Causal relationship between Alzheimer's disease and retinal nerve fiber layer thickness, visual disturbance: a two-sample Mendelian randomization study.

Causal relationship between Alzheimer's disease and retinal nerve fiber layer thickness, visual disturbance: a two-sample Mendelian randomization study. | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Causal relationship between Alzheimer's disease and retinal nerve fiber layer thickness, visual disturbance: a two-sample Mendelian randomization study. Xuan Han, Xiaojuan Su, Jinyan Wang, Xingyu Guo, Hejiang Ye This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4253388/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Alzheimer's disease (AD) is a chronic, progressive, irreversible degenerative disorder of the central nervous system caused by multiple factors.Previous studies have demonstrated that patients with AD experience visual disturbances and reduced retinal nerve fiber layer (RNFL) thickness in the early stages. If a causal relationship can be established between AD and visual disturbances as well as RNFL thickness, then AD can be diagnosed by early visual disturbances and RNFL thickness changes.To investigate the causal relationship between AD and visual disturbances as well as RNFL thickness, we conducted a Mendelian randomization study.In this MR study, the main approach was inverse variance weighted (IVW) and evaluate the robustness of the results via sensitivity analysis. The IVW results revealed no significant evidence supporting a causal association between AD and RNFL thickness (P = 0.283, odds ratio [OR] [95% confidence interval (CI)] = 0.926[0.806-1.065]) as well as visual disturbances(P = 0.205, odds ratio [OR] [95% confidence interval (CI)] = 1.070[0.964-1.187]) .Consequently, the potential for diagnosing early-stage AD through alterations in RNFL thickness and visual disturbances remains controversial. Health sciences/Diseases/Eye diseases Health sciences/Neurology/Neurological disorders alzheimer's disease retinal nerve fiber layer thickness visual disturbance mendelian randomization causality Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Alzheimer's disease (AD)is a chronic, progressive, irreversible degenerative disorder of the central nervous system caused by multiple factors [1] . AD is the leading cause of dementia, accounting for approximately 50–70% of global dementia cases and has been recognized as a major public health concern by the World Health Organization [2] .Recent research indicates that the global prevalence of dementia is projected to reach 152 million individuals by mid-century, thereby exerting unprecedented social and economic burdens on society [3–4] .The primary methods for early detection of AD are neuroimaging techniques (e.g., magnetic resonance imaging and positron emission tomography imaging) and cerebrospinal fluid biomarker analysis [5] . However, the widespread clinical application of neuroimaging techniques has been limited due to their high cost and restricted resolution [5] . Moreover, the invasive nature of cerebrospinal fluid collection further impedes its extensive use in clinical practice [5] . Therefore, the future advancement of novel biomarkers is imperative for early AD diagnosis. The retina originates from the forebrain and is regarded as an integral component of the central nervous system [6] .There is compelling evidence indicating varying degrees of pathological alterations in the retinal tissue of patients with AD [6] .Using optical coherence tomography(OCT),Jagan A Pillai et al. conducted a comparative analysis of the retinal nerve fiber layer (RNFL) thickness between 21 patients with AD and 34 healthy subjects, revealing a significant reduction in RNFL thickness among individuals with AD [7] .Elena Salobrar-García et al. evaluated visual acuity, contrast sensitivity, color perception, and visual integration in 39 patients with mild AD, 21 patients with moderate AD, and 40 age-matched healthy subjects [8] . The results demonstrated significantly lower levels of visual acuity, contrast sensitivity, color perception, and visual integration rate among individuals with AD compared to those in the healthy control group [8] . Notably, studies have demonstrated that pathological changes in the retina of patients with AD precede those in the brain [9] . If a causal relationship between AD and alterations in RNFL thickness, as well as visual disturbances, can be established, early diagnosis of AD may be facilitated based on these changes.However, current investigations regarding the impact of AD on RNFL thickness and visual disturbances are observational in nature, leaving the existence of a causal relationship uncertain. Mendelian randomization (MR) analysis is a statistical methodology that primarily employs genetic variation, particularly single nucleotide polymorphisms (SNPs), to evaluate the potential causal relationship between exposure and outcome [10] .Diverging from the traditional randomized controlled trials (RCTs), MR analysis can mitigate the influence of confounding factors on experimental results by leveraging randomly assorted genetic variants at conception, which are independent of postnatal environmental factors [11] . Furthermore, MR methods can attenuate the impact of reverse causation on outcomes as germline genotypes are unlikely to change concomitantly with the development of diseases [12] . 2. Methods 2.1 Study design and ethics statement To investigate the potential causal relationship between AD and RNFL thickness, as well as visual disturbance, a two-sample MR analysis was conducted. The MR analysis necessitates adherence to three fundamental assumptions(Fig. 1 ): (1)Assumption of instrumental variable relevance, which requires a robust correlation between SNPs and exposure factors; (2) Assumption of instrumental variable independence, where SNPs are independent of confounding factors; (3) Assumption of instrumental variable exclusivity, indicating that SNPs solely influence the outcome through the exposure factor. Given the utilization of publicly accessible repositories, ethical sanctions or informed consent from participants were not required. 2.2 Data sources The data utilized in this study were obtained from the publicly available GWAS database. Specifically, we considered AD as an exposure factor and RNFL thickness and visual disturbance as outcomes. To obtain GWAS summary data related to these factors, we accessed the IEU database ( https://gwas.mrcieu.ac.uk/ ). Further details can be found in Table 1 . Table 1 Characteristics of samples used for MR analysis Traits Used as Population IEU GWAS id Number of SNPs AD exposure European ebi-a-GCST90027158 20,921,626 RNFL thickness outcome European ebi-a-GCST90014266 9,121,075 Subjective visual disturbances outcome European finn-b-H7_VISUDISTURBSUB 16,380,464 2.3 IVs selection Based on the GWAS data mentioned above, we implemented a rigorous screening procedure to identify high-quality SNPs as instrumental variables (IVs). Firstly, we selected SNPs strongly associated with the exposure factors using a filtering condition of P value < 5*10 − 8. Secondly, in order to mitigate the impact of linkage disequilibrium among SNPs, we set R 2 and KB values at < 0.001 and 10000 KB respectively. Thirdly, to address potential pleiotropy effects, SNPs independence were ensured by querying the PhenoScanner database (version 2.0) ( http://phenoscanner.medschl.cam.ac.uk/ ) to exclude any SNPs associated with confounders and outcome variables. Fourthly, we calculated the F-value for each SNP using the equation F = β 2 /SE 2 and only selected those with F-values greater than 10 to