Genetic evidence for a causal relationship between Alzheimer’s disease and age-related macular degeneration: A Mendelian randomization study

Genetic evidence for a causal relationship between Alzheimer’s disease and age-related macular degeneration: A 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 Research Article Genetic evidence for a causal relationship between Alzheimer’s disease and age-related macular degeneration: A Mendelian randomization study Yu Huang, Xueli Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3916453/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 Background Previous observational studies have established a bi-directional association between age-related macular degeneration (AMD) and Alzheimer’s disease (AD). However, these associations might be induced by confounding factors. Methods We conducted a bi-directional MR study to evaluate potential causal associations between AMD and AD using GWAS data. 39,106 clinically diagnosed AD cases, 46,828 proxy AD and related dementia, and 14,034 AMD patients were included in this study. Results Increased AMD exposure due to germline genetic variation was generally associated with decreased risk for AD. A causal effect was observed between early AMD and AD. However, reverse–direction MR analysis depicted generally little evidence of an association between genetically increased AD exposure and risk of early AMD with 57 SNPs and risk of AMD progression. Conclusions Our MR study confirmed the causal effect of early AMD on AD, and early AMD could reduce the risk for AD. Ophthalmology Alzheimer’s disease age–related macular degeneration Mendelian randomization Figures Figure 1 Figure 2 Figure 3 Figure 4 What is already known on this topic Observational studies have highlighted connections between age-related macular degeneration (AMD) and Alzheimer’s disease (AD); however, their causal relationship remains uncertain. What this study adds: This study reveals that a genetic predisposition to early-onset AMD is causally associated with reduce risk of AD, and there is no evidence supporting AD's causal role in AMD. How this study might affect research, practice or policy: Our findings hold significance for leveraging the retina or retinal diseases as tools for early AD diagnosis. Additionally, implications for drug development are notable, suggesting the need for targeted drugs for AD or AMD, considering potential side effects on the other condition. 1. Introduction The aging population is rising rapidly worldwide. According to the WHO, the proportion of the population over 60 years will nearly double between 2015 and 2050. 1 Aging could cause various functional changes to the neuron system, which includes the brain and retina. As a consequence, the prevalence of age-related macular degeneration (AMD) and Alzheimer’s disease (AD) is likely to increase with age. 2 AD, an irreversible neuron degenerative disease with high heritability (~ 60–80%), has gradually become the major type of dementia and a considerable public health concern. 3 AMD is also an irreversible neuron degenerative disease in the macular area and is the leading common cause of irreversible blindness worldwide. In developed countries, AMD has become the major cause of vision loss of the elderly over 65 years old in developed countries. 4 AD and AMD are common comorbidities in chronic diseases and represent major global public health challenges. 5, 6 Previous epidemiological studies have revealed associations between AD and AMD, suggesting they may have common pathogenesis. 7, 8 A cohort study with 4,097 patients in the eye disease group and 20,475 in the control group revealed that AMD was associated with an increased risk of AD (adjusted hazard ratio [HR] = 36.94, 95% confidence interval [CI] = 4.62–295.46). 9 A recent retrospective cohort study based on AREDS2 (3,157 participants; mean age 72.7 years) revealed that the risk of late AMD was higher in participants with cognitive impairment at baseline (at five years, HR = 1.24, 95% CI = 1.08–1.43; at ten years, HR = 1.20, 95% CI = 1.05–1.37). 10 Although evidence has revealed that the two diseases may share similar pathological mechanisms, the existence of a causal relationship between them is still uncertain. 7 The direction of this causal relationship remains to be explored as well. 8–11 Conventional observational studies are susceptible to residual confounding (due to unmeasured or imprecisely measured confounders), reverse causation, and other forms of bias that undermine robust causal inference, which makes it unclear if the relationship is truly causal. 12 The gold standard to determine a causal relationship is the randomized controlled study (RCT). However, RCT has numerous restrictions, which have limited its conduction among different populations. 13 Mendelian randomization (MR) is an analytical approach that uses germline genetic variants as instruments for risk factors to evaluate the causal effects of these factors on disease outcomes in observational settings. 14 Since germline genetic variants, such as single-nucleotide polymorphisms (SNPs), are randomly assorted at meiosis, MR analyses should be less prone to confounding by lifestyle and environmental factors than conventional observational studies. Furthermore, since germline genetic variants are fixed at conception, MR analyses are not subject to reverse causation bias and ethical issues. 5, 15 The validity of causal estimates obtained using MR are dependent on three key assumptions (Fig. 1 ): 1) The instrumental variants (IVs) are associated with the risk factor of interest (the relevance assumption); 2) They share no common cause with the outcome (the independence assumption); and ( 3 ) They do not affect the outcome except through the risk factor (the exclusion restriction assumption). 16 To date, despite numerous studies that have explored the shared genetic variations between AD and AMD, 17, 18 none of these studies has investigated their causal relationship under an MR framework. Therefore, the causality behind these two diseases remains largely ambiguous. In this study, we conducted a two–sample MR approach to investigate the associations of AMD with the risk of AD. A bi–directional MR was used to infer reverse causality. This study may provide insights into the mechanism of AD and AMD’s comorbidity. 2. Methods 2.1 Study design Figure 1 presents the study overview. In this study, we conducted a bi–directional two–sample MR to determine the causal association between AMD and AD using summary–level datasets generated from large GWAS studies. The IVs were extracted based on the GWAS summary statistics of AD or AMD. We first performed an MR analysis to quantify the effect of genetically proxied early AMD on the risk of AD. After that, we investigated the causal effect of genetically proxied AD on the risk of AD in reverse-direction MR. 2.2 Data source Both summary–level data of AD and early AMD were obtained from previously published GWAS. In the GWAS for early AMD, data were obtained from 11 different centers, including the International AMD Genomics Consortium and UK Biobank (accession numbers: GCST010723). 19 Among the participants, 14,034 were cases, while 91,214 were controls. Another AMD progression dataset was used for validation. The 2127 Caucasian participants from the Age–Related Eye Disease Study were between 55 and 81 years at enrollment (accession numbers: GCST009144). 20 For the GWAS for AD, summary statistics were obtained from The European Alzheimer & Dementia Biobank (EADB) stage I (GWAS meta–analysis; accession numbers: GCST90027158). 21 The dataset comprised 39,106 clinically diagnosed AD cases, 46,828 proxy AD and related dementia (proxy–ADD), and 401,577 controls. Participation of both AD and AMD was restricted to European ancestry to avoid population bias (e.g., concerning age, sex, and ancestry). The GWAS on early onset AMD and AD utilized an overlapping dataset of 57,802 participants from the UK Biobank. Because all analyses discussed herein are based on publicly available summary data, no additional ethical approval from an institutional review board was required for this study. 2.3 Genetic instrument selection Genetic variants associated with AD and AMD at the genome–wide significance level ( p < 5 × 10 –8 ), weak linkage disequilibrium (LD; r 2 < 0.001), and with a minor allele frequency (MAF) greater than 0.01 were selected as IVs. We restricted genetic variants to cis–acting SNPs, i.e., in or within ± 10,000 kb from the gene encoding the protein. 12 Based on the above rules, eight SNPs were rigorously selected as IVs of early AMD, and their causal role on AMD was further analyzed by a series of MR approaches. Similarly, 59 different SNPs were used as genetic proxies of AD to explore the causal effect of AD on AMD in the reverse–direction MR analysis. IV was excluded if it’s effect on the outcome is missing. 2.4 Statistical analysis The two–sample MR method was used to evaluate the causal relationship between genetically predicted early AMD and AD. As the main analysis, inverse–variance weighting (IVW) 22 was the primary method to establish the overall casual effect estimates. For sensitivity analyses, weighted median (WMed) estimation, 23 MR–Egger regression, 24 and weighted mode (WMod) estimation 25 were conducted. Leave–one–out analyses were conducted to evaluate SNPs with extreme effect. 26 Before MR analysis, the MR assumptions were tested. The selected IVs were required to have a strong relationship with exposure (AD). The strength of the IVs was estimated based on the F –statistic, 27 calculated using the following formula: $$\begin{array}{c}F=\frac{{R}^{2}\left(n-k-1\right)}{k\left(1-{R}^{2}\right)}\#\left(1.\right)\end{array}$$ ( R 2 : variance of exposure explained by selected instrumental variables, n : sample size, k : number of instrumental variables) R 2 was calculated using the following formula: $$\begin{array}{c}{R}^{2}={\sum }_{i=1}^{K}\frac{{\beta }_{i}^{2}}{{\beta }_{i}^{2}+2\ast n\ast se{\left({\beta }_{i}\right)}^{2}}\#\left(2.\right)\end{array}$$ , K : number of the selected genetic variants). 28 We estimated the F –statistic for each SNP as the square of the SNP–exposure association divided by the variance of the SNP–exposure association. 24 We also generated the mean F -statistic for exposure. 29 Those with an F –statistic of 10 or less were regarded as weak instruments. 30 It is recommended to choose a higher F statistic corresponding to a smaller bias than the weak instrumental variable bias having a slim chance. 