Retinal manifestations and their diagnostic significance in Alzheimer's disease

Abstract

Alzheimer's disease (AD) is a neurodegenerative condition manifesting as cognitive decline, memory deterioration, and behavioral alterations. Late-onset AD accounts for most diagnosed cases, with the onset of symptoms usually occurring after 65 years. At present, there are no proven treatments that alter the course of AD. For early detection and intervention, it is crucial to understand the underlying mechanisms and identify promising biomarkers for AD. Research suggests that the pathological processes of AD initiate years before the emergence of noticeable symptoms, which makes the early diagnosis more challenging. While various biomarkers, such as cognitive tests, imaging, and biological markers in blood and cerebrospinal fluid, have been proposed for early detection, their reliability, as matched with symptomatic stages, varies significantly. As a component of the central nervous system, the retina has attracted attention as a potential site for studying AD-related changes. Studies from human and animal models have revealed structural, vascular, functional, and metabolic changes in the retina through the early phases of AD. Furthermore, advances in ophthalmic technologies have facilitated the identification and characterization of AD-related changes such as amyloid-β and tau-protein deposition. This review provides an overview and perspective on AD as they relate to the retina and highlights the importance of ocular changes as surrogates for understanding and diagnosing AD.

Keywords

Alzheimer's disease amyloid-β neurodegeneration non-invasive biomarkers ocular imaging retina retina-brain axis retinal biomarkers retinal pathology tau pathology

Overview of Alzheimer's disease (AD)

At least 55 million people worldwide are affected by dementia (World Health Organization, WHO World Alzheimer Report 2021). AD is the most prevalent form of dementia, accounting for approximately 60–80% of cases worldwide. In 2023, in the USA, about 6.7 million people aged 65 years and older were estimated to have Alzheimer's dementia.

AD is indicated by a progressive loss of memory, learning capability, and executive function,^1,2 thus individuals with AD may have difficulties making decisions, talking, problem solving, and caring for themselves.³ Neuropathologically, AD is distinguished by amyloid-β (Aβ) plaque accumulation and tau tangles in the brain, leading to neuronal dysfunction and ultimately cell death.^4–7 Aβ plaque aggregation begins in the trans-entorhinal region and spreads to other brain regions.⁸ The development of tau-associated neurofibrillary tangles also follows a similar pattern.⁸ As the disease progresses, there is widespread neurodegeneration, neuroinflammation and synaptic loss, which can occur prior to cognitive decline and memory impairment.⁹ Also, vascular pathologies such as hemorrhages, microinfarcts, hypoperfusion, atherosclerosis, and cerebral amyloid angiopathy have been observed in around one-third of AD patients.^10,11

Age is the greatest risk factor for developing AD,¹² although a rare form of AD also exists in people between 30–50, which is referred to as early-onset AD (EOAD). EOAD is less well-studied than late-onset AD (LOAD)¹³ and typically results in a more rapid progression of the disease than LOAD.¹⁴ While both EOAD and LOAD share the same essential neuropathological traits, such as amyloid plaques and neurofibrillary tangles, they differ in several features.¹⁴ EOAD is predominantly determined by genetics, with heritability ranging between 92% to 100% polymorphisms linked to presenilin 1 (PSEN1), presenilin 2 (PSEN 2) and amyloid-β precursor protein (AβPP) genes being important.¹⁵ Abnormal Aβ protein is produced upon mutation in these genes, which leads to the development of EOAD.^16–18 Phenotypically, EOAD may have a different clinical course, including non-amnestic syndromes that affect various cognitive domains.¹⁹ Another distinction between EOAD and LOAD is that EOAD has a less complex etiology (combining genetic, environmental, and lifestyle factors) than LOAD. In terms of clinical presentation and disease progression, EOAD tends to progress faster and has more severe cognitive decline than LOAD.^14,20–22 Moreover, EOAD often begins with non-memory symptoms such as language difficulties or visual disturbances, while memory impairment usually marks the onset of LOAD.^14,20–22

LOAD accounts for approximately 95% of all AD cases,²³ with a prevalence of around 30% in individuals over the age of 85.²⁴ Although LOAD mainly occurs after the age of 65, it can occur earlier.^25,26 LOAD is a multifaceted disease with diverse causation and heritability is estimated at 70% to 80%. Apolipoprotein E4 (APOE4) is a key genetic risk factor, albeit not linked to all LOAD cases.¹⁵ Studies have shown that because of hormonal and genetic factors such as APOE4, women have an increased risk of developing LOAD compared to men.^27,28 Also, cardiovascular health, education level, social engagement, and other lifestyle factors are associated with LOAD,²⁶ as is the environmental exposure to air pollution,^29,30 metals³¹ and pesticides.^32,33 This association affects several AD-related pathology hallmarks and levels of AD-specific biomarkers, as reviewed by Santiago et al.³⁴

A genome-wide association study has detected variants associated with LOAD, suggesting a mixture of numerous genetic variants with subtle impacts and environmental factors as the cause.³⁵ The APOE ε4 allele has been recognized as a strong genetic risk factor for LOAD.^36,37 56% of patients diagnosed with AD in the USA possessed one copy of the APOE4 gene and 11% harbored two copies of the APOE4 gene.^24,38 The APOE gene has three alleles: ε2, ε3, and ε4. APOE ε4 allele is associated with an increased risk of developing LOAD, while ε2 allele is known to be protective.^39,40 Individuals inheriting one copy of the ε4 allele have a higher risk, and those who inherit two copies have an even higher risk.^25,41 According to genetic and epidemiological research, allelic segregation of the APOE was found in families with an increased risk of sporadic LOAD.^39,42,43 The prevalence of APOE4 is >50% in sporadic AD, and it impacts the age of onset by 7 to 9 years with each allele copy, increasing the risk for AD.³⁹ Biochemical and histological studies of AD brains and the brains of transgenic mice carrying human APOE3 (the non-pathogenic APOE allele for AD), and APOE4 showed that APOE4 is linked to synaptic disease and reduced neuronal plasticity.^44–50 A case-control study from central Norway has shown that the APOE4 allele was significantly linked with an earlier onset of LOAD, with a mean age for the onset for LOAD with the APOE4 allele being 73.2 years compared to 77.6 years for those individuals without the allele.²⁵ In addition to APOE, other genes have been implicated in LOAD,⁵¹ including clusterin (CLU),^52–54 phosphatidylinositol-binding clathrin assembly protein (PICALM),^55–57 and triggering receptor expressed on myeloid cells 2 (TREM2)^58,59 gene.

The currently available therapies for AD focus on reducing the symptoms and decelerating the progression of the disease.⁶⁰ In comparison to other diseases, AD progression is multifaceted and subtle. Therefore, accurately tracking cognitive decline over time and identifying significant biomarkers for progression and treatment is challenging.⁶⁰ Initial brain changes in AD may already be substantial by the time symptoms start to appear; this makes intervention more difficult before damage has occurred.⁶¹ AD exhibits mixed proteinopathies, and its interactions with other age-related diseases complicate drug development.⁶² Furthermore, drug development is hindered by the high cost of diagnostic testing and a lack of efficient therapies.⁶³ Cholinesterase inhibitors such as donepezil, rivastigmine, and galantamine are commonly recommended to improve cognitive abilities and delay the deterioration of daily activities. Memantine has been shown to help with cognitive symptoms by regulating the brain's glutamate activity.² Aducanumab, donanemab and lecanemab target and remove Aβ, thus helping reduce cognitive and functional decline in early AD.⁶⁴ Additionally, suvorexant,⁶⁵ a dual orexin receptor antagonist and brexpiprazole,⁶⁶ an atypical anti-psychotic, have been approved to treat insomnia and agitation associated with AD. In the near future, improved precision around AD phenotyping will provide improved diagnostic tools and patient-matched therapeutics.

Whilst AD is classically characterized by cerebral pathology, recent evidence has suggested that similar pathological processes may occur in the retina. The underlying hypothesis that retinal biomarkers may serve as a non-invasive surrogate for the detection and diagnosis of central nervous system (CNS) pathology in AD will be explored in this review.

The retina as a window to the brain

As a facet of AD precision phenotyping and improved diagnosis, there is a strong rationale for studying the retina of patients with AD. This is because the retina shares many similarities with the brain in terms of embryological origin, neural connectivity, neurovasculature and immune cell regulation.

The retina contains multiple specialized cells that act in concert to facilitate vision (Figure 1). At the interface with the vitreous lies the retinal nerve fiber layer (RNFL) and the retinal ganglion cell (RGC) layer.⁶⁷ RGCs transmit visual information from the retina to the brain. The inner nuclear layer (INL) and the inner plexiform layer (IPL) contain the nuclei and synapses of retinal neurons, including bipolar, horizontal, and amacrine cells. These cells synthesize and regulate the relay of information between the rod and cone photoreceptors of the outer nuclear layer (ONL) to the RGCs. Adjacent to the photoreceptor outer segments (photoreceptor layer- PRL) is the retinal pigment epithelium (RPE) layer, and along with the choroid, it forms the outer blood-retinal barrier (oBRB). The inner blood-retinal barrier (iBRB) is formed by the tight junctions of the retinal blood vessels.⁶⁷ Spanning the retinal layers are the retina-specific glia cells – Müller cells (MCs). MCs provide structural and metabolic support to the retina its’ vasculature and, thus, are integral to neurovascular coupling. MCs also play roles in metabolism, water, and ion transport in the retina.⁶⁸ Retinal blood vessels span the retina in three distinct layers – the superficial, intermediate, and deep vascular plexuses. The retinal vessels are supported by surrounding cells in a functional unit known as the neurovascular unit (NVU). The NVU comprises the interdependent vascular, glial, neuronal, and immune cells that maintain homeostasis in the brain and retina. Astrocytes play a key role in the function of the retinal neurovascular unit, specifically contributing to the structure and maintenance of the blood-retinal barrier at the inner retinal layers. Retinal microglia are most commonly found surveilling the tissue from the plexiform layers but can also migrate to sites of damage and inflammation. Microglia also interact with the NVU by regulating immune responses in the retina and are a vital driver of neuroinflammation.

Figure 1.

Summary of retinal abnormalities in Alzheimer's disease (AD). Left: Normal retinal structure and cell types. Right: Pathological changes in the retina associated with AD. Reduced nerve fiber layer thickness has been reported in AD, alongside death of retinal ganglion cells (RGCs). Amyloid-β (Aβ) deposition is observed around the RGCs and around retinal blood vessels, and Aβ plaques are apparent. There is a loss of blood vessel density in AD, alongside reduced arterial diameter. Blood-retinal barrier breakdown increases inflammation in the retina. Increased numbers of activated microglia are observed in AD patients, and Müller cell gliosis is reported in animal models of AD. (Created with BioRender.com)

Notably, the retina often reflects pathophysiological changes that occur in parallel in the brain and is considered an accessible and non-invasive window into the CNS.^69,70 In a study of 56 healthy participants (aged between 20–80 years, without cognitive, psychiatric, or neurological impairments), the thickness of the primary visual cortex and other cortical regions was measured via T1-weighted MRI, alongside retinal thickness using OCT. It was found that age-related thinning in the primary visual cortex correlated significantly with reductions in retinal thickness, indicating that the atrophy of both structures may contribute to age-related declines in visual abilities and suggesting that retinal health reflects overall cortical integrity.⁷¹ Furthermore, a recent study found that retinal imaging biomarkers share genetic links with brain diseases and traits in 65 genomic regions, including 18 regions that overlap with brain MRI traits. Mendelian randomization showed a two-way genetic relationship between retinal structures and neurological disorders such as AD, suggesting that retinal images could help identify genetic risks for brain conditions and changes in brain structure and function.⁷²

Retinal pathology as biomarkers of AD

Amyloidopathy in the retina

A body of research from the Koronyo-Hamaoui lab has shown Aβ deposition in the retina in plaques and drusen,⁷³ around RGCs,⁷⁴ and in association with the retinal vasculature, such as capillary degeneration⁷⁵ and abundant arteriolar Aβ deposition.⁷⁶ Using a novel imaging technique involving curcumin, a fluorochrome that binds Aβ, retinal Aβ plaques were observed earlier than brain Aβ plaques in amyloid precursor protein (APP) APPSWE/PS1ΔE9 mice⁷³ and in human AD retinas, a 4.7-fold increase in Aβ was observed.⁷⁷ Interestingly, in mice, the retinal Aβ deposits appeared to be correlated with brain Aβ burden,⁷⁸ and in APPSWE/PS1ΔE9 mice, retinal Aβ was detected before brain Aβ was observed.⁷³ In line with these findings, other groups have reported Aβ accumulation in various AD animal models, such as aged APP/PS1 mice, which demonstrated APP proteolytic products and Aβ deposition in the RPE,⁷⁹ while thioflavin-S staining (fluorescent probe binds to Aβ) confirmed age-dependent retinal Aβ increases, particularly in the RGC layer^80,81 and 5xFAD mice, both soluble and insoluble Aβ deposits were detected as early as three months, progressing from inner to outer retinal layers with disease severity.^82,83 While the above reports are promising, some studies showed conflicting results in the retina of human AD samples, though signals for Aβ were detected in multiple retinal cell types,⁸⁴ no typical Aβ deposits were observed in the retinas.^84–86 This could be due to a low sample size and possibly to a high binding affinity of Aβ for other neurodegenerative proteins such as α-synuclein.⁸⁷ Therefore, large-scale human validation studies are necessary before introducing this method as a biomarker for AD.

