Brain amyloid and vascular risk are related to distinct white matter hyperintensity patterns

Abstract

White matter hyperintensities (WMHs) are associated with vascular risk and Alzheimer's disease. In this study, we examined relations between WMH load and distribution, amyloid pathology and vascular risk in 339 controls and cases with either subjective (SCD) or mild cognitive impairment (MCI). Regional deep (DWMH) and periventricular (PWMH) WMH loads were determined using an automated algorithm. We stratified on Aβ1-42 pathology (Aβ+/-) and analyzed group differences, as well as associations with Framingham Risk Score for cardiovascular disease (FRS-CVD) and age. Occipital PWMH (p = 0.001) and occipital DWMH (p = 0.003) loads were increased in SCD-Aβ+ compared with Aβ- controls. In MCI-Aβ+ compared with Aβ- controls, there were differences in global WMH (p = 0.003), as well as occipital DWMH (p = 0.001) and temporal DWMH (p = 0.002) loads. FRS-CVD was associated with frontal PWMHs (p = 0.003) and frontal DWMHs (p = 0.005), after adjusting for age. There were associations between global and all regional WMH loads and age. In summary, posterior WMH loads were increased in SCD-Aβ+ and MCI-Aβ+ cases, whereas frontal WMHs were associated with vascular risk. The differences in WMH topography support the use of regional WMH load as an early-stage marker of etiology.

Keywords: Alzheimer’s; cerebrospinal fluid; cognitive impairment/decline; small vessel disease; white matter disease.

Figure 1.

WMH segmentation. Example of the brain segmentation for one of the SCD-Aβ+ cases, a 72-year-old woman. The segmentation of WMHs is coloured green in the 2nd column. In the 3rd and 4th columns, the layers and lobes are shown, respectively. The inner and outer two layers were added to estimate periventricular and deep WMHs, respectively.

Figure 2.

Flow chart of subject selection.

Figure 3.

Regional WMH loads. Barplots of regression coefficients with regional WMH loads as dependent variables and group dummy variables as independent variables, showing the differences in SCD-Aβ+, MCI-Aβ− and MCI-Aβ+ compared with NC-Aβ−, adjusted for age and scanners, with error bars marking 95% confidence intervals. * p<0.05. ** p<0.01

Figure 4.

Effects of age and FRS-CVD on regional WMH. Barplots of regression coefficients with regional WMH loads as dependent variables and age and FRS-CVD_woa as independent variables, in univariable (a) or multivariable (b) models, all models adjusted for scanners, with error bars marking 95% confidence intervals. *p<0.05. **p<0.01. ***p<0.001.