Educational Attainment and Supraventricular Tachycardia: A Mendelian Randomization Study

Educational Attainment and Supraventricular Tachycardia: 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 Educational Attainment and Supraventricular Tachycardia: A Mendelian Randomization Study Ruochen Xu, Zhuen Zhong, Qiushi Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4084844/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 Objective : The objective of this study was to explore whether there was a causal relationship between educational attainment (EA) and supraventricular tachycardia (SVT) using Mendelian randomization (MR) analysis. Method : A two‐sample Mendelian randomization (MR) analysis using the inverse‐ variance weighted (IVW), weighted median, MR‐Egger regression, simple model, weighted mode and MR pleiotropy residual sum and outlier (MR-PRESSO) methods were performed. A mediation analysis using multivariate MR methods was also conducted. We used the publicly available summary statistics data sets of genome‐ wide association studies (GWAS) meta‐analyses for EA in individuals of European descent (n = 766 345; SSGAC consortium) as the exposure and a GWAS for Diagnoses - main ICD10: I47.1 SVT from the individuals included in the UK Biobank (total n = 463 010; case = 1306, control = 461704) as the outcome. Results : The IVW analysis results supported an inverse causative association between EA and SVT (β=−0.0018, SE=0.00066, p=0.0066), which was consistent with the results of weighted median, as well as MR-PRESSO. Common cardiovascular risk factors such as body mass index (BMI), type 2 diabetes mellitus (T2DM), systolic blood pressure (SBP) and smoking behaviour did not mediate the association between EA and SVT. Conclusion : The results of MR analysis suggest a potential negative causal association between EA and the occurrence of SVT. education supraventricular tachycardia mendelian randomization analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Supraventricular tachycardia (SVT) is the most common category of arrhythmia encountered in clinical practice and comprises a heterogeneous group of arrhythmias with different electrophysiological characteristics. Common subtypes of SVT include atrioventricular nodal re-entrant tachycardia, atrioventricular re-entrant tachycardia, and atrial tachycardia. With the continuous improvement and introduction of new medical technologies, catheter ablation has become the preferred treatment for SVT, with its good effectiveness and safety [ 1 ]. SVT affects all age groups and is a common cause of hospital admissions, which can lead to severe discomfort and distress[ 2 ]. According to previous reports, the occurrence of SVT is related to age and sex, and its subtypes are distributed differently across different age groups and sexes[ 3 , 4 ]. Nonetheless, the identification of other potentially modifiable SVT risk factors is critical and has the potential to facilitate early intervention in patients with SVT and improve their prognosis. Educational attainment (EA) is one factor that may influence the onset of SVT. Several studies have shown that improving education levels can effectively reduce the risk of cardiovascular disease[ 5 , 6 ]. Cardiovascular diseases share many common causes and mechanisms, so we hypothesized that education levels were also associated with SVT. However, it is not known whether there is a causal relationship between education level and SVT. Mendelian randomization (MR) uses genetic variation as an instrumental variable to test whether there is a causal relationship between a modifiable exposure and a particular outcome[ 7 ]. MR studies are based on instrumental variable assumptions, which require that genetic variation be independent of other confounding factors to establish a strong correlation with the exposure and can only influence the outcome by influencing the exposure[ 8 ]. Compared to observational studies, MR studies can overcome the effects of reverse causality and confounding bias[ 9 ]. Previous MR studies have reported the protective effects of education on cardiovascular diseases such as coronary heart disease, myocardial infarction, stroke and heart failure[ 6 , 10 ]. The objective of this study was to explore whether there was a causal relationship between EA and SVT using MR analysis. We performed a two-sample MR analysis and mediation analysis through multivariate MR analysis methods. And the results suggest a potential negative causal association between EA and the occurrence of SVT. Materials and methods Instrumental Variable Selection We extracted genetic variants of EA from a large genome-wide association