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Genome-wide association study and polygenic risk scores of retinal thickness across the cognitive continuum: data from the NORFACE cohort

Alzheimer's Research & Therapy volume 16, Article number: 38 (2024) Cite this article

Abstract

Background

Several studies have reported a relationship between retinal thickness and dementia. Therefore, optical coherence tomography (OCT) has been proposed as an early diagnosis method for Alzheimer’s disease (AD). In this study, we performed a genome-wide association study (GWAS) aimed at identifying genes associated with retinal nerve fiber layer (RNFL) and ganglion cell inner plexiform layer (GCIPL) thickness assessed by OCT and exploring the relationships between the spectrum of cognitive decline (including AD and non-AD cases) and retinal thickness.

Methods

RNFL and GCIPL thickness at the macula were determined using two different OCT devices (Triton and Maestro). These determinations were tested for association with common single nucleotide polymorphism (SNPs) using adjusted linear regression models and combined using meta-analysis methods. Polygenic risk scores (PRSs) for retinal thickness and AD were generated.

Results

Several genetic loci affecting retinal thickness were identified across the genome in accordance with previous reports. The genetic overlap between retinal thickness and dementia, however, was weak and limited to the GCIPL layer; only those observable with all-type dementia cases were considered.

Conclusions

Our study does not support the existence of a genetic link between dementia and retinal thickness.

Background

Alzheimer’s disease (AD) is the most common form of dementia in the elderly, responsible for 60–80% of cases. AD is a slowly progressive neurodegenerative condition that irreversibly impairs cognition and results in a complete loss of autonomy [1]. Its main neuropathological hallmarks are β-amyloid plaques and neurofibrillary tangles composed of hyper-phosphorylated tau [2]. It is known that these brain changes start developing up to two decades before the dementia onset, and, therefore, the identification of early biological markers of the disease is currently the focus of intensive research.

Nowadays, it is possible to detect these AD-core neuropathological changes while individuals are alive and have not yet developed dementia by using different imaging and fluid biomarker techniques, such as positron emission tomography, magnetic resonance imaging, and cerebrospinal fluid [3]. The currently available biomarkers are either expensive or invasive, and, thus, there is great interest in identifying novel ones that are sensitive and specific to early AD changes but also easy to administer, inexpensive, non-invasive, and widely accessible. Research efforts are now focused on the fields of plasma, genomics, and retinal imaging, among others [4,5,6].

The retina is the neuronal structure of the eye and is embryologically derived from the diencephalon, as is the optic nerve, and both are considered part of the central nervous system [7]. Retinal structures can be visualized in vivo using non-invasive high-resolution imaging methods, such as optical coherence tomography (OCT), which has been employed for decades in the ophthalmology field to diagnose and monitor common ocular pathologies, such as glaucoma, diabetes retinopathy, and age-related macular degeneration [8]. In the past few years, changes in thickness and volume of different retinal layers have also been observed in several neurological disorders, such as optic neuritis, multiple sclerosis, Parkinson’s disease, and AD [9]. In AD in particular, degeneration of the retinal nerve fiber layer (RNFL) and ganglion cell layer (GCL) has been observed in postmortem tissue [10]. Multiple publications have shown that patients with AD dementia (ADD) and mild cognitive impairment (MCI) present RNFL and GCL thinning compared to healthy controls, as measured in vivo by OCT, although other articles have shown no significant differences [11, 12]. Interestingly, a recent study reported that both retinal structural and vascular measures were significantly decreased in dementia patients vs. cognitively healthy (CH) and MCI individuals but found no differences between amyloid positive and negative dementia patients [13].

Recent advances in the field of AD genetics [6, 14] have permitted the identification of several genetic factors associated with AD risk. To date, up to 82 different AD loci have been uncovered. Furthermore, the linear combination of existing loci using polygenic risk scores (PRS) has been demonstrated to be instrumental in the identification of high-risk populations and as a promising tool for selecting patients for clinical trials [6].

Regarding the association of genomics with retinal structures, several recent genome-wide association studies (GWAS) have identified genetic loci determining retinal thickness [15,16,17]. First, a GWAS using OCT images from 68,423 participants from the UK Biobank cohort identified 139 genetic loci associated with macular thickness, the most significant ones being highly expressed in the retina [16]. In another GWAS also using OCT images from 31,434 UK Biobank participants, 46 genetic loci associated with the thickness of the RNFL or ganglion cell inner plexiform layer (GCIPL, GCL + inner plexiform layer) were established, three of which were related to foveal hypoplasia and visual acuity [15]. More recently, Currant et al. have also described 111 loci associated with the thickness of one or more of the photoreceptor cell layers in a GWAS using OCT images from 31,135 participants from the UK Biobank [17], with a significant enrichment of genes involved in rare eye pathologies, such as retinitis pigmentosa.

In the present study, we performed a GWAS aimed at identifying genes associated with RNFL and GCIPL thickness assessed by OCT in a Spanish population with different degrees of cognitive impairment, including CH, MCI, and dementia (most of them ADD), evaluated in a memory clinic. We also constructed polygenic risk scores (PRS) for retinal thickness and AD and explored their association with RNFL and GCIPL thickness, as well as with cognitive status.

Methods

Study population

The study population comprised 3170 participants from the Neuro-Ophthalmology Research at Fundació ACE (NORFACE) cohort, which was founded in 2014 to search for retinal biomarkers of AD and examine the relationships between retinal changes and different types of neurodegenerative disorders [12]. Consecutive individuals evaluated due to cognitive decline at Ace Alzheimer Center Barcelona between September 2014 and March 2019 with a diagnosis of CH, MCI, or dementia, and who had available GWAS data from the GR@ACE study [18] were included in the present analysis.

