Retinal vascular alterations in cognitive impairment: A multicenter study in China

Abstract

Introduction: Foundational models suggest Alzheimer's disease (AD) can be diagnosed using retinal images, but the specific structural features remain poorly understood. This study investigates retinal vascular changes in individuals with cognitive impairment in three East Asian regions.

Methods: A multicenter study was conducted in Shanghai, Hong Kong, and Ningxia, collecting retinal images from 176 patients with mild cognitive impairment (MCI) or AD and 264 controls. The VC-Net deep learning model segmented arterial/venous networks, extracting 36 vascular features.

Results: Significant reductions in vessel length, segment number, and vascular density were observed in cognitively impaired patients, while venous structure and complexity were correlated with the level of cognitive function.

Discussion: Retinal vascular changes may serve as indicators of cognitive impairment, requiring validation in larger cohorts and exploration of the underlying mechanisms.

Highlights: A deep learning segmentation model extracted diverse retinal vascular features. Significant alterations in the structure of retinal arterial/venous networks were identified. Partitioning vessel-rich retinal zones improved detection of vascular changes. Decreases in vessel length, segment number, and vascular density were found in CI individuals.

Keywords: Alzheimer's disease; cognitive impairment; early biomarkers; interpretable deep learning; retinal imaging; vascular structure.

FIGURE 1

Example images of retinal vessel segmentation and zone division of retinal vascular area. (A) Left, original retinal fundus image; middle, binary vessel image; right, binary segmentation of arteries and veins. Red: arteries. Blue: veins. Green: overlaps of arteries and veins. (B) Left, the radius of the optic disc is used as a unit to define areas of the retinal fundus image; middle, Zone B is the area 2x to 3x radii away from the disc margin; right, Zone C is the area 2x to 5x radii away from the disc margin.

FIGURE 2

Comparison of data distribution of overall retinal vascular features showing significant changes. (A) Schematic diagram of the vascular skeleton (gray) and crossing points (green) in a processed fundus image. (B) Schematic diagram showing the differentiation of each vascular segment in the vascular skeleton and the calculation of the mean width of each segment. (C) Schematic diagram showing the differentiation of each vascular segment in the vascular skeleton in Zone B. DensityA (C), densityV (D), skeleton_lenA (E), and skeleton_lenV (F) decreased significantly in the CI group compared to the NC group. n_cross (H) in both eyes increased significantly in the CI group compared to the NC group. n_crossV (I) for the right eye decreased significantly in the CI group compared to the NC group. Data are presented using violin plots and box plots with median (line) and quartiles (box). ANCOVA p‐values adjusted for FDR are reported as Q‐values: *Q < 0.1; ** Q < 0.01; *** Q < 0.001.

FIGURE 3

Comparison of data distribution of retinal vascular features with significant changes in Zone B and Zone C. For Zone B, zb_len (A), zb_lenV (B), zb_num (C), and zb_numV (D) for the right eye decreased significantly in the CI group compared to the NC group while zb_width (E) for the right eye increased significantly. For Zone C, zc_len (F), zc_lenA (G), zc_lenV (H), and zc_numV (I) in some eyes decreased significantly in the CI group compared to the NC group while zc_width (J) for both eyes increased significantly. Data are presented using violin plots and box plots with median (line) and quartiles (box). ANCOVA p‐values adjusted for FDR are reported as Q‐values: *Q < 0.1; **Q < 0.01; ***Q < 0.001.

FIGURE 4

Significantly different vascular features in the left and right eyes. Heatmaps depict the pairwise Pearson correlation between extracted vascular features with significant differences in both eyes for the left (A) or right (B) eyes. Features are hierarchically clustered and a dendrogram with cutoff at 1 for clustering is shown. Volcano plots show the significance and magnitude of change in the extracted vascular features for the left (C) and right (D) eyes separately. The red dotted line indicates an FDR cutoff of 0.1, with features elevated in the CI group compared to the NC group further to the right (positive logFC).

FIGURE 5

Correlation of vascular features with cognitive performance and classification results of NC and CI. (A) Bar plot of Pearson correlation coefficients between each feature (demographic and vascular) and MoCA scores. (B) Forest plot of beta coefficients of GLM regression models, demographics and one additional vascular feature, assessing their association with MoCA scores. Only models where the Q‐value of the vascular feature coefficient is less than 0.1 are included. The forest plot shows each regressors’ standardized beta coefficient, with 95% confidence interval, enabling the effect size of variables to be compared. Five‐fold cross‐validation results for classification between NC and CI are reported as mean (C) accuracy, (D) AUC, and (E) Brier score with 95% error bars. Performance metrics are compared across models (logistic regression, SVM, and random Forest) and different feature sets (legend right). The dotted gray line indicates the performance of a dummy classifier that always guesses the majority class (NC).

FIGURE 6

Comparison of data distribution of retinal vascular features with significant changes in NC, MCI, or SD groups. Statistically significant differences were found in at least one pairwise comparison for densityV (A), n_cross (B), n_crossV (C), skeleton_lenV (D), zb_len (E), zb_lenA (F), zb_num (G), zb_numA (H), zc_width (I). Data are presented using violin plots and box plots with median (line) and quartiles (box). ANCOVA Tukey post‐hoc p‐values adjusted for FDR are reported as Q‐values: *Q < 0.1; **Q < 0.01; ***Q < 0.001.