27–30 Independent variants ( r 2 < 0.001, window size = 10,000 kb) were selected using the “clump” function (EUR population) of the “TwoSampleMR” R package to maximize instrument strength. 31 The exclusion restriction assumption is generally violated when a genetic variant influences an outcome through a biological pathway independent of the exposure. Cochran’s Q statistic was calculated to evaluate heterogeneity. 32 Besides, the MR–Egger intercept test provided stable estimates when considering the horizontal pleiotropy between the IVs and confounders. 33 Finally, MR pleiotropy residual sum and outlier (MR–PRESSO) test were employed to estimate the effect if the violated IVs were removed. 34 All statistical analyses were performed using the R software with “TwoSampleMR” packages and “MRPRESSO” (version 4.2.0, R Foundation for Statistical Computing, Vienna, Austria), and p < 0.05 was considered a significant association. 3. Results 3.1 Genetic instruments In MR analysis, eight SNPs that were genome-wide significant ( p < 5 × 10 − 8 ), independent (LD, r 2 0.01 were selected as IV for MR analyses of the effect of early AMD on AD risk, as displayed in Table 1 . None of the candidate SNPs was palindromic SNP for being palindromic with intermediate allele frequencies (MAF > 0.42). The F -statistic values for instruments and estimated power are illustrated in Table 2 . The F –statistic value was 55.22, indicating an excellent strength of the used genetic instruments. For reverse causality, we incorporated 57 independent SNPs that were genome-wide significant ( p < 5 × 10 − 8 ) and independent (LD, r 2 0.01, suggestive as IVs for AD (Supplementary Table S1). Table 1 Characteristics of SNPs used in forward-direction MR analysis for causal effect of early AMD on AD risk. Target SNPs Chr EA OA MAF R 2 Association with AMD Association with AD β SE p β SE p rs3750847 10 T C 0.7755 2.53E-03 0.3838 0.0166 3.6E − 118 −0.0363 0.0099 2.51E-04 rs247617 16 A C 0.6769 1.87E-04 0.0922 0.0147 3.39E − 10 0.0072 0.0087 0.41 rs11569415 19 A G 0.7859 2.15E-04 0.1158 0.018 1.32E − 10 −0.0249 0.0109 0.02 rs4658046 1 T C 0.3902 2.5E-03 −0.3213 0.014 8.52E − 116 0.0116 0.0083 0.17 rs4844620 1 A G 0.7865 1.43E-04 −0.0949 0.0173 3.79E − 08 0.0233 0.0099 0.02 rs547154 6 T G 0.9118 3.73E-04 −0.2178 0.0246 7.62E − 19 0.0012 0.0135 0.93 rs943080 6 T C 0.4932 1.53E-04 0.0797 0.0145 3.838E − 08 0.0047 0.0081 0.56 rs13278062 8 T G 0.4793 1.60E-04 0.0801 0.0142 1.637E − 08 0.0142 0.0081 0.08 Abbreviations : SNP: single-nucleotide polymorphism; Chr = chromosome; EA: effect allele; OA: other allele; β: regression effect size; EAF = effect allele frequency; MAF = minor allele frequency; SE = standard error Table 2 MR estimates for causal effect of early AMD on AD with five MR methods. Method No. Of SNPs β SE p OR (95% CI) MR PRESSO pval R 2 combined F statistic MR–Egger 8 −0.091 0.047 0.099 0.913 (0.833–1.000) Egger intercept 8 0.009 0.009 0.399 WMed 8 −0.058 0.022 0.008 0.944 (0.905–0.985) IVW 8 −0.056 0.026 0.032 0.945 (0.898–0.995) 0.1 0.0063 55.22 WMod 8 −0.060 0.020 0.019 0.942 (0.907–0.979) Abbreviations : SNP: single-nucleotide polymorphism; β: regression effect size; CI = confidence interval; OR = odds ratio; PRESSO = Pleiotropy RESidual Sum and Outlier; SE = standard error; R2 combined: variance of exposure explained by selected instrumental variables; WMed = weighted median; IVW = inverse-variance weighting; WMod = weighted mode. 3.2 Evaluating the effect of early AMD on AD risk In our MR analyses, we found moderate evidence for a protective effect of early AMD on the risk of AD (IVW, p = 0.032, OR = 0.945, 95% CI = 0.898–0.995 per SD–increase; (Figs. 2 and 3 and Table 2 ). The estimates were broadly consistent with the estimates yielded by the WMed ( p = 0.008, OR = 0.944, 95% CI = 0.905–0.985 per SD–increase) and WMod ( p = 0.019, OR = 0.942, 95% CI = 0.907–0.979 per SD–increase) analyses. The MR–Egger regression method is robust to invalid instruments, and the WMed yields robust estimates if more than half of the information comes from invalid IVs. 23 We noted heterogeneity in the effect of early AMD–associated SNPs on AD (IVW: Q = 16.06, p = 0.013; MR–Egger: Q = 18.26, p = 0.011; Supplementary Table S2). However, the MR–Egger intercept analysis did not indicate horizontal pleiotropy (coefficient β = 0.009, SE = 0.009, p = 0.399). The MR–PRESSO analysis did not identify any SNP as an outlier. Conventional IVW leave–one–out analysis did not identify any high leverage points with strong influence (Fig. 4 ). The F –statistics ( F = 55.22 ) for the genetic instruments were consistent with an absence of weak instrument bias. 3.3 Evaluating the effect of AD on early AMD risk We further evaluated the effect of AD on AMD. We extracted 60 SNPs as IVs for AD from the same GWAS summary data. We removed one SNP for being palindromic with intermediate allele frequencies. Next, we selected strong IVs ( F –statistics > 10) to study reverse causality to test whether AD was causally associated with early AMD. We performed MR analyses following the above–described methods (IVW, MR–Egger regression, WMed regression, simple mode, WMod, and leave–one–out analysis) to evaluate the causal relationship. Most of the methods indicated no causal effect of genetically predicted AD on early AMD risk (IVW, p = 0.432, OR = 0.974, 95% CI = 0.911–1.041 per SD–increase; WMed, p = 0.602, OR = 0.977, 95% CI = 0.905–1.066 per SD–increase; WMod, p = 0.186, OR = 0.899, 95% CI = 0.768–1.051 per SD–increase; Supplementary Table S3, Supplementary Figures S1, S2 and S3). However, MR–Egger regression displayed different results ( p = 0.009, OR = 0.855, 95% CI = 0.763–0.958 per SD–increase). Meanwhile, the MR–Egger intercept test (coefficient β = 0.015, SE = 0.005, p = 0.009) indicated horizontal pleiotropy for the SNPs (Supplementary Table S3). For validation, the MR–PRESSO analysis (causal estimate = − 0.027, SE = 0.034, p = 0.00017) confirmed strong pleiotropy. Two outliers (SNP: rs1800978, rs61679753) were picked out according to MR–PRESSO analysis (outlier–corrected p = 0.172), and after removing the outliers, MR–PRESSO yielded a causal estimation of AD on early AMD risk (Supplementary Table S4) which showed no causal relationship between them. For a further validation analysis, we analyzed the causal relationship between AD and AMD progression. We still found no causal effect of genetically predicted AD on AMD progression with 45 SNPs estimated (IVW, p = 0.760, OR = 1.034, 95% CI = 0.835–1.279 per SD–increase; MR–Egger regression, p = 0.573, OR = 0.861, 95% CI = 0.514–1.442per SD–increase; WMed, p = 0.486, OR = 0.899, 95% CI = 0.666–1.213 per SD–increase; WMod, p = 0.560, OR = 0.908, 95% CI = 0.659–1.252 per SD–increase; Supplementary Table S5). 4. Discussion Our MR analyses first investigated the causal association between genetically predicted early AMD with the risk of AD. The results from the major estimation method of MR analyses indicated a causal role of early AMD in AD onset, suggesting that early AMD may play a protective role in the onset of AD. The reverse–direction MR analysis also revealed no evidence of genetic liability to AD being related to early AMD. Our MR analyses demonstrated that early AMD could be a protective factor against AD, which is consistent with the observation that persons with low cognitive function scores were more likely to have early AMD than persons with higher scores. 35, 36 However, the influence of confounders may have led to bias in these observational estimates of association. Age is one of the potential confounders, with a positive association with AMD and AD. Jiang et al. 37 conducted a two-sample bidirectional MR study recently aiming to evaluate the causal relationship between advanced AMD (16,144 advanced AMD cases, 17,832controls) and AD in discovery data set (late-onset AD, 21,982 cases and 41,944 controls) and validation data set (71,880 clinically diagnosed AD/AD-by-proxy cases and 383,378 control). Interestingly, they found no evidence to support a bidirectional causal association between advanced AMD and AD and then concluded that the MR study decreased the probability of a clinically relevant relationship between the two neurodegenerative diseases and that the associations observed in epidemiological studies should not be considered causal. However, we took it with a grain of salt. Therefore, we performed an MR study to further explore the causal association between AMD and AD. As expected, Our MR study confirmed a moderate causal effect of early AMD on AD development after avoiding these age–related risk factors. This may give us a little prompt message that we should account for the influence of phenotype (IVW, p = 0.032, OR = 0.945, 95% CI = 0.898–0.995 per SD–increase). Contrary to previous studies that adopted a cross-sectional study design, some longitudinal studies investigated the association between AD and AMD. A cohort study with 4,097 patients in the eye disease group and 20,475 in the control group depicted that general AMD was associated with an increased risk of AD (adjusted HR = 36.94, 95% CI = 4.62–295.46). 9 Our study focused on genetic risk factors and demonstrated some shared genes between AMD and AD. However, the same genes may play a different role in AMD and AD, for example, the famous gene ApoE , which has three alleles ( ε2 , ε3 , and ε4 ). Individuals carrying the APOE-ε4 allele seem to have a lower risk for AMD but the strongest thus far identified risk factor for AD. This may be one of the reasons causing the result that we obtained. 38 In reverse–direction MR analysis, we could not determine a causal association between AD and AMD (IVW, p = 0.432, OR = 0.974, 95% CI = 0.911–1.041 per SD–increase). The MR analysis result was consistent with the above in validation (IVW, p = 0.760, OR = 1.034, 95% CI = 0.835–1.279 per SD–increase; MR–Egger regression, p = 0.573, OR = 0.861, 95% CI = 0.514-1–442 per SD–increase; WMed, p = 0.486, OR = 0.899, 95% CI = 0.666–1.213 per SD–increase; WMod, p = 0.560, OR = 0.908, 95% CI = 0.659–1.252 per SD–increase). A cross–sectional study that included 592 AD patients aged 50 and above showed AD was not associated with AMD. 2 A similar conclusion was reported in a population–based study with 2,088 participants aged 69 to 97 years, which demonstrated that there was no association of AD with early AMD. 35 Interestingly, MR–Egger analysis suggests a pleiotropic effect between AD and AMD ( p = 0.009, OR = 0.855, 95% CI = 0.763–0.958 per SD–increase), suggested by MR–PRESSO, 2 pleiotropic SNP—rs1800978, encoding ABCA1 and rs61679753, and encoding TOMM40 might influence the result. ATP–binding cassette transporter A1 ( ABCA1 ) is one of several proteins involved in cholesterol homeostasis. ABCA1 gene variation has been demonstrated to contribute to AMD susceptibility. 