Retinal imaging in Alzheimer's disease

Outer retinal thinning is associated with brain atrophy in early age-related macular degeneration

PURPOSE

Both retinal changes and age-related macular degeneration (AMD) have been shown to be associated with Alzheimer's disease and related dementias (ADRD). In AMD, the outer retina is impacted significantly and early, but little is known about its association with cognition or changes in brain morphometry. This study investigates the relationship between retinal and brain morphometry in older adults with early and intermediate age-related macular degeneration (AMD).

DESIGN

Cross-sectional study.

METHODS

Adults ≥ 70 years with normal, early, and intermediate AMD were recruited from Callahan Eye Hospital Clinics at the University of Alabama at Birmingham. Participants underwent cognitive testing, optical coherence tomography (OCT), and magnetic resonance imaging (MRI). Associations of retinal layer thickness with brain volume and thickness of specific brain regions were evaluated utilizing multivariable linear regression. The relevance of retinal thickness variables in brain volumetrics was quantified using least absolute shrinkage and selection operator (LASSO) regression models. Correlations between demographic variables, cognitive scores, and brain morphometry were evaluated.

RESULTS

Participants with thinner outer retina had significantly smaller hippocampus (β = 0.019, p = 0.022), lower occipital cortex regions of interest (occipital ROIs) thickness (β = 5.68, p = 0.020), and lower cortical thickness in ADRD-related brain regions (β = 7.72, p = 0.006). People with thinner total retina had significantly lower occipital ROIs (β = 3.19, p = 0.009) and ADRD-related brain region (β = 3.94, p = 0.005) thickness. Outer retinal thickness in the outer Early Treatment of Diabetic Retinopathy Study (ETDRS) ring was the most frequently reported retinal variable associated with brain morphometry on LASSO regression. Total gray matter volume showed positive correlations with education (Pearson's r = 0.30, p = 0.022).

CONCLUSION

In older adults with normal retinal aging and early and intermediate AMD, thinner outer retina had specific associations with brain regions primarily involved in vision and cognition, such as lower hippocampal volume and lower thickness of the occipital ROIs and brain regions known to show early structural changes in dementia.

Longitudinal Assessment of Retinal Microvasculature in Preclinical Alzheimer's Disease

Purpose

To investigate if changes in vessel density (VD) and the foveal avascular zone (FAZ) occur in the preclinical phase of Alzheimer's disease (pAD) over time.

Methods

Optical coherence tomography angiography (OCTA) was used to image VD and FAZ at baseline and for a follow-up period of 2 years. Positron emission tomography (PET) was used to determine the amyloid beta (Aβ) status of participants.

Results

The VD and FAZ of 148 participants (54% female) were analyzed at baseline and follow-up (mean time between measurements, 2.24 ± 0.35 years). The mean age of the participants was 68.3 ± 6.0 years at baseline and 70.3 ± 5.9 years at follow-up. Participants were divided into three groups: control group, participants who had negative Aβ status at both measurements (Aβ-, n = 116); converter group, participants who transitioned from negative to positive between baseline and follow-up (Aβ-+, n = 18); and participants who were consistently positive at both visits (Aβ++, n = 14). The VD of both Aβ+ groups demonstrated non-significant increases over time in both macula and optic nerve head (ONH) regions. The Aβ- group was found to be significantly higher in both ONH and macular regions. The VD of the Aβ++ group was significantly higher in the macula inner and outer rings compared to the Aβ-+ and Aβ- groups. No significant change was found in FAZ values over time.

Conclusions

Alterations in VD seem to manifest already in pAD, exhibiting distinct variations between the ONH and macula. Further longitudinal studies with a longer follow-up design and known amyloid pathology should be undertaken to validate these observations.

Oculomic stratification of COVID-19 patients' intensive therapy unit admission status and mortality by retinal morphological findings

To investigate if retinal thickness has predictive utility in COVID-19 outcomes by evaluating the statistical association between retinal thickness using OCT and of COVID-19-related mortality. Secondary outcomes included associations between retinal thickness and length of stay (LoS) in hospital. In this retrospective cohort study, OCT scans from 230 COVID-19 patients admitted to the Intensive Care Unit (ITU) were compared with age and gender-matched patients with pneumonia from before March 2020. Total retinal, GCL + IPL, and RNFL thicknesses were recorded, and analysed with systemic measures collected at the time of admission and mortality outcomes, using linear regression models, Pearson's R correlation, and Principal Component Analysis. Retinal thickness was significantly associated with all-time mortality on follow up in the COVID-19 group (p = 0.015), but not 28-day mortality (p = 0.151). Retinal and GCL + IPL layer thicknesses were both significantly associated with LoS in hospital for COVID-19 patients (p = 0.006 for both), but not for patients with pneumonia (p = 0.706 and 0.989 respectively). RNFL thickness was not associated with LoS in either group (COVID-19 p = 0.097, pneumonia p = 0.692). Retinal thickness associated with LoS in hospital and long-term mortality in COVID-19 patients, suggesting that retinal structure could be a surrogate marker for frailty and predictor of disease severity in this group of patients, but not in patients with pneumonia from other causes.

Retinal and Choroidal Phenotypes Across Novel Subtypes of Type 2 Diabetes Mellitus

PURPOSE

To investigate longitudinal changes in choroidal thickness (CT) and ganglion cell-inner plexiform layer thickness (GC-IPLT) across distinct phenotypes of type 2 diabetes mellitus (T2DM) patients.

DESIGN

Prospective cohort study.

METHODS

T2DM patients were categorized into 5 groups (SAID, SIDD, SIRD, MOD, and MARD) using K-means clustering based on β-cell function and insulin resistance. Swept-source optical coherence tomography measured baseline and 4-year follow-up CT and GC-IPLT. Linear mixed-effects models assessed absolute and relative changes in CT and GC-IPLT across subtypes.

RESULTS

Over a median 4.11-year follow-up, CT and GC-IPLT decreased significantly across all groups. Choroidal thinning rates were most pronounced in SIDD (-6.5 ± 0.53 µm/year and -3.5 ± 0.24%/year) and SAID (-6.27 ± 0.8 µm/year and -3.19 ± 0.37%/year), while MARD showed the slowest thinning rates (-3.63 ± 0.34 µm/year and -1.98 ± 0.25%/year). SIRD exhibited the greatest GC-IPLT loss (-0.66 ± 0.05 µm/year and -0.91 ± 0.07%/year), with the least in SIDD (-0.36 ± 0.05 µm/year and -0.49 ± 0.07%/year), all statistically significant (all P < 0.001). Adjusted for confounding variables, SIDD and SAID groups showed faster CT thinning than MARD [-2.57 µm/year (95% CI: -4.16 to -0.97; P = 0.002) and -2.89 µm/year (95% CI: -4.12 to -1.66; P < 0.001), respectively]. GC-IPLT thinning was notably accelerated in SIRD versus MARD, but slowed in SIDD relative to MARD [differences of -0.16 µm/year (95% CI: -0.3 to -0.03; P = 0.015) and 0.15 µm/year (95% CI: 0.03 to 0.27; P = 0.015), respectively].

CONCLUSIONS

Microvascular damage in the choroid is associated with SIDD patients, whereas early signs of retinal neurodegeneration are evident in SIRD patients. All these changes may precede the onset of DR.

Optical coherence tomography angiography of the retina and choroid in systemic diseases

Optical coherence tomography angiography (OCTA) has transformed ocular vascular imaging, revealing microvascular changes linked to various systemic diseases. This review explores its applications in diabetes, hypertension, cardiovascular diseases, and neurodegenerative diseases. While OCTA provides a valuable window into the body's microvasculature, interpreting the findings can be complex. Additionally, challenges exist due to the relative non-specificity of its findings where changes observed in OCTA might not be unique to a specific disease, variations between OCTA machines, the lack of a standardized normative database for comparison, and potential image artifacts. Despite these limitations, OCTA holds immense potential for the future. The review highlights promising advancements like quantitative analysis of OCTA images, integration of artificial intelligence for faster and more accurate interpretation, and multi-modal imaging combining OCTA with other techniques for a more comprehensive characterization of the ocular vasculature. Furthermore, OCTA's potential future role in personalized medicine, enabling tailored treatment plans based on individual OCTA findings, community screening programs for early disease detection, and longitudinal studies tracking disease progression over time is also discussed. In conclusion, OCTA presents a significant opportunity to improve our understanding and management of systemic diseases. Addressing current limitations and pursuing these exciting future directions can solidify OCTA as an indispensable tool for diagnosis, monitoring disease progression, and potentially guiding treatment decisions across various systemic health conditions.

Value proposition of retinal imaging in Alzheimer's disease screening: A review of eight evolving trends

Alzheimer's disease (AD) is the leading cause of dementia worldwide. Current diagnostic modalities of AD generally focus on detecting the presence of amyloid β and tau protein in the brain (for example, positron emission tomography [PET] and cerebrospinal fluid testing), but these are limited by their high cost, invasiveness, and lack of expertise. Retinal imaging exhibits potential in AD screening and risk stratification, as the retina provides a platform for the optical visualization of the central nervous system in vivo, with vascular and neuronal changes that mirror brain pathology. Given the paradigm shift brought by advances in artificial intelligence and the emergence of disease-modifying therapies, this article aims to summarize and review the current literature to highlight 8 trends in an evolving landscape regarding the role and potential value of retinal imaging in AD screening.