Participants were recruited from four sources within Ace Alzheimer Center Barcelona (ACE): (1) the Memory Clinic, an outpatient diagnostic unit for individuals with cognitive decline that has an agreement with the Catalan Public Health System [19], (2) Ace’s Open House Initiative [20], a social program that provides free assessment of the cognitive status of individuals from the community without the need for a physician’s referral, (3) the Fundació ACE Healthy Brain Initiative (FACEHBI), a longitudinal research study of aging, lifestyle, cognition, and biomarkers in individuals with subjective cognitive decline (SCD) [21], and (4) the BIOFACE project, a research study of novel biomarkers focused on individuals with early-onset MCI [22].

Clinical diagnostic groups

Study participants completed neurological, neuropsychological, and social evaluations at ACE. For each individual, a consensus-based diagnosis of cognitive status was reached at the time of study recruitment by a multidisciplinary team of professionals that included neurologists, neuropsychologists, and social workers [19].

Dementia was defined according to the DSM-V criteria [23]. Within the dementia group, ADD [1] represented 84.3% of cases, followed by vascular dementia [24] (8.8%), while the remaining 6.9% included other types of dementia, such as dementia with Lewy bodies [25] (2.2%) and frontotemporal [26] (0.9%). MCI was defined using Petersen’s [27] and the Cardiovascular Health and Cognition Study criteria [28]. All individuals in the CH group had a Clinical Dementia Rating (CDR) score [29] of 0, a preserved performance (score ≥ 27) on the Mini-Mental State Examination (MMSE) [30, 31] and a strictly normal performance on the neuropsychological battery of Fundació ACE (NBACE) [32, 33].

Neuro-ophthalmological evaluation

In parallel with the cognitive assessment, study participants underwent a complete neuro-ophthalmological evaluation, which lasted about 20 min and was performed by an optometrist. The evaluation comprised (1) a review of past ophthalmological diseases, treatments, and surgeries, (2) a monocular visual acuity assessment, with the participants wearing their habitual correction for refractive error using a pinhole occluder and the Early Treatment of Diabetic Retinopathy Study (ETDRS) chart [34, 35], and (3) an intraocular pressure (IOP) measurement by Icare tonometry [36]. More details can be found elsewhere [37]. The ophthalmologist and neurologists were blind to each other’s diagnoses.

Optical coherence tomography

Participants were imaged with a 3D-OCT Maestro® (Fast Map software version 8.40) and/or a DRI OCT Triton—Swept Source (SS) OCT (software v.1.22.1; Topcon Co. Tokyo, Japan). In the final cohort, 1789 individuals were scanned with the OCT Maestro, 1123 with the OCT Triton, and 582 with both OCT devices performed the same day. The OCT exam was completed in about 5–10 min. Both eyes were scanned separately. No pupil dilation was required. Retinal layer segmentation was performed using the Topcon Advanced Boundary Segmentation TM (TABS) algorithm as part of the Fast Map software [38]. Data from RNFL and GCIPL thickness at the macula were analyzed.

Statistical analysis

RNFL and GCIPL thickness measures were scaled before analysis. Correlation between macular RNFL and GCIPL thickness by eye and OCT dataset were explored by means of Spearman correlation analysis. Correlogram plots were generated using the corrplot R package [39].

The associations of RNFL and GCIPL thickness measures with cognitive status were explored by means of logistic regression models adjusted by the OCT image quality parameter provided by the OCT device, age, sex, years of education, APOE genotype, genotyping batch (I/II), and concomitant ocular diseases/surgeries. A meta-analysis of the Maestro and Triton OCT cohorts was run using meta R package [40] and a fixed effect model. P values below the 0.05 threshold were considered significant.

Genome-wide association study (GWAS)

Genotyping was conducted using the Axiom 815 K Spanish biobank array (Thermo Fisher) at the Spanish National Center for Genotyping (CeGEN, Santiago de Compostela, Spain), which contains rare population-specific variations observed in the Spanish population. Genotyping, quality control (QC), and imputation procedures have been described elsewhere [18]. For the OCT Triton dataset, 1465 samples were available after QC; for the OCT Maestro cohort, after QC and exclusion of samples included in the Triton dataset, 1705 remained available for GWAS.

Association analyses between single-nucleotide polymorphisms (SNPs) and retinal thickness measurements were performed independently by each eye (left, right) and OCT device (Maestro, Triton) using a linear regression procedure, assuming an additive model in Plink 2 [41]. Only SNPs with minor allele frequency (MAF) above 0.01 were kept for analysis. All analyses were adjusted by age, sex, genotyping batch (I/II), and concomitant ocular diseases/surgeries that could affect retinal thickness, OCT image quality, the first 10 principal component (PC) vectors, and cognitive status (dementia, MCI, or CH). Ocular conditions included as adjusting factors in the model were the following: open angle glaucoma, maculopathy, retinal surgery, amblyopia, high myopia (< −6Dp) or hyperopia (> +6Dp), and intraocular pressure > 24 mmHg. The top results were aggregated by genomic region and linkage disequilibrium blocks using the clump procedure implemented in Plink 2. Additionally, an interaction term with dementia status (presence vs. absence) was included to assess whether the presence of the disease modulated the association of the SNPs with the retinal thickness measurements. The fixed-effects meta-analysis procedures implemented in Plink 2 were used to combine the association analysis from both datasets and both eyes.

The GWAS significance threshold was established in p = 5e−8; p values below p = 10e−5 were considered suggestive of association. The genomic inflation factor (λ) was calculated as the median of the resulting chi-square test statistics divided by the expected median of the chi-square distribution. Manhattan and QQ plots were generated using qqman R package [42].

Functional analyses

An over-representation analysis was performed, aimed at identifying the Gene Ontology (GO) categories and signaling pathways using gprofiler2 R package [43].