39–42 A GWAS study found that TOMM40 was associated with advanced AMD ( p = 3.1 × 10 − 6 ) and combined replication samples ( p = 8.4 × 10 − 8 ) with European ancestry. 43 This variant has been identified, showing an association with early AMD in a GWAS meta-analysis of the European population (p = 1.1 × 10 − 6 ), and the signal remained in a secondary meta-analysis combining two Singapore-based Asian cohorts ( p = 3.2 × 10 − 6 ). 44 However, previous observational studies investigated the influence of neurological diagnoses such as AD on AMD but with inconclusive results. The potential causality between AD and AMD remains unclear owing to bias or various confounders inherent to observational studies. To date, no RCT study has been performed to assess the effect of AD on AMD. Our MR study adds to the existing body of knowledge by confirming the causal effect of AD on AMD, which is informative. The causal association between AMD and AD has implications for understanding disease pathogenesis, referral, and treatments and can make the most effective use of scarce resources. The major strength of our study is its two–sample MR design, which could circumvent the limitations of observational studies with measurement errors and residual confounding. It can minimize residual confounding and reverse causality and genetic validation of the wide range of AD, which can afford high–quality evidence. However, our study has a few limitations that should be addressed. First, the estimates might be biased toward the observational associations if the exposure and outcome data came from the same sample. 45 Second, residual pleiotropy might remain, despite the range of sensitivity analyses (i.e., WM, MR–PRESSO, and MR–Egger intercept) conducted to explore and account for pleiotropy. Third, we could not distinguish the effects of all different kinds of AD and AMD and thus only examined general AD, early AMD and AMD progression. A further limitation was that our study was restricted to participants of European ancestry; however, the observational and causal effects of AMD on AD may differ in other ethnic groups. Therefore, the causal effect in our principal MR analysis (IVW) was robust and unbiased, which was evidenced by neither the MR–Egger intercept test nor the MR–PRESSO analysis. Conclusively, our primary MR and sensitivity analyses, with IVW, WMed, and WMod methods, consistently establish the existence of a causal effect of suffering early AMD on the decreased risk of AD occurrence. 5. Conclusions In conclusion, our comprehensive MR analysis provides insight into potential causal mechanisms linking excess early AMD to decreased AD risk. Our results elevated that early AMD causes a moderately decreased risk of AD than that reported in previous conventional observational studies. Future well-designed RCT studies need to be conducted to clarify the role of individual AD on AMD. Abbreviations AMD Age-related macular degeneration AD Alzheimer’s disease MR Mendelian randomization HR Hazard ratio CI Confidence interval RCT Randomized controlled study SNPs Single-nucleotide polymorphisms IVs Instrumental variants LD Linkage disequilibrium MAF Minor allele frequency IVW Inverse–variance weighting WMed Weighted median WMod Weighted mode MR–PRESSO MR pleiotropy residual sum and outlier Chr Chromosome EA Effect allele OA Other allele β Regression effect size EAF Effect allele frequency SE Standard error Declarations Availability of Data and Materials AD GWAS: http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90027001-GCST90028000/GCST90027158/ Early AMD GWAS: http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST010001-GCST011000/GCST010723/ AMD progression GWAS: http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009144/ Acknowledgments We would like to express our sincere appreciation for the MRC–IEU OpenGWAS project. Funding The present work was supported by the National Natural Science Foundation of China (32200545), and the GDPH Supporting Fund for Talent Program (KJ2020633). Author information Shuo Ma and Yu Huang contributed equally to this work. Authors and Affiliations Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 510080 Guangzhou, China Yu Huang, Shunming Liu, Xianwen Shang, Honghua Yu, Mingguang He, Xueli Zhang Medical Big Data Center, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 510080 Guangzhou, China Shuo Ma Clinical Data Center, Guangzhou Women and Children’s Medical Center, Guangzhou Medical University, Guangzhou, 51023, China Shuo Ma Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 510080 Guangzhou, China Ke Zhao State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, 510060 Guangzhou, China Mingguang He Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, 3002 Melbourne, Australia Mingguang He Medical Research Center, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences Xueli Zhang Contributions XLZ conceptualized and designed the study with XLZ did the literature and YH and SM did the MR method and prepared the manuscript. MGH did the supervision. SM had full access to all of the data. SM and SML did the visualization. XLZ was the guarantor. All authors commented on and approved the final manuscript. Corresponding author Correspondence to Xueli Zhang Contributions Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. References Ageing and health. 26 October, 2022. Accessed 1 October, 2022. https://www.who.int/news-room/fact-sheets/detail/ageing-and-health Chua J, Zhang Z, Wong D, et al. Age-Related Eye Diseases in Individuals With Mild Cognitive Impairment and Alzheimer's Disease. Front Aging Neurosci . 2022;14:933853. doi:10.3389/fnagi.2022.933853 Gatz M, Reynolds CA, Fratiglioni L, et al. Role of genes and environments for explaining Alzheimer disease. Article. Arch Gen Psychiatry . Feb 2006;63(2):168-74. doi:10.1001/archpsyc.63.2.168 Dewan A, Liu M, Hartman S, et al. 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Search for age-related macular degeneration risk variants in Alzheimer disease genes and pathways. Neurobiol Aging . Jun 2014;35(6):1510.e7-18. doi:10.1016/j.neurobiolaging.2013.12.007 Winkler TW, Grassmann F, Brandl C, et al. Genome-wide association meta-analysis for early age-related macular degeneration highlights novel loci and insights for advanced disease. BMC Medical Genomics . 2020/08/26 2020;13(1):120. doi:10.1186/s12920-020-00760-7 Yan Q, Ding Y, Liu Y, et al. Genome-wide analysis of disease progression in age-related macular degeneration. Hum Mol Genet . Mar 1 2018;27(5):929-940. doi:10.1093/hmg/ddy002 Bellenguez C, Küçükali F, Jansen IE, et al. New insights into the genetic etiology of Alzheimer’s disease and related dementias. Nature Genetics . 2022/04/01 2022;54(4):412-436. doi:10.1038/s41588-022-01024-z Burgess S, Bowden J, Fall T, Ingelsson E, Thompson SG. Sensitivity Analyses for Robust Causal Inference from Mendelian Randomization Analyses with Multiple Genetic Variants. Epidemiology . Jan 2017;28(1):30-42. doi:10.1097/ede.0000000000000559 Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol . May 2016;40(4):304-14. doi:10.1002/gepi.21965 Bowden J, Del Greco MF, Minelli C, Davey Smith G, Sheehan NA, Thompson JR. Assessing the suitability of summary data for two-sample Mendelian randomization analyses using MR-Egger regression: the role of the I2 statistic. Int J Epidemiol . Dec 1 2016;45(6):1961-1974. doi:10.1093/ije/dyw220 Hartwig FP, Davey Smith G, Bowden J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol . Dec 1 2017;46(6):1985-1998. doi:10.1093/ije/dyx102 Ma M, Yang F, Wang Z, Bao Q, Shen J, Xie X. Association of plasma polyunsaturated fatty acids with arterial blood pressure: A Mendelian randomization study. Medicine (Baltimore) . Jan 22 2021;100(3):e24359. doi:10.1097/md.0000000000024359 Burgess S, Thompson SG. Avoiding bias from weak instruments in Mendelian randomization studies. Int J Epidemiol . Jun 2011;40(3):755-64. doi:10.1093/ije/dyr036 Shim H, Chasman DI, Smith JD, et al. A multivariate genome-wide association analysis of 10 LDL subfractions, and their response to statin treatment, in 1868 Caucasians. PLoS One . 2015;10(4):e0120758. doi:10.1371/journal.pone.0120758 Richardson TG, Sanderson E, Palmer TM, et al. Evaluating the relationship between circulating lipoprotein lipids and apolipoproteins with risk of coronary heart disease: A multivariable Mendelian randomisation analysis. PLoS Med . Mar 2020;17(3):e1003062. doi:10.1371/journal.pmed.1003062 Pierce BL, Ahsan H, Vanderweele TJ. Power and instrument strength requirements for Mendelian randomization studies using multiple genetic variants. Int J Epidemiol . Jun 2011;40(3):740-52. doi:10.1093/ije/dyq151 Hemani G, Tilling K, Davey Smith G. Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLoS Genet . Nov 2017;13(11):e1007081. doi:10.1371/journal.pgen.1007081 Bowden J, Del Greco MF, Minelli C, Davey Smith G, Sheehan N, Thompson J. A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization. Stat Med . May 20 2017;36(11):1783-1802. doi:10.1002/sim.7221 Bowden J, Del Greco MF, Minelli C, et al. Improving the accuracy of two-sample summary-data Mendelian randomization: moving beyond the NOME assumption. Int J Epidemiol . Jun 1 2019;48(3):728-742. doi:10.1093/ije/dyy258 Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet . May 2018;50(5):693-698. doi:10.1038/s41588-018-0099-7 Baker ML, Wang JJ, Rogers S, et al. Early age-related macular degeneration, cognitive function, and dementia: the Cardiovascular Health Study. Arch Ophthalmol . May 2009;127(5):667-73. doi:10.1001/archophthalmol.2009.30 Wong TY, Klein R, Nieto FJ, et al. Is early age-related maculopathy related to cognitive function? The Atherosclerosis Risk in Communities Study. Am J Ophthalmol . Dec 2002;134(6):828-35. doi:10.1016/s0002-9394(02)01672-0 Jiang L, Li JC, Tang BS, Guo JF, Shen L. Lack of bidirectional association between age-related macular degeneration and Alzheimer's disease: A Mendelian randomization study. Alzheimers Dement . Aug 25 2022;doi:10.1002/alz.12775 Kaarniranta K, Salminen A, Haapasalo A, Soininen H, Hiltunen M. Age-related macular degeneration (AMD): Alzheimer's disease in the eye? J Alzheimers Dis . 