The retinal ganglion cell layer reflects neurodegenerative changes in cognitively unimpaired individuals

To date, there is no disease-modifying treatment for Alzheimer’s disease (AD). In particular, clinical trials with anti-amyloid drugs have consistently failed to show efficacy in clinical endpoints. However, it is likely that these studies have been conducted in individuals in advanced stages of the disease, in whom lowering amyloid levels might not be enough to halt the disease progression [1]. There is a consensus in the field that clinical trials with potentially disease-modifying treatments should be performed in the early stages of AD. Therefore, it is necessary to develop tests to identify those individuals who are asymptomatic or very mildly symptomatic, but who have a high risk of progressing to cognitive impairment.

The core biomarkers used in AD research can be divided into three categories: (1) biomarkers of beta-amyloid (Aβ) brain deposition: high ligand retention on amyloid positron emission tomography (PET) or low cerebrospinal fluid (CSF) beta-amyloid 1-42 (Aβ 1–42) and Aβ 1–42/40 ratio [2‐4]; (2) biomarkers of AD-associated Tau pathology: elevated CSF phosphorylated Tau (pTau) and Tau-PET [4, 5], and (3) biomarkers of neurodegeneration or neuronal injury: CSF total Tau (tTau), ¹⁸F-fluorodeoxyglucose (FDG)-PET hypometabolism, and atrophy on structural magnetic resonance imaging (MRI) [5, 6]. Advances in the technique and standardization of CSF biomarkers and the emergence of amyloid and Tau-PET have considerably improved the ability to detect preclinical individuals with AD pathological changes and currently are considered as gold standard tests [7‐10]. However, despite the increasing attempts to integrate biomarkers into clinical decision-making, diagnosis supported by them is still considered appropriate only for research-related purposes [11]. The clinical environment has not yet evolved properly for this to occur due to challenges with cost, standardization, and accessibility [12, 13]. Therefore, there is an urgent need to develop non-invasive, affordable, and scalable biomarkers. The aim would be to detect preclinical individuals at high risk of cognitive deterioration, as well as to allow monitoring of the effects of disease-modifying treatments.

The retina, a developmental outgrowth of the brain, is considered a window to study disorders in the central nervous system [14‐16]. The link between the eye and AD has been established clinically, histologically, and through technological devices such as optical coherence tomography (OCT) [17]. OCT provides a cross-sectional structure of the retina with extremely high resolution, typically on the micrometer scale. The main advantages of OCT for diagnosing probable AD include its non-invasive nature, its wide availability, and the resulting retinal images, which can be analyzed both objectively and qualitatively [18]. There is growing evidence supporting the incorporation of OCT technology into clinical settings managing neurological diseases [19‐21]. Currently, the identification of retinal biomarkers in AD using OCT remains an area of active research, and a growing number of studies indicate that OCT reflects AD pathology in individuals with dementia and those in prodromal stages [22‐28]. However, few studies have assessed the role of OCT as an AD biomarker in preclinical individuals [22, 29, 30], and even fewer have used CSF AD biomarkers as a gold standard [29]. Based on the above evidence, we propose to evaluate OCT for its potential applications as a population screening tool. The objective of the present study is to explore a wide range of OCT parameters in a well-phenotyped group of community dwellers. OCT measurements included were the retinal ganglion cell layer (GCL) thickness, the peripapillary retinal nerve fiber layer (RFNL) thickness, the Bruch’s membrane opening-minimum rim width (BMO-MRW), and the choroidal thickness (CT). We evaluated their potential relationships with established CSF core AD biomarkers (tTau, pTau, Aβ 1–42, and Aβ 1–40) and MRI (hippocampal volume).

Overall, 99 individuals (191 eyes) were consecutively evaluated in the final analysis. Females were predominant (71%), and the mean age was 64.7 ± 6.3 years. The demographic and clinical characteristics of the study participants are summarized in Table 1. The majority were cognitively healthy (the mean Mini-Mental State Examination score was 28.94 ± 1.3), and the mean FCSRT delayed total recall was 15.01 ± 2.43.

Ophthalmological examination parameters did not show significant associations with CSF AD biomarkers (Additional file 1). Subsequently, we did not consider them as confounding variables for the analysis.

Our main results show a nominally significant association between Tau (pTau) and both neurodegeneration biomarkers (tTau and, especially, hippocampal volume) and GCL degeneration. To a lesser degree, we detected an association betweent Tau and hippocampal volume with RFNL. Despite the exploratory nature of our design, the consistency of our results is remarkable, as all of the GCL associated areas showed the same pattern: the more tTau or pTau levels in the CSF, the thinner was the GCL layer. The opposite was found for the hippocampal volume: larger hippocampal volumes were associated with thicker GCL layers and vice versa. Moreover, the same patterns were present in the RFNL analysis. However, after adjusting the p-values using FDR correction, only the hippocampal volume was associated with GCL and RFNL parameters. In contrast, CT did not show any significant correlation with CSF biomarkers, and BMO-MRW measurements were associated with pTau and tTau status in isolated sectors, but p-values did not survive multivariate analysis. Overall, we found that none of the OCT measurements was associated with the amyloid biomarker (CSF Aβ 1–42/40 ratio). Finally, we found no association between the episodic memory test, FCSRT, and any of the OCT biomarkers, which is concordant with the hypothesis that retinal changes precede cognitive problems.

