Causal Influence of Plasma Metabolites on Age-Related Macular Degeneration: A Mendelian Randomization Study.

Causal Influence of Plasma Metabolites on 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 Causal Influence of Plasma Metabolites on Age-Related Macular Degeneration: A Mendelian Randomization Study. tao wang, chun huang, jinshuai li, Xiangjian wu, Xiaoyan fu, Yimin Hu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3812922/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 Age-related macular degeneration (AMD) is a prevalent eye condition in the elderly, profoundly affecting their quality of life. Despite its multifactorial nature, the precise mechanisms underlying AMD remain elusive. The potential role of metabolites as biomarkers has become a focal point of recent research. Utilizing Mendelian randomization analysis, this study aims to decipher the complex metabolic mechanisms associated with AMD, laying the groundwork for novel diagnostic and therapeutic approaches. Methods Employing Mendelian randomization (MR) analysis, this study leveraged single nucleotide polymorphisms (SNPs) significantly associated with plasma metabolites as instrumental variables (IVs). This approach established a causal link between metabolites and AMD. Analytical methods such as Inverse Variance Weighted (IVW), MR-Egger, and Weighted Median were applied to validate causality. MR-PRESSO was utilized for outlier detection and correction, and Cochran's Q test was conducted to assess heterogeneity. To delve deeper into the metabolic characteristics of AMD, metabolic enrichment analysis was performed using Metabo Analyst 5.0. These combined methods provided a robust framework for elucidating the metabolic underpinnings of AMD. Results The two-sample MR analysis, after meticulous screening, identified causal relationships between 88 metabolites and AMD. Of these, 16 metabolites showed a significant causal association. Following FDR correction, three metabolites remained significantly associated, with Androstenediol (3beta,17beta) disulfate (2) exhibiting the most potent protective effect against AMD. Further exploration using Metabo Analyst 5.0 highlighted four metabolic pathways potentially implicated in AMD pathogenesis. Conclusion This pioneering MR study has unravelled the causal connections between plasma metabolites and AMD. It identified several metabolites with a causal impact on AMD, with three maintaining significance after FDR correction. These insights offer robust causal evidence for future clinical applications and underscore the potential of these metabolites as clinical biomarkers in AMD screening, treatment, and prevention strategies. Plasma Metabolites AMD GWAS Mendelian randomization analysis Metabolic pathway analysis Figures Figure 1 Figure 2 1. Introduction Age-related macular degeneration (AMD) is a prevalent chronic and degenerative eye condition that leads to central vision loss in individuals over 55, significantly impairing their quality of life. It emerges from a complex interplay of age, environmental, genetic, and metabolic factors[ 1 ]. According to a meta-analysis by The Lancet, the number of AMD patients is projected to rise to 288 million by 2040[ 2 ], a figure that could be an underestimate considering increasing life expectancy and an aging global population. AMD is associated with heightened risks of depression and cognitive impairment[ 3 , 4 ] and a 20% increase in all-cause mortality[ 5 ]. The economic impact of AMD-induced blindness is substantial, encompassing both direct healthcare costs and indirect expenses such as caregiving and lost productivity. In 2020, the societal cost of AMD-related blindness in the US was estimated at 20 billion dollars, projected to triple by 2050[ 6 ]. AMD typically progresses from early and intermediate stages, characterized by small and medium pigment deposits in the macula, to advanced stages. Advanced AMD is classified into two types: non-neovascular (dry) and neovascular (wet). Dry AMD mainly presents with geographic atrophy, leading to central blind spots and visual distortion, while wet AMD is marked by the growth of new blood vessels under or in the retina, causing severe visual distortion, large central blind spots, and a rapid decline in vision[ 7 ]. Genome-Wide Association (GWA) studies are extensively used to explore the relationships between genes and diseases. However, they fall short in elucidating the mechanisms underlying disease onset. In this context, metabolites serve as functional intermediates, shedding light on how genetic variations can impact metabolic processes and disease mechanisms[ 8 ]. A notable study from China has identified 29 distinct metabolites across various metabolic pathways, including caffeine metabolism, biosynthesis of unsaturated fatty acids, and purine metabolism. These include 4-hydroxybenzoic acid, adrenic acid, and palmitic acid in plasma, which are promising candidates as biomarkers for neovascular Age-related Macular Degeneration (nAMD). Changes in these metabolites may indicate metabolic imbalances in nAMD patients, offering insights into the disease's molecular mechanism[ 9 ].For effective management of Age-related Macular Degeneration (AMD), diagnostic tests must be both accessible and predictive of disease progression. Current research has delved into identifying serum and plasma biomarkers, particularly those associated with inflammation and lipid levels, due to their critical role in AMD's pathogenesis[ 10 – 13 ]. However, inconsistencies in research findings highlight the challenges in pinpointing reliable biological fluid biomarkers for AMD.Moreover, existing studies on AMD's metabolome face limitations, including small sample sizes and the challenge of isolating dietary effects on AMD[ 14 ]. These issues underscore the need for a deeper understanding of plasma metabolites' roles in AMD. MR is a statistical method that leverages genetic variations as instrumental variables to ascertain causal relationships between exposures and outcomes[ 15 ]. Utilizing Mendel's Second Law for random gene allocation, MR minimizes confounding factors, offering a clearer view of exposure effects on disease risk[ 16 ]. This approach surpasses traditional observational studies by reducing confounder influence and avoiding reverse causality, providing more robust evidence in the absence of randomized trials. MR employs genetic variations as proxies for long-term exposures, thus circumventing common errors and biases in observational studies[ 17 ]. With these premises, MR can be effectively used in large-scale studies to elucidate the causal relationship between plasma metabolites and AMD. Employing KEGG pathways for GSEA analysis, MR helps identify metabolic pathways related to AMD pathogenesis. This research could significantly contribute to understanding the causal links between plasma metabolites and AMD, aiding in the development of new biomarkers and therapeutics, ultimately improving AMD patient care and prevention strategies. 