Mendelian randomization (MR) analysis

With the aim or investigating the causal relationships between retinal structures and dementia, we calculated individual polygenic risk scores (PRS) for RNFL and GCIPL thickness in the full GR@ACE cohort (N = 17,089, including 5195 ADD patients, 2509 non-AD dementia patients, 794 MCI, and 8579 CH) based on the genetic variants below the genome-wide significance threshold described by [15]. For RNFL, 23 available SNPs of the 30 reported by Currant et al. were used to compute the PRS, while, for GCIPL, the full 22-SNP set was used. Similarly, the PRS for AD was calculated in the study sample using 83 SNPs showing genome-wide significant association with AD according to [14].

Additive PRSs were computed for AD, RNFL, and GCIPL as \({\sum }_{i=0}^{n}\beta x SNPij\), where β is the weight for each of the variants in the GWAS, and \(SNPij\) is the number of alleles (0–2 range). PRSs were scaled prior to analysis.

Spearman correlation and linear regression were used to explore the relationship between AD PRS and retinal PRSs (RNFL PRS and GCIPL PRS) and for retinal determinations and AD PRs and retinal PRSs. The associations of AD PRS, RNFL PRS, and GCIPL PRS with cognitive status were explored by means of logistic regression analysis. All regression models were adjusted for age, sex, APOE genotype, years of education, genotyping batch (I/II), and concomitant ocular diseases; regression models for RNFL or GCIPL thickness and AD status were additionally adjusted by the quality of retinal measures. Meta-analysis of the OCT Maestro and Triton cohorts was performed using meta R package. A p value < 0.05 was considered significant in this analysis.

Results

Study population and retinal measurements

The study population included 1847 dementia patients, 764 MCI patients, and 559 CH individuals from the NORFACE cohort (Table 1). As expected, dementia cases were older and had lower MMSE scores and fewer years of education but a higher prevalence of the APOE E4 allele and a larger proportion of females.

Table 1 Characteristics of the study population
Full size table

The analysis of raw RNFL and GCIPL thickness showed significant differences among diagnostic groups, with dementia patients showing the lowest median values for both retinal measures in both eyes. In the adjusted logistic models including only ADD cases, however, retinal measurements were not significantly associated with dementia status, with the exception of GCIPL measurements in the OCT Maestro cohort, although with a reduced effect size (Fig. 1). After meta-analysis, the association of GCIPL thickness with dementia remained significant, with a similar effect in both eyes (β = 0.98, p = 1e−4 and β = 0.98, p = 1.2e−5 for left and right eye, respectively); exclusion of non-AD dementia patients generated similar results (β = 0.98, p = 5.6e−6 and β = 0.98, p = 2.1e−6 for left/right eye).

Fig. 1
figure 1

Regression models for all-type dementia and ADD vs. retinal thickness. Beta regression and standard error (SE) are represented

Full size image

Correlation analysis in individuals with retinal measures from both OCT devices (N = 582) showed a strong correlation between left and right RNFL thickness measures (r = 0.89 for OCT Maestro, r = 0.63 OCT for Triton; Supplementary Fig. 1). Similarly, left and right GCIPL measures were also strongly correlated in both OCT datasets (r = 0.77 for OCT Maestro and r = 0.82 for OCT Triton). By contrast, correlations of retinal thickness measures between the two OCT devices were much lower, in the 0.26–0.40 range. Interestingly, while in the OCT Triton a positive correlation between GCIPL and RNFL thickness measures was observed, these two determinations were inversely correlated in the OCT Maestro cohort.

Genome-wide association study (GWAS)

A GWAS of RNFL and GCIPL thickness was performed independently by OCT cohort and eye, then results from both OCT devices were combined by eye; suggestive signals from the left eye (p < e−5) were explored for replication in the right eye, with the aim of filtering out spurious associations.

For GCIPL thickness, the meta-analysis of left eye OCT Triton and OCT Maestro GWAS results (λ = 1.01) identified 72 suggestive signals with a single SNP on chromosome 18, rs147136024, showing borderline genome-wide significance (p = 5.78 × 10 − 8; Fig. 2). When compared with results from the right eye (λ = 0.99), 69 SNPs were consistently associated in both eyes; these SNPs were clustered in 26 suggestive independent loci (top SNP p < 5e−8) showing similar effects across OCT datasets and eyes, reaching 7 of genome-wide significance in the combined meta-analysis (Table 2, Supplementary Table 1). Of note, two intragenic regions showed the largest evidence of association: a 13.6-kb region on chromosome 5q33.1, encoding two lncRNA genes with opposite transcription directions (LINC01470 and ENSG00000286749), and a 57.7-kb region on chromosome 22q11.1 around TPTE pseudogene 1 (TPTEP1). Other suggestive signals were located near loci previously associated with retinal features, myopia, or abnormality of refraction, such as the TSPAN10/NPLOC4/PDE6G locus or the retinoid acid receptor beta RARB, associated with retinal vasculature in a previous GWAS. Enrichment analysis showed an over-representation of molecules with binding properties, many of them involved in transport across membranes.

