Causal relationship of serum micronutrient with autoimmune neurological diseases: a Mendelian randomization study

Causal relationship of serum micronutrient with autoimmune neurological diseases: 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 relationship of serum micronutrient with autoimmune neurological diseases: a Mendelian randomization study Yang Zhou, Zhenyu Wei, Liya Zhan, Yiping Bao, Ping Zhong, Chunhua Jin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4590504/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 The relationship between micronutrients and autoimmune neurological diseases such as multiple sclerosis (MS), myasthenia gravis (MG), and Guillain-Barré syndrome (GBS) remains poorly understood. This study aims to elucidate the causal relationships between specific micronutrients and these diseases using Mendelian randomization (MR) analysis with publicly available genome-wide association study (GWAS) data. Methods We utilized data from Open GWAS to identify genetic instruments associated with 15 micronutrients, including copper, calcium, carotene, folate, iron, magnesium, potassium, selenium, zinc, vitamin A, vitamin B12, vitamin B6, vitamin C, vitamin D, and vitamin E in European populations. For outcome data, we sourced GWAS datasets from the Finnish database comprising 2409 MS cases and 408561 controls, 461 MG cases and 408430 controls, and 445 GBS cases and 405136 controls. Single nucleotide polymorphisms (SNPs) with P-values less than 5 × 10^-6 were selected as instrumental variables (IVs), ensuring minimal linkage disequilibrium. Statistical analysis was performed using inverse-variance weighted (IVW) method complemented by weighted mode, weighted median estimate, MR-Egger regression, and simple mode approaches. Sensitivity analyses included Cochran's Q test for heterogeneity, MR-Egger intercept and MR-PRESSO for horizontal pleiotropy, and the one-by-one exclusion method for assessing the influence of specific SNPs on the MR analysis results. Results Our findings indicate a significant inverse association between blood magnesium levels and MS risk (OR = 0.47; 95% CI: 0.27–0.81; P = 0.007). Similarly, blood iron levels showed a significant inverse association with MG risk (OR = 0.19; 95% CI: 0.04–0.87; P = 0.032). No statistically significant causal relationships were observed between any of the studied micronutrients and GBS. Conclusion In conclusion, our MR analysis suggests that higher blood levels of magnesium may reduce the risk of MS and higher blood levels of iron may reduce the risk of MG. These findings warrant further investigation into the potential therapeutic roles of these micronutrients in autoimmune neurological diseases. Future research should focus on elucidating the underlying biological mechanisms and exploring potential clinical applications based on these associations. micronutrients autoimmune neurological diseases Mendelian randomization analysis genome-wide association study data iron magnesium Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction Multiple Sclerosis (MS), Myasthenia Gravis (MG), and Guillain-Barré Syndrome (GBS) are three debilitating autoimmune neurological disorders that significantly impact patients' quality of life. MS is characterized by the immune system attacking the central nervous system, leading to demyelination and neurodegeneration, affecting approximately 2.8 million people worldwide [1] . MG is a chronic autoimmune neuromuscular disorder that manifests as muscle weakness and fatigue, with an estimated prevalence of 20 per 100,000 population [2] . GBS is an acute condition in which the body's immune system attacks the peripheral nerves, with an incidence rate of 1-2 per 100,000 annually [3] . Despite advances in medical treatments, these diseases remain incurable, and current therapies primarily focus on symptom management and immunosuppression, which can have significant side effects and variable efficacy [4-6] . Therefore, it is imperative to uncover the potential causal risk factors for autoimmune neurological disorders. The etiology of these autoimmune diseases is multifactorial, involving genetic, environmental, and immunological factors. Genome-Wide Association Studies (GWAS) have identified numerous genetic loci associated with MS, MG, and GBS, suggesting a complex genetic architecture underlying these conditions [7-9] . However, the role of environmental factors, particularly micronutrients, in the pathogenesis of these diseases remains less clear. Micronutrients are essential for various biological functions, including immune regulation, and deficiencies or imbalances may contribute to autoimmune disease development [10] . Previous studies have highlighted the potential role of micronutrients in other autoimmune diseases. For instance, vitamin D deficiency has been linked to an increased risk of rheumatoid arthritis and type 1 diabetes [11-13] . Similarly, zinc and selenium deficiencies have been associated with autoimmune thyroid diseases [14, 15] . These findings suggest that micronutrients may also play a role in the pathogenesis of MS, MG, and GBS, but the evidence is limited and often conflicting. To address this gap, we conducted a Mendelian Randomization (MR) analysis using publicly available GWAS data to investigate the causal relationship between 15 micronutrients (including copper, calcium, carotene, folate, iron, magnesium, potassium, selenium, zinc, vitamins A, B12, B6, C, D, and E) and the risk of the three autoimmune neurological diseases(MS, MG, and GBS). MR is a powerful method that uses genetic variants as instrumental variables (IVs) to infer causality, minimizing confounding and reverse causation biases [16] . By leveraging large-scale GWAS data, we aimed to provide robust evidence on the potential causal effects of micronutrients on these autoimmune neurological diseases. 2 Methods 2.1 Study Design This study utilized publicly available data from GWAS databases to identify appropriate instrumental variables (IVs) for conducting Mendelian randomization (MR) analysis, aiming to investigate the causal relationship between micronutrients and three autoimmune neurological disorders (MS, MG, and GBS) (Fig. 1 ). To ensure the accuracy of the results, strict adherence to three assumptions was followed: 1) IVs should be related to the exposure [ 17 ] ; 2) they should not be associated with any confounding factors [ 18 ] ; 3) their effect on the outcome can only be mediated through the exposure [ 19 ] . Ethical approval and informed consent were obtained as provided in the original studies. 2.2 Data Sources 2.2.1 Exposure Data We retrieved genetic instruments associated with copper, calcium, Carotene, Folate, Iron, Magnesium, Potassium, Selenium, Zinc, Vitamin A, Vitamin B12, Vitamin B6, Vitamin C, Vitamin D, and Vitamin E in individuals of European ancestry from the Open GWAS database. For copper GWAS, a total of 2,603 Europeans were included, with 6 significant SNPs identified. For calcium GWAS, 64,979 Europeans were included, with 20 significant SNPs identified. For Carotene GWAS, 64,979 Europeans were included, with 15 significant SNPs identified. For Folate GWAS, 64,979 Europeans were included, with 13 significant SNPs identified. For Iron GWAS, 64,979 Europeans were included, with 12 significant SNPs identified. For Magnesium GWAS, 64,979 Europeans were included, with 17 significant SNPs identified. For Potassium GWAS, 64,979 Europeans were included, with 15 significant SNPs identified. For Selenium GWAS, 2,603 Europeans were included, with 6 significant SNPs identified. For Zinc GWAS, 2,603 Europeans were included, with 8 significant SNPs identified. For Vitamin A GWAS, 460,351 Europeans were included, with 13 significant SNPs identified. For Vitamin B12 GWAS, 64,979 Europeans were included, with 9 significant SNPs identified. For Vitamin B6 GWAS, 64,979 Europeans were included, with 18 significant SNPs identified. For Vitamin C GWAS, 64,979 Europeans were included, with 10 significant SNPs identified. For Vitamin D GWAS, 64,979 Europeans were included, with 14 significant SNPs identified. For Vitamin E GWAS, 64,979 Europeans were included, with 12 significant SNPs identified. More information on these phenotypes is available in the supplementary material (Supplementary table 1 and 2). 