2. Methods 2.1 Exposure Data We acquired comprehensive summary data on 1,400 serum metabolites from a large-scale GWAS study conducted by Yiheng Chen et al. This dataset included 1,091 blood metabolites and 309 metabolite ratios, sourced from the Canadian Longitudinal Study on Aging (CLSA). The CLSA encompassed data from over 50,000 middle-aged and older Canadian participants. For the metabolomics analysis, ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was utilized. In our study, we focused on 1,091 metabolites that were measured in less than 50% of the samples. This subset included 850 known metabolites and 241 metabolites yet to be identified[ 18 ]. These metabolites are found within eight key metabolic pathways: lipids, amino acids, xenobiotic metabolism, nucleotides, cofactors and vitamins, carbohydrates, peptides, and energy. This distribution aligns with the classifications in the KEGG database (Kyoto Encyclopedia of Genes and Genomes)[ 19 ]. 2.2 Outcome Data Comprehensive GWAS data on AMD were obtained from the Finnish Biobank, involving 8,913 Finnish AMD patients and 348,936 controls ( https://r9.finngen.fi/ )[ 20 ]. 2.3 Selection of Instrumental Variables For MR studies, three core assumptions must be met: (a) The instrumental variable (IV) is significantly associated with the exposure, (b) The IV is not associated with confounding factors between the exposure and outcome, (c) The IV affects the outcome solely through the exposure. For our analysis of 1,400 serum and plasma metabolites, SNPs with P-values < 5×10^−5 were selected as IVs. Following the 1000 Genomes Project, we applied a linkage disequilibrium threshold of r^2 < 0.001 and a physical distance of 1000kb. The F-statistic \((F=\frac{N-K-1}{K}\times \frac{{R}^{2}}{1-{R}^{2}})\) was used to evaluate IV effectiveness, excluding those with an F-statistic < 10 [ 21 , 22 ]. 2.4 Mendelian Randomization Analysis and Sensitivity Testing The primary analysis method was IVW, aggregating weighted SNP causal effects for accurate causal inference[ 23 , 24 ]. For sensitivity, the MR-Egger method estimated causal parameters and assessed overall causality[ 25 ]. The Weighted Median method, assuming 50% of IVs are valid, was also used for robust causal effect estimation[ 26 ]. MR-PRESSO detected and corrected horizontal pleiotropy outliers[ 27 ]. MR-Egger intercept estimation tested for horizontal pleiotropy, with a non-zero intercept indicating its presence[ 28 ]. Cochran's Q test assessed heterogeneity[ 24 , 29 ]. Leave-one-out sensitivity analysis removed one SNP at a time, enhancing the reliability of the analysis. Consistency in p-values and directions across the three MR methods indicated sufficient evidence for causality. We applied FDR correction to IVW p-values; a q-value < 0.05 suggested a potential AMD candidate metabolite[ 30 ]. Analyses were conducted using R version 4.3.1 (R Studio), with TwoSampleMR (version 0.5.7) and MR-PRESSO (version 1.0.0) for two-sample MR analysis. 2.5 Metabolic Pathway Analysis Metabolic enrichment analysis for AMD was conducted using Metabo Analyst 5.0 ( https://www.metaboanalyst.ca/ ). By analyzing metabolite collections in KEGG and SMPDB, we explored the metabolic characteristics of AMD[ 31 ]. 3. Results 3.1 MR analysis Our analysis encompassed 1,400 metabolites, each represented by 11–82 SNPs, all F-statistics are greater than 10(Supplementary1 Table S1 ).Utilizing the IVW method in MR analysis, we identified 88 serum and plasma metabolites associated with AMD. Of these, 80 are known and 8 are unknown (Supplementary1 Table S2 ) As shown in Fig. 2 , after sensitivity analysis (MR-Egger and WM method), 16 metabolites showed significant p-values and consistent directions across all three methods(Supplementary1 Table S3), with 15 known and 1 unknown. These include 9 lipids, 5 amino acids, and 1 carbohydrate. Known AMD risk factors identified include the N-palmitoyl-sphingosine (d18:1 to 16:0) to N-stearoyl-sphingosine (d18:1 to 18:0) ratio (p:0.0026, OR:1.12, 95%CI:1.04–1.20) and Mannonate levels (p:0.0017, OR:1.07, 95%CI:1.02–1.11). In contrast, several metabolites emerged as protective factors against AMD. Androstenediol (3beta,17beta) disulfate (2) levels showed a significant protective effect (p: 0.000018, OR:0.89, 95%CI:0.84–0.94), followed by 1-stearoyl-GPE (18:0) (p:0.000069, OR:0.82, 95%CI:0.74–0.90), 1-palmitoyl-2-docosahexaenoyl-GPE (16:0/22:6) (p:0.000086, OR:0.81, 95%CI:0.73–0.90), 1-stearoyl-2-oleoyl-GPE (18:0/18:1) (p: 0.00018, OR:0.80, 95%CI:0.71–0.90), N-acetylkynurenine (2) (p:0.00075, OR:0.91, 95%CI:0.86–0.96), 1-stearoyl-2-arachidonoyl-GPE (18:0/20:4) (p:0.00015, OR:0.85, 95%CI:0.78–0.92), 1-palmitoyl-2-arachidonoyl-GPE (16:0/20:4) (p:0.0026, OR:0.84, 95%CI:0.75–0.94), 1-stearoyl-2-docosahexaenoyl-GPE (18:0/22:6) (p: 0.00063, OR:0.84, 95%CI:0.78–0.93), 1-oleoyl-2-arachidonoyl-GPE (18:1/20:4) (p:0.00046, OR:0.83, 95%CI:0.75–0.92), N-acetylleucine (p:0.023, OR:0.94, 95%CI:0.89–0.99), N-acetyltyrosine (p:0.00049, OR:0.92, 95%CI:0.88–0.97), N-acetylasparagine (p:0.024, OR:0.96, 95%CI:0.92–0.99), and N-acetylcitrulline (p:0.021, OR:0.96, 95%CI:0.92–0.99). The metabolites did not show signs of heterogeneity or pleiotropy (Supplementary1 Table S4-5), All passed the MR-PRESSO test without evidence of horizontal pleiotropy(Supplementary1 Table S6). The leave-one-out plot (Supplementary2) confirms that excluding individual instrumental variables did not significantly alter the results, reinforcing the analysis's reliability. The scatter plots, funnel plots, and retention sensitivity analyses of the 16 metabolites are shown in Fig. 1 –48.Post-FDR correction, three metabolites with adjusted p-values below 0.05 were identified: 1-stearoyl-GPE (18:0) levels, Androstenediol (3beta,17beta) disulfate (2) levels, and 1-palmitoyl-2-docosahexaenoyl-GPE (16:0/22:6) levels. 3.2 Metabolic Pathway Analysis Utilizing Metabo Analyst 5.0, we selected 80 metabolites significant via the IVW method, with 19 identified for further metabolic pathway analysis to investigate their association with AMD. The analysis in SMPDB and KEGG yielded specific results, as shown in Table 1 . For more details on the metabolic pathways, refer to Supplementary1 Table S7. Table 1 Significant metabolic pathways involved in the pathogenesis of AMD.。 Metabolic pathway Metabolites Involved P value Database Lysine degradation 5-hydroxylysine N(6),N(6),N(6)-trimethyl-L-lysine 0.01257 KEGG SMP Glycerophospholipid metabolism 1-stearoyl-2-arachidonoyl-sn-glycero-3-phosphoethanolamine PC(18:1(9Z)/22:6(4Z,7Z,10Z,13Z,16Z,19Z)) 0.025294 KEGG Phenylalanine,tyrosine and tryptophan biosynthesis Linoleic acid metabolism Tyrosine PC(18:1(9Z)/22:6(4Z,7Z,10Z,13Z,16Z,19Z)) 0.028113 0.035028 KEGG SMP KEGG SMP KEGG: Kyoto Encyclopedia of Genes and Genomes; SMPDB: Small Molecule Pathway Database. 