Association Between Retinal Layer Thickness and Cognitive Decline in Older Adults

Genome-wide association study of subfoveal choroidal thickness in a longitudinal cohort of older adults

To identify genetic influences on subfoveal choroidal thickness of older adults using a genome-wide association study (GWAS). We recruited 300 participants from the population-based Korean Longitudinal Study on Health and Aging (KLoSHA) and Korean Longitudinal Study on Cognitive Aging and Dementia (KLOSCAD) cohort studies and 500 participants from the Bundang age-related macular degeneration (AMD) cohort study dataset. We conducted a GWAS on older adult populations in the KLoSHA and KLOSCAD cohorts. Single nucleotide polymorphisms (SNPs) associated with choroidal thickness were identified with P values < 1.0 × 10-4 in both the right and left eyes, followed by validation using the Bundang AMD cohort dataset. This association was further confirmed by a functional in vitro study using human umbilical vein endothelial cells (HUVECs). The ages of the cohort participants in the discovery and validation datasets were 73.5 ± 3.3 and 71.3 ± 7.9 years, respectively. In the discovery dataset, three SNPs (rs1916762, rs7587019, and rs13320098) were significantly associated with choroidal thickness in both eyes. This association was confirmed for rs1916762 (genotypes GG, GA, and AA) and rs7587019 (genotypes GG, GA, and AA), but not for rs13320098. The mean choroidal thickness decreased by 56.7 μm (AA, 73.8%) and 31.1 μm (GA, 85.6%) compared with that of the GG genotype of rs1916762, and by 55.4 μm (AA, 74.2%) and 28.2 μm (GA, 86.7%) compared with that of the GG genotype of rs7587019. The SNPs rs1916762 and rs7587019 were located close to the FAM124B gene near its cis-regulatory region. Moreover, FAM124B was highly expressed in vascular endothelial cells. In vitro HUVEC experiments showed that the inhibition of FAM124B was associated with decreased vascular endothelial proliferation, suggesting a potential mechanism of choroidal thinning. FAM124B was identified as a susceptibility gene affecting subfoveal choroidal thickness in older adults. This gene may be involved in mechanisms underlying retinal diseases associated with altered choroidal thickness, such as age-related macular degeneration.

LONGITUDINAL CHANGE OF RETINAL LAYER THICKNESS IN COGNITIVELY NORMAL ELDERLY SUBJECTS: Population-Based Cohort Study

PURPOSE

To identify longitudinal retinal layer thickness changes in normal eyes of cognitively healthy elderly people.

METHODS

Post hoc analysis was performed on 57 cognitively healthy elderly participants from the population-based Korean Longitudinal Study on Health and Aging and Korean Longitudinal Study on Cognitive Aging and Dementia cohort studies who underwent baseline and final optical coherence tomography scans. The peripapillary retinal nerve fiber layer, subfoveal choroid, and average retinal layer thickness at four quadrant (nasal, temporal, superior, and inferior) points 1 mm, 2 mm, and 3 mm from the center of the fovea were measured.

RESULTS

The mean age of subjects was 75.1 years and the mean follow-up period was 55.9 months. Among the analyzed retinal layers, both the ganglion cell-inner plexiform layer and the outer nuclear layer at all 1 mm, 2 mm, and 3 mm points showed a statistically significant decrease in thickness at the final visit compared with baseline. The annual decrease rates were -1.2 µm/year at 1 mm (total -6.6%), -1.3 µm/year at 2 mm (total -8.4%), and -1.1 µm/year at 3 mm (total -9.7%) for ganglion cell-inner plexiform layer and -0.6 µm/year at 1 mm (total -4.2%), -0.5 µm/year at 2 mm (total -3.9%), and -0.4 µm/year at 3 mm (total -4.1%) for outer nuclear layer.

CONCLUSION

Aging plays a significant role in the reduction of ganglion cell-inner plexiform layer and outer nuclear layer thicknesses in cognitively healthy elderly individuals.

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.

