The Contribution of Ocular Biomarkers in the Differential Diagnosis of Alzheimer’s Disease versus Other Types of Dementia and Future Prospects

Abstract

With dementia becoming increasingly prevalent, there is a pressing need to become better equipped with accurate diagnostic tools that will favorably influence its course via prompt and specific intervention. The overlap in clinical manifestation, imaging, and even pathological findings between different dementia syndromes is one of the most prominent challenges today even for expert physicians. Since cerebral microvasculature and the retina share common characteristics, the idea of identifying potential ocular biomarkers to facilitate diagnosis is not a novel one. Initial efforts included studying less quantifiable parameters such as aspects of visual function, extraocular movements, and funduscopic findings. However, the really exciting prospect of a non-invasive, safe, fast, reproducible, and quantifiable method of pinpointing novel biomarkers has emerged with the advent of optical coherence tomography (OCT) and, more recently, OCT angiography (OCTA). The possibility of analyzing multiple parameters of retinal as well as retinal microvasculature variables in vivo represents a promising opportunity to investigate whether specific findings can be linked to certain subtypes of dementia and aid in their earlier diagnosis. The existing literature on the contribution of the eye in characterizing dementia, with a special interest in OCT and OCTA parameters will be reviewed and compared, and we will explicitly focus our effort in advancing our understanding and knowledge of relevant biomarkers to facilitate future research in the differential diagnosis between Alzheimer’s disease and common forms of cognitive impairment, including vascular dementia, frontotemporal dementia, and dementia with Lewy bodies.

Keywords

Alzheimer’s disease biomarkers dementia optical coherence tomography retina

INTRODUCTION

With dementia rates quickly rising to pandemic status in an increasingly older population [1], there is an urgent need for the scientific community to address the issue of early and accurate diagnosis. Sensitive biomarkers of AD aiming at detecting amyloid-β (Aβ) and tau pathology are already in use, chiefly in the forms of cerebrospinal fluid (CSF) testing and positron emission tomography (PET) brain imaging, but their cost, invasiveness and complexity make them less applicable to everyday clinical practice [2].

The concept of studying the eye, which represents a veritable extension of the brain, is naturally not a novel one. Several structural and functional characteristics have thus already been studied, although with questionable clinical relevance. It is through the advent of optical coherence angiography (OCT) and the powerful complementary technology of OCT angiography (OCTA) in recent years, however, that the possibility of isolating quantifiable and reproducible retinal biomarkers has surfaced. OCT uses reflected light to compose a three-dimensional image of ocular tissues of either the anterior segment or the fundus of the eye in great resolution, while automatic layer segmentation of the retina is possible. OCTA software gives the additional option of noninvasively and dynamically imaging and quantifying the vascularization of the retina. OCT and OCTA technology has proven to be among the most significant advancements in modern ophthalmology [3]. Chan et al. [4] have even proposed a protocol to standardize the way OCT scans can be obtained, analyzed, and quantified for future studies. The applications of the aforementioned modalities in the field of neurology are quickly becoming evident as well, as great promise is shown in the investigation of several neurological conditions, revealing possible correlations to underlying pathology. It is thus with optimism that the utilization of OCT/OCTA in the search for dementia biomarkers is moving forward [3].

In this literature review, we will venture to concentrate all relevant and important evidence on the information that the ocular examination can provide to aid in the diagnosis of Alzheimer’s disease (AD), vascular dementia (VaD), Lewy body dementia (LBD), Parkinson’s disease dementia (PDD), and frontotemporal dementia (FTD). Special interest will be directed towards identifying potential OCT and OCTA biomarkers which might facilitate the differential diagnosis between these types of dementia, with the goal to elucidate correlations which may have emerged from current research and guide future studies in this exciting domain. Studies using spectral-domain OCT (SD-OCT) and swept-source OCT (SS-OCT) have been included in this review.

ALZHEIMER’S DISEASE

AD is diagnosed in an estimated 80% demented patients, making it by far the most common dementia syndrome [5]. AD is more common in individuals of 65 years of age or older and, in view of the incessant rise in life expectancy, it can be foreseen that it might soon become virtually pandemic [1].