avoid bias caused by weak instrumental variables in MR analysis results. Finally, in order to ensure consistency of effect alleles, any SNPs exhibiting palindromic properties or intermediate allele frequencies were eliminated through harmonization of exposure and outcome data. 2.4 Statiscal analysis The causal relationship between AD and RNFL thickness as well as visual disturbance was evaluated using five methods: Instrumental variable weighting(IVW), MR-Egger, simple model, weight model, and weight median.IVW is the primary approach for pooling MR data, employing the Wald ratio method to estimate the causal effect of each IV, followed by a weighted aggregation analysis, which provides a highly accurate reference for causal inference [13] . The MR-Egger method is utilized to assess horizontal pleiotropy and ensure consistent estimation of causal effects under the assumption of Instrument Strength Independent of Direct Effect (InSIDE) [14] . The weight median method requires that at least 50% of the weights originate from valid IVs in order to estimate causal effects reliably [15] . The weight model incorporates similarity information among SNPs to enhance the robustness of MR analysis results [16] . Additionally, a simple model serves as a supplementary approach for evaluating potential causal relationships [16] . All analyses were performed using version 4.3.2 of R with the 'TwoSampleMR' package. 2.5 Sensitivity analysis The sensitivity analysis is divided into three main areas, which are heterogeneity test, horizontal multiple validity test and leave-one-out sensitivity analysis.Cochran's Q statistic was calculated using IVW and MR-Egger regression, with P value > 0.05 indicating no significant heterogeneity. Validation of horizontal pleiotropy was done through MR-Egger intercept test and MR-PRESSO test. If the MR-Egger intercept test shows p > 0.05, it proves that there is no horizontal pleiotropy. MR-PRESSO can identify SNPs with horizontal pleiotropy (i.e., outliers) and assess whether there is a significant difference in the causal effect estimates before and after removal of the outliers. The global test of MR-PRESSO should not be significant ( P > 0.05). MR- Egger intercept test and MR-PRESSO test can further validate Hypotheses (2) and (3).In addition, a leave-one-out sensitivity analysis was used to assess the impact of individual SNPs on MR results as a means of assessing the robustness of the results of the MR analysis. 3. Results 3.1 Causal Effects of AD and RNFL 3.1.1 IVs selection We identified 59 SNPs that exhibit a strong correlation with AD ( p < 5 * 10 − 8), ensuring their independence from linkage imbalance effects under the condition of r2 < 0.001 and Kb = 10000Kb. One outlier SNP (rs1799777) was excluded using the MR-PRESSO test. SNPs associated with outcome and confounders, namely rs7384878 and rs199451, were eliminated based on Phenoscanner. Additionally, palindromic SNPs with intermediate allele frequencies, including rs10933431, rs1385742, rs1065712, rs11500477, rs148601586, and rs61679753 were excluded to enhance the robustness of our Mendelian randomization analysis. Ultimately, a total of 50 SNPs were included in the final analysis.The selected IVs all possess F values greater than 10,as detailed in Supplementary Table 1. 3.1.2 MR analysis The IVW method was primarily employed to assess the presence of a causal relationship between AD and RNFL thickness. However, the IVW results revealed no significant evidence supporting a causal association between AD and RNFL thickness ( P = 0.283, odds ratio [OR] [95% confidence interval (CI)] = 0.926[0.806–1.065]) as depicted in Fig. 2 . Furthermore, the findings from MR-Egger analysis, simple model analysis, weight model analysis, and weight median analysis consistently indicated an absence of causality between AD and RNFL thickness. These outcomes are visually presented in Fig. 3 and summarized in Table 2 . Table 2 Mendelian randomization estimates for AD on RNFL thickness. Methods OR 95%CI P- value MR Egger 0.806 0.613–1.060 0.130 Weighted median 0.982 0.794–1.213 0.864 Inverse variance weighted 1.086 0.806–1.065 0.283 Simple mode 0.957 0.723–1.631 0.693 Weighted mode 0.240 0.721–1.271 0.763 3.1.3 Sensitivity analysis Examination of MR-Egger regression (Cochran's Q = 46.973, p = 0.474) and IVW analysis (Cochran's Q = 48.301, p = 0.461) revealed no signs of heterogeneity.The MR intercept( p > 0.05 ) test indicates a very low probability of horizontal pleiotropy, thereby minimizing the potential for bias effects in the MR analysis results. Furthermore, the graphical symmetry observed in Figure (4) of the funnel plot demonstrates an absence of systematic bias between study effects and their accuracy. Additionally, we conducted leave-one-out sensitivity analysis to assess the influence of individual SNPs on our results. This method involves gradually eliminating each SNP and calculating meta-effects based on the remaining SNPs to observe any significant changes in outcomes. As depicted in Figure (5), minimal alterations were observed in the overall error line after eliminating each SNP (all error lines are positioned to the left of 0). These findings affirm that our MR results can be deemed reliable. 3.2 Causal Effects of AD and visual disturbance 3.2.1 IVs selection We identified 59 SNPs that exhibit a strong correlation with AD ( p < 5 * 10 − 8 ), ensuring their independence from linkage imbalance effects under the condition of r 2 < 0.001 and Kb = 10000Kb. No outlier SNPs were excluded using the MR-PRESSO test. SNPs associated with outcome and confounders, namely rs7384878 and rs199451, were eliminated based on Phenoscanner. Additionally, palindromic SNPs with intermediate allele frequencies, including rs1065712, rs10933431, rs11500477, rs148601586, and rs61679753 were excluded to enhance the robustness of our Mendelian randomization analysis. Ultimately, a total of 52 SNPs were included in the final analysis.The selected IVs all possess F values greater than 10, as detailed in Supplementary Table 1. 3.2.2 MR analysis The IVW method was primarily employed to assess the presence of a causal relationship between AD and RNFL thickness. However, the IVW results revealed no significant evidence supporting a causal association between AD and visual disturbance ( P = 0.205, odds ratio [OR] [95% confidence interval (CI)] = 1.070[0.964–1.187]) as depicted in Fig. 6 . Furthermore, the findings from MR-Egger analysis, simple model analysis, weight model analysis, and weight median analysis consistently indicated an absence of causality between AD and RNFL thickness. These outcomes are visually presented in Fig. 7 and summarized in Table 3 . Table 3 Mendelian randomization estimates for AD on visual disturbance. Methods OR 95%CI P- value MR Egger 1.002 0.819–1.226 0.986 Weighted median 1.103 0.949–1.281 0.201 Inverse variance weighted 1.070 0.964–1.187 0.205 Simple mode 1.213 0.938–1.570 0.148 Weighted mode 1.086 0.919–1.284 0.085 3.2.3 Sensitivity analysis Examination of MR-Egger regression (Cochran's Q = 52.364, p = 0.308) and IVW analysis (Cochran's Q = 52.967, p = 0.324) revealed no signs of heterogeneity.The MR intercept( p > 0.05 ) test indicates a very low probability of horizontal pleiotropy, thereby minimizing the potential for bias effects in the MR analysis results. Furthermore, the graphical symmetry observed in Figure (8) of the funnel plot demonstrates an absence of systematic bias between study effects and their accuracy. Additionally, we conducted leave-one-out sensitivity analysis to assess the influence of individual SNPs on our results. This method involves gradually eliminating each SNP and calculating meta-effects based on the remaining SNPs to observe any significant changes in outcomes. As depicted in Figure (9), minimal alterations were observed in the overall error line after eliminating each SNP (all error lines are positioned to the left of 0). These findings affirm that our MR results can be deemed reliable. 