Standardization and clinical applications of retinal imaging biomarkers for cardiovascular disease: a Roadmap from an NHLBI workshop

The accessibility of the retina with the use of non-invasive and relatively low-cost ophthalmic imaging techniques and analytics provides a unique opportunity to improve the detection, diagnosis and monitoring of systemic diseases. The National Heart, Lung, and Blood Institute conducted a workshop in October 2022 to examine this concept. On the basis of the discussions at that workshop, this Roadmap describes current knowledge gaps and new research opportunities to evaluate the relationships between the eye (in particular, retinal biomarkers) and the risk of cardiovascular diseases, including coronary artery disease, heart failure, stroke, hypertension and vascular dementia. Identified gaps include the need to simplify and standardize the capture of high-quality images of the eye by non-ophthalmic health workers and to conduct longitudinal studies using multidisciplinary networks of diverse at-risk populations with improved implementation and methods to protect participant and dataset privacy. Other gaps include improving the measurement of structural and functional retinal biomarkers, determining the relationship between microvascular and macrovascular risk factors, improving multimodal imaging 'pipelines', and integrating advanced imaging with 'omics', lifestyle factors, primary care data and radiological reports, by using artificial intelligence technology to improve the identification of individual-level risk. Future research on retinal microvascular disease and retinal biomarkers might additionally provide insights into the temporal development of microvascular disease across other systemic vascular beds.

Disparities in glaucoma and macular degeneration healthcare utilization among persons living with dementia in the United States

PURPOSE

Dementia is common among patients with primary open angle glaucoma (POAG) and neovascular age-related macular degeneration (nAMD). This study compares visit frequency, diagnostic test utilization, and treatment patterns for POAG and nAMD among persons with vs. without dementia.

METHODS

Optum's de-identified Clinformatics® Data Mart Database (January 1, 2000-June 30, 2022) was used for this study. Two cohorts were created from newly diagnosed POAG or nAMD patients. Within each cohort, an exposure cohort was created of newly diagnosed dementia patients. The primary outcome was the number of visits to an eye care provider. Secondary analyses for the POAG cohort assessed the number of visual field tests, optical coherence tomography (OCT), and glaucoma medication prescription coverage. The secondary analysis for the nAMD cohort included the number of injections performed. Poisson regression was used to determine the relative rates of outcomes.

RESULTS

POAG patients with dementia had reduced rates of eye care visits (RR 0.76, 95% CI: 0.75-0.77), lower rates of testing utilization for visual fields (RR 0.66, 95% CI: 0.63-0.68) and OCT (RR 0.67, 95% CI: 0.64-0.69), and a lower rate of glaucoma prescription medication coverage (RR 0.83, 95% CI: 0.83-0.83). nAMD patients with dementia had reduced rates of eye care visits (RR 0.74, 95% CI: 0.70-0.79) and received fewer intravitreal injections (RR 0.64, 95% CI: 0.58-0.69) than those without dementia.

CONCLUSIONS

POAG and nAMD patients with dementia obtained less eye care and less monitoring and treatment of their disease. These findings suggest that this population may be vulnerable to gaps in ophthalmic care.

Alzheimer's disease pathophysiology in the Retina

The retina is an emerging CNS target for potential noninvasive diagnosis and tracking of Alzheimer's disease (AD). Studies have identified the pathological hallmarks of AD, including amyloid β-protein (Aβ) deposits and abnormal tau protein isoforms, in the retinas of AD patients and animal models. Moreover, structural and functional vascular abnormalities such as reduced blood flow, vascular Aβ deposition, and blood-retinal barrier damage, along with inflammation and neurodegeneration, have been described in retinas of patients with mild cognitive impairment and AD dementia. Histological, biochemical, and clinical studies have demonstrated that the nature and severity of AD pathologies in the retina and brain correspond. Proteomics analysis revealed a similar pattern of dysregulated proteins and biological pathways in the retina and brain of AD patients, with enhanced inflammatory and neurodegenerative processes, impaired oxidative-phosphorylation, and mitochondrial dysfunction. Notably, investigational imaging technologies can now detect AD-specific amyloid deposits, as well as vasculopathy and neurodegeneration in the retina of living AD patients, suggesting alterations at different disease stages and links to brain pathology. Current and exploratory ophthalmic imaging modalities, such as optical coherence tomography (OCT), OCT-angiography, confocal scanning laser ophthalmoscopy, and hyperspectral imaging, may offer promise in the clinical assessment of AD. However, further research is needed to deepen our understanding of AD's impact on the retina and its progression. To advance this field, future studies require replication in larger and diverse cohorts with confirmed AD biomarkers and standardized retinal imaging techniques. This will validate potential retinal biomarkers for AD, aiding in early screening and monitoring.

Deep Learning-Based Vascular Aging Prediction From Retinal Fundus Images

Purpose

The purpose of this study was to establish and validate a deep learning model to screen vascular aging using retinal fundus images. Although vascular aging is considered a novel cardiovascular risk factor, the assessment methods are currently limited and often only available in developed regions.

Methods

We used 8865 retinal fundus images and clinical parameters of 4376 patients from two independent datasets for training a deep learning algorithm. The gold standard for vascular aging was defined as a pulse wave velocity ≥1400 cm/s. The probability of the presence of vascular aging was defined as deep learning retinal vascular aging score, the Reti-aging score. We compared the performance of the deep learning model and clinical parameters by calculating the area under the receiver operating characteristics curve (AUC). We recruited clinical specialists, including ophthalmologists and geriatricians, to assess vascular aging in patients using retinal fundus images, aiming to compare the diagnostic performance between deep learning models and clinical specialists. Finally, the potential of Reti-aging score for identifying new-onset hypertension (NH) and new-onset carotid artery plaque (NCP) in the subsequent three years was examined.

Results

The Reti-aging score model achieved an AUC of 0.826 (95% confidence interval [CI] = 0.793-0.855) and 0.779 (95% CI = 0.765-0.794) in the internal and external dataset. It showed better performance in predicting vascular aging compared with the prediction with clinical parameters. The average accuracy of ophthalmologists (66.3%) was lower than that of the Reti-aging score model, whereas geriatricians were unable to make predictions based on retinal fundus images. The Reti-aging score was associated with the risk of NH and NCP (P < 0.05).

Conclusions

The Reti-aging score model might serve as a novel method to predict vascular aging through analysis of retinal fundus images. Reti-aging score provides a novel indicator to predict new-onset cardiovascular diseases.

Translational Relevance

Given the robust performance of our model, it provides a new and reliable method for screening vascular aging, especially in undeveloped areas.

Targeting complement C3a receptor resolves mitochondrial hyperfusion and subretinal microglial activation in progranulin-deficient frontotemporal dementia

Mutations in progranulin ( GRN ) cause frontotemporal dementia ( GRN -FTD) due to deficiency of the pleiotropic protein progranulin. GRN -FTD exhibits diverse pathologies including lysosome dysfunction, lipofuscinosis, microgliosis, and neuroinflammation. Yet, how progranulin loss causes disease remains unresolved. Here, we report that non-invasive retinal imaging of GRN -FTD patients revealed deficits in photoreceptors and the retinal pigment epithelium (RPE) that correlate with cognitive decline. Likewise, Grn -/- mice exhibit early RPE dysfunction, microglial activation, and subsequent photoreceptor loss. Super-resolution live imaging and transcriptomic analyses identified RPE mitochondria as an early driver of retinal dysfunction. Loss of mitochondrial fission protein 1 (MTFP1) in Grn -/- RPE causes mitochondrial hyperfusion and bioenergetic defects, leading to NF-kB-mediated activation of complement C3a-C3a receptor signaling, which drives further mitochondrial hyperfusion and retinal inflammation. C3aR antagonism restores RPE mitochondrial integrity and limits subretinal microglial activation. Our study identifies a previously unrecognized mechanism by which progranulin modulates mitochondrial integrity and complement-mediated neuroinflammation.

Prognostic potentials of AI in ophthalmology: systemic disease forecasting via retinal imaging

BACKGROUND

Artificial intelligence (AI) that utilizes deep learning (DL) has potential for systemic disease prediction using retinal imaging. The retina's unique features enable non-invasive visualization of the central nervous system and microvascular circulation, aiding early detection and personalized treatment plans for personalized care. This review explores the value of retinal assessment, AI-based retinal biomarkers, and the importance of longitudinal prediction models in personalized care.

MAIN TEXT

This narrative review extensively surveys the literature for relevant studies in PubMed and Google Scholar, investigating the application of AI-based retina biomarkers in predicting systemic diseases using retinal fundus photography. The study settings, sample sizes, utilized AI models and corresponding results were extracted and analysed. This review highlights the substantial potential of AI-based retinal biomarkers in predicting neurodegenerative, cardiovascular, and chronic kidney diseases. Notably, DL algorithms have demonstrated effectiveness in identifying retinal image features associated with cognitive decline, dementia, Parkinson's disease, and cardiovascular risk factors. Furthermore, longitudinal prediction models leveraging retinal images have shown potential in continuous disease risk assessment and early detection. AI-based retinal biomarkers are non-invasive, accurate, and efficient for disease forecasting and personalized care.