Tauopathy in the retina

The presence of tau aggregates in the retina of patients with AD and other tauopathies has been documented, suggesting that the retina may serve as a site for presymptomatic stage imaging and indicating that AD is both a cerebral and an ocular disease.⁸⁸ Tau aggregates in the RGCs have been associated with neurodegeneration.^89,90 Moreover, research has shown that tauopathies are related to dysfunction of glutamatergic photoreceptor synapses linked to RGC loss.⁹¹ It should be noted that tauopathy in the retina is not a consistent finding, and some studies have reported the absence of tau aggregates in AD retinas,⁹² highlighting the need for further research to elucidate the role of tau in the retina of AD patients.

Retinal neuronal loss and synaptic dysfunction

Many reports have shown that RGCs are depleted in AD patients,^93–95 with the possibility that melanopsin-expressing RGCs are explicitly affected,⁷⁴ thus linking to potential circadian disruption in AD.⁹⁶ Studies suggested varying degrees of RNFL thinning, particularly in the superior and inferior quadrants, which correlate with cognitive decline and brain atrophy.^97–100 The reduced thickness at the GC-IPL correlates with lower brain grey matter volume in AD.¹⁰¹ However, some studies have suggested that retinal layer thickness is not a suitable biomarker for early AD detection.^102,103 Van De Kreeke et al. found no significant difference in the retinal thickness at the macula or peripheral NFL when comparing pre-clinical AD to healthy controls,¹⁰² suggesting that these measurements may only be promising biomarkers for later stages of AD. RNFL thickness is also reduced in cases of brain atrophy¹⁰⁴ and other neurodegenerative diseases such as AMD,¹⁰⁵ glaucoma,¹⁰⁶ and multiple sclerosis,¹⁰⁷ further reducing its strength as an AD-specific biomarker.

Hinton et al. described a significant optic nerve degeneration in post-mortem samples from patients with AD.¹⁰⁸ Synapse pathology, an early event in the AD brain¹⁰⁹ has also been observed in retinal synapses in clinical studies,¹¹⁰ AD mouse models, P301S⁹¹ and APOE4^111,112 and chicken embryo retinal neuronal cultures in vitro.¹¹³

Retinal vascular changes and blood-retinal barrier disruption

Several retinal vascular changes have been identified in patients with AD, especially since the development of OCT-angiography (OCT-A) as a non-invasive way to measure retinal vasculature parameters. The leading reported candidates for retinal vessel AD biomarkers include reduced vascular density^114–117 and foveal avascular zone (FAZ) area,^117–120 although other vascular changes have been reported such as hemorrhages, hypoperfusion, and atherosclerosis. AD patients have increased vessel branching in the mid-peripheral retina, increased artery thinning,¹²¹ and reduced arterial dilation.¹¹⁶ In patients with mild cognitive impairment (MCI), significant arteriolar and venular thinning was also observed in the retina.^121,122 Although the FAZ significantly increased in some studies,^117–120 these findings are contradicted in other reports.^95,115,123

As is observed in the brain in AD,¹²⁴ the retina of AD patients has a lower perfusion density^115,125 and lower retinal arterial blood flow¹²⁶ when compared to age-matched, healthy controls. Some studies differentiated vessel density differences into the superficial and deep vascular plexus (the superficial vascular complex is composed of a central retinal artery and larger arteries, capillaries, and veins predominantly in GCL, the deeper capillary networks below INL are known as the deep capillary plexus¹²⁷). Parafoveal superficial vessel density was significantly reduced in AD patients,^114,115,117 specifically in the superior quadrant of OCT-A.¹¹⁸ One study found a significant reduction in vessel density in the deep vascular plexus,¹¹⁴ but others did not observe this.^115,116 Studies in patients have not identified changes in the intermediate vascular plexus. However, in mouse models, the vascular density of the intermediate plexus decreased with aging.^128,129 The studies so far have relatively small sample sizes,¹³⁰ but they agree that OCT-A can detect vascular density changes and changes to the FAZ in AD patients.

Patients with AD also have reduced microvascular density,^{125,131–135} specifically at the macula,¹³⁶ and choroid^133,137 which correlates with brain changes such as cognition and memory function^138,139 and structural damage visible by MRI.¹⁴⁰ Vascular degeneration is also reported in AD mouse models.^128,129,141

Post-mortem studies have enabled a detailed understanding of underlying retinal vascular pathology in AD patients, such as loss of tight junctional proteins and pericytes⁷⁵ and deposition of Aβ.⁷⁷ Shi et al. recently reported that tight junction proteins Zonula occludens-1 (ZO-1) and Claudin-5 were also significantly reduced in the retinas of patients with MCI and AD, and correlated with the levels of retinal arteriolar Aβ deposition.⁷⁶ Interestingly, the retina arteriolar Aβ deposition levels were also correlated to cerebral amyloid angiopathy—a deposition of Aβ in the brain blood vessel walls often reported in individuals with AD.

Glial dysfunction and inflammation

A few studies have focused on retinal astrocyte changes during AD, where they were found surrounding a blood vessel positive for 4G8⁺ Aβ deposits, and they were rich near GC containing Aβ. In post-mortem human retinal samples, there was an increased number of astrocytes in the nerve fiber layer of AD patients.^93,142 A more recent study found that astrocytosis, alongside increased expression of the pro-inflammatory molecule complement C3, in the retinas of patients with AD.¹⁴³ In a mouse model of tauopathy, activated astrocytes were found in the brain and retina close to tau oligomers.¹⁴⁴ In another model (3xTG-AD), retinal astrocyte hypertrophic morphology was observed.¹⁴⁵ The retinas of AD patients and tauopathy mice (P301L tau mice) showed colocalization of oligomeric tau with glial fibrillary acidic protein (GFAP, gliosis marker)-labeled astrocytes and Iba1 (microglia marker)-labeled microglia.¹⁴⁴

MC gliosis is characterized by both general and potentially protective responses, such as increased expression of GFAP and reduced expression of glutamine synthetase (GS).¹⁴⁶ Several AD mouse models, such as 5xFAD, App^NL-G-F, 3xTG and APOE4, reported MC gliosis,^{83,141,145,147} suggesting MC activation is a prominent feature of AD. Consistent with these findings, lower GS levels have been found in AD brains^148–151 and retinas.^152,153 However, in postmortem retina samples from AD patients, Xu et al. found reduced immunoreactivity for GFAP and GS, alongside increased Iba-1.¹⁵² Other studies have shown elevated levels of GS in the CSF and prefrontal cortex.^154,155 While elevated levels of GS in the brain may indicate astrogliosis processes, decreased GS levels in the retina could signify a MC degeneration,¹⁵² perhaps linked to Aβ exposure, underscoring possible disparity in gliotic responses between the brain and the retina in AD.

Microglia in the CNS respond dynamically to insults and inflammation. A recent study by Koronyo et al. compared retina and brain tissue from individuals with AD and found that retinal microgliosis is strongly associated with cognitive status.¹⁵⁶ They also found that female AD patients had increased levels of microgliosis compared to males and that retinal microglia were involved in Aβ uptake.¹⁵⁶ Microglial activation was observed before brain changes in 3xTg-AD mice,¹⁵⁷ further highlighting the possibility of retinal pathology as a predictor for brain changes in AD. Previously, microglia were profiled into two distinct subtypes – M1 (pro-inflammatory) and M2 (anti-inflammatory). However, recent work has shown that microglia subtypes are much more nuanced. Specifically in the context of AD, disease-associated microglia (DAMs) have been identified in the brains of 5xFAD mice, specifically located around Aβ plaques.¹⁵⁸ DAMs were found to have increased expression of genes related to lipid metabolism, phagocytosis, and upregulation of APOE.¹⁵⁸ A similar subset of microglia was found in the human AD brain, termed human AD microglia (HAMs). These cells had a similar profile to DAMs, indicating an important and conserved role in the pathogenesis of AD.^158,159 DAMs were found to have specific sub-sets related to Aβ and tau pathology, respectively.¹⁶⁰ However, very little work has been focused on identifying DAMs or HAMs in the AD retina. One recent study targeted DAMs in the AD retina by conditionally depleting miR-155, a known regulator of the TREM2-APOE pathway, which was highlighted as a key pathway in AD DAMs.¹⁶¹ The mice were found to have reduced pro-inflammatory signaling in the retina alongside improved blood vessel tight junction integrity. Further research is needed to fully comprehend the intricate roles of these cells in AD pathology before their potential as a retinal biomarker can be determined.

A glia-assisted lymphatic system (termed the glymphatic system) is involved in clearing brain solutes,¹⁶² which is involved in the clearance of excess Aβ.^162,163 In a mouse model of AD, glymphatic clearance of Aβ was significantly reduced.¹⁶³ Recently, it was discovered that a retinal glymphatic system also exists in mice¹⁶⁴ with a strong dependency on Aquaporin 4 (AQP4) proteins on retinal MCs. Experimental data demonstrated glymphatic-mediated clearance of Aβ along a paravascular pathway from the retina to the optic nerve.¹⁶⁴ A summary of the key retinal changes observed in AD are shown in Table 1.

Table 1.

Summary of key retinal changes in Alzheimer's disease (AD) patients and mouse models.

Key Retinal Changes in AD		In AD Individuals	In AD Mouse Models
Visual Function	Loss of visual acuity	^165,166	Aged APP/PS1 - ¹⁶⁷ 5xFAD - ¹⁶⁸
	Loss of contrast sensitivity	^166,169	APP/PS1 - ¹⁷⁰
	Loss of color sensitivity	^166,169	APP/PS1 - ¹⁷⁰
Retinal Structure	Reduced Nerve fiber layer	^{74,166,171–173}	5xFAD - ¹⁷⁴
	Reduced retinal thickness	¹⁶⁶	3xTg-AD - ¹⁷⁵
	Reduced inner plexiform layer	¹⁶⁶	-
Amyloid-β deposition	Retinal deposition	^73,74,77,176	3xTg-AD - ¹⁵⁷ APP/PS1 - ^73,177
Amyloid-β deposition	Vascular deposition	^76,77,178	APP/PS1 - ⁷⁵
Neurodegeneration	Loss of retinal ganglion cells	⁷⁴	APP/PS1 - ¹⁷⁹
Vessel damage	Loss of vessel density	^{125,131,132,134,135}	3xTg-AD- ¹²⁸ P301S- ¹²⁹ APP^NL−G−F- ¹⁴¹
Vessel damage	Loss of pericytes	¹⁷⁸	APP/PS1 - ⁷⁵
Inflammation	Increased Microgliosis	^152,156	APP/PS1 - ^157,179 3xTg-AD - ¹⁵⁷

Retinal imaging for AD pathogenesis and diagnosis

The understanding of retinal changes associated with AD has been revolutionized by the use of non-invasive retinal imaging techniques. These imaging modalities provide insights into structural and functional alterations in the retina, facilitating early detection, monitoring, and potential diagnostic markers for AD (Figure 2).¹⁸⁰

Figure 2.

Diagrammatic representation highlighting various methods for detecting, monitoring, and diagnosing Alzheimer's disease (AD) changes using the visual system. Functioning as an extension of the central nervous system, the retina has emerged as a promising domain for investigating AD-related changes and uncovering potential biomarkers. LGN: lateral geniculate nucleus; OCT: optical coherence tomography; OCTA: optical coherence tomography angiography; HIS: hyperspectral imaging; FLIO: Fluorescence Lifetime Imaging Ophthalmoscopy; SLO: scanning laser ophthalmoscopy. (Created with BioRender.com).

Fundus photography and autofluorescence

Fundus photography is a common technique that involves retinal imaging and has been studied for diagnosing AD. Hart et al. (2016) compiled studies on ocular biomarkers of AD and demonstrated retinal alterations in AD patients.⁸⁸

Fundus autofluorescence (FAF) is an imaging tool that captures intrinsic fluorescent light from the retina.¹⁸¹ FAF provides information about cellular metabolism and can detect pathological changes related to AD, including lipofuscin deposition, a hallmark of aging cells, and oxidative stress within the RPE in patients with AD. Lipofuscin accumulation may reflect neuronal degeneration and metabolic dysfunction in AD.¹⁸² FAF abnormalities have been observed in preclinical stages of AD and correlate with cognitive decline, making FAF a promising tool for early detection and monitoring of AD-related retinal alterations.

