Protein and Imaging Biomarkers in the Eye for Early Detection of Alzheimer's Disease

Potential Retinal Biomarkers in Alzheimer's Disease

Alzheimer's disease (AD) represents a major diagnostic challenge, as early detection is crucial for effective intervention. This review examines the diagnostic challenges facing current AD evaluations and explores the emerging field of retinal alterations as early indicators. Recognizing the potential of the retina as a noninvasive window to the brain, we emphasize the importance of identifying retinal biomarkers in the early stages of AD. However, the examination of AD is not without its challenges, as the similarities shared with other retinal diseases introduce complexity in the search for AD-specific markers. In this review, we address the relevance of using the retina for the early diagnosis of AD and the complex challenges associated with the search for AD-specific retinal biomarkers. We provide a comprehensive overview of the current landscape and highlight avenues for progress in AD diagnosis by retinal examination.

The Eye as a Diagnostic Tool for Alzheimer’s Disease

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder impacting cognition, function, and behavior in the elderly population. While there are currently no disease-modifying agents capable of curing AD, early diagnosis and management in the preclinical stage can significantly improve patient morbidity and life expectancy. Currently, the diagnosis of Alzheimer’s disease is a clinical one, often supplemented by invasive and expensive biomarker testing. Over the last decade, significant advancements have been made in our understanding of AD and the role of ocular tissue as a potential biomarker. Ocular biomarkers hold the potential to provide noninvasive and easily accessible diagnostic and monitoring capabilities. This review summarizes current research for detecting biomarkers of Alzheimer’s disease in ocular tissue.

Disruption of early visual processing in amyloid-positive healthy individuals and mild cognitive impairment

BACKGROUND

Amyloid deposition is a primary predictor of Alzheimer's disease (AD) and related neurodegenerative disorders. Retinal changes involving the structure and function of the ganglion cell layer are increasingly documented in both established and prodromal AD. Visual event-related potentials (vERP) are sensitive to dysfunction in the magno- and parvocellular visual systems, which originate within the retinal ganglion cell layer. The present study evaluates vERP as a function of amyloid deposition in aging, and in mild cognitive impairment (MCI).

METHODS

vERP to stimulus-onset, motion-onset, and alpha-frequency steady-state (ssVEP) stimuli were obtained from 16 amyloid-positive and 41 amyloid-negative healthy elders and 15 MCI individuals and analyzed using time-frequency approaches. Social cognition was assessed in a subset of individuals using The Awareness of Social Inference Test (TASIT).

RESULTS

Neurocognitively intact but amyloid-positive participants and MCI individuals showed significant deficits in stimulus-onset (theta) and motion-onset (delta) vERP generation relative to amyloid-negative participants (all p < .01). Across healthy elders, a composite index of these measures correlated highly (r =  - .52, p < .001) with amyloid standardized uptake value ratios (SUVR) and TASIT performance. A composite index composed of vERP measures significant differentiated amyloid-positive and amyloid-negative groups with an overall classification accuracy of > 70%.

DISCUSSION

vERP may assist in the early detection of amyloid deposition among older individuals without observable neurocognitive impairments and in linking previously documented retinal deficits in both prodromal AD and MCI to behavioral impairments in social cognition.

Proteostasis in aging-associated ocular disease

Vision impairment has devastating consequences for the quality of human life. The cells and tissues associated with the visual process must function throughout one's life span and maintain homeostasis despite exposure to a variety of insults. Maintenance of the proteome is termed proteostasis, and is vital for normal cellular functions, especially at an advanced age. Here we describe basic aspects of proteostasis, from protein synthesis and folding to degradation, and discuss the current status of the field with a particular focus on major age-related eye diseases: age-related macular degeneration, cataract, and glaucoma. Our intent is to allow vision scientists to determine where and how to harness the proteostatic machinery for extending functional homeostasis in the aging retina, lens, and trabecular meshwork. Several common themes have emerged despite these tissues having vastly different metabolisms. Continued exposure to insults, including chronic stress with advancing age, increases proteostatic burden and reduces the fidelity of the degradation machineries including the ubiquitin-proteasome and the autophagy-lysosome systems that recognize and remove damaged proteins. This "double jeopardy" results in an exponential accumulation of cytotoxic proteins with advancing age. We conclude with a discussion of the challenges in maintaining an appropriate balance of protein synthesis and degradation pathways, and suggest that harnessing proteostatic capacities should provide new opportunities to design interventions for attenuating age-related eye diseases before they limit sight.

Artificial intelligence in dementia

PURPOSE OF REVIEW

Artificial intelligence tools are being rapidly integrated into clinical environments and may soon be incorporated into dementia diagnostic paradigms. A comprehensive review of emerging trends will allow physicians and other healthcare providers to better anticipate and understand these powerful tools.