4. Discussion Our study successfully identified 88 metabolites causally linked to AMD, with 16 showing significant causal relationships after rigorous sensitivity analysis. Remarkably, three metabolites − 1-stearoyl-GPE (18:0), Androstenediol (3beta,17beta) disulfate (2), and 1-palmitoyl-2-docosahexaenoyl-GPE (16:0/22:6) - stood out, remaining significant even after FDR correction (P < 0.05). These metabolites are protective against AMD, with Androstenediol (3beta,17beta) disulfate (2) exhibiting the most potent protective effect. This study is pioneering in using metabolomics techniques and MR analysis to explore the potential causal impact of serum and plasma metabolites on AMD. Our findings underscore the protective role of high levels of Androstenediol (3beta,17beta) monosulfate in slowing AMD progression. Notably, Androstenediol (3beta,17beta) disulfate (2), a steroid sulfate derived from androstenediol, possesses both androgenic and estrogenic properties. Synthesized in the adrenal glands from DHEA via the Δ5 pathway[ 32 ], this compound functions in the central nervous system through both traditional androgen receptor (AR) signaling and alternative pathways. The traditional AR pathway, found in the fovea but not in the macula, and alternative pathways involving specific binding proteins, are present in various parts of the retina and are closely associated with sex hormones. Androgens, including metabolic derivatives of androstenediol, can also interact with GABAA receptors, which are located in areas of the central nervous system, including the retina[ 33 ]. The neuroprotective role of androgens, such as Androstenediol (3beta,17beta) monosulfate, is evident in their influence on neural differentiation, survival, and development through the AR pathway[ 34 ]. Testosterone, a derivative of androstanediol, has been shown to protect neurons from serum deprivation-induced cellular death, even when its conversion to estrogen is inhibited, a process that is suppressed by anti-androgen drugs like flutamide[ 35 ]. This suggests that Androstenediol (3beta,17beta) monosulfate's protective effect against AMD may be due to its neuroprotective efficacy. Androgens, by activating their receptors, can reduce oxidative stress, inflammation, and cell apoptosis, thus potentially preventing AMD [ 35 , 36 ]. To date, no studies have directly linked 1-stearoyl-GPE (18:0) and 1-palmitoyl-2-docosahexaenoyl-GPE (16:0/22:6) levels with AMD. Both of these are glycerophosphoethanolamines, integral to phospholipid structures. Research involving the quantification of mixed extracts from the macula and surrounding retinal areas has investigated the relationship between lipofuscin accumulation and age in the retinal pigment epithelium. Notably, A2-Glycerophosphoethanolamine, a type of retinoid acid, showed a 7.1-fold increase in these areas, although the increase was not statistically significant[ 37 ]. This implies that A2-Glycerophosphoethanolamine, despite being a retinoid acid, may not contribute to lipofuscin accumulation[ 38 ]].In a separate study, a plasma metabolomics analysis was conducted on AMD patients and healthy controls using mass spectrometry. This study highlighted significant metabolite differences, particularly in lipid metabolism pathways. The glycerophospholipid pathway, in particular, exhibited significant metabolic variations in AMD patients. Glycerophospholipids, crucial for cell membrane structure, can impact membrane stability and function. Their levels may trigger processes like oxidative stress and influence the proliferation, differentiation, and migration of neuronal cells, ultimately affecting ocular cell health. The study also noted a correlation between reduced levels of diacylglycerol and phosphatidylcholine and neuronal cell membrane damage[ 39 ].Our research suggests that 1-stearoyl-GPE (18:0) and 1-palmitoyl-2-docosahexaenoyl-GPE (16:0/22:6) levels might also influence AMD through the glycerophosphate pathway. We found that both metabolites exhibit protective effects, potentially delaying AMD progression. However, the precise relationship and protective mechanisms of these metabolites in AMD are yet to be fully elucidated, highlighting a gap in experimental research in this area. In our study, we performed a metabolic pathway analysis on 19 metabolites, uncovering several pathways associated with AMD. Notably, the KEGG highlighted Lysine degradation as the most significant metabolic pathway related to AMD, with a p-value of 0.01257. This discovery underscores the potential importance of amino acid metabolism in AMD. Additionally, other relevant metabolic pathways were identified, including Phenylalanine, tyrosine, and tryptophan biosynthesis (p = 0.028113), Glycerophospholipid metabolism (p = 0.025294), and Linoleic acid metabolism (p = 0.034). These findings suggest a complex interplay of various metabolic pathways in the progression and development of AMD, warranting further investigation to elucidate their specific roles and impacts. The primary strength of our study is its pioneering approach of utilizing two extensive Genome-Wide Association Study (GWAS) summary datasets to establish a causal link between plasma metabolites and AMD. This approach enabled us to identify 88 metabolites causally related to AMD, including 16 of significant relevance, with 3 maintaining significance post-False Discovery Rate (FDR) adjustment. This is particularly noteworthy considering that AMD often presents no symptoms in its early stages. Although Optical Coherence Tomography (OCT) and telemedicine technologies have shown potential in screening, their effectiveness is still under evaluation for specificity, sensitivity, and accuracy[ 40 ]. Moreover, the high costs and advanced technology required for these methods may limit their applicability, especially in resource-constrained settings. Our findings hold substantial promise for early AMD diagnosis by identifying potential biomarkers. These biomarkers not only improve diagnostic accuracy but also unveil biological pathways implicated in AMD, paving the way for novel treatment approaches. Additionally, understanding metabolite variations enables more personalized treatment plans, risk assessment, preventive measures, and monitoring of disease progression, thereby enriching our comprehension of AMD and enhancing current treatment and management strategies. Furthermore, our study employed instrumental variables with F-statistics exceeding 10 to mitigate weak instrument bias. We also incorporated multiple methods to address heterogeneity and pleiotropy, bolstering the reliability of our results. FDR testing further reinforced this, adjusting the significance threshold to minimize false positives and elevate the credibility of our findings. However, the study is not without limitations. Despite employing MR-egger and MR-presso methods to account for pleiotropy, potential confounding factors like nutritional status and smoking habits could still introduce biases[ 41 ]. Additionally, while we identified three metabolites associated with AMD, the underlying biological mechanisms of how plasma metabolites influence AMD require further exploration. Lastly, since our data primarily pertains to European populations, the generalizability of our results to other ethnicities remains uncertain, highlighting the need for future studies to encompass a broader demographic range. 