2011;24(4):615-31. doi:10.3233/jad-2011-101908 Wang Y, Wang M, Han Y, Zhang R, Ma L. ABCA1 rs1883025 polymorphism and risk of age-related macular degeneration. Graefes Arch Clin Exp Ophthalmol . Feb 2016;254(2):323-32. doi:10.1007/s00417-015-3211-z Dietzel M, Pauleikhoff D, Arning A, et al. The contribution of genetic factors to phenotype and progression of drusen in early age-related macular degeneration. Graefes Arch Clin Exp Ophthalmol . Aug 2014;252(8):1273-81. doi:10.1007/s00417-014-2690-7 Yu Y, Reynolds R, Fagerness J, Rosner B, Daly MJ, Seddon JM. Association of variants in the LIPC and ABCA1 genes with intermediate and large drusen and advanced age-related macular degeneration. Invest Ophthalmol Vis Sci . Jun 28 2011;52(7):4663-70. doi:10.1167/iovs.10-7070 Chen W, Stambolian D, Edwards AO, et al. Genetic variants near TIMP3 and high-density lipoprotein-associated loci influence susceptibility to age-related macular degeneration. Proc Natl Acad Sci U S A . Apr 20 2010;107(16):7401-6. doi:10.1073/pnas.0912702107 Cipriani V, Leung HT, Plagnol V, et al. Genome-wide association study of age-related macular degeneration identifies associated variants in the TNXB-FKBPL-NOTCH4 region of chromosome 6p21.3. Hum Mol Genet . Sep 15 2012;21(18):4138-4150. doi:10.1093/hmg/dds225 Holliday EG, Smith AV, Cornes BK, et al. Insights into the genetic architecture of early stage age-related macular degeneration: a genome-wide association study meta-analysis. PLoS One . 2013;8(1):e53830. doi:10.1371/journal.pone.0053830 Burgess S, Davies NM, Thompson SG. Bias due to participant overlap in two-sample Mendelian randomization. Genet Epidemiol . Nov 2016;40(7):597-608. doi:10.1002/gepi.21998 Additional Declarations The authors declare no competing interests. Supplementary Files FigureS1.pdf Supplementary Figure S1. Forrest plot depicting MR estimates of the causal effect of AD on early AMD risk, suggesting a causal relationship between genetically proxied AD on early AMD risk based on MR–Egger ( p = 0.111) and IVW methods ( p = 0.615) using a full set of 57 SNPs. FigureS2.pdf Supplementary Figure S2. Scatter plot illustrating SNP–AD association against SNP–early AMD association using different MR methods. FigureS3.pdf Supplementary Figure S3. Plot for “leave–one–out” analysis of the causal effect of genetically proxied AD on early AMD risk, revealing no single SNP altering MR estimates when each SNP is removed from the principal MR analysis. The red line is representative of the significance contributed by the overall MR estimate using the IVW method. SupplementaryTables.docx Supplementary Table S1. Characteristics of SNPs selected for the causal effect of early AMD on AD risk. Supplementary Table S2. Heterogeneity analysis of SNPs selected for a causal effect of early AMD on AD risk. Supplementary Table S3. MR estimates the causal effect of AD on early AMD with five MR methods. Supplementary Table S4. MR estimates for the causal effect of AD on early AMD with five MR methods after outliers-corrected. Supplementary Table S5. MR estimates the causal effect of AD on AMD progression with five MR methods in the validation dataset. 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. 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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-3916453","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":270402354,"identity":"ff0bb035-dc32-4249-998d-1df3a6cc092d","order_by":0,"name":"Yu Huang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYBACAyjNI8/MfPABiMFHrBYZw3a2ZBCHh41YLTYM53nUJEAsglrMJZKfPfzadpiHsZmHrfJrjp0MGwPzw0c38GixnJFmbiwL1MLOzHvstuy2ZKDD2IyNc/A57EaCmbQk2Ba+tNuS25iBWnjYpPFrSf8G1sJwmMesWHJbPTFacswkP0K1MH7cdpgILWfelEkznEvnMWxmS5Zm3Hach42ZkF+Op2+T/FFmbS/Pf/jgx5/bqu352ZsfPsanBQSYeaFxwcwDJgkoBwHGH39gDCJUj4JRMApGwcgDADzQQsqQPOYFAAAAAElFTkSuQmCC","orcid":"","institution":"Guangdong Provincial People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yu","middleName":"","lastName":"Huang","suffix":""},{"id":270402467,"identity":"aec828f0-0cce-4afd-83e5-c07035c36622","order_by":1,"name":"Xueli Zhang","email":"","orcid":"","institution":"Guangdong Provincial People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xueli","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-02-01 07:25:17","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-3916453/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3916453/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50569599,"identity":"158bc92c-4604-4f9a-8eea-78d0e6d9cd83","added_by":"auto","created_at":"2024-02-02 15:33:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":101693,"visible":true,"origin":"","legend":"\u003cp\u003eParadigm and schematic model of bi-directional MR analysis. Three key MR assumptions: (1) genetic variants strongly associated with the exposure (the relevance assumption); (2) there is no confounding of the variant–outcome relationships (the independence assumption); (3) genetic variants only affect the outcome through the exposure (the exclusion restriction assumption).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3916453/v1/265e6eef9b201ccd7af05951.png"},{"id":50570058,"identity":"8fa98bd4-beed-4fd4-83f9-1a20ed72a00d","added_by":"auto","created_at":"2024-02-02 15:41:12","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":69962,"visible":true,"origin":"","legend":"\u003cp\u003eForrest plot\u003cstrong\u003e \u003c/strong\u003edepicting MR estimates of the causal effect of early AMD on AD risk, suggesting a causal relationship between genetically proxied early AMD on AD risk based on WMed (\u003cem\u003ep\u003c/em\u003e\u0026nbsp;\u0026lt; 0.05) and IVW methods (\u003cem\u003ep\u003c/em\u003e\u0026nbsp;\u0026lt; 0.05) using a full set of eight SNPs.\u003c/p\u003e","description":"","filename":"Figure200.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3916453/v1/e4272b2d9363d856a1b1dc7c.jpg"},{"id":50569602,"identity":"909c7bf3-8981-4639-9af6-1a10c4ae6429","added_by":"auto","created_at":"2024-02-02 15:33:12","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":81084,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot illustrating SNP–early AMD association against SNP–AD association using different MR methods.\u003c/p\u003e","description":"","filename":"Figure300.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3916453/v1/b110ecbb294369810726f5a8.jpg"},{"id":50569600,"identity":"ab32b54d-e0ae-42df-a53c-490fd662a2d5","added_by":"auto","created_at":"2024-02-02 15:33:12","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":74610,"visible":true,"origin":"","legend":"\u003cp\u003ePlot for “leave–one–out” analysis of the causal effect of genetically proxied early AMD on AD risk, revealing no single SNP altering MR estimates when each SNP is removed from the principal MR analysis. The red line is representative of the significance contributed by the overall MR estimate using the IVW method.\u003c/p\u003e","description":"","filename":"Figure400.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3916453/v1/6518140c74ef0fd3f4efa18e.jpg"},{"id":50570421,"identity":"5beccfe8-b551-4559-811c-6a4eb14fe518","added_by":"auto","created_at":"2024-02-02 15:49:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":876621,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3916453/v1/91b60a88-291e-403d-aea5-9a810c207e69.pdf"},{"id":50569596,"identity":"61e6e6d9-6bf3-4483-8dad-afcb9afa2c61","added_by":"auto","created_at":"2024-02-02 15:33:12","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":11219,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure S1. \u003c/strong\u003eForrest plot\u003cstrong\u003e \u003c/strong\u003edepicting MR estimates of the causal effect of AD on early AMD risk, suggesting a causal relationship between genetically proxied AD on early AMD risk based on MR–Egger (\u003cem\u003ep\u003c/em\u003e = 0.111) and IVW methods (\u003cem\u003ep\u003c/em\u003e = 0.615) using a full set of 57 SNPs.\u003c/p\u003e","description":"","filename":"FigureS1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3916453/v1/5775228abf73fe935bf4aaea.pdf"},{"id":50569597,"identity":"f10d100d-afa8-46f3-b92e-e964145fa737","added_by":"auto","created_at":"2024-02-02 15:33:12","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":10937,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure S2.\u003c/strong\u003e Scatter plot illustrating SNP–AD association against SNP–early AMD association using different MR methods.\u003c/p\u003e","description":"","filename":"FigureS2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3916453/v1/b90fc29a0fb2e7df7a8910e4.pdf"},{"id":50569603,"identity":"678c0925-d334-4672-aa13-733961be472a","added_by":"auto","created_at":"2024-02-02 15:33:12","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":10815,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure S3. \u003c/strong\u003ePlot for “leave–one–out” analysis of the causal effect of genetically proxied AD on early AMD risk, revealing no single SNP altering MR estimates when each SNP is removed from the principal MR analysis. The red line is representative of the significance contributed by the overall MR estimate using the IVW method.\u003c/p\u003e","description":"","filename":"FigureS3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3916453/v1/10d88715a312a822ffb75c6e.pdf"},{"id":50570059,"identity":"51ad10a3-36a9-4390-a14d-41eb5caa485e","added_by":"auto","created_at":"2024-02-02 15:41:12","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":37002,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table S1.\u003c/strong\u003e Characteristics of SNPs selected for the causal effect of early AMD on AD risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table S2.\u003c/strong\u003e Heterogeneity analysis of SNPs selected for a causal effect of early AMD on AD risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table S3. \u003c/strong\u003eMR estimates the causal effect of AD on early AMD with five MR methods.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table S4.\u003c/strong\u003e MR estimates for the causal effect of AD on early AMD with five MR methods after outliers-corrected.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary Table S5.\u003c/strong\u003e MR estimates the causal effect of AD on AMD progression with five MR methods in the validation dataset.\u003c/p\u003e","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-3916453/v1/dee8f04f137945e32aaadb32.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eGenetic evidence for a causal relationship between Alzheimer’s disease and age-related macular degeneration: A Mendelian randomization study\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"What is already known on this topic","content":"\u003cul\u003e\n \u003cli\u003eObservational studies have highlighted connections between age-related macular degeneration (AMD) and Alzheimer\u0026rsquo;s disease (AD); however, their causal relationship remains uncertain.