Retinal imaging and Alzheimer's disease: a future powered by Artificial Intelligence

Alzheimer's disease (AD) is a neurodegenerative condition that primarily affects brain tissue. Because the retina and brain share the same embryonic origin, visual deficits have been reported in AD patients. Artificial Intelligence (AI) has recently received a lot of attention due to its immense power to process and detect image hallmarks and make clinical decisions (like diagnosis) based on images. Since retinal changes have been reported in AD patients, AI is being proposed to process images to predict, diagnose, and prognosis AD. As a result, the purpose of this review was to discuss the use of AI trained on retinal images of AD patients. According to previous research, AD patients experience retinal thickness and retinal vessel density changes, which can occasionally occur before the onset of the disease's clinical symptoms. AI and machine vision can detect and use these changes in the domains of disease prediction, diagnosis, and prognosis. As a result, not only have unique algorithms been developed for this condition, but also databases such as the Retinal OCTA Segmentation dataset (ROSE) have been constructed for this purpose. The achievement of high accuracy, sensitivity, and specificity in the classification of retinal images between AD and healthy groups is one of the major breakthroughs in using AI based on retinal images for AD. It is fascinating that researchers could pinpoint individuals with a positive family history of AD based on the properties of their eyes. In conclusion, the growing application of AI in medicine promises its future position in processing different aspects of patients with AD, but we need cohort studies to determine whether it can help to follow up with healthy persons at risk of AD for a quicker diagnosis or assess the prognosis of patients with AD.

Eye-brain connections revealed by multimodal retinal and brain imaging genetics

The retina, an anatomical extension of the brain, forms physiological connections with the visual cortex of the brain. Although retinal structures offer a unique opportunity to assess brain disorders, their relationship to brain structure and function is not well understood. In this study, we conducted a systematic cross-organ genetic architecture analysis of eye-brain connections using retinal and brain imaging endophenotypes. We identified novel phenotypic and genetic links between retinal imaging biomarkers and brain structure and function measures from multimodal magnetic resonance imaging (MRI), with many associations involving the primary visual cortex and visual pathways. Retinal imaging biomarkers shared genetic influences with brain diseases and complex traits in 65 genomic regions, with 18 showing genetic overlap with brain MRI traits. Mendelian randomization suggests bidirectional genetic causal links between retinal structures and neurological and neuropsychiatric disorders, such as Alzheimer's disease. Overall, our findings reveal the genetic basis for eye-brain connections, suggesting that retinal images can help uncover genetic risk factors for brain disorders and disease-related changes in intracranial structure and function.

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.

Correlation between serum biomarkers, brain volume, and retinal neuronal loss in early-onset Alzheimer's disease

PURPOSE

To compare the peripapillary retinal nerve fiber layer (pRNFL), retinal nerve fiber layer (RNFL), and ganglion cell complex (GCC) thickness measurement in early-onset Alzheimer's disease (EOAD) and controls using spectral domain optical coherence tomography (SD-OCT). We also assessed the relationship between SD-OCT measurements and cognitive measures, serum biomarkers for Alzheimer's disease (AD), and cerebral microstructural volume.

METHODS

pRNFL, RNFL, and GCC thicknesses were measured in 43 EOAD and 42 controls using SD-OCT. Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE) were used to assess cognitive status, magnetic resonance imaging (MRI) tool was used to quantify cerebral microstructural volume, and serum biomarkers were quantified from peripheral blood.

RESULTS

EOAD patients had thinner pRNFL (P < 0.001), RNFL (P = 0.008), and GCC (P = 0.018) thicknesses compared to controls after adjusting for multiple factors. pRNFL thickness correlated (P = 0.016) with serum t-tau level. Serum Aβ42 (P < 0.05) concentration correlated with RNFL thickness. Importantly, occipital lobe volume (P = 0.010) correlated with GCC thicknesses in EOAD patients.

CONCLUSION

Our findings suggest that retinal thickness may be useful markers for assessing neurodegenerative process in EOAD.

Potential ocular indicators to distinguish posterior cortical atrophy and typical Alzheimer's disease: a cross-section study using optical coherence tomography angiography

BACKGROUND

Posterior cortical atrophy (PCA) is a form of dementia that frequently displays significant visual dysfunction and relatively preserved cognitive and executive functions, thus hindering early diagnosis and treatment. This study aimed to investigate possible fundus markers in PCA patients and compare them with those of typical Alzheimer's disease (AD) patients to seek potential diagnostic patterns.

METHODS

Age-matched PCA and AD patients and healthy controls (HC) completed optometry, intraocular pressure measurement, neuropsychologic assessments, optical coherence tomography (OCT), and optical coherence tomography angiography (OCTA) examination in one visit. Overall, six outcomes of thicknesses of various retinal layers and seven outcomes of the retinal microvascular network were calculated. After adjusting for age, sex, and years of education, the OCT and OCTA results were analyzed using analysis of covariance and generalized linear models. Correlation analyses were performed using Spearman correlation, and ROC curves were plotted.