4. Discussion Numerous studies have consistently reported that individuals with AD may exhibit visual disturbances and a reduction in RNFL thickness; however, the causal relationship between these factors remains inconclusive. To the best of our knowledge, this is the first two-sample MR study aiming to investigate the potential causal association between AD and RNFL thickness as well as visual disturbance. The findings from our study suggest that there is no definitive evidence supporting a direct causal link between genetic predisposition to AD and an increased risk of RNFL thinning and visual disturbance. The existing literature highlights the strong association between AD and RNFL thickness, as well as visual disturbances. R N Guliyeva et al. utilized OCT to study 45 AD patients and 60 healthy subjects, confirming a significant decrease in RNFL thickness among AD patients ( p <0.001) [17] .AD is a chronic degenerative disease, and early visual disturbance (such as visual field defects, abnormal contrast sensitivity, color vision abnormalities, depth perception defects) are also typical clinical manifestations of AD [18] . Electroretinogram (ERG) and visual evoked potentials (VEP) can effectively reflect visual function. Qi Zhe Ngoo et al. demonstrated that graphic ERG showed a significant decrease in the amplitude of P50 and N95 waves in AD patients [19] . Furthermore, this study revealed a decreased amplitude of P100 and increased latency in pattern VEP among AD patients [19] . The main pathogenesis of AD involves cholinergic neuron dysfunction, with flash visual evoked potential (FVEP) P2 being associated with cholinergic function in the brain [20] . It has been shown that FVEP-P2 latency is prolonged in AD patients [21] . These findings collectively confirm the presence of visual disturbances experienced by individuals with AD. This study did not reveal a significant genetic causal relationship between AD and RNFL thickness, as well as visual disturbances. The authors suggest that the observed association between AD and thinning RNFL thickness, as well as visual disturbances reported in clinical studies, may be driven by similar pathogenesis mechanisms. Abnormal deposition of amyloid-β (Aβ) protein in the brain is a typical manifestation of AD patients, which can be detected decades before the onset of clinical symptoms. Recent studies have identified Aβ protein deposits in retinas of AD patients [22-23] . Qinyuan Alis Xu et al. found higher levels of Aβ protein deposits in the retinas of AD patients compared to control retinas [24] . Sieun Lee et al. also observed significantly increased intracellular and extracellular Aβ protein deposits in the retinas of AD patients compared to controls [25] . Yosef Koronyo et al. utilized curcumin-based amyloid probes for labeling and successfully visualized retinal Aβ proteins using a modified laser-scanning ophthalmoscope, showing 2.1 times higher levels of retinal Aβ proteins in AD patients than healthy controls [26] . Another study employed hyperspectral imaging to directly detect retinal Aβ protein content in AD patients, demonstrating its potential for predicting brain accumulation of Aβ proteins [27] .It is worth noting that researchers have discovered an earlier presence of retinal Aβ protein compared to neurodegeneration and the subsequent accumulation of brain Aβ protein [28-29] . The buildup of Aβ protein in the brain can induce an elevation in reactive oxygen species (ROS), impair mitochondrial function, and provoke oxidative stress, ultimately leading to cellular demise [30] .Similar to the brain, the glycolysis process in the retina of patients with AD is impacted by Aβ protein, resulting in an elevation of ROS [31] . This increase in ROS induces mutations in mitochondrial DNA, leading to the production of defective proteins that subsequently decrease ATP levels and enhance ROS generation, ultimately causing oxidative stress, mitochondrial impairment, apoptosis [31] .These aberrant reactions lead to degeneration of retinal ganglion cells (RGCs) and a decrease in the thickness of the RNFL in patients with AD [31] . Additionally, Aβ protein can activate glial cells within both the brain and retina which undergo morphological changes and alter their immune phenotype [32] . These activated glial cells proliferate and migrate towards injury sites while releasing various inflammatory factors such as nitric oxide (NO), tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), etc [32-34] . The presence of these inflammatory factors directly damages RGCs and RNFLs [35] . Degeneration of axonal cells within RGCs along with a reduction in RNFL thickness are closely associated with visual disturbances observed among AD patients [36] . The advantage of this study over traditional observational studies lies in its ability to optimize the impact of reverse causality and confounding factors on experimental outcomes. Furthermore, the utilization of a large-scale GWAS dataset and multiple sensitivity analysis methods enhances the reliability of the findings. However, there are certain limitations to consider in this study. Firstly, all cases included were from European populations, necessitating further investigation into its applicability to other human populations in future research. Secondly, due to limited availability of GWAS datasets for RNFL thickness and visual disturbance, validating differences in causal effects between various datasets becomes challenging. Conclusions Numerous observational studies have consistently demonstrated a robust correlation between AD and RNFL thickness, as well as visual impairment; however, our study did not establish a causal relationship between AD and changes in RNFL thickness or visual disturbance. Therefore, further investigations are warranted to elucidate the precise impact of AD on RNFL thickness and visual disturbances. Consequently, the potential for diagnosing early-stage AD through alterations in RNFL thickness and visual disturbances remains controversial. Abbreviations Alzheimer's disease (AD) optical coherence tomography(OCT) retinal nerve fiber layer (RNFL) Mendelian randomization (MR) single nucleotide polymorphisms (SNPs) randomized controlled trials (RCTs) Instrumental variable weighting(IVW) Electroretinogram (ERG) visual evoked potentials (VEP flash visual evoked potential (FVEP) amyloid-β (Aβ) reactive oxygen species (ROS) retinal ganglion cells (RGCs) nitric oxide (NO) tumor necrosis factor-alpha (TNF-α) interleukin-6 (IL-6) Declarations Ethical Approval: Given the utilization of publicly accessible repositories, ethical sanctions or informed consent from participants were not required. Funding: We don't get fund support. Data availability: All of the data analyzed in this study can be found in the https://gwas.mrcieu.ac.uk/. Conflict of interest: I declare that the authors have no competing interests as defined by Nature Research, or other interests that might be perceived to influence the results and/or discussion reported in this paper. Acknowledgments: The authors would like to thank all the colleagues who contributed to this work. Author Contributions Statement:Xuan Han wrote the main manuscript text.Xiaojuan Su,Jinyan Wang and Xingyu Guo prepared figures 1-9,Table1-3 and Supplementary table 1-2.All authors reviewed the manuscript. Consent for publication: Not applicable. References McKhann GM, Knopman DS, Chertkow H, et al. The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement J Alzheimers Assoc. 