study (GWAS) conducted by the Social Science Genetic Association Consortium (SSGAC)[ 11 ]. This GWAS was a meta-analysis of 71 core-level studies involving 1,131,881 individuals of European ancestry. In the current study, the degree of EA was measured as the number of years of education that an individual had completed. Then, single-nucleotide polymorphisms (SNPs) were selected as instrumental variables (IVs) based on their genome-wide significance ( P < 5 × 10 –8 ). We ascertained the independence of SNPs after pruning for linkage disequilibrium (LD) (r2 < 0.001; distance 0.8) was used as a substitute. Ultimately, 317 SNPs were included in the analysis as instrumental variables. Summary statistics from the Medical Research Council Integrative Epidemiology Unit (MRC-IEU) consortium [ 12 ] were used as the data indicating SVT (GWAS ID “ukb-b-11748”), which included 463010 European individuals. The remaining genetic variants involved are detailed in Table 1 . Table 1 Overview of genome-wide association studies used Genetic variants Trait GWAS ID Population Consortium Sample size Numbers of SNPs Education attainment Years of schooling IEU-A-1239 European SSGAC 766,345 10,101,242 Supraventricular tachycardia Diagnoses - main ICD10: I47.1 Supraventricular tachycardia ukb-b-11748 European MRC-IEU 463,010 9,851,867 BMI Body mass index (BMI) ukb-b-19953 European MRC-IEU 461.140 9,851,867 T2DM Type 2 diabetes, strict (exclude DM1) finn-b-E4_DM2_STRICT European Finngen 212,351 16,380,434 SBP Systolic blood pressure ieu-b-38 European International Consortium of Blood Pressure 757,601 7,088,083 Smoking behaviour Smoking behaviour (cigarettes smoked per day) ebi-a-GCST009968 European NA 4,772 8,648,224 Statistical method for MR analysis Because MR analysis requires that the genetic variation be related to exposure, we assessed whether SNPs were independently associated with EA and whether each SNP was associated with the occurrence of SVT. Then, we combined these findings to assess a clear causal relationship between EA and the occurrence of SVT using MR analysis. We performed our analysis using two-sample MR[ 13 ], an approach for estimating the causal effect of one factor on another, in this case EA on SVT, using 184 SNPs as IVs to assess the causal relationship between EA and the development of SVT. We used inverse variance weighting (IVW) as the primary analysis method[ 14 ]. This approach leverages a random effects meta-analysis approach in which the Wald ratio estimates for each SNP are combined into one causal estimate for each exposure[ 15 ]; this represents the most efficient combination of variable-specific rate estimates and accounts for the heterogeneity of causal estimates obtained from individual variables[ 14 , 16 , 17 ]. We performed multivariate MR analysis to investigate potential mediators of the relationship between EA and SVT[ 18 ]. Multivariate MR allows the use of genetic variations associated with primary and secondary exposures (mediating) as an analytical tool and can be used to decompose the total causal effect of exposure on outcomes into indirect effects through mediation and direct effects of exposure on outcomes that are not mediated. All multivariate MR analyses need to satisfy the following: (1) The genetic variation must be closely related to the exposure in the univariate MR analysis and must be closely related to at least one of the exposures in the multivariate MR analysis; (2) Genetic variants must not be associated with confounders of the associations between instruments of each exposure and SVT; and (3) The effect of genetic variation on SVT must be mediated by each exposure. As in the univariate MR analysis, we also used IVW as the primary multivariate MR analysis method[ 14 ]. We calculated the F statistics of the 184 selected SNPs to detect strong IVs at a threshold of F > 10, as is typically recommended in MR analysis[ 19 ]. The F statistic was calculated as F = R 2 (n − 2) /(1 − R 2 ), where n represents the sample size and R 2 represents the proportion of variation explained by the SNPs in the exposure[ 20 ]. To verify the robustness of the IVW results in univariate MR analysis, we employed several methods, namely, the weighted median, MR Egger, simple model, weighted mode and MR pleiotropy residual sum and outlier (MR-PRESSO) methods. We also used the MR-Egger intercept to test the magnitude of horizontal pleiotropy. Heterogeneity and sensitivity analysis In addition to conducting analysis with several robust MR methods, we tested the heterogeneity between SNPs by using Cochran's Q statistic and funnel plots[ 21 ]. The funnel plot of the IV precisions vs the IV estimates should