Tauopathy and Aβ deposition are the main cau-sative pathologies recognized in current literature, while its diagnosis, although supported by imaging and various bodily fluid markers and definitely confirmed post-mortem, to this day remains mainly clinical [5]. There is a poorly understood genetic component predisposing for the disease both in her-editary and in sporadic cases, which represent the vast majority. In the latter, apolipoprotein E (APOE) ɛ4 alleles of the APOE gene is the main culprit identified [1], which, intriguingly, also plays a role in the development of age-related macular degeneration [6]. The clinical spectrum of AD, however, is broad and inconsistent, while adding to the difficulty of reaching an accurate clinical diagnosis are the facts that it does not rarely coexist with other causes of neurocognitive decline, as well as that its manifestations can be significantly misleading when younger individuals are afflicted [1].

The course of AD is a complex one, with the clinical picture of dementia likely representing just the tip of the iceberg. While the classical diagnostic scheme for AD largely depends on a well-established clinical picture, there has recently been a shift toward recognizing early processes that take place before they result in dementia and incorporating these in the diagnostic criteria. In DSM-IV, AD is regarded as a syndrome which, depending on the level of the clinician’s certainty in its diagnosis, is classified as either probable, possible or definite [7]. The term mild cognitive impairment (MCI) has been used for patients displaying a level of impaired cognition, mostly affecting memory, of an intermediate severity between healthy controls and AD sufferers [8] and is a prodromal of AD [9]. However, taking into account modern developments in our understanding and sensitive modalities in the diagnosis of the disease, Dubois et al. [10] challenged this by introducing a proposed set of criteria in which, among other changes, the verification of at least one specific biomarker is necessary for diagnosis. Namely, these biomarkers include magnetic resonance imaging (MRI) findings of atrophy, predominantly of the hippocampus, amygdala, or entorhinal cortex, CSF protein biomarkers, PET findings, and specific gene mutations [7, 10]. This new perspective did prove to successfully identify prodromal stages of the disease more efficiently [7], and thus the notion that sensitive biomarkers, able to distinguish AD from other conditions rather than making it a diagnosis of exclusion, as well as from other types of dementia, are sine qua non in the future of treating AD patients, is strongly supported. These will hopefully enable the physician to take decisions modifying the disease course before the patient has already reached the stage of severe mental impairment.

The eye has been studied in AD with the hope that more disease-specific and cost-effective and less invasive biomarkers might surface. Several aspects of visual function seem to be affected in AD patients, notably contrast sensitivity, color perception, visual attention, and visual memory [11], and this impairment was found to correlate with the severity of cognitive dysfunction as well as negatively affect patients’ performance in some cognitive tasks [12]. What is more, visual and cognitive disability seem to be strongly correlated. The recognition of posterior cortical atrophy, or “visual variant”, as a rare form of AD, implies that this might represent the extreme of a spectrum in which the visual cortex is implicated to a varying degree in all AD patients [11]. There has been some interest in the study of numerous parameters of ocular motility, mostly focusing on saccadic eye movement impairment in AD, and studies have reported a correlation between such impairment and the level of cognitive disability [13, 14]. Ocular surface and anterior segment potential biomarkers have also been researched in recent literature, because of their accessibility and ease of sample collection. The study of tear film and tears, which are protein-rich, in AD patients, has led to the isolation of lipocalin-1, dermcidin, lysozyme C, and lacritin, as well as the elongation initiation factor 4E (eIF4E), as potent candidates as AD biomarkers, providing very satisfyingly specific and sensitive results [15]. Total microRNA content in tears was significantly increased in AD patients according to a study, and micoRNA-200b-5p in particular displayed significantly higher levels versus controls, adding it to the aforementioned list of potential novel markers [16]. Accumulation of Aβ, especially of the Aβ_1–40 and Aβ_1–42 isoforms, paralleling the changes found in cerebral tissue, has been found in the crystalline lens of the eye [15]. A technique applying laser scanning to quantify such changes in the lens in vivo was even developed and yielded promising results [17], although the high prevalence of cataract and the need for lens extraction in older individuals unfortunately undermines the usefulness of these findings. Amyloid-β protein precursor (AβPP) and β-secretase 1 (BACE1) have been identified in the hu-man corneal epithelium and corneal fibroblasts, a fin-ding confirming similar earlier findings in animal models [15]. Pathologic changes observed in the ret-ina of AD subjects include amyloid plaques and high levels of Aβ_1–42, in the noninvasive identification of which hyperspectral imaging microscopy can aid [15], as well as decreased levels of melanopsin retinal ganglion cells with Aβ accumulated around these cells [18].

