White matter hyperintensities and imaging patterns of brain ageing in the general population

Abstract

White matter hyperintensities are associated with increased risk of dementia and cognitive decline. The current study investigates the relationship between white matter hyperintensities burden and patterns of brain atrophy associated with brain ageing and Alzheimer's disease in a large populatison-based sample (n = 2367) encompassing a wide age range (20-90 years), from the Study of Health in Pomerania. We quantified white matter hyperintensities using automated segmentation and summarized atrophy patterns using machine learning methods resulting in two indices: the SPARE-BA index (capturing age-related brain atrophy), and the SPARE-AD index (previously developed to capture patterns of atrophy found in patients with Alzheimer's disease). A characteristic pattern of age-related accumulation of white matter hyperintensities in both periventricular and deep white matter areas was found. Individuals with high white matter hyperintensities burden showed significantly (P < 0.0001) lower SPARE-BA and higher SPARE-AD values compared to those with low white matter hyperintensities burden, indicating that the former had more patterns of atrophy in brain regions typically affected by ageing and Alzheimer's disease dementia. To investigate a possibly causal role of white matter hyperintensities, structural equation modelling was used to quantify the effect of Framingham cardiovascular disease risk score and white matter hyperintensities burden on SPARE-BA, revealing a statistically significant (P < 0.0001) causal relationship between them. Structural equation modelling showed that the age effect on SPARE-BA was mediated by white matter hyperintensities and cardiovascular risk score each explaining 10.4% and 21.6% of the variance, respectively. The direct age effect explained 70.2% of the SPARE-BA variance. Only white matter hyperintensities significantly mediated the age effect on SPARE-AD explaining 32.8% of the variance. The direct age effect explained 66.0% of the SPARE-AD variance. Multivariable regression showed significant relationship between white matter hyperintensities volume and hypertension (P = 0.001), diabetes mellitus (P = 0.023), smoking (P = 0.002) and education level (P = 0.003). The only significant association with cognitive tests was with the immediate recall of the California verbal and learning memory test. No significant association was present with the APOE genotype. These results support the hypothesis that white matter hyperintensities contribute to patterns of brain atrophy found in beyond-normal brain ageing in the general population. White matter hyperintensities also contribute to brain atrophy patterns in regions related to Alzheimer's disease dementia, in agreement with their known additive role to the likelihood of dementia. Preventive strategies reducing the odds to develop cardiovascular disease and white matter hyperintensities could decrease the incidence or delay the onset of dementia.

Keywords: Alzheimer’s disease; brain ageing; cardiovascular disease; mild cognitive impairment; white matter hyperintensities.

White matter hyperintensities (WMH) are associated with increased risk of cognitive decline. In a large population-based sample, Habes et al. reveal that WMH burden contributes to atrophy in regions typically affected by beyond-normal brain ageing and Alzheimer’s disease. Strategies aimed at preventing WMH development could delay the onset of dementia.

Figure 1

WMH volume as function of age for the whole SHIP sample included in this study ( n = 2367) .

Figure 2

SHIP individuals with high and low WMH burden and the corresponding SPARE indices . Top row : WMH volume for SHIP subjects in middle (40–65 years old) and old (>65 years old) age categories. Based upon WMH volume, we grouped these subjects into subjects with high WMH load (red dots; above the 80th percentile of WMH volume as a function of age, n = 282 for middle and n = 102 for old ages) and low WMH load (black dots; below the 20th percentile of WMH volume as a function of age, n = 282 for middle and n = 101 for old ages). Middle row: The relationship between age and SPARE-BA (reflecting ageing patterns of brain atrophy) in both groups. Bottom row: The relationship between age and SPARE-AD (capturing patterns of atrophy in Alzheimer’s disease-related regions) in both groups. Individuals with high WMH load have higher SPARE-AD and lower SPARE-BA values ( P < 0.0001).

Figure 3

Frequency maps of WMH in the high burden groups in middle ( n = 282), and old ( n = 102) age categories. Upper row presents axial view and the lower , sagittal view.

Figure 4

Path diagram of the factors associated with the SPARE-BA index. WMH had 10.4% [95% confidence interval (CI): 8.4–12.4%] mediation of age effect on the SPARE-BA index (paths ab and dcb). CVD-RS had a 21.6% (95% CI: 17.4–25.7%) mediation of age effect on the SPARE-BA index (paths de and dcb). The common path through CVD and WMH (path dcb) shows 2.3% (95% CI: 1.3–3.3%) mediation of age effect on brain atrophy. The total mediation of age effect through WMH and CVD-RS together (path ab, dcb and de) was 28.9 (95% CI: 25.4–34.1%). The direct age effect on SPARE-BA (path f) was 70.2% (95% CI: 65.9–74.6%). All results were statistically significant ( P < 0.0001).

Figure 5

Path diagram of the factors associated the SPARE-AD index. WMH had 32.8% (95% CI: 25.6–40.3%) mediation of age effect on the SPARE-AD index (paths ab and dcb). CVD-RS had 8.4% (95% CI: −6–23.1%) mediation of age effect on the SPARE-AD index (paths de and dcb). The common path through CVD and WMH (path dcb) shows 7.3% (95% CI: 4.4–10.8%) mediation of age effect on brain atrophy. The total mediation of age effect through WMH and CVD-RS together (path ab, dcb and de) was 34.0% (95% CI: 18.7–50.1%). The direct age effect on SPARE-AD (path f) was 66.0% (95% CI: 49.9–81.2%).

Figure 6

Regions of significant group differences in gray matter atrophy between individuals with high and low WMH burden in relation to advanced brain aging patterns of atrophy. Blue: regions displaying significant regional atrophy patterns between resilient ( n = 439 middle age, n = 167 old age) and advanced brain ageing ( n = 393 middle age, n = 158 old age) individuals. Orange: regions displaying significant WMH-related patterns of atrophy in high ( n = 282 middle age, n =102 old age) versus low WMH ( n = 282 middle age, n = 101 old age) individuals. Green: overlap of the blue and orange regions (false discovery rate correction with q < 0.05).