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eWhat this study adds:\u0026nbsp;\u003c/strong\u003eThis study reveals that a genetic predisposition to early-onset AMD is causally associated with reduce risk of AD, and there is no evidence supporting AD\u0026apos;s causal role in AMD.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eHow this study might affect research, practice or policy:\u0026nbsp;\u003c/strong\u003eOur findings hold significance for leveraging the retina or retinal diseases as tools for early AD diagnosis. Additionally, implications for drug development are notable, suggesting the need for targeted drugs for AD or AMD, considering potential side effects on the other condition.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"1. Introduction","content":"\u003cp\u003eThe aging population is rising rapidly worldwide. According to the WHO, the proportion of the population over 60 years will nearly double between 2015 and 2050.\u003csup\u003e1\u003c/sup\u003e Aging could cause various functional changes to the neuron system, which includes the brain and retina. As a consequence, the prevalence of age-related macular degeneration (AMD) and Alzheimer\u0026rsquo;s disease (AD) is likely to increase with age.\u003csup\u003e2\u003c/sup\u003e AD, an irreversible neuron degenerative disease with high heritability (~\u0026thinsp;60\u0026ndash;80%), has gradually become the major type of dementia and a considerable public health concern.\u003csup\u003e3\u003c/sup\u003e AMD is also an irreversible neuron degenerative disease in the macular area and is the leading common cause of irreversible blindness worldwide. In developed countries, AMD has become the major cause of vision loss of the elderly over 65 years old in developed countries.\u003csup\u003e4\u003c/sup\u003e AD and AMD are common comorbidities in chronic diseases and represent major global public health challenges.\u003csup\u003e5, 6\u003c/sup\u003e\u003c/p\u003e \u003cp\u003ePrevious epidemiological studies have revealed associations between AD and AMD, suggesting they may have common pathogenesis.\u003csup\u003e7, 8\u003c/sup\u003e A cohort study with 4,097 patients in the eye disease group and 20,475 in the control group revealed that AMD was associated with an increased risk of AD (adjusted hazard ratio [HR]\u0026thinsp;=\u0026thinsp;36.94, 95% confidence interval [CI]\u0026thinsp;=\u0026thinsp;4.62\u0026ndash;295.46).\u003csup\u003e9\u003c/sup\u003e A recent retrospective cohort study based on AREDS2 (3,157 participants; mean age 72.7 years) revealed that the risk of late AMD was higher in participants with cognitive impairment at baseline (at five years, HR\u0026thinsp;=\u0026thinsp;1.24, 95% CI\u0026thinsp;=\u0026thinsp;1.08\u0026ndash;1.43; at ten years, HR\u0026thinsp;=\u0026thinsp;1.20, 95% CI\u0026thinsp;=\u0026thinsp;1.05\u0026ndash;1.37).\u003csup\u003e10\u003c/sup\u003e Although evidence has revealed that the two diseases may share similar pathological mechanisms, the existence of a causal relationship between them is still uncertain.\u003csup\u003e7\u003c/sup\u003e The direction of this causal relationship remains to be explored as well.\u003csup\u003e8\u0026ndash;11\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eConventional observational studies are susceptible to residual confounding (due to unmeasured or imprecisely measured confounders), reverse causation, and other forms of bias that undermine robust causal inference, which makes it unclear if the relationship is truly causal.\u003csup\u003e12\u003c/sup\u003e The gold standard to determine a causal relationship is the randomized controlled study (RCT). However, RCT has numerous restrictions, which have limited its conduction among different populations.\u003csup\u003e13\u003c/sup\u003e Mendelian randomization (MR) is an analytical approach that uses germline genetic variants as instruments for risk factors to evaluate the causal effects of these factors on disease outcomes in observational settings.\u003csup\u003e14\u003c/sup\u003e Since germline genetic variants, such as single-nucleotide polymorphisms (SNPs), are randomly assorted at meiosis, MR analyses should be less prone to confounding by lifestyle and environmental factors than conventional observational studies. Furthermore, since germline genetic variants are fixed at conception, MR analyses are not subject to reverse causation bias and ethical issues.\u003csup\u003e5, 15\u003c/sup\u003e The validity of causal estimates obtained using MR are dependent on three key assumptions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e): 1) The instrumental variants (IVs) are associated with the risk factor of interest (the relevance assumption); 2) They share no common cause with the outcome (the independence assumption); and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) They do not affect the outcome except through the risk factor (the exclusion restriction assumption).\u003csup\u003e16\u003c/sup\u003e To date, despite numerous studies that have explored the shared genetic variations between AD and AMD,\u003csup\u003e17, 18\u003c/sup\u003e none of these studies has investigated their causal relationship under an MR framework. Therefore, the causality behind these two diseases remains largely ambiguous.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn this study, we conducted a two\u0026ndash;sample MR approach to investigate the associations of AMD with the risk of AD. A bi\u0026ndash;directional MR was used to infer reverse causality. This study may provide insights into the mechanism of AD and AMD\u0026rsquo;s comorbidity.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Study design\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the study overview. In this study, we conducted a bi–directional two–sample MR to determine the causal association between AMD and AD using summary–level datasets generated from large GWAS studies. The IVs were extracted based on the GWAS summary statistics of AD or AMD. We first performed an MR analysis to quantify the effect of genetically proxied early AMD on the risk of AD. After that, we investigated the causal effect of genetically proxied AD on the risk of AD in reverse-direction MR.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Data source\u003c/h2\u003e\n \u003cp\u003eBoth summary–level data of AD and early AMD were obtained from previously published GWAS. In the GWAS for early AMD, data were obtained from 11 different centers, including the International AMD Genomics Consortium and UK Biobank (accession numbers: GCST010723).\u003csup\u003e19\u003c/sup\u003e Among the participants, 14,034 were cases, while 91,214 were controls. Another AMD progression dataset was used for validation. The 2127 Caucasian participants from the Age–Related Eye Disease Study were between 55 and 81 years at enrollment (accession numbers: GCST009144).\u003csup\u003e20\u003c/sup\u003e For the GWAS for AD, summary statistics were obtained from The European Alzheimer \u0026amp; Dementia Biobank (EADB) stage I (GWAS meta–analysis; accession numbers: GCST90027158).\u003csup\u003e21\u003c/sup\u003e The dataset comprised 39,106 clinically diagnosed AD cases, 46,828 proxy AD and related dementia (proxy–ADD), and 401,577 controls.\u003c/p\u003e\n \u003cp\u003eParticipation of both AD and AMD was restricted to European ancestry to avoid population bias (e.g., concerning age, sex, and ancestry). The GWAS on early onset AMD and AD utilized an overlapping dataset of 57,802 participants from the UK Biobank. Because all analyses discussed herein are based on publicly available summary data, no additional ethical approval from an institutional review board was required for this study.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Genetic instrument selection\u003c/h2\u003e\n \u003cp\u003eGenetic variants associated with AD and AMD at the genome–wide significance level (\u003cem\u003ep\u003c/em\u003e \u0026lt; 5 × 10\u003csup\u003e–8\u003c/sup\u003e), weak linkage disequilibrium (LD; \u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e \u0026lt; 0.001), and with a minor allele frequency (MAF) greater than 0.01 were selected as IVs. We restricted genetic variants to cis–acting SNPs, i.e., in or within ± 10,000 kb from the gene encoding the protein.\u003csup\u003e12\u003c/sup\u003e Based on the above rules, eight SNPs were rigorously selected as IVs of early AMD, and their causal role on AMD was further analyzed by a series of MR approaches. Similarly, 59 different SNPs were used as genetic proxies of AD to explore the causal effect of AD on AMD in the reverse–direction MR analysis. IV was excluded if it’s effect on the outcome is missing.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e\n \u003cp\u003eThe two–sample MR method was used to evaluate the causal relationship between genetically predicted early AMD and AD. As the main analysis, inverse–variance weighting (IVW)\u003csup\u003e22\u003c/sup\u003e was the primary method to establish the overall casual effect estimates. For sensitivity analyses, weighted median (WMed) estimation,\u003csup\u003e23\u003c/sup\u003e MR–Egger regression,\u003csup\u003e24\u003c/sup\u003e and weighted mode (WMod) estimation\u003csup\u003e25\u003c/sup\u003e were conducted. Leave–one–out analyses were conducted to evaluate SNPs with extreme effect.\u003csup\u003e26\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eBefore MR analysis, the MR assumptions were tested. The selected IVs were required to have a strong relationship with exposure (AD). The strength of the IVs was estimated based on the \u003cem\u003eF\u003c/em\u003e–statistic,\u003csup\u003e27\u003c/sup\u003e calculated using the following formula:\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\begin{array}{c}F=\\frac{{R}^{2}\\left(n-k-1\\right)}{k\\left(1-{R}^{2}\\right)}\\#\\left(1.\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003e(\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e: variance of exposure explained by selected instrumental variables, \u003cem\u003en\u003c/em\u003e: sample size, \u003cem\u003ek\u003c/em\u003e: number of instrumental variables)\u003c/p\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e \u003csup\u003e2\u003c/sup\u003e was calculated using the following formula:\u003c/p\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$\\begin{array}{c}{R}^{2}={\\sum }_{i=1}^{K}\\frac{{\\beta }_{i}^{2}}{{\\beta }_{i}^{2}+2\\ast n\\ast se{\\left({\\beta }_{i}\\right)}^{2}}\\#\\left(2.