Association of Vascular Risk Factors and Cerebrovascular Pathology With Alzheimer Disease Pathologic Changes in Individuals Without Dementia

Abstract

Background and objectives: Vascular risk factors (VRFs) and cerebral small vessel disease (cSVD) are common in patients with Alzheimer disease (AD). It remains unclear whether this coexistence reflects shared risk factors or a mechanistic relationship and whether vascular and amyloid pathologies have independent or synergistic influence on subsequent AD pathophysiology in preclinical stages. We investigated links between VRFs, cSVD, and amyloid levels (Aβ_1-42) and their combined effect on downstream AD biomarkers, that is, CSF hyperphosphorylated tau (P-tau₁₈₁), atrophy, and cognition.

Methods: This retrospective study included nondemented participants (Clinical Dementia Rating < 1) from the European Prevention of Alzheimer's Dementia (EPAD) cohort and assessed VRFs with the Framingham risk score (FRS) and cSVD features on MRI using visual scales and white matter hyperintensity volumes. After preliminary linear analysis, we used structural equation modeling (SEM) to create a "cSVD severity" latent variable and assess the direct and indirect effects of FRS and cSVD severity on Aβ_1-42, P-tau₁₈₁, gray matter volume (baseline and longitudinal), and cognitive performance (baseline and longitudinal).

Results: A total cohort of 1,592 participants were evaluated (mean age = 65.5 ± 7.4 years; 56.16% F). We observed positive associations between FRS and all cSVD features (all p < 0.05) and a negative association between FRS and Aβ_1-42 (β = -0.04 ± 0.01). All cSVD features were negatively associated with CSF Aβ_1-42 (all p < 0.05). Using SEM, the cSVD severity fully mediated the association between FRS and CSF Aβ_1-42 (indirect effect: β = -0.03 ± 0.01), also when omitting vascular amyloid-related markers. We observed a significant indirect effect of cSVD severity on P-tau₁₈₁ (indirect effect: β = 0.12 ± 0.03), baseline and longitudinal gray matter volume (indirect effect: β = -0.10 ± 0.03; β = -0.12 ± 0.05), and baseline cognitive performance (indirect effect: β = -0.16 ± 0.03) through CSF Aβ_1-42.

Discussion: In a large nondemented population, our findings suggest that cSVD is a mediator of the relationship between VRFs and CSF Aβ_1-42 and affects downstream neurodegeneration and cognitive impairment. We provide evidence of VRFs indirectly affecting the pathogenesis of AD, highlighting the importance of considering cSVD burden in memory clinics for AD risk evaluation and as an early window for intervention. These results stress the role of VRFs and cerebrovascular pathology as key biomarkers for accurate design of anti-amyloid clinical trials and offer new perspectives for patient stratification.

Figure 1. Mediation Analysis and cSVD Burden Latent Factor

(A) Illustration of the mediation analysis, including total and mediated effects. After SEM convention, observed variables are depicted as rectangles and latent variables as ovals. Red arrows are used to illustrate direct effects. Blue arrows are used to illustrate indirect (mediated) effects. Black arrows are used to illustrate confirmatory factor analysis (CFA) loadings. *p < 0.05, **p < 0.01, and ***p < 0.001. (B) Distribution (histogram) of the estimated cSVD latent factor through CFA. (C) Association between Framingham scores and raw CSF Aβ_1-42 values stratified by scores at the cSVD latent factor. (D) Association between Framingham scores and log-scaled CSF Aβ_1-42 values stratified by scores at the cSVD latent factor. (E) Illustration of FLAIR and T2w scans from a participant with a high score in the cSVD latent factor. (F) Illustration of FLAIR and T2w scans from a participant with a low score in the cSVD latent factor. CMB = cerebral microbleed; cSVD = cerebral small vessel disease; D = deep; FZKS DWMH = Fazekas deep white matter hyperintensity; PV = periventricular; FZKS PVH = Fazekas periventricular hyperintensity; PVS-BG = perivascular spaces in basal ganglia; PVS-CS = perivascular spaces in centrum semiovale.

Figure 2. Structural Equation Model

After SEM convention, observed variables are depicted as rectangles and latent variables as ovals. Arrows are used to illustrate relations included in the model. (A) The structural equation model fitted to the data after model selection. (B) The significant (p < 0.05) paths and standardized coefficients of the model fitted on cross-sectional data. (C) The significant (p < 0.05) paths and standardized coefficients of the model fitted using rates of change of gray matter atrophy and cognitive performance. Direct effects are shown in red, and indirect effects are shown in dotted blue arrows. All model coefficients are listed in eTables 17 and 18 of supplementary materials. AD = Alzheimer disease; CMB = cerebral microbleed; cSVD = cerebral small vessel disease; FZKS DWMH = Fazekas deep white matter hyperintensity; FZKS PVH = Fazekas periventricular hyperintensity; PVS-BG = perivascular spaces in basal ganglia; PVS-CS = perivascular spaces in centrum semiovale; ROC = rates of change.

Figure 3. Association of CSF Aβ_1-42 With cSVD Radiologic Scores

Boxplot showing the association of CSF Aβ_1-42 with PVS (BG and CS), lacunes, CMB (lobar and deep), and Fazekas scores (DWMH and PVH). p Values for the global association of cSVD scores and CSF Aβ_1-42 are listed in the bottom left corner of boxplots. The association of CSF Aβ_1-42 with WMH volumes is shown using barplots reporting the β coefficients and 95% confidence intervals of the models. Asterisks refer to significance levels as found in the multivariable general linear models correcting for age, sex, and APOE, compared with reference groups. Between-group comparisons are presented in eTable 9. CMB = cerebral microbleed; D = deep; DWMH = deep white matter hyperintensity; PV = periventricular; PVH = periventricular hyperintensity; PVS-BG = perivascular spaces in basal ganglia; PVS-CS = perivascular spaces in centrum semiovale. *p < 0.05, **p < 0.01, and ***p < 0.001.