Patterns revealed through fundus photography in AD include retinopathy, decreased central retinal ar-tery equivalent, a measure of arteriolar widening, ven-ular narrowing, increased or decreased central retinal vein equivalent and vessel tortuosity, according to the study, and a decrease in retinal fractal dimension, which is translated as scant retinal vasculature [19]. Virtually all OCT and OCTA parameters have been studied in patients with AD in numerous studies, with promising and mostly conclusive results. Peripapillary RNFL thickness is reduced in those patients in all quadrants, with the decrease more pronounced temporally, while no safe conclusion can be drawn for the same parameter in MCI patients [20]. It is also suggested that an association exists between thinner peripapillary retinal nerve fiber layer (RNFL) and prevalent cognitive decline and AD [2 , 22]. RNFL values, however, did not seem to correlate with brain imaging in AD subjects [23]. It has been reported that macular RNFL is significantly thinner as well, but some disparity is observed in study findings when this region is examined in both in AD and MCI patients. Alber et al. argue that the diagnostic value of this parameter may be limited as it represents a thin layer which can also be affected by vitreoretinal interaction [28]. A thinning in peripapillary RNFL was found in MCI patients as well, implying a potential value as a biomarker in prodromal stages [24]. Ganglion cell-inner plexiform layer (GC-IPL), but not RNFL thickness, were found to predict cognitive scores in AD patients [25]. A decreased GC-IPL thickness has been noted in several studies, while the same region also displays a degree of thinning in MCI, although not always statistically significant. Studies examining the ganglion cell complex (GCC) of persons with AD separately have found it to be significantly thinner as well [20]. Measurements of macular thickness and macular volume, defined as the additive thickness value of all nine sectors in the macular OCT scan [26], also showed a thinning in AD versus healthy controls [20], while a correlation between parietal cortical thickness in AD and macular thickness has been observed [27]. Choroidal thickness has been investigated as a biomarker and was indeed found to be reduced in MCI and AD [20, 28]. More recent studies focusing on OCTA parameters reported intriguing associations as well. Vessel density (VD) and perfusion density (PD) of the superficial capillary plexus (SCP) have significantly lower values in the AD group compared to cognitively normal controls and MCI patients alike, although the MCI group failed to show a significant difference in the same parameters compared to normals [29, 30]. Another study also found capillary networks to be scarcer in both AD and MCI patients, with more pronounced changes observed in the deep retinal capillary plexus (DRCP) [31]. VD might be important in identifying preclinical AD, as a recent study demonstrated that higher values in this parameter was correlated with Aβ positivity in PET scans [32]. Significantly, reduced flow density in the superficial retinal plexus and in the peripapillary region found in AD sufferers was shown to correlate with vascular white matter brain lesions, underlining the concomitant vascular component in AD pathophysiology [33]. There is some discordance among researchers’ findings about changes in peripapillary vascularity, reflected by the radial peripapillary capillary (RPC) density value, in AD patients, with most studies reporting a decreased capillary density [23]. Finally, the foveal avascular zone (FAZ) seems to display significant enlargement in AD versus cognitively healthy subjects, making this a biomarker also worthy of consideration in the study of dementia [30].