CONCLUSION

AI-based retinal imaging hold promise in transforming primary care and systemic disease management. Together, the retina's unique features and the power of AI enable early detection, risk stratification, and help revolutionizing disease management plans. However, to fully realize the potential of AI in this domain, further research and validation in real-world settings are essential.

Predicting the cognitive impairment with multimodal ophthalmic imaging and artificial neural network for community screening

BACKGROUND/AIMS

To investigate the comprehensive prediction ability for cognitive impairment in a general elder population using the combination of the multimodal ophthalmic imaging and artificial neural networks.

METHODS

Patients with cognitive impairment and cognitively healthy individuals were recruited. All subjects underwent medical history, blood pressure measurement, the Montreal Cognitive Assessment, medical optometry, intraocular pressure and custom-built multimodal ophthalmic imaging, which integrated pupillary light reaction, multispectral imaging, laser speckle contrast imaging and retinal oximetry. Multidimensional parameters were analysed by Student's t-test. Logistic regression analysis and back-propagation neural network (BPNN) were used to identify the predictive capability for cognitive impairment.

RESULTS

This study included 104 cognitive impairment patients (61.5% female; mean (SD) age, 68.3 (9.4) years), and 94 cognitively healthy age-matched and sex-matched subjects (56.4% female; mean (SD) age, 65.9 (7.6) years). The variation of most parameters including decreased pupil constriction amplitude (CA), relative CA, average constriction velocity, venous diameter, venous blood flow and increased centred retinal reflectance in 548 nm (RC548) in cognitive impairment was consistent with previous studies while the reduced flow acceleration index and oxygen metabolism were reported for the first time. Compared with the logistic regression model, BPNN had better predictive performance (accuracy: 0.91 vs 0.69; sensitivity: 93.3% vs 61.70%; specificity: 90.0% vs 68.66%).

CONCLUSIONS

This study demonstrates retinal spectral signature alteration, neurodegeneration and angiopathy occur concurrently in cognitive impairment. The combination of multimodal ophthalmic imaging and BPNN can be a useful tool for predicting cognitive impairment with high performance for community screening.

Spectral Fluorescence Pathology of Protein Misfolding Disorders

Protein misfolding has been extensively studied in the context of neurodegenerative disorders and systemic amyloidoses. Due to misfolding and aggregation of proteins being highly heterogeneous and generating a variety of structures, a growing body of evidence illustrates numerous ways how the aggregates contribute to progression of diseases such as Alzheimer's disease, Parkinson's disease, and prion disorders. Different misfolded species of the same protein, commonly referred to as strains, appear to play a significant role in shaping the disease clinical phenotype and clinical progression. The distinct toxicity profiles of various misfolded proteins underscore their importance. Current diagnostics struggle to differentiate among these strains early in the disease course. This review explores the potential of spectral fluorescence approaches to illuminate the complexities of protein misfolding pathology and discusses the applications of advanced spectral methods in the detection and characterization of protein misfolding disorders. By examining spectrally variable probes, current data analysis approaches, and important considerations for the use of these techniques, this review aims to provide an overview of the progress made in this field and highlights directions for future research.

Artificial intelligence in the diagnosis of glaucoma and neurodegenerative diseases

Artificial Intelligence is a rapidly expanding field within computer science that encompasses the emulation of human intelligence by machines. Machine learning and deep learning - two primary data-driven pattern analysis approaches under the umbrella of artificial intelligence - has created considerable interest in the last few decades. The evolution of technology has resulted in a substantial amount of artificial intelligence research on ophthalmic and neurodegenerative disease diagnosis using retinal images. Various artificial intelligence-based techniques have been used for diagnostic purposes, including traditional machine learning, deep learning, and their combinations. Presented here is a review of the literature covering the last 10 years on this topic, discussing the use of artificial intelligence in analysing data from different modalities and their combinations for the diagnosis of glaucoma and neurodegenerative diseases. The performance of published artificial intelligence methods varies due to several factors, yet the results suggest that such methods can potentially facilitate clinical diagnosis. Generally, the accuracy of artificial intelligence-assisted diagnosis ranges from 67-98%, and the area under the sensitivity-specificity curve (AUC) ranges from 0.71-0.98, which outperforms typical human performance of 71.5% accuracy and 0.86 area under the curve. This indicates that artificial intelligence-based tools can provide clinicians with useful information that would assist in providing improved diagnosis. The review suggests that there is room for improvement of existing artificial intelligence-based models using retinal imaging modalities before they are incorporated into clinical practice.

Retinal vessel caliber and cognitive performance: the multi-ethnic study of atherosclerosis (MESA)

Retinal vessel calibers share anatomic and physiologic characteristics with the cerebral vasculature and can be visualized noninvasively. In light of the known microvascular contributions to brain health and cognitive function, we aimed to determine if, in a community based-study, retinal vessel calibers and change in caliber over 8 years are associated with cognitive function or trajectory. Participants in the Multi-Ethnic Study of Atherosclerosis (MESA) cohort who completed cognitive testing at Exam 5 (2010-2012) and had retinal vascular caliber measurements (Central Retinal Artery and Vein Equivalents; CRAE and CRVE) at Exam 2 (2002-2004) and Exam 5 were included. Using multivariable linear regression, we evaluated the association of CRAE and CRVE from Exam 2 and Exam 5 and their change between the two exams with scores on tests of global cognitive function (Cognitive Abilities Screening Instrument; CASI), processing speed (Digit Symbol Coding; DSC) and working memory (Digit Span; DS) at Exam 5 and with subsequent change in cognitive scores between Exam 5 and Exam 6 (2016-2018).The main effects are reported as the difference in cognitive test score per SD increment in retinal vascular caliber with 95% confidence intervals (CI). A total of 4334 participants (aged 61.6 ± 9.2 years; 53% female; 41% White) completed cognitive testing and at least one retinal assessment. On multivariable analysis, a 1 SD larger CRAE at exam 5 was associated with a lower concomitant CASI score (- 0.24, 95% CI - 0.46, - 0.02). A 1 SD larger CRVE at exam 2 was associated with a lower subsequent CASI score (- 0.23, 95%CI - 0.45, - 0.01). A 1 SD larger CRVE at exam 2 or 5 was associated with a lower DSC score [(- 0.56, 95% CI - 1.02, - 0.09) and - 0.55 (95% CI - 1.03, - 0.07) respectively]. The magnitude of the associations was relatively small (2.8-3.1% of SD). No significant associations were found between retinal vessel calibers at Exam 2 and 5 with the subsequent score trajectory of cognitive tests performance over an average of 6 years. Wider retinal venular caliber was associated with concomitant and future measures of slower processing speed but not with later cognitive trajectory. Future studies should evaluate the utility of these measures in risk stratification models from a clinical perspective as well as for screening on a population level.

Consensus on rapid screening for prodromal Alzheimer's disease in China

Alzheimer's disease (AD) is a common cause of dementia, characterised by cerebral amyloid-β deposition, pathological tau and neurodegeneration. The prodromal stage of AD (pAD) refers to patients with mild cognitive impairment (MCI) and evidence of AD's pathology. At this stage, disease-modifying interventions should be used to prevent the progression to dementia. Given the inherent heterogeneity of MCI, more specific biomarkers are needed to elucidate the underlying AD's pathology. Although the uses of cerebrospinal fluid and positron emission tomography are widely accepted methods for detecting AD's pathology, their clinical applications are limited by their high costs and invasiveness, particularly in low-income areas in China. Therefore, to improve the early detection of Alzheimer's disease (AD) pathology through cost-effective screening methods, a panel of 45 neurologists, psychiatrists and gerontologists was invited to establish a formal consensus on the screening of pAD in China. The supportive evidence and grades of recommendations are based on a systematic literature review and focus group discussion. National meetings were held to allow participants to review, vote and provide their expert opinions to reach a consensus. A majority (two-thirds) decision was used for questions for which consensus could not be reached. Recommended screening methods are presented in this publication, including neuropsychological assessment, peripheral biomarkers and brain imaging. In addition, a general workflow for screening pAD in China is established, which will help clinicians identify individuals at high risk and determine therapeutic targets.

Quantitative Analysis of Retinal Perfusion in Patients with Frontotemporal Dementia Using Optical Coherence Tomography Angiography

BACKGROUND

Optical coherence tomography angiography (OCT-A) provides detailed visualization of the perfusion of the vascular network of the eye. While in other forms of dementia, such as Alzheimer's disease and mild cognitive impairment, reduced retinal perfusion was frequently reported, data of patients with frontotemporal dementia (FTD) are lacking.

OBJECTIVE

Retinal and optic nerve head perfusion was evaluated in patients with FTD with OCT-A. Quantitative OCT-A metrics were analyzed and correlated with clinical markers and vascular cerebral lesions in FTD patients.

METHODS

OCT-A was performed in 18 eyes of 18 patients with FTD and 18 eyes of 18 healthy participants using RTVue XR Avanti with AngioVue. In addition, patients underwent a detailed ophthalmological, neurological, and neuropsychological examination, cerebral magnetic resonance imaging (MRI), and lumbar puncture.

RESULTS

The flow density in the optic nerve head (ONH) and in the superficial capillary plexus (SCP) of the macula of patients was significantly lower compared to that of healthy controls (p < 0.001). Similarly, the VD in the deep capillary plexus (DCP) of the macula of patients was significantly lower compared to that of healthy controls (p < 0.001). There was no significant correlation between the flow density data, white matter lesions in brain MRI, cognitive deficits, and cerebrospinal fluid markers of dementia.