Fig. 2
figure 2

Meta-analysis of GCIPL and RNFL thickness GWAS (both eyes). Manhattan plots representing log10p values from adjusted PLINK linear regression models by genomic location

Full size image
Table 2 GCL and RNFL thickness meta-analysis: top concordant signals across datasets and eyes
Full size table

Left eye meta-analysis of OCT Triton and Maestro RNFL thickness genome-wide association results (λ = 0.98) identified 609 SNPs suggestive of association, 45 below the genome-wide significance threshold (Fig. 2). Only 388 of these SNPs showed the same direction of effect in all the analyses, representing 71 suggestive genome regions, including PTPRD, previously associated with RNFL, or FGFR2 and FHIT, both associated with retinal vasculature (Table 2, Supplementary Table 1). Of these regions, 14 reached the 5e−8 threshold after meta-analysis, including a 28.1-kb region on 13q13.3 overlapping the LINC00571 gene, a lncRNA gene previously associated with myopia, schizophrenia, and, interestingly, educational attainment and tau measures. Signals arising from the APOC1/APOE/TOMM40 locus were also observed on the left eye meta-analysis but showed opposite effect size directions in the OCT Triton and Maestro cohorts on the right eye, not reaching even the suggestive significance threshold on final RNFL thickness meta-analysis of both eyes. Enrichment analysis also highlighted proteins with binding properties involved in cellular metabolism.

We did not find overlap between RNFL and GCIPL thickness variants. We found no evidence of interaction between variants associated with either RNFL or GCIPL and either all-type dementia or ADD.

Polygenic risk scores and Mendelian randomization

Correlation between retinal PRSs and retinal thickness measures

RNFL and GCIPL PRSs showed significant but weak correlations with RNFL and GCIPL thickness measures in the range of ρ = 0.06–0.09 for RNFL PRS vs RNFL thickness to ρ = 0.16–0.23 for GCIPL PRS and GCIPL thickness (Supplementary Fig. 2). Adjusted models confirmed the association of retinal PRSs with retinal thickness measures, while AD PRS was not associated with RNFL or GCIPL thickness (Fig. 3D, Supplementary Fig. 3, Supplementary Table 3). We then investigated whether any of the SNPs reported by Bellenguez et al. included in the AD PRS were associated with RNFL or GCIPL measures at the nominal significance level of 0.05 by fitting adjusted linear regression models with allelic dosage as a predictor variable (Supplementary Tables 4–7, Supplementary Figs. 4–6). After a meta-analysis of the Maestro and Triton OCT results, only the rs587709 SNP, located within the leukocyte immunoglobulin-like receptor (LIR) cluster on 19q13.4, was consistently associated with RNFL thickness in both eyes, while two SNPs, rs7384878 (SPDYE3) and rs6586028 (TSPAN14), were associated with GCIPL thickness in both eyes.

Fig. 3
figure 3

Polygenic risk scores (AD PRS, RNFL PRS, and GCIPL PRS). A Pearson’s correlation coefficient between PRSs (GR@ACE). B Adjusted regression models for RNFL PRS as predictor of dementia (GR@ACE). C Adjusted regression models for GCIPL PRS as predictor of dementia (GR@ACE). D Adjusted regression models for RNFL PRS, GCIPL PRS, and AD PRS as predictors of retinal thickness (meta-analysis Maestro and Triton) and dementia (GR@ACE); beta regression coefficients and log10p values are shown. OI: left eye; OD: right eye

Full size image

Correlation between retinal PRSs and AD PRS

Aiming to further explore the relationship between cognitive status and retinal thickness, AD, RNFL, and GCIPL PRSs were calculated in the entire GR@ACE cohort, which comprises more than 17,000 individuals. Correlation between RNFL PRS and GCIPL PRS was 0.34 (Fig. 3A) in the GR@ACE dataset, with little variation according to cognitive status (Supplementary Fig. 7). The AD and RNFL PRSs showed a weak negative correlation in the entire cohort (ρ = −0.019, p = 0.014); in the stratified analysis by cognitive status, the largest absolute correlation was observed among MCI patients (ρ = −0.072, p = 0.0473), followed by non-AD dementia cases (ρ = −0.049) and CH (ρ = −0.026), while this correlation was positive in the ADD group (ρ = +0.021), although it did not reach statistical significance (p = 0.13). No significant correlations were observed between AD PRS and GCIPL PRS for any of the analysis performed (Supplementary Fig. 7).

Retinal and AD PRSs as predictors of dementia

We also assessed in the GR@ACE cohort whether dementia (all-type, ADD, and non-ADD) or dementia plus MCI status were predicted by the AD PRS, RNFL PRS (Fig. 3B), or GCIPL PRS (Fig. 3C) using adjusted logistic regression models (Supplementary Table 8, Supplementary Fig. 8). As expected, the risk of all-type dementia increased with increasing AD PRS scores (β = 0.04, p = 8.5e−50); this effect remained significant in the non-ADD group (β = 0.01, p = 5.0e−6), although it was smaller in this group than in the ADD cohort (β = 0.03, p = 5.4e−26), probably due to the fact that the study from Bellenguez et al. included not only ADD, but also other types of related dementia, offering a broader spectrum for genetic associations. By contrast, neither the RNFL PRS nor the GCIPL PRS were associated with the risk of dementia, with the exception of the GCIPL PRS and all-type dementia (β = 0.01, p = 3.0e−2) and for dementia plus MCI (β = 0.01, p = 4.5e−2). Moreover, none of the SNPs reported by Currant et al. were associated with dementia, apart from two SNPs in chromosome 6: rs9398171 within the FOXO3 and KIF6 genes, conferring a decreased and increased risk, respectively, for all-type dementia, although none passed the multiple testing correction (Supplementary Tables 9–10, Supplementary Figs. 9–10).

Discussion

This article presents the results of a genome-wide association study for macular RNFL and GCIPL thickness on more than 3000 individuals with different degrees of cognitive decline (CH, MCI, and different types of dementia, mostly ADD). We identified several genetic loci affecting retinal thickness, some previously associated with these structures or related phenotypes that were replicated across eyes and OCT cohorts. Both retinal measures showed a strong correlation, but only GCIPL thickness showed evidence of association with all-type dementia. We assessed genetic overlap between cognitive status and retinal thickness using RNFL and GCIPL PRSs as predictors of dementia (all-type, ADD, and non-ADD), but only a weak association was observed between GCIPL PRS and all-type dementia. On the other hand, AD PRS was not a predictor of RNFL or GCIPL thickness, despite three ADD candidate SNPs’ being associated with RNFL (rs587709, within the LILRB2 gene) and GCIPL (rs7384878, within the SPDYE3 gene, and rs6586028, within the TSPAN14 gene).