VASCULAR DEMENTIA

VaD is the second leading cause of dementia after AD [23, 34], with a prevalence of 15–30% of all cases [35]. It represents a neurocognitive disorder of vascular cause and great heterogeneity, the diagnosis of which requires the exclusion of other conditions [36]. Cognitive decline has been shown to correlate with white matter lesions, microinfarcts, lacunar infarcts, and larger thromboembolic infarcts [37]. Although a pure vascular etiology is rare when dementia is dia-gnosed, up to 75% of dementia patients have a mixed form where vascular pathology is detected either clinically or postmortem [35]. Its hallmarks include a stepwise cognitive decline, a history positive for numerous strokes, focal neurological signs and un-equal distribution of cognitive impairment along with corresponding neuroimaging evidence of cerebrovascular pathology [35, 37]. It can result in diffuse as well as focal brain atrophy as in patients with AD [34].

VaD has recently been categorized under the um-brella term vascular cognitive impairment and dem-entia, along with vascular cognitive impairment, subcortical VaD, and vascular cognitive disorder [37]. The most common underlying pathophysiology in VaD is cerebral small vessel disease (CSVD), an un-derlying factor which becomes more common in older age, accounting for 40–50% of clinically diagnosed VaD [36]. Its cornerstones are classically des-cribed as MRI findings of white matter lesions (WML) and lacunar infarcts [38], while cerebral mic-robleeds (CMB) and cerebral atrophy also represent more recently associated features [39, 40]. It is followed by large artery disease resulting from large vessel thrombus or embolus. Infarcts in ‘strategic’ areas, i.e., thalamus, hippocampus, etc., are a less co-mmon subtype, as well as VaD due to hereditary and other rare causes. SVD can be linked to metabolic syndrome, hyperhomocysteinemia, chronic kidney disease, infective causes, and obstructive sleep apnea and usually has a substrate of arteriosclerotic and hypertensive changes, amyloid or collagen vascul-opathy, and lipohyalinosis. Carotid artery stenosis, coronary artery disease, hypertension, and atrial fibrillation, on the other hand, predispose to large artery disease-related VaD. Monogenic stroke disorders including cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL), cerebral autosomal recessive arteriopathy with subcortical infarcts and leukoencephalopathy (CARASIL) and hereditary angiopathy, as well as monogenic disorders involving stroke such as Fabry disease, Marfan syndrome, vascular Ehlers-Danlos syndrome, pseudoxanthoma elasticum, etc., can be rare causes of vascular cognitive impairment [34].

VaD is also sometimes subdivided into two main forms, subcortical vascular dementia (SVaD), which accounts for more than half of VaD cases (including both sporadic cases which represent the majority of VaD, as well as CADASIL), and cortical or multi-infarct dementia [41]. The term subcortical vascular cognitive impairment (SVCI) is used by some authors to represent both SVaD and subcortical vascular mild cognitive impairment which is its early stage [42].

Both WMLs and subclinical infarcts have been shown to be more likely in the presence of retinal microvascular changes such as retinopathy, arteriovenous nicking, and focal arteriolar narrowing [43]. Retinopathy changes including hemorrhages, microaneurysms, and exudates have been shown to be associated with multiple CMBs [44]. However, retinopathy seems to be a marker reflecting late mic-rovascular damage. The coexistence of CMBs, which is a sign appearing earlier in patients with CSVD, and fundus changes in the density and tortuosity of retinal microvasculature, which could represent earlier biomarkers, has more recently been reported [39]. In addition, retinal vascular fractal dimension, which reflects complexity of retinal vessels, was shown to be associated with MRI findings in SVD [45] and has been identified as a potential biomarker to differentiate AD from VaD [42] and its diagnostic potential has also been shown in CADASIL patients [46].

As AD and VaD, especially SVaD, significantly overlap, with just under 90% of VaD patients having an AD pathology in postmortem studies and with vascular pathology in the brain of 30–60% of patients clinically diagnosed with AD [42], the importance of a reliable biomarker to differentiate the two is apparent. Since retinal arteries and small cerebral arteries share common characteristics [38], the pathology of those vessels could reflect changes detected in VaD. Despite the aforementioned evidence of a correlation between funduscopic findings and VaD, there has been no study of the retinal vasculature via fluorescein angiography for this spectrum of diseases. However, with the advent of OCTA, the existing literature, although sparse, has fittingly focused on either the SVCI (the commonest form of VaD), or on rare hereditary arteriopathies, which can provide insight into underlying pathophysiology, to screen for CSVD, thus exploring it as a tool for the identification of potential biomarkers.