2011;7(3):263–269. doi: 10.1016/j.jalz.2011.03.005. Zhang XX, Tian Y, Wang ZT, Ma YH, Tan L, Yu JT. 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J Optom. 2019 Jul-Sep;12(3):198-207. doi: 10.1016/j.optom.2018.07.001. García-Bermúdez MY, Vohra R, Freude K, Wijngaarden PV, Martin K, Thomsen MS, Aldana BI, Kolko M. Potential Retinal Biomarkers in Alzheimer's Disease. Int J Mol Sci. 2023 Oct 31;24(21):15834. doi: 10.3390/ijms242115834. Ashok A, Singh N, Chaudhary S, Bellamkonda V, Kritikos AE, Wise AS, Rana N, McDonald D, Ayyagari R. Retinal Degeneration and Alzheimer's Disease: An Evolving Link. Int J Mol Sci. 2020 Oct 2;21(19):7290. doi: 10.3390/ijms21197290. Ramirez AI, de Hoz R, Salobrar-Garcia E, Salazar JJ, Rojas B, Ajoy D, López-Cuenca I, Rojas P, Triviño A, Ramírez JM. The Role of Microglia in Retinal Neurodegeneration: Alzheimer's Disease, Parkinson, and Glaucoma. Front Aging Neurosci. 2017 Jul 6;9:214. doi: 10.3389/fnagi.2017.00214. Additional Declarations No competing interests reported. Supplementary Files Supplementarytable1.xlsx Supplementarytable2.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4253388","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":302541053,"identity":"49278601-c6bd-40c3-86ad-c06482f137c0","order_by":0,"name":"Xuan Han","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xuan","middleName":"","lastName":"Han","suffix":""},{"id":302541054,"identity":"70fad3fb-8f39-4204-af58-41a68426614b","order_by":1,"name":"Xiaojuan Su","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xiaojuan","middleName":"","lastName":"Su","suffix":""},{"id":302541055,"identity":"20ad5a95-be5a-466d-851e-6a882154433f","order_by":2,"name":"Jinyan Wang","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Jinyan","middleName":"","lastName":"Wang","suffix":""},{"id":302541056,"identity":"318ec5a1-ea83-4af0-b92e-6f2509165dce","order_by":3,"name":"Xingyu Guo","email":"","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xingyu","middleName":"","lastName":"Guo","suffix":""},{"id":302541057,"identity":"bbec333e-2449-440f-ae40-1c26d6f200af","order_by":4,"name":"Hejiang Ye","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYFACHgYGCQMgzd7AwMDYQJIWngNQLWzEaAEDiQQitRgc7z34wKLALk8+8o3hg587GPL45Qm4zuDMuWQDCYPkYsPbOcaGvWcYiiXbCNlyI8dMQsKAOXHjbCCDt40hccMxQlruvzH/IWFQn7hx5hnzn3+BWvYT1HKDxwwYYocT50vwmDGDbSHkfckzOcZAhx1P3MCTViwt2yaROONYAn4tfMfPGH6W+FOdOL/98MaPb9tsEvubD+DXogCUZ5YAufAAByhCJQi4CghAkcD4Acxgf0BY+SgYBaNgFIxIAABAv0XQpj4i5AAAAABJRU5ErkJggg==","orcid":"","institution":"Chengdu University of Traditional Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Hejiang","middleName":"","lastName":"Ye","suffix":""}],"badges":[],"createdAt":"2024-04-11 15:44:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4253388/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4253388/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":56520970,"identity":"0a7afda1-8856-43f1-ba3c-58526a64fa34","added_by":"auto","created_at":"2024-05-15 08:50:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":117516,"visible":true,"origin":"","legend":"\u003cp\u003eThe key assumptions for our Mendelian randomization study.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4253388/v1/448a1faf548820587290264f.png"},{"id":56522379,"identity":"a23f4f41-48ab-4222-b056-662002af4acf","added_by":"auto","created_at":"2024-05-15 09:06:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":586718,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of the effect of AD on RNFL thickness.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4253388/v1/6607cf78468f042ed93335c6.png"},{"id":56521701,"identity":"17fe195d-40ef-4729-a6fe-3a67446ae6fe","added_by":"auto","created_at":"2024-05-15 08:58:56","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":312278,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of the effect of AD on RNFL thickness.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4253388/v1/c8c51fc610c5a26a4ecfe35c.png"},{"id":56520973,"identity":"cd6738f8-59d9-4aca-82d9-4a9a19204822","added_by":"auto","created_at":"2024-05-15 08:50:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":329803,"visible":true,"origin":"","legend":"\u003cp\u003eFunnel plot of the effect of AD on RNFL thickness.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4253388/v1/a855e6bcf8f92f109579e96b.png"},{"id":56521704,"identity":"f698d967-8742-4cdd-a0f5-141c4dccfb4f","added_by":"auto","created_at":"2024-05-15 08:58:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":592300,"visible":true,"origin":"","legend":"\u003cp\u003eLeave-one-out plot of the effect of AD on RNFL thickness.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4253388/v1/e0ecb39e69878757b3b47729.png"},{"id":56522932,"identity":"ecabc238-5dc7-441c-90a4-67cd31edbf06","added_by":"auto","created_at":"2024-05-15 09:14:56","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":501651,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of the effect of AD on visual disturbance.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4253388/v1/1ee6c1f6b92bf4ef366a4c0c.png"},{"id":56520978,"identity":"759bb34a-5b00-4b66-84d7-53636142106e","added_by":"auto","created_at":"2024-05-15 08:50:56","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":312993,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot of the effect of AD on visual disturbance.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4253388/v1/bdb1e542aea0e584c06950e1.png"},{"id":56520980,"identity":"f3c0088d-fd28-4deb-ab4f-dc4e4fd68c2c","added_by":"auto","created_at":"2024-05-15 08:50:57","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":248192,"visible":true,"origin":"","legend":"\u003cp\u003eFunnel plot of the effect of AD on visual disturbance.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4253388/v1/935645bc7bf17852adb2c1b9.png"},{"id":56520976,"identity":"f346edaf-e7bb-486a-8843-5a0949c95ee8","added_by":"auto","created_at":"2024-05-15 08:50:56","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":530459,"visible":true,"origin":"","legend":"\u003cp\u003eLeave-one-out plot of the effect of AD on visual disturbance.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-4253388/v1/43a575de3049ff20c0ec6819.png"},{"id":66835946,"identity":"121c17a8-5cb9-4431-be1b-9064a94fc3e8","added_by":"auto","created_at":"2024-10-17 03:54:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3586524,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4253388/v1/7f28360e-5969-48d0-9261-f086a86353b5.pdf"},{"id":56520971,"identity":"5527acfe-eae5-47ee-b53b-e88e46261461","added_by":"auto","created_at":"2024-05-15 08:50:56","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":13403,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4253388/v1/fb9fa1a2feb0458656ac0cdd.xlsx"},{"id":56520972,"identity":"9330ee0f-d417-41a9-a3eb-a148ba7a4076","added_by":"auto","created_at":"2024-05-15 08:50:56","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":13387,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4253388/v1/38134beefe9fd6041a9cdd72.