The recent research framework for AD proposes an A/T/N classification system based on biomarkers in living patients, independently of cognitive status: “A” refers to the Aβ biomarker (Aβ PET or CSF), “T” refers to pathologic Tau (CSF pTau or PET-Tau), and “N” refers to neurodegeneration (CSF tTau, FDG-PET, or structural MRI) [42]. This new research framework is based on the rationale that AD is a continuum and the A/T/N classification captures the sequential pathological changes starting with Aβ deposition that would produce Tau pathology and, finally, neurodegeneration. Our results support the concept of AD as a continuous biological process [42, 43]. While no OCT measurements correlated with the Aβ biomarker, changes in the retina became significant in those individuals with positive pTau, especially in relation with positive neurodegeneration biomarkers like tTau and hippocampal atrophy. As shown in Fig. 1, the GCL damage was more extensive in those individuals whose biomarker profile showed a more advanced disease course. Moreover, we found that the correlation between CSF pTau and tTau (6 TS, 9 TI, 13 TI and 9 TI, 13 TI, 14 TI, respectively) with GCL was strongest in the macular peripheral sectors. The same pattern, with even larger effects, was found in the hippocampal volume analysis.

Only a handful of studies have suggested a link between AD biomarkers and retinal OCT measurements in cognitively unimpaired individuals [22, 29, 30]. Particularly, Asanad et al. [29] reported that the mean RFNL was thinner in individuals that had CSF biomarkers of AD pathology before cognitive deficits. Santos et al. [30] also found larger RNFL damage in preclinical AD relative to controls and related this damage to increased neocortical amyloid accumulation detected by ¹⁸F-florbetapir Aβ PET. In contrast to our study, Santos and co-authors did not find an association between GCL thinning and AD biomarkers [30]. One possible explanation for these conflicting results is that some of the aforementioned studies have less statistical power. While our population included 99 individuals (mean of both eyes, 191 eyes in total), these previous studies evaluated 43 participants and 56 eyes, respectively. In line with our results, Golzan et al. reported a significant difference in GCL thickness between AD, preclinical AD, and healthy controls [22]. However, they found no association between OCT measurements and PET imaging evidence of brain amyloidosis [22]. In comparison, in our study, we used CSF biomarkers, which are known to detect Alzheimer’s pathological changes earlier than PET [44].

Other studies have investigated the relationship between MRI and retinal thickness in cognitively normal subjects, describing the association between gray matter volume or temporal lobe atrophy and retinal layer [28, 45, 46]. Casaletto et al. demonstrated an association between GCL loss and RNFL thinning with MTL atrophy in neurologically normal older adults [45]. Also, in line with our results, another group found that macular GCL and inner plexiform layer (IPL) thinning was associated with lower gray matter volume of the occipital and temporal lobes in elderly subjects [47]. Our results, adjusted by age, show that GCL damage is related not only to hippocampal atrophy but also to increased CSF pTau and tTau, suggesting that the most likely mechanism would be preclinical AD changes. According to this, Bevan et al. [48] reported, in a transgenic AD model, that degeneration in GCL happens simultaneously with the loss of hippocampal dendritic spines. Interestingly, this constitutes a key hallmark of AD research models, with spine loss particularly acute in the vicinity of amyloid plaques [49].

Despite the cross-sectional design, the predominant association between pTau CSF levels and thinning of the macular temporal peripheral sector of GCL would suggest that it is in this area where we might find the earliest AD-related degenerative changes in the retina of cognitively unimpaired individuals. Retinal ganglion cells, neurons located in the retinal GCL (mostly in the macula), are characterized by a soma from which the originating axon runs initially into the RNFL. Subsequently, these axons converge turning into the optic disc. There is increasing evidence describing the existence of AD pathology in the retina of AD patients [17, 50‐58], specifically in the GCL [59]. However, the localization of these changes has been controversial. Neuroretinal damage (RFNL or GCL) in the upper, lower, or temporal macular sectors has been described [24, 27, 28, 60, 61], most likely reflecting that the tissues belonged to individuals at different time points in the Alzheimer’s continuum. Koronyo et al studied the distribution of Aβ plaques in the retina, reporting that Aβ deposits were frequently concentrated in the middle and far periphery of the superior quadrants along the blood vessels [17]. In line with our results, a recent study found that the mid-peripheral retina showed more AD pathology than the central retina [62]. Furthermore, this study showed that the temporal retina had the strongest correlations with brain neuritic plaques and cerebral amyloid angiopathy, and this area showed the greatest contrast between AD and controls, leading the authors to suggest that it might be the optimal location for in vivo ocular imaging [62].

A potential technical limitation of our study is the fact that CT thickness was measured manually after the EDI-OCT scan, providing us with a choroidal analysis based on subjective, non-automated measurements. To help overcome this hurdle, we tested the agreement in a control sample, and as shown in a previous publication, we proved to have high intra-observer and inter-observer reproducibilities [25]. A more general limitation of the present study is the relatively small sample size in comparison with the large number of OCT parameters measured, which might increase the probability of false positives. However, with our two-step design, we have sought to minimize the type 1 error. Additionally, we performed a FDR correction to account for multiple testing due to the large number of areas tested. Most importantly, we think that the consistency of the OCT associations across several biomarkers with similar meaning, like tTau and hippocampal volume, makes it highly unlikely that our results are explained by chance alone.

Our main finding was the significant association between GCL thickness, measured by OCT, with Tau and neurodegeneration biomarkers in cognitively unimpaired individuals. According to our results, macular temporal peripheral sectors of GCL may represent the areas with the greatest clinical potential for early screening; but this hypothesis requires sequential studies in a larger population to evaluate their clinical utility. Even though our study represents the largest cohort of cognitively unimpaired individuals assessed with OCT and multimodal AD biomarkers to date, our results should be considered exploratory. Future investigations including larger samples and analyzing prospectively the spatiotemporal changes of OCT measurements are needed to optimize the diagnostic utility of retinal imaging modalities in the diagnosis of preclinical AD.