In a study where a potential association between retinal vessel diameters and CSVD was investigated, a larger diameter of retinal veins was found to predict CSVD progression as evidenced by a change in WML severity or new lacunar infarcts, but neither arteriolar nor venular diameter predicted CSVD severity [47]. A recent study utilizing OCTA technology reported that the capillary density (CD) in the RPC network has distinguishing values in different quadrants when patients with SVCI were compared to both cognitively normal controls and AD patients, with CD values being significantly lower in the SVCI group. Moreover, a positive association was found between the same OCTA parameter and CSVD severity. In contrast, RNFL did not seem to play an important role in differentiating SVCI patients from AD patients and normals [23]. These findings suggest that RPC density could help both in the diagnosis and differentiation of VaD from AD as well as in predicting CSVD burden in particular.

Studies focusing on OCTA vascular parameters in patients with Fabry disease and CADASIL, both inherited diseases with manifestations of vasculopathy and ocular involvement among else [48, 49] have also yielded some interesting, although partially conflicting, results. Studies have consistently so far shown a significant difference in VD values between normal subjects and Fabry disease patients with macular VD either reduced [50, 51] or increased [48] in the SCP of the patient group and also reduced [50] or increased [51] in the DRCP versus controls, while an enlargement of the FAZ area was unanimously reported [48, 50]. The finding of a statistically significant reduction of VD in the DRCP was replicated in CADASIL patients as well [49]. Thus, further investigation into the macular vasculature of VaD patients might be warranted based on current evidence.

DEMENTIA WITH LEWY BODIES AND PARKINSON’S DISEASE DEMENTIA

Although both neurodegenerative diseases share many of their core features, especially when in their fully established forms, DLB and PDD are clinically distinguished by their natural history. Where DLB patients develop cognitive impairment followed by motor signs at the same time or within a year, in PDD these are the first to emerge, preceding dementia [52, 53]. Impaired cognition, behavioral changes, visual hallucinations, motor problems, and autonomic dysfunction are shared features [52 –54]. In addition to similarities in their clinical presentation, these nosologic entities are also neuropathologically related, notably in that α-synuclein/Lewy body accumulation and Aβ cortical deposition and tauopathy play a key role in both [53]. Unsurprisingly, a debate over the validity of the notion that DLB and PDD represent distinct types of dementia is ongoing, and the addition of sensitive biomarkers in our diagnostic armamentarium would likely be beneficial in settling it [53].

DLB patients can exhibit a plethora of ocular symptomatology besides visual hallucinations, incl-uding visual field defects, ocular motility problems, and difficulty in complex visual tasks [55]. An equally broad array of visual symptoms has been found in patients with PD, ranging from contrast sensitivity and color perception alterations to glaucomatous damage, eye movement defects and complex perceptual problems [56, 57], with studies finding visual problems to be more ubiquitous in the PDD group than in PD patients not suffering from dementia, which implies a possible connection between disease progression and visual pathology [58, 59]. Visual perception seems to be comparably impaired in DLB and PDD, while this is not the case with AD patients. This can in part be attributed to pathological changes in the occipital lobe [60], but the possible coexistence of retinal changes mediated by dopaminergic activity should not be disregarded and has been addressed in literature [60, 61]. Indeed, postmortem retinal examination has pointed towards structural changes in the retina, notably the outer plexiform layer, of DLB patients [62].