Berisha et al. (2007) reported RNFL thinning and altered retinal blood vessels in the early stages of AD compared to the controls.¹⁸³ This was further adapted for telemedicine screening for retinal diseases using a handheld portable non-mydriatic fundus camera, showing the feasibility and effectiveness of portable fundus cameras in emergency settings.¹⁸⁴ Toslak et al. (2018) developed a low-cost, portable fundus camera that could use smartphones to capture wide-field fundus images.¹⁸⁵ Thus, fundus photography can help diagnose, monitor, and screen various ocular and systemic conditions, including AD.

OCT and OCT-A

OCT is a real-time, in situ, non-invasive imaging technique providing cross-sectional high spatial resolution images with substantial penetration depth for biological tissues in high scattering media situations (sub-micrometer; 10 mm or less).¹⁸⁶ To visualize and quantify retinal layers, OCT uses low-coherence interferometry to measure backscattered light echoes. In patients with AD, OCT has demonstrated thinning of the peripapillary NFL, macular volume loss and reduced nerve fiber density.^88,187–189

Parisi et al. (2001) utilized a cross-sectional OCT imaging study and reported primary structural alterations in the retina in AD patients.¹⁹⁰ A three-year longitudinal cohort study from the United Kingdom Biobank used OCT scans and found that changes in RNFL thickness are linked to cognitive deterioration as assessed with reaction time, prospective memory, and reasoning-based analysis.¹⁹¹ Measurements of spectral domain OCT in AD patients have been shown to correlate with visual dysfunctions and ocular disturbances.^{169,192–194} Using OCT-enhanced depth scanning, several studies have reported choroidal attenuation in dementia cases.^195,196 The localized atrophy of retinal and choroidal arteries or endothelial breakdown caused by Aβ toxicity could be attributed to the visual impairments in AD.¹⁸³ A meta-analysis conducted by Mejia-Vergara et al. (2020) on using OCT in MCI and AD found that OCT can detect inner retinal degeneration.¹⁹⁷ Recent studies have also observed hyper-reflective foci in the OCT of AD patients, which may be representative of retinal Aβ plaques.¹³⁷ OCT-A can evaluate both retinal and choroidal vasculature as well as capture retinal capillary microcirculation and provide high-resolution images of the retina, optic nerve, choroid, and vessel density at the macula without any need for contrast agents, leading to identification and monitoring mechanisms associated with AD.^{119,198–202} Furthermore, studies using OCT-A in individuals with AD and AD-like dementia showed a strong association between cognitive performance and peripapillary vessel density,¹³⁹ reductions in capillary vessels and perfusion density,⁹⁵ fovea centralis changes and alterations in the retinal vasculature.¹¹⁷ The University of Liverpool's 2025 study analyzed retinal microvascular density and inner retinal thickness in AD and MCI patients. Significant reductions in vascular density around the fovea were observed in both AD and MCI groups compared to healthy controls. Additionally, thinning of the inner and full retinal layers, as well as enlargement of the FAZ perimeter, were significantly associated with cognitive decline.²⁰³ A recent study by Jin et al. (2025) introduced retinal OCT intensity spatial correlation features (ISCF) as novel biomarkers for AD detection. The study demonstrated that ISCF achieved an area under the curve of 0.935 for AD dementia (ADD) and 0.830 for MCI, outperforming traditional retinal thickness measures, which had AUCs of 0.795 and 0.705, respectively. Moreover, ISCF showed stronger correlations with brain Aβ burden and Montreal Cognitive Assessment (MoCA) scores than retinal thickness, suggesting enhanced diagnostic efficacy.²⁰⁴

Scanning laser ophthalmoscopy (SLO)

SLO provides a comprehensive view of the fundus, which can be explored by studying leukocyte-retinal endothelial interactions in AD.²⁰⁵ The retina is scanned by a laser beam, which generates detailed images comprising layers of the retina, blood vessels, and other microvascular features. This method offers advantages, including reduced light scattering and, thus, better contrasting effects, especially when gathering minute retinal pathological details.²⁰⁶ When combined with other techniques, such as OCT and adaptive optics (AO), SLO provides detailed and thorough retinal imaging.^207–209

SLO can be helpful in several aspects of AD; SLO can aid in detecting Aβ deposits in the retina.²¹⁰ Researchers can visualize retinal Aβ plaques by targeting them with specific contrast agents.²¹⁰ Retinal thickness and morphology, indicative of degeneration markers such as loss of RGCs, RNFL alterations, can be easily measured using SLO.²¹¹ Precise imaging of retinal blood vessels by SLO allows insights into vascular changes associated with AD. These changes include vessel density, tortuosity, and blood flow, which may be potential disease progression biomarkers.²¹² SLO is non-invasive, allowing for repeated imaging sessions and providing a means for tracking sequential retinal changes.

Retinal hyperspectral imaging

Retinal hyperspectral imaging (HSI) is an emerging technique that combines spectral and spatial information to characterize retinal tissue at multiple wavelengths. HSI detects variations in retinal blood vessels, oxygen saturation levels, and metabolic activity using a unique signature spectrum from the retina.^213,214 Thus, by studying subtle changes in the retina, HSI helps to gain a deeper understanding of biochemical and metabolic changes in AD. Guided by the Rayleigh scatter principle, this method leverages the light scattering properties of even small particles within a tissue. Digital and optical processing of sequential HSI images enables the non-invasive identification of biochemical and structural alterations within the tissue.^213,214 HSI studies have demonstrated abnormalities in AD patients’ retinal oxygen metabolism and microvascular parameters.

Recently, attempts were made to distinguish Aβ accumulation in the retina via HSI^215–218 based on capturing spectra adjacent within a specific range through integrative wavelength measurements. Small soluble Aβ oligomers in the retina are thought to produce a distinct HSI signature, particularly in the short visible wavelength spectrum.²¹⁵ While HSI cannot precisely visualize Aβ, it can identify retinal Aβ via its distinct spectral signature. A study by More et al. (2015) showed that HSI imaging of the retina of mouse model at various disease stages corroborated these findings, showing notable distinctions at four months, which intensified by 6 months. By 8 months, APP/PS1 mice displayed Aβ plaque formation, leading to noticeable HSI differences between control and transgenic mice.²¹⁷ Remarkably, at 4 months, amyloid deposition was not apparent in the brains of APP/PS1 mice, suggesting that the light scattering of small soluble Aβ sustains the HSI signature, making it a potentially valuable tool for early AD detection.

This method has been used clinically in a few small studies.^215,219,220 One study showed that HSI was most helpful in differentiating MCI patients from healthy controls and, therefore, maybe a more useful biomarker for pre-clinical AD cases.²¹⁹ The transition of HSI in human imaging has posed challenges primarily due to substantial spectral variations between subjects. Hadoux et al. (2019) showed that HSI can distinguish individuals with more Aβ burden from those with lower Aβ burden on cerebral PET imaging.²¹⁵ Nonetheless, due to considerable ocular reflectance variability, unprocessed HSI data showed limited distinction between control subjects and AD patients. After normalizing the spectral features for interference from ocular tissues, spectral differentiation between AD and control subjects was observed, aligning with the spectral profile of Aβ in solution.²¹⁵ Processed data successfully distinguished the retinal environment of AD and control subjects, with pronounced variation depending on the examined region. Higher HSI scores were observed in the superior and foveal regions among individuals with AD and controls, consistent with previous reports of elevated Aβ plaque-like formations in the superior quadrant.⁷⁷

While the distinctive retinal HSI signature in human AD individuals and AD mouse models probably arises from Aβ accumulation, other factors, including inflammatory processes, pTau, metal ion accumulation, and various structural and biochemical changes, could influence spectral shifts.^89,221 HSI has been combined with other imaging modalities, strengthening its use as a biomarker.

Adaptive optics (AO)

AO imaging is an advanced technique that overcomes optical aberrations in the eye, enabling dynamic, microscopic ophthalmoscopy and projection of light stimuli onto the retina on a minute scale.²²² By reducing optical aberrations, AO technology enhances the performance of optical systems. This technique has revealed detailed morphological changes in individual retinal cells, thereby increasing our understanding of the cellular aspects of neurodegeneration.²²³ In AD research, AO could revolutionize the visualization of cellular and subcellular aspects since it provides exceptional clarity. AO imaging has shown morphological changes in RGCs and AD photoreceptors, highlighting microscopical variations within the retina.²²³ Retinal microvasculature, including nerve fiber bundles, rods, cones, and capillaries, can be directly visualized when incorporating AO into other retinal imaging techniques.²²⁴ SLO can monitor early pathological changes within the retina and response to the treatment at the cellular levels.^224,225

Fluorescence lifetime imaging ophthalmoscopy (FLIO)

FLIO is a novel strategy that measures the fluorescence lifetime emitted by retinal fluorophores. It helps understand metabolic and biochemical alterations in retinal cells’.²²⁶ FLIO could detect localized or global alterations in fluorescence lifetimes among patients suffering from DR, AMD, or vessel occlusion.²²⁷ Jentsch et al. (2015) demonstrated that FLIO measures in the retina were dependent on the severity of AD²²⁸ and a pilot study indicated that fluorescence lifetime (τ_m) in AD patients correlated with clinically significant biomarkers such as Aβ, tau and OCT thickness.²²⁹ Adaptive optics FLIO could differentiate spectrally overlapping fluorophores in the mouse retina, indicating its potential for assessing retinal health.²³⁰

Multimodal imaging

Multimodal imaging integrates data from different modalities such as OCT, SLO, and AO, which provides a complex understanding of neurodegenerative changes.^231,232 This permits the relationship of structural shifts with functional deficits, leading to greater diagnostic precision. A multimodal adaptive optics flood-illumination ophthalmoscope for high-resolution retinal imaging offers valuable information about pathological processes within the retina,²³³ indicating that multimodal retinal imaging may become a biomarker for diagnosing and monitoring neurodegenerative diseases like AD and Parkinson's.²³⁴ A study by Ravichandran et al. 2025 explored the integration of retinal imaging and plasma biomarkers for preclinical AD detection. The multimodal model, combining retinal parameters (gliosis area and inner retinal nerve fiber layer thickness) using spectral domain OCT (SD-OCT) with plasma biomarkers (p-tau217 and Aβ₄₂/Aβ₄₀ ratio), achieved an AUC of 0.97 (95% CI: 0.93- 1.01) in distinguishing Aβ-positive individuals from controls. This approach significantly outperformed models using either retinal or plasma biomarkers alone, demonstrating the enhanced diagnostic accuracy of multimodal biomarker integration.²³⁵

Widefield imaging

While traditional imaging techniques narrow down to specific areas in the retina, widefield imaging captures a broader panoramic view. Widefield imaging helps study signs of neurodegeneration at the retina's periphery.²³⁶ Widefield imaging can be combined with AO to acquire detailed spatial information on individual cones and capillaries, which may improve resolution.²³⁷ A number of studies have highlighted the utility of widefield imaging in AD,^121,122,238 including the observations of peripheral vascular changes correlated to changes in cognition¹²¹ and peripheral drusen formation.¹²²

Diagnostic accuracy and clinical utility

Efforts are undertaken to develop and validate retinal biomarkers for the early stages of AD. Different studies have investigated non-invasive retinal biomarkers to diagnose and monitor AD. A recent study by Hao et al. (2023) discovered that retinal thickness and MRI significantly differed in AD patients relative to healthy controls, indicating the potential diagnostic value of combining retinal and brain imaging techniques.²³⁹ Moreover, RGC degeneration has been found to correlate with hippocampal spine loss in experimental AD, underscoring the possibility of using retinal imaging as a non-invasive means of monitoring AD progression or testing the efficacy of AD treatments.²⁴⁰ The pathogenesis of AD has been associated with retinal amyloid pathology; thus, the detection of retinal Aβ in vivo could offer a practical method for widespread AD diagnosis and monitoring.⁷⁷

Combining the biomarker approaches above may enhance AD detection, and a few studies have explored this possibility. For example, Sharafi et al. combined HSI for Aβ detection alongside vascular measurements to report a higher tortuosity of retinal venules in Aβ+ patients; this combination approach, in particular, helped with differentiating Aβ+ patients from those of Aβ−.²¹⁸ Another study on a smaller scale by Lemmens et al. combined HSI with OCT imaging, concluding that combining these two retinal imaging modalities could discriminate between AD patients and controls.²²⁰ A recent pilot study uncovered that FLIO parameters correlated with Aβ in the CSF and GC-IPL retinal thickness on OCT. Aβ imaging via curcumin with vascular markers using a confocal scanning ophthalmoscope has also been explored suggesting that vascular changes did not correlate with Aβ deposition in the proximal mid-periphery; however, Aβ deposition did correlate with cognition.¹⁷⁶ Polarimetric imaging and OCT-A are promising in detecting retinal Aβ deposits and other AD pathologies.^241,242 This promising approach combines the detection and quantification of Aβ deposits and related pathologies via non-invasive retinal imaging with different pathological features identified through plasma-sensitive AD biomarkers, offering great promise for diagnostic screening, progression monitoring, and therapeutic efficacy evaluation.²⁴³

Successful validation of AD biomarkers will pave the way for validating retinal biomarkers, thus ensuring an affordable, non-invasive diagnostic approach to AD prevention.²⁴⁴ Prospective, longitudinal studies seek to identify possible patterns of early diagnosis or prognosis assessment and monitoring of AD (Table 2).