2.2.2 Outcome Data The generated outcome data were obtained from publicly accessible databases that provide comprehensive descriptions of the data from the original sources. The Finnish database provided GWAS data for MS, MG, and GBS. The MS group consisted of 2,409 cases and 408,561 controls, making a total of 410,970 individuals. The MG group included 461 cases and 408,430 controls, with a total of 408,891 individuals. The GBS group comprised 445 cases and 405,136 controls, totaling 405,581 individuals. To ensure consistency in the study, all GWAS result data used were from individuals of European descent. Supplementary table 2 provides detailed information on all the GWAS data used in the analysis. 2.3 Instrumental Variable Selection To confirm the causal relationship between the micronutrients and autoimmune neurologic disorders, we utilized quality control modalities to select the relevant IVs.. First, there must be a strong correlation between genetic variations and micronutrients. Therefore, we selected SNPs associated with each micronutrient, with a threshold of P-value less than 5 × 10 − 6 , as our genetic instruments. Second, during the process of instrument variable clustering, a linkage disequilibrium (R 2 ) threshold of 0.001 and a genetic distance of 10 MB were set to eliminate linkage disequilibrium between SNPs. Additionally, we computed the F-statistic to assess the strength of the instrumental variables. The formula for calculating the F-statistic is: F = R 2 (N − 2) / (1 - R 2 ), where R 2 represents the proportion of variance in micronutrient explained by SNP, N is the sample size [ 20 ] . R 2 was estimated using the formula: R 2 = [2 × Beta 2 × (1 - EAF) × EAF] / [2 × Beta 2 × (1 - EAF) × EAF + 2 × SE 2 × N × (1 - EAF) × EAF], where Beta represents the genetic effect of the SNP on the micronutrient, EAF represents the effect allele frequency, SE represents the standard error, and N represents the sample size [ 21 ] . An F-statistic value exceeding 10 indicates a strong instrumental variable. Lastly, to account for confounding factors, we assessed the horizontal pleiotropy for each instrumental variable using the PhenoScanner database ( http://www.phenoscanner.medschl.cam.ac.uk/ ). 2.4 Statistical Analysis 2.4.1 MR Analysis In this study, we employed two-sample MR analysis to assess the causal relationship between micronutrients and three autoimmune neurological disorders (MS, MG, and GBS). For traits with multiple instrumental variables, the commonly used methods include inverse variance-weighted (IVW) test, weighted mode, weighted median, MR-Egger regression, and simple mode. Previous research has demonstrated that IVW method yields more meaningful results compared to other methods, thus IVW method was primarily used in this study, while other methods provide supplementary explanations [ 22 ] . The effect size was expressed as odds ratio (OR) with a 95% confidence interval (CI). IVW involves weighted linear regression of the genetic instruments on the exposure outcome, with the intercept set to zero. In the absence of horizontal pleiotropy, IVW method tends to provide unbiased results. 2.4.2 Sensitivity Analysis To assess the robustness of MR results, a series of sensitivity analyses were conducted. Cochran's Q test was used to evaluate heterogeneity, with a significance level of P < 0.05 indicating heterogeneity [ 23 ] . MR-Egger intercept and MR-PRESSO methods were employed to detect the presence of horizontal pleiotropy in causal inferences, with a p-value less than 0.05 indicating the presence of horizontal pleiotropy [ 24 ] . MR-PRESSO consists of three components: (I) detection of horizontal pleiotropy, (II) correction of horizontal pleiotropy by removing outliers, and (III) analysis of causal estimates before and after outlier correction to determine the presence of differences. One-by-one exclusion method was used to assess the influence of specific SNPs on the MR analysis results [ 23 ] . The MR analysis was performed using the R software (version 4.4.0) with the "TwoSampleMR" package (version 0.6.1) and the "MRPRESSO" package (version 1.0). 3 Results In order to investigate the causal relationship between SNPs associated with copper, calcium, Carotene, Folate, Iron, Magnesium, Potassium, Selenium, Zinc, Vitamin A, Vitamin B12, Vitamin B6, Vitamin C, Vitamin D, and Vitamin E, and three autoimmune neurological disorders (MS, MG, GBS), we conducted a two-sample MR study using genetic data from participants of European ancestry. The IVW method was primarily used as the main analysis method, with Weighted median, MR-Egger, Simple mode, and Weighted mode as supplementary analysis methods. The SNPs associated with the micronutrients that were identified for each microbial taxa are provided(Supplementary Table S1 ). Details for each SNP include the effect allele, the other allele, the beta, the p-value,the R 2 and the F-value. 3.1 The impact of serum micronutrients on MS When analyzing the relationship between micronutrients and MS, the only statistically significant factor is the level of blood magnesium. The results from MR analyses, including IVW, MR-egger, Weighted median, Simple mode, and Weighted mode methods, consistently demonstrate the same direction of effect (Fig. 2 and Table 1). The IVW method shows a significant association between blood magnesium level and multiple sclerosis (OR = 0.47, 95%CI: 0.27–0.81, P = 0.007). Specific information on the correlation of genetic variants with blood magnesium and MS is shown in Figure S1 . Insufficient evidence is found regarding the causal impact of other factors on MS (Fig. 3 and Supplementary table 3 ). 3.2 The impact of serum micronutrients on MG When analyzing the relationship between micronutrients and MG, the only statistically significant factor is the level of blood iron. MR analyses, including IVW, MR-egger, Weighted median, Simple mode, and Weighted mode methods, consistently demonstrate the same direction of effect ( Fig. 4 and Table 1). The IVW method shows a significant association between blood iron level and myasthenia gravis (OR = 0.19, 95%CI: 0.04–0.87, P = 0.032). Specific information on the correlation of genetic variants with blood magnesium and MS is shown in Figure S2 . Insufficient evidence is found regarding the causal impact of other factors on MG (Fig. 5 and Supplementary table 4 ). 3.3 The impact of serum micronutrients on GBS When analyzing the relationship between micronutrients and GBS, we did not find any causal association between micronutrients and GBS (Fig. 6 and Supplementary table 5 ). 3.4 Sensitivity analysis Cochran's Q test was used to detect heterogeneity, and MR-PRESSO test and MR-Egger intercept test were performed to assess horizontal pleiotropy. The results indicate that the included SNPs exhibit no significant heterogeneity or horizontal pleiotropy(table 2). Furthermore, the leave-one-out analysis did not reveal any individual SNP that strongly influenced the MR analysis results, further demonstrating the robustness of our MR analysis (Figure S3 and S4). We also employed five methods to measure the results of the MR analysis and generated scatter plots for the three diseases(Figure S5 and S6). Additionally, the funnel plots exhibit a symmetrical shape, providing further confirmation of the reliability of our results(Figure S7 and S8). 