5. Conclusion This study represents a groundbreaking MR investigation into the causal relationship between plasma metabolites and AMD. We successfully identified several metabolites with causal effects on AMD, with three demonstrating significant associations even after FDR correction. These findings are anticipated to contribute high-quality causal evidence to clinical practice, laying a foundation for using these metabolites as clinical circulating biomarkers in the screening, treatment, and prevention of AMD. Declarations Ethics approval and consent to participate : Not applicable Consent for publication : Not applicable Availability of data and materials : The datasets utilized in our study are accessible in an online repository. The genetic association data for 1400 plasma metabolites were sourced from the GWAS catalog (https://www.ebi.ac.uk/gwas/), identified under the ID number GCST90199621-90201020. These original study results are available in a public database, allowing users to download the relevant data for research purposes at no cost. Further inquiries can be directed to the corresponding author. Competing interests : None Funding : This research was supported by the Shenzhen Science and Technology Plan Project Innovation and Entrepreneurship Special Fund (KCXFZ20201221173209027) and the Guangdong Provincial Administration of Traditional Chinese Medicine (2022201). Author Contributions : TW and CH formulated the research concept and design; data collection was conducted by TW, CH, JL, CY, XW, XF, GW,YH,ML, SC; TW, CH, JL undertook the data analysis; and the manuscript was authored by TW. 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Clinical effectiveness of screening for age-related macular degeneration: A systematic review. PloS one. 2023; doi: 10.1371/journal.pone.0294398 . Hyman, Neborsky. Risk factors for age-related macular degeneration: an update. Current opinion in ophthalmology. 2002; doi: 10.1097/00055735-200206000-00007 . Additional Declarations No competing interests reported. Supplementary Files Supplementary1.xlsx Supplementary2.pdf 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-3812922","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":264940919,"identity":"38e219d1-754d-4c9c-a6a0-8bb78dcf0de1","order_by":0,"name":"tao wang","email":"","orcid":"","institution":"Shenzhen Traditional Chinese Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"tao","middleName":"","lastName":"wang","suffix":""},{"id":264940920,"identity":"e0f1bb30-d59d-41e4-9518-353b59970ddb","order_by":1,"name":"chun huang","email":"","orcid":"","institution":"Shenzhen Traditional Chinese Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"chun","middleName":"","lastName":"huang","suffix":""},{"id":264940921,"identity":"4d61ff39-d2f6-406b-a06c-dae8f4e8bc4d","order_by":2,"name":"jinshuai li","email":"","orcid":"","institution":"Shenzhen Traditional Chinese Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"jinshuai","middleName":"","lastName":"li","suffix":""},{"id":264940922,"identity":"71effc98-7e9e-4c60-a8ac-bd26e892f543","order_by":3,"name":"Xiangjian wu","email":"","orcid":"","institution":"Shenzhen Traditional Chinese Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiangjian","middleName":"","lastName":"wu","suffix":""},{"id":264940923,"identity":"f8a6884a-9549-430f-ad6a-1cc6528073d2","order_by":4,"name":"Xiaoyan fu","email":"","orcid":"","institution":"Shenzhen Traditional Chinese Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyan","middleName":"","lastName":"fu","suffix":""},{"id":264940924,"identity":"517720fd-3558-4293-8551-6b396c9e9466","order_by":5,"name":"Yimin Hu","email":"","orcid":"","institution":"Shenzhen Traditional Chinese Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yimin","middleName":"","lastName":"Hu","suffix":""},{"id":264940925,"identity":"7f825d76-c42b-4266-876c-58f7054cb8ab","order_by":6,"name":"Geping Wu","email":"","orcid":"","institution":"School of Life Sciences,Beijing University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Geping","middleName":"","lastName":"Wu","suffix":""},{"id":264940926,"identity":"df9d34e7-badd-47dd-9056-b650f3812ff6","order_by":7,"name":"Chunfeng Yang","email":"","orcid":"","institution":"Shenzhen Traditional Chinese Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"Chunfeng","middleName":"","lastName":"Yang","suffix":""},{"id":264940927,"identity":"532ca83f-101c-4261-bd7d-3e4647f79e42","order_by":8,"name":"minfang Li","email":"","orcid":"","institution":"Shenzhen Traditional Chinese Medicine Hospital","correspondingAuthor":false,"prefix":"","firstName":"minfang","middleName":"","lastName":"Li","suffix":""},{"id":264940928,"identity":"2326dd64-219f-47fb-9b1d-fa72d7425191","order_by":9,"name":"sheng chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuUlEQVRIiWNgGAWjYBADOTb25gOkaTHm4zmWQJqWxHkSOQrEKZXvP/5MmqemLr2NIYeB4UfFNsJaDA4cSJPmOXY4t43h7AHGnjO3idDC2HBMmrfhQG4bY18CM2MbEVrkmxnbgFrq0tmYeQyI08JwjJkNqIU5gY2NWC0GZ9iYLeccO2zYxsOWcJAovwBD7OGNNzV18vLzHx988KOCGIcxMLBI8UBZB4hSDwTMH38Qq3QUjIJRMApGJgAAoI83HZp4luQAAAAASUVORK5CYII=","orcid":"","institution":"Shenzhen Traditional Chinese Medicine Hospital","correspondingAuthor":true,"prefix":"","firstName":"sheng","middleName":"","lastName":"chen","suffix":""}],"badges":[],"createdAt":"2023-12-27 15:44:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3812922/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3812922/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49169355,"identity":"b7d07eda-c0c7-4427-a027-738b0b1a3bf8","added_by":"auto","created_at":"2024-01-04 09:40:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":368832,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCircular Heatmap Illustrating Metabolite Effects on AMD as Determined by the IVW Method\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3812922/v1/59dc4502927c432712705f50.png"},{"id":49169356,"identity":"ce273033-8385-4f38-b83c-958e7087da9b","added_by":"auto","created_at":"2024-01-04 09:40:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":227174,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eForest Plot of Metabolite Effects on AMD as Determined by the IVW Method\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3812922/v1/7326cf2cc46571769b40348b.png"},{"id":49171644,"identity":"efb1e6a2-e4ac-42af-9430-ca456323d5d9","added_by":"auto","created_at":"2024-01-04 10:37:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":923208,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3812922/v1/eae7f489-43ff-4198-a623-6f3eba8e6241.pdf"},{"id":49169358,"identity":"414cf71e-a044-4a09-9cd2-46d301a360e3","added_by":"auto","created_at":"2024-01-04 09:40:45","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":970334,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3812922/v1/dd9c114829e93408f4fbcd65.xlsx"},{"id":49169528,"identity":"f9003d6f-c5e9-4574-969d-b6338bf959b0","added_by":"auto","created_at":"2024-01-04 09:48:45","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":195317,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementary2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3812922/v1/b38551a96879f91fad984a61.