Association between age-related macular degeneration and the gut microbiota: A two-sample Mendelian randomization analysis

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Methods Mendelian randomization (MR) analysis was performed on summary data from genome-wide association studies (GWASs) of the gut microbiota and AMD. The gut microbiota was considered the exposure. Instrumental variables (IVs) were identified from a GWAS involving 7,738 participants. The GWAS for AMD from European cohorts served as the outcome dataset, comprising 8,931 AMD patients and 348,936 controls. The primary analysis employed the inverse-variance weighted (IVW) method, with sensitivity analysis conducted to assess the robustness and reliability of the MR analysis results. Results IVW revealed significant associations between specific microbial families/genera and AMD. Notably, the family Peptococcaceae (OR=1.176, 95% CI 1.012 to 1.366, P=0.004), genus Parasutterella (OR=1.167, 95% CI 1.011 to 1.347, P=0.035), and genus Faecalibacterium (OR=1.247, 95% CI 1.071 to 1.451, P=0.034) demonstrated positive causal associations with AMD, while the class Melainabacteria (OR=0.886, 95% CI 0.789 to 0.995, P=0.041) and family Rikenellaceae (OR=0.844, 95% CI 0.726 to 0.981, P=0.027) showed negative causal associations. Sensitivity analysis did not reveal evidence of reverse causality, pleiotropy, or heterogeneity. Conclusion Utilizing MR, we conducted a comprehensive assessment to investigate the causal effect of 412 gut microbiota species spanning from the phylum to the genus level on AMD. Our study revealed significant positive associations between specific genetic variants related to the family Peptococcaceae , genus Parasutterella , and genus Faecalibacterium and an increased risk of AMD. Our findings provide strong evidence supporting a protective role of certain genetic variants related to the class Melainabacteria and family Rikenellaceae against AMD. These results highlight a potential causal relationship between several gut microbiota taxa and AMD. However, future studies employing MR in larger cohorts and incorporating functional analyses are warranted to elucidate the underlying mechanisms by which genetic variants related to the gut microbiota influence the development of AMD. Age-related macular degeneration Gut microbiota Mendelian randomization Single nucleotide polymorphism. Figures Figure 1 Figure 2 Introduction AMD, a progressive fundus disease, significantly impairs vision and visual function, making it a major cause of blindness among elderly people. With the increasing aging population, AMD poses a considerable public health challenge, boasting a worldwide prevalence of 8.7% and ranking as the primary cause of severe visual impairment in developed nations. Clinically, AMD manifests in two main forms: dry and wet. Patients with wet AMD typically experience weeks or months of central visual impairment, often leading to loss. Pathologically, wet AMD is characterized by neovascularization, wherein fragile new blood vessels infiltrate the subretinal space through the choroid, leading to serous fluid leakage and triggering inflammatory reactions in surrounding tissues [1] . All age-related retinal diseases share two common features: disruption of retinal homeostasis and low-grade chronic inflammation (para-inflammation) [2] . The pathogenesis of AMD is multifaceted and involves inflammation, apoptosis, autophagy, mitochondrial dysfunction, gut microbiota, and lipid disorders [3] . This study aimed to explore the intricate relationship between AMD and the gut microbiota. The gut microbiota is a diverse ecosystem comprised of bacteria, yeast, and viruses. Within the realm of taxonomy, bacteria are classified into various hierarchical levels, including phyla, classes, orders, families, genera, and species [1] . Recent research has underscored the significance of the gut microbiota as a pivotal organ, establishing intricate connections and bidirectional or multidirectional communication pathways with various bodily organs. These connections occur via neural, endocrine, immune, humoral, and metabolic pathways [4] . Dysregulation of the gut microbiota can precipitate a range of gastrointestinal disorders and affect distant tissues beyond the intestinal tract, such as joints, mucosa, and eyes—commonly implicated sites [5] . The discovery of the "gut-retina axis" by Rowan et al. [6] emphasized the vital role of the gut microbiota in regulating eye health. This axis indicates a two-way communication pathway between the gut and retina. This finding suggested that changes in the gut microbiota can affect eye conditions. Recent evidence shows that alterations in the gut microbiome are linked to various eye diseases, such as age-related macular degeneration, retinal artery occlusion, central serous chorioretinopathy, and uveitis [7-12] . These findings highlight the complex relationship between gut health and eye diseases, suggesting potential therapeutic approaches involving gut microbiota modulation for preventing and managing eye diseases. MR serves as a data analysis technique employed in epidemiological studies to assess causal inference. It utilizes genetic variation as an IV in nonexperimental data to estimate the causal relationship between the exposure factor of interest and the outcome of interest. MR becomes particularly valuable when confirming the impact of exposure factors on outcomes and poses challenges, including the presence of confounding factors, the potential reversal of the true causal relationship between 'exposure' and 'outcome', or the ethical constraints limiting routine randomized controlled trials. At present, GWASs have identified hundreds of thousands, if not millions, of genetic variations associated with disease outcomes, forming the foundation for MR analysis. MR is essentially a technique that evaluates the causal effects of modifiable nongenetic exposure factors using genetic data. Its theoretical basis lies in the immutable nature of genes and the principles of Mendelian inheritance laws. During meiotic gamete formation, parental alleles are randomly assigned to offspring, ensuring that the relationship between genes and outcomes remains unaffected by common confounding factors such as the postnatal environment, socioeconomic factors, and behavioral habits. This inherent stability allows for the derivation of logical causal relationship time series. In our exploration of the causal relationship between the gut microbiota and AMD, we selected the gut microbiota classification group as the exposure group and AMD as the outcome group for MR analysis. Methods Assumptions and Study Design of MR In this study, we conducted a two-sample MR analysis to evaluate the causal relationship between the gut microbiota and AMD, utilizing publicly accessible summary-level data from (GWASs) for both the exposures (gut microbiota) and the outcome (AMD). To guarantee the validity of the MR analysis, three assumptions had to be met: (1) the genetic variants used in the analysis should have a significant association with the exposure; (2) the genetic variants selected as IVs for exposure should be uncorrelated with confounding factors that are linked to both the exposure and outcome; and (3) there should be no horizontal pleiotropy, meaning that IVs can only affect AMD through gut microbiota taxa [13] (Fig. 1). Ethics Statement This study used deidentified public summary-level data, which can be downloaded for free, to analyse the relationships between gut microbiota taxa and AMD. The GWASs used in this investigation were all approved by their respective institutional ethics committees. Data Source In this study, gut microbiota taxa from the Dutch Microbiome [14] were subjected to an exposure study, and the host genotypes of 7738 participants were analysed. The summary-level data for AMD were extracted from 8931 patients and 348936 controls and from the FinnGen biobank (https://www.finngen.fi/en). Instrumental Variable Selection To satisfy the first key hypothesis (the correlating hypothesis) and ensure the accuracy of the results, IVs should be significantly correlated with exposure factors. The single nucleotide polymorphisms (SNPs) associated with exposure reached the genome-wide significance threshold (P<5×10 -8 ). Additionally, a separate group of SNPs below the locus-wide significance level (1 × 10 −5 ) were selected as instrumental variables to ensure a comprehensive conclusion. It is important to ensure that all the instrumental SNPs for the exposure are not in linkage disequilibrium (LD), and LD analysis (R 2 <0.001, clumping distance = 10,000 kb) was also performed to meet the MR assumptions. To avoid the influence of alleles on the causal relationships between gut microbiota taxa and AMD, palindromes and ambiguous SNPs were excluded from the above-selected instrumental SNPs. SNPs with a minor allele frequency (MAF) < 0.01 were also removed. To alleviate the risk of potential weak instrumental bias, the strength of the IVs was evaluated using the F statistic, which was calculated by the following formula: F = R 2 × (N – 2)/(1 – R 2 ), where N refers to the sample size and R 2 refers to the variance of exposure explained by selected IVs, for which we obtained the value of R 2 in the MR Steiger directionality test. The possibility of weak IV bias is small if the F statistic exceeds 10 [13] . MR analysis Inverse variance-weighted (IVW) analysis is mainly used in this study. MR-weighted Egger regression and the weighted median method (WME) were used for MR analysis. Under the assumption that all SNPs are valid IVs, the IVW method can provide the most accurate effect estimates [14] . MR regression with Egger regression can detect and adjust the multiplicity, but the estimation accuracy produced by this method is very low [15] . The weighted median is based on the assumption that at least 50% of the IVs are valid and provides an accurate estimate [16] . Because the IVW method is more effective than the other two methods, this study uses the IVW method for the main effect analysis [17] . Sensitivity Analysis The “leave-one-out” sensitivity analysis examines whether each SNP drives causal correlation by eliminating each IV in turn. If the elimination of a single SNP has a great impact on MR analysis, it shows that MR analysis is disturbed by a single IV. Second, the MR alternative Egger intercept is used to evaluate the multiple relationships between IVs and other potential confounding factors to ensure that the selected IVs do not affect the outcome variables in ways other than exposure factors. If MR‒Egger intercept analysis showed that there was a significant difference (P < 0.05), then there was horizontal pleiotropy. Finally, this study also used Cochran Q statistics to test heterogeneity. If the Cochran Q statistics test was statistically significant (P < 0.05), the analysis results were considered to have significant heterogeneity [18] . Statistical analysis The two-sample MR package in RS studio was utilized to conduct all the statistical analyses in this study. The results showed that the causal effect of the gut microbiota on AMD was explained by β and the corresponding 95% confidence interval (CI). P<0.05 indicated that the difference was statistically significant. Results Results of MR Analysis First, SNPs related to the gut microbiota at the phylum, class, order, family and genus levels were identified. After a series of quality control steps, the selected IVs were selected for the MR study. The IVW results showed that the family Peptococcaceae (OR=1.176, 95% CI=1.012 to 1.366, P=0.004), genus Parasutterella (OR=1.167, 95% CI=1.011 to 1.347, P=0.035) and genus Faecalibacterium (OR=1.247, 95% CI=1.071 to 1.451, P=0.034) may be risk factors for AMD, and the class Melainabacteria (OR=0.886, 95% CI=0.789 to 0.995, P=0.041) and family Rikenellaceae (OR=0.844, 95% CI=0.726 to 0.981, P=0.027) may be protective factors against AMD, as shown in Fig. 2. At the same time, the relationship between the gut microbiota and AMD was analysed by MR inverse Egger regression and the weighted median method, as shown in Table 1. Table 1 MR effect values of the gut microbiota significantly correlated with AMD incidence Gut Microbiome Method Beta OR 95% CI P Melainabacteria Rikenellaceae Peptococcaceae Faecalibacterium Parasutterella Inverse variance weighted MR Egger Weighted median Weighted mode Inverse variance weighted MR Egger Weighted median Weighted mode Inverse variance weighted MR Egger Weighted median Weighted mode Inverse variance weighted MR Egger Weighted median Weighted mode Inverse variance weighted MR Egger Weighted median Weighted mode -0.121 -0.092 -0.054 -0.012 -0.170 -0.132 -0.147 -0.123 0.162 0.190 0.176 0.234 0.221 0.074 0.292 0.334 0.154 0.034 0.108 0.028 0.886 0.912 0.948 0.988 0.844 0.876 0.863 0.885 1.176 1.209 1.193 1.263 1.247 1.076 1.339 1.396 1.167 1.035 1.114 1.029 0.789-0.995 0.641-1.297 0.813-1.105 0.785-1.243 0.726-0.981 0.552-1.389 0.701-1.063 0.607-1.289 1.012-1.366 0.798-1.832 0.975-1.459 0.915-1.743 1.071-1.451 0.801-1.446 1.075-1.667 1.029-1.894 1.011-1.347 0.687-1.557 0.934-1.327 0.809-1.307 0.040 0.622 0.492 0.920 0.027 0.583 0.167 0.532 0.034 0.400 0.087 0.193 0.004 0.638 0.009 0.061 0.035 0.874 0.228 0.821 Sensitivity Analysis The results of Cochran Q and MR Egger regression showed that there was no significant heterogeneity or pleiotropy in this study (Table 2). The results of the leave-one-out method showed that the results did not change after the SNPs were eliminated one by one. These analyses prove the robustness of the results of this study to some extent. Table 2 Sensitivity analysis results Gut Microbiome MR‒Egger Cochran Q test Intercept P Q value P Melainabacteria Rikenellaceae Peptococcaceae Faecalibacterium Parasutterella -0.003 -0.003 -0.003 0.018 0.010 0.868 0.868 0.891 0.288 0.547 7.651 9.102 5.458 7.557 17.115 0.468 0.428 0.604 0.478 0.145 Discussion In this study, through MR analysis, we found that there is a potential causal relationship between the gut microbiota and exudative AMD, which provides valuable insight and serves as a solid foundation for future research in this field. The human gut microbiota is a sophisticated ecosystem populated by diverse microorganisms, including bacteria, viruses, archaea, and eukaryotes, that reside in the gastrointestinal tract. The indispensable functions carried out by the gut microbiota for the human host highlight its profound importance [19] . Integral to numerous host functions, the human gut microbiota contributes significantly to nutritional metabolism, immune system modulation, protection against pathogens, and the maintenance of intestinal barrier integrity [20] . In 2005, Eckburg et al. conducted pioneering metagenomic research to classify the gut microbiota into six primary phyla: Firmicutes , Bacteroidetes , Proteobacteria , Actinobacteria , Verrucomicrobia , and Fusobacteria . Among them, Bacteroidetes and Firmicutes were the main dominant bacterial groups. In 2010, the EU Meta HIT project team published a gene catalogue of human gut microbiota in Nature, obtaining a total of 3.3 million effective reference genes for human gut metagenomes, representing an approximately 150-fold increase over the size of the human genome. From this gene set, it is estimated that there are at least 1000-1150 bacterial species present in the human gut, with an average of approximately 160 dominant bacterial species per host. Subsequent investigations categorized the gut microbiota in populations of varying ages, body weights, sexes, and nationalities into three main types: Bacteroides , Prevotella , and Ruminococcus [21] . However, ongoing research is refining our understanding of gut microbiota diversity, and these classifications may evolve with further investigation. A study of the microbiota in the intraocular environment of healthy individuals and patients with ocular diseases provided preliminary evidence that the intraocular environment of patients with AMD exhibits disease-specific microbial characteristics, indicating that either spontaneous or pathogenic bacterial translocation may be associated with these common sight-threatening conditions [22] . These findings provide preliminary evidence that microbial characteristics within the intraocular environment of AMD patients may differ from those of healthy individuals, suggesting a potential association between microbial translocation and the development or progression of this sight-threatening condition. Although the precise mechanisms regulating the gut-eye axis remain