\\right)\\end{array}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e, \u003cem\u003eK\u003c/em\u003e: number of the selected genetic variants).\u003csup\u003e28\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eWe estimated the \u003cem\u003eF\u003c/em\u003e–statistic for each SNP as the square of the SNP–exposure association divided by the variance of the SNP–exposure association.\u003csup\u003e24\u003c/sup\u003e We also generated the mean \u003cem\u003eF\u003c/em\u003e-statistic for exposure.\u003csup\u003e29\u003c/sup\u003e Those with an \u003cem\u003eF\u003c/em\u003e–statistic of 10 or less were regarded as weak instruments.\u003csup\u003e30\u003c/sup\u003e It is recommended to choose a higher \u003cem\u003eF\u003c/em\u003e statistic corresponding to a smaller bias than the weak instrumental variable bias having a slim chance.\u003csup\u003e27–30\u003c/sup\u003e Independent variants (\u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e \u0026lt; 0.001, window size = 10,000 kb) were selected using the “clump” function (EUR population) of the “TwoSampleMR” R package to maximize instrument strength.\u003csup\u003e31\u003c/sup\u003e\u003c/p\u003e\n \u003cp\u003eThe exclusion restriction assumption is generally violated when a genetic variant influences an outcome through a biological pathway independent of the exposure. Cochran’s Q statistic was calculated to evaluate heterogeneity.\u003csup\u003e32\u003c/sup\u003e Besides, the MR–Egger intercept test provided stable estimates when considering the horizontal pleiotropy between the IVs and confounders.\u003csup\u003e33\u003c/sup\u003e Finally, MR pleiotropy residual sum and outlier (MR–PRESSO) test were employed to estimate the effect if the violated IVs were removed.\u003csup\u003e34\u003c/sup\u003e All statistical analyses were performed using the R software with “TwoSampleMR” packages and “MRPRESSO” (version 4.2.0, R Foundation for Statistical Computing, Vienna, Austria), and \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05 was considered a significant association.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Genetic instruments\u003c/h2\u003e \u003cp\u003eIn MR analysis, eight SNPs that were genome-wide significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e), independent (LD, \u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and with MAF\u0026thinsp;\u0026gt;\u0026thinsp;0.01 were selected as IV for MR analyses of the effect of early AMD on AD risk, as displayed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. None of the candidate SNPs was palindromic SNP for being palindromic with intermediate allele frequencies (MAF\u0026thinsp;\u0026gt;\u0026thinsp;0.42). The \u003cem\u003eF\u003c/em\u003e-statistic values for instruments and estimated power are illustrated in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The \u003cem\u003eF\u003c/em\u003e\u0026ndash;statistic value was 55.22, indicating an excellent strength of the used genetic instruments. For reverse causality, we incorporated 57 independent SNPs that were genome-wide significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) and independent (LD, \u003cem\u003er\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, window size\u0026thinsp;=\u0026thinsp;10,000 kb) and had MAF\u0026thinsp;\u0026gt;\u0026thinsp;0.01, suggestive as IVs for AD (Supplementary Table S1).\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 SNPs used in forward-direction MR analysis for causal effect of early AMD on AD risk.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTarget SNPs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eChr\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMAF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eAssociation with AMD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c12\" namest=\"c10\"\u003e \u003cp\u003eAssociation with AD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers3750847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.53E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.3838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.6E\u0026thinsp;\u0026minus;\u0026thinsp;118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;0.0363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.0099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e2.51E-04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers247617\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.87E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.39E\u0026thinsp;\u0026minus;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.0087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers11569415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7859\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.15E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.1158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.32E\u0026thinsp;\u0026minus;\u0026thinsp;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u0026minus;0.0249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.0109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers4658046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.3902\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.5E-03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.3213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.52E\u0026thinsp;\u0026minus;\u0026thinsp;116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.0083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers4844620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.7865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.43E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.0949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.79E\u0026thinsp;\u0026minus;\u0026thinsp;08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.0099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers547154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.73E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;0.2178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.62E\u0026thinsp;\u0026minus;\u0026thinsp;19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.0135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers943080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.53E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0797\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e3.838E\u0026thinsp;\u0026minus;\u0026thinsp;08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.0081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.56\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ers13278062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.4793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.60E-04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.637E\u0026thinsp;\u0026minus;\u0026thinsp;08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.0142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.0081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"12\"\u003e\u003cb\u003eAbbreviations\u003c/b\u003e: SNP: single-nucleotide polymorphism; Chr\u0026thinsp;=\u0026thinsp;chromosome; EA: effect allele; OA: other allele; β: regression effect size; EAF\u0026thinsp;=\u0026thinsp;effect allele frequency; MAF\u0026thinsp;=\u0026thinsp;minor allele frequency; SE\u0026thinsp;=\u0026thinsp;standard error\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMR estimates for causal effect of early AMD on AD with five MR methods.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethod\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. Of SNPs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMR\u003c/p\u003e \u003cp\u003ePRESSO\u003c/p\u003e \u003cp\u003epval\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e combined\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e statistic\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMR\u0026ndash;Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.913 (0.833\u0026ndash;1.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEgger intercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.399\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWMed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.944 (0.905\u0026ndash;0.985)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIVW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.945 (0.898\u0026ndash;0.995)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.0063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e55.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWMod\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.942 (0.907\u0026ndash;0.979)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cb\u003eAbbreviations\u003c/b\u003e: SNP: single-nucleotide polymorphism; β: regression effect size; CI\u0026thinsp;=\u0026thinsp;confidence interval; OR\u0026thinsp;=\u0026thinsp;odds ratio; PRESSO\u0026thinsp;=\u0026thinsp;Pleiotropy RESidual Sum and Outlier; SE\u0026thinsp;=\u0026thinsp;standard error; R2 combined: variance of exposure explained by selected instrumental variables; WMed\u0026thinsp;=\u0026thinsp;weighted median; IVW\u0026thinsp;=\u0026thinsp;inverse-variance weighting; WMod\u0026thinsp;=\u0026thinsp;weighted mode.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Evaluating the effect of early AMD on AD risk\u003c/h2\u003e \u003cp\u003eIn our MR analyses, we found moderate evidence for a protective effect of early AMD on the risk of AD (IVW, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032, OR\u0026thinsp;=\u0026thinsp;0.945, 95% CI\u0026thinsp;=\u0026thinsp;0.898\u0026ndash;0.995 per SD\u0026ndash;increase; (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The estimates were broadly consistent with the estimates yielded by the WMed (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008, OR\u0026thinsp;=\u0026thinsp;0.944, 95% CI\u0026thinsp;=\u0026thinsp;0.905\u0026ndash;0.985 per SD\u0026ndash;increase) and WMod (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.019, OR\u0026thinsp;=\u0026thinsp;0.942, 95% CI\u0026thinsp;=\u0026thinsp;0.907\u0026ndash;0.979 per SD\u0026ndash;increase) analyses. The MR\u0026ndash;Egger regression method is robust to invalid instruments, and the WMed yields robust estimates if more than half of the information comes from invalid IVs.