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causal Influence of Plasma Metabolites on Age-Related Macular Degeneration: A Mendelian Randomization Study.","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAge-related macular degeneration (AMD) is a prevalent chronic and degenerative eye condition that leads to central vision loss in individuals over 55, significantly impairing their quality of life. It emerges from a complex interplay of age, environmental, genetic, and metabolic factors[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. According to a meta-analysis by The Lancet, the number of AMD patients is projected to rise to 288\u0026nbsp;million by 2040[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], a figure that could be an underestimate considering increasing life expectancy and an aging global population. AMD is associated with heightened risks of depression and cognitive impairment[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] and a 20% increase in all-cause mortality[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The economic impact of AMD-induced blindness is substantial, encompassing both direct healthcare costs and indirect expenses such as caregiving and lost productivity. In 2020, the societal cost of AMD-related blindness in the US was estimated at 20\u0026nbsp;billion dollars, projected to triple by 2050[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. AMD typically progresses from early and intermediate stages, characterized by small and medium pigment deposits in the macula, to advanced stages. Advanced AMD is classified into two types: non-neovascular (dry) and neovascular (wet). Dry AMD mainly presents with geographic atrophy, leading to central blind spots and visual distortion, while wet AMD is marked by the growth of new blood vessels under or in the retina, causing severe visual distortion, large central blind spots, and a rapid decline in vision[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGenome-Wide Association (GWA) studies are extensively used to explore the relationships between genes and diseases. However, they fall short in elucidating the mechanisms underlying disease onset. In this context, metabolites serve as functional intermediates, shedding light on how genetic variations can impact metabolic processes and disease mechanisms[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. A notable study from China has identified 29 distinct metabolites across various metabolic pathways, including caffeine metabolism, biosynthesis of unsaturated fatty acids, and purine metabolism. These include 4-hydroxybenzoic acid, adrenic acid, and palmitic acid in plasma, which are promising candidates as biomarkers for neovascular Age-related Macular Degeneration (nAMD). Changes in these metabolites may indicate metabolic imbalances in nAMD patients, offering insights into the disease's molecular mechanism[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].For effective management of Age-related Macular Degeneration (AMD), diagnostic tests must be both accessible and predictive of disease progression. Current research has delved into identifying serum and plasma biomarkers, particularly those associated with inflammation and lipid levels, due to their critical role in AMD's pathogenesis[\u003cspan additionalcitationids=\"CR11 CR12\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, inconsistencies in research findings highlight the challenges in pinpointing reliable biological fluid biomarkers for AMD.Moreover, existing studies on AMD's metabolome face limitations, including small sample sizes and the challenge of isolating dietary effects on AMD[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These issues underscore the need for a deeper understanding of plasma metabolites' roles in AMD.\u003c/p\u003e \u003cp\u003eMR is a statistical method that leverages genetic variations as instrumental variables to ascertain causal relationships between exposures and outcomes[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Utilizing Mendel's Second Law for random gene allocation, MR minimizes confounding factors, offering a clearer view of exposure effects on disease risk[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This approach surpasses traditional observational studies by reducing confounder influence and avoiding reverse causality, providing more robust evidence in the absence of randomized trials. MR employs genetic variations as proxies for long-term exposures, thus circumventing common errors and biases in observational studies[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. With these premises, MR can be effectively used in large-scale studies to elucidate the causal relationship between plasma metabolites and AMD. Employing KEGG pathways for GSEA analysis, MR helps identify metabolic pathways related to AMD pathogenesis. This research could significantly contribute to understanding the causal links between plasma metabolites and AMD, aiding in the development of new biomarkers and therapeutics, ultimately improving AMD patient care and prevention strategies.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Exposure Data\u003c/h2\u003e \u003cp\u003eWe acquired comprehensive summary data on 1,400 serum metabolites from a large-scale GWAS study conducted by Yiheng Chen et al. This dataset included 1,091 blood metabolites and 309 metabolite ratios, sourced from the Canadian Longitudinal Study on Aging (CLSA). The CLSA encompassed data from over 50,000 middle-aged and older Canadian participants. For the metabolomics analysis, ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was utilized. In our study, we focused on 1,091 metabolites that were measured in less than 50% of the samples. This subset included 850 known metabolites and 241 metabolites yet to be identified[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These metabolites are found within eight key metabolic pathways: lipids, amino acids, xenobiotic metabolism, nucleotides, cofactors and vitamins, carbohydrates, peptides, and energy. This distribution aligns with the classifications in the KEGG database (Kyoto Encyclopedia of Genes and Genomes)[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Outcome Data\u003c/h2\u003e \u003cp\u003eComprehensive GWAS data on AMD were obtained from the Finnish Biobank, involving 8,913 Finnish AMD patients and 348,936 controls (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://r9.finngen.fi/\u003c/span\u003e\u003cspan address=\"https://r9.finngen.fi/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Selection of Instrumental Variables\u003c/h2\u003e \u003cp\u003eFor MR studies, three core assumptions must be met: (a) The instrumental variable (IV) is significantly associated with the exposure, (b) The IV is not associated with confounding factors between the exposure and outcome, (c) The IV affects the outcome solely through the exposure. For our analysis of 1,400 serum and plasma metabolites, SNPs with P-values\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10^\u0026minus;5 were selected as IVs. Following the 1000 Genomes Project, we applied a linkage disequilibrium threshold of r^2\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and