incompletely understood, the impact of the gut microbiota on eye diseases cannot be overlooked. It holds potential as a therapeutic target for certain ocular conditions. "Dysbiosis of the gut microbiome, characterized by an imbalance in microbial communities, is linked to chronic inflammation and increased intestinal permeability. This dysbiosis can profoundly affect local metabolic and inflammatory pathways with systemic consequences, potentially extending to peripheral tissues such as the eye. The gut microbiota exerts its influence through local metabolic and inflammatory pathways, which have systemic implications. These systemic effects may extend to peripheral tissues, including the eye, impacting the pathogenesis of eye diseases. With advancing age, changes in the microbiome composition occur, potentially contributing to age-related degenerative diseases such as AMD. Dysfunction of the gut microbiota can affect the metabolism and absorption of constant and trace nutrients in the intestinal barrier and is associated with increased intestinal permeability. Metabolites produced by the gut microbiota can potentially initiate autoimmune reactions in the eyes through the activation of retinal-specific T cells. The gut microbiota plays a crucial role in metabolic diseases, influencing factors such as blood glucose control and fat metabolism, which are significant considerations in AMD development [23] . Prolonged consumption of a high-fat diet and obesity can compromise the integrity of the intestinal barrier, leading to systemic inflammation and contributing to the development of various biological disorders. Microbial molecular pattern molecules and proinflammatory cytokines, which originate from the compromised intestinal barrier, can enter the systemic circulation and initiate immune responses in the retina. The reactivity of microglia and recruitment of inflammatory macrophages contribute to stromal support, promoting angiogenesis and ultimately leading to choroidal neovascularization. Dysbiosis in AMD patients disrupts intestinal homeostasis, leading to the accumulation of stimulator of interferon genes (STING) in the gut. Subsequent translocation of microbial products into the blood allows access to the retina via the impaired blood‒retinal barrier, resulting in chronic activation of the STING pathway in the retina and contributing to disease progression [24] . Some articles [25, 26] have demonstrated that a high-sucrose and high-fat diet exacerbates choroidal neovascularization by altering the gut microbiota. Intestinal dysbiosis leads to increased intestinal permeability and chronic low-grade inflammation characterized by the production of inflammatory factors, including IL-6, IL-1b, and TNF-α, and the production of vascular endothelial growth factor-A increases, ultimately exacerbating pathological angiogenesis. An MR analysis was conducted in this study using summary statistics data on the gut microbiota extracted from the Dutch microbiome, while summary-level data for AMD were obtained from the FinnGen biobank. The objective of this study was to identify the causal relationship between the gut microbiota and AMD. The family Peptococcaceae , genus Parasutterella and genus Faecalibacterium are related to an increased risk of AMD, while the class Melainabacteria and family Rikenellaceae can reduce the risk of AMD. Several articles have studied the relationship between the gut microbiota and AMD. Li et al [27] indicated that Eubacterium , Parabacteroides , Ruminococcaceae and Lachnospiracea may have a protective effect against AMD. Conversely, both the weighted median and IVW estimates suggest that Dorea may increase the risk of AMD. Mao et al. [28] demonstrated that the genera Anaerotruncus , Candidatus Soleaferrea , and unknown id.2071 were protective factors against AMD. The Eubacterium oxidoreducens group, genus Faecalibacterium , and genus Ruminococcaceae UCG-011 were risk factors for AMD. Liu [29] demonstrated that the order Rhodospirillales , family Victivallaceae , family Rikenellaceae , genus Slackia , genus Faecalibacterium , genus Bilophila , and genus Candidatus Soleaferreaw were suggestively associated with AMD. In the replication stage, only the order Rhodospirillales passed validation. Both Faecalibacterium and Rikenellaceae have consistently been shown to be related to AMD. Parasutterella occupies a specific intestinal niche and affects microflora and host metabolism. Changes in bile acid levels are accompanied by alterations in bile acid transport genes in the ileum and bile acid synthesis genes in the liver, indicating a potential role for bacteria in maintaining bile acid homeostasis and cholesterol metabolism. The metabolism of L-cysteine may be related to the development of type 2 diabetes, while the link with the fatty acid biosynthesis pathway is related to weight gain in carbohydrate-rich diets during the development of obesity [30] . Faecalibacterium , a normal intestinal symbiotic bacterium and a dominant member of Clostridium softeners, is considered a crucial bacterial indicator of healthy intestines, accounting for more than 5% of the total number of bacteria in the intestines of healthy individuals. Faecalibacterium is capable of producing butyric acid, which plays a crucial role in regulating the intestinal immune system, reducing oxidative stress, and modulating the metabolism of colonic epithelial cells. Furthermore, Faecalibacterium secretes anti-inflammatory compounds into the surrounding environment, which has been shown to reduce the incidence of inflammatory diseases in mice. Recently, a study showed that oral administration of Faecalibacterium could significantly improve fatty liver in mice [31] . The presence of genes for vitamin biosynthesis in gut Melainabacteria members suggests their potential utility to the host, with potential connections to neurodevelopment, neurodegeneration, obesity, allergic rhinitis, and gastrointestinal, respiratory, and eye diseases. [32] The first study [33] on gut bacterial ClpB-like gene function in humans revealed that the relative abundance of Rikenellaceae was lower in subjects with obesity, while it was positively associated with gut bacterial ClpB-like gene function. All of these findings prove the existence of a gut-eye axis. Enhanced intestinal permeability or a dysregulated microbiota can impair nutrient absorption in the intestinal barrier, leading to increased mobility of bacteria, including endotoxins and lipopolysaccharides. These can trigger low-level inflammation in various tissues by activating pattern recognition receptors. When these processes occur in the retina, they can induce the expression of macrophages and retinal pigmented epithelial cells, ultimately causing eye inflammation, such as age-related macular degeneration. With the discovery of a large number of genetic variations closely related to specific traits in the field of biology, researchers have gained valuable insights into disease etiology. Large-scale GWASs have provided researchers with hundreds of thousands of aggregate data points, facilitating the study of relationships between exposure, disease, and genetic variation in large sample datasets. Moreover, these advancements enable researchers to estimate genetic associations in large sample datasets efficiently and at low cost, primarily through MR studies. Our study included only participants of European ancestry, which limits the generalizability of our results to individuals of non-European ancestry. Further research is necessary to assess the association between the gut microbiota and AMD risk in other ethnic groups. Further research is needed to determine the universality of the association between the gut microbiota and the risk of AMD in other ethnic groups. Additionally, our study is subject to methodological limitations, including but not limited to issues such as linkage disequilibrium, pleiotropy, and developmental compensation. From a clinical perspective, this article presents a promising avenue for the treatment of AMD by targeting the gut microbiota. Adjusting the balance of the intestinal microbiota through dietary changes may lower the incidence of AMD or slow its progression. Additionally, advancements in medical technology offer the possibility of developing novel methods to enhance the human gut microbiota, facilitating the treatment of various diseases, including AMD. However, it is essential to acknowledge that this field is still in its early stages, and further research is needed to confirm and establish the best treatment strategies. Furthermore, given the complexity and diversity of the human gut microbiota, personalized and targeted intervention measures are necessary to effectively address individual patient needs. 