CONCLUSIONS

Patients with FTD showed a reduced flow density in the ONH, and in the superficial and deep retinal capillary plexus of the macula, when compared with that of healthy controls. Quantitative analyses of retinal perfusion using OCT-A may therefore help in the diagnosis and monitoring of FTD. Larger and longitudinal studies are necessary to evaluate if OCT-A is a suitable biomarker for patients with FTD.

Alzheimer's Disease: Models and Molecular Mechanisms Informing Disease and Treatments

Alzheimer's Disease (AD) is a complex neurodegenerative disease resulting in progressive loss of memory, language and motor abilities caused by cortical and hippocampal degeneration. This review captures the landscape of understanding of AD pathology, diagnostics, and current therapies. Two major mechanisms direct AD pathology: (1) accumulation of amyloid β (Aβ) plaque and (2) tau-derived neurofibrillary tangles (NFT). The most common variants in the Aβ pathway in APP, PSEN1, and PSEN2 are largely responsible for early-onset AD (EOAD), while MAPT, APOE, TREM2 and ABCA7 have a modifying effect on late-onset AD (LOAD). More recent studies implicate chaperone proteins and Aβ degrading proteins in AD. Several tests, such as cognitive function, brain imaging, and cerebral spinal fluid (CSF) and blood tests, are used for AD diagnosis. Additionally, several biomarkers seem to have a unique AD specific combination of expression and could potentially be used in improved, less invasive diagnostics. In addition to genetic perturbations, environmental influences, such as altered gut microbiome signatures, affect AD. Effective AD treatments have been challenging to develop. Currently, there are several FDA approved drugs (cholinesterase inhibitors, Aß-targeting antibodies and an NMDA antagonist) that could mitigate AD rate of decline and symptoms of distress.

Retinal and Conjunctival Vessels in the Diagnosis and Assessment of Behcet's Disease: A New Approach

BACKGROUND AND OBJECTIVE

The study aimed to investigate the alterations of retinal and conjunctival vessels in patients with Behcet's disease (BD).

PATIENTS AND METHODS

In this case-control study, 17 patients (34 eyes) diagnosed with BD and 17 healthy volunteers (34 eyes) matched by age, sex, blood pressure, and intraocular pressure were recruited. Optical coherence tomography angiography examinations were performed to calculate the vessel density of the retina and conjunctiva according to different sizes of vessels and different zones divided by three segmentation methods of the retina: hemispheric segmentation, Early Treatment Diabetic Retinopathy Study, and central annulus segmentation.

RESULTS

The vessel densities of the superficial macrovascular (P = 0.050), superficial microvascular (P < 0.001), superficial total microvascular (P < 0.001), deep total microvascular (P < 0.001), and deep total microvascular (P < 0.001) were significantly lower in the BD group. The conjunctival vessel density was significantly higher in the BD group (P < 0.001). The receiver operating characteristic curve analysis showed that the area under the curve of vessel density of the superior right (0.993, 95% CI 0.980-1) and right zones (0.996, 95% CI 0.987-1) were the largest in the superficial and deep retina, respectively. Otherwise, the area under the curve of conjunctival vessel density was 0.728 (95% CI 0.607-0.848).

CONCLUSIONS

In patients with BD, retinal vessel density decreases, while conjunctival vessel density increases. Optical coherence tomography angiography provides a new noninvasive and quantitative assessment for retinal and conjunctival vessels. [Ophthalmic Surg Lasers Imaging Retina 2024;55:13-21.].

Automated Retinal Vessel Analysis Based on Fundus Photographs as a Predictor for Non-Ophthalmic Diseases-Evolution and Perspectives

The study of retinal vessels in relation to cardiovascular risk has a long history. The advent of a dedicated tool based on digital imaging, i.e., the retinal vessel analyzer, and also other software such as Integrative Vessel Analysis (IVAN), Singapore I Vessel Assessment (SIVA), and Vascular Assessment and Measurement Platform for Images of the Retina (VAMPIRE), has led to the accumulation of a formidable body of evidence regarding the prognostic value of retinal vessel analysis (RVA) for cardiovascular and cerebrovascular disease (including arterial hypertension in children). There is also the potential to monitor the response of retinal vessels to therapies such as physical activity or bariatric surgery. The dynamic vessel analyzer (DVA) remains a unique way of studying neurovascular coupling, helping to understand the pathogenesis of cerebrovascular and neurodegenerative conditions and also being complementary to techniques that measure macrovascular dysfunction. Beyond cardiovascular disease, retinal vessel analysis has shown associations with and prognostic value for neurological conditions, inflammation, kidney function, and respiratory disease. Artificial intelligence (AI) (represented by algorithms such as QUantitative Analysis of Retinal vessel Topology and siZe (QUARTZ), SIVA-DLS (SIVA-deep learning system), and many others) seems efficient in extracting information from fundus photographs, providing prognoses of various general conditions with unprecedented predictive value. The future challenges will be integrating RVA and other qualitative and quantitative risk factors in a unique, comprehensive prediction tool, certainly powered by AI, while building the much-needed acceptance for such an approach inside the medical community and reducing the "black box" effect, possibly by means of saliency maps.

Retinal ganglion cell-inner plexiform layer, white matter hyperintensities, and their interaction with cognition in older adults

Purpose

We explored the interaction of optical coherence tomography (OCT) parameters and white matter hyperintensities with cognitive measures in our older adult cohort.

Methods

This observational study enrolled participants who underwent a comprehensive neuropsychological battery, structural 3-T brain magnetic resonance imaging (MRI), visual acuity examination, and OCT imaging. Cerebral small vessel disease (CSVD) markers were read on MR images; lacune, cerebral microbleeds (CMB), white matter hyperintensities (WMH), and enlarged perivascular spaces (EPVS), were defined according to the STRIVE standards. Retinal nerve fiber layer (RNFL) and ganglion cell-inner plexiform layer (GCIPL) thicknesses (μm) were measured on the OCT tool.

Results

Older adults with cognitive impairment (CI) showed lower RNFL (p = 0.001), GCIPL (p = 0.009) thicknesses, and lower hippocampal volume (p = 0.004) when compared to non-cognitively impaired (NCI). RNFL (p = 0.006) and GCIPL thicknesses (p = 0.032) correlated with MoCA scores. GCIPL thickness (p = 0.037), total WMH (p = 0.003), PWMH (p = 0.041), and DWMH (p = 0.001) correlated with hippocampal volume in our older adults after adjusting for covariates. With hippocampal volume as the outcome, a significant interaction (p < 0.05) between GCIPL and PWMH and total WMH was observed in our older adults.

Conclusion

Both GCIPL thinning and higher WMH burden (especially PWMH) are associated with hippocampal volume and older adults with both pathologies are more susceptible to subclinical cognitive decline.

Vascular cognitive impairment - Past, present, and future challenges

Vascular cognitive impairment (VCI) is a lifelong process encompassing a broad spectrum of cognitive disorders, ranging from subtle or mild deficits to prodromal and fully developed dementia, originating from cerebrovascular lesions such as large and small vessel disease. Genetic predisposition and environmental exposure to risk factors such as unhealthy lifestyles, hypertension, cardiovascular disease, and metabolic disorders will synergistically interact, yielding biochemical and structural brain changes, ultimately culminating in VCI. However, little is known about the pathological processes underlying VCI and the temporal dynamics between risk factors and disease mechanisms (biochemical and structural brain changes). This narrative review aims to provide an evidence-based summary of the link between individual vascular risk/disorders and cognitive dysfunction and the potential structural and biochemical pathophysiological processes. We also discuss some key challenges for future research on VCI. There is a need to shift from individual risk factors/disorders to comorbid vascular burden, identifying and integrating imaging and fluid biomarkers, implementing a life-course approach, considering possible neuroprotective influences of positive life exposures, and addressing biological sex at birth and gender differences. Finally, this review highlights the need for future researchers to leverage and integrate multidimensional data to advance our understanding of the mechanisms and pathophysiology of VCI.

APOE ε4 Gene Carriers Demonstrate Reduced Retinal Capillary Densities in Asymptomatic Older Adults

Early identification of Apolipoprotein E (APOE)-related microvascular pathology will help to study the microangiopathic contribution to Alzheimer's disease and provide a therapeutic target for early intervention. To evaluate the differences in retinal microvasculature parameters between APOE ε4 carriers and non-carriers, asymptomatic older adults aged ≥ 55 years underwent APOE ε4 genotype analysis, neuropsychological examination, and optical coherence tomography angiography (OCTA) imaging. One hundred sixty-three older adults were included in the data analysis. Participants were also defined as cognitively impaired (CI) and non-cognitively impaired (NCI) according to their MoCA scores and educational years. APOE ε4 carriers demonstrated reduced SVC (p = 0.023) compared to APOE ε4 non-carriers. Compared to NCI, CI participants showed reduced SVC density (p = 0.006). In the NCI group, no significant differences (p > 0.05) were observed in the microvascular densities between APOE ε4 carriers and non-carriers. In the CI group, APOE ε4 carriers displayed reduced microvascular densities compared to non-carriers (SVC, p = 0.006; DVC, p = 0.048). We showed that CI and APOE ε4 affect retinal microvasculature in older adults. Quantitative measures of the retinal microvasculature could serve as surrogates for brain microcirculation, providing an opportunity to study microvascular contributions to AD.