4 Discussion Multiple sclerosis (MS), myasthenia gravis (MG), and Guillain-Barré syndrome (GBS) are debilitating autoimmune neurological disorders that significantly impact patients' health and quality of life. These diseases not only impose a substantial burden on patients but also on healthcare systems due to their chronic nature and the need for long-term management. Understanding the etiological factors contributing to these diseases is crucial for developing effective prevention and treatment strategies. To our knowledge, this study represents the first utilization of published GWAS meta-analysis data for conducting two-sample Mendelian randomization analysis, aiming to explore the causal relationship between micronutrients and autoimmune neurological disorders. By focusing on the phenotypic manifestations of MS, MG, and GBS, this research aims to uncover potential nutritional interventions that could mitigate disease risk or progression. The use of MR analysis, which employs genetic variants as instrumental variables, helps to address confounding factors and reverse causation, providing more robust evidence for causal inference [ 16 ] . This approach holds significant promise for identifying modifiable risk factors and informing clinical guidelines, ultimately improving disease management and patient outcomes. Magnesium and Multiple Sclerosis Magnesium plays a crucial role in numerous biological processes, including DNA synthesis, energy production, and regulation of muscle and nerve function. Our Mendelian Randomization (MR) analysis revealed a significant inverse association between blood magnesium levels and the risk of multiple sclerosis (MS) (OR = 0.47, 95%CI: 0.27–0.81, P = 0.007). This finding is consistent with previous studies suggesting that magnesium deficiency may exacerbate inflammatory responses and oxidative stress, both of which are implicated in the pathogenesis of MS [ 25 ] . Furthermore, magnesium has been shown to modulate immune function by influencing the activity of T cells and cytokine production, which are critical in the autoimmune response observed in MS [ 26 ] . These results underscore the potential therapeutic value of magnesium supplementation in MS management and warrant further investigation into the underlying mechanisms through which magnesium exerts its protective effects against MS. Iron and Myasthenia Gravis Iron is an essential micronutrient involved in oxygen transport, DNA synthesis, and electron transport in mitochondria. Our MR analysis identified a significant inverse association between blood iron levels and the risk of myasthenia gravis (MG) (OR = 0.19, 95%CI: 0.04–0.87, P = 0.032). This finding aligns with existing literature that suggests iron deficiency can impair immune function and increase susceptibility to infections, which may trigger or exacerbate autoimmune conditions like MG [ 27 ] . Iron is also critical for the proper functioning of acetylcholine receptors, which are targeted by autoantibodies in MG [28] . The observed association highlights the importance of maintaining adequate iron levels for immune homeostasis and neuromuscular function. Further research is needed to elucidate the precise mechanisms by which iron influences the pathophysiology of MG and to explore the potential benefits of iron supplementation in patients with MG. Micronutrients and Guillain-Barré Syndrome Our study did not find any statistically significant causal relationships between the 15 micronutrients analyzed and the risk of Guillain-Barré Syndrome (GBS). This lack of association suggests that the etiology of GBS may be more complex and not directly influenced by the micronutrient levels assessed in this study. GBS is known to be triggered by infections, particularly Campylobacter jejuni, which suggests that environmental and infectious factors play a more prominent role in its pathogenesis [29] . While micronutrients are essential for overall immune function, their specific impact on GBS risk may be minimal or overshadowed by other more dominant factors. Future studies should consider a broader range of environmental and genetic factors to fully understand the multifactorial nature of GBS and identify potential preventive strategies. Overall, our study underscores the importance of micronutrient homeostasis in the context of autoimmune neurological diseases. The significant associations between magnesium and MS, as well as iron and MG, suggest potential therapeutic avenues for these conditions. Future research should focus on clinical trials to validate these findings and explore the underlying mechanisms through which these micronutrients influence disease risk and progression. Understanding these pathways could lead to novel interventions aimed at modulating micronutrient levels to prevent or treat autoimmune neurological disorders. Despite the robustness of our Mendelian Randomization (MR) analysis, several limitations should be acknowledged. First, our study relies solely on publicly available GWAS data, and we did not incorporate wet lab experiments to validate our findings. Second, the sample sizes for MG and GBS are relatively small compared to MS, which may affect the statistical power and generalizability of our results. Third, our analysis lacks clinical validation, which is crucial for translating genetic findings into clinical practice. Lastly, the use of multiple datasets might introduce batch effects, potentially influencing the consistency of our results. 5 Conclusion In conclusion, our study provides valuable insights into the causal relationships between various micronutrients and three autoimmune neurological diseases: MS, MG, and GBS. By leveraging publicly available GWAS data and employing rigorous MR methods, we have identified potential causal links that warrant further investigation. Future research should focus on clinical validation and wet lab experiments to confirm these findings and explore their potential therapeutic implications. Our results lay the groundwork for future studies aimed at understanding the role of micronutrients in autoimmune neurological diseases, ultimately contributing to the development of targeted nutritional interventions. Declarations Acknowledgments The authors appreciate the valuable suggestions from other members of their teams. Authors’ contributions CJ conceived and designed the study. YZ performed data analysis and wrote the draft. ZW, YB, and LZ contributed to data analysis. CJ and PZ critically revised the manuscript. All authors read and approved the final manuscript. Funding This study was supported by grants from the Zhejiang Medical Science and Technology Project (Grant No.2023RC287 and No.2023KY1240), and Shaoxing Basic Public Welfare Program (Grant No.2022A14018). Consent for publication Not applicable. Competing interests The authors declare no competing interests. Data availability The study utilized exclusively data that is accessible to the public, with the sources of this data detailed in the Materials and Methods section. Ethical approval Not applicable. Only publicly available summary statistics were used. References Walton C, King R, Rechtman L, et al. Rising prevalence of multiple sclerosis worldwide: Insights from the Atlas of MS, third edition[J]. Mult Scler. 2020;26(14):1816–21. 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Supplementary Files FigureS1.tif FigureS2.tif FigureS3.tif FigureS4.tif FigureS5.tif FigureS6.tif FigureS7.tif FigureS8.tif Supplementarytable1.xlsx Supplementarytable2.xlsx Supplementarytable3.xlsx Supplementarytable4.xlsx Supplementarytable5.xlsx SupplementaryLegend.docx table1.xlsx Table1 MR results of causal links between micronutrients and MS, MG. table2.xlsx Table2 Sensitivity analyses results of micronutrients and MS, MG,GBS. 