Several studies have reported significant RNFL thinning in AD compared to CH individuals, while others have not (reviewed by Majeed et al. [44]), but, as retinal thickness decreases with age, it is difficult to establish a causal relationship between both events. In fact, unadjusted analyses from our dataset showed that AD cases had thinner RNFL and GCIPL than the CH and MCI subjects, but this difference was not observed after adjustment for confounding variables. Only a small reduction in the GCIPL thickness in dementia patients was observed, but this association was no longer significant when only patients with ADD were considered, in accordance with previous results from our group [12, 45].

Mendelian randomization (MR) uses genetic data as an instrumental variable for testing the association between the exposure of interest (GCIPL and RNFL thickness) and the study outcome (dementia). To our knowledge, this was the first time MR was applied to explore this association, and our results do not support the existence of common genetic factors for retinal thickness and dementia, either of AD or non-AD dementia types. A recent report by Sekimitsu et al. [46] in the UK Biobank population described a thicker inner nuclear layer (INL), chorio-scleral interface (CSI), and inner plexiform layer (IPL) among individuals with higher AD PRS scores, although the observed effect was very weak, below our statistical power; moreover, neither the individual association of the AD SNPs, nor the association of retinal PRSs and AD, were explored to fully clarify the relationship between retinal thickness and dementia.

Another potential limitation of our study would be the differences observed between retinal measures generated by the OCT Maestro and Triton devices. These outputs are determined by a complex combination of technical parameters differing by vendors and OCT technicians that may result in discordant estimations of retinal layer thickness [47]. To minimize this effect, both datasets were analyzed independently, and the association results were combined using meta-analysis. Moreover, only signals showing concordant effects in both eyes were considered.

Another interesting conclusion from our study is the suggestion, derived from the adjusted regression models for AD PRS as a predictor of dementia, that AD and non-AD dementia share genetic factors, as previously described by other authors [48].

Conclusions

Our results do not support the existence of a genetic link between dementia and retinal thickness, as suggested by the lack of association of AD genetic risk factors with macular RNFL and GCIPL measures, and, conversely, of RNFL and GCIPL genetic determinants with dementia, potentially limiting the utility of OCT measures as early biomarkers of cognitive decline. Reported associations between retinal phenotypes and cognitive status may occur via non-genetic risk exposures or may be diluted because are mediated by a common risk factor (for example diabetes). These mechanisms are probably complex enough (due to multiple environmental and genetic factors) that they cannot be disentangled using our current sample size. Much larger efforts are necessary to dissect such complex relationships.

Availability of data and materials

Available upon reasonable request.

Abbreviations

AD:

Alzheimer’s disease

ADD:

Alzheimer’s disease dementia

CH:

Cognitively healthy

Dp:

Dioptres

ETDRS:

Early Treatment of Diabetic Retinopathy Study

FACEHBI:

Fundació ACE Healthy Brain Initiative

GCIPL:

Ganglion cell inner plexiform layer

GCL:

Ganglion cell layer

GO:

Gene Ontology

GWAS:

Genome-wide association study

GR@CE:

Genome Research at Fundació ACE

IOP:

Intraocular pressure

MAF:

Minor allele frequency

MCI:

Mild cognitive impairment

MR:

Mendelian randomization

NORFACE:

Neuro-Ophthalmology Research at Fundació ACE

OCT:

Optical coherence tomography

PC:

Principal component

QC:

Quality control

PRS:

Polygenic risk score

RNFL:

Retinal Nerve fiber layer

SCD:

Subjective cognitive decline

SNP:

Single-nucleotide polymorphism

SS:

Swept source

TABS:

Topcon Advanced Boundary Segmentation TM

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Acknowledgements

The authors are grateful to all participants from the NORFACE and GR@CE studies and their families. We thank Topcon for its reliability as a technological partnership. Ace Alzheimer Center Barcelona is one of the participating centers of the Dementia Genetics Spanish Consortium (DEGESCO). Some control samples and data from patients included in this study were provided in part by the National DNA Bank Carlos III (www.bancoadn.org, University of Salamanca, Spain) and Hospital Universitario Virgen de Valme (Sevilla, Spain); they were processed following standard operating procedures with the appropriate approval of the Ethical and Scientific Committee.

The GR@CE study group:

Aguilera Núria1, Appiani Franco1, Aritón Diana M1, Bojaryn Urzsula1, Buendia Mar1, Cañabate Pilar1,2, Carracedo Angel3,4, Corbatón-Anchuelo A5, Diego Susana1, Gailhajenet Anna1, Guitart Marina1, González-Pérez Antonio6, Ibarria Marta1, Lafuente Asunción1, Lleonart Núria,1 Macias Juan7, Maroñas Olalla3, Martín Elvira1, Martínez Maria Teresa5, Montrreal Laura1, Moreno-Grau Sonia1,2, Moreno Mariona1, Muñoz Nathalia,1 Nuñez-Llaves Raúl1, Orellana Adelina1, Ortega Gemma1,2, Pancho Ana1, Pelejà Ester1, Pérez-Cordon Alba1, Pineda Juan A8, Preckler Silvia1, Quintela Inés3, Real Luis Miguel7,8, Rosende-Roca Maitee1, Sanabria Angela1,2, Seguer Susanna,1 Serrano-Rios Manuel5, Sotolongo-Grau Oscar1, Tartari Juan Pablo,1 Vargas Liliana1.