\u003csup\u003e23\u003c/sup\u003e We noted heterogeneity in the effect of early AMD\u0026ndash;associated SNPs on AD (IVW: Q\u0026thinsp;=\u0026thinsp;16.06, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013; MR\u0026ndash;Egger: Q\u0026thinsp;=\u0026thinsp;18.26, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011; Supplementary Table S2). However, the MR\u0026ndash;Egger intercept analysis did not indicate horizontal pleiotropy (coefficient β\u0026thinsp;=\u0026thinsp;0.009, SE\u0026thinsp;=\u0026thinsp;0.009, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.399). The MR\u0026ndash;PRESSO analysis did not identify any SNP as an outlier. Conventional IVW leave\u0026ndash;one\u0026ndash;out analysis did not identify any high leverage points with strong influence (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The \u003cem\u003eF\u003c/em\u003e\u0026ndash;statistics (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;55.22\u003cem\u003e)\u003c/em\u003e for the genetic instruments were consistent with an absence of weak instrument bias.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Evaluating the effect of AD on early AMD risk\u003c/h2\u003e \u003cp\u003eWe further evaluated the effect of AD on AMD. We extracted 60 SNPs as IVs for AD from the same GWAS summary data. We removed one SNP for being palindromic with intermediate allele frequencies.\u003c/p\u003e \u003cp\u003eNext, we selected strong IVs (\u003cem\u003eF\u003c/em\u003e\u0026ndash;statistics\u0026thinsp;\u0026gt;\u0026thinsp;10) to study reverse causality to test whether AD was causally associated with early AMD. We performed MR analyses following the above\u0026ndash;described methods (IVW, MR\u0026ndash;Egger regression, WMed regression, simple mode, WMod, and leave\u0026ndash;one\u0026ndash;out analysis) to evaluate the causal relationship. Most of the methods indicated no causal effect of genetically predicted AD on early AMD risk (IVW, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.432, OR\u0026thinsp;=\u0026thinsp;0.974, 95% CI\u0026thinsp;=\u0026thinsp;0.911\u0026ndash;1.041 per SD\u0026ndash;increase; WMed, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.602, OR\u0026thinsp;=\u0026thinsp;0.977, 95% CI\u0026thinsp;=\u0026thinsp;0.905\u0026ndash;1.066 per SD\u0026ndash;increase; WMod, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.186, OR\u0026thinsp;=\u0026thinsp;0.899, 95% CI\u0026thinsp;=\u0026thinsp;0.768\u0026ndash;1.051 per SD\u0026ndash;increase; Supplementary Table S3, Supplementary Figures S1, S2 and S3).\u003c/p\u003e \u003cp\u003eHowever, MR\u0026ndash;Egger regression displayed different results (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009, OR\u0026thinsp;=\u0026thinsp;0.855, 95% CI\u0026thinsp;=\u0026thinsp;0.763\u0026ndash;0.958 per SD\u0026ndash;increase). Meanwhile, the MR\u0026ndash;Egger intercept test (coefficient β\u0026thinsp;=\u0026thinsp;0.015, SE\u0026thinsp;=\u0026thinsp;0.005, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009) indicated horizontal pleiotropy for the SNPs (Supplementary Table S3). For validation, the MR\u0026ndash;PRESSO analysis (causal estimate\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.027, SE\u0026thinsp;=\u0026thinsp;0.034, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.00017) confirmed strong pleiotropy. Two outliers (SNP: rs1800978, rs61679753) were picked out according to MR\u0026ndash;PRESSO analysis (outlier\u0026ndash;corrected \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.172), and after removing the outliers, MR\u0026ndash;PRESSO yielded a causal estimation of AD on early AMD risk (Supplementary Table S4) which showed no causal relationship between them.\u003c/p\u003e \u003cp\u003eFor a further validation analysis, we analyzed the causal relationship between AD and AMD progression. We still found no causal effect of genetically predicted AD on AMD progression with 45 SNPs estimated (IVW, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.760, OR\u0026thinsp;=\u0026thinsp;1.034, 95% CI\u0026thinsp;=\u0026thinsp;0.835\u0026ndash;1.279 per SD\u0026ndash;increase; MR\u0026ndash;Egger regression, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.573, OR\u0026thinsp;=\u0026thinsp;0.861, 95% CI\u0026thinsp;=\u0026thinsp;0.514\u0026ndash;1.442per SD\u0026ndash;increase; WMed, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.486, OR\u0026thinsp;=\u0026thinsp;0.899, 95% CI\u0026thinsp;=\u0026thinsp;0.666\u0026ndash;1.213 per SD\u0026ndash;increase; WMod, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.560, OR\u0026thinsp;=\u0026thinsp;0.908, 95% CI\u0026thinsp;=\u0026thinsp;0.659\u0026ndash;1.252 per SD\u0026ndash;increase; Supplementary Table S5).\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur MR analyses first investigated the causal association between genetically predicted early AMD with the risk of AD. The results from the major estimation method of MR analyses indicated a causal role of early AMD in AD onset, suggesting that early AMD may play a protective role in the onset of AD. The reverse\u0026ndash;direction MR analysis also revealed no evidence of genetic liability to AD being related to early AMD.\u003c/p\u003e \u003cp\u003eOur MR analyses demonstrated that early AMD could be a protective factor against AD, which is consistent with the observation that persons with low cognitive function scores were more likely to have early AMD than persons with higher scores.\u003csup\u003e35, 36\u003c/sup\u003e However, the influence of confounders may have led to bias in these observational estimates of association. Age is one of the potential confounders, with a positive association with AMD and AD. Jiang et al.\u003csup\u003e37\u003c/sup\u003e conducted a two-sample bidirectional MR study recently aiming to evaluate the causal relationship between advanced AMD (16,144 advanced AMD cases, 17,832controls) and AD in discovery data set (late-onset AD, 21,982 cases and 41,944 controls) and validation data set (71,880 clinically diagnosed AD/AD-by-proxy cases and 383,378 control). Interestingly, they found no evidence to support a bidirectional causal association between advanced AMD and AD and then concluded that the MR study decreased the probability of a clinically relevant relationship between the two neurodegenerative diseases and that the associations observed in epidemiological studies should not be considered causal. However, we took it with a grain of salt. Therefore, we performed an MR study to further explore the causal association between AMD and AD. As expected, Our MR study confirmed a moderate causal effect of early AMD on AD development after avoiding these age\u0026ndash;related risk factors. This may give us a little prompt message that we should account for the influence of phenotype (IVW, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032, OR\u0026thinsp;=\u0026thinsp;0.945, 95% CI\u0026thinsp;=\u0026thinsp;0.898\u0026ndash;0.995 per SD\u0026ndash;increase). Contrary to previous studies that adopted a cross-sectional study design, some longitudinal studies investigated the association between AD and AMD. A cohort study with 4,097 patients in the eye disease group and 20,475 in the control group depicted that general AMD was associated with an increased risk of AD (adjusted HR\u0026thinsp;=\u0026thinsp;36.94, 95% CI\u0026thinsp;=\u0026thinsp;4.62\u0026ndash;295.46).\u003csup\u003e9\u003c/sup\u003e Our study focused on genetic risk factors and demonstrated some shared genes between AMD and AD. However, the same genes may play a different role in AMD and AD, for example, the famous gene \u003cem\u003eApoE\u003c/em\u003e, which has three alleles (\u003cem\u003eε2\u003c/em\u003e, \u003cem\u003eε3\u003c/em\u003e, and \u003cem\u003eε4\u003c/em\u003e). Individuals carrying the \u003cem\u003eAPOE-ε4\u003c/em\u003e allele seem to have a lower risk for AMD but the strongest thus far identified risk factor for AD. This may be one of the reasons causing the result that we obtained.\u003csup\u003e38\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eIn reverse\u0026ndash;direction MR analysis, we could not determine a causal association between AD and AMD (IVW, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.432, OR\u0026thinsp;=\u0026thinsp;0.974, 95% CI\u0026thinsp;=\u0026thinsp;0.911\u0026ndash;1.041 per SD\u0026ndash;increase). The MR analysis result was consistent with the above in validation (IVW, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.760, OR\u0026thinsp;=\u0026thinsp;1.034, 95% CI\u0026thinsp;=\u0026thinsp;0.835\u0026ndash;1.279 per SD\u0026ndash;increase; MR\u0026ndash;Egger regression, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.573, OR\u0026thinsp;=\u0026thinsp;0.861, 95% CI\u0026thinsp;=\u0026thinsp;0.514-1\u0026ndash;442 per SD\u0026ndash;increase; WMed, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.486, OR\u0026thinsp;=\u0026thinsp;0.899, 95% CI\u0026thinsp;=\u0026thinsp;0.666\u0026ndash;1.213 per SD\u0026ndash;increase; WMod, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.560, OR\u0026thinsp;=\u0026thinsp;0.908, 95% CI\u0026thinsp;=\u0026thinsp;0.659\u0026ndash;1.252 per SD\u0026ndash;increase). A cross\u0026ndash;sectional study that included 592 AD patients aged 50 and above showed AD was not associated with AMD.\u003csup\u003e2\u003c/sup\u003e A similar conclusion was reported in a population\u0026ndash;based study with 2,088 participants aged 69 to 97 years, which demonstrated that there was no association of AD with early AMD.\u003csup\u003e35\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eInterestingly, MR\u0026ndash;Egger analysis suggests a pleiotropic effect between AD and AMD (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009, OR\u0026thinsp;=\u0026thinsp;0.855, 95% CI\u0026thinsp;=\u0026thinsp;0.763\u0026ndash;0.958 per SD\u0026ndash;increase), suggested by MR\u0026ndash;PRESSO, 2 pleiotropic SNP\u0026mdash;rs1800978, encoding ABCA1 and rs61679753, and encoding \u003cem\u003eTOMM40\u003c/em\u003e might influence the result. ATP\u0026ndash;binding cassette transporter A1 (\u003cem\u003eABCA1\u003c/em\u003e) is one of several proteins involved in cholesterol homeostasis. \u003cem\u003eABCA1\u003c/em\u003e gene variation has been demonstrated to contribute to AMD susceptibility.\u003csup\u003e39\u0026ndash;42\u003c/sup\u003e A GWAS study found that \u003cem\u003eTOMM40\u003c/em\u003e was associated with advanced AMD (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e) and combined replication samples (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8.4 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) with European ancestry.