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Additional Declarations No competing interests reported. Supplementary Files res.csv MRanalysiswithpositiveresults.csv Summarizetheresultsofpleiotropy.csv Detailsofallfloradata.csv heterogeneityanalysis.csv Summarizetheresultsofmranalysis.csv SummarizetheresultsofmranalysiswithFDRcorrection.csv Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4117483","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":288130343,"identity":"0444fd9a-2dfc-498d-adde-9f68740a60ae","order_by":0,"name":"Maozhu He","email":"","orcid":"","institution":"Sichuan Taikang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Maozhu","middleName":"","lastName":"He","suffix":""},{"id":288130344,"identity":"d80903b2-5785-4373-ab92-9ff6eeb9297c","order_by":1,"name":"Min Nie","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYDACZiBOYJDgYWNmPnDgww/itdjI8bO3JR6c2UO8XWnGkj1njA9zsBGh1pyd+eiGhzsOJ264kfPhMAMPgzy/2AH8Wiyb2dJuJJ4BacndcLjAgsFw5uwE/FoMDvOY3Uhsg2qZwcOQYHCboBb+b1AtOQ8O87ARpYWHDagF7H0GYrWwgRwGDmQDYCBLEOGX84ef3fzZBo7Kxx8+/LCR55cmoAUdSJCmfBSMglEwCkYBdgAAA/BLZbH9bKoAAAAASUVORK5CYII=","orcid":"","institution":"Sichuan Taikang Hospital","correspondingAuthor":true,"prefix":"","firstName":"Min","middleName":"","lastName":"Nie","suffix":""}],"badges":[],"createdAt":"2024-03-17 14:29:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4117483/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4117483/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54445660,"identity":"faace592-eb3b-4745-a81b-8ac2f9fd4b60","added_by":"auto","created_at":"2024-04-10 16:16:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":31752,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of two-sample Mendelian randomization models\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4117483/v1/3190fbcaad6336153c26c67f.png"},{"id":54445654,"identity":"477279c2-0a27-44db-a089-b8f9c52ea67c","added_by":"auto","created_at":"2024-04-10 16:16:48","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":134202,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of gut microbiota effect values with causal relationships with AMD\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4117483/v1/550b8819a9372e758f556021.png"},{"id":54818994,"identity":"e6f16fd7-8f09-4577-a001-aa3ce0a46afe","added_by":"auto","created_at":"2024-04-17 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16:16:42","extension":"csv","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":35387,"visible":true,"origin":"","legend":"","description":"","filename":"Detailsofallfloradata.csv","url":"https://assets-eu.researchsquare.com/files/rs-4117483/v1/6bc27dbeb76a530afd939daa.csv"},{"id":54445610,"identity":"5bebadaf-2316-4b2d-9480-c762cdb9813c","added_by":"auto","created_at":"2024-04-10 16:16:45","extension":"csv","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":68706,"visible":true,"origin":"","legend":"","description":"","filename":"heterogeneityanalysis.csv","url":"https://assets-eu.researchsquare.com/files/rs-4117483/v1/6f4217b6eb95f651af657a30.csv"},{"id":54445666,"identity":"09dacef3-3f62-4d84-beb5-5893b98811ff","added_by":"auto","created_at":"2024-04-10 16:17:01","extension":"csv","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":220993,"visible":true,"origin":"","legend":"","description":"","filename":"Summarizetheresultsofmranalysis.csv","url":"https://assets-eu.researchsquare.com/files/rs-4117483/v1/99daacc0562ca19dbe280de7.csv"},{"id":54445659,"identity":"8ed970fa-2dd6-4a38-bf1f-6b055328d1f8","added_by":"auto","created_at":"2024-04-10 16:16:51","extension":"csv","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":234836,"visible":true,"origin":"","legend":"","description":"","filename":"SummarizetheresultsofmranalysiswithFDRcorrection.csv","url":"https://assets-eu.researchsquare.com/files/rs-4117483/v1/97faf8e6a7c88bf0742a1e6f.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between age-related macular degeneration and the gut microbiota: A two-sample Mendelian randomization analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAMD, a progressive fundus disease, significantly impairs vision and visual function, making it a major cause of blindness among elderly\u0026nbsp;people. With the\u0026nbsp;increasing\u0026nbsp;aging population, AMD poses a considerable public health challenge, boasting a worldwide prevalence of 8.7% and ranking as the primary cause of severe visual impairment in developed nations. Clinically, AMD manifests in two main forms: dry and wet. Patients with wet AMD typically experience weeks or months of central visual impairment, often leading to loss. Pathologically, wet AMD is characterized by neovascularization, wherein fragile new blood vessels infiltrate the subretinal space through the choroid, leading to serous fluid leakage and triggering inflammatory reactions in surrounding tissues\u003csup\u003e[1]\u003c/sup\u003e. All age-related retinal diseases share two common features: disruption of retinal homeostasis and low-grade chronic inflammation (para-inflammation)\u0026nbsp;\u003csup\u003e[2]\u003c/sup\u003e. The pathogenesis of AMD is multifaceted\u0026nbsp;and involves\u0026nbsp;inflammation, apoptosis, autophagy, mitochondrial dysfunction, gut microbiota, and lipid disorders\u003csup\u003e[3]\u003c/sup\u003e. This study\u0026nbsp;aimed\u0026nbsp;to explore the intricate relationship between AMD and\u0026nbsp;the\u0026nbsp;gut microbiota.\u003c/p\u003e\n\u003cp\u003eThe gut microbiota is a diverse ecosystem comprised of bacteria, yeast, and viruses. Within the realm of taxonomy, bacteria are classified into various hierarchical levels, including phyla, classes, orders, families, genera, and species\u003csup\u003e[1]\u003c/sup\u003e. Recent research has underscored the significance of\u0026nbsp;the\u0026nbsp;gut microbiota as a pivotal organ, establishing intricate connections and bidirectional or\u0026nbsp;multidirectional\u0026nbsp;communication pathways with various bodily organs. These connections occur via neural, endocrine, immune, humoral, and metabolic pathways\u003csup\u003e[4]\u003c/sup\u003e. Dysregulation of\u0026nbsp;the\u0026nbsp;gut microbiota can precipitate a range of gastrointestinal disorders and affect distant tissues beyond the intestinal tract, such as joints, mucosa, and eyes—commonly implicated sites\u003csup\u003e[5]\u003c/sup\u003e. The discovery of the \"gut-retina axis\" by Rowan et al.\u0026nbsp;\u003csup\u003e[6]\u003c/sup\u003e emphasized the vital role of\u0026nbsp;the\u0026nbsp;gut microbiota in regulating eye health. This axis indicates a two-way communication pathway between the gut and retina.\u0026nbsp;This finding suggested\u0026nbsp;that changes in\u0026nbsp;the\u0026nbsp;gut microbiota can affect eye conditions. Recent evidence shows that alterations in the gut microbiome are linked to various eye diseases,\u0026nbsp;such as age-related macular degeneration, retinal artery occlusion, central serous chorioretinopathy, and uveitis\u003csup\u003e[7-12]\u003c/sup\u003e. These findings highlight the complex relationship between gut health and eye diseases, suggesting potential therapeutic approaches involving gut microbiota modulation for preventing and managing eye diseases.