1Ace Alzheimer Center Barcelona – Universitat Internacional de Catalunya, Spain. 2CIBERNED, Center for Networked Biomedical Research on Neurodegenerative Diseases, National Institute of Health Carlos III, Ministry of Economy and Competitiveness, Spain, 3Grupo de Medicina Xenómica, Centro Nacional de Genotipado (CEGEN-PRB3-ISCIII). Universidad de Santiago de Compostela, Santiago de Compostela, Spain. 4Fundación Pública Galega de Medicina Xenómica- CIBERER-IDIS, Santiago de Compostela, Spain. 5Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, CIBERDEM, Spain, Hospital Clínico San Carlos, Madrid, Spain. 6CAEBI. Centro Andaluz de Estudios Bioinformáticos, Sevilla, Spain. 7Unidad Clínica de Enfermedades Infecciosas y Microbiología. Hospital Universitario de Valme, Sevilla, Spain. 8Dept. of Surgery, Biochemistry and Molecular Biology, School of Medicine. University of Málaga. Málaga, Spain.

The DEGESCO consortium:

Adarmes-Gómez Astrid Daniela1,2, Aguilar Miquel3,4, Aguilera Nuria5, Alarcón-Martín Emilio5, Alcolea Daniel6,2, Alegret Montserrat5,2, Alonso María Dolores7, Alvarez Ignacio3,4, Álvarez Victoria8,9, Amer-Ferrer Guillermo10, Antequera Martirio11, Antonell Anna12, Antúnez Carmen13, Arias Pastor Alfonso14,15, Baquero Miquel16, Belbin Olivia6,2, Bernal Sánchez-Arjona María17, Boada Mercè5,2, Buendia Mar5, Buiza-Rueda Dolores1,2, Bullido María Jesús18,2,19,20, Buongiorno Mariateresa3,4, Burguera Juan Andrés21, Calero Miguel22,2,23, Cano Amanda5, Cañabate Pilar5, Cardona Serrate Fernando24,2,25, Carracedo Ángel26,27, Carrillo Fátima1,2, Casajeros María José28, Clarimon Jordi6,2, Corbatón-Anchuelo Arturo29,30, Corma-Gómez Anaïs31, De la Guía Paz32, de Rojas Itziar5,2, del Ser Teodoro33, Diego Susana5, Diez-Fairen Mónica3,4, Dols-Icardo Oriol6,2, Espinosa Ana5,2, Fernández-Fuertes Marta31, Fortea Juan6,2, Franco-Macías Emilio17,2, Frank-García Ana34,2,35,20, Gailhajenet Anna5, García-Alberca Jose María32, García-González Pablo5,2, García-Madrona Sebastián28, Garcia-Ribas Guillermo28, Garrote-Espina Lorena1,2, Gómez-Garre Pilar1,2, González-Pérez Antonio36, Guitart Marina5, Huerto Vilas Raquel14,15, Ibarria Marta5, Jesús Silvia1,2, Labrador Espinosa Miguel Angel1,2, Lafuente Asunción5, Lage Carmen37,2, Legaz Agustina11, Lleó Alberto6,2, López-García Sara38,2, Lopez de Munain Adolfo39,40,41,2, Macías Juan31, Macias-García Daniel1,2, Manzanares Salvadora11, Marín Marta17, Marín-Muñoz Juan11, Maroñas Olalla26, Marquié Marta5,2, Martín Elvira5, Martín Montés Angel42,2,35, Martínez Begoña11, Martínez Victoriana11, Martínez-Lage Álvarez Pablo43, Martínez-Larrad María Teresa29,30, Martinez de Pancorbo Marian44, Martínez Rodríguez Carmen45,9, Medina Miguel2,22, Mendioroz Iriarte Maite46, Mendoza Silvia32, Menéndez-González Manuel47,9,48, Mir Pablo1,2,49, Molina-Porcel Laura50,12, Montrreal Laura5, Moreno Mariola5, Moreno Fermin39,2,41, Muñoz-Delgado Laura1,2, Noguera Perea Fuensanta11, Núñez-Llaves Raúl5, Olivé Clàudia5, Ortega Gemma5,2, Pancho Ana5, Pastor Ana Belén22,52, Pastor Pau53,54, Pelejà Ester5, Pérez-Cordón Alba5, Pérez-Tur Jordi24,2,25, Periñán María Teresa1,2, Pineda Juan Antonio31, Pineda-Sánchez Rocío1,2, Piñol-Ripoll Gerard14,15, Preckler Silvia5, Puerta Raquel5, Quintela Inés26, Rábano Alberto22,51,2, Real Luis Miguel31,54, Real de Asúa Diego55, Rodriguez-Rodriguez Eloy37,2, Rosas Allende Irene8,9, Rosende-Roca Maitée5,2, Royo Jose Luís56, Ruiz Agustín5,2, Sáez María Eugenia36, Sanabria Ángela5,2, Sánchez-Juan Pascual57,2,58, Sánchez-Valle Raquel12, Sastre Isabel18,2, Serrano-Ríos Manuel29,30, Sotolongo-Grau Oscar5, Tárraga Lluís5,2, Valero Sergi5,2, Vargas Liliana5, Vicente María Pilar11, Vivancos-Moreau Laura11, Zulaica Miren41,2.