a physical distance of 1000kb. The F-statistic \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\((F=\\frac{N-K-1}{K}\\times \\frac{{R}^{2}}{1-{R}^{2}})\\)\u003c/span\u003e\u003c/span\u003e was used to evaluate IV effectiveness, excluding those with an F-statistic\u0026thinsp;\u0026lt;\u0026thinsp;10 [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Mendelian Randomization Analysis and Sensitivity Testing\u003c/h2\u003e \u003cp\u003eThe primary analysis method was IVW, aggregating weighted SNP causal effects for accurate causal inference[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. For sensitivity, the MR-Egger method estimated causal parameters and assessed overall causality[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The Weighted Median method, assuming 50% of IVs are valid, was also used for robust causal effect estimation[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. MR-PRESSO detected and corrected horizontal pleiotropy outliers[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. MR-Egger intercept estimation tested for horizontal pleiotropy, with a non-zero intercept indicating its presence[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Cochran's Q test assessed heterogeneity[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Leave-one-out sensitivity analysis removed one SNP at a time, enhancing the reliability of the analysis.\u003c/p\u003e \u003cp\u003eConsistency in p-values and directions across the three MR methods indicated sufficient evidence for causality. We applied FDR correction to IVW p-values; a q-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 suggested a potential AMD candidate metabolite[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Analyses were conducted using R version 4.3.1 (R Studio), with TwoSampleMR (version 0.5.7) and MR-PRESSO (version 1.0.0) for two-sample MR analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Metabolic Pathway Analysis\u003c/h2\u003e \u003cp\u003eMetabolic enrichment analysis for AMD was conducted using Metabo Analyst 5.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.metaboanalyst.ca/\u003c/span\u003e\u003cspan address=\"https://www.metaboanalyst.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). By analyzing metabolite collections in KEGG and SMPDB, we explored the metabolic characteristics of AMD[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 MR analysis\u003c/h2\u003e \u003cp\u003eOur analysis encompassed 1,400 metabolites, each represented by 11\u0026ndash;82 SNPs, all F-statistics are greater than 10(Supplementary1 Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).Utilizing the IVW method in MR analysis, we identified 88 serum and plasma metabolites associated with AMD. Of these, 80 are known and 8 are unknown (Supplementary1 Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, after sensitivity analysis (MR-Egger and WM method), 16 metabolites showed significant p-values and consistent directions across all three methods(Supplementary1 Table S3), with 15 known and 1 unknown. These include 9 lipids, 5 amino acids, and 1 carbohydrate. Known AMD risk factors identified include the N-palmitoyl-sphingosine (d18:1 to 16:0) to N-stearoyl-sphingosine (d18:1 to 18:0) ratio (p:0.0026, OR:1.12, 95%CI:1.04\u0026ndash;1.20) and Mannonate levels (p:0.0017, OR:1.07, 95%CI:1.02\u0026ndash;1.11).\u003c/p\u003e \u003cp\u003eIn contrast, several metabolites emerged as protective factors against AMD. Androstenediol (3beta,17beta) disulfate (2) levels showed a significant protective effect (p: 0.000018, OR:0.89, 95%CI:0.84\u0026ndash;0.94), followed by 1-stearoyl-GPE (18:0) (p:0.000069, OR:0.82, 95%CI:0.74\u0026ndash;0.90), 1-palmitoyl-2-docosahexaenoyl-GPE (16:0/22:6) (p:0.000086, OR:0.81, 95%CI:0.73\u0026ndash;0.90), 1-stearoyl-2-oleoyl-GPE (18:0/18:1) (p: 0.00018, OR:0.80, 95%CI:0.71\u0026ndash;0.90), N-acetylkynurenine (2) (p:0.00075, OR:0.91, 95%CI:0.86\u0026ndash;0.96), 1-stearoyl-2-arachidonoyl-GPE (18:0/20:4) (p:0.00015, OR:0.85, 95%CI:0.78\u0026ndash;0.92), 1-palmitoyl-2-arachidonoyl-GPE (16:0/20:4) (p:0.0026, OR:0.84, 95%CI:0.75\u0026ndash;0.94), 1-stearoyl-2-docosahexaenoyl-GPE (18:0/22:6) (p: 0.00063, OR:0.84, 95%CI:0.78\u0026ndash;0.93), 1-oleoyl-2-arachidonoyl-GPE (18:1/20:4) (p:0.00046, OR:0.83, 95%CI:0.75\u0026ndash;0.92), N-acetylleucine (p:0.023, OR:0.94, 95%CI:0.89\u0026ndash;0.99), N-acetyltyrosine (p:0.00049, OR:0.92, 95%CI:0.88\u0026ndash;0.97), N-acetylasparagine (p:0.024, OR:0.96, 95%CI:0.92\u0026ndash;0.99), and N-acetylcitrulline (p:0.021, OR:0.96, 95%CI:0.92\u0026ndash;0.99).\u003c/p\u003e \u003cp\u003eThe metabolites did not show signs of heterogeneity or pleiotropy (Supplementary1 Table S4-5), All passed the MR-PRESSO test without evidence of horizontal pleiotropy(Supplementary1 Table S6). The leave-one-out plot (Supplementary2) confirms that excluding individual instrumental variables did not significantly alter the results, reinforcing the analysis's reliability. The scatter plots, funnel plots, and retention sensitivity analyses of the 16 metabolites are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;48.Post-FDR correction, three metabolites with adjusted p-values below 0.05 were identified: 1-stearoyl-GPE (18:0) levels, Androstenediol (3beta,17beta) disulfate (2) levels, and 1-palmitoyl-2-docosahexaenoyl-GPE (16:0/22:6) levels.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Metabolic Pathway Analysis\u003c/h2\u003e \u003cp\u003eUtilizing Metabo Analyst 5.0, we selected 80 metabolites significant via the IVW method, with 19 identified for further metabolic pathway analysis to investigate their association with AMD. The analysis in SMPDB and KEGG yielded specific results, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. For more details on the metabolic pathways, refer to Supplementary1 Table S7.\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\u003eSignificant metabolic pathways involved in the pathogenesis of AMD.。\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolic pathway\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMetabolites Involved\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDatabase\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLysine degradation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5-hydroxylysine\u003c/p\u003e \u003cp\u003eN(6),N(6),N(6)-trimethyl-L-lysine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.01257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKEGG SMP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlycerophospholipid metabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1-stearoyl-2-arachidonoyl-sn-glycero-3-phosphoethanolamine\u003c/p\u003e \u003cp\u003ePC(18:1(9Z)/22:6(4Z,7Z,10Z,13Z,16Z,19Z))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.025294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKEGG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhenylalanine,tyrosine and tryptophan biosynthesis\u003c/p\u003e \u003cp\u003eLinoleic acid\u003c/p\u003e \u003cp\u003emetabolism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTyrosine\u003c/p\u003e \u003cp\u003ePC(18:1(9Z)/22:6(4Z,7Z,10Z,13Z,16Z,19Z))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.028113\u003c/p\u003e \u003cp\u003e0.035028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKEGG SMP\u003c/p\u003e \u003cp\u003eKEGG SMP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eKEGG: Kyoto Encyclopedia of Genes and Genomes; SMPDB: Small Molecule Pathway Database.