be a symmetric funnel, indicating that more precise estimates are less variable[ 22 ]. We also performed a “leave-one-out” sensitivity analysis to examine whether the causal association was driven by a single SNP. Tests were considered statistically significant at P < 0.05. All MR analyses were conducted using R packages “TwoSampleMR”, “MRPRESSO” and “MVMR” in R software (version 4.3.2; the R Foundation for Statistical Computing, Vienna, Austria). Results Validity of Instrumental Variables We included 184 SNPs that explained 1.3% (R2) of the EA variation as IVs for EA–SVT causal estimations (Table S1 ). The strength of the genetic instruments denoted by the F -statistic was thresholded at ≥ 10 for all the EA variants (Table S2 ). Mendelian Randomization The IVW analysis results supported an inverse causative association between EA and SVT (β=−0.0018, SE = 0.00066, p = 0.0066). The results obtained from the MR-PRESSO approach also supported the inverse causative association between EA and SVT (β=−0.0018, SE = 0.00065, p = 0.0064) while the weighted median analysis results suggested a negative causative association between EA and SVT (β=−0.950, SE = 0.355, p = 0.008). MR-Egger regression analysis further revealed that directional pleiotropy does not seem to bias the results (intercept = 3.5e − 5 ; p = 0.301). However, the MR-Egger analysis showed no causative relationship between EA and SVT (β=−0.0045, SE = 0.00265, p = 0.094). The findings of the simple model (β=−0.0042, SE = 0.00303, p = 0.1645) and weighted model approaches (β=−0.0038, SE = 0.00249, p = 0.1296) also showed no causative relationship between EA and SVT. All details were shown in Table 2 and Fig. 1 . Table 2 Overview of genome-wide association studies used MR method No. of SNPs Beta SE P OR (95%CI) Inverse variance weighted 184 -0.0018 0.0007 0.0066 0.9982(0.9969–0.9995) MR Egger 184 -0.0045 0.0027 0.0948 0.9955(0.9904–1.0007) Weighted median 184 -0.0024 0.001 0.0169 0.9976(0.9956–0.9996) Simple mode 184 -0.0042 0.003 0.1645 0.9958(0.9899–1.0017) Weighted mode 184 -0.0038 0.0025 0.1296 0.9962(0.9914–1.0011) MR-PRESSO 184 -0.0018 0.0007 0.0064 0.9982(0.9969–0.9995) Overall, the findings of the IVW, weighted median and MR-PRESSO methods suggest a negative causal effect of EA on SVT risk, whereas the MR‒Egger, simple model and weighted model methods suggest a null causal effect. Nevertheless, the results of single SNP analysis (Fig. 2 ) and the results of all methods tend show a negative correlation between EA and SVT (Fig. 3 ). As a result, we conclude that the results of the MR analysis support a reverse causal relationship between EA and SVT. The association between low EA and SVT risk may be mediated by increased cardiometabolic risk factors and unhealthy behaviours[ 23 ]. Previous MR analyses have shown that genetic susceptibility to EA is inversely associated with factors such as body mass index (BMI), type 2 diabetes mellitus (T2DM), systolic blood pressure (SBP) and smoking behaviour[ 24 ]. Using multivariate MR methods, we conducted a mediation analysis. Adjusting for the genetic correlation T2DM and smoking behaviour via multivariate MR analyses did not change the association between EA and SVT. Nevertheless, the effect sizes were slightly attenuated after correcting for the genetic liability. After adjusting for genetic association of BMI and SBP by multivariate MR analysis, the relationship between EA and AF was no longer significant. However, the effect of mediation was not statistically significant. Heterogeneity and sensitivity test Cochran's Q test showed that there was no evidence of heterogeneity between the estimates of IVs based on individual variables. There is no evidence of asymmetry in funnel plots and MR-Egger regression analysis in this study (Fig. 4 ). The results of the “leave one out” analysis showed that no single SNP drives IVW point estimation (Fig. 5 ). Discussion The attainment of many years of education has been considered a protective factor against cardiovascular events. However, it is not known whether EA has a causative relationship with the occurrence of SVT. The objective of this study was to explore whether there was a causal relationship between education and SVT using MR analysis. We used six distinct estimation methods (IVW, weighted median, simple model, weighted model, MR-Egger regression test and MR-PRESSO) for the MR analyses. The results of the IVW, weighted median analysis and MR-PRESSO methods suggested a negative causative association between EA and SVT. Considering the advantage of the weighted median method compared to the MR-Egger method, all methods above showed a negative trend. Common