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-4590504","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":318899194,"identity":"fa6876a8-0b2d-459d-ac21-f9126da8f288","order_by":0,"name":"Yang Zhou","email":"","orcid":"","institution":"Shaoxing People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Zhou","suffix":""},{"id":318899196,"identity":"327b1aa6-c058-4b01-8623-512f94c4fd7f","order_by":1,"name":"Zhenyu Wei","email":"","orcid":"","institution":"Shanghai Yangpu District Shidong Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhenyu","middleName":"","lastName":"Wei","suffix":""},{"id":318899197,"identity":"1d6dc717-7b45-4d05-a65c-912b804e749b","order_by":2,"name":"Liya Zhan","email":"","orcid":"","institution":"Shaoxing People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Liya","middleName":"","lastName":"Zhan","suffix":""},{"id":318899200,"identity":"93dbac8c-8056-4f59-92cd-5d42782c9152","order_by":3,"name":"Yiping Bao","email":"","orcid":"","institution":"Shaoxing People's Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yiping","middleName":"","lastName":"Bao","suffix":""},{"id":318899201,"identity":"0d354523-4f67-4057-87aa-20de702d1e72","order_by":4,"name":"Ping Zhong","email":"","orcid":"","institution":"Shanghai Yangpu District Shidong Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Zhong","suffix":""},{"id":318899203,"identity":"f47d3e21-70ef-4fd2-bc00-e6e42ff017fa","order_by":5,"name":"Chunhua Jin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAx0lEQVRIie3RsQrCMBCA4ZSDTmldrxSKj3AQCA59E5d2yaYUXDsIDh19FcEXEA+cBF/AoZM4xt3BdnLsjYL5hkz3D5dTKgh+EIJSkaeymAFwL04AG2eyLnYkS8ZH+3NNNz1HUZJ1CT+RTpVhrUi15XIyySF1i4bua8vJqVcXt9pOJQVoS0iPjeW0omjLwkQT18edJhQlOWjTj8kBpEm20xaQnEEePrmS7IK3q3n593DKPXPv23I6GcTfc1SC8RF44WAQBMG/+gDp9DoUNHQYbAAAAABJRU5ErkJggg==","orcid":"","institution":"Shaoxing People's Hospital","correspondingAuthor":true,"prefix":"","firstName":"Chunhua","middleName":"","lastName":"Jin","suffix":""}],"badges":[],"createdAt":"2024-06-16 16:53:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4590504/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4590504/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60599266,"identity":"6312e400-73a0-43a8-a371-4bd2743d9bfd","added_by":"auto","created_at":"2024-07-18 15:56:53","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":636031,"visible":true,"origin":"","legend":"\u003cp\u003eThe flowchart of the present study. MR, Mendelian randomization; MS, Multiple Sclerosis; GBS, Guillain-Barré Syndrome; MG, Myasthenia Gravis.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4590504/v1/cfa0a70d166cdd177ac22995.png"},{"id":60599280,"identity":"725a89bd-8624-4b2f-87a2-142d697ad836","added_by":"auto","created_at":"2024-07-18 15:56:55","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":454907,"visible":true,"origin":"","legend":"\u003cp\u003eThe impact of serum Magnesium on MS. MS, Multiple Sclerosis.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4590504/v1/20f9f252ded840bc5872636b.png"},{"id":60601248,"identity":"bad406a1-b6a0-436a-a270-f5150cc65918","added_by":"auto","created_at":"2024-07-18 16:04:54","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":596289,"visible":true,"origin":"","legend":"\u003cp\u003eThe impact of serum micronutrients on MS. MS, Multiple Sclerosis; IVW, Inverse variance weighted.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4590504/v1/73d0769738fffbb704dbe765.png"},{"id":60599274,"identity":"f0925c1e-83ff-4fe5-b8d5-20f41b5054f0","added_by":"auto","created_at":"2024-07-18 15:56:54","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":433067,"visible":true,"origin":"","legend":"\u003cp\u003eThe impact of serum Iron on MG. MG, Myasthenia Gravis.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4590504/v1/c761248d539e70cab098dd8d.png"},{"id":60599275,"identity":"75ef7302-9b26-46c0-974e-aaca9aa88f2b","added_by":"auto","created_at":"2024-07-18 15:56:54","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":587050,"visible":true,"origin":"","legend":"\u003cp\u003eThe impact of serum micronutrients on MG. MG, Myasthenia Gravis; IVW, Inverse variance weighted.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4590504/v1/21814d1df37917b5d32d8bbe.png"},{"id":60599282,"identity":"45bb6741-22c0-4e36-b935-0bd19e2d0cd0","added_by":"auto","created_at":"2024-07-18 15:56:56","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":585111,"visible":true,"origin":"","legend":"\u003cp\u003eThe impact of serum micronutrients on GBS. GBS, Guillain-Barré Syndrome; IVW, Inverse variance weighted.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4590504/v1/46de8f591bdc9b022712ec91.png"},{"id":76554244,"identity":"31de8376-c0dc-4b7c-8b5f-36a8407692c4","added_by":"auto","created_at":"2025-02-18 10:24:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3457430,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4590504/v1/64b58df8-5932-4a21-b075-c04f5291220a.pdf"},{"id":60599268,"identity":"90a7c78b-af9b-4325-bbb1-61fd2c3d568d","added_by":"auto","created_at":"2024-07-18 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15:56:54","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":30441,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4590504/v1/bde87d4557550aa93016c9b4.xlsx"},{"id":60599285,"identity":"fb8fde32-632a-48ad-a224-7acde91b9aee","added_by":"auto","created_at":"2024-07-18 15:56:56","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":11002,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4590504/v1/8b66b8cbc3457f7363182784.xlsx"},{"id":60599278,"identity":"4e51e2ac-5715-4102-9c5e-3b7d2585a569","added_by":"auto","created_at":"2024-07-18 15:56:55","extension":"xlsx","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":18371,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4590504/v1/62378cf27d450473323fcaaa.xlsx"},{"id":60599299,"identity":"f108cf94-a7a4-48a6-b41d-c478df6c457b","added_by":"auto","created_at":"2024-07-18 15:56:57","extension":"xlsx","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":18402,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4590504/v1/5d5cdcf26c8dcbbe0dccff4b.xlsx"},{"id":60599265,"identity":"011553d2-0b79-48af-8b3e-2672794d00a7","added_by":"auto","created_at":"2024-07-18 15:56:53","extension":"xlsx","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":18389,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4590504/v1/e995c2d9cf04aaa81e3241f1.xlsx"},{"id":60599284,"identity":"45a79412-6d7c-4595-89ba-9f482b8d489f","added_by":"auto","created_at":"2024-07-18 15:56:56","extension":"docx","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":14316,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryLegend.docx","url":"https://assets-eu.researchsquare.com/files/rs-4590504/v1/b152def774eb1711db6f6c7c.docx"},{"id":60599286,"identity":"caf04435-3edb-45a8-b8f1-9e177d58bc82","added_by":"auto","created_at":"2024-07-18 15:56:56","extension":"xlsx","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":11542,"visible":true,"origin":"","legend":"\u003cp\u003eTable1 MR results of causal links between micronutrients and MS, MG.\u003c/p\u003e","description":"","filename":"table1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4590504/v1/0852a3027ff7901c7ce2116b.xlsx"},{"id":60599281,"identity":"20c553c9-fd96-4b76-940f-06669326d820","added_by":"auto","created_at":"2024-07-18 15:56:56","extension":"xlsx","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":11237,"visible":true,"origin":"","legend":"\u003cp\u003eTable2 Sensitivity analyses results of micronutrients and MS, MG,GBS.\u003c/p\u003e","description":"","filename":"table2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4590504/v1/983693804a4156dfb636d56a.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causal relationship of serum micronutrient with autoimmune neurological diseases: a Mendelian randomization study","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eMultiple Sclerosis (MS), Myasthenia Gravis (MG), and Guillain-Barr\u0026eacute; Syndrome (GBS) are three debilitating autoimmune neurological disorders that significantly impact patients\u0026apos; quality of life. MS is characterized by the immune system attacking the central nervous system, leading to demyelination and neurodegeneration, affecting approximately 2.8 million people worldwide\u003csup\u003e[1]\u003c/sup\u003e.