RECENT FINDINGS

Machine learning models that utilize cerebral biomarkers are demonstrably effective for dementia identification and prediction; however, cerebral biomarkers are relatively expensive and not widely available. As eye images harbor several ophthalmic biomarkers that mirror the state of the brain and can be clinically observed with routine imaging, eye-based machine learning models are an emerging area, with efficacy comparable with cerebral-based machine learning models. Emerging machine learning architectures like recurrent, convolutional, and partially pretrained neural networks have proven to be promising frontiers for feature extraction and classification with ocular biomarkers.

SUMMARY

Machine learning models that can accurately distinguish those with symptomatic Alzheimer's dementia from those with mild cognitive impairment and normal cognition as well as predict progressive disease using relatively inexpensive and accessible ocular imaging inputs are impactful tools for the diagnosis and risk stratification of Alzheimer's dementia continuum. If these machine learning models can be incorporated into clinical care, they may simplify diagnostic efforts. Recent advancements in ocular-based machine learning efforts are promising steps forward.

Solving neurodegeneration: common mechanisms and strategies for new treatments

Across neurodegenerative diseases, common mechanisms may reveal novel therapeutic targets based on neuronal protection, repair, or regeneration, independent of etiology or site of disease pathology. To address these mechanisms and discuss emerging treatments, in April, 2021, Glaucoma Research Foundation, BrightFocus Foundation, and the Melza M. and Frank Theodore Barr Foundation collaborated to bring together key opinion leaders and experts in the field of neurodegenerative disease for a virtual meeting titled "Solving Neurodegeneration". This "think-tank" style meeting focused on uncovering common mechanistic roots of neurodegenerative disease and promising targets for new treatments, catalyzed by the goal of finding new treatments for glaucoma, the world's leading cause of irreversible blindness and the common interest of the three hosting foundations. Glaucoma, which causes vision loss through degeneration of the optic nerve, likely shares early cellular and molecular events with other neurodegenerative diseases of the central nervous system. Here we discuss major areas of mechanistic overlap between neurodegenerative diseases of the central nervous system: neuroinflammation, bioenergetics and metabolism, genetic contributions, and neurovascular interactions. We summarize important discussion points with emphasis on the research areas that are most innovative and promising in the treatment of neurodegeneration yet require further development. The research that is highlighted provides unique opportunities for collaboration that will lead to efforts in preventing neurodegeneration and ultimately vision loss.

Alzheimer's Disease Seen through the Eye: Ocular Alterations and Neurodegeneration

Alzheimer’s Disease (AD) is one of the main neurodegenerative diseases worldwide. Unfortunately, AD shares many similarities with other dementias at early stages, which impedes an accurate premortem diagnosis. Therefore, it is urgent to find biomarkers to allow for early diagnosis of the disease. There is increasing scientific evidence highlighting the similarities between the eye and other structures of the CNS, suggesting that knowledge acquired in eye research could be useful for research and diagnosis of AD. For example, the retina and optic nerve are considered part of the central nervous system, and their damage can result in retrograde and anterograde axon degeneration, as well as abnormal protein aggregation. In the anterior eye segment, the aqueous humor and tear film may be comparable to the cerebrospinal fluid. Both fluids are enriched with molecules that can be potential neurodegenerative biomarkers. Indeed, the pathophysiology of AD, characterized by cerebral deposits of amyloid-beta (Aβ) and tau protein, is also present in the eyes of AD patients, besides numerous structural and functional changes observed in the structure of the eyes. Therefore, all this evidence suggests that ocular changes have the potential to be used as either predictive values for AD assessment or as diagnostic tools.

Effect of virtual reality exercise on interventions for patients with Alzheimer's disease: A systematic review

Virtual reality (VR) interventions are increasingly being used in rehabilitating and treating patients with neurological disorders. This study aimed to explore the effects of VR exercise interventions for patients with Alzheimer's disease (AD). A systematic review of the published literature on randomized controlled trials of VR technology applied to patients with AD was conducted using the preferred reporting entry for systematic reviews and Meta-analysis guidelines. Descriptive analyses were performed to assess the quality of the studies in terms of the characteristics of the included studies, samples, diagnoses, types of VR technologies, subjective and objective levels of immersion, and quality of studies. Eight studies were included, including a pooled sample of 362 patients with AD. A systematic review showed that most studies focused on patients with AD's cognitive and physical functions. The main finding was that VR interventions could help improve cognitive and physical balance in patients with AD. However, future studies should emphasize design and use well-accepted assessment tools to validate the effects of VR interventions further.

Sensing Using Light: A Key Area of Sensors

This invited featured paper offers a Doctrinal Conception of sensing using Light (SuL) as an "umbrella" in which any sensing approach using Light Sciences and Technologies can be easily included. The key requirements of a sensing system will be quickly introduced by using a bottom-up methodology. Thanks to this, it will be possible to get a general conception of a sensor using Light techniques and know some related issues, such as its main constituted parts and types. The case in which smartness is conferred to the device is also considered. A quick "flight" over 10 significant cases using different principles, techniques, and technologies to detect diverse measurands in various sector applications is offered to illustrate this general concept. After reading this paper, any sensing approach using Light Sciences and Technologies may be easily included under the umbrella: sensing using Light or photonic sensors (PS).