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causal relationship between Alzheimer's disease and retinal nerve fiber layer thickness, visual disturbance: a two-sample Mendelian randomization study.","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAlzheimer's disease (AD)is a chronic, progressive, irreversible degenerative disorder of the central nervous system caused by multiple factors\u003csup\u003e[1]\u003c/sup\u003e. AD is the leading cause of dementia, accounting for approximately 50\u0026ndash;70% of global dementia cases and has been recognized as a major public health concern by the World Health Organization\u003csup\u003e[2]\u003c/sup\u003e .Recent research indicates that the global prevalence of dementia is projected to reach 152\u0026nbsp;million individuals by mid-century, thereby exerting unprecedented social and economic burdens on society\u003csup\u003e[3\u0026ndash;4]\u003c/sup\u003e.The primary methods for early detection of AD are neuroimaging techniques (e.g., magnetic resonance imaging and positron emission tomography imaging) and cerebrospinal fluid biomarker analysis\u003csup\u003e[5]\u003c/sup\u003e. However, the widespread clinical application of neuroimaging techniques has been limited due to their high cost and restricted resolution\u003csup\u003e[5]\u003c/sup\u003e. Moreover, the invasive nature of cerebrospinal fluid collection further impedes its extensive use in clinical practice\u003csup\u003e[5]\u003c/sup\u003e. Therefore, the future advancement of novel biomarkers is imperative for early AD diagnosis. The retina originates from the forebrain and is regarded as an integral component of the central nervous system\u003csup\u003e[6]\u003c/sup\u003e.There is compelling evidence indicating varying degrees of pathological alterations in the retinal tissue of patients with AD\u003csup\u003e[6]\u003c/sup\u003e.Using optical coherence tomography(OCT),Jagan A Pillai et al. conducted a comparative analysis of the retinal nerve fiber layer (RNFL) thickness between 21 patients with AD and 34 healthy subjects, revealing a significant reduction in RNFL thickness among individuals with AD\u003csup\u003e[7]\u003c/sup\u003e.Elena Salobrar-Garc\u0026iacute;a et al. evaluated visual acuity, contrast sensitivity, color perception, and visual integration in 39 patients with mild AD, 21 patients with moderate AD, and 40 age-matched healthy subjects\u003csup\u003e[8]\u003c/sup\u003e. The results demonstrated significantly lower levels of visual acuity, contrast sensitivity, color perception, and visual integration rate among individuals with AD compared to those in the healthy control group\u003csup\u003e[8]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eNotably, studies have demonstrated that pathological changes in the retina of patients with AD precede those in the brain\u003csup\u003e[9]\u003c/sup\u003e. If a causal relationship between AD and alterations in RNFL thickness, as well as visual disturbances, can be established, early diagnosis of AD may be facilitated based on these changes.However, current investigations regarding the impact of AD on RNFL thickness and visual disturbances are observational in nature, leaving the existence of a causal relationship uncertain.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) analysis is a statistical methodology that primarily employs genetic variation, particularly single nucleotide polymorphisms (SNPs), to evaluate the potential causal relationship between exposure and outcome\u003csup\u003e[10]\u003c/sup\u003e.Diverging from the traditional randomized controlled trials (RCTs), MR analysis can mitigate the influence of confounding factors on experimental results by leveraging randomly assorted genetic variants at conception, which are independent of postnatal environmental factors\u003csup\u003e[11]\u003c/sup\u003e. Furthermore, MR methods can attenuate the impact of reverse causation on outcomes as germline genotypes are unlikely to change concomitantly with the development of diseases\u003csup\u003e[12]\u003c/sup\u003e.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design and ethics statement\u003c/h2\u003e \u003cp\u003eTo investigate the potential causal relationship between AD and RNFL thickness, as well as visual disturbance, a two-sample MR analysis was conducted. The MR analysis necessitates adherence to three fundamental assumptions(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e): (1)Assumption of instrumental variable relevance, which requires a robust correlation between SNPs and exposure factors; (2) Assumption of instrumental variable independence, where SNPs are independent of confounding factors; (3) Assumption of instrumental variable exclusivity, indicating that SNPs solely influence the outcome through the exposure factor. Given the utilization of publicly accessible repositories, ethical sanctions or informed consent from participants were not required.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data sources\u003c/h2\u003e \u003cp\u003eThe data utilized in this study were obtained from the publicly available GWAS database. Specifically, we considered AD as an exposure factor and RNFL thickness and visual disturbance as outcomes. To obtain GWAS summary data related to these factors, we accessed the IEU database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Further details can be found in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of samples used for MR analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTraits\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUsed as\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIEU GWAS id\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNumber of SNPs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eexposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eebi-a-GCST90027158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20,921,626\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRNFL\u003c/p\u003e \u003cp\u003ethickness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eoutcome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eebi-a-GCST90014266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9,121,075\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubjective visual disturbances\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eoutcome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003efinn-b-H7_VISUDISTURBSUB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16,380,464\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 IVs selection\u003c/h2\u003e \u003cp\u003eBased on the GWAS data mentioned above, we implemented a rigorous screening procedure to identify high-quality SNPs as instrumental variables (IVs). Firstly, we selected SNPs strongly associated with the exposure factors using a filtering condition of \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;5*10\u0026thinsp;\u0026minus;\u0026thinsp;8. Secondly, in order to mitigate the impact of linkage disequilibrium among SNPs, we set R\u003csup\u003e2\u003c/sup\u003e and KB values at \u0026lt;\u0026thinsp;0.001 and 10000 KB respectively. Thirdly, to address potential pleiotropy effects, SNPs independence were ensured by querying the PhenoScanner database (version 2.0) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://phenoscanner.medschl.cam.ac.uk/\u003c/span\u003e\u003cspan address=\"http://phenoscanner.medschl.cam.ac.uk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to exclude any SNPs associated with confounders and outcome variables. Fourthly, we calculated the F-value for each SNP using the equation F\u0026thinsp;=\u0026thinsp;β\u003csup\u003e2\u003c/sup\u003e/SE\u003csup\u003e2\u003c/sup\u003e and only selected those with F-values greater than 10 to avoid bias caused by weak instrumental variables in MR analysis results. Finally, in order to ensure consistency of effect alleles, any SNPs exhibiting palindromic properties or intermediate allele frequencies were eliminated through harmonization of exposure and outcome data.