Ratiometric Analysis of In Vivo Optical Coherence Tomography Retinal Layer Thicknesses for Detection of Changes in Alzheimer's Disease

We analyzed ophthalmic retinal optical coherence tomography (OCT) images from patients with Alzheimer's disease (AD) to identify retinal layer thickness and ratio changes that may serve as image-based biomarkers for the disease. One three-dimensional volume before and one after diagnosis for each of 48 patients were segmented to identify retinal layer and total retinal thicknesses. Between before- and after-diagnosis retinal OCT images, there were significant thickness changes in six of ten (60%) retinal layers across all 48 patients. Through a comparison with age-matched healthy subjects, the significant changes were attributed to AD only (NFL and PR2 layers), age only (GCL, IPL, and RPE layers), or both AD and age (OPL layer). Analyzing ratios of retinal layer thicknesses, 53 of 90 (58.89%) ratios had significant changes. The four independently non-significant layers were assessed to be affected by neither AD nor age (INL layer) or both AD and age (ELM, PR1, and BM layers). The demonstrated image segmentation, measurement, and ratiometric analysis of retinal layers in AD patients may yield a noninvasive OCT image-based retinal biomarker that can be used to detect retinal changes associated with this disease.

Optical Coherence Tomography Assessment of Macular Thickness in Alzheimer's Dementia with Different Neuropsychological Severities

This retrospective case-control study aimed to investigate associations between disease severity of Alzheimer's dementia (AD) and macular thickness. Data of patients with AD who were under medication (n = 192) between 2013 and 2020, as well as an age- and sex-matched control group (n = 200) with normal cognitive function, were included. AD patients were divided into subgroups according to scores of the Mini-Mental State Examination (MMSE) and Clinical Dementia Rating (CDR). Macular thickness was analyzed via the Early Treatment Diabetic Retinopathy Study (ETDRS) grid map. AD patients had significant reductions in full macula layers, including inner circle, outer inferior area, and outer nasal area of the macula. Similar retinal thinning was noted in ganglion cells and inner plexiform layers. Advanced AD patients (MMSE score < 18 or CDR ≥ 1) showed more advanced reduction of macular thickness than the AD group (CDR = 0.5 or MMSE ≥ 18), indicating that severe cognitive impairment was associated with thinner macular thickness. Advanced AD is associated with significant macula thinning in full retina and inner plexiform layers, especially at the inner circle of the macula. Macular thickness may be a useful biomarker of AD disease severity. Retinal imaging may be a non-invasive, low-cost surrogate for AD.

Ethnicity is not biology: retinal pigment score to evaluate biological variability from ophthalmic imaging using machine learning

Background

Few metrics exist to describe phenotypic diversity within ophthalmic imaging datasets, with researchers often using ethnicity as an inappropriate marker for biological variability.

Methods

We derived a continuous, measured metric, the retinal pigment score (RPS), that quantifies the degree of pigmentation from a colour fundus photograph of the eye. RPS was validated using two large epidemiological studies with demographic and genetic data (UK Biobank and EPIC-Norfolk Study).

Findings

A genome-wide association study (GWAS) of RPS from UK Biobank identified 20 loci with known associations with skin, iris and hair pigmentation, of which 8 were replicated in the EPIC-Norfolk cohort. There was a strong association between RPS and ethnicity, however, there was substantial overlap between each ethnicity and the respective distributions of RPS scores.

Interpretation

RPS serves to decouple traditional demographic variables, such as ethnicity, from clinical imaging characteristics. RPS may serve as a useful metric to quantify the diversity of the training, validation, and testing datasets used in the development of AI algorithms to ensure adequate inclusion and explainability of the model performance, critical in evaluating all currently deployed AI models. The code to derive RPS is publicly available at: https://github.com/uw-biomedical-ml/retinal-pigmentation-score.

Funding

The authors did not receive support from any organisation for the submitted work.

Relationships between quantitative retinal microvascular characteristics and cognitive function based on automated artificial intelligence measurements

Introduction: The purpose of this study is to assess the relationship between retinal vascular characteristics and cognitive function using artificial intelligence techniques to obtain fully automated quantitative measurements of retinal vascular morphological parameters. Methods: A deep learning-based semantic segmentation network ResNet101-UNet was used to construct a vascular segmentation model for fully automated quantitative measurement of retinal vascular parameters on fundus photographs. Retinal photographs centered on the optic disc of 3107 participants (aged 50-93 years) from the Beijing Eye Study 2011, a population-based cross-sectional study, were analyzed. The main parameters included the retinal vascular branching angle, vascular fractal dimension, vascular diameter, vascular tortuosity, and vascular density. Cognitive function was assessed using the Mini-Mental State Examination (MMSE). Results: The results showed that the mean MMSE score was 26.34 ± 3.64 (median: 27; range: 2-30). Among the participants, 414 (13.3%) were classified as having cognitive impairment (MMSE score < 24), 296 (9.5%) were classified as mild cognitive impairment (MMSE: 19-23), 98 (3.2%) were classified as moderate cognitive impairment (MMSE: 10-18), and 20 (0.6%) were classified as severe cognitive impairment (MMSE < 10). Compared with the normal cognitive function group, the retinal venular average diameter was significantly larger (p = 0.013), and the retinal vascular fractal dimension and vascular density were significantly smaller (both p < 0.001) in the mild cognitive impairment group. The retinal arteriole-to-venular ratio (p = 0.003) and vascular fractal dimension (p = 0.033) were significantly decreased in the severe cognitive impairment group compared to the mild cognitive impairment group. In the multivariate analysis, better cognition (i.e., higher MMSE score) was significantly associated with higher retinal vascular fractal dimension (b = 0.134, p = 0.043) and higher retinal vascular density (b = 0.152, p = 0.023) after adjustment for age, best corrected visual acuity (BCVA) (logMAR) and education level. Discussion: In conclusion, our findings derived from an artificial intelligence-based fully automated retinal vascular parameter measurement method showed that several retinal vascular morphological parameters were correlated with cognitive impairment. The decrease in retinal vascular fractal dimension and decreased vascular density may serve as candidate biomarkers for early identification of cognitive impairment. The observed reduction in the retinal arteriole-to-venular ratio occurs in the late stages of cognitive impairment.

Epidemiology, Translation and Clinical Research of Ophthalmology

The human eye is a complex and vital organ that plays a significant role in maintaining a high quality of human life [...].

Vitreous Humor Biomarkers Reflect Pathological Changes in the Brain for Alzheimer's Disease and Chronic Traumatic Encephalopathy

BACKGROUND

Patients with eye disease have an increased risk for developing neurodegenerative disease. Neurodegenerative proteins can be measured in the eye; however, correlations between biomarker levels in eye fluid and neuropathological diagnoses have not been established.

OBJECTIVE

This exploratory, retrospective study examined vitreous humor from 41 postmortem eyes and brain tissue with neuropathological diagnoses of Alzheimer's disease (AD, n = 7), chronic traumatic encephalopathy (CTE, n = 15), both AD + CTE (n = 10), and without significant neuropathology (controls, n = 9).

METHODS

Protein biomarkers i.e., amyloid-β (Aβ 40,42), total tau (tTau), phosphorylated tau (pTau181,231), neurofilament light chain (NfL), and eotaxin-1 were quantitatively measured by immunoassay. Non-parametric tests were used to compare vitreous biomarker levels between groups. Spearman's rank correlation tests were used to correlate biomarker levels in vitreous and cortical tissue. The level of significance was set to α= 0.10.

RESULTS

In pairwise comparisons, tTau levels were significantly increased in AD and CTE groups versus controls (p = 0.08 for both) as well as AD versus AD+CTE group and CTE versus AD+CTE group (p = 0.049 for both). Vitreous NfL levels were significantly increased in low CTE (Stage I/II) versus no CTE (p = 0.096) and in low CTE versus high CTE stage (p = 0.03). Vitreous and cortical tissue levels of pTau 231 (p = 0.02, r = 0.38) and t-Tau (p = 0.04, r = -0.34) were significantly correlated.

CONCLUSION

The postmortem vitreous humor biomarker levels significantly correlate with AD and CTE pathology in corresponding brains, while vitreous NfL was correlated with the CTE staging. This exploratory study indicates that biomarkers in the vitreous humor may serve as a proxy for neuropathological disease.

Associations of Microvascular Dysfunction with Mild Cognitive Impairment and Cognitive Function Among Rural-Dwelling Older Adults in China

BACKGROUND

Microvascular dysfunction (MVD) may contribute to cognitive impairment and Alzheimer's disease, but evidence is limited.

OBJECTIVE

To investigate the association of composite and organ-specific MVD burden with mild cognitive impairment (MCI) and cognition among rural-dwelling Chinese older adults.

METHODS

In this population-based cross-sectional study, we assessed MVD makers using optical coherence tomographic angiography for retinal microvasculature features, brain magnetic resonance imaging scans for cerebral small vessel disease (CSVD), and serum biomarkers for MVD. A composite MVD score was generated from the aforementioned organ-specific parameters. We used a neuropsychological test battery to assess memory, verbal fluency, attention, executive function, and global cognitive function. MCI, amnestic MCI (aMCI), and non-amnestic MCI (naMCI) were diagnosed following the Petersen's criteria. Data was analyzed with the linear and logistic regression models.