cardiovascular risk factors did not mediate the association between EA and SVT. Therefore, our study provides supportive evidence of an inverse causative association of years of education with SVT. The association between education and cardiovascular disease has been reported by several previous studies[ 23 , 25 , 26 ]. Studies have shown that in low-income countries, low education is associated with a higher risk of cardiovascular disease and death than in higher-income countries[ 25 ]. A previous MR analysis also demonstrated a negative causal relationship between education and cardiovascular disease[ 10 ]. Similar to our results, they identified a negative causal relationship between education and atrial fibrillation[ 27 ]. Education influences a variety of conditions from childhood via exposure to community-level factors (such as living or working in a healthier environment) and better access to health and social resources[ 25 ]. Our findings support that the completion of more years of education is related to a reduced risk of SVT. However, the potential biological pathway between EA and SVT remains unknown. MR minimizes the possibility of bias inherent to observational studies[ 28 ]. However, genetic variants may be associated with more than one genetic phenotype, resulting in pleiotropy[ 29 ]. Since pleiotropy may bias the causal estimation of MR, we also used sensitivity analysis to verify the validity of the MR results. In the sensitivity analysis, the weighted median estimator and MR-Egger regression gave opposite results. However, MR-Egger regression has less precision and power, while the weighted median estimator provides valid estimates even if 50% of the SNPs are not valid instruments[ 30 ]; thus, the estimation results of the weighted median method may be more accurate than those of the MR-Egger analysis. Meanwhile, the results of the weighted median method were consistent with those of the IVW and MR-PRESSO results. MR-PRESSO methods detect outlier SNPS of potential level multipotency and assess whether exclusion of outlier SNPS affects the causal estimate[ 31 ]. Moreover, in all sensitivity analyses, the results tended to show a negative relationship between EA and SVT. There are several limitations of this study. First, the population involved in this study only consisted of individuals with European ancestry. As ethnicity may affect causality, more studies involving different populations are required to verify our conclusions. Second, EA in this study was determined by the number of years of education, which may not be completely representative of an individual’s EA. Importantly, without social and political reforms, it will be difficult to achieve the goal of direct intervention in education. Based on the results of this study, it is reasonable to conclude that increased screening for SVT among people with low EA would reduce its public health burden. In conclusion, MR analysis suggests a potential negative causal association between EA and the occurrence of SVT, ultimately indicating that education may serve as a protective factor in the pathogenesis of SVT. Further research may build upon these findings to identify potential mechanisms underlying the impact of EA on the risk of SVT. The results of this study have significant implications for policymakers, as they identify potential strategies for reducing health education inequalities. Declarations Ethics approval and consent to participate All participants signed an informed consent document. Our study used only publicly available data. Ethical endorsement can be found in the original publication. Consent for publication All authors have consented for publication. Availability of data and materials The data supporting the findings of this study are openly available in IEU OpenGWAS (https://gwas.mrcieu.ac.uk/), with reference numbers ieu-a-1239, ukb-b-11748, ukb-b-19953, and ieu-b-38; Finngen (https://www.finngen.fi/fi), with reference number E4_DM2_STRICT; and PubMed (doi: 10.1038/s41380-020-0702-z). Competing interests All authors report that they have no conflicts of interest to disclose. Funding This work was not supported by grants. Authors' contributions Ruochen.Xu wrote the manuscript text, prepared all figures and did the main statistical work. Zhuen Zhong helped do the statistical work. Qiushi Chen provided thoughts support. All authors reviewed the manuscript. Acknowledgements We thank the IEU OpenGWAS, UK Biobank, SSGAC and FinnGen consortiums for providing aggregated statistics for the analysis. References Brugada J, et al. 2019 ESC Guidelines for the management of patients with supraventricular tachycardiaThe Task Force for the management of patients with supraventricular tachycardia of the European Society of Cardiology (ESC). Eur Heart J. 2020;41(5):655–720. Kotadia ID, Williams SE, O'Neill M. Supraventricular tachycardia: An overview of diagnosis and management. Clin Med (Lond). 