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statiscal analysis\u003c/h2\u003e \u003cp\u003eThe causal relationship between AD and RNFL thickness as well as visual disturbance was evaluated using five methods: Instrumental variable weighting(IVW), MR-Egger, simple model, weight model, and weight median.IVW is the primary approach for pooling MR data, employing the Wald ratio method to estimate the causal effect of each IV, followed by a weighted aggregation analysis, which provides a highly accurate reference for causal inference\u003csup\u003e[13]\u003c/sup\u003e. The MR-Egger method is utilized to assess horizontal pleiotropy and ensure consistent estimation of causal effects under the assumption of Instrument Strength Independent of Direct Effect (InSIDE)\u003csup\u003e[14]\u003c/sup\u003e. The weight median method requires that at least 50% of the weights originate from valid IVs in order to estimate causal effects reliably\u003csup\u003e[15]\u003c/sup\u003e. The weight model incorporates similarity information among SNPs to enhance the robustness of MR analysis results\u003csup\u003e[16]\u003c/sup\u003e. Additionally, a simple model serves as a supplementary approach for evaluating potential causal relationships\u003csup\u003e[16]\u003c/sup\u003e. All analyses were performed using version 4.3.2 of R with the 'TwoSampleMR' package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Sensitivity analysis\u003c/h2\u003e \u003cp\u003eThe sensitivity analysis is divided into three main areas, which are heterogeneity test, horizontal multiple validity test and leave-one-out sensitivity analysis.Cochran's Q statistic was calculated using IVW and MR-Egger regression, with \u003cem\u003eP\u003c/em\u003e value\u0026thinsp;\u0026gt;\u0026thinsp;0.05 indicating no significant heterogeneity. Validation of horizontal pleiotropy was done through MR-Egger intercept test and MR-PRESSO test. If the MR-Egger intercept test shows \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, it proves that there is no horizontal pleiotropy. MR-PRESSO can identify SNPs with horizontal pleiotropy (i.e., outliers) and assess whether there is a significant difference in the causal effect estimates before and after removal of the outliers. The global test of MR-PRESSO should not be significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). MR- Egger intercept test and MR-PRESSO test can further validate Hypotheses (2) and (3).In addition, a leave-one-out sensitivity analysis was used to assess the impact of individual SNPs on MR results as a means of assessing the robustness of the results of the MR analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Causal Effects of AD and RNFL\u003c/h2\u003e\u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e3.1.1 IVs selection\u003c/strong\u003e We identified 59 SNPs that exhibit a strong correlation with AD (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5 * 10\u0026thinsp;\u0026minus;\u0026thinsp;8), ensuring their independence from linkage imbalance effects under the condition of r2\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and Kb\u0026thinsp;=\u0026thinsp;10000Kb. One outlier SNP (rs1799777) was excluded using the MR-PRESSO test. SNPs associated with outcome and confounders, namely rs7384878 and rs199451, were eliminated based on Phenoscanner. Additionally, palindromic SNPs with intermediate allele frequencies, including rs10933431, rs1385742, rs1065712, rs11500477, rs148601586, and rs61679753 were excluded to enhance the robustness of our Mendelian randomization analysis. Ultimately, a total of 50 SNPs were included in the final analysis.The selected IVs all possess F values greater than 10,as detailed in Supplementary Table\u0026nbsp;1.\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e3.1.2 MR analysis\u003c/strong\u003e The IVW method was primarily employed to assess the presence of a causal relationship between AD and RNFL thickness. However, the IVW results revealed no significant evidence supporting a causal association between AD and RNFL thickness (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.283, odds ratio [OR] [95% confidence interval (CI)]\u0026thinsp;=\u0026thinsp;0.926[0.806\u0026ndash;1.065]) as depicted in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Furthermore, the findings from MR-Egger analysis, simple model analysis, weight model analysis, and weight median analysis consistently indicated an absence of causality between AD and RNFL thickness. These outcomes are visually presented in Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and summarized in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\n \u003c/span\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMendelian randomization estimates for AD on RNFL thickness.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMethods\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.613\u0026ndash;1.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.794\u0026ndash;1.213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.864\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInverse variance weighted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.806\u0026ndash;1.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.283\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSimple mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.723\u0026ndash;1.631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.693\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.721\u0026ndash;1.271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.763\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cstrong\u003e3.1.3 Sensitivity analysis\u003c/strong\u003e Examination of MR-Egger regression (Cochran\u0026apos;s Q\u0026thinsp;=\u0026thinsp;46.973, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.474) and IVW analysis (Cochran\u0026apos;s Q\u0026thinsp;=\u0026thinsp;48.301, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.461) revealed no signs of heterogeneity.The MR intercept( \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05 ) test indicates a very low probability of horizontal pleiotropy, thereby minimizing the potential for bias effects in the MR analysis results. Furthermore, the graphical symmetry observed in Figure (4) of the funnel plot demonstrates an absence of systematic bias between study effects and their accuracy. Additionally, we conducted leave-one-out sensitivity analysis to assess the influence of individual SNPs on our results. This method involves gradually eliminating each SNP and calculating meta-effects based on the remaining SNPs to observe any significant changes in outcomes. As depicted in Figure (5), minimal alterations were observed in the overall error line after eliminating each SNP (all error lines are positioned to the left of 0). These findings affirm that our MR results can be deemed reliable.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Causal Effects of AD and visual disturbance\u003c/h2\u003e\u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e3.2.1 IVs selection\u003c/strong\u003e We identified 59 SNPs that exhibit a strong correlation with AD (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5 * 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e), ensuring their independence from linkage imbalance effects under the condition of r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and Kb\u0026thinsp;=\u0026thinsp;10000Kb. No outlier SNPs were excluded using the MR-PRESSO test. SNPs associated with outcome and confounders, namely rs7384878 and rs199451, were eliminated based on Phenoscanner. Additionally, palindromic SNPs with intermediate allele frequencies, including rs1065712, rs10933431, rs11500477, rs148601586, and rs61679753 were excluded to enhance the robustness of our Mendelian randomization analysis. Ultimately, a total of 52 SNPs were included in the final analysis.The selected IVs all possess F values greater than 10, as detailed in Supplementary Table\u0026nbsp;1.