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOur study successfully identified 88 metabolites causally linked to AMD, with 16 showing significant causal relationships after rigorous sensitivity analysis. Remarkably, three metabolites \u0026minus;\u0026thinsp;1-stearoyl-GPE (18:0), Androstenediol (3beta,17beta) disulfate (2), and 1-palmitoyl-2-docosahexaenoyl-GPE (16:0/22:6) - stood out, remaining significant even after FDR correction (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These metabolites are protective against AMD, with Androstenediol (3beta,17beta) disulfate (2) exhibiting the most potent protective effect. This study is pioneering in using metabolomics techniques and MR analysis to explore the potential causal impact of serum and plasma metabolites on AMD.\u003c/p\u003e \u003cp\u003eOur findings underscore the protective role of high levels of Androstenediol (3beta,17beta) monosulfate in slowing AMD progression. Notably, Androstenediol (3beta,17beta) disulfate (2), a steroid sulfate derived from androstenediol, possesses both androgenic and estrogenic properties. Synthesized in the adrenal glands from DHEA via the Δ5 pathway[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], this compound functions in the central nervous system through both traditional androgen receptor (AR) signaling and alternative pathways. The traditional AR pathway, found in the fovea but not in the macula, and alternative pathways involving specific binding proteins, are present in various parts of the retina and are closely associated with sex hormones. Androgens, including metabolic derivatives of androstenediol, can also interact with GABAA receptors, which are located in areas of the central nervous system, including the retina[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The neuroprotective role of androgens, such as Androstenediol (3beta,17beta) monosulfate, is evident in their influence on neural differentiation, survival, and development through the AR pathway[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Testosterone, a derivative of androstanediol, has been shown to protect neurons from serum deprivation-induced cellular death, even when its conversion to estrogen is inhibited, a process that is suppressed by anti-androgen drugs like flutamide[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. This suggests that Androstenediol (3beta,17beta) monosulfate's protective effect against AMD may be due to its neuroprotective efficacy. Androgens, by activating their receptors, can reduce oxidative stress, inflammation, and cell apoptosis, thus potentially preventing AMD [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo date, no studies have directly linked 1-stearoyl-GPE (18:0) and 1-palmitoyl-2-docosahexaenoyl-GPE (16:0/22:6) levels with AMD. Both of these are glycerophosphoethanolamines, integral to phospholipid structures. Research involving the quantification of mixed extracts from the macula and surrounding retinal areas has investigated the relationship between lipofuscin accumulation and age in the retinal pigment epithelium. Notably, A2-Glycerophosphoethanolamine, a type of retinoid acid, showed a 7.1-fold increase in these areas, although the increase was not statistically significant[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This implies that A2-Glycerophosphoethanolamine, despite being a retinoid acid, may not contribute to lipofuscin accumulation[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]].In a separate study, a plasma metabolomics analysis was conducted on AMD patients and healthy controls using mass spectrometry. This study highlighted significant metabolite differences, particularly in lipid metabolism pathways. The glycerophospholipid pathway, in particular, exhibited significant metabolic variations in AMD patients. Glycerophospholipids, crucial for cell membrane structure, can impact membrane stability and function. Their levels may trigger processes like oxidative stress and influence the proliferation, differentiation, and migration of neuronal cells, ultimately affecting ocular cell health. The study also noted a correlation between reduced levels of diacylglycerol and phosphatidylcholine and neuronal cell membrane damage[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].Our research suggests that 1-stearoyl-GPE (18:0) and 1-palmitoyl-2-docosahexaenoyl-GPE (16:0/22:6) levels might also influence AMD through the glycerophosphate pathway. We found that both metabolites exhibit protective effects, potentially delaying AMD progression. However, the precise relationship and protective mechanisms of these metabolites in AMD are yet to be fully elucidated, highlighting a gap in experimental research in this area.\u003c/p\u003e \u003cp\u003eIn our study, we performed a metabolic pathway analysis on 19 metabolites, uncovering several pathways associated with AMD. Notably, the KEGG highlighted Lysine degradation as the most significant metabolic pathway related to AMD, with a p-value of 0.01257. This discovery underscores the potential importance of amino acid metabolism in AMD. Additionally, other relevant metabolic pathways were identified, including Phenylalanine, tyrosine, and tryptophan biosynthesis (p\u0026thinsp;=\u0026thinsp;0.028113), Glycerophospholipid metabolism (p\u0026thinsp;=\u0026thinsp;0.025294), and Linoleic acid metabolism (p\u0026thinsp;=\u0026thinsp;0.034). These findings suggest a complex interplay of various metabolic pathways in the progression and development of AMD, warranting further investigation to elucidate their specific roles and impacts.\u003c/p\u003e \u003cp\u003eThe primary strength of our study is its pioneering approach of utilizing two extensive Genome-Wide Association Study (GWAS) summary datasets to establish a causal link between plasma metabolites and AMD. This approach enabled us to identify 88 metabolites causally related to AMD, including 16 of significant relevance, with 3 maintaining significance post-False Discovery Rate (FDR) adjustment. This is particularly noteworthy considering that AMD often presents no symptoms in its early stages. Although Optical Coherence Tomography (OCT) and telemedicine technologies have shown potential in screening, their effectiveness is still under evaluation for specificity, sensitivity, and accuracy[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Moreover, the high costs and advanced technology required for these methods may limit their applicability, especially in resource-constrained settings.