RESULTS

Of the 274 dementia-free participants (age≥65 years), 56 were diagnosed with MCI, including 47 with aMCI and 9 with naMCI. A composite MVD score was statistically significantly associated with an odds ratio (OR) of 2.70 (95% confidence interval 1.12-6.53) for MCI and β-coefficient of -0.29 (-0.48--0.10) for global cognitive score after adjustment for socio-demographics, lifestyle factors, APOE genotype, the Geriatric Depression Scale score, serum inflammatory biomarkers, and cardiovascular comorbidity. A composite score of retinal microvascular morphology was associated with a multivariable-adjusted OR of 1.72 (1.09-2.73) for MCI and multivariable-adjusted β-coefficient of -0.11 (-0.22--0.01) for global cognitive score. A composite CSVD score was associated with a lower global cognitive score (β= -0.10; -0.17--0.02).

CONCLUSION

Microvascular dysfunction, especially in the brain and retina, is associated with MCI and poor cognitive function among rural-dwelling older adults.

Retinal photoreceptor layer thickness has disease specificity and distinguishes predicted FTLD-Tau from biomarker-determined Alzheimer's disease

While Alzheimer's disease (AD) is associated with inner retina thinning (retinal nerve fiber layer and ganglion cell layer), we have observed photoreceptor outer nuclear layer (ONL) thinning in patients with frontotemporal lobar degeneration tauopathy (FTLD-Tau) compared to normal controls. We hypothesized that ONL thinning may distinguish FTLD-Tau from patients with biomarker evidence of AD neuropathologic change (ADNC) and will correlate with FTLD-Tau disease severity. Predicted FTLD-Tau (pFTLD-Tau; n = 21; 33 eyes) and predicted ADNC (pADNC; n = 24; 46 eyes) patients were consecutively enrolled, underwent optical coherence tomography macula imaging, and disease was categorized (pFTLD-Tau vs. pADNC) with cerebrospinal fluid biomarkers, genetic testing, and autopsy data when available. Adjusting for age, sex, and race, pFTLD-Tau patients had a thinner ONL compared to pADNC, while retinal nerve fiber layer and ganglion cell layer were not significantly different. Reduced ONL thickness correlated with worse performance on Folstein Mini-Mental State Examination and clinical dementia rating plus frontotemporal dementia sum of boxes for pFTLD-Tau but not pADNC. Photoreceptor ONL thickness may serve as an important noninvasive diagnostic marker that distinguishes FTLD-Tau from AD neuropathologic change.

Retinal thickness as a biomarker of cognitive impairment in manifest Huntington's disease

BACKGROUND

Cognitive decline has been reported in premanifest and manifest Huntington's disease but reliable biomarkers are lacking. Inner retinal layer thickness seems to be a good biomarker of cognition in other neurodegenerative diseases.

OBJECTIVE

To explore the relationship between optical coherence tomography-derived metrics and global cognition in Huntington's Disease.

METHODS

Thirty-six patients with Huntington's disease (16 premanifest and 20 manifest) and 36 controls matched by age, sex, smoking status, and hypertension status underwent macular volumetric and peripapillary optical coherence tomography scans. Disease duration, motor status, global cognition and CAG repeats were recorded in patients. Group differences in imaging parameters and their association with clinical outcomes were analyzed using linear mixed-effect models.

RESULTS

Premanifest and manifest Huntington's disease patients presented thinner retinal external limiting membrane-Bruch's membrane complex, and manifest patients had thinner temporal peripapillary retinal nerve fiber layer compared to controls. In manifest Huntington's disease, macular thickness was significantly associated with MoCA scores, inner nuclear layer showing the largest regression coefficients. This relationship was consistent after adjusting for age, sex, and education and p-value correction with False Discovery Rate. None of the retinal variables were related to Unified Huntington's Disease Rating Scale score, disease duration, or disease burden. Premanifest patients did not show a significant association between OCT-derived parameters and clinical outcomes in corrected models.

CONCLUSIONS

In line with other neurodegenerative diseases, OCT is a potential biomarker of cognitive status in manifest HD. Future prospective studies are needed to evaluate OCT as a potential surrogate marker of cognitive decline in HD.

Childhood Social Isolation as a Predictor of Retinal Neuronal Thickness in Middle Age: A Lifecourse Birth Cohort Study

OBJECTIVE

We investigated whether childhood social isolation was associated with retinal neural layer changes in adulthood, and whether this association was independent of other childhood or adulthood risk factors, including adult social isolation.

METHODS

Participants were members of the Dunedin Multidisciplinary Health and Development Study, a longitudinal population-based birth cohort from Aotearoa New Zealand ( n = 1037), born 1972 to 1973 and followed until age 45 years, with 94% of the living cohort still participating. Social isolation was recorded prospectively at ages 5, 7, 9, and 11 years, from teacher and parent report. Retinal nerve fiber layer (RNFL) and ganglion cell-inner plexiform layer thicknesses were measured via optical coherence tomography at age 45 years.

RESULTS

Childhood social isolation was associated with thinner average RNFL ( B = -0.739, p = .02), nasal RNFL ( B = -1.118, p = .005), and inferior RNFL ( B = -1.524, p = .007), although only nasal RNFL remained significant after adjustment. These associations were not fully explained by other psychosocial or physical health risk factors in childhood or adulthood, nor were they mediated by adult loneliness or social support.

CONCLUSIONS

Childhood social isolation was an independent predictor of RNFL thickness in middle age. Highlighting prospective links between childhood psychosocial adversity and retinal neuronal measures will help to inform future research into the utility of retinal neuronal thickness as a biomarker for neurodegeneration.

Associations Between Thinner Retinal Neuronal Layers and Suboptimal Brain Structural Integrity in a Middle-Aged Cohort

Purpose

The retina has potential as a biomarker of brain health and Alzheimer's disease (AD) because it is the only part of the central nervous system which can be easily imaged and has advantages over brain imaging technologies. Few studies have compared retinal and brain measurements in a middle-aged sample. The objective of our study was to investigate whether retinal neuronal measurements were associated with structural brain measurements in a middle-aged population-based cohort.

Participants and Methods

Participants were members of the Dunedin Multidisciplinary Health and Development Study (n=1037; a longitudinal cohort followed from birth and at ages 3, 5, 7, 9, 11, 13, 15, 18, 21, 26, 32, 38, and most recently at age 45, when 94% of the living Study members participated). Retinal nerve fibre layer (RNFL) and ganglion cell-inner plexiform layer (GC-IPL) thickness were measured by optical coherence tomography (OCT). Brain age gap estimate (brainAGE), cortical surface area, cortical thickness, subcortical grey matter volumes, white matter hyperintensities, were measured by magnetic resonance imaging (MRI).

Results

Participants with both MRI and OCT data were included in the analysis (RNFL n=828, female n=413 [49.9%], male n=415 [50.1%]; GC-IPL n=825, female n=413 [50.1%], male n=412 [49.9%]). Thinner retinal neuronal layers were associated with older brain age, smaller cortical surface area, thinner average cortex, smaller subcortical grey matter volumes, and increased volume of white matter hyperintensities.

Conclusion

These findings provide evidence that the retinal neuronal layers reflect differences in midlife structural brain integrity consistent with increased risk for later AD, supporting the proposition that the retina may be an early biomarker of brain health.

Retinal Alterations as Potential Biomarkers of Structural Brain Changes in Alzheimer’s Disease Spectrum Patients

Retinal imaging being a potential biomarker for Alzheimer’s disease is gradually attracting the attention of researchers. However, the association between retinal parameters and AD neuroimaging biomarkers, particularly structural changes, is still unclear. In this cross-sectional study, we recruited 25 cognitively impaired (CI) and 21 cognitively normal (CN) individuals. All subjects underwent retinal layer thickness and microvascular measurements with optical coherence tomography angiography (OCTA). Gray matter and white matter (WM) data such as T1-weighted magnetic resonance imaging and diffusion tensor imaging, respectively, were also collected. In addition, hippocampal subfield volumes and WM tract microstructural alterations were investigated as classical AD neuroimaging biomarkers. The microvascular and retinal features and their correlation with brain structural imaging markers were further analyzed. We observed a reduction in vessel density (VD) at the inferior outer (IO) sector (p = 0.049), atrophy in hippocampal subfield volumes, such as the subiculum (p = 0.012), presubiculum (p = 0.015), molecular_layer_HP (p = 0.033), GC-ML-DG (p = 0.043) and whole hippocampus (p = 0.033) in CI patients. Altered microstructural integrity of WM tracts in CI patients was also discovered in the cingulum hippocampal part (CgH). Importantly, we detected significant associations between retinal VD and gray matter volumes of the hippocampal subfield in CI patients. These findings suggested that the retinal microvascular measures acquired by OCTA may be markers for the early prediction of AD-related structural brain changes.