\u003csup\u003e43\u003c/sup\u003e This variant has been identified, showing an association with early AMD in a GWAS meta-analysis of the European population (p\u0026thinsp;=\u0026thinsp;1.1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e), and the signal remained in a secondary meta-analysis combining two Singapore-based Asian cohorts (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3.2 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e).\u003csup\u003e44\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eHowever, previous observational studies investigated the influence of neurological diagnoses such as AD on AMD but with inconclusive results. The potential causality between AD and AMD remains unclear owing to bias or various confounders inherent to observational studies. To date, no RCT study has been performed to assess the effect of AD on AMD. Our MR study adds to the existing body of knowledge by confirming the causal effect of AD on AMD, which is informative. The causal association between AMD and AD has implications for understanding disease pathogenesis, referral, and treatments and can make the most effective use of scarce resources.\u003c/p\u003e \u003cp\u003eThe major strength of our study is its two\u0026ndash;sample MR design, which could circumvent the limitations of observational studies with measurement errors and residual confounding. It can minimize residual confounding and reverse causality and genetic validation of the wide range of AD, which can afford high\u0026ndash;quality evidence. However, our study has a few limitations that should be addressed. First, the estimates might be biased toward the observational associations if the exposure and outcome data came from the same sample.\u003csup\u003e45\u003c/sup\u003e Second, residual pleiotropy might remain, despite the range of sensitivity analyses (i.e., WM, MR\u0026ndash;PRESSO, and MR\u0026ndash;Egger intercept) conducted to explore and account for pleiotropy. Third, we could not distinguish the effects of all different kinds of AD and AMD and thus only examined general AD, early AMD and AMD progression. A further limitation was that our study was restricted to participants of European ancestry; however, the observational and causal effects of AMD on AD may differ in other ethnic groups.\u003c/p\u003e \u003cp\u003eTherefore, the causal effect in our principal MR analysis (IVW) was robust and unbiased, which was evidenced by neither the MR\u0026ndash;Egger intercept test nor the MR\u0026ndash;PRESSO analysis. Conclusively, our primary MR and sensitivity analyses, with IVW, WMed, and WMod methods, consistently establish the existence of a causal effect of suffering early AMD on the decreased risk of AD occurrence.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn conclusion, our comprehensive MR analysis provides insight into potential causal mechanisms linking excess early AMD to decreased AD risk. Our results elevated that early AMD causes a moderately decreased risk of AD than that reported in previous conventional observational studies. Future well-designed RCT studies need to be conducted to clarify the role of individual AD on AMD.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAMD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAge-related macular degeneration\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eAD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAlzheimer\u0026rsquo;s disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMendelian randomization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHazard ratio\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eRCT\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRandomized controlled study\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSNPs\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSingle-nucleotide polymorphisms\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIVs\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInstrumental variants\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eLD\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLinkage disequilibrium\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMAF\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMinor allele frequency\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eIVW\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInverse\u0026ndash;variance weighting\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eWMed\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eWMod\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMR\u0026ndash;PRESSO\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMR pleiotropy residual sum and outlier\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eChr\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eChromosome\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eEA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEffect allele\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eOA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOther allele\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eβ\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRegression effect size\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eEAF\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEffect allele frequency\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSE\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard error\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAD GWAS: http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90027001-GCST90028000/GCST90027158/\u003c/p\u003e\n\u003cp\u003eEarly AMD GWAS: http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST010001-GCST011000/GCST010723/\u003c/p\u003e\n\u003cp\u003eAMD progression GWAS: http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009144/\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our sincere appreciation for the MRC\u0026ndash;IEU OpenGWAS project.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present work was supported by the National Natural Science Foundation of China (32200545), and the GDPH Supporting Fund for Talent Program (KJ2020633).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShuo Ma and Yu Huang contributed equally to this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGuangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People\u0026rsquo;s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 510080 Guangzhou, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYu Huang, Shunming Liu, Xianwen Shang, Honghua Yu, Mingguang He, Xueli Zhang\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMedical Big Data Center, Guangdong Provincial People\u0026rsquo;s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 510080 Guangzhou, China \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShuo Ma\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Data Center, Guangzhou Women and Children\u0026rsquo;s Medical Center, Guangzhou Medical University, Guangzhou, 51023, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShuo Ma\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDepartment of Radiology, Guangdong Provincial People\u0026rsquo;s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, 510080 Guangzhou, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKe Zhao\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eState Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, 510060 Guangzhou, China\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMingguang He\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCentre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, 3002 Melbourne, Australia\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMingguang He\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMedical Research Center, Guangdong Provincial People\u0026apos;s Hospital, Guangdong Academy of Medical Sciences\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXueli Zhang\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXLZ conceptualized and designed the study with XLZ did the literature and YH and SM did the MR method and prepared the manuscript. MGH did the supervision. SM had full access to all of the data. SM and SML did the visualization. XLZ was the guarantor. All authors commented on and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Xueli Zhang\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions Ethics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAgeing and health. 26 October, 2022. 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Nov 2016;40(7):597-608. doi:10.1002/gepi.21998\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"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, age–related macular degeneration, Mendelian randomization","lastPublishedDoi":"10.21203/rs.3.rs-3916453/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3916453/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePrevious observational studies have established a bi-directional association between age-related macular degeneration (AMD) and Alzheimer\u0026rsquo;s disease (AD). However, these associations might be induced by confounding factors.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a bi-directional MR study to evaluate potential causal associations between AMD and AD using GWAS data. 39,106 clinically diagnosed AD cases, 46,828 proxy AD and related dementia, and 14,034 AMD patients were included in this study.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIncreased AMD exposure due to germline genetic variation was generally associated with decreased risk for AD. A causal effect was observed between early AMD and AD. However, reverse\u0026ndash;direction MR analysis depicted generally little evidence of an association between genetically increased AD exposure and risk of early AMD with 57 SNPs and risk of AMD progression.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur MR study confirmed the causal effect of early AMD on AD, and early AMD could reduce the risk for AD.\u003c/p\u003e","manuscriptTitle":"Genetic evidence for a causal relationship between Alzheimer’s disease and age-related macular degeneration: A Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-02 15:33:08","doi":"10.21203/rs.3.rs-3916453/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":"c41171e2-6cd0-4f49-83cf-ab7022134f5f","owner":[],"postedDate":"February 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28501342,"name":"Ophthalmology"}],"tags":[],"updatedAt":"2024-02-02T15:33:08+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-02 15:33:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3916453","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3916453","identity":"rs-3916453","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}