Peripheral Retinal Imaging Biomarkers for Alzheimer’s Disease: A Pilot Study

Abstract

Purpose: To examine whether ultra-widefield (UWF) retinal imaging can identify biomarkers for Alzheimer’s disease (AD) and its progression. Methods: Images were taken using a UWF scanning laser ophthalmoscope (Optos P200C AF) to determine phenotypic variations in 59 patients with AD and 48 healthy controls at baseline (BL). All living participants were invited for a follow-up (FU) after 2 years and imaged again (if still able to participate). All participants had blood taken for genotyping at BL. Images were graded for the prevalence of age-related macular degeneration-like pathologies and retinal vascular parameters. Comparison between AD patients and controls was made using the Student t test and the χ² test. Results: Analysis at BL revealed a significantly higher prevalence of a hard drusen phenotype in the periphery of AD patients (14/55; 25.4%) compared to controls (2/48; 4.2%) [χ² = 9.9, df = 4, p = 0.04]. A markedly increased drusen number was observed at the 2-year FU in patients with AD compared to controls. There was a significant increase in venular width gradient at BL (zone C: 8.425 × 10^–3 ± 2.865 × 10^–3 vs. 6.375 × 10^–3 ± 1.532 × 10^–3, p = 0.008; entire image: 8.235 × 10^–3 ± 2.839 × 10^–3 vs. 6.050 × 10^–3 ± 1.414 × 10^–3, p = 0.004) and a significant decrease in arterial fractal dimension in AD at BL (entire image: 1.250 ± 0.086 vs. 1.304 ± 0.089, p = 0.049) with a trend for both at FU. Conclusions: UWF retinal imaging revealed a significant association between AD and peripheral hard drusen formation and changes to the vasculature beyond the posterior pole, at BL and after clinical progression over 2 years, suggesting that monitoring pathological changes in the peripheral retina might become a valuable tool in AD monitoring.

Systematic Review on Fractal Dimension of the Retinal Vasculature in Neurodegeneration and Stroke: Assessment of a Potential Biomarker

“…In this review, the outcomes of fractal analysis in neurodegeneration studies show a decreased FD in various degrees and etiologies of CI, including AD and vascular dementia, as opposed to cognitively normal controls. Despite rather small sample sizes, findings are consistent with effect sizes ranging from −0.018 (Csincsik et al, 2018) to −1.33 (Cabrera DeBuc et al, 2018). The findings in studies with stroke patients are less straightforward, probably due to the heterogeneity in both study design and methodological set-up.…”

“…Three of these focused on CI, and even with differences in ROI, imaging device, vessel segmentation software, FD calculation method and software, all three confirmed a significant decrease of FD measures in CI (including dementia) (Cabrera DeBuc et al, 2018), mild CI (Jiang et al, 2018) and AD (Csincsik et al, 2018). After a follow-up period of 2 years, Csincsik et al reported that the significant difference in FD between AD and controls at baseline had become a trend (Csincsik et al, 2018), whereas Jiang et al described a trend of vascular density loss from controls over MCI to AD cases (Jiang et al, 2018). A significant association between fractal dimension and Montreal Cognitive Assessment (MoCA) could not be confirmed (Cabrera DeBuc et al, 2018).…”

Insights into Systemic Disease through Retinal Imaging-Based Oculomics

“…44 The peripheral retina may also hold clues-patients with AD appear to have both a higher baseline and an increase in peripheral hard drusen over two years of follow-up. 45 Anatomically, however, changes in the optic nerve and retinal nerve fiber layer would seem most plausible representatives of neurodegenerative disease. Early on, it was noted that red free retinal nerve fiber layer (RNFL) photographs reveal a higher proportion of defects in patients with AD, but this has not always been consistent, perhaps owing to the limited number of cases in many studies.…”

Application of Big Data and Artificial Intelligence technologies to dementia prevention research: an opportunity for low-and-middle-income countries

“…A systematic review by Pellegrini et al [19] found over 110 publications on various initiatives where ML approaches have been employed to develop prediction models for cognitive impairment and AD using neuroimaging data. These research efforts are being extended to other data sources and domains such as linguistic analysis of text messages; speech analysis [20,21]; and also through the human eyes using retinal imaging [22] with promising results.…”