\u003c/p\u003e\n \u003c/span\u003e \u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e3.2.2 MR analysis\u003c/strong\u003e The IVW method was primarily employed to assess the presence of a causal relationship between AD and RNFL thickness. However, the IVW results revealed no significant evidence supporting a causal association between AD and visual disturbance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.205, odds ratio [OR] [95% confidence interval (CI)]\u0026thinsp;=\u0026thinsp;1.070[0.964\u0026ndash;1.187]) as depicted in Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e. Furthermore, the findings from MR-Egger analysis, simple model analysis, weight model analysis, and weight median analysis consistently indicated an absence of causality between AD and RNFL thickness. These outcomes are visually presented in Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e and summarized in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\n \u003c/span\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMendelian randomization estimates for AD on visual disturbance.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMethods\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.819\u0026ndash;1.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.986\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.949\u0026ndash;1.281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInverse variance weighted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.964\u0026ndash;1.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSimple mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.938\u0026ndash;1.570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWeighted mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.919\u0026ndash;1.284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.085\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cstrong\u003e3.2.3 Sensitivity analysis\u003c/strong\u003e Examination of MR-Egger regression (Cochran\u0026apos;s Q\u0026thinsp;=\u0026thinsp;52.364, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.308) and IVW analysis (Cochran\u0026apos;s Q\u0026thinsp;=\u0026thinsp;52.967, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.324) revealed no signs of heterogeneity.The MR intercept( \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05 ) test indicates a very low probability of horizontal pleiotropy, thereby minimizing the potential for bias effects in the MR analysis results. Furthermore, the graphical symmetry observed in Figure (8) of the funnel plot demonstrates an absence of systematic bias between study effects and their accuracy. Additionally, we conducted leave-one-out sensitivity analysis to assess the influence of individual SNPs on our results. This method involves gradually eliminating each SNP and calculating meta-effects based on the remaining SNPs to observe any significant changes in outcomes. As depicted in Figure (9), minimal alterations were observed in the overall error line after eliminating each SNP (all error lines are positioned to the left of 0). These findings affirm that our MR results can be deemed reliable.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eNumerous studies have consistently reported that individuals with AD may exhibit visual disturbances and a reduction in RNFL thickness; however, the causal relationship between these factors remains inconclusive. To the best of our knowledge, this is the first two-sample MR study aiming to investigate the potential causal association between AD and RNFL thickness as well as visual disturbance. The findings from our study suggest that there is no definitive evidence supporting a direct causal link between genetic predisposition to AD and an increased risk of RNFL thinning and visual disturbance.\u003c/p\u003e\n\u003cp\u003eThe existing literature highlights the strong association between AD and RNFL thickness, as well as visual disturbances. R N Guliyeva et al. utilized OCT to study 45 AD patients and 60 healthy subjects, confirming a significant decrease in RNFL thickness among AD patients (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.001)\u003csup\u003e[17]\u003c/sup\u003e.AD is a chronic degenerative disease, and early visual disturbance (such as visual field defects, abnormal contrast sensitivity, color vision abnormalities, depth perception defects) are also typical clinical manifestations of AD\u003csup\u003e[18]\u003c/sup\u003e. Electroretinogram (ERG) and visual evoked potentials (VEP) can effectively reflect visual function. Qi Zhe Ngoo et al. demonstrated that graphic ERG showed a significant decrease in the amplitude of P50 and N95 waves in AD patients\u003csup\u003e[19]\u003c/sup\u003e. Furthermore, this study revealed a decreased amplitude of P100 and increased latency in pattern VEP among AD patients\u003csup\u003e[19]\u003c/sup\u003e. The main pathogenesis of AD involves cholinergic neuron dysfunction, with flash visual evoked potential (FVEP) P2 being associated with cholinergic function in the brain\u003csup\u003e[20]\u003c/sup\u003e. It has been shown that FVEP-P2 latency is prolonged in AD patients\u003csup\u003e[21]\u003c/sup\u003e. These findings collectively confirm the presence of visual disturbances experienced by individuals with AD.\u003c/p\u003e\n\u003cp\u003eThis study did not reveal a significant genetic causal relationship between AD and RNFL thickness, as well as visual disturbances. The authors suggest that the observed association between AD and thinning RNFL thickness, as well as visual disturbances reported in clinical studies, may be driven by similar pathogenesis mechanisms. Abnormal deposition of amyloid-\u0026beta; (A\u0026beta;) protein in the brain is a typical manifestation of AD patients, which can be detected decades before the onset of clinical symptoms. Recent studies have identified A\u0026beta; protein deposits in retinas of AD patients\u003csup\u003e[22-23]\u003c/sup\u003e. \u003ca href=\"https://pubmed.99885.net/?term=Xu+QA\u0026cauthor_id=36199154\"\u003eQinyuan Alis Xu\u003c/a\u003e et al. found higher levels of A\u0026beta; protein deposits in the retinas of AD patients compared to control retinas\u003csup\u003e[24]\u003c/sup\u003e. \u003ca href=\"https://pubmed.99885.net/?term=Lee+S\u0026cauthor_id=32848548\"\u003eSieun Lee\u003c/a\u003e et al. also observed significantly increased intracellular and extracellular A\u0026beta; protein deposits in the retinas of AD patients compared to controls\u003csup\u003e[25]\u003c/sup\u003e. \u003ca href=\"https://pubmed.99885.net/?term=Koronyo+Y\u0026cauthor_id=28814675\"\u003eYosef Koronyo\u003c/a\u003e et al. utilized curcumin-based amyloid probes for labeling and successfully visualized retinal A\u0026beta; proteins using a modified laser-scanning ophthalmoscope, showing 2.1 times higher levels of retinal A\u0026beta; proteins in AD patients than healthy controls\u003csup\u003e[26]\u003c/sup\u003e. Another study employed hyperspectral imaging to directly detect retinal A\u0026beta; protein content in AD patients, demonstrating its potential for predicting brain accumulation of A\u0026beta; proteins\u003csup\u003e[27]\u003c/sup\u003e.It is worth noting that researchers have discovered an earlier presence of retinal A\u0026beta; protein compared to neurodegeneration and the subsequent accumulation of brain A\u0026beta; protein\u003csup\u003e[28-29]\u003c/sup\u003e. The buildup