The effort to isolate markers to predict cognitive deterioration in PD has returned results underscoring the value of clinical picture, genetics, CSF biomarkers and dopamine transporter imaging in this task [63, 64], and there has been equal interest in MRI findings [64, 65]. An impairment in saccadic eye movements has recently been added to the list of useful clinical markers in the prediction of cognitive impairment in PD subjects [66]. Since the advent of the OCT, a number of studies have concluded that there is significant retinal thinning in PD patients as compared to controls, which are more pronounced in the inner layers, as well as a thinning in most RNFL sectors [67], although visual fields might not correlate well with RNFL thickness according to another study, with PD patients exhibiting glaucomatous-like defects even in the absence of structural RNFL abnormalities [68]. RNFL has also been found to be thinner in both PDD and DLB than in cognitively normal controls [69], even corresponding with Mini-Mental State Examination (MMSE) scores in PDD patients [59], but although there is evidence that this thinning might be more pronounced in DLB than in PDD and AD, no statistical significance has been proven [69]. In addition, while current bibliography has reached no consensus regarding parafoveal macular thickness of inner layers in PD patients when compared to controls, this area (1–3mm around the fovea) has been found to be significantly thinner in DLB [70]. However, GC-IPL thinning was suggested to be a potential marker of future visual decline in PD patients [70], as well as a predictor of increased risk for PDD in patients diagnosed with PD [59]. A significance of parafoveal GC-IPL as a biomarker is thus suggested for both DLB and PD and future studies looking into its role in differentiating DLB from PDD and AD might as well as in predicting the risk of developing dementia in PD patients might be of benefit.

FRONTOTEMPORAL DEMENTIA

FTD or frontotemporal lobar degeneration is a term that unites three clinical forms of dementia with a strong genetic component, initially affecting the frontal and temporal lobes, in which pathological protein deposition results in an insidious installment of cognitive impairment, manifesting mainly as behavioral changes, executive dysfunction, or language deficits [71]. In behavioral-variant frontotemporal dementia (bvFTD), early and severe changes in behavior and personality, including disinhibition and increasing compulsiveness predominate [72]; grammatical mistakes along with speech errors are core features of non-fluent variant primary progressive aphasia (nfvPPA); while semantic loss mainly in language is a key finding associated with semantic primary progressive aphasia (svPPA) [71]. Distinct patterns of atrophy can aid in the differential diagnosis of different forms of FTD and rule out other dementias via imaging [72]. Patients are usually younger than 65 years old and FTD can masquerade as [71] and overlap with an array of psychiatric conditions [72], thus making its diagnosis a challenge for clinicians and the contribution of objective biomarkers of utmost importance.

Visuospatial impairment has been studied in an effort to discriminate between AD and FTD, since this represents an aftermath of parietal lobe atrophy which is a feature of the former but rarely of the latter. However, out of several visuospatial tasks studied, only pentagons/loops copy was found to distinguish the AD group, in which performance was significantly poorer, from FTD patients in a reliable manner [73]. Ocular motility has also been tested in FTD, the rationale being that frontal lobe impairment can be reflected in a dysfunction in saccadic eye movements. A poorer performance in reflexive prosaccades inhibition, as well as in withholding voluntary saccadic movements, was indeed observed in FTD patients [74]. Anti-saccades have also been reported to be subnormal in both AD and most FTD subtypes, although only AD subjects failed to self-correct erroneous anti-saccades as opposed to the FTD group, suggesting a potential place for the study of anti-saccades and vertical saccades in the differentiation of FTD from other types of dementia [75].

A study comparing the ocular histopathologic findings of an FTD mouse model to human FTD patients demonstrated that visual changes in this form of dementia might be explained by tauopathy of the optic nerve and retina, thus underlying that their evaluation could be relevant in the examination of FTD patients [76]. OCT examination of RNFL and retinal layers initially revealed significant RNFL thinning in FTD patients versus both cognitively normal controls and mild AD, while GC-IPL was also found to be decreased in FTD when compared to the same groups [25]. The latter finding was, however, challenged by more recent studies, which found no difference in the thickness of inner layers in FTD sufferers, but instead reported a thinning of the outer retina, a measure which even correlates to MMSE scores according to the same study. Specifically, the thinning seems to affect both the outer nuclear layer and ellipsoid zone of FTD patients, including the tauopathy subgroup [77]. The aforementioned observations were replicated by the authors in a follow-up study which, in addition to these, reported a correlation between disease progression and persistent thinning of the outer layers of the retina in FTD patients, an observation more marked in the tauopathy subgroup [78]. The thinning of different retinal layers in FTD versus AD, if validated, could therefore guide the search to establish reliable biomarkers that differentiate the two.