\u003c/p\u003e\n\u003cp\u003eMR serves as a data analysis technique employed in epidemiological studies to assess causal inference. It utilizes genetic variation as an IV in nonexperimental data to estimate the causal relationship between the exposure factor of interest and the outcome of interest. MR becomes particularly valuable when confirming the impact of exposure factors on outcomes and poses challenges, including the presence of confounding factors, the potential reversal of the true causal relationship between 'exposure' and 'outcome', or the ethical constraints limiting routine randomized controlled trials. At present, GWASs have identified hundreds of thousands, if not millions, of genetic variations associated with disease outcomes, forming the foundation for MR analysis. MR is essentially a technique that evaluates the causal effects of modifiable nongenetic exposure factors using genetic data. Its theoretical basis lies in the immutable nature of genes and the principles of Mendelian inheritance laws. During meiotic gamete formation, parental alleles are randomly assigned to offspring, ensuring that the relationship between genes and outcomes remains unaffected by common confounding factors such as the postnatal environment, socioeconomic factors, and behavioral habits. This inherent stability allows for the derivation of logical causal relationship time series. In our exploration of the causal relationship between the gut microbiota and AMD, we selected the gut microbiota classification group as the exposure group and AMD as the outcome group for MR analysis.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eAssumptions and Study Design of MR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we conducted a two-sample MR analysis\u003c/p\u003e\n\u003cp\u003eto evaluate the causal relationship between the gut microbiota and AMD, utilizing publicly accessible summary-level data from (GWASs) for both the exposures (gut microbiota) and the outcome (AMD). To guarantee the validity of the MR analysis, three assumptions had to be met: (1) the genetic variants used in the analysis should have a significant association with the exposure; (2) the genetic variants selected as IVs for exposure should be uncorrelated with confounding factors that are linked to both the exposure and outcome; and (3) there should be\u003c/p\u003e\n\u003cp\u003eno horizontal pleiotropy, meaning that IVs can only affect\u003c/p\u003e\n\u003cp\u003eAMD through gut microbiota taxa \u003csup\u003e[13]\u003c/sup\u003e\u003csup\u003e\u0026nbsp;\u003c/sup\u003e(Fig. 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used deidentified public summary-level data, which can be downloaded for free, to analyse the relationships between gut microbiota taxa and AMD. The GWASs used in this investigation were all approved by their respective institutional ethics committees.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Source\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, gut microbiota taxa from the Dutch Microbiome\u003csup\u003e[14]\u003c/sup\u003e were subjected to an exposure study, and the host genotypes of 7738 participants were analysed. The summary-level data for AMD were extracted from 8931 patients and 348936 controls and from the FinnGen biobank (https://www.finngen.fi/en).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstrumental Variable Selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo satisfy the first key hypothesis (the correlating hypothesis) and ensure the accuracy of the results, IVs should be significantly correlated with exposure factors. The single nucleotide polymorphisms (SNPs) associated with exposure reached the genome-wide significance threshold (P\u0026lt;5\u0026times;10\u003csup\u003e-8\u003c/sup\u003e).\u0026nbsp;Additionally, a separate group of SNPs below the locus-wide significance level (1 \u0026times; 10\u003csup\u003e\u0026minus;5\u003c/sup\u003e) were selected as instrumental variables to ensure a comprehensive conclusion. It is important to ensure that all the instrumental SNPs for the exposure are not in linkage disequilibrium (LD), and LD analysis (R\u003csup\u003e2\u003c/sup\u003e\u0026lt;0.001, clumping distance = 10,000 kb) was also performed to meet the MR assumptions. To avoid the influence of alleles on the causal relationships between gut microbiota taxa and AMD, palindromes and ambiguous SNPs were excluded from the above-selected instrumental SNPs. SNPs with a minor allele frequency (MAF) \u0026lt; 0.01 were also removed. To alleviate the risk of potential weak instrumental bias, the strength of the IVs was evaluated using the F statistic, which was calculated by the following formula: F = R\u003csup\u003e2\u003c/sup\u003e \u0026times; (N \u0026ndash; 2)/(1 \u0026ndash; R\u003csup\u003e2\u003c/sup\u003e), where N refers to the sample size and \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e refers to the variance of exposure explained by selected IVs, for which we obtained the value of \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u0026nbsp;\u003c/sup\u003ein the MR Steiger directionality test. The possibility of weak IV bias is small if the F statistic exceeds 10\u003csup\u003e[13]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMR analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInverse variance-weighted (IVW) analysis is mainly used in this study. MR-weighted Egger regression and the weighted median method (WME) were used for MR analysis. Under the assumption that all SNPs are valid IVs, the IVW method can provide the most accurate effect estimates\u003csup\u003e[14]\u003c/sup\u003e. MR regression with Egger regression can detect and adjust the multiplicity, but the estimation accuracy produced by this method is very low\u003csup\u003e[15]\u003c/sup\u003e. The weighted median is based on the assumption that at least 50% of the IVs are valid and provides an accurate estimate\u003csup\u003e[16]\u003c/sup\u003e. Because the IVW method is more effective than the other two methods, this study uses the IVW method for the main effect analysis\u003csup\u003e[17]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSensitivity Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe \u0026ldquo;leave-one-out\u0026rdquo; sensitivity analysis examines whether each SNP drives causal correlation by eliminating each IV in turn. If the elimination of a single SNP has a great impact on MR analysis, it shows that MR analysis is disturbed by a single IV. Second, the MR alternative Egger intercept is used to evaluate the multiple relationships between IVs and other potential confounding factors to ensure that the selected IVs do not affect the outcome variables in ways other than exposure factors. If MR‒Egger intercept analysis showed that there was a significant difference (P \u0026lt; 0.05), then there was horizontal pleiotropy. Finally, this study also used Cochran Q statistics to test heterogeneity. If the Cochran Q statistics test was statistically significant (P \u0026lt; 0.05), the analysis results were considered to have significant heterogeneity\u003csup\u003e[18]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe two-sample MR package in RS studio was utilized to conduct all the statistical analyses in this study. The results showed that the causal effect of the gut microbiota on AMD was explained by \u0026beta; and the corresponding 95% confidence interval (CI). P\u0026lt;0.05 indicated that the difference was statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eResults of MR Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFirst, SNPs related to the gut microbiota at the phylum, class, order, family and genus levels were identified. After a series of quality control steps, the selected IVs were selected for the MR study. The IVW results showed that the \u003cem\u003efamily Peptococcaceae\u0026nbsp;\u003c/em\u003e(OR=1.176, 95% CI=1.012 to 1.366, P=0.004), genus Parasutterella (OR=1.167, 95% CI=1.011 to 1.347, P=0.035) and \u003cem\u003egenus Faecalibacterium\u0026nbsp;\u003c/em\u003e(OR=1.247, 95% CI=1.071 to 1.451, P=0.034) may be risk factors for AMD, and the \u003cem\u003eclass Melainabacteria\u0026nbsp;\u003c/em\u003e(OR=0.886, 95% CI=0.789 to 0.995, P=0.041) and \u003cem\u003efamily Rikenellaceae\u0026nbsp;\u003c/em\u003e(OR=0.844, 95% CI=0.726 to 0.981, P=0.027) may be protective factors against AMD, as shown in Fig. 2. At the same time, the relationship between the gut microbiota and AMD was analysed by MR inverse Egger regression and the weighted median method, as shown in Table 1.