Table 2.

List of clinical trials investigating ocular changes in AD retina. Data acquired from ClinicalTrials.gov database.

Number	Study Title	Status	Interventions	Enrollment	Study Type	Start Date	End Date	Sponsor/ Collaborators/ Locations
NCT04567745	Automated Retinal Image Analysis System (EyeQuant) for Computation of Vascular Biomarkers	Completed	Diagnostic test: Retinal fundus photography	80	Interventional	2020-09-29	2021-09-30	Mayo Clinic; Eyenuk, Inc., Malavika Bhaskaranand, Kaushal Solanki, AZ, USA
NCT02360527	Retinal Neurodegeneration in Type 2 Diabetes as Biomarker for Alzheimer's Disease	Completed		126	Observational	2014-09	2015-12	Hospital Universitari Vall d'Hebron Research Institute, Barcelona, Spain
NCT01555827	Retinal Neurodegenerative Signs in Alzheimer's Diseases	Completed	Other: Ophthalmological examination and questionnaire	200	Interventional	2012-03-12	2014-06-07	University Hospital, Bordeaux, France
NCT02141971	Down Syndrome Biomarker Initiative (DSBI)	Completed		12	Observational	2013-06-01	2017-04-15	Michael Rafii; Janssen Research & Development, LLC, University of California, CA, USA
NCT03420807	Retinal Metabolic Imaging of Alzheimer's Patient	Completed	Device: MHRC	49	Interventional	2017-12-04	2020-03-12	Optina Diagnostics Inc., McGill University, Sunnybrook Health Sciences Centre, Toronto, Canada
NCT01937221	Novel Retinal Imaging Biomarkers in Early Alzheimer's Disease	Completed	Other: spectral-domain optical coherence tomography (SD-OCT)	60	Observational	2013-09	2017-07	Duke Institute for Brain Sciences, Alzheimer's Association, Duke-NUS Graduate Medical School, NC, USA
NCT05475158	Comparison of OCTA Factors in Patients with or Without Amyloid Pathology: A Prospective Study	Completed		117	Observational	2019-09-01	2021-07-31	Pusan National University Hospital, Korea
NCT04794634	Relationship Between Alzheimer Disease and Diminution of the Three Macular Nervous Retinal Layers	Completed	Diagnostic test: Optical coherence tomography (OCT), Optical coherence tomography angiography (OCTA)	55	Interventional	2021-03-09	2023-09-01	Centre Hospitalier Universitaire, Amiens, France
NCT02264899	MEMENTO-VAScular COmponents of Dementia	Completed	Other: in Memento-VASCOD	332	Interventional	2014-11-04	2021-10-29	CHU d'Amiens, Amiens, France\|CHU de Bordeaux - Pellegrin, Bordeaux, 33,000, France\|CHU de Dijon, Dijon, France\|CHU de Lille, Lille, France\|Hospices civils de Lyon, Lyon, France\|AP-HM, Marseille, France\|CHU de Montpellier, Montpellier, France\|AP-HP - HÃ´pital BROCA, Paris, France\|AP-HP - HÃ´pital LARIBOISIERE, Paris, France\|CHU de Strasbourg, Strasbourg, France
NCT06023446	Can (Optical Coherence Tomography) Pictures of the Retina Detect Alzheimer's Disease at Its Earliest Stages?	Enrolling by invitation	Diagnostic test: Optical Coherence Tomography	100	Observational	2023-09-11	2028-02-28	University of California, Davis; National Institute on Aging (NIA), CA, USA
NCT06022068	Evaluating Rapamycin Treatment in Alzheimer's Disease Using Positron Emission Tomography	Enrolling by invitation	Drug: Sirolimus	15	Interventional	2023-09-01	2025-01-31	Karolinska Institutet, Karolinska University Hospital, Stockholm, Sweden
NCT05007353	The SINgapore GERiatric Intervention Study to Reduce Cognitive Decline and Physical Frailty (SINGER) Study	Recruiting	Behavioral: Structured Lifestyle Intervention, Self-Guided Intervention	1200	Interventional	2021-08-23	2026-01-31	National University of Singapore, National Medical Research Council (NMRC), Yong Loo Lin School of Medicine, National Neuroscience Institute, Singapore
NCT03233646	Retinal Imaging in Neurodegenerative Disease	Recruiting	Device: Retinal and Choroidal Imaging	2000	Observational	2017-07-20	2025-12-31	Duke University NA, USA; University of Edinburgh, Scotland; Tan Tock Seng Hospital, Singapore; Queens University of Belfast, UK
NCT03466177	Multimodal Retinal Imaging in the Detection and Follow-up of Alzheimer's Disease	Recruiting	Diagnostic test: Non-invasive, multimodal retinal imaging	320	Observational	2018-03-01	2025-12-31	Universitaire Ziekenhuizen KU Leuven, Belgium
NCT05655650	Identifying Biomarkers in Alzheimer's Disease	Recruiting		300	Observational	2018-09-01	2030-12-31	Chinese University of Hong Kong, Hong Kong
NCT05886114	A Multi-domain Lifestyle Intervention Among Aged Community-residents in Zhejiang, China	Recruiting	Behavioral: Structured Multi-domain Intervention, Self-Guided Intervention	1200	Interventional	2023-05-28	2027-04-28	School of Public Health and The Second Affiliated Hospital of School of Medicine, Zhejiang University, Hangzhou, China
NCT05655793	The TearAD Study: Tear Biomarkers for Alzheimer's Disease (AD) Screening and Diagnosis	Recruiting	Diagnostic test: Tear Fluid collection (Schirmer's strip), Retinal imaging	200	Observational	2022-06-09	2025-07-01	Academic Hospital Maastricht, Amsterdam University Medical Center, Netherlands
NCT02524405	BEAM: Brain-Eye Amyloid Memory Study	Recruiting	Other: Pittsburgh Compound B [11C]-PIB	345	Observational	2016-02	2024-12	Sunnybrook Health Sciences Centre, St Michael's Hospital, University Health Network, Baycrest Health Sciences, Centre for Addiction and Mental Health (CAMH), Toronto, Canada
NCT03862222	Atlas of Retinal Imaging in Alzheimer's Study	Recruiting	Other: Retinal Imaging, Pupillometry, Contrast Sensitivity, Neuropsychological Evaluation, APOE genotyping, Blood draw, Gait Assessment, Actigraphy	330	Observational	2019-12-16	2025-06	Stuart Sinoff; Peter J Snyder; BayCare Health System; Morton Plant Hospital, FL, USA; St Anthony's Hospital, FL, USA; University of Rhode Island, FL, USA
NCT05067010	Finding Retinal Biomarkers in Alzheimer's Disease	Recruiting	Other: Detailed ophthalmologic examination	160	Observational	2021-06-10	2024-12-24	Assistance Publique - HÃ´pitaux de Paris; Centre MÃ^©moire de Ressources et de Recherche Paris Nord, France
NCT05457998	BioFINDER-Brown: Examination of Alzheimer's Disease Biomarkers	Recruiting	Diagnostic test: Flutemetamol F18 Injection, [18F]-RO6958948 Injection, [18F]-MK-6240 Injection	200	Observational	2023-06-14	2028-06	The Warren Alpert Foundation; Brown University; University of Rhode Island; Hoffmann-La Roche; Eli Lilly and Company; GE Healthcare; Butler Hospital Memory and Aging Program, Rhode Island, USA
NCT04277767	The Role of Ophthalmologic Tests and EEG Imaging in Alzheimer's Disease	Unknown	Diagnostic test: observational	60	Observational	2022-09-01	2023-07-01	Zhongshan Ophthalmic Center, Guangzhou, China
NCT04229186	Pilot Study About Extra Virgin Olive Oil “Coratina” in Mild Cognitive Impairment and Alzheimer's Disease Patients	Unknown	Dietary supplement: EVOO-C, ROO	24	Interventional	2020-01	2022-01	University of Bari Aldo Moro; Med & Food and Schena Foundation
NCT02051244	Measurement of Retinal Nerve Fiber Layer Thickness - a Biomarker for the Early Detection of Alzheimer's Disease?	Unknown		60	Observational	2014-02	2015-02	Carmel Medical Center, Haifa, Israel
NCT05977712	Circadian Rhythm and Other Factors in Memory Clinic Patients	Not yet recruiting	Other: Questionnaire, Clinical examination, Accelerometer port, Eye examination	1500	Observational	2024-01	2041-12	Assistance Publique - HÃ´pitaux de Paris; Institut National de la SantÃ^© Et de la Recherche MÃ^©dicale, France; Fondation Rothschild, France; Centre de neurologie Cognitive, France

Role of artificial intelligence and machine learning

Artificial intelligence (AI) is helpful in AD diagnosis using retinal imaging biomarkers²⁴¹ and Lin et al. (2021) have shown that AI may enable the screening of multiple retinal alterations in real-world settings.²⁴⁵ Characteristic retinal changes in AD can be detected using AI approaches such as machine learning and deep learning (DL).²⁴⁶ DL algorithms can detect complex patterns and features in retinal images, which can help identify AD-related retinal changes that may not be visible to the human eye.²⁴⁷

A multi-center, retrospective, case-control study by Cheung et al. identified a DL algorithm and its potential to detect AD-related dementia by analyzing retinal images.²⁴⁸ The data were gathered from 11 studies from various countries that recruited individuals with AD-related dementia and those without this condition. This study showed that the DL model could detect patients with eye diseases with an accuracy of 89.6% (with a standard deviation (SD) of 12.5%) and with an accuracy of 71.7% (SD 11.6%) for the ones without eye diseases.²⁴⁸ Kesu et al. (2025) developed advanced deep learning models, TransNetOCT and Swin Transformer, for classifying AD using retinal OCT images. TransNetOCT achieved an average accuracy of 98.18% for raw OCT images and 98.91% for segmented images in five-fold cross-validation, while the Swin Transformer model attained an accuracy of 93.54%. These results underscore the potential of deep learning techniques in enhancing the diagnostic capabilities of retinal imaging for AD.²⁴⁹ Further research in this area can be pivotal and help to detect and monitor AD.

Challenges and limitations of retinal biomarkers in clinical practice

Despite the research on retinal biomarkers, the challenges remain in interpreting and validating them.²⁴⁸ For example, HSI and curcumin fluorescence are promising techniques for identifying retinal Aβ, but they require further validation in larger study groups. Additionally, there are still problems in differentiating Aβ labeled with curcumin from background fluorescence and establishing consistent dosing and measurement methods.²⁴⁶ Similarly, changes in retinal reflectance spectra among individuals may cause difficulty in interpreting HSI results.²⁴⁶

Retinal biomarkers may be affected by variables such as age-related changes and comorbidities. Choroidal capillary bed loss in aging individuals has been observed using OCT-A.²⁵⁰ Elevated levels of Aβ have been observed in PRs, the RPE, adjacent capillaries and subretinal macrophages with age, both in human tissues and rodent models.²⁵¹ Haan et al. (2019) found that retinal thickness measured using OCT did not differ significantly between posterior cortical atrophy (PCA), typical AD, and control groups. Similarly, subgroup analyses with MRI and CSF biomarkers did not show significant differences in retinal thickness.²⁵² Moreover, multiple AD pathological facets intersect with AMD and glaucoma, including the accumulation of characteristic tau and Aβ in the retina.²⁵³ Aβ buildup is associated with approximately 40% of drusen and subretinal deposits in patients with AMD.^253–255 Animal and human postmortem investigations have shown elevated levels of neurotoxic Aβ aggregates in the retina, which align with the apoptosis of RGCs observed in glaucoma and AD.²⁵⁶ Further research is necessary to establish the clinical efficacy of the retinal biomarkers.

Conclusions

As an extension of CNS, the retina has the potential to provide insights into the early stages of AD. Recent research has revealed that AD has significant retinal alterations, including loss of RGCs, NFL thinning, changes in vasculature, neurodegeneration, and elevated Aβ and tau deposits. Earlier retinal changes may indicate the onset of AD. These retinal alterations mimic CNS pathology, thereby supporting the idea that the retina is a biomarker for AD.

However, significant gaps remain in the field that must be addressed. Currently, there is a pressing need to improve the diagnostic accuracy and specificity of retinal imaging and to advance its integration into routine clinical practice for AD diagnosis. Improvement of standardization across retinal imaging modalities is essential to ensure reproducibility and further reliable diagnostic accuracy for AD. Moreover, a deep understanding of multi-modal retinal biomarker integration will improve the potential for more personalized AD diagnostics, especially for early detection of the disease.