1Unidad de Trastornos del Movimiento, Servicio de Neurología y Neurofisiología. Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Seville, Spain, 2CIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, National Institute of Health Carlos III, Madrid, Spain, 3Fundació Docència i Recerca MútuaTerrassa, Terrassa, Barcelona, Spain, 4Memory Disorders Unit, Department of Neurology, Hospital Universitari Mutua de Terrassa, Terrassa, Barcelona, Spain, 5Ace Alzheimer Center Barcelona – Universitat Internacional de Catalunya, Spain, 6Department of Neurology, II B Sant Pau, Hospital de la Santa Creu i Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain, 7Servei de Neurologia. Hospital Clínic Universitari de València, 8Laboratorio de Genética. Hospital Universitario Central de Asturias, Oviedo, Spain, 9Instituto de Investigación Sanitaria del Principado de Asturias (ISPA), 10Department of Neurology, Hospital Universitario Son Espases, Palma, Spain, 11Unidad de Demencias. Hospital Clínico Universitario Virgen de la Arrixaca, Palma, Spain, 12Alzheimer’s disease and other cognitive disorders unit. Service of Neurology. Hospital Clínic of Barcelona. Institut d’Investigacions Biomèdiques August Pi i Sunyer, University of Barcelona, Barcelona, Spain, 13Unidad de Demencias, Hospital Clínico Universitario Virgen de la Arrixaca, Murcia, Spain, 14Unitat Trastorns Cognitius, Hospital Universitari Santa Maria de Lleida, Lleida, Spain, 15Institut de Recerca Biomedica de Lleida (IRBLLeida), Lleida, Spain, 16Servei de Neurologia, Hospital Universitari i Politècnic La Fe, Valencia, Spain, 17Unidad de Demencias, Servicio de Neurología y Neurofisiología. Instituto de Biomedicina de Sevilla (IBiS), Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, Seville, Spain, 18Centro de Biología Molecular Severo Ochoa (UAM-CSIC), 19Instituto de Investigacion Sanitaria “Hospital la Paz” (IdIPaz), Madrid, Spain, 20Universidad Autónoma de Madrid, 21Servei de Neurologia, Hospital Universitari i Politècnic La Fe, Valencia, Spain, 22CIEN Foundation/Queen Sofia Foundation Alzheimer Center, 23UFIEC, Instituto de Salud Carlos III, 24Unitat de Genètica Molecular, Institut de Biomedicina de València-CSIC, Valencia, Spain, 25Unidad Mixta de Neurologia Genètica, Instituto de Investigación Sanitaria La Fe, Valencia, Spain, 26Grupo de Medicina Xenómica, Centro Nacional de Genotipado (CEGEN-PRB3-ISCIII). Universidade de Santiago de Compostela, Santiago de Compostela, Spain, 27Fundación Pública Galega de Medicina Xenómica- CIBERER-IDIS, Santiago de Compostela, Spain, 28Hospital Universitario Ramon y Cajal, IRYCIS, Madrid, Spain, 29Instituto de Investigación Sanitaria, Hospital Clínico San Carlos (IdISSC), Madrid, Spain, 30Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders(CIBERDEM), Madrid, Spain, 31Unidad Clínica de Enfermedades Infecciosas y Microbiología. Hospital Universitario de Valme, Sevilla, Spain, 32Alzheimer Research Center & Memory Clinic, Instituto Andaluz de Neurociencia, Málaga, Spain, 33Department of Neurology/CIEN Foundation/Queen Sofia Foundation Alzheimer Center, 34Department of Neurology, La Paz University Hospital. Instituto de Investigación Sanitaria del Hospital Universitario La Paz. IdiPAZ, 35Hospital La Paz Institute for Health Research, IdiPAZ, Madrid, Spain, 36CAEBI, Centro Andaluz de Estudios Bioinformáticos, Sevilla, Spain, 37Neurology Service, Marqués de Valdecilla University Hospital (University of Cantabria and IDIVAL), Santander, Spain, 38Service of Neurology, University Hospital Marqués de Valdecilla, IDIVAL, University of Cantabria, Santander, Spain, 39Department of Neurology. Hospital Universitario Donostia. San Sebastian, Spain, 40Department of Neurosciences. Faculty of Medicine and Nursery. University of the Basque Country, San Sebastián, Spain, 41Neurosciences Area. Instituto Biodonostia. San Sebastian, Spain, 42Department of Neurology, La Paz University Hospital, 43Centro de Investigación y Terapias Avanzadas. Fundación CITA-alzheimer, San Sebastian, Spain, 44BIOMICS País Vasco; Centro de investigación Lascaray, Universidad del País Vasco UPV/EHU, Vitoria-Gasteiz, Spain, 45Hospital de Cabueñes, Gijón, Spain, 46Navarrabiomed, Pamplona, Spain, 47Servicio de Neurología. Hospital Universitario Central de Asturias, Oviedo, Spain, 48Departamento de Medicina, Universidad de Oviedo, Oviedo, Spain, 49Departamento de Medicina, Facultad de Medicina, Universidad de Sevilla, Seville, Spain, 51Neurological Tissue Bank of the Biobanc-Hospital Clinic-IDIBAPS, Institut d’Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain, 50BT-CIEN, 51Unit of Neurodegenerative diseases, Department of Neurology, Hospital Germans Trias i Pujol, Badalona, Barcelona, 52Neurodegenerative Diseases Research Laboratory, Germans Trias i Pujol Research Laboratory, Badalona, Barcelona, 53Departamento de Especialidades Quirúrgicas, Bioquímica e Inmunología. Facultad de Medicina. Universidad de Málaga. Málaga, Spain, 54Hospital Universitario La Princesa, Madrid, Spain, 55Departamento de Especialidades Quirúrgicas, Bioquímica e Inmunología. School of Medicine. University of Malaga. Málaga, Spain, 56Alzheimer’s Centre Reina Sofia-CIEN Foundation, Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain, 57IDIVAL.