Through the eyes into the brain, using artificial intelligence

Introduction: Detection of neurological conditions is of high importance in the current context of increasingly ageing populations. Imaging of the retina and the optic nerve head represents a unique opportunity to detect brain diseases, but requires specific human expertise. We review the current outcomes of artificial intelligence (AI) methods applied to retinal imaging for the detection of neurological and neuro-ophthalmic conditions. Method: Current and emerging concepts related to the detection of neurological conditions, using AI-based investigations of the retina in patients with brain disease were examined and summarised. Results: Papilloedema due to intracranial hypertension can be accurately identified with deep learning on standard retinal imaging at a human expert level. Emerging studies suggest that patients with Alzheimer’s disease can be discriminated from cognitively normal individuals, using AI applied to retinal images. Conclusion: Recent AI-based systems dedicated to scalable retinal imaging have opened new perspectives for the detection of brain conditions directly or indirectly affecting retinal structures. However, further validation and implementation studies are required to better understand their potential value in clinical practice. Keywords: Alzheimer’s disease, deep learning, dementia, optic neuropathy, papilloedema

Beta-Amyloid Peptide in Tears: An Early Diagnostic Marker of Alzheimer's Disease Correlated with Choroidal Thickness

We aimed to evaluate the diagnostic role of Alzheimer's disease (AD) biomarkers in tears as well as their association with retinal and choroidal microstructures. In a cross-sectional study, 35 subjects (age 71.7 ± 6.9 years) were included: 11 with prodromal AD (MCI), 10 with mild-to-moderate AD, and 14 healthy controls. The diagnosis of AD and MCI was confirmed according to a complete neuropsychological evaluation and PET or MRI imaging. After tear sample collection, β-amyloid peptide Aβ1-42 concentration was analyzed using ELISA, whereas C-terminal fragments of the amyloid precursor protein (APP-CTF) and phosphorylated tau (p-tau) were assessed by Western blot. Retinal layers and choroidal thickness (CT) were acquired by spectral-domain optical coherence tomography (SD-OCT). Aβ1-42 levels in tears were able to detect both MCI and AD patients with a specificity of 93% and a sensitivity of 81% (AUC = 0.91). Tear levels of Aβ1-42 were lower, both in the MCI (p < 0.01) and in the AD group (p < 0.001) when compared to healthy controls. Further, Aβ1-42 was correlated with psychometric scores (p < 0.001) and CT (p < 0.01). CT was thinner in the affected patients (p = 0.035). No differences were observed for APP-CTF and p-tau relative abundance in tears. Testing Aβ1-42 levels in tears seems to be a minimally invasive, cost-saving method for early detection and diagnosis of AD.

Correlation between retinal structure and brain multimodal magnetic resonance imaging in patients with Alzheimer's disease

Background

The retina imaging and brain magnetic resonance imaging (MRI) can both reflect early changes in Alzheimer's disease (AD) and may serve as potential biomarker for early diagnosis, but their correlation and the internal mechanism of retinal structural changes remain unclear. This study aimed to explore the possible correlation between retinal structure and visual pathway, brain structure, intrinsic activity changes in AD patients, as well as to build a classification model to identify AD patients.

Methods

In the study, 49 AD patients and 48 healthy controls (HCs) were enrolled. Retinal images were obtained by optical coherence tomography (OCT). Multimodal MRI sequences of all subjects were collected. Spearman correlation analysis and multiple linear regression models were used to assess the correlation between OCT parameters and multimodal MRI findings. The diagnostic value of combination of retinal imaging and brain multimodal MRI was assessed by performing a receiver operating characteristic (ROC) curve.

Results

Compared with HCs, retinal thickness and multimodal MRI findings of AD patients were significantly altered (p < 0.05). Significant correlations were presented between the fractional anisotropy (FA) value of optic tract and mean retinal thickness, macular volume, macular ganglion cell layer (GCL) thickness, inner plexiform layer (IPL) thickness in AD patients (p < 0.01). The fractional amplitude of low frequency fluctuations (fALFF) value of primary visual cortex (V1) was correlated with temporal quadrant peripapillary retinal nerve fiber layer (pRNFL) thickness (p < 0.05). The model combining thickness of GCL and temporal quadrant pRNFL, volume of hippocampus and lateral geniculate nucleus, and age showed the best performance to identify AD patients [area under the curve (AUC) = 0.936, sensitivity = 89.1%, specificity = 87.0%].

Conclusion

Our study demonstrated that retinal structure change was related to the loss of integrity of white matter fiber tracts in the visual pathway and the decreased LGN volume and functional metabolism of V1 in AD patients. Trans-synaptic axonal retrograde lesions may be the underlying mechanism. Combining retinal imaging and multimodal MRI may provide new insight into the mechanism of retinal structural changes in AD and may serve as new target for early auxiliary diagnosis of AD.

Deep Reinforcement Learning-Based Retinal Imaging in Alzheimer's Disease: Potential and Perspectives

Alzheimer's disease (AD) remains a global health challenge in the 21st century due to its increasing prevalence as the major cause of dementia. State-of-the-art artificial intelligence (AI)-based tests could potentially improve population-based strategies to detect and manage AD. Current retinal imaging demonstrates immense potential as a non-invasive screening measure for AD, by studying qualitative and quantitative changes in the neuronal and vascular structures of the retina that are often associated with degenerative changes in the brain. On the other hand, the tremendous success of AI, especially deep learning, in recent years has encouraged its incorporation with retinal imaging for predicting systemic diseases. Further development in deep reinforcement learning (DRL), defined as a subfield of machine learning that combines deep learning and reinforcement learning, also prompts the question of how it can work hand in hand with retinal imaging as a viable tool for automated prediction of AD. This review aims to discuss potential applications of DRL in using retinal imaging to study AD, and their synergistic application to unlock other possibilities, such as AD detection and prediction of AD progression. Challenges and future directions, such as the use of inverse DRL in defining reward function, lack of standardization in retinal imaging, and data availability, will also be addressed to bridge gaps for its transition into clinical use.

Identification of ocular refraction based on deep learning algorithm as a novel retinoscopy method

BACKGROUND

The evaluation of refraction is indispensable in ophthalmic clinics, generally requiring a refractor or retinoscopy under cycloplegia. Retinal fundus photographs (RFPs) supply a wealth of information related to the human eye and might provide a promising approach that is more convenient and objective. Here, we aimed to develop and validate a fusion model-based deep learning system (FMDLS) to identify ocular refraction via RFPs and compare with the cycloplegic refraction. In this population-based comparative study, we retrospectively collected 11,973 RFPs from May 1, 2020 to November 20, 2021. The performance of the regression models for sphere and cylinder was evaluated using mean absolute error (MAE). The accuracy, sensitivity, specificity, area under the receiver operating characteristic curve, and F1-score were used to evaluate the classification model of the cylinder axis.

RESULTS

Overall, 7873 RFPs were retained for analysis. For sphere and cylinder, the MAE values between the FMDLS and cycloplegic refraction were 0.50 D and 0.31 D, representing an increase of 29.41% and 26.67%, respectively, when compared with the single models. The correlation coefficients (r) were 0.949 and 0.807, respectively. For axis analysis, the accuracy, specificity, sensitivity, and area under the curve value of the classification model were 0.89, 0.941, 0.882, and 0.814, respectively, and the F1-score was 0.88.

CONCLUSIONS

The FMDLS successfully identified the ocular refraction in sphere, cylinder, and axis, and showed good agreement with the cycloplegic refraction. The RFPs can provide not only comprehensive fundus information but also the refractive state of the eye, highlighting their potential clinical value.

Association of retinal thickness and microvasculature with cognitive performance and brain volumes in elderly adults

BACKGROUND

Retinal structural and microvascular changes can be visualized and have been linked with cognitive decline and brain changes in cerebral age-related disorders. We investigated the association between retinal structural and microvascular changes with cognitive performance and brain volumes in elderly adults.

MATERIALS AND METHODS

All participants underwent magnetic resonance imaging (MRI), and a battery of neuropsychological examinations. Macula retinal thicknesses (retinal nerve fiber layer, mRNFL, and ganglion cell-inner plexiform layer, GCIPL) were imaged and measured with swept-source optical coherence tomography (SS-OCT) while Optical Coherence Tomography Angiography (OCTA) imaged and measured the superficial vascular complex (SVC) and deep vascular complex (DVC) of the retina.

RESULTS

Out of the 135 participants, 91 (67.41%) were females and none had dementia. After adjusting for risk factors, Shape Trail Test (STT)-A correlated with SVC (P < 0.001), DVC (P = 0.015) and mRNFL (P = 0.013) while STT-B correlated with SVC (P = 0.020) and GCIPL (P = 0.015). mRNFL thickness correlated with Montreal Cognitive Assessment (MoCA) (P = 0.007) and Stroop A (P = 0.030). After adjusting for risk factors and total intracranial volume, SVC correlated with hippocampal volume (P < 0.001). Hippocampal volume correlated (P < 0.05) with most cognitive measures. Stroop B (P < 0.001) and Stroop C (P = 0.020) correlated with white matter volume while Stroop measures and STT-A correlated with gray matter volume (P < 0.05).

CONCLUSION

Our findings suggest that the retinal structure and microvasculature can be useful pointers for cognitive performance, giving a choice for early discovery of decline in cognition and potential early treatment.

A deep learning model for detection of Alzheimer's disease based on retinal photographs: a retrospective, multicentre case-control study

BACKGROUND

There is no simple model to screen for Alzheimer's disease, partly because the diagnosis of Alzheimer's disease itself is complex-typically involving expensive and sometimes invasive tests not commonly available outside highly specialised clinical settings. We aimed to develop a deep learning algorithm that could use retinal photographs alone, which is the most common method of non-invasive imaging the retina to detect Alzheimer's disease-dementia.