\u003c/p\u003e\n\u003cp\u003eTable 1 MR effect values of the gut microbiota significantly correlated with AMD incidence\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.924187725631768%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;Gut Microbiome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.588447653429604%\" valign=\"top\"\u003e\n \u003cp\u003eMethod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.288808664259928%\" valign=\"top\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.288808664259928%\" valign=\"top\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.342960288808664%\" valign=\"top\"\u003e\n \u003cp\u003e95%\u003cem\u003eCI\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.566787003610107%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.924187725631768%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMelainabacteria\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eRikenellaceae\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ePeptococcaceae\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eFaecalibacterium\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eParasutterella\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"31.588447653429604%\" valign=\"top\"\u003e\n \u003cp\u003eInverse variance weighted\u003c/p\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003cp\u003eWeighted mode\u003c/p\u003e\n \u003cp\u003eInverse variance weighted\u003c/p\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003cp\u003eWeighted mode\u003c/p\u003e\n \u003cp\u003eInverse variance weighted\u003c/p\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003cp\u003eWeighted mode\u003c/p\u003e\n \u003cp\u003eInverse variance weighted\u003c/p\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003cp\u003eWeighted mode\u003c/p\u003e\n \u003cp\u003eInverse variance weighted\u003c/p\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003cp\u003eWeighted mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.288808664259928%\" valign=\"top\"\u003e\n \u003cp\u003e-0.121\u003c/p\u003e\n \u003cp\u003e-0.092\u003c/p\u003e\n \u003cp\u003e-0.054\u003c/p\u003e\n \u003cp\u003e-0.012\u003c/p\u003e\n \u003cp\u003e-0.170\u003c/p\u003e\n \u003cp\u003e-0.132\u003c/p\u003e\n \u003cp\u003e-0.147\u003c/p\u003e\n \u003cp\u003e-0.123\u003c/p\u003e\n \u003cp\u003e0.162\u003c/p\u003e\n \u003cp\u003e0.190\u003c/p\u003e\n \u003cp\u003e0.176\u003c/p\u003e\n \u003cp\u003e0.234\u003c/p\u003e\n \u003cp\u003e0.221\u003c/p\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003cp\u003e0.292\u003c/p\u003e\n \u003cp\u003e0.334\u003c/p\u003e\n \u003cp\u003e0.154\u003c/p\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003cp\u003e0.108\u003c/p\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.288808664259928%\" valign=\"top\"\u003e\n \u003cp\u003e0.886\u003c/p\u003e\n \u003cp\u003e0.912\u003c/p\u003e\n \u003cp\u003e0.948\u003c/p\u003e\n \u003cp\u003e0.988\u003c/p\u003e\n \u003cp\u003e0.844\u003c/p\u003e\n \u003cp\u003e0.876\u003c/p\u003e\n \u003cp\u003e0.863\u003c/p\u003e\n \u003cp\u003e0.885\u003c/p\u003e\n \u003cp\u003e1.176\u003c/p\u003e\n \u003cp\u003e1.209\u003c/p\u003e\n \u003cp\u003e1.193\u003c/p\u003e\n \u003cp\u003e1.263\u003c/p\u003e\n \u003cp\u003e1.247\u003c/p\u003e\n \u003cp\u003e1.076\u003c/p\u003e\n \u003cp\u003e1.339\u003c/p\u003e\n \u003cp\u003e1.396\u003c/p\u003e\n \u003cp\u003e1.167\u003c/p\u003e\n \u003cp\u003e1.035\u003c/p\u003e\n \u003cp\u003e1.114\u003c/p\u003e\n \u003cp\u003e1.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.342960288808664%\" valign=\"top\"\u003e\n \u003cp\u003e0.789-0.995\u003c/p\u003e\n \u003cp\u003e0.641-1.297\u003c/p\u003e\n \u003cp\u003e0.813-1.105\u003c/p\u003e\n \u003cp\u003e0.785-1.243\u003c/p\u003e\n \u003cp\u003e0.726-0.981\u003c/p\u003e\n \u003cp\u003e0.552-1.389\u003c/p\u003e\n \u003cp\u003e0.701-1.063\u003c/p\u003e\n \u003cp\u003e0.607-1.289\u003c/p\u003e\n \u003cp\u003e1.012-1.366\u003c/p\u003e\n \u003cp\u003e0.798-1.832\u003c/p\u003e\n \u003cp\u003e0.975-1.459\u003c/p\u003e\n \u003cp\u003e0.915-1.743\u003c/p\u003e\n \u003cp\u003e1.071-1.451\u003c/p\u003e\n \u003cp\u003e0.801-1.446\u003c/p\u003e\n \u003cp\u003e1.075-1.667\u003c/p\u003e\n \u003cp\u003e1.029-1.894\u003c/p\u003e\n \u003cp\u003e1.011-1.347\u003c/p\u003e\n \u003cp\u003e0.687-1.557\u003c/p\u003e\n \u003cp\u003e0.934-1.327\u003c/p\u003e\n \u003cp\u003e0.809-1.307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.566787003610107%\" valign=\"top\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003cp\u003e0.622\u003c/p\u003e\n \u003cp\u003e0.492\u003c/p\u003e\n \u003cp\u003e0.920\u003c/p\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003cp\u003e0.583\u003c/p\u003e\n \u003cp\u003e0.167\u003c/p\u003e\n \u003cp\u003e0.532\u003c/p\u003e\n \u003cp\u003e0.034\u003c/p\u003e\n \u003cp\u003e0.400\u003c/p\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003cp\u003e0.193\u003c/p\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003cp\u003e0.638\u003c/p\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003cp\u003e0.061\u003c/p\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003cp\u003e0.874\u003c/p\u003e\n \u003cp\u003e0.228\u003c/p\u003e\n \u003cp\u003e0.821\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eSensitivity Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of Cochran Q and MR Egger regression showed that there was no significant heterogeneity or pleiotropy in this study (Table 2). The results of the leave-one-out method showed that the results did not change after the SNPs were eliminated one by one. These analyses prove the robustness of the results of this study to some extent.\u003c/p\u003e\n\u003cp\u003eTable 2 Sensitivity analysis results\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.07233273056058%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eGut Microbiome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.96383363471971%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eMR‒Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"39.96383363471971%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eCochran Q test\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003eQ value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eMelainabacteria\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eRikenellaceae\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003ePeptococcaceae\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eFaecalibacterium\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eParasutterella\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003cp\u003e-0.003\u003c/p\u003e\n \u003cp\u003e0.018\u003c/p\u003e\n \u003cp\u003e0.010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0.868\u003c/p\u003e\n \u003cp\u003e0.868\u003c/p\u003e\n \u003cp\u003e0.891\u003c/p\u003e\n \u003cp\u003e0.288\u003c/p\u003e\n \u003cp\u003e0.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e7.651\u003c/p\u003e\n \u003cp\u003e9.102\u003c/p\u003e\n \u003cp\u003e5.458\u003c/p\u003e\n \u003cp\u003e7.557\u003c/p\u003e\n \u003cp\u003e17.115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20%\" valign=\"top\"\u003e\n \u003cp\u003e0.468\u003c/p\u003e\n \u003cp\u003e0.428\u003c/p\u003e\n \u003cp\u003e0.604\u003c/p\u003e\n \u003cp\u003e0.478\u003c/p\u003e\n \u003cp\u003e0.145\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, through MR analysis, we found that there is a potential causal relationship between the gut microbiota and exudative AMD, which provides valuable insight and serves as a solid foundation for future research in this field.\u003c/p\u003e\n\u003cp\u003eThe human gut microbiota is a sophisticated ecosystem populated by diverse microorganisms, including bacteria, viruses, archaea, and eukaryotes, that reside in the gastrointestinal tract. The indispensable functions carried out by the gut microbiota for the human host highlight its profound importance\u003csup\u003e[19]\u003c/sup\u003e. Integral to numerous host functions, the human gut microbiota contributes significantly to nutritional metabolism, immune system modulation, protection against pathogens, and the maintenance of intestinal barrier integrity\u003csup\u003e[20]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIn 2005, Eckburg et al. conducted pioneering metagenomic research to classify the gut microbiota into six primary phyla: \u003cem\u003eFirmicutes\u003c/em\u003e, \u003cem\u003eBacteroidetes\u003c/em\u003e, \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eActinobacteria\u003c/em\u003e, \u003cem\u003eVerrucomicrobia\u003c/em\u003e, and \u003cem\u003eFusobacteria\u003c/em\u003e. Among them, Bacteroidetes and Firmicutes were the main dominant bacterial groups. In 2010, the EU Meta HIT project team published a gene catalogue of human gut microbiota in Nature, obtaining a total of 3.3 million effective reference genes for human gut metagenomes, representing an approximately 150-fold increase over the size of the human genome. From this gene set, it is estimated that there are at least 1000-1150 bacterial species present in the human gut, with an average of approximately 160 dominant bacterial species per host.