\u0026nbsp;MG is a chronic autoimmune neuromuscular disorder that manifests as muscle weakness and fatigue, with an estimated prevalence of 20 per 100,000 population\u003csup\u003e[2]\u003c/sup\u003e. GBS is an acute condition in which the body\u0026apos;s immune system attacks the peripheral nerves, with an incidence rate of 1-2 per 100,000 annually\u003csup\u003e[3]\u003c/sup\u003e. Despite advances in medical treatments, these diseases remain incurable, and current therapies primarily focus on symptom management and immunosuppression, which can have significant side effects and variable efficacy\u003csup\u003e[4-6]\u003c/sup\u003e. Therefore, it is imperative to uncover the potential causal risk factors for autoimmune neurological disorders.\u003c/p\u003e\n\u003cp\u003eThe etiology of these autoimmune diseases is multifactorial, involving genetic, environmental, and immunological factors. Genome-Wide Association Studies (GWAS) have identified numerous genetic loci associated with MS, MG, and GBS, suggesting a complex genetic architecture underlying these conditions\u003csup\u003e[7-9]\u003c/sup\u003e. However, the role of environmental factors, particularly micronutrients, in the pathogenesis of these diseases remains less clear. Micronutrients are essential for various biological functions, including immune regulation, and deficiencies or imbalances may contribute to autoimmune disease development\u003csup\u003e[10]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003ePrevious studies have highlighted the potential role of micronutrients in other autoimmune diseases. For instance, vitamin D deficiency has been linked to an increased risk of rheumatoid arthritis and type 1 diabetes\u003csup\u003e[11-13]\u003c/sup\u003e. Similarly, zinc and selenium deficiencies have been associated with autoimmune thyroid diseases\u003csup\u003e[14, 15]\u003c/sup\u003e. These findings suggest that micronutrients may also play a role in the pathogenesis of MS, MG, and GBS, but the evidence is limited and often conflicting.\u003c/p\u003e\n\u003cp\u003eTo address this gap, we conducted a Mendelian Randomization (MR) analysis using publicly available GWAS data to investigate the causal relationship between 15 micronutrients (including copper, calcium, carotene, folate, iron, magnesium, potassium, selenium, zinc, vitamins A, B12, B6, C, D, and E) and the risk of the three autoimmune neurological diseases(MS, MG, and GBS). MR is a powerful method that uses genetic variants as instrumental variables (IVs) to infer causality, minimizing confounding and reverse causation biases\u003csup\u003e[16]\u003c/sup\u003e. By leveraging large-scale GWAS data, we aimed to provide robust evidence on the potential causal effects of micronutrients on these autoimmune neurological diseases.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1 Study Design\u003c/h2\u003e\n \u003cp\u003eThis study utilized publicly available data from GWAS databases to identify appropriate instrumental variables (IVs) for conducting Mendelian randomization (MR) analysis, aiming to investigate the causal relationship between micronutrients and three autoimmune neurological disorders (MS, MG, and GBS) (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). To ensure the accuracy of the results, strict adherence to three assumptions was followed: 1) IVs should be related to the exposure\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e; 2) they should not be associated with any confounding factors\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e; 3) their effect on the outcome can only be mediated through the exposure\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Ethical approval and informed consent were obtained as provided in the original studies.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2 Data Sources\u003c/h2\u003e\n \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.1 Exposure Data\u003c/h2\u003e\n \u003cp\u003eWe retrieved genetic instruments associated with copper, calcium, Carotene, Folate, Iron, Magnesium, Potassium, Selenium, Zinc, Vitamin A, Vitamin B12, Vitamin B6, Vitamin C, Vitamin D, and Vitamin E in individuals of European ancestry from the Open GWAS database. For copper GWAS, a total of 2,603 Europeans were included, with 6 significant SNPs identified. For calcium GWAS, 64,979 Europeans were included, with 20 significant SNPs identified. For Carotene GWAS, 64,979 Europeans were included, with 15 significant SNPs identified. For Folate GWAS, 64,979 Europeans were included, with 13 significant SNPs identified. For Iron GWAS, 64,979 Europeans were included, with 12 significant SNPs identified. For Magnesium GWAS, 64,979 Europeans were included, with 17 significant SNPs identified. For Potassium GWAS, 64,979 Europeans were included, with 15 significant SNPs identified. For Selenium GWAS, 2,603 Europeans were included, with 6 significant SNPs identified. For Zinc GWAS, 2,603 Europeans were included, with 8 significant SNPs identified. For Vitamin A GWAS, 460,351 Europeans were included, with 13 significant SNPs identified. For Vitamin B12 GWAS, 64,979 Europeans were included, with 9 significant SNPs identified. For Vitamin B6 GWAS, 64,979 Europeans were included, with 18 significant SNPs identified. For Vitamin C GWAS, 64,979 Europeans were included, with 10 significant SNPs identified. For Vitamin D GWAS, 64,979 Europeans were included, with 14 significant SNPs identified. For Vitamin E GWAS, 64,979 Europeans were included, with 12 significant SNPs identified. More information on these phenotypes is available in the supplementary material (Supplementary table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e and 2).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n \u003ch2\u003e2.2.2 Outcome Data\u003c/h2\u003e\n \u003cp\u003eThe generated outcome data were obtained from publicly accessible databases that provide comprehensive descriptions of the data from the original sources. The Finnish database provided GWAS data for MS, MG, and GBS. The MS group consisted of 2,409 cases and 408,561 controls, making a total of 410,970 individuals. The MG group included 461 cases and 408,430 controls, with a total of 408,891 individuals. The GBS group comprised 445 cases and 405,136 controls, totaling 405,581 individuals. To ensure consistency in the study, all GWAS result data used were from individuals of European descent. Supplementary table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e provides detailed information on all the GWAS data used in the analysis.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3 Instrumental Variable Selection\u003c/h2\u003e\n \u003cp\u003eTo confirm the causal relationship between the micronutrients and autoimmune neurologic disorders, we utilized quality control modalities to select the relevant IVs.. First, there must be a strong correlation between genetic variations and micronutrients. Therefore, we selected SNPs associated with each micronutrient, with a threshold of P-value less than 5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e, as our genetic instruments. Second, during the process of instrument variable clustering, a linkage disequilibrium (R\u003csup\u003e2\u003c/sup\u003e) threshold of 0.001 and a genetic distance of 10 MB were set to eliminate linkage disequilibrium between SNPs. Additionally, we computed the F-statistic to assess the strength of the instrumental variables. The formula for calculating the F-statistic is: F\u0026thinsp;=\u0026thinsp;R\u003csup\u003e2\u003c/sup\u003e (N \u0026minus;\u0026thinsp;2) / (1 - R\u003csup\u003e2\u003c/sup\u003e), where R\u003csup\u003e2\u003c/sup\u003e represents the proportion of variance in micronutrient explained by SNP, N is the sample size\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. R\u003csup\u003e2\u003c/sup\u003e was estimated using the formula: R\u003csup\u003e2\u003c/sup\u003e = [2 \u0026times; Beta\u003csup\u003e2\u003c/sup\u003e \u0026times; (1 - EAF) \u0026times; EAF] / [2 \u0026times; Beta\u003csup\u003e2\u003c/sup\u003e \u0026times; (1 - EAF) \u0026times; EAF\u0026thinsp;+\u0026thinsp;2 \u0026times; SE\u003csup\u003e2\u003c/sup\u003e \u0026times; N \u0026times; (1 - EAF) \u0026times; EAF], where Beta represents the genetic effect of the SNP on the micronutrient, EAF represents the effect allele frequency, SE represents the standard error, and N represents the sample size\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. An F-statistic value exceeding 10 indicates a strong instrumental variable. Lastly, to account for confounding factors, we assessed the horizontal pleiotropy for each instrumental variable using the PhenoScanner database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.phenoscanner.medschl.cam.ac.uk/\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4 Statistical Analysis\u003c/h2\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003e2.4.1 MR Analysis\u003c/h2\u003e\n \u003cp\u003eIn this study, we employed two-sample MR analysis to assess the causal relationship between micronutrients and three autoimmune neurological disorders (MS, MG, and GBS). For traits with multiple instrumental variables, the commonly used methods include inverse variance-weighted (IVW) test, weighted mode, weighted median, MR-Egger regression, and simple mode. Previous research has demonstrated that IVW method yields more meaningful results compared to other methods, thus IVW method was primarily used in this study, while other methods provide supplementary explanations\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. The effect size was expressed as odds ratio (OR) with a 95% confidence interval (CI). IVW involves weighted linear regression of the genetic instruments on the exposure outcome, with the intercept set to zero. In the absence of horizontal pleiotropy, IVW method tends to provide unbiased results.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\n \u003ch2\u003e2.4.2 Sensitivity Analysis\u003c/h2\u003e\n \u003cp\u003eTo assess the robustness of MR results, a series of sensitivity analyses were conducted. Cochran\u0026apos;s Q test was used to evaluate heterogeneity, with a significance level of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating heterogeneity\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. MR-Egger intercept and MR-PRESSO methods were employed to detect the presence of horizontal pleiotropy in causal inferences, with a p-value less than 0.05 indicating the presence of horizontal pleiotropy\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. MR-PRESSO consists of three components: (I) detection of horizontal pleiotropy, (II) correction of horizontal pleiotropy by removing outliers, and (III) analysis of causal estimates before and after outlier correction to determine the presence of differences. One-by-one exclusion method was used to assess the influence of specific SNPs on the MR analysis results\u003csup\u003e[\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n \u003cp\u003eThe MR analysis was performed using the R software (version 4.4.0) with the \u0026quot;TwoSampleMR\u0026quot; package (version 0.6.1) and the \u0026quot;MRPRESSO\u0026quot; package (version 1.0).\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"3 Results","content":"\u003cp\u003eIn order to investigate the causal relationship between SNPs associated with copper, calcium, Carotene, Folate, Iron, Magnesium, Potassium, Selenium, Zinc, Vitamin A, Vitamin B12, Vitamin B6, Vitamin C, Vitamin D, and Vitamin E, and three autoimmune neurological disorders (MS, MG, GBS), we conducted a two-sample MR study using genetic data from participants of European ancestry. The IVW method was primarily used as the main analysis method, with Weighted median, MR-Egger, Simple mode, and Weighted mode as supplementary analysis methods. The SNPs associated with the micronutrients that were identified for each microbial taxa are provided(Supplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Details for each SNP include the effect allele, the other allele, the beta, the p-value,the R\u003csup\u003e2\u003c/sup\u003e and the F-value.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 The impact of serum micronutrients on MS\u003c/h2\u003e \u003cp\u003eWhen analyzing the relationship between micronutrients and MS, the only statistically significant factor is the level of blood magnesium. The results from MR analyses, including IVW, MR-egger, Weighted median, Simple mode, and Weighted mode methods, consistently demonstrate the same direction of effect (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;1). The IVW method shows a significant association between blood magnesium level and multiple sclerosis (OR\u0026thinsp;=\u0026thinsp;0.47, 95%CI: 0.27\u0026ndash;0.81, P\u0026thinsp;=\u0026thinsp;0.007). Specific information on the correlation of genetic variants with blood magnesium and MS is shown in Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Insufficient evidence is found regarding the causal impact of other factors on MS (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Supplementary table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 The impact of serum micronutrients on MG\u003c/h2\u003e \u003cp\u003eWhen analyzing the relationship between micronutrients and MG, the only statistically significant factor is the level of blood iron. MR analyses, including IVW, MR-egger, Weighted median, Simple mode, and Weighted mode methods, consistently demonstrate the same direction of effect ( Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Table\u0026nbsp;1). The IVW method shows a significant association between blood iron level and myasthenia gravis (OR\u0026thinsp;=\u0026thinsp;0.19, 95%CI: 0.04\u0026ndash;0.87, P\u0026thinsp;=\u0026thinsp;0.032). Specific information on the correlation of genetic variants with blood magnesium and MS is shown in Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e. Insufficient evidence is found regarding the causal impact of other factors on MG (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Supplementary table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 The impact of serum micronutrients on GBS\u003c/h2\u003e \u003cp\u003eWhen analyzing the relationship between micronutrients and GBS, we did not find any causal association between micronutrients and GBS (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Supplementary table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Sensitivity analysis\u003c/h2\u003e \u003cp\u003eCochran's Q test was used to detect heterogeneity, and MR-PRESSO test and MR-Egger intercept test were performed to assess horizontal pleiotropy. The results indicate that the included SNPs exhibit no significant heterogeneity or horizontal pleiotropy(table 2). Furthermore, the leave-one-out analysis did not reveal any individual SNP that strongly influenced the MR analysis results, further demonstrating the robustness of our MR analysis (Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e and S4). We also employed five methods to measure the results of the MR analysis and generated scatter plots for the three diseases(Figure \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e and S6). Additionally, the funnel plots exhibit a symmetrical shape, providing further confirmation of the reliability of our results(Figure \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e and S8).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eMultiple sclerosis (MS), myasthenia gravis (MG), and Guillain-Barr\u0026eacute; syndrome (GBS) are debilitating autoimmune neurological disorders that significantly impact patients' health and quality of life. These diseases not only impose a substantial burden on patients but also on healthcare systems due to their chronic nature and the need for long-term management. Understanding the etiological factors contributing to these diseases is crucial for developing effective prevention and treatment strategies.