CONCLUSION

Recent literature taking advantage of OCT and OCTA to gain insight into and differentiate between different forms of dementia is already yielding encouraging results. These have been the main focus of this review and key potentially discriminatory findings which could aid in the conception and design of future studies are summarized in Table 1.

Table 1

Key differentiating OCT/OCTA parameters identified in this review for five types of dementia

	RNFL	Inner retinal layers	Outer retinal layers	FAZ	VD/PD	RPC density
AD, MCI	Thinning (also in MCI)	GCC thinning		Enlarged	Decreased in the SCP,	Decreased
		GC-IPL thinning			DRCP (maybe also in MCI)
		(maybe also in MCI)
VaD	Probably not useful in			Further investigation	Further investigation	Decreased versus
	the differential diagnosis			warranted	warranted	controls and AD
	with AD
DLB, PDD	Thinning (both, maybe	Thinning in DLB,
	more pronounced in	possible role in PDD
	DLB versus PDD and AD)
FTD	Thinning versus controls and AD		ONL and EZ thinning

AD, Alzheimer’s disease; DLB, dementia with Lewy bodies; DRCP, deep retinal capillary plexus; EZ, ellipsoid zone; FAZ, foveal avascular zone; FTD, frontotemporal dementia; GC-IPL, ganglion cell-inner plexiform layer; GCC, ganglion cell complex; MCI, mild cognitive impairment; ONL, outer nuclear layer; PD, perfusion density; PDD, Parkinson’s disease dementia; RNFL, retinal nerve fiber layer; RPC, retinal peripapillary capillaries; SCP, superficial capillary plexus; VaD, vascular dementia; VD, vessel density.

As this literature review clearly demonstrates, there is a growing interest in the exploration of ocular biomarkers in accurately characterizing all types of dementia. Although there is already an array of biomarkers in the hands of the clinician to support or complement clinical findings, none of them are pathognomonic for any type or stage of dementia. Their use is also sometimes limited in clinical practice due to their cost and invasiveness. The eye is regarded as a “window to the brain”, and its transparent media along the visual axis provides the advantage of easy access to the retina. This neural tissue shares common structural and biochemical characteristics with the cerebrum and emerging technology makes its study increasingly efficient, detailed and accurate. The hypothesis is that cerebral pathology and processes specific to different types of dementia will be reflected in comparable changes in the retina, which can in turn be noninvasively studied, quantified and used to both diagnose dementia before its late and irreversible clinical manifestations occur, as well as to aid in the differential diagnosis of different types of dementia which might be amenable to different treatment modalities.

While the idea of identifying ocular biomarkers is anything but new, initial efforts were limited by technology and their focus included studying aspects of vision, ocular motility, pupillary reactions and fundus photography and it is unlikely they ever made it past the laboratory. The promise of swift and easily quantifiable measurements emerged with the advent of OCT, and the more recent OCTA technology, sparking new interest in the domain of potential noninvasive ocular biomarkers. Although other ocular markers were discussed as well, putting together the pieces of up-to-date knowledge on what these novel ocular imaging techniques have to offer in distinguishing AD, VaD, DLB, PDD, and FTD has been the main focus of this study.

