Reconsidering harbingers of dementia: progression of parietal lobe white matter hyperintensities predicts Alzheimer's disease incidence

Abstract

Accumulating evidence implicates small vessel cerebrovascular disease, visualized as white matter hyperintensities (WMH) on T2-weighted magnetic resonance imaging, in the pathogenesis and diagnosis of Alzheimer's disease (AD). Cross-sectional volumetric measures of WMH, particularly in the parietal lobes, are associated with increased risk of AD. In the present study, we sought to determine whether the longitudinal regional progression of WMH predicts incident AD above-and-beyond traditional radiological markers of neurodegeneration (i.e., hippocampal atrophy and cortical thickness). Three hundred three nondemented older adults (mean age = 79.24 ± 5.29) received high-resolution magnetic resonance imaging at baseline and then again 4.6 years (standard deviation = 1.01) later. Over the follow-up interval 26 participants progressed to AD. Using structural equation modeling, we calculated latent difference scores of parietal and nonparietal WMH, hippocampus volumes, and cortical thickness values in AD-related regions. Within the structural equation modeling framework, we determined whether baseline or change scores or both predicted AD conversion, while controlling for several time-invariant relevant variables. Smaller baseline hippocampus volume, change in hippocampus volume (i.e., atrophy), higher baseline parietal lobe WMH, and increasing parietal lobe WMH volume but not WMH in other regions or measures of cortical thickness, independently predicted progression to AD. The findings provide strong evidence that regionally accumulating WMH predict AD onset in addition to hallmark neurodegenerative changes typically associated with AD.

Keywords: Alzheimer's disease; Hippocampus atrophy; Longitudinal; White matter hyperintensities.

Figure 1

Unconditional latent difference score (LDS) model. Squares represent measured variables at the two time points. The circle represents the latent change variable. The triangle represents a fixed constant of 1.0 for every individual. Paths from this constant reflect mean or intercept.