\u003c/p\u003e \u003cp\u003eTo our knowledge, this study represents the first utilization of published GWAS meta-analysis data for conducting two-sample Mendelian randomization analysis, aiming to explore the causal relationship between micronutrients and autoimmune neurological disorders. By focusing on the phenotypic manifestations of MS, MG, and GBS, this research aims to uncover potential nutritional interventions that could mitigate disease risk or progression. The use of MR analysis, which employs genetic variants as instrumental variables, helps to address confounding factors and reverse causation, providing more robust evidence for causal inference\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. This approach holds significant promise for identifying modifiable risk factors and informing clinical guidelines, ultimately improving disease management and patient outcomes.\u003c/p\u003e \u003cp\u003eMagnesium and Multiple Sclerosis\u003c/p\u003e \u003cp\u003eMagnesium plays a crucial role in numerous biological processes, including DNA synthesis, energy production, and regulation of muscle and nerve function. Our Mendelian Randomization (MR) analysis revealed a significant inverse association between blood magnesium levels and the risk of multiple sclerosis (MS) (OR\u0026thinsp;=\u0026thinsp;0.47, 95%CI: 0.27\u0026ndash;0.81, P\u0026thinsp;=\u0026thinsp;0.007). This finding is consistent with previous studies suggesting that magnesium deficiency may exacerbate inflammatory responses and oxidative stress, both of which are implicated in the pathogenesis of MS\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. Furthermore, magnesium has been shown to modulate immune function by influencing the activity of T cells and cytokine production, which are critical in the autoimmune response observed in MS\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. These results underscore the potential therapeutic value of magnesium supplementation in MS management and warrant further investigation into the underlying mechanisms through which magnesium exerts its protective effects against MS.\u003c/p\u003e \u003cp\u003eIron and Myasthenia Gravis\u003c/p\u003e \u003cp\u003eIron is an essential micronutrient involved in oxygen transport, DNA synthesis, and electron transport in mitochondria. Our MR analysis identified a significant inverse association between blood iron levels and the risk of myasthenia gravis (MG) (OR\u0026thinsp;=\u0026thinsp;0.19, 95%CI: 0.04\u0026ndash;0.87, P\u0026thinsp;=\u0026thinsp;0.032). This finding aligns with existing literature that suggests iron deficiency can impair immune function and increase susceptibility to infections, which may trigger or exacerbate autoimmune conditions like MG\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Iron is also critical for the proper functioning of acetylcholine receptors, which are targeted by autoantibodies in MG\u003csup\u003e[28]\u003c/sup\u003e. The observed association highlights the importance of maintaining adequate iron levels for immune homeostasis and neuromuscular function. Further research is needed to elucidate the precise mechanisms by which iron influences the pathophysiology of MG and to explore the potential benefits of iron supplementation in patients with MG.\u003c/p\u003e \u003cp\u003eMicronutrients and Guillain-Barr\u0026eacute; Syndrome\u003c/p\u003e \u003cp\u003eOur study did not find any statistically significant causal relationships between the 15 micronutrients analyzed and the risk of Guillain-Barr\u0026eacute; Syndrome (GBS). This lack of association suggests that the etiology of GBS may be more complex and not directly influenced by the micronutrient levels assessed in this study. GBS is known to be triggered by infections, particularly Campylobacter jejuni, which suggests that environmental and infectious factors play a more prominent role in its pathogenesis\u003csup\u003e[29]\u003c/sup\u003e. While micronutrients are essential for overall immune function, their specific impact on GBS risk may be minimal or overshadowed by other more dominant factors. Future studies should consider a broader range of environmental and genetic factors to fully understand the multifactorial nature of GBS and identify potential preventive strategies.\u003c/p\u003e \u003cp\u003eOverall, our study underscores the importance of micronutrient homeostasis in the context of autoimmune neurological diseases. The significant associations between magnesium and MS, as well as iron and MG, suggest potential therapeutic avenues for these conditions. Future research should focus on clinical trials to validate these findings and explore the underlying mechanisms through which these micronutrients influence disease risk and progression. Understanding these pathways could lead to novel interventions aimed at modulating micronutrient levels to prevent or treat autoimmune neurological disorders.\u003c/p\u003e \u003cp\u003eDespite the robustness of our Mendelian Randomization (MR) analysis, several limitations should be acknowledged. First, our study relies solely on publicly available GWAS data, and we did not incorporate wet lab experiments to validate our findings. Second, the sample sizes for MG and GBS are relatively small compared to MS, which may affect the statistical power and generalizability of our results. Third, our analysis lacks clinical validation, which is crucial for translating genetic findings into clinical practice. Lastly, the use of multiple datasets might introduce batch effects, potentially influencing the consistency of our results.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn conclusion, our study provides valuable insights into the causal relationships between various micronutrients and three autoimmune neurological diseases: MS, MG, and GBS. By leveraging publicly available GWAS data and employing rigorous MR methods, we have identified potential causal links that warrant further investigation. Future research should focus on clinical validation and wet lab experiments to confirm these findings and explore their potential therapeutic implications. Our results lay the groundwork for future studies aimed at understanding the role of micronutrients in autoimmune neurological diseases, ultimately contributing to the development of targeted nutritional interventions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors appreciate the valuable suggestions from other members of their teams.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCJ conceived and designed the study. YZ performed data analysis and wrote the draft. ZW, YB, and LZ contributed to data analysis. CJ and PZ critically revised the manuscript. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by grants from the Zhejiang Medical Science and Technology Project (Grant No.2023RC287 and No.2023KY1240), and Shaoxing Basic Public Welfare Program (Grant No.2022A14018).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e The study utilized exclusively data that is accessible to the public, with the sources of this data detailed in the Materials and Methods section.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e Not applicable. Only publicly available summary statistics were used.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWalton C, King R, Rechtman L, et al. Rising prevalence of multiple sclerosis worldwide: Insights from the Atlas of MS, third edition[J]. Mult Scler. 2020;26(14):1816\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarr AS, Cardwell CR, McCarron PO, et al. A systematic review of population based epidemiological studies in Myasthenia Gravis[J]. BMC Neurol. 2010;10:46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSejvar JJ, Baughman AL, Wise M, et al. Population incidence of Guillain-Barr\u0026eacute; syndrome: a systematic review and meta-analysis[J]. Neuroepidemiology. 