Peripapillary RNFL seems to be more reliable and useful as a biomarker than macular RNFL. A decrease in peripapillary RNFL values has been noted in AD, DLB, PDD, and FTD. Even among healthy individuals, RNFL seems to predict a significantly increased risk for developing cognitive decline and dementia and is directly related to cognitive performance [22]. In 2016, a study by Pillai et al. concluded that RNFL, among other OCT parameters, namely GCC thickness and macular volume, does not distinguish between AD, non-AD dementia, and amnesic MCI [79]. More recently, Lee et al. also reported no difference in RNFL values between AD, SVCI patients and controls and the authors found no correlation with cortical thickness either [23]. However, evidence suggests there might be more pronounced RNFL thinning in DLB than in both PDD and AD, although further studies need to confirm this as the difference is not statistically significant. RNFL thinning is also more pronounced in FTD versus mild AD according to current literature. Moreover, since MCI patients appear to have decreased RNFL thickness and this parameter is also associated with cognitive decline in AD, it might also serve as an early biomarker in dementia. Dementia seems to be associated with a reduction in retinal thickness as well [80], but there is no strong evidence for its utilization in the differential diagnosis between different types of dementia in current literature. Automatic segmentation of separate retinal layers, on the other hand, might provide more useful information. Thinning of the inner layers (GC-IPL) appears to be a feature of prevalent dementia [21]. This marker is correlated with AD, in which it also correlates with cognitive score, and it is also a feature of DLB. In PD, GC-IPL thinning might predict risk for developing PDD. GC-IPL thinning has also been found to coincide with gray matter volume loss in the occipital and temporal lobes of cognitively normal persons, possibly reflecting early neurodegenerative changes [81]. With regards to FTD, the evidence on thinning of different retinal layers is partly contradictory, but outer layer thinning might serve in differentiating this type of dementia from others and further studies are needed to confirm the validity of this biomarker. Choroidal thickness is another parameter mainly studied in AD. Its reduction both in MCI and to a greater level in AD makes it a potential early biomarker for the disease. OCTA parameters reflecting retinal vasculature have mostly been examined in AD and, fittingly, VaD patients. In AD, both VD and PD values are reported to be lower than in controls, while the SCP as well as the DRCP seem to be affected, according to different studies. VD may prove to be useful as an early AD biomarker. There is also a reduction in the RPC density in AD patients, which is reported as well, and might be more pronounced, in SVCI. This parameter might therefore be of value in distinguishing AD from VaD. Finally, the FAZ seems to be enlarged according to most studies both in AD and in cases of monogenic SVD.

Not surprisingly, the largest volume of research has so far concentrated in AD, which represents the vast majority of diagnosed cases. An interesting, even though at times conflicting, body of evidence on virtually all parameters which can be analyzed already exists for both AD as well as its prodromal stages. The same is true to a noticeably lesser extent for rarer forms of dementia. Particularly with regards to OCTA, apart from AD and its precursor, MCI, and very limited evidence from studies in monogenic SVD, this technology has not been employed to look into retinal vasculature in all other types and subtypes of dementia [82]. This opens the door to wide and varied areas for future investigation. There is also still significant ground to be covered in the effort to compare OCT and OCTA findings among well-characterized patients diagnosed with different types of dementia. Of note, are several challenges in this. Most researchers studying ocular biomarkers in dem-entia exclude patients with coexistent ophthalmic pathology. However, ocular conditions related to ag-ing such as age-related macular degeneration, glaucoma, and diabetic retinopathy have in fact been found to be comorbid and possibly related to dementia including AD [2]. An added concern regarding the interpretation of the values of ocular parameters is that abnormal results might in fact be attributed to normal aging. This point was indeed demonstrated by a study that failed to show a difference in any ocular biomarker among demented patients and cognitively normal subjects when all participants were aged over 90 (nonagerians), while the same biomarkers differed significantly among nonagerians of any cognitive state when compared to younger individuals [83].

To the best of our knowledge, this is the first review to comprehensibly examine the vast majority of studied ocular biomarkers and directly compare their application in five of the most prevalent causes of dementia in an effort to pinpoint potential biomarkers and areas of future research which might specifically aid in the differential diagnosis of these subtypes. As we have demonstrated, although there are significant gaps in the existing literature, which we have identified and discussed, research results indicate that there are several biomarkers which could be used not only to diagnose early but also to differentiate forms of dementia between them. We have isolated and discussed specific biomarkers, chiefly OCT and OCTA values, which could efficiently be utilized for specific indications should future research support current findings or hypotheses. As OCTA technology is advancing with software and algorithms constantly updated and artifacts minimized, the contribution of this rapid, noninvasive, and relatively cheap modality is not unlikely to prove invaluable in better understanding and managing dementia.

DISCLOSURE STATEMENT

Authors’ disclosures available online (https://www.j-alz.com/manuscript-disclosures/20-1516r1).

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