Collectively, these advancements have the potential to transform the way we diagnose and manage AD, as well as inform the design of future clinical trials. Future directions should prioritize large-scale clinical trials to improve diagnostic accuracy and specificity, ultimately revolutionizing our approaches towards the diagnosis and management of dementia and AD. With combined multi-modal and interdisciplinary approaches, the retina may serve as a critical tool in early diagnosis, disease monitoring, and personalized intervention for AD.

Footnotes

ORCID iDs

Surabhi D Abhyankar

Karis Little

Alan Stitt

Ashay D Bhatwadekar

Author contributions

Surabhi D. Abhyankar: Writing – original draft; Writing – review & editing

Karis Little: Writing – original draft; Writing – review & editing

Alan Stitt: Writing – review & editing

Ashay Dilip Bhatwadekar: Writing – review & editing

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The authors would like to acknowledge the funding support from the National Institute of Health (NIH)—National Eye Institute (NEI) grants, R01EY027779, R01EY027779-S1, and R01EY032080 to AB and a Challenge grant to the Department of Ophthalmology from Indiana University.

Declaration of conflicting interests

AB is an ad hoc District Support Pharmacist at CVS Health/Aetna and a consultant for the Office of Continuing Education and Professional Development, Purdue University College of Pharmacy. The contents of this study do not reflect those of CVS Health/Aetna. SA, KL, and AS do not have any conflicts to declare.

References

Bondi

Edmonds

Salmon

. Alzheimer's disease: past, present, and future. J Int Neuropsychol Soc 2017; 23: 818–831.

Breijyeh

Karaman

. Comprehensive review on Alzheimer's disease: causes and treatment. Molecules 2020; 25: 5789.

Silva

MVF

Loures

CMG

Alves

LCV

, et al. Alzheimer's disease: risk factors and potentially protective measures. J Biomed Sci 2019; 26: 33.

Ballard

Gauthier

Corbett

, et al. Alzheimer's disease. Lancet 2011; 377: 1019–1031.

Abubakar

Sanusi

Ugusman

, et al. Alzheimer's disease: an update and insights into pathophysiology. Front Aging Neurosci 2022; 14: 742408.

Knopman

Amieva

Petersen

, et al. Alzheimer disease. Nat Rev Dis Primers 2021; 7: 33.

Golde

. Alzheimer's disease - the journey of a healthy brain into organ failure. Mol Neurodegener 2022; 17: 18.

Thal

Rüb

Orantes

, et al. Phases of Aβ-deposition in the human brain and its relevance for the development of AD. Neurology 2002; 58: 1791–1800.

Montine

Phelps

Beach

, et al. National institute on aging–Alzheimer’s association guidelines for the neuropathologic assessment of Alzheimer’s disease: a practical approach. Acta Neuropathol 2012; 123: 1–11.

10.

Roh

Lee

. Recent updates on subcortical ischemic vascular dementia. J Stroke 2014; 16: 18–26.

11.

Iadecola

. The pathobiology of vascular dementia. Neuron 2013; 80: 844–866.

12.

Corrada

Brookmeyer

Paganini-Hill

, et al. Dementia incidence continues to increase with age in the oldest old: the 90 + study. Ann Neurol 2010; 67: 114–121.

13.

Ayodele

Rogaeva

Kurup

, et al.

Early-onset Alzheimer’s disease: what is missing in research?

Curr Neurol Neurosci Rep 2021; 21: 4.

14.

Tort-Merino

Falgàs

Allen

, et al. Early-onset Alzheimer's disease shows a distinct neuropsychological profile and more aggressive trajectories of cognitive decline than late-onset. Ann Clin Transl Neurol 2022; 9: 1962–1973.

15.

Cacace

Sleegers

Broeckhoven

. Molecular genetics of early-onset Alzheimer's disease revisited. Alzheimers Dement 2016; 12: 733–748.

16.

Liang

, et al. Clinical and genetic characteristics in a central-southern Chinese cohort of early-onset Alzheimer's disease. Front Neurol 2023; 14: 1119326.

17.

Park

Lee

, et al. Identification of PSEN1 and APP gene mutations in Korean patients with early-onset Alzheimer's disease. J Korean Med Sci 2008; 23: 213–217.

18.

Bagaria

Bagyinszky

SSA

. Genetics, functions, and clinical impact of Presenilin-1 (PSEN1) gene. Int J Mol Sci 2022; 23: 10970.

19.

Mendez

. Early-onset Alzheimer disease and its variants. Continuum (Minneap Minn) 2019; 25: 34–51.

20.

Tellechea

Pujol

Esteve-Belloch

, et al.

Early- and late-onset Alzheimer disease: are they the same entity?

Neurologia (Engl Ed) 2018; 33: 244–253.

21.

Wattmo

Wallin

ÅK

. Early- versus late-onset Alzheimer's disease in clinical practice: cognitive and global outcomes over 3 years. Alzheimers Res Ther 2017; 9: 70.

22.

van der Flier

Pijnenburg

Fox

, et al. Early-onset versus late-onset Alzheimer's disease: the case of the missing APOE ɛ4 allele. Lancet Neurol 2011; 10: 280–288.

23.

Boutajangout

Wısnıewskı

. The innate immune system in Alzheimer’s disease. Int J Cell Biol 2013; 2013: 576383.

24.

2023 Alzheimer's disease facts and figures. Alzheimers Dement 2023; 19: 1598–1695.

25.

Sando

Melquist

Cannon

, et al. APOE Ε4 lowers age at onset and is a high risk factor for Alzheimer's disease; A case control study from central Norway. BMC Neurol 2008; 8: 9.

26.

Rabinovici

. Late-onset Alzheimer disease. Continuum (Minneap Minn) 2019; 25: 14–33.

27.

Payami

Montee

Grimslid

, et al. Increased risk of familial late-onset Alzheimer's disease in women. Neurology 1996; 46: 12–129.

28.

Scheyer

Rahman

Hristov

, et al. Female sex and Alzheimer’s risk: the menopause connection. J Prev Alzheimers Dis 2018; 5: 225–230.

29.

Power

. Growing evidence links air pollution exposure to risk of Alzheimer's disease and related dementia. Brain 2020; 143: 8–10.

30.

Shi

Steenland

, et al. A national cohort study (2000–2018) of long-term air pollution exposure and incident dementia in older adults in the United States. Nat Commun 2021; 12: 6754.

31.

Babić Leko

Jurasović

Nikolac Perković

, et al. The association of essential metals with APOE genotype in Alzheimer's disease. J Alzheimers Dis 2021; 82: 661–672.

32.

Richardson

Roy

Shalat

, et al. Elevated serum pesticide levels and risk for Alzheimer disease. JAMA Neurol 2014; 71: 284–290.

33.

Tang

. Neuropathological mechanisms associated with pesticides in Alzheimer's disease. Toxics 2020; 8: 21.

34.

Santiago

Potashkin

. Biological and clinical implications of sex-specific differences in Alzheimer's disease. Handb Exp Pharmacol 2023; 282: 181–197.

35.

Wightman

Jansen

Savage

, et al. Largest GWAS (N=1,126,563) of Alzheimer’s disease implicates microglia and immune cells. medRxiv 2020: 2020.2011.2020.20235275 [Preprint]. Posted November 23, 2020.

36.

Uddin

Kabir

Al Mamun

, et al. APOE And Alzheimer’s disease: evidence mounts that targeting APOE4 may combat Alzheimer’s pathogenesis. Mol Neurobiol 2018; 56: 2450–2465.

37.

Yamazaki

Zhao

Caulfield

, et al. Apolipoprotein E and Alzheimer disease: pathobiology and targeting strategies. Nat Rev Neurol 2019; 15: 501–518.

38.

Rajan

Weuve

Barnes

, et al. Population estimate of people with clinical Alzheimer's disease and mild cognitive impairment in the United States (2020–2060). Alzheimers Dement 2021; 17: 1966–1975.

39.

Roses

. Apolipoprotein E alleles as risk factors in Alzheimer's disease. Annu Rev Med 1996; 47: 387–400.

40.

Husain

Laurent

Plourde

. APOE And Alzheimer’s disease: from lipid transport to physiopathology and therapeutics. Front Neurosci 2021; 15: 630502.

41.

Wang

, et al. Correlation analysis between APOE gene polymorphism and Alzheimer's disease. Int J Clin Exp Med 2018; 11: 3672–3678.

42.

Corder

Saunders

Strittmatter

, et al. Gene dose of apolipoprotein E type 4 allele and the risk of Alzheimer's disease in late onset families. Science 1993; 261: 921–923.

43.

Saunders

Strittmatter

Schmechel

, et al. Association of apolipoprotein E allele epsilon 4 with late-onset familial and sporadic Alzheimer's disease. Neurology 1993; 43: 1467–1472.

44.

Arendt

Schindler

Brückner

, et al. Plastic neuronal remodeling is impaired in patients with Alzheimer's disease carrying apolipoprotein epsilon 4 allele. J Neurosci 1997; 17: 516–529.

45.

Buttini

Shockley

, et al. Modulation of Alzheimer-like synaptic and cholinergic deficits in transgenic mice by human apolipoprotein E depends on isoform, aging, and overexpression of amyloid beta peptides but not on plaque formation. J Neurosci 2002; 22: 10539–10548.

46.

Sen

Alkon

Nelson

. Apolipoprotein E3 (ApoE3) but not ApoE4 protects against synaptic loss through increased expression of protein kinase C epsilon. J Biol Chem 2012; 287: 15947–15958.

47.

Gong

Gan

, et al. Apolipoprotein E isoform-specific regulation of dendritic spine morphology in apolipoprotein E transgenic mice and Alzheimer's disease patients. Neuroscience 2003; 122: 305–315.

48.

Wang

Wilson

Moore

, et al. Human apoE4-targeted replacement mice display synaptic deficits in the absence of neuropathology. Neurobiol Dis 2005; 18: 390–398.

49.

Blain

Sullivan

Poirier

. A deficit in astroglial organization causes the impaired reactive sprouting in human apolipoprotein E4 targeted replacement mice. Neurobiol Dis 2006; 21: 505–514.

50.

Zhong

Scearce-Levie

Ramaswamy

, et al. Apolipoprotein E4 domain interaction: synaptic and cognitive deficits in mice. Alzheimers Dement 2008; 4: 179–192.

51.

Efthymiou

Goate

. Late onset Alzheimer's disease genetics implicates microglial pathways in disease risk. Mol Neurodegener 2017; 12: 43.

52.

Moon

Herring

Zhao

. Clusterin: a multifaceted protein in the brain. Neural Regen Res 2021; 16: 1438–1439.

53.

Milinkeviciute

Green

. Clusterin/apolipoprotein J, its isoforms and Alzheimer's disease. Front Aging Neurosci 2023; 15: 1167886.

54.

Herring

Moon

Rawal

, et al. Brain clusterin protein isoforms and mitochondrial localization. Elife 2019; 8: e48255.

55.

Ando

Nagaraj

Küçükali

, et al. PICALM and Alzheimer's disease: an update and perspectives. Cells 2022; 11: 3994.

56.

Xiao

Gil

Yan

, et al. Role of phosphatidylinositol clathrin assembly lymphoid-myeloid leukemia (PICALM) in intracellular amyloid precursor protein (APP) processing and amyloid plaque pathogenesis. J Biol Chem 2012; 287: 21279–21289.

57.

Tan

. The role of PICALM in Alzheimer's disease. Mol Neurobiol 2015; 52: 399–413.

58.

Jonsson

Stefansson

Steinberg

, et al. Variant of TREM2 associated with the risk of Alzheimer's disease. N Engl J Med 2013; 368: 107–116.

59.

Kulkarni

Kumar

Cruz-Martins

, et al. Role of TREM2 in Alzheimer's disease: a long road ahead. Mol Neurobiol 2021; 58: 5239–5252.

60.

Hsu

Marshall

. Primary and secondary prevention trials in Alzheimer disease: looking back, moving forward. Curr Alzheimer Res 2017; 14: 426–440.

61.

Douaud

Refsum

Jager

, et al. Preventing Alzheimer’s disease-related gray matter atrophy by B-vitamin treatment. Proc Natl Acad Sci 2013; 110: 9523–9528.

62.

DeTure

Dickson

. The neuropathological diagnosis of Alzheimer’s disease. Mol Neurodegener 2019; 14: 32.

63.

Qiang

Duan

Miao

, et al. Identification of genetic markers and immune infiltration characteristics of Alzheimer’s disease through weighted gene co-expression network analysis. Front Neurol 2022; 13: 947781.

64.

Cummings

Osse

AML

Cammann

, et al. Anti-amyloid monoclonal antibodies for the treatment of Alzheimer’s disease. BioDrugs 2024; 38: 5–22.

65.

Lucey

Liu

Toedebusch

, et al. Suvorexant acutely decreases tau phosphorylation and aβ in the human CNS. Ann Neurol 2023; 94: 27–40.

66.