RESULTS

Twelve PCA patients, nineteen AD patients, and thirty HC, aged 45-80 years were included. Fifty HC, thirty AD, and twenty PCA eyes were available for foveal avascular zone (FAZ) area analysis; forty-nine HC, thirty-four AD, and eighteen PCA eyes were available for OCT and OCTA assessments. PCA patients had thinner retinal nerve fiber layer and ganglion cell layer + inner plexiform layer than HC in the 0-3 mm circle and 1-3 mm ring. Few structural differences were observed between the AD group and the other two groups. The flow area of the superficial capillary plexus and the intermediate capillary plexus was smaller in the PCA group than in the HC group in the 0-1 mm circle, 0-3 mm circle. MMSE performed better than any combination of optical parameters in identifying AD and PCA from HC (AUC = 1), while the combination of MoCA, retinal thickness and vascular density of ICP in the 1-3 mm ring, with flow area of ICP in the 0-1 mm circle showed the strongest ability to distinguish PCA from AD (AUC = 0.944).

CONCLUSIONS

PCA patients exhibited similar impairment patterns to AD patients in the fundus structure and microvascular network. OCTA may aid in the non-invasive detection of AD and PCA, but still remains to be substantiated.

Retinal OCT biomarkers and their association with cognitive function-clinical and AI approaches

Retinal optical coherence tomography (OCT) biomarkers have the potential to serve as early, noninvasive, and cost-effective markers for identifying individuals at risk for cognitive impairments and neurodegenerative diseases. They may also aid in monitoring disease progression and evaluating the effectiveness of interventions targeting cognitive decline. The association between retinal OCT biomarkers and cognitive performance has been demonstrated in several studies, and their importance in cognitive assessment is increasingly being recognized. Machine learning (ML) is a branch of artificial intelligence (AI) with an exponential number of applications in the medical field, particularly its deep learning (DL) subset, which is widely used for the analysis of medical images. These techniques efficiently deal with novel biomarkers when their outcome for the applications of interest is unclear, e.g., for diagnosis, prognosis prediction, disease staging, or any other relevance to clinical practice. However, using AI-based tools for medical purposes must be approached with caution, despite the many efforts to address the black-box nature of such approaches, especially due to the general underperformance in datasets other than those used for their development. Retinal OCT biomarkers are promising as potential indicators for decline in cognitive function. The underlying mechanisms are currently being explored to gain deeper insights into this relationship linking retinal health and cognitive function. Insights from neurovascular coupling and retinal microvascular changes play an important role. Further research is needed to establish the validity and utility of retinal OCT biomarkers as early indicators of cognitive decline and neurodegenerative diseases in routine clinical practice. Retinal OCT biomarkers could then provide a new avenue for early detection, monitoring and intervention in cognitive impairment with the potential to improve patient care and outcomes.

[Retinal optical coherence tomography biomarkers and their association with cognitive functions : Clinical and artificial intelligence approaches. German version]

Retinal optical coherence tomography (OCT) biomarkers have the potential to serve as early, noninvasive, and cost-effective markers for identifying individuals at risk for cognitive impairments and neurodegenerative diseases. They may also aid in monitoring disease progression and evaluating the effectiveness of interventions targeting cognitive decline. The association between retinal OCT biomarkers and cognitive performance has been demonstrated in several studies, and their importance in cognitive assessment is increasingly being recognized. Machine learning (ML) is a branch of artificial intelligence (AI) with an exponential number of applications in the medical field, particularly its deep learning (DL) subset, which is widely used for the analysis of medical images. These techniques efficiently deal with novel biomarkers when their outcome for the applications of interest are unclear, e.g., for the diagnosis, prognosis prediction and disease staging. However, using AI-based tools for medical purposes must be approached with caution, despite the many efforts to address the black-box nature of such approaches, especially due to the general underperformance in datasets other than those used for their development. Retinal OCT biomarkers are promising as potential indicators for decline in cognitive function. The underlying mechanisms are currently being explored to gain deeper insights into this relationship linking retinal health and cognitive function. Insights from neurovascular coupling and retinal microvascular changes play an important role. Further research is needed to establish the validity and utility of retinal OCT biomarkers as early indicators of cognitive decline and neurodegenerative diseases in routine clinical practice. Retinal OCT biomarkers could then provide a new avenue for early detection, monitoring and intervention in cognitive impairment with the potential to improve patient care and outcomes.