of A\u0026beta; protein in the brain can induce an elevation in reactive oxygen species (ROS), impair mitochondrial function, and provoke oxidative stress, ultimately leading to cellular demise\u003csup\u003e[30]\u003c/sup\u003e.Similar to the brain, the glycolysis process in the retina of patients with AD is impacted by A\u0026beta; protein, resulting in an elevation of ROS\u003csup\u003e[31]\u003c/sup\u003e. This increase in ROS induces mutations in mitochondrial DNA, leading to the production of defective proteins that subsequently decrease ATP levels and enhance ROS generation, ultimately causing oxidative stress, mitochondrial impairment, apoptosis\u003csup\u003e[31]\u003c/sup\u003e.These aberrant reactions lead to degeneration of retinal ganglion cells (RGCs) and a decrease in the thickness of the RNFL in patients with AD\u003csup\u003e[31]\u003c/sup\u003e. Additionally, A\u0026beta; protein can activate glial cells within both the brain and retina which undergo morphological changes and alter their immune phenotype\u003csup\u003e[32]\u003c/sup\u003e. These activated glial cells proliferate and migrate towards injury sites while releasing various inflammatory factors such as nitric oxide (NO), tumor necrosis factor-alpha (TNF-\u0026alpha;), interleukin-6 (IL-6), etc\u003csup\u003e[32-34]\u003c/sup\u003e. The presence of these inflammatory factors directly damages RGCs and RNFLs\u003csup\u003e[35]\u003c/sup\u003e. Degeneration of axonal cells within RGCs along with a reduction in RNFL thickness are closely associated with visual disturbances observed among AD patients\u003csup\u003e[36]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe advantage of this study over traditional observational studies lies in its ability to optimize the impact of reverse causality and confounding factors on experimental outcomes. Furthermore, the utilization of a large-scale GWAS dataset and multiple sensitivity analysis methods enhances the reliability of the findings. However, there are certain limitations to consider in this study. Firstly, all cases included were from European populations, necessitating further investigation into its applicability to other human populations in future research. Secondly, due to limited availability of GWAS datasets for RNFL thickness and visual disturbance, validating differences in causal effects between various datasets becomes challenging.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eNumerous observational studies have consistently demonstrated a robust correlation between AD and RNFL thickness, as well as visual impairment; however, our study did not establish a causal relationship between AD and changes in RNFL thickness or visual disturbance. Therefore, further investigations are warranted to elucidate the precise impact of AD on RNFL thickness and visual disturbances. Consequently, the potential for diagnosing early-stage AD through alterations in RNFL thickness and visual disturbances remains controversial.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAlzheimer's disease (AD)\u003c/p\u003e\n\u003cp\u003eoptical coherence tomography(OCT)\u003c/p\u003e\n\u003cp\u003eretinal nerve fiber layer (RNFL)\u003c/p\u003e\n\u003cp\u003eMendelian randomization (MR)\u003c/p\u003e\n\u003cp\u003esingle nucleotide polymorphisms (SNPs)\u003c/p\u003e\n\u003cp\u003erandomized controlled trials (RCTs)\u003c/p\u003e\n\u003cp\u003eInstrumental variable weighting(IVW)\u003c/p\u003e\n\u003cp\u003eElectroretinogram (ERG)\u003c/p\u003e\n\u003cp\u003evisual evoked potentials (VEP\u003c/p\u003e\n\u003cp\u003eflash visual evoked potential (FVEP)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;amyloid-β (Aβ)\u003c/p\u003e\n\u003cp\u003ereactive oxygen species (ROS)\u003c/p\u003e\n\u003cp\u003eretinal ganglion cells (RGCs)\u003c/p\u003e\n\u003cp\u003enitric oxide (NO)\u003c/p\u003e\n\u003cp\u003etumor necrosis factor-alpha (TNF-α)\u003c/p\u003e\n\u003cp\u003einterleukin-6 (IL-6)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Approval:\u0026nbsp;\u003c/strong\u003eGiven the utilization of publicly accessible repositories, ethical sanctions or informed consent from participants were not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eWe don\u0026apos;t get fund support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eAll of the data analyzed in this study can be found in the https://gwas.mrcieu.ac.uk/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u0026nbsp;\u003c/strong\u003eI declare that the authors have no competing interests as defined by Nature Research, or other interests that might be perceived to influence the results and/or discussion reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u0026nbsp;\u003c/strong\u003eThe authors would like to thank all the colleagues who contributed to this work.\u003c/p\u003e\n\u003cp\u003eAuthor Contributions Statement:Xuan Han wrote the main manuscript text.Xiaojuan Su,Jinyan Wang and Xingyu Guo prepared figures 1-9,Table1-3 and Supplementary table 1-2.All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eMcKhann GM, Knopman DS, Chertkow H, et al. 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Front Aging Neurosci. 2017 Jul 6;9:214. doi: 10.3389/fnagi.2017.00214.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"alzheimer's disease, retinal nerve fiber layer thickness, visual disturbance, mendelian randomization, causality","lastPublishedDoi":"10.21203/rs.3.rs-4253388/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4253388/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAlzheimer's disease (AD) is a chronic, progressive, irreversible degenerative disorder of the central nervous system caused by multiple factors.Previous studies have demonstrated that patients with AD experience visual disturbances and reduced retinal nerve fiber layer (RNFL) thickness in the early stages. If a causal relationship can be established between AD and visual disturbances as well as RNFL thickness, then AD can be diagnosed by early visual disturbances and RNFL thickness changes.To investigate the causal relationship between AD and visual disturbances as well as RNFL thickness, we conducted a Mendelian randomization study.In this MR study, the main approach was inverse variance weighted (IVW) and evaluate the robustness of the results via sensitivity analysis. The IVW results revealed no significant evidence supporting a causal association between AD and RNFL thickness (P = 0.283, odds ratio [OR] [95% confidence interval (CI)] = 0.926[0.806-1.065]) as well as visual disturbances(P = 0.205, odds ratio [OR] [95% confidence interval (CI)] = 1.070[0.964-1.187]) .Consequently, the potential for diagnosing early-stage AD through alterations in RNFL thickness and visual disturbances remains controversial.\u003c/p\u003e","manuscriptTitle":"Causal relationship between Alzheimer's disease and retinal nerve fiber layer thickness, visual disturbance: a two-sample Mendelian randomization study.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-05-15 08:50:51","doi":"10.21203/rs.3.rs-4253388/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d7842fe3-666b-4c33-9ae0-4dfbb590a33f","owner":[],"postedDate":"May 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":31922975,"name":"Health sciences/Diseases/Eye diseases"},{"id":31922976,"name":"Health sciences/Neurology/Neurological disorders"}],"tags":[],"updatedAt":"2024-10-17T03:53:51+00:00","versionOfRecord":[],"versionCreatedAt":"2024-05-15 08:50:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4253388","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4253388","identity":"rs-4253388","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}