Lee

Slomkowski

Hefting

, et al. Brexpiprazole for the treatment of agitation in Alzheimer dementia: a randomized clinical trial. JAMA Neurol 2023; 80: 1307–1316.

67.

Mahabadi

Al Khalili

. Neuroanatomy, retina. In: StatPearls. Treasure Island (FL): StatPearls Publishing, 2024. PMID: 31424894. Bookshelf ID: NBK545310.

68.

Newman

Reichenbach

. The Müller cell: a functional element of the retina. Trends Neurosci 1996; 19: 307–312.

69.

Erskine

Herrera

. Connecting the retina to the brain. ASN Neuro 2014; 6: 1759091414562107.

70.

Yücel

Zhang

Weinreb

, et al. Effects of retinal ganglion cell loss on magno-, parvo-, koniocellular pathways in the lateral geniculate nucleus and visual cortex in glaucoma. Prog Retin Eye Res 2003; 22: 465–481.

71.

Jorge

Canário

Quental

, et al. Is the retina a mirror of the aging brain? Aging of neural retina layers and primary visual cortex across the lifespan. Front Aging Neurosci 2019; 11: 360.

72.

Zhao

Fan

, et al. Eye-brain connections revealed by multimodal retinal and brain imaging genetics. Nat Commun 2024; 15: 6064.

73.

Koronyo-Hamaoui

Koronyo

Ljubimov

, et al. Identification of amyloid plaques in retinas from Alzheimer's patients and noninvasive in vivo optical imaging of retinal plaques in a mouse model. Neuroimage 2011; 54: S204–S217.

74.

La Morgia

Ross-Cisneros

Koronyo

, et al. Melanopsin retinal ganglion cell loss in Alzheimer disease. Ann Neurol 2016; 79: 90–109.

75.

Shi

Koronyo

Fuchs

, et al. Retinal capillary degeneration and blood-retinal barrier disruption in murine models of Alzheimer’s disease. Acta Neuropathol Commun 2020; 8: 202.

76.

Shi

Koronyo

Fuchs

, et al. Retinal arterial Aβ40 deposition is linked with tight junction loss and cerebral amyloid angiopathy in MCI and AD patients. Alzheimers Dement 2023; 19: 5185–5197.

77.

Koronyo

Biggs

Barron

, et al. Retinal amyloid pathology and proof-of-concept imaging trial in Alzheimer’s disease. JCI Insight 2017; 2: e93621.

78.

Koronyo

Salumbides

Black

, et al. Alzheimer’s disease in the retina: imaging retinal Aβ plaques for early diagnosis and therapy assessment. Neurodegener Dis 2012; 10: 285–293.

79.

Dong

Gan

, et al. Amyloid beta deposition related retinal pigment epithelium cell impairment and subretinal microglia activation in aged APPswePS1 transgenic mice. Int J Ophthalmol 2018; 11: 747–755.

80.

Georgevsky

Retsas

Raoufi

, et al. A longitudinal assessment of retinal function and structure in the APP/PS1 transgenic mouse model of Alzheimer's disease. Transl Neurodegener 2019; 8: 30.

81.

Gupta

Chitranshi

Gupta

, et al. Amyloid β accumulation and inner retinal degenerative changes in Alzheimer's disease transgenic mouse. Neurosci Lett 2016; 623: 52–56.

82.

Lim

JKH

, et al. Retinal functional and structural changes in the 5xFAD mouse model of Alzheimer’s disease. Front Neurosci 2020; 14: 862.

83.

Zhang

Zhong

Han

, et al. Brain and retinal abnormalities in the 5xFAD mouse model of Alzheimer's disease at early stages. Front Neurosci 2021; 15: 681831.

84.

den Haan

Morrema

THJ

Verbraak

, et al. Amyloid-beta and phosphorylated tau in post-mortem Alzheimer's disease retinas. Acta Neuropathol Commun 2018; 6: 147.

85.

Troncoso

Knox

, et al. Beta-amyloid, phospho-tau and alpha-synuclein deposits similar to those in the brain are not identified in the eyes of Alzheimer's and Parkinson's disease patients. Brain Pathol 2014; 24: 25–32.

86.

Williams

McGuone

Frosch

, et al. Absence of Alzheimer disease neuropathologic changes in eyes of subjects with Alzheimer disease. J Neuropathol Exp Neurol 2017; 76: 376–383.

87.

Singh

Kotia

Ghosh

, et al. Curcumin modulates α-synuclein aggregation and toxicity. ACS Chem Neurosci 2013; 4: 393–407.

88.

Hart

Koronyo

Black

, et al. Ocular indicators of Alzheimer's: exploring disease in the retina. Acta Neuropathol 2016; 132: 767–787.

89.

Chiasseu

Alarcon-Martinez

Belforte

, et al. Tau accumulation in the retina promotes early neuronal dysfunction and precedes brain pathology in a mouse model of Alzheimer's disease. Mol Neurodegener 2017; 12: 58.

90.

Lacomme

Hales

Brown

, et al. Numb regulates tau levels and prevents neurodegeneration in tauopathy mouse models. Sci Adv 2022; 21: eabm4295.

91.

Arouche-Delaperche

Cadoni

Joffrois

, et al. Dysfunction of the glutamatergic photoreceptor synapse in the P301S mouse model of tauopathy. Acta Neuropathol Commun 2023; 11: 5.

92.

Fernández-Albarral

Salobrar-García

Matamoros

, et al. Microglial hemoxygenase-1 deletion reduces inflammation in the retina of old mice with tauopathy. Antioxidants (Basel) 2022; 11: 2151.

93.

Blanks

Schmidt

Torigoe

, et al. Retinal pathology in Alzheimer's disease. II. Regional neuron loss and glial changes in GCL. Neurobiol Aging 1996; 17: 385–395.

94.

Blanks

Torigoe

Hinton

, et al. Retinal degeneration in the macula of patients with Alzheimer's disease. Ann N Y Acad Sci 1991; 640: 44–46.

95.

Yoon

Grewal

Thompson

, et al. Retinal microvascular and neurodegenerative changes in Alzheimer’s disease and mild cognitive impairment compared with control participants. Ophthalmol Retina 2019; 3: 489–499.

96.

Esquiva

Hannibal

. Melanopsin-expressing retinal ganglion cells in aging and disease. Histol Histopathol 2019; 34: 1299–1311.

97.

, et al. Retinal biomarkers in Alzheimer's disease and mild cognitive impairment: a systematic review and meta-analysis. Ageing Res Rev 2021; 69: 101361.

98.

Kirbas

Turkyilmaz

Anlar

, et al. Retinal nerve fiber layer thickness in patients with Alzheimer disease. J Neuroophthalmol 2013; 33: 58–61.

99.

Asanad

Fantini

Sultan

, et al. Retinal nerve fiber layer thickness predicts CSF amyloid/tau before cognitive decline. PLoS One 2020; 15: e0232785.

100.

Carazo-Barrios

Archidona-Arranz

Claros-Ruiz

, et al. Correlation between retinal nerve fibre layer thickness and white matter lesions in Alzheimer's disease. Int J Geriatr Psychiatry 2021; 36: 935–942.

101.

Liu

Ong

Hilal

, et al. The association between retinal neuronal layer and brain structure is disrupted in patients with cognitive impairment and Alzheimer's disease. J Alzheimers Dis 2016; 54: 585–595.

102.

van de Kreeke

Nguyen

den Haan

, et al. Retinal layer thickness in preclinical Alzheimer's disease. Acta Ophthalmol 2019; 97: 798–804.

103.

Pillai

Bermel

Bonner-Jackson

, et al. Retinal nerve fiber layer thinning in Alzheimer's disease: a case-control study in comparison to normal aging, Parkinson's disease, and non-Alzheimer's dementia. Am J Alzheimers Dis Other Demen 2016; 31: 430–436.

104.

Shi

Zheng

, et al. Retinal nerve fiber layer thinning is associated with brain atrophy: a longitudinal study in nondemented older adults. Front Aging Neurosci 2019; 11: 69.

105.

Mathalone

Geyer

. Retinal nerve fiber layer thinning in advanced age related macular degeneration. Invest Ophthalmol Vis Sci 2007; 48: 5111.

106.

Bowd

Weinreb

Williams

, et al. The retinal nerve fiber layer thickness in ocular hypertensive, normal, and glaucomatous eyes with optical coherence tomography. Arch Ophthalmol 2000; 118: 22–26.

107.

Bsteh

Hegen

Teuchner

, et al. Peripapillary retinal nerve fibre layer thinning rate as a biomarker discriminating stable and progressing relapsing-remitting multiple sclerosis. Eur J Neurol 2019; 26: 865–871.

108.

Hinton

Sadun

Blanks

, et al. Optic-nerve degeneration in Alzheimer's disease. N Engl J Med 1986; 315: 485–487.

109.

Scheff

Price

Schmitt

, et al. Hippocampal synaptic loss in early Alzheimer's disease and mild cognitive impairment. Neurobiol Aging 2006; 27: 1372–1384.

110.

Jorge

Canário

Martins

, et al. The retinal inner plexiform synaptic layer mirrors grey matter thickness of primary visual cortex with increased Amyloid β load in early Alzheimer's disease. Neural Plast 2020; 2020: 8826087.

111.

Antes

Ezra-Elia

Weinberger

, et al. Apoe4 induces synaptic and ERG impairments in the retina of young targeted replacement apoE4 mice. PLoS One 2013; 8: e64949.

112.

Abhyankar

Luo

Hartman

, et al. Retinal dysfunction in APOE4 knock-in mouse model of Alzheimer's disease. Alzheimers Dement 2025; 21: e14433.

113.

Oliveira

Louzada

de Mello

, et al.

Amyloid-β decreases nitric oxide production in cultured retinal neurons: a possible mechanism for synaptic dysfunction in Alzheimer's disease?

Neurochem Res 2011; 36: 163–169.

114.

Jiang

Wei

Shi

, et al. Altered macular microvasculature in mild cognitive impairment and Alzheimer disease. J Neuroophthalmol 2018; 38: 292–298.

115.

Lahme

Esser

Mihailovic

, et al. Evaluation of ocular perfusion in Alzheimer's disease using optical coherence tomography angiography. J Alzheimers Dis 2018; 66: 1745–1752.

116.

Querques

Borrelli

Sacconi

, et al. Functional and morphological changes of the retinal vessels in Alzheimer's disease and mild cognitive impairment. Sci Rep 2019; 9: 63.

117.

Bulut

Kurtuluş

Gözkaya

, et al. Evaluation of optical coherence tomography angiographic findings in Alzheimer's type dementia. Br J Ophthalmol 2018; 102: 233–237.

118.

Zhang

Azhati

, et al. Retinal microvascular attenuation in mental cognitive impairment and Alzheimer's disease by optical coherence tomography angiography. Acta Ophthalmol 2020; 98: e781–e787.

119.

O'Bryhim

Apte

Kung

, et al. Association of preclinical Alzheimer disease with optical coherence tomographic angiography findings. JAMA Ophthalmol 2018; 136: 1242–1248.

120.

Zabel

Kaluzny

Wilkosc-Debczynska

, et al. Comparison of retinal microvasculature in patients with Alzheimer's disease and primary open-angle glaucoma by optical coherence tomography angiography. Invest Ophthalmol Vis Sci 2019; 60: 3447–3455.

121.

Pead

Thompson

Grewal

, et al. Retinal vascular changes in Alzheimer's dementia and mild cognitive impairment: a pilot study using ultra-widefield imaging. Transl Vis Sci Technol 2023; 12: 13.

122.

Csincsik

MacGillivray

Flynn

, et al. Peripheral retinal imaging biomarkers for Alzheimer's disease: a pilot study. Ophthalmic Res 2018; 59: 182–192.

123.

den Haan

van de Kreeke

van Berckel

, et al.

Is retinal vasculature a biomarker in amyloid proven Alzheimer's disease?

Alzheimers Dement (Amst) 2019; 11: 383–391.

124.

Mazza

Marano

Traversi

, et al. Primary cerebral blood flow deficiency and Alzheimer's disease: shadows and lights. J Alzheimers Dis 2011; 23: 375–389.

125.

Xie

Wang

, et al. A cross-sectional study of retinal vessel changes based on optical coherence tomography angiography in Alzheimer's disease and mild cognitive impairment. Front Aging Neurosci 2023; 15: 1101950.

126.

Szegedi

Dal-Bianco

Stögmann

, et al. Anatomical and functional changes in the retina in patients with Alzheimer's disease and mild cognitive impairment. Acta Ophthalmol 2020; 98: e914–e921.

127.

Campbell

Zhang

Hwang

, et al. Detailed vascular anatomy of the human retina by projection-resolved optical coherence tomography angiography. Sci Rep 2017; 7: 42201.

128.

Nazari

Karimaghaei

van der Merwe

, et al. Age dependence of retinal vascular plexus attenuation in the triple transgenic mouse model of Alzheimer's disease. Exp Eye Res 2022; 214: 108879.

129.