Retinal Alterations Predict Early Prodromal Signs of Neurodegenerative Disease

Neurodegenerative diseases are an increasingly common group of diseases that occur late in life with a significant impact on personal, family, and economic life. Among these, Alzheimer's disease (AD) and Parkinson's disease (PD) are the major disorders that lead to mild to severe cognitive and physical impairment and dementia. Interestingly, those diseases may show onset of prodromal symptoms early after middle age. Commonly, the evaluation of these neurodegenerative diseases is based on the detection of biomarkers, where functional and structural magnetic resonance imaging (MRI) have shown a central role in revealing early or prodromal phases, although it can be expensive, time-consuming, and not always available. The aforementioned diseases have a common impact on the visual system due to the pathophysiological mechanisms shared between the eye and the brain. In Parkinson's disease, α-synuclein deposition in the retinal cells, as well as in dopaminergic neurons of the substantia nigra, alters the visual cortex and retinal function, resulting in modifications to the visual field. Similarly, the visual cortex is modified by the neurofibrillary tangles and neuritic amyloid β plaques typically seen in the Alzheimer's disease brain, and this may reflect the accumulation of these biomarkers in the retina during the early stages of the disease, as seen in postmortem retinas of AD patients. In this light, the ophthalmic evaluation of retinal neurodegeneration could become a cost-effective method for the early diagnosis of those diseases, overcoming the limitations of functional and structural imaging of the deep brain. This analysis is commonly used in ophthalmic practice, and interest in it has risen in recent years. This review will discuss the relationship between Alzheimer's disease and Parkinson's disease with retinal degeneration, highlighting how retinal analysis may represent a noninvasive and straightforward method for the early diagnosis of these neurodegenerative diseases.

Association of retinal neurodegeneration with the progression of cognitive decline in Parkinson's disease

Retinal thickness may serve as a biomarker in Parkinson's disease (PD). In this prospective longitudinal study, we aimed to determine if PD patients present accelerated thinning rate in the parafoveal ganglion cell-inner plexiform layer (pfGCIPL) and peripapillary retinal nerve fiber layer (pRNFL) compared to controls. Additionally, we evaluated the relationship between retinal neurodegeneration and clinical progression in PD. A cohort of 156 PD patients and 72 controls underwent retinal optical coherence tomography, visual, and cognitive assessments between February 2015 and December 2021 in two Spanish tertiary hospitals. The pfGCIPL thinning rate was twice as high in PD (β [SE] = -0.58 [0.06]) than in controls (β [SE] = -0.29 [0.06], p < 0.001). In PD, the progression pattern of pfGCIPL atrophy depended on baseline thickness, with slower thinning rates observed in PD patients with pfGCIPL below 89.8 µm. This result was validated with an external dataset from Moorfields Eye Hospital NHS Foundation Trust (AlzEye study). Slow pfGCIPL progressors, characterized by older at baseline, longer disease duration, and worse cognitive and disease stage scores, showed a threefold increase in the rate of cognitive decline (β [SE] = -0.45 [0.19] points/year, p = 0.021) compared to faster progressors. Furthermore, temporal sector pRNFL thinning was accelerated in PD (βtime x group [SE] = -0.67 [0.26] μm/year, p = 0.009), demonstrating a close association with cognitive score changes (β [SE] = 0.11 [0.05], p = 0.052). This study suggests that a slower pattern of pfGCIPL tissue loss in PD is linked to more rapid cognitive decline, whereas changes in temporal pRNFL could track cognitive deterioration.

Hyperspectral Retinal Imaging as a Non-Invasive Marker to Determine Brain Amyloid Status

Background

As an extension of the central nervous system (CNS), the retina shares many similarities with the brain and can manifest signs of various neurological diseases, including Alzheimer's disease (AD).

Objective

To investigate the retinal spectral features and develop a classification model to differentiate individuals with different brain amyloid levels.

Methods

Sixty-six participants with varying brain amyloid-β protein levels were non-invasively imaged using a hyperspectral retinal camera in the wavelength range of 450-900 nm in 5 nm steps. Multiple retina features from the central and superior views were selected and analyzed to identify their variability among individuals with different brain amyloid loads.