\u0026nbsp;Subsequent investigations categorized the gut microbiota in populations of varying ages, body weights, sexes, and nationalities into three main types: \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003ePrevotella\u003c/em\u003e, and \u003cem\u003eRuminococcus\u003c/em\u003e\u003csup\u003e[21]\u003c/sup\u003e. However, ongoing research is refining our understanding of gut microbiota diversity, and these classifications may evolve with further investigation.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;A study of the microbiota in the intraocular environment of healthy individuals and patients with ocular diseases provided preliminary evidence that the intraocular environment of patients with AMD exhibits disease-specific microbial characteristics, indicating that either spontaneous or pathogenic bacterial translocation may be associated with these common sight-threatening conditions\u003csup\u003e[22]\u003c/sup\u003e. These findings provide preliminary evidence that microbial characteristics within the intraocular environment of AMD patients may differ from those of healthy individuals, suggesting a potential association between microbial translocation and the development or progression of this sight-threatening condition. Although the precise mechanisms regulating the gut-eye axis remain incompletely understood, the impact of the gut microbiota on eye diseases cannot be overlooked. It holds potential as a therapeutic target for certain ocular conditions.\u0026nbsp;\u0026quot;Dysbiosis of the gut microbiome, characterized by an imbalance in microbial communities, is linked to chronic inflammation and increased intestinal permeability. This dysbiosis can profoundly affect local metabolic and inflammatory pathways with systemic consequences, potentially extending to peripheral tissues such as the eye.\u0026nbsp;The gut microbiota exerts its influence through local metabolic and inflammatory pathways, which have systemic implications. These systemic effects may extend to peripheral tissues, including the eye, impacting the pathogenesis of eye diseases.\u003c/p\u003e\n\u003cp\u003eWith advancing age, changes in the microbiome composition occur, potentially contributing to age-related degenerative diseases such as AMD.\u0026nbsp;Dysfunction of the gut microbiota can affect the metabolism and absorption of constant and trace nutrients in the intestinal barrier and is associated with increased intestinal permeability. Metabolites produced by the gut microbiota can potentially initiate autoimmune reactions in the eyes through the activation of retinal-specific T cells. The gut microbiota plays a crucial role in metabolic diseases, influencing factors such as blood glucose control and fat metabolism, which are significant considerations in AMD development\u003csup\u003e[23]\u003c/sup\u003e. Prolonged consumption of a high-fat diet and obesity can compromise the integrity of the intestinal barrier, leading to systemic inflammation and contributing to the development of various biological disorders. Microbial molecular pattern molecules and proinflammatory cytokines, which originate from the compromised intestinal barrier, can enter the systemic circulation and initiate immune responses in the retina. The reactivity of microglia and recruitment of inflammatory macrophages contribute to stromal support, promoting angiogenesis and ultimately leading to choroidal neovascularization.\u0026nbsp;Dysbiosis in AMD patients disrupts intestinal homeostasis, leading to the accumulation of stimulator of interferon genes (STING) in the gut. Subsequent translocation of microbial products into the blood allows access to the retina via the impaired blood‒retinal barrier, resulting in chronic activation of the STING pathway in the retina and contributing to disease progression\u003csup\u003e[24]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eSome articles\u003csup\u003e[25, 26]\u003c/sup\u003e have demonstrated that a high-sucrose and high-fat diet exacerbates choroidal neovascularization by altering the gut microbiota. Intestinal dysbiosis leads to increased intestinal permeability and chronic low-grade inflammation characterized by the production of inflammatory factors, including IL-6, IL-1b, and TNF-\u0026alpha;, and the production of vascular endothelial growth factor-A increases, ultimately exacerbating pathological angiogenesis.\u003c/p\u003e\n\u003cp\u003eAn MR analysis was conducted in this study using summary statistics data on the gut microbiota extracted from the Dutch microbiome, while summary-level data for AMD were obtained from the FinnGen biobank. The objective of this study was to identify the causal relationship between the gut microbiota and AMD. The \u003cem\u003efamily Peptococcaceae\u003c/em\u003e, \u003cem\u003egenus Parasutterella\u0026nbsp;\u003c/em\u003eand \u003cem\u003egenus Faecalibacterium\u003c/em\u003e are related to an increased risk of AMD, while the \u003cem\u003eclass Melainabacteria\u003c/em\u003e and \u003cem\u003efamily Rikenellaceae\u003c/em\u003e can reduce the risk of AMD. Several articles have studied the relationship between the gut microbiota and AMD. Li et al\u003csup\u003e[27]\u003c/sup\u003e indicated that \u003cem\u003eEubacterium\u003c/em\u003e, \u003cem\u003eParabacteroides\u003c/em\u003e, \u003cem\u003eRuminococcaceae\u0026nbsp;\u003c/em\u003eand \u003cem\u003eLachnospiracea may\u003c/em\u003e have a protective effect against AMD. Conversely, both the weighted median and IVW estimates suggest that \u003cem\u003eDorea\u003c/em\u003e may increase the risk of AMD.\u0026nbsp;Mao et al.\u003csup\u003e[28]\u003c/sup\u003e demonstrated that the genera \u003cem\u003eAnaerotruncus\u003c/em\u003e, \u003cem\u003eCandidatus Soleaferrea\u003c/em\u003e, and unknown id.2071 were protective factors against AMD. The \u003cem\u003eEubacterium oxidoreducens\u003c/em\u003e group, \u003cem\u003egenus Faecalibacterium\u003c/em\u003e, and \u003cem\u003egenus Ruminococcaceae UCG-011\u003c/em\u003e were risk factors for AMD. Liu\u003csup\u003e[29]\u003c/sup\u003e demonstrated that the order \u003cem\u003eRhodospirillales\u003c/em\u003e, \u003cem\u003efamily Victivallaceae\u003c/em\u003e, \u003cem\u003efamily Rikenellaceae\u003c/em\u003e, \u003cem\u003egenus Slackia\u003c/em\u003e, \u003cem\u003egenus Faecalibacterium\u003c/em\u003e, \u003cem\u003egenus Bilophila\u003c/em\u003e, and \u003cem\u003egenus Candidatus Soleaferreaw were\u003c/em\u003e suggestively associated with AMD. In the replication stage, only\u003cem\u003e\u0026nbsp;the order Rhodospirillales\u003c/em\u003e passed validation.\u003c/p\u003e\n\u003cp\u003eBoth \u003cem\u003eFaecalibacterium\u003c/em\u003e and \u003cem\u003eRikenellaceae\u003c/em\u003e have consistently been shown to be related to AMD.\u0026nbsp;\u003cem\u003eParasutterella\u003c/em\u003e occupies a specific intestinal niche and affects microflora and host metabolism. Changes in bile acid levels are accompanied by alterations in bile acid transport genes in the ileum and bile acid synthesis genes in the liver, indicating a potential role\u0026nbsp;for\u0026nbsp;bacteria in maintaining bile acid homeostasis and cholesterol metabolism. The metabolism of L-cysteine may be related to the development of type 2 diabetes, while the link with\u0026nbsp;the\u0026nbsp;fatty acid biosynthesis pathway is related to weight gain in carbohydrate-rich diets during the development of obesity\u003csup\u003e[30]\u003c/sup\u003e.\u0026nbsp;\u003cem\u003eFaecalibacterium\u003c/em\u003e, a normal intestinal symbiotic bacterium and a dominant member of Clostridium softeners, is considered a crucial bacterial indicator of healthy intestines, accounting for more than 5% of the total number of bacteria in the intestines of healthy individuals. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFaecalibacterium\u0026nbsp;\u003c/em\u003eis capable of producing butyric acid, which plays a crucial role in regulating the intestinal immune system, reducing oxidative stress, and modulating the metabolism of colonic epithelial cells. \u003cem\u003eFurthermore, Faecalibacterium\u003c/em\u003e secretes anti-inflammatory compounds into the surrounding environment, which has been shown to reduce the incidence of inflammatory diseases in mice. Recently, a study showed that oral administration of \u003cem\u003eFaecalibacterium\u003c/em\u003e could significantly improve fatty liver in mice\u003csup\u003e[31]\u003c/sup\u003e.\u0026nbsp;The presence of genes for vitamin biosynthesis in gut \u003cem\u003eMelainabacteria\u003c/em\u003e members suggests their potential utility to the host, with potential connections to neurodevelopment, neurodegeneration, obesity, allergic rhinitis, and gastrointestinal, respiratory, and eye diseases.\u003csup\u003e[32]\u003c/sup\u003e The first study\u003csup\u003e[33]\u003c/sup\u003e on gut bacterial ClpB-like gene function in humans revealed that the relative abundance of \u003cem\u003eRikenellaceae\u003c/em\u003e was lower in subjects with obesity, while it was positively associated with gut bacterial ClpB-like gene function. All of these findings prove the existence of a gut-eye axis.\u003c/p\u003e\n\u003cp\u003eEnhanced intestinal permeability or a dysregulated microbiota can impair nutrient absorption in the intestinal barrier, leading to increased mobility of bacteria, including endotoxins and lipopolysaccharides. These can trigger low-level inflammation in various tissues by activating pattern recognition receptors. When these processes occur in the retina, they can induce the expression of macrophages and retinal pigmented epithelial cells, ultimately causing eye inflammation, such as age-related macular degeneration.\u003c/p\u003e\n\u003cp\u003eWith the discovery of a large number of genetic variations closely related to specific traits in the field of biology, researchers have gained valuable insights into disease etiology. Large-scale GWASs have provided researchers with hundreds of thousands of aggregate data points, facilitating the study of relationships between exposure, disease, and genetic variation in large sample datasets. Moreover, these advancements enable researchers to estimate genetic associations in large sample datasets efficiently and at low cost, primarily through MR studies. Our study included only participants of European ancestry, which limits the generalizability of our results to individuals of non-European ancestry. Further research is necessary to assess the association between the gut microbiota and AMD risk in other ethnic groups. Further research is needed to determine the universality of the association between the gut microbiota and the risk of AMD in other ethnic groups.\u0026nbsp;Additionally, our study is subject to methodological limitations, including but not limited to issues such as linkage disequilibrium, pleiotropy, and developmental compensation.\u003c/p\u003e\n\u003cp\u003eFrom a clinical perspective, this article presents a promising avenue for the treatment of AMD by targeting the gut microbiota. Adjusting the balance of the intestinal microbiota through dietary changes may lower the incidence of AMD or slow its progression. Additionally, advancements in medical technology offer the possibility of developing novel methods to enhance the human gut microbiota, facilitating the treatment of various diseases, including AMD. However, it is essential to acknowledge that this field is still in its early stages, and further research is needed to confirm and establish the best treatment strategies. Furthermore, given the complexity and diversity of the human gut microbiota, personalized and targeted intervention measures are necessary to effectively address individual patient needs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eabsence of any commercial or financial relationships that could\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ebe construed as a potential conflict of interest. This statement ensures transparency and integrity in the conduct and reporting of the research findings.\u003c/p\u003e"},{"header":"参考文献","content":"\u003col\u003e\n\u003cli\u003eChu L, Bi C, Wang C, et al. The Relationship between Complements and Age-Related Macular Degeneration and Its Pathogenesis. J Ophthalmol, 2024,2024:6416773.\u003c/li\u003e\n\u003cli\u003eChen M, Luo C, Zhao J, et al. 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Microbiome, 2020,8(1):59.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Age-related macular degeneration, Gut microbiota, Mendelian randomization, Single nucleotide polymorphism.","lastPublishedDoi":"10.21203/rs.3.rs-4117483/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4117483/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective \u003c/strong\u003eTo assess any potential associations between age-related macular degeneration (AMD) and the gut microbiota.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e Mendelian randomization (MR) analysis was performed on summary data from genome-wide association studies (GWASs) of the gut microbiota and AMD. The gut microbiota was considered the exposure. Instrumental variables (IVs) were identified from a GWAS involving 7,738 participants. The GWAS for AMD from European cohorts served as the outcome dataset, comprising 8,931 AMD patients and 348,936 controls.\u003c/p\u003e\n\u003cp\u003eThe primary analysis employed the inverse-variance weighted (IVW) method, with sensitivity analysis conducted to assess the robustness and reliability of the MR analysis results.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e IVW revealed significant associations between specific microbial families/genera and AMD. Notably, the \u003cem\u003efamily Peptococcaceae \u003c/em\u003e(OR=1.176, 95% CI 1.012 to 1.366, P=0.004), \u003cem\u003egenus Parasutterella\u003c/em\u003e (OR=1.167, 95% CI 1.011 to 1.347, P=0.035), and\u003cem\u003e genus Faecalibacterium\u003c/em\u003e (OR=1.247, 95% CI 1.071 to 1.451, P=0.034) demonstrated positive causal associations with AMD, while\u003cem\u003e the class Melainabacteria\u003c/em\u003e(OR=0.886, 95% CI 0.789 to 0.995, P=0.041) and \u003cem\u003efamily Rikenellaceae \u003c/em\u003e(OR=0.844, 95% CI 0.726 to 0.981, P=0.027) showed negative causal associations. Sensitivity analysis did not reveal evidence of reverse causality, pleiotropy, or heterogeneity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e Utilizing MR, we conducted a comprehensive assessment to investigate the causal effect of 412 gut microbiota species spanning from the phylum to the genus level on AMD. Our study revealed significant positive associations between specific genetic variants related to the family \u003cem\u003ePeptococcaceae\u003c/em\u003e, \u003cem\u003egenus Parasutterella\u003c/em\u003e, and \u003cem\u003egenus Faecalibacterium\u003c/em\u003e and an increased risk of AMD. Our findings provide strong evidence supporting a protective role of certain genetic variants related to the \u003cem\u003eclass Melainabacteria\u003c/em\u003e and \u003cem\u003efamily Rikenellaceae\u003c/em\u003e against AMD. These results highlight a potential causal relationship between several gut microbiota taxa and AMD. However, future studies employing MR in larger cohorts and incorporating functional analyses are warranted to elucidate the underlying mechanisms by which genetic variants related to the gut microbiota influence the development of AMD.\u003c/p\u003e","manuscriptTitle":"Association between age-related macular degeneration and the gut microbiota: A two-sample Mendelian randomization analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-10 16:16:29","doi":"10.21203/rs.3.rs-4117483/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":"fa3a859c-a1a8-4db1-9c17-54296a70d1ec","owner":[],"postedDate":"April 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-17T08:07:08+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-10 16:16:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4117483","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4117483","identity":"rs-4117483","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}