2020;20(1):43–7. Porter MJ, et al. Influence of age and gender on the mechanism of supraventricular tachycardia. Heart Rhythm. 2004;1(4):393–6. Orejarena LA, et al. Paroxysmal supraventricular tachycardia in the general population. J Am Coll Cardiol. 1998;31(1):150–7. Zeng L, et al. Genetically modulated educational attainment and coronary disease risk. Eur Heart J. 2019;40(29):2413–20. Liao LZ, et al. 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Verbanck M, et al. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50(5):693–8. Additional Declarations No competing interests reported. Supplementary Files TableS1.xlsx TableS2.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4084844","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":280744429,"identity":"aae712c9-0c1a-4f53-bf79-7414e8100fdb","order_by":0,"name":"Ruochen Xu","email":"","orcid":"","institution":"The First Affiliated Hospital with Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ruochen","middleName":"","lastName":"Xu","suffix":""},{"id":280744430,"identity":"30fd1b7e-83cc-4a2c-87f2-19f450a0bf76","order_by":1,"name":"Zhuen Zhong","email":"","orcid":"","institution":"The First Affiliated Hospital with Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhuen","middleName":"","lastName":"Zhong","suffix":""},{"id":280744431,"identity":"7fffe1ab-a0c2-4ae2-ab4a-29f735070cf6","order_by":2,"name":"Qiushi Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYDACCTBpw8PP3tj48AMJWtJkJHsONxtLkKDlsI3BjfQ2AR5idPDPbj724M2fwzySMx+2AfXbyek2ELLkzrF0w7lt6Tz80oltDwoYko3NDhDQYiCRYybN22DNIzk7sd1AguFA4jbCWvK/SfP8YeYxuHmwTYKHOC05bNI8bM48BjcYidQicSPNTHJuWxqPZE8iMJANiPAL/4zkZxJv/tjY87Mff/jwQ4WdHEEtYICIDgNilKNqGQWjYBSMglGABQAAxCI+bxUEjlMAAAAASUVORK5CYII=","orcid":"","institution":"The First Affiliated Hospital with Nanjing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Qiushi","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-03-12 14:32:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4084844/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4084844/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53193333,"identity":"8a2f1550-ccc0-4803-ae33-c52f88386ebc","added_by":"auto","created_at":"2024-03-21 18:00:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":336616,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation between genetically determined EA and SVT.\u003c/strong\u003e \u0026nbsp;MR, mendelian randomization; IVW, inverse variance–weighted; PRESSO, Pleiotropy Residual Sum and Outlier; MVMR, multivariable MR; BMI, body mass index; T2DM, type 2 diabetes mellitus (T2DM); SBP systolic blood pressure.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4084844/v1/5888eb3b2c75b6f3661298c5.png"},{"id":53193332,"identity":"77e55c73-4030-42bb-b6f9-b50aacc9ca86","added_by":"auto","created_at":"2024-03-21 18:00:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":93168,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of the causal effects of EA associated single nucleotide polymorphisms on SVT.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4084844/v1/375e6d219df478e2dabe612c.png"},{"id":53193335,"identity":"fe62b850-e026-406c-ac10-c162c4b16430","added_by":"auto","created_at":"2024-03-21 18:00:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":35220,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plots of genetic associations with EA against the genetic associations with SVT. The slopes of each line represent the causal association for each method.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4084844/v1/9a9c9b340315f9a923ef6e20.png"},{"id":53193334,"identity":"73bbab4e-d122-45f8-8c8d-62f0ae061ac2","added_by":"auto","created_at":"2024-03-21 18:00:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":14614,"visible":true,"origin":"","legend":"\u003cp\u003eFunnel plot assessing heterogeneity. Pink line represents the inverse-variance weighted estimate, and dark blue line represents the Mendelian randomisation-Egger estimate.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4084844/v1/17c7c66f9d9f16573189d35f.png"},{"id":53193339,"identity":"a0fbfa00-f970-4edf-a798-b2f7dcc45540","added_by":"auto","created_at":"2024-03-21 18:00:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":87520,"visible":true,"origin":"","legend":"\u003cp\u003eThe results of the “leave one out” analysis of genetic associations with EA against the genetic associations with SVT.