previous studies in general population and patient cohorts demonstrated that next to age, hypertension represents the most important risk factor for WMH [7,8] and cognitive impairment [9] with strict blood pressure (BP) control preventing progression of WMH [10] and cognitive decline [11]. These studies, however, did not look at BP/treatment categories (normotension without antihypertensive treatment, treated to normotension, untreated hypertension, hypertension despite treatment), but analysed hypertension defined as high blood pressure or presence of antihypertensive treatment, included only patients receiving antihypertensive treatment [9][10][11][12], or patients without a need for antihypertensive treatment [13].…”

Epigenetic Clock Explains White Matter Hyperintensity Burden Irrespective of Chronological Age

“…For instance, the location of WMH within the brain might correspond to different pathophysiological mechanisms, as reported recently [4,35,36], and thus present different risk factors and epigenetic signatures. To this aim, we may need to combine EWAS with new neuroimaging analytical approaches that improve the ability to capture the true nature of WMH [37,38].…”