Funding

This project has received funding from the Instituto de Salud Carlos III (ISCIII) Acción Estratégica en Salud, integrated in the Spanish National RCDCI Plan and financed by ISCIII-Subdirección General de Evaluación and the Fondo Europeo de Desarrollo Regional (FEDER - Una manera de hacer Europa) grant PI19/00335 awarded to MM, grant PI17/01474 awarded to MB, and grants AC17/00100, PI19/01301, PI22/01403, and PMP22/00022 awarded to AR. AR and MB received support from the European Union/EFPIA Innovative Medicines Initiative joint undertaking ADAPTED and MOPEAD projects (grant numbers 115975 and 115985, respectively). This project is also funded by the European Union Joint Programme – Neurodegenerative Disease Research (JPND) Multinational research projects on Personalized Medicine for Neurodegenerative Diseases/Instituto de Salud Carlos III grant AC19/00097 awarded to AR. IdR received funding support from the ISCIII grant FI20/00215. AC received funding support from the ISCIII grant Sara Borrell (CD22/00125) and Ministerio de Ciencia e Innovación, Proyectos de Generación de Conocimiento grant PID2021-122473OA-I00. PGG was supported by CIBERNED employment plan CNV-304-PRF-866. The GR@ACE project is supported by Grifols SA, Fundación bancaria “La Caixa”, Ace Alzheimer Center Barcelona, and CIBERNED.

Author information

Author notes

  1. Agustín Ruiz and Marta Marquié contributed equally to this work.

Authors and Affiliations

  1. Ace Alzheimer Center Barcelona, Universitat Internacional de Catalunya (UIC), Barcelona, Spain

    María Eugenia Sáez, Ainhoa García-Sánchez, Itziar de Rojas, Emilio Alarcón-Martín, Joan Martínez, Amanda Cano, Pablo García-González, Raquel Puerta, Clàudia Olivé, Maria Capdevila, Fernando García-Gutiérrez, Ana Espinosa, Montserrat Alegret, Mario Ricciardi, Vanesa Pytel, Sergi Valero, Lluís Tárraga, Mercè Boada, Agustín Ruiz & Marta Marquié

  2. Centro Andaluz de Estudios Bioinformáticos (CAEBI), Seville, Spain

    María Eugenia Sáez

  3. Networking Research Center on Neurodegenerative Diseases (CIBERNED), Instituto de Salud Carlos III, Madrid, Spain

    Itziar de Rojas, Amanda Cano, Ana Espinosa, Montserrat Alegret, Sergi Valero, Lluís Tárraga, Mercè Boada, Agustín Ruiz & Marta Marquié

  4. Clínica Oftalmológica Dr. Castilla, Barcelona, Spain

    Miguel Castilla-Martí

  5. Vista Alpina Eye Clinic, Visp, Switzerland

    Miguel Castilla-Martí

  6. PhD Programme in Surgery and Morphological Sciences, Universitat Autònoma de Barcelona, Barcelona, Spain

    Luis Castilla-Martí

  7. Hôpital ophtalmique Jules-Gonin, Fondation asiles des aveugles, University of Lausanne, Lausanne, Switzerland

    Luis Castilla-Martí

  8. Biggs Institute for Alzheimer’s and Neurodegenerative Diseases, University of Texas Health Science Center, San Antonio, Texas, USA

    Agustín Ruiz

Authors
  1. María Eugenia Sáez
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  2. Ainhoa García-Sánchez
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  3. Itziar de Rojas
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  4. Emilio Alarcón-Martín
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  5. Joan Martínez
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Contributions

MES, MB, LT, AR, and MM were involved in the study design and conduct. MES oversaw the statistical analysis. MES, AR, and MM wrote the manuscript. EA-M, AG-S, MM, IdR, and PGG prepared the databases. JM, MA, AE, MR, VP, and MM were involved in the acquisition of data. IdR, AC, PGG, RP, CO, MC, FGG, MC-M, LC-M, and MM were involved in data analysis and interpretation. All authors contributed to critical review of the manuscript and approval of the final manuscript and agree to be accountable for all aspects of the work.

Corresponding author

Correspondence to Agustín Ruiz.

Ethics declarations

Ethics approval and consent to participate

This study and its informed consent were approved by the Ethics Committees of the Hospital Clínic i Provincial de Barcelona in accordance with Spanish biomedical laws (Law 14/2007, July 3rd, about biomedical research; Royal Decree 1716/2011, November 18th) and followed the recommendations of the Declaration of Helsinki. The patients/participants provided their written informed consent to participate in this study.

Consent for publication

All authors have read and approved the final manuscript for publication.

Competing interests

MB has consulted for Araclon, Avid, Grifols, Lilly, Nutricia, Roche, Eisai, and Servier. She has received fees from lectures and funds for research from Araclon, Biogen, Grifols, Nutricia, Roche, and Servier. She reports grants/research funding from Abbvie, Araclon, Biogen Research Limited, Bioiberica, Grifols, Lilly, S.A, Merck Sharp & Dohme, Kyowa Hakko Kirin, Laboratorios Servier, Nutricia SRL, Oryzon Genomics, Piramal Imaging Limited, Roche Pharma SA, and Schwabe Farma Iberica SLU, all outside the submitted work. She has not received personal compensation from these organizations. AR is a member of the scientific advisory board of Landsteiner Genmed and Grifols SA. AR has stocks in Landsteiner Genmed. MM has consulted for F. Hoffmann-La Roche Ltd. The rest of authors declare that they have no competing interests.

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Sáez, M.E., García-Sánchez, A., de Rojas, I. et al. Genome-wide association study and polygenic risk scores of retinal thickness across the cognitive continuum: data from the NORFACE cohort. Alz Res Therapy 16, 38 (2024). https://doi.org/10.1186/s13195-024-01398-8

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Keywords

Alzheimer's Research & Therapy

ISSN: 1758-9193