2011;36(2):123\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHauser SL, Chan JR, Oksenberg JR. Multiple sclerosis: Prospects and promise[J]. Ann Neurol. 2013;74(3):317\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGilhus NE, Tzartos S, Evoli A, et al. Myasthenia gravis[J]. Nat Rev Dis Primers. 2019;5(1):30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMagy L, Frachet S. Therapeutic issues in Guillain-Barr\u0026eacute; syndrome[J]. Expert Rev Neurother. 2023;23(6):549\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInternational Multiple Sclerosis Genetics Consortium. Multiple sclerosis genomic map implicates peripheral immune cells and microglia in susceptibility[J]. Science, 2019,365(6460).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRenton AE, Pliner HA, Provenzano C, et al. A genome-wide association study of myasthenia gravis[J]. JAMA Neurol. 2015;72(4):396\u0026ndash;404.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlum S, Ji Y, Pennisi D, et al. Genome-wide association study in Guillain-Barr\u0026eacute; syndrome[J]. J Neuroimmunol. 2018;323:109\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLahoda BH, Klempir J, Zavora J et al. The Role of Micronutrients in Neurological Disorders[J]. Nutrients, 2023,15(19).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHolick MF. Vitamin D deficiency[J]. N Engl J Med. 2007;357(3):266\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTv P, Kumar B, Chidambaram Y, et al. Correlation of Rheumatoid arthritis disease severity with serum vitamin D levels[J]. Clin Nutr ESPEN. 2023;57:697\u0026ndash;702.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJanner M, Ballinari P, Mullis PE, et al. High prevalence of vitamin D deficiency in children and adolescents with type 1 diabetes[J]. Swiss Med Wkly. 2010;140:w13091.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVentura M, Melo M, Carrilho F. Selenium and Thyroid Disease: From Pathophysiology to Treatment[J]. Int J Endocrinol. 2017;2017:1297658.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKravchenko V, Zakharchenko T. Thyroid hormones and minerals in immunocorrection of disorders in autoimmune thyroid diseases[J]. Front Endocrinol (Lausanne). 2023;14:1225494.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavey SG, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies[J]. Hum Mol Genet. 2014;23(R1):R89\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVass K. Current immune therapies of autoimmune disease of the nervous system with special emphasis to multiple sclerosis[J]. Curr Pharm Des. 2012;18(29):4513\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRubin DB, Batra A, Vaitkevicius H, et al. Autoimmune Neurologic Disorders[J]. Am J Med. 2018;131(3):226\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiaz-Marugan L, Kantsj\u0026ouml; JB, Rutsch A, et al. Microbiota, diet, and the gut-brain axis in multiple sclerosis and stroke[J]. Eur J Immunol. 2023;53(11):e2250229.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgess S, Thompson SG. Avoiding bias from weak instruments in Mendelian randomization studies[J]. Int J Epidemiol. 2011;40(3):755\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgess S, Dudbridge F, Thompson SG. Combining information on multiple instrumental variables in Mendelian randomization: comparison of allele score and summarized data methods[J]. Stat Med. 2016;35(11):1880\u0026ndash;906.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data[J]. Genet Epidemiol. 2013;37(7):658\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarbagallo M, Dominguez LJ. Magnesium and aging[J]. Curr Pharm Des. 2010;16(7):832\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheltova AA, Kharitonova MV, Iezhitsa IN, et al. Magnesium deficiency and oxidative stress: an update[J]. Biomed (Taipei). 2016;6(4):20.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeiss G, Goodnough LT. Anemia of chronic disease[J]. N Engl J Med. 2005;352(10):1011\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang P. The relationship between serum iron levels and AChR-Ab and IL-6 in patients with myasthenia gravis[J]. Eur Rev Med Pharmacol Sci. 2023;27(1):98\u0026ndash;102.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWillison HJ, Jacobs BC, van Doorn PA. Guillain-Barr\u0026eacute; syndrome[J] Lancet. 2016;388(10045):717\u0026ndash;27.\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":"micronutrients, autoimmune neurological diseases, Mendelian randomization analysis, genome-wide association study data, iron, magnesium","lastPublishedDoi":"10.21203/rs.3.rs-4590504/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4590504/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe relationship between micronutrients and autoimmune neurological diseases such as multiple sclerosis (MS), myasthenia gravis (MG), and Guillain-Barr\u0026eacute; syndrome (GBS) remains poorly understood. This study aims to elucidate the causal relationships between specific micronutrients and these diseases using Mendelian randomization (MR) analysis with publicly available genome-wide association study (GWAS) data.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe utilized data from Open GWAS to identify genetic instruments associated with 15 micronutrients, including copper, calcium, carotene, folate, iron, magnesium, potassium, selenium, zinc, vitamin A, vitamin B12, vitamin B6, vitamin C, vitamin D, and vitamin E in European populations. For outcome data, we sourced GWAS datasets from the Finnish database comprising 2409 MS cases and 408561 controls, 461 MG cases and 408430 controls, and 445 GBS cases and 405136 controls. Single nucleotide polymorphisms (SNPs) with P-values less than 5 \u0026times; 10^-6 were selected as instrumental variables (IVs), ensuring minimal linkage disequilibrium. Statistical analysis was performed using inverse-variance weighted (IVW) method complemented by weighted mode, weighted median estimate, MR-Egger regression, and simple mode approaches. Sensitivity analyses included Cochran's Q test for heterogeneity, MR-Egger intercept and MR-PRESSO for horizontal pleiotropy, and the one-by-one exclusion method for assessing the influence of specific SNPs on the MR analysis results.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur findings indicate a significant inverse association between blood magnesium levels and MS risk (OR\u0026thinsp;=\u0026thinsp;0.47; 95% CI: 0.27\u0026ndash;0.81; P\u0026thinsp;=\u0026thinsp;0.007). Similarly, blood iron levels showed a significant inverse association with MG risk (OR\u0026thinsp;=\u0026thinsp;0.19; 95% CI: 0.04\u0026ndash;0.87; P\u0026thinsp;=\u0026thinsp;0.032). No statistically significant causal relationships were observed between any of the studied micronutrients and GBS.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn conclusion, our MR analysis suggests that higher blood levels of magnesium may reduce the risk of MS and higher blood levels of iron may reduce the risk of MG. These findings warrant further investigation into the potential therapeutic roles of these micronutrients in autoimmune neurological diseases. Future research should focus on elucidating the underlying biological mechanisms and exploring potential clinical applications based on these associations.\u003c/p\u003e","manuscriptTitle":"Causal relationship of serum micronutrient with autoimmune neurological diseases: a Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-18 15:56:43","doi":"10.21203/rs.3.rs-4590504/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":"35b24d61-ccf2-4eba-9d24-273cc53d691f","owner":[],"postedDate":"July 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-18T10:23:26+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-18 15:56:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4590504","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4590504","identity":"rs-4590504","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}