\u003c/p\u003e \u003cp\u003eOur findings hold substantial promise for early AMD diagnosis by identifying potential biomarkers. These biomarkers not only improve diagnostic accuracy but also unveil biological pathways implicated in AMD, paving the way for novel treatment approaches. Additionally, understanding metabolite variations enables more personalized treatment plans, risk assessment, preventive measures, and monitoring of disease progression, thereby enriching our comprehension of AMD and enhancing current treatment and management strategies.\u003c/p\u003e \u003cp\u003eFurthermore, our study employed instrumental variables with F-statistics exceeding 10 to mitigate weak instrument bias. We also incorporated multiple methods to address heterogeneity and pleiotropy, bolstering the reliability of our results. FDR testing further reinforced this, adjusting the significance threshold to minimize false positives and elevate the credibility of our findings.\u003c/p\u003e \u003cp\u003eHowever, the study is not without limitations. Despite employing MR-egger and MR-presso methods to account for pleiotropy, potential confounding factors like nutritional status and smoking habits could still introduce biases[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Additionally, while we identified three metabolites associated with AMD, the underlying biological mechanisms of how plasma metabolites influence AMD require further exploration. Lastly, since our data primarily pertains to European populations, the generalizability of our results to other ethnicities remains uncertain, highlighting the need for future studies to encompass a broader demographic range.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study represents a groundbreaking MR investigation into the causal relationship between plasma metabolites and AMD. We successfully identified several metabolites with causal effects on AMD, with three demonstrating significant associations even after FDR correction. These findings are anticipated to contribute high-quality causal evidence to clinical practice, laying a foundation for using these metabolites as clinical circulating biomarkers in the screening, treatment, and prevention of AMD.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e:\u0026nbsp;Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e:\u0026nbsp;Not applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e: The datasets utilized in our study are accessible in an online repository. The genetic association data for 1400 plasma metabolites were sourced from the GWAS catalog (https://www.ebi.ac.uk/gwas/), identified under the ID number GCST90199621-90201020. These original study results are available in a public database, allowing users to download the relevant data for research purposes at no cost. Further inquiries can be directed to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e: None\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: This research was supported by the Shenzhen Science and Technology Plan Project Innovation and Entrepreneurship Special Fund (KCXFZ20201221173209027) and the Guangdong Provincial Administration of Traditional Chinese Medicine (2022201).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e: TW and CH formulated the research concept and design; data collection was conducted by TW, CH, JL, CY, XW, XF, GW,YH,ML, SC; TW, CH, JL undertook the data analysis; and the manuscript was authored by TW.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e: Our gratitude extends to all the studies that provided public GWAS summary data, as well as the researchers and participants involved in these GWAS studies.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKeenan, Cukras, Chew. 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Current opinion in ophthalmology. 2002; doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/00055735-200206000-00007\u003c/span\u003e\u003cspan address=\"10.1097/00055735-200206000-00007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\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":"Plasma Metabolites, AMD, GWAS, Mendelian randomization analysis, Metabolic pathway analysis","lastPublishedDoi":"10.21203/rs.3.rs-3812922/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3812922/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAge-related macular degeneration (AMD) is a prevalent eye condition in the elderly, profoundly affecting their quality of life. Despite its multifactorial nature, the precise mechanisms underlying AMD remain elusive. The potential role of metabolites as biomarkers has become a focal point of recent research. Utilizing Mendelian randomization analysis, this study aims to decipher the complex metabolic mechanisms associated with AMD, laying the groundwork for novel diagnostic and therapeutic approaches.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eEmploying Mendelian randomization (MR) analysis, this study leveraged single nucleotide polymorphisms (SNPs) significantly associated with plasma metabolites as instrumental variables (IVs). This approach established a causal link between metabolites and AMD. Analytical methods such as Inverse Variance Weighted (IVW), MR-Egger, and Weighted Median were applied to validate causality. MR-PRESSO was utilized for outlier detection and correction, and Cochran's Q test was conducted to assess heterogeneity. To delve deeper into the metabolic characteristics of AMD, metabolic enrichment analysis was performed using Metabo Analyst 5.0. These combined methods provided a robust framework for elucidating the metabolic underpinnings of AMD.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe two-sample MR analysis, after meticulous screening, identified causal relationships between 88 metabolites and AMD. Of these, 16 metabolites showed a significant causal association. Following FDR correction, three metabolites remained significantly associated, with Androstenediol (3beta,17beta) disulfate (2) exhibiting the most potent protective effect against AMD. Further exploration using Metabo Analyst 5.0 highlighted four metabolic pathways potentially implicated in AMD pathogenesis.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis pioneering MR study has unravelled the causal connections between plasma metabolites and AMD. It identified several metabolites with a causal impact on AMD, with three maintaining significance after FDR correction. These insights offer robust causal evidence for future clinical applications and underscore the potential of these metabolites as clinical biomarkers in AMD screening, treatment, and prevention strategies.\u003c/p\u003e","manuscriptTitle":"Causal Influence of Plasma Metabolites on Age-Related Macular Degeneration: A Mendelian Randomization Study.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-04 09:40:40","doi":"10.21203/rs.3.rs-3812922/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":"df88cf11-0a9c-4370-a083-6dde87ee07b5","owner":[],"postedDate":"January 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-01-04T10:29:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-04 09:40:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3812922","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3812922","identity":"rs-3812922","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}