Buscho

Palacios

Xia

, et al. Longitudinal characterization of retinal vasculature alterations with optical coherence tomography angiography in a mouse model of tauopathy. Exp Eye Res 2022; 224: 109240.

130.

Rifai

McGrory

Robbins

, et al. The application of optical coherence tomography angiography in Alzheimer's disease: a systematic review. Alzheimers Dement (Amst) 2021; 13: e12149.

131.

Katsimpris

Papadopoulos

Voulgari

, et al. Optical coherence tomography angiography in Parkinson's disease: a systematic review and meta-analysis. Eye (London) 2023; 37: 2847–2854.

132.

Chua

, et al. Retinal microvasculature dysfunction is associated with Alzheimer's disease and mild cognitive impairment. Alzheimers Res Ther 2020; 12: 161.

133.

Robbins

Grewal

Thompson

, et al. Choroidal structural analysis in Alzheimer disease, mild cognitive impairment, and cognitively healthy controls. Am J Ophthalmol 2021; 223: 359–367.

134.

Zabel

Kaluzny

Zabel

, et al. Quantitative assessment of retinal thickness and vessel density using optical coherence tomography angiography in patients with Alzheimer's disease and glaucoma. PloS One 2021; 16: e0248284.

135.

Robbins

Grewal

Stinnett

, et al. Assessing the retinal microvasculature in individuals with early and late-onset Alzheimer's disease. Ophthalmic Surg Lasers Imaging Retina 2021; 52: 336–344.

136.

Wang

Liu

, et al. Macular microvascular density as a diagnostic biomarker for Alzheimer's disease. J Alzheimers Dis 2022; 90: 139–149.

137.

Moussa

Falfoul

Nasri

, et al. Optical coherence tomography and angiography in Alzheimer's disease and other cognitive disorders. Eur J Ophthalmol 2023; 33: 1706–1717.

138.

Yan

Wang

, et al. The retinal vessel density can reflect cognitive function in patients with Alzheimer's disease: evidence from optical coherence tomography angiography. J Alzheimers Dis 2021; 79: 1307–1316.

139.

Zhang

Zhou

Knoll

, et al. Parafoveal vessel loss and correlation between peripapillary vessel density and cognitive performance in amnestic mild cognitive impairment and early Alzheimer’s disease on optical coherence tomography angiography. Plos One 2019; 14: e0214685.

140.

Yoon

Thompson

Polascik

, et al. Correlation of OCTA and volumetric MRI in mild cognitive impairment and Alzheimer's disease. Ophthalmic Surg Lasers Imaging Retina 2019; 50: 709–718.

141.

Vandenabeele

Veys

Lemmens

, et al. The App(NL-G-F) mouse retina is a site for preclinical Alzheimer's disease diagnosis and research. Acta Neuropathol Commun 2021; 9: 6.

142.

Blanks

Torigoe

Hinton

, et al. Retinal pathology in Alzheimer's disease. I. Ganglion cell loss in foveal/parafoveal retina. Neurobiol Aging 1996; 17: 377–384.

143.

Grimaldi

Pediconi

Oieni

, et al. Neuroinflammatory processes, A1 astrocyte activation and protein aggregation in the retina of Alzheimer's disease patients, possible biomarkers for early diagnosis. Front Neurosci 2019; 13: 925.

144.

Nilson

English

Gerson

, et al. Tau oligomers associate with inflammation in the brain and retina of tauopathy mice and in neurodegenerative diseases. J Alzheimers Dis 2017; 55: 1083–1099.

145.

Edwards

Rodríguez

Gutierrez-Lanza

, et al. Retinal macroglia changes in a triple transgenic mouse model of Alzheimer's disease. Exp Eye Res 2014; 127: 252–260.

146.

Bringmann

Pannicke

Grosche

, et al. Müller cells in the healthy and diseased retina. Prog Retin Eye Res 2006; 25: 397–424.

147.

Abhyankar

Xiao

Mahajan

, et al. Müller glial Kir4.1 channel dysfunction in APOE4-KI model of Alzheimer’s disease. bioRxiv 2025: 2025.2002.2026.640427 [Preprint]. Posted February 28, 2025.

148.

Olabarria

Noristani

Verkhratsky

, et al.

Age-dependent decrease in glutamine synthetase expression in the hippocampal astroglia of the triple transgenic Alzheimer's disease mouse model: mechanism for deficient glutamatergic transmission?

Mol Neurodegener 2011; 6: 55.

149.

Kulijewicz-Nawrot

Syková

Chvátal

, et al. Astrocytes and glutamate homoeostasis in Alzheimer's disease: a decrease in glutamine synthetase, but not in glutamate transporter-1, in the prefrontal cortex. ASN Neuro 2013; 5: 273–282.

150.

Robinson

. Changes in the cellular distribution of glutamine synthetase in Alzheimer's disease. J Neurosci Res 2001; 66: 972–980.

151.

Le Prince

Delaere

Fages

, et al. Glutamine Synthetase (GS) expression is reduced in senile dementia of the Alzheimer type. Neurochem Res 1995; 20: 859–862.

152.

Boerkoel

Hirsch-Reinshagen

, et al. Müller cell degeneration and microglial dysfunction in the Alzheimer's retina. Acta Neuropathol Commun 2022; 10: 145.

153.

Tams

ALM

Sanz-Morello

Westi

, et al. Decreased glucose metabolism and glutamine synthesis in the retina of a transgenic mouse model of Alzheimer’s disease. Cell Mol Neurobiol 2022; 42: 291–303.

154.

Burbaeva

Boksha

Tereshkina

, et al. Glutamate metabolizing enzymes in prefrontal cortex of Alzheimer’s disease patients. Neurochem Res 2005; 30: 1443–1451.

155.

Tumani

Shen

Peter

, et al.

Glutamine synthetase in cerebrospinal fluid, serum, and brain: a diagnostic marker for Alzheimer disease?

Arch Neurol 1999; 56: 1241–1246.

156.

Koronyo

Rentsendorj

Mirzaei

, et al. Retinal pathological features and proteome signatures of Alzheimer's disease. Acta Neuropathol 2023; 145: 409–438.

157.

Grimaldi

Brighi

Peruzzi

, et al. Inflammation, neurodegeneration and protein aggregation in the retina as ocular biomarkers for Alzheimer's disease in the 3xTg-AD mouse model. Cell Death Dis 2018; 9: 685.

158.

Keren-Shaul

Spinrad

Weiner

, et al. A unique microglia type associated with restricting development of Alzheimer's disease. Cell 2017; 169: 1276–1290.e1217.

159.

Srinivasan

Friedman

Etxeberria

, et al. Alzheimer's patient microglia exhibit enhanced aging and unique transcriptional activation. Cell Rep 2020; 31: 107843.

160.

Gerrits

Brouwer

Kooistra

, et al. Distinct amyloid-β and tau-associated microglia profiles in Alzheimer's disease. Acta Neuropathol 2021; 141: 681–696.

161.

Shi

Yin

Koronyo

, et al. Regulating microglial miR-155 transcriptional phenotype alleviates Alzheimer’s-induced retinal vasculopathy by limiting Clec7a/Galectin-3 + neurodegenerative microglia. Acta Neuropathol Commun 2022; 10: 136.

162.

Iliff

Wang

Liao

, et al. A paravascular pathway facilitates CSF flow through the brain parenchyma and the clearance of interstitial solutes, including amyloid beta. Sci Transl Med 2012; 4: 147ra111.

163.

Peng

Achariyar

, et al. Suppression of glymphatic fluid transport in a mouse model of Alzheimer's disease. Neurobiol Dis 2016; 93: 215–225.

164.

Wang

Lou

Eberhardt

, et al. An ocular glymphatic clearance system removes β-amyloid from the rodent eye. Sci Transl Med 2020; 12: eaaw3210.

165.

Risacher

WuDunn

Pepin

, et al. Visual contrast sensitivity in Alzheimer’s disease, mild cognitive impairment, and older adults with cognitive complaints. Neurobiol Aging 2013; 34: 1133–1144.

166.

Salobrar-García

de Hoz

Ramírez

, et al. Changes in visual function and retinal structure in the progression of Alzheimer's disease. PloS One 2019; 14: e0220535.

167.

Stover

Brown

. Age-related changes in visual acuity, learning and memory in the APPswe/PS1dE9 mouse model of Alzheimer's disease. Behav Brain Res 2012; 231: 75–85.

168.

Criscuolo

Cerri

Fabiani

, et al. The retina as a window to early dysfunctions of Alzheimer's disease following studies with a 5xFAD mouse model. Neurobiol Aging 2018; 67: 181–188.

169.

Polo

Rodrigo

Garcia-Martin

, et al. Visual dysfunction and its correlation with retinal changes in patients with Alzheimer's disease. Eye (Lond) 2017; 31: 1034–1041.

170.

Vit

Fuchs

Angel

, et al. Color and contrast vision in mouse models of aging and Alzheimer's disease using a novel visual-stimuli four-arm maze. Sci Rep 2021; 11: 1255.

171.

Ascaso

Cruz

Modrego

, et al. Retinal alterations in mild cognitive impairment and Alzheimer's disease: an optical coherence tomography study. J Neurol 2014; 261: 1522–1530.

172.

Costanzo

Lengyel

Parravano

, et al. Ocular biomarkers for Alzheimer disease dementia: an umbrella review of systematic reviews and meta-analyses. JAMA Ophthalmol 2023; 141: 84–91.

173.

Ferrari

Huang

Magnani

, et al. Optical coherence tomography reveals retinal neuroaxonal thinning in frontotemporal dementia as in Alzheimer's disease. J Alzheimers Dis 2017; 56: 1101–1107.

174.

Matei

Leahy

Blair

, et al. Retinal vascular physiology biomarkers in a 5XFAD mouse model of Alzheimer's disease. Cells 2022; 11: 2413.

175.

Ferreira

Martins

Moreira

, et al. Longitudinal normative OCT retinal thickness data for wild-type mice, and characterization of changes in the 3×tg-AD mice model of Alzheimer's disease. Aging 2021; 13: 9433–9454.

176.

Dumitrascu

Rosenberry

Sherman

, et al. Retinal venular tortuosity jointly with retinal amyloid burden correlates with verbal memory loss: a pilot study. Cells 2021; 10: 2926.

177.

Habiba

Descallar

Kreilaus

, et al. Detection of retinal and blood Aβ oligomers with nanobodies. Alzheimers Dement (Amst) 2021; 13: e12193.

178.

Shi

Koronyo

Rentsendorj

, et al. Identification of early pericyte loss and vascular amyloidosis in Alzheimer's disease retina. Acta Neuropathol 2020; 139: 813–836.

179.

Gao

Chen

Tang

, et al. Neuroprotective effect of memantine on the retinal ganglion cells of APPswe/PS1ΔE9 mice and its immunomodulatory mechanisms. Exp Eye Res 2015; 135: 47–58.

180.

Cheung

Chan

VTT

Mok

, et al.

Potential retinal biomarkers for dementia: what is new?

Curr Opin Neurol 2019; 32: 82–91.

181.

Midena

Vujosevic

Convento

, et al. Microperimetry and fundus autofluorescence in patients with early age-related macular degeneration. Br J Ophthalmol 2007; 91: 1499–1503.

182.

Kayabasi

Sergott

Rispoli

. Retinal examination for the diagnosis of Alzheimer's disease. Int J Ophthal Pathol 2014; 2014: 1–4.

183.

Berisha

Feke

Trempe

, et al. Retinal abnormalities in early Alzheimer’s disease. Invest Opthalmol Vis Sci 2007; 48: 2285–2289.

184.

Jin

, et al. Telemedicine screening of retinal diseases with a handheld portable non-mydriatic fundus camera. BMC Ophthalmol 2017; 17: 89.

185.

Toslak

Ayata

Liu

, et al. Wide-field smartphone fundus video camera based on miniaturized indirect ophthalmoscopy. Retina 2018; 38: 438–441.

186.

Fujimoto

Pitris

Boppart

, et al. Optical coherence tomography: an emerging technology for biomedical imaging and optical biopsy. Neoplasia 2000; 2: 9–25.

187.

Uchida

Pillai

Bermel

, et al. Outer retinal assessment using spectral-domain optical coherence tomography in patients with Alzheimer's and Parkinson's disease. Invest Opthalmol Vis Sci 2018; 59: 2768–2777.

188.

Thomson

Yeo

Waddell

, et al. A systematic review and meta-analysis of retinal nerve fiber layer change in dementia, using optical coherence tomography. Alzheimers Dement (Amst) 2015; 1: 136–143.

189.

Sánchez

Castilla-Marti

Marquié

, et al. Evaluation of macular thickness and volume tested by optical coherence tomography as biomarkers for Alzheimer's disease in a memory clinic. Sci Rep 2020; 10: 1580.

190.

Parisi

Restuccia

Fattapposta

, et al. Morphological and functional retinal impairment in Alzheimer's disease patients. Clin Neurophysiol 2001; 112: 1860–1867.

191.