Results

The retinal reflectance spectra in the 450-585 nm wavelengths exhibited a significant difference in individuals with increasing brain amyloid. The retinal features in the superior view showed higher inter-subject variability. A classification model was trained to differentiate individuals with varying amyloid levels using the spectra of extracted retinal features. The performance of the spectral classification model was dependent upon retinal features and showed 0.758-0.879 accuracy, 0.718-0.909 sensitivity, 0.764-0.912 specificity, and 0.745-0.891 area under curve for the right eye.

Conclusions

This study highlights the spectral variation of retinal features associated with brain amyloid loads. It also demonstrates the feasibility of the retinal hyperspectral imaging technique as a potential method to identify individuals in the preclinical phase of AD as an inexpensive alternative to brain imaging.

A Systematic Review on Retinal Biomarkers to Diagnose Dementia from OCT/OCTA Images

Background

Traditional methods for diagnosing dementia are costly, time-consuming, and somewhat invasive. Since the retina shares significant anatomical similarities with the brain, retinal abnormalities detected via optical coherence tomography (OCT) and OCT angiography (OCTA) have been studied as a potential non-invasive diagnostic tool for neurodegenerative disorders; however, the most effective retinal changes remain a mystery to be unraveled in this review.

Objective

This study aims to explore the relationship between retinal abnormalities in OCT/OCTA images and cognitive decline as well as evaluating biomarkers' effectiveness in detecting neurodegenerative diseases.

Methods

A systematic search was conducted on PubMed, Web of Science, and Scopus until December 2022, resulted in 64 papers using agreed search keywords, and inclusion/exclusion criteria.

Results

The superior peripapillary retinal nerve fiber layer (pRNFL) is a trustworthy biomarker to identify most Alzheimer's disease (AD) cases; however, it is inefficient when dealing with mild AD and mild cognitive impairment (MCI). The global pRNFL (pRNFL-G) is another reliable biomarker to discriminate frontotemporal dementia from mild AD and healthy controls (HCs), moderate AD and MCI from HCs, as well as identifing pathological Aβ42/tau in cognitively healthy individuals. Conversely, pRNFL-G fails to realize mild AD and the progression of AD. The average pRNFL thickness variation is considered a viable biomarker to monitor the progression of AD. Finally, the superior and average pRNFL thicknesses are considered consistent for advanced AD but not for early/mild AD.

Conclusions

Retinal changes may indicate dementia, but further research is needed to confirm the most effective biomarkers for early and mild AD.

Ocular images-based artificial intelligence on systemic diseases

PURPOSE

To provide a summary of the research advances on ocular images-based artificial intelligence on systemic diseases.

METHODS

Narrative literature review.

RESULTS

Ocular images-based artificial intelligence has been used in a variety of systemic diseases, including endocrine, cardiovascular, neurological, renal, autoimmune, and hematological diseases, and many others. However, the studies are still at an early stage. The majority of studies have used AI only for diseases diagnosis, and the specific mechanisms linking systemic diseases to ocular images are still unclear. In addition, there are many limitations to the research, such as the number of images, the interpretability of artificial intelligence, rare diseases, and ethical and legal issues.

CONCLUSION

While ocular images-based artificial intelligence is widely used, the relationship between the eye and the whole body should be more clearly elucidated.

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.

Eye-brain connections revealed by multimodal retinal and brain imaging genetics in the UK Biobank

As an anatomical extension of the brain, the retina of the eye is synaptically connected to the visual cortex, establishing physiological connections between the eye and the brain. Despite the unique opportunity retinal structures offer for assessing brain disorders, less is known about their relationship to brain structure and function. Here we present a systematic cross-organ genetic architecture analysis of eye-brain connections using retina and brain imaging endophenotypes. Novel phenotypic and genetic links were identified between retinal imaging biomarkers and brain structure and function measures derived from multimodal magnetic resonance imaging (MRI), many of which were involved in the visual pathways, including the primary visual cortex. In 65 genomic regions, retinal imaging biomarkers shared genetic influences with brain diseases and complex traits, 18 showing more genetic overlaps with brain MRI traits. Mendelian randomization suggests that retinal structures have bidirectional genetic causal links with neurological and neuropsychiatric disorders, such as Alzheimer's disease. Overall, cross-organ imaging genetics reveals a genetic basis for eye-brain connections, suggesting that the retinal images can elucidate genetic risk factors for brain disorders and disease-related changes in intracranial structure and function.

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.