BACKGROUND. Noninvasive detection of Alzheimer's disease (AD) with high specificity and sensitivity can greatly facilitate identification of at-risk populations for earlier, more effective intervention. AD patients exhibit a myriad of retinal pathologies, including hallmark amyloid β-protein (Aβ) deposits. METHODS.Burden, distribution, cellular layer, and structure of retinal Aβ plaques were analyzed in flat mounts and cross sections of definite AD patients and controls (n = 37). In a proof-of-concept retinal imaging trial (n = 16), amyloid probe curcumin formulation was determined and protocol was established for retinal amyloid imaging in live patients. RESULTS.Histological examination uncovered classical and neuritic-like Aβ deposits with increased retinal Aβ 42 plaques (4.7-fold; P = 0.0063) and neuronal loss (P = 0.0023) in AD patients versus matched controls. Retinal Aβ plaque mirrored brain pathology, especially in the primary visual cortex (P = 0.0097 to P = 0.0018; Pearson's r = 0.84-0.91). Retinal deposits often associated with blood vessels and occurred in hot spot peripheral regions of the superior quadrant and innermost retinal layers. Transmission electron microscopy revealed retinal Aβ assembled into protofibrils and fibrils. Moreover, the ability to image retinal amyloid deposits with solid-lipid curcumin and a modified scanning laser ophthalmoscope was demonstrated in live patients. A fully automated calculation of the retinal amyloid index (RAI), a quantitative measure of increased curcumin fluorescence, was constructed. Analysis of RAI scores showed a 2.1-fold increase in AD patients versus controls (P = 0.0031). CONCLUSION.The geometric distribution and increased burden of retinal amyloid pathology in AD, together with the feasibility to noninvasively detect discrete retinal amyloid deposits in living patients, may lead to a practical approach for large-scale AD diagnosis and monitoring.

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The Precision Neurology development process implements systems theory with system biology and neurophysiology in a parallel, bidirectional research path: a combined hypothesis-driven investigation of systems dysfunction within distinct molecular, cellular and large-scale neural network systems in both animal models as well as through tests for the usefulness of these candidate dynamic systems biomarkers in different diseases and subgroups at different stages of pathophysiological progression. This translational research path is paralleled by an “omics”-based, hypothesis-free, exploratory research pathway, which will collect multimodal data from progressing asymptomatic, preclinical and clinical neurodegenerative disease (ND) populations, within the wide continuous biological and clinical spectrum of ND, applying high-throughput and high-content technologies combined with powerful computational and statistical modeling tools, aimed at identifying novel dysfunctional systems and predictive marker signatures associated with ND. The goals are to identify common biological denominators or differentiating classifiers across the continuum of ND during detectable stages of pathophysiological progression, characterize systems-based intermediate endophenotypes, validate multi-modal novel diagnostic systems biomarkers, and advance clinical intervention trial designs by utilizing systems-based intermediate endophenotypes and candidate surrogate markers. Achieving these goals is key to the ultimate development of early and effective individualized treatment of ND, such as Alzheimer’s disease (AD). The Alzheimer Precision Medicine Initiative (APMI) and cohort program (APMI-CP), as well as the Paris based core of the Sorbonne University Clinical Research Group “Alzheimer Precision Medicine” (GRC-APM) were recently launched to facilitate the passageway from conventional clinical diagnostic and drug development towards breakthrough innovation based on the investigation of the comprehensive biological nature of aging individuals. The APMI movement is gaining momentum to systematically apply both systems neurophysiology and systems biology in exploratory translational neuroscience research on ND.

Introduction Despite advances in imaging retinal amyloidosis, a quantitative and topographical investigation of retinal amyloid beta burden in patients with cognitive decline has never been reported. Methods We used the specific amyloid‐binding fluorophore curcumin and laser ophthalmoscopy to assess retinal amyloid imaging (RAI) in 34 patients with cognitive decline. We automatically quantified retinal amyloid count (RAC) and area in the superotemporal retinal sub‐regions and performed correlation analyses with cognitive and brain volumetric parameters. Results RAC significantly and inversely correlated with hippocampal volume (HV; r = ‐0.39, P = .04). The proximal mid‐periphery (PMP) RAC and RA areas were significantly greater in patients with Montreal Cognitive Assessment (MOCA) score < 26 ( P = .01; Cohen d = 0.83 and 0.81, respectively). PMP showed significantly more RAC and area in subjects with amnestic mild cognitive impairment (MCI) and Alzheimer's disease (AD) compared to cognitively normal ( P = .04; Cohen d = 0.83). Conclusion Quantitative RAI is a feasible technique and PMP RAC may predict HV. Future larger studies should determine RAI's potential as a biomarker of early AD.

Our findings suggest that cognitively intact older men compared with women have higher resilience to pathophysiological processes of Alzheimer's disease.

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Introduction: Retinal imaging is a non-invasive tool to study both retinal vasculature and neurodegeneration. In this exploratory retinal curcumin-fluorescence imaging (RFI) study, we sought to determine whether retinal vascular features combined with retinal amyloid burden correlate with the neurocognitive status. Methods: We used quantitative RFI in a cohort of patients with cognitive impairment to automatically compute retinal amyloid burden. Retinal blood vessels were segmented, and the vessel tortuosity index (VTI), inflection index, and branching angle were quantified. We assessed the correlations between retinal vascular and amyloid parameters, and cognitive domain Z-scores using linear regression models. Results: Thirty-four subjects were enrolled and twenty-nine (55% female, mean age 64 ± 6 years) were included in the combined retinal amyloid and vascular analysis. Eleven subjects had normal cognition and 18 had impaired cognition. Retinal VTI was discriminated among cognitive scores. The combined proximal mid-periphery amyloid count and venous VTI index exhibited significant differences between cognitively impaired and cognitively normal subjects (0.49 ± 1.1 vs. 0.91 ± 1.4, p = 0.006), and correlated with both the Wechsler Memory Scale-IV and SF-36 mental component score Z-scores (p < 0.05). Conclusion: This pilot study showed that retinal venular VTI combined with the proximal mid-periphery amyloid count could predict verbal memory loss. Future research is needed to finesse the clinical application of this retinal imaging-based technology.

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