White matter hyperintensities and the risk of vascular dementia: a systematic review and meta-analysis.

BACKGROUND

White matter hyperintensities (WMHs) are hyperintense lesions observed on magnetic resonance imaging (MRI) and are unique imaging indicators of cerebral small vessel diseases. WMH-related white matter alterations have been correlated with cognitive impairment and cerebrovascular pathology. Some studies suggest that vascular hemodynamic changes contribute to WMH development, ultimately leading to vascular dementia (VaD). However, the association between WMH burden and VaD remains inconclusive. This meta-analysis aimed to quantify the relationship between WMH volume and VaD severity and to clarify the role of WMHs in VaD pathogenesis.

METHODS

A systematic literature search was performed using the MEDLINE, EMBASE, and Cochrane Library databases. A total of 15 studies with 4,061 patients were selected. The meta-analysis was performed using the RevMan software (version 5.4) and Stata software (version 14.0). All the patients underwent brain MRI to assess WMH volumes or levels, and compared the differences in WMH levels among the VaD group, the non-cognitively impaired (NCI) group, the cognitively impaired no dementia (CIND) group, and the Alzheimer's disease (AD) group.

RESULTS

The meta-analysis showed that all patients in the VaD group had high white matter signals on brain MRI. They also had higher WMH volumes compared to patients in the NCI, CIND, and AD groups. WMH correlated with cerebrovascular pathology, with irregular and periventricular WMHs being more specific to VaD. Sensitivity analyses were performed to identify sources of heterogeneity, while funnel plot and Egger's test suggested potential publication bias.

CONCLUSIONS

Patients with VaD exhibit significantly greater WMH than those with AD, NCI, and CIND, reinforcing the role of cerebrovascular pathology in VaD. These findings emphasize the need for standardized imaging assessments, multi-modal biomarkers, and the development of predictive models to enhance early diagnosis, personalized risk assessment, and targeted therapeutic strategies for VaD.