METHODS

In this retrospective, multicentre case-control study, we trained, validated, and tested a deep learning algorithm to detect Alzheimer's disease-dementia from retinal photographs using retrospectively collected data from 11 studies that recruited patients with Alzheimer's disease-dementia and people without disease from different countries. Our main aim was to develop a bilateral model to detect Alzheimer's disease-dementia from retinal photographs alone. We designed and internally validated the bilateral deep learning model using retinal photographs from six studies. We used the EfficientNet-b2 network as the backbone of the model to extract features from the images. Integrated features from four retinal photographs (optic nerve head-centred and macula-centred fields from both eyes) for each individual were used to develop supervised deep learning models and equip the network with unsupervised domain adaptation technique, to address dataset discrepancy between the different studies. We tested the trained model using five other studies, three of which used PET as a biomarker of significant amyloid β burden (testing the deep learning model between amyloid β positive vs amyloid β negative).

FINDINGS

12 949 retinal photographs from 648 patients with Alzheimer's disease and 3240 people without the disease were used to train, validate, and test the deep learning model. In the internal validation dataset, the deep learning model had 83·6% (SD 2·5) accuracy, 93·2% (SD 2·2) sensitivity, 82·0% (SD 3·1) specificity, and an area under the receiver operating characteristic curve (AUROC) of 0·93 (0·01) for detecting Alzheimer's disease-dementia. In the testing datasets, the bilateral deep learning model had accuracies ranging from 79·6% (SD 15·5) to 92·1% (11·4) and AUROCs ranging from 0·73 (SD 0·24) to 0·91 (0·10). In the datasets with data on PET, the model was able to differentiate between participants who were amyloid β positive and those who were amyloid β negative: accuracies ranged from 80·6 (SD 13·4%) to 89·3 (13·7%) and AUROC ranged from 0·68 (SD 0·24) to 0·86 (0·16). In subgroup analyses, the discriminative performance of the model was improved in patients with eye disease (accuracy 89·6% [SD 12·5%]) versus those without eye disease (71·7% [11·6%]) and patients with diabetes (81·9% [SD 20·3%]) versus those without the disease (72·4% [11·7%]).

INTERPRETATION

A retinal photograph-based deep learning algorithm can detect Alzheimer's disease with good accuracy, showing its potential for screening Alzheimer's disease in a community setting.

FUNDING

BrightFocus Foundation.

Microvascular Changes in the Retina Correlate with MRI Markers in Patients with Early-Onset Dementia

Background and Aims: Recent reports suggest that results from imaging retinal microvascular changes with optical coherence tomography angiography (OCTA) in dementia patients reflect cerebral microcirculation changes that occur during dementia. Macula microvascular impairment has been shown in dementia patients compared to controls, but very little is known about its correlation with radiological visual rating scores associated with dementia. We aimed to explore the association between retinal microvasculature and radiological visual rating in early-onset dementia (EOD) patients. Methods: Swept-source OCTA (SS-OCTA) was used to image the retinal microvasculature of all EOD patients. Automated software in the OCTA tool segmented and measured the densities in the superficial vascular plexus (SVC) and deep vascular plexus (DVC) and foveal avascular zone (FAZ) areas. Radiological visual rating scores were evaluated on all MR images. Results: Medial temporal lobe atrophy (MTA) scores significantly correlated with FAZ area (p = 0.031) in EOD patients after adjusting for risk factors. PWMH correlated with SVC (p = 0.032) while DWMH significantly correlated with SVC (p = 0.007), DVC (p = 0.018) and FAZ (p = 0.001) in EOD patients. Discussion: FAZ changes correlated with MTA scores in EOD patients, while retinal microvasculature correlated with white matter hyperintensity. Our report suggests that microvascular changes in the retina may reflect cortical changes in the brain of EOD patients.

Deep-learning retinal vessel calibre measurements and risk of cognitive decline and dementia

Previous studies have explored the associations of retinal vessel calibre, measured from retinal photographs or fundus images using semi-automated computer programs, with cognitive impairment and dementia, supporting the concept that retinal blood vessels reflect microvascular changes in the brain. Recently, artificial intelligence deep-learning algorithms have been developed for the fully automated assessment of retinal vessel calibres. Therefore, we aimed to determine whether deep-learning-based retinal vessel calibre measurements are predictive of risk of cognitive decline and dementia. We conducted a prospective study recruiting participants from memory clinics at the National University Hospital and St. Luke's Hospital in Singapore; all participants had comprehensive clinical and neuropsychological examinations at baseline and annually for up to 5 years. Fully automated measurements of retinal arteriolar and venular calibres from retinal fundus images were estimated using a deep-learning system. Cox regression models were then used to assess the relationship between baseline retinal vessel calibre and the risk of cognitive decline and developing dementia, adjusting for age, gender, ethnicity, education, cerebrovascular disease status, hypertension, hyperlipidemia, diabetes, and smoking. A total of 491 participants were included in this study, of whom 254 developed cognitive decline over 5 years. In multivariable models, narrower retinal arteriolar calibre (hazard ratio per standard deviation decrease = 1.258, P = 0.008) and wider retinal venular calibre (hazard ratio per standard deviation increase = 1.204, P = 0.037) were associated with increased risk of cognitive decline. Among participants with cognitive impairment but no dementia at baseline (n = 212), 44 progressed to have incident dementia; narrower retinal arteriolar calibre was also associated with incident dementia (hazard ratio per standard deviation decrease = 1.624, P = 0.021). In summary, deep-learning-based measurement of retinal vessel calibre was associated with risk of cognitive decline and dementia.

Decreased Vessel Density in Retinal Capillary Plexus and Thinner Ganglion Cell Complex Associated With Cognitive Impairment

Background

To determine the association of the retinal capillary plexus (RCP) and ganglion cell complex (GCC) with cognitive impairment using optical coherence tomography angiography (OCTA).

Methods

A cross-sectional, community-based study utilizing data from the participants enrolled between August 2019 and January 2020 in the Jidong Eye Cohort Study. We assessed the vessel density in RCP and GCC thickness using OCTA, and cognitive testing using the Montreal Cognitive Assessment (MoCA). Cognitive impairment in this study was defined as MoCA score &lt; 24. We used multivariable analysis to evaluate the association of RCP and GCC with cognitive impairment after adjusting for confounders.

Results

This study analyzed 1555 participants. The mean age of participants was 52.3 (8.4) years, and 861 (55.4%) were women. Cognitive impairment was observed in 268 (17.2%) participants. The adjusted odds ratio (OR) with 95% confidence interval (95% CI) for parafovea vessel density in the deep RCP with cognitive impairment was 1.20 (1.03–1.39). For vessel area and length density surrounding foveal avascular zone with cognitive impairment, the ORs with 95% CIs were 1.23 (1.07–1.41) and 1.30 (1.13–1.49), respectively. For thickness in the superior GCC with cognitive impairment, the OR with 95% CI was 1.16 (1.01–1.32).

Conclusion

Lower vessel density in the RCP and thinner GCC were associated with cognitive impairment. Our results suggest that alterations in the RCP and GCC could provide further evidence when assessing the cognitive function and may even be potentially useful biomarkers in the detection of cognitive impairment.

Hypertensive eye disease

Hypertensive eye disease includes a spectrum of pathological changes, the most well known being hypertensive retinopathy. Other commonly involved parts of the eye in hypertension include the choroid and optic nerve, sometimes referred to as hypertensive choroidopathy and hypertensive optic neuropathy. Together, hypertensive eye disease develops in response to acute and/or chronic elevation of blood pressure. Major advances in research over the past three decades have greatly enhanced our understanding of the epidemiology, systemic associations and clinical implications of hypertensive eye disease, particularly hypertensive retinopathy. Traditionally diagnosed via a clinical funduscopic examination, but increasingly documented on digital retinal fundus photographs, hypertensive retinopathy has long been considered a marker of systemic target organ damage (for example, kidney disease) elsewhere in the body. Epidemiological studies indicate that hypertensive retinopathy signs are commonly seen in the general adult population, are associated with subclinical measures of vascular disease and predict risk of incident clinical cardiovascular events. New technologies, including development of non-invasive optical coherence tomography angiography, artificial intelligence and mobile ocular imaging instruments, have allowed further assessment and understanding of the ocular manifestations of hypertension and increase the potential that ocular imaging could be used for hypertension management and cardiovascular risk stratification.

Retinal Biomarkers for Alzheimer Disease: The Facts and the Future

ABSTRACT

Alzheimer disease (AD) is a significant cause of morbidity and mortality worldwide, with limited treatment options and considerable diagnostic challenges. Identification and validation of retinal changes that correlate with clinicopathologic features of AD could provide a noninvasive method of screening and monitoring progression of disease, with notable implications for developing new therapies, particularly in its preclinical stages. Retinal biomarkers that have been studied to date include structural changes in neurosensory retinal layers, alterations in vascular architecture and function, and pathologic deposition of proteins within the retina, which have all demonstrated variable correlation with the presence of preclinical or clinical AD. Evolution of specialized retinal imaging modalities and advances in artificial intelligence hold great promise for future study in this burgeoning field. The current status of research in retinal biomarkers, and some of the challenges that will need to be addressed in future work, are reviewed herein.