Muthy

Gallacher

, et al. Association of retinal nerve fiber layer thinning with current and future cognitive decline: a study using optical coherence tomography. JAMA Neurol 2018; 75: 1198–1205.

192.

Chang

LYL

Lowe

Ardiles

, et al. Alzheimer's disease in the human eye. Clinical tests that identify ocular and visual information processing deficit as biomarkers. Alzheimers Dement 2014; 10: 251–261.

193.

Cronin-Golomb

. Visuospatial function in Alzheimer's disease and related disorders. In: Mostofsky

Budson

Kowall

(eds) The handbook of Alzheimer's disease and other dementias. Chichester, UK: Blackwell Publishing Ltd, 2011, pp.457–482.

194.

Tzekov

Mullan

. Vision function abnormalities in Alzheimer disease. Surv Ophthalmol 2014; 59: 414–433.

195.

Wong

Koizumi

Lai

. Enhanced depth imaging optical coherence tomography. Ophthalmic Surg Lasers Imaging 2011; 42: S75–S84.

196.

Cunha

Proença

Dias-Santos

, et al. Choroidal thinning: Alzheimer's disease and aging. Alzheimers Dement (Amst) 2017; 8: 11–17.

197.

Mejia-Vergara

Restrepo-Jiménez

Pelak

. Optical coherence tomography in mild cognitive impairment: a systematic review and meta-analysis. Front Neurol 2020; 11: 578698.

198.

Paulose

Chhablani

. Optical coherence tomography angiography. Kerala J Ophthalmol 2016; 28: 158–163.

199.

Katsimpris

Karamaounas

Sideri

, et al. Optical coherence tomography angiography in Alzheimer’s disease: a systematic review and meta-analysis. Eye (Lond) 2022; 36: 1419–1426.

200.

Pellegrini

Vagge

Desideri

, et al. Optical coherence tomography angiography in neurodegenerative disorders. J Clin Med 2020; 9: 1706.

201.

Lei

Ruoxin

, et al. A systematic review and meta-analysis of retinal microvascular features in Alzheimer's disease. Front Aging Neurosci 2021; 13: 683824.

202.

Abraham

Guo

Arsiwala

, et al.

Cognitive decline in older adults: what can we learn from optical coherence tomography (OCT)-based retinal vascular imaging?

J Am Geriatr Soc 2021; 69: 2524–2535.

203.

Ibrahim

Macerollo

Sardone

, et al. Retinal microvascular density and inner thickness in Alzheimer's disease and mild cognitive impairment. Front Aging Neurosci 2025; 17: 1477008.

204.

Jin

Wang

Lang

, et al. Retinal optical coherence tomography intensity spatial correlation features as new biomarkers for confirmed Alzheimer's disease. Alzheimers Res Ther 2025; 17: 33.

205.

Tsujikawa

Ogura

. Evaluation of leukocyte-endothelial interactions in retinal diseases. Ophthalmologica 2012; 227: 68–79.

206.

Shirazi

Andilla

Lefaudeux

, et al. Multi-modal and multi-scale clinical retinal imaging system with pupil and retinal tracking. Sci Rep 2022; 12: 9577.

207.

Pircher

Kroisamer

Felberer

, et al. Temporal changes of human cone photoreceptors observed in vivo with SLO/OCT. Biomed Opt Express 2010; 2: 110–112.

208.

Medeiros

Zangwill

Bowd

, et al. Comparison of the GDx VCC scanning laser polarimeter, HRT II ConfocalScanning laser ophthalmoscope, and stratus OCT optical coherence tomographfor the detection of glaucoma. Arch Ophthalmol 2004; 122: 827–837.

209.

Zawadzki

Zhang

Zam

, et al. Adaptive-optics SLO imaging combined with widefield OCT and SLO enables precise 3D localization of fluorescent cells in the mouse retina. Biomed Opt Express 2015; 6: 2191–2210.

210.

Sidiqi

Wahl

Lee

, et al. In vivo retinal fluorescence imaging with curcumin in an Alzheimer mouse model. Front Neurosci 2020; 14: 713.

211.

Swanson

King

Burns

. Interpreting retinal nerve fiber layer reflectance defects based on presence of retinal nerve fiber bundles. Optom Vis Sci 2021; 98: 531–541.

212.

Mainster

Desmettre

Querques

, et al. Scanning laser ophthalmoscopy retroillumination: applications and illusions. Int J Retina Vitreous 2022; 8: 71.

213.

Beach

Rizvi

Lichtenfels

, et al. Topical review: studies of ocular function and disease using hyperspectral imaging. Optom Vis Sci 2022; 99: 101–113.

214.

Pallua

Brunner

Zelger

, et al. New perspectives of hyperspectral imaging for clinical research. NIR News 2021; 32: 5–13.

215.

Hadoux

Hui

Lim

JKH

, et al. Non-invasive in vivo hyperspectral imaging of the retina for potential biomarker use in Alzheimer’s disease. Nat Commun 2019; 10: 4227.

216.

Shi

Koronyo

Rentsendorj

, et al. Retinal vasculopathy in Alzheimer’s disease. Front Neurosci 2021; 15: 731614.

217.

Vince

. Hyperspectral imaging signatures detect amyloidopathy in Alzheimer’s mouse retina well before onset of cognitive decline. ACS Chem Neurosci 2015; 6: 306–315.

218.

Sharafi

Sylvestre

Chevrefils

, et al. Vascular retinal biomarkers improves the detection of the likely cerebral amyloid status from hyperspectral retinal images. Alzheimers Dement (N Y) 2019; 5: 610–617.

219.

Beach

McClelland

, et al. In vivo assessment of retinal biomarkers by hyperspectral imaging: early detection of Alzheimer’s disease. ACS Chem Neurosci 2019; 10: 4492–4501.

220.

Lemmens

Van Craenendonck

Van Eijgen

, et al. Combination of snapshot hyperspectral retinal imaging and optical coherence tomography to identify Alzheimer’s disease patients. Alzheimers Res Ther 2020; 12: 144.

221.

Gupta

Anitha

Hegde

, et al.

Aluminium in Alzheimer’s disease: are we still at a crossroad?

Cell Mol Life Sci 2005; 62: 143–158.

222.

Balas

Ramalingam

Pandya

, et al. Adaptive optics imaging in ophthalmology: redefining vision research and clinical practice. JFO Open Ophthalmol 2024; 7: 100116.

223.

Hampson

Turcotte

Miller

, et al. Adaptive optics for high-resolution imaging. Nat Rev Methods Primers 2021; 1: 68.

224.

Lombardo

Serrao

Devaney

, et al. Adaptive optics technology for high-resolution retinal imaging. Sensors (Basel) 2012; 13: 334–366.

225.

Abozaid

. The use of adaptive optics for retinal imaging with microscopic resolution. J Egyptian Ophthalmol Soc 2016; 109: 145–152.

226.

Dysli

Wolf

Berezin

, et al. Fluorescence lifetime imaging ophthalmoscopy. Prog Retin Eye Res 2017; 60: 120–143.

227.

Bernstein

Dysli

Fischer

, et al. Fluorescence lifetime imaging ophthalmoscopy (FLIO). In: Bille

(ed.) High resolution imaging in microscopy and ophthalmology: new frontiers in biomedical optics. Cham: Springer, 2019, pp.213–235.

228.

Jentsch

Schweitzer

Schmidtke

, et al. Retinal fluorescence lifetime imaging ophthalmoscopy measures depend on the severity of Alzheimer's disease. Acta Ophthalmol (Copenh) 2015; 93: e241–e247.

229.

Sadda

Borrelli

Fan

, et al. A pilot study of fluorescence lifetime imaging ophthalmoscopy in preclinical Alzheimer’s disease. Eye 2019; 33: 1271–1279.

230.

Feeks

Hunter

. Adaptive optics two-photon excited fluorescence lifetime imaging ophthalmoscopy of exogenous fluorophores in mice. Biomed Opt Express 2017; 8: 2483–2495.

231.

Young

Moinuddin

Khandwala

, et al. Exploring retinal imaging as a novel biomarker of Alzheimer’s disease. Alzheimers Dement 2020; 16: e044895.

232.

Ringel

Tang

Tao

. Advances in multimodal imaging in ophthalmology. Ther Adv Ophthalmol 2021; 13: 25158414211002400.

233.

Krafft

Senée

Gofas

, et al. Multimodal high-resolution retinal imaging using a camera-based DMD-integrated adaptive optics flood-illumination ophthalmoscope. Opt Lett 2023; 48: 3785–3788.

234.

Moons

Groef

. Multimodal retinal imaging to detect and understand Alzheimer’s and Parkinson’s disease. Curr Opin Neurobiol 2021; 72: 1–7.

235.

Ravichandran

Snyder

Alber

, et al. Association and multimodal model of retinal and blood-based biomarkers for detection of preclinical Alzheimer's disease. Alzheimers Res Ther 2025; 17: 19.

236.

Kozák

Shoughy

Arevalo

. Update on wide- and ultra-widefield retinal imaging. Indian J Ophthalmol 2015; 63: 575–581.

237.

Glanc

Gendron

Lacombe

, et al. Towards wide-field retinal imaging with adaptive optics. Optics Commun 2004; 230: 225–238.

238.

Csincsik

Quinn

Yong

KXX

, et al. Retinal phenotyping of variants of Alzheimer's disease using ultra-widefield retinal images. Alzheimers Dement (Amst) 2021; 13: e12232.

239.

Hao

Zhang

Jiao

, et al. Correlation between retinal structure and brain multimodal magnetic resonance imaging in patients with Alzheimer’s disease. Front Aging Neurosci 2023; 15: 1088829.

240.

Bevan

Hughes

Williams

, et al. Retinal ganglion cell degeneration correlates with hippocampal spine loss in experimental Alzheimer’s disease. Acta Neuropathol Commun 2020; 8: 216.

241.

Snyder

Alber

Alt

, et al. Retinal imaging in Alzheimer's and neurodegenerative diseases. Alzheimers Dement 2021; 17: 103–111.

242.

López-Cuenca

Salobrar-García

Elvira-Hurtado

, et al. The value of OCT and OCTA as potential biomarkers for preclinical Alzheimer’s disease: a review study. Life (Basel) 2021; 11: 712.

243.

Mirzaei

Shi

Oviatt

, et al. Alzheimer’s retinopathy: seeing disease in the eyes. Front Neurosci 2020; 14: 921.

244.

Alber

Goldfarb

Thompson

, et al. Developing retinal biomarkers for the earliest stages of Alzheimer's disease: what we know, what we don't, and how to move forward. Alzheimers Dement 2020; 16: 229–243.

245.

Lin

Xiong

Liu

, et al. Application of comprehensive artificial intelligence retinal expert (CARE) system: a national real-world evidence study. Lancet Digit Health 2021; 3: e486–e495.

246.

Tang

Blazes

Lee

. Imaging amyloid and tau in the retina: current research and future directions. J Neuroophthalmol 2023; 43: 168–179.

247.

Wang

Deng

, et al. Deep learning for quality assessment of retinal OCT images. Biomed Opt Express 2019; 10: 6057–6072.

248.

Cheung

Ran

Wang

, et al. A deep learning model for detection of Alzheimer's disease based on retinal photographs: a retrospective, multicentre case-control study. Lancet Digit Health 2022; 4: e806–e815.

249.

Kesu

SMR

Sinha

Ramasangu

, et al. Alzheimer's disease classification using retinal OCT: TransnetOCT and swin transformer models. arXiv 2025, preprint arXiv:250311511.

250.

Camino

Guo

You

, et al. Detecting and measuring areas of choriocapillaris low perfusion in intermediate, non-neovascular age-related macular degeneration. Neurophotonics 2019; 6: 041108.

251.

Hoh Kam

Lenassi

Jeffery

. Viewing ageing eyes: diverse sites of amyloid Beta accumulation in the ageing mouse retina and the up-regulation of macrophages. PloS One 2010; 5: e13127.

252.

den Haan

Csinscik

Parker

, et al. Retinal thickness as potential biomarker in posterior cortical atrophy and typical Alzheimer’s disease. Alzheimers Res Ther 2019; 11: 62.

253.

Gupta

Chitranshi

, et al. One protein, multiple pathologies: multifaceted involvement of amyloid β in neurodegenerative disorders of the brain and retina. Cell Mol Life Sci 2016; 73: 4279–4297.

254.

Curcio

. Soft drusen in age-related macular degeneration: biology and targeting via the oil spill strategies. Invest Ophthalmol Vis Sci 2018; 59: AMD160–AMD181.

255.

Zhao

Wei

, et al. The enhancement of unipolar resistive switching behavior via an amorphous TiOx layer formation in Dy2O3-based forming-free RRAM. Solid State Electron 2013; 89: 12–16.

256.

Gupta

You

, et al. BDNF Impairment is associated with age-related changes in the inner retina and exacerbates experimental glaucoma. Biochim Biophys Acta 2014; 1842: 1567–1578.