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4084844/v1/6fc6ff24da6cad99c71e52de.png"},{"id":59401107,"identity":"2ffa86fb-a34d-43b1-98d0-e3fa2160a465","added_by":"auto","created_at":"2024-07-01 10:20:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":977211,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4084844/v1/f55172fd-faf9-4b86-9d51-fe525b2eb044.pdf"},{"id":53193341,"identity":"cfd1d364-e1f9-45ee-bb51-04635816cc7f","added_by":"auto","created_at":"2024-03-21 18:00:25","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":23295,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4084844/v1/ac0ce9b7fd3cc646543ec612.xlsx"},{"id":53193340,"identity":"9792f3cd-37f2-4124-85fc-7c0392eee622","added_by":"auto","created_at":"2024-03-21 18:00:25","extension":"xlsx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":27053,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4084844/v1/bcf2f97b00bae2b4b67b9eb1.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Educational Attainment and Supraventricular Tachycardia: A Mendelian Randomization Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSupraventricular tachycardia (SVT) is the most common category of arrhythmia encountered in clinical practice and comprises a heterogeneous group of arrhythmias with different electrophysiological characteristics. Common subtypes of SVT include atrioventricular nodal re-entrant tachycardia, atrioventricular re-entrant tachycardia, and atrial tachycardia. With the continuous improvement and introduction of new medical technologies, catheter ablation has become the preferred treatment for SVT, with its good effectiveness and safety [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. SVT affects all age groups and is a common cause of hospital admissions, which can lead to severe discomfort and distress[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. According to previous reports, the occurrence of SVT is related to age and sex, and its subtypes are distributed differently across different age groups and sexes[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Nonetheless, the identification of other potentially modifiable SVT risk factors is critical and has the potential to facilitate early intervention in patients with SVT and improve their prognosis.\u003c/p\u003e \u003cp\u003eEducational attainment (EA) is one factor that may influence the onset of SVT. Several studies have shown that improving education levels can effectively reduce the risk of cardiovascular disease[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Cardiovascular diseases share many common causes and mechanisms, so we hypothesized that education levels were also associated with SVT. However, it is not known whether there is a causal relationship between education level and SVT.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) uses genetic variation as an instrumental variable to test whether there is a causal relationship between a modifiable exposure and a particular outcome[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. MR studies are based on instrumental variable assumptions, which require that genetic variation be independent of other confounding factors to establish a strong correlation with the exposure and can only influence the outcome by influencing the exposure[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Compared to observational studies, MR studies can overcome the effects of reverse causality and confounding bias[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Previous MR studies have reported the protective effects of education on cardiovascular diseases such as coronary heart disease, myocardial infarction, stroke and heart failure[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe objective of this study was to explore whether there was a causal relationship between EA and SVT using MR analysis. We performed a two-sample MR analysis and mediation analysis through multivariate MR analysis methods. And the results suggest a potential negative causal association between EA and the occurrence of SVT.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eInstrumental Variable Selection\u003c/h2\u003e \u003cp\u003eWe extracted genetic variants of EA from a large genome-wide association study (GWAS) conducted by the Social Science Genetic Association Consortium (SSGAC)[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This GWAS was a meta-analysis of 71 core-level studies involving 1,131,881 individuals of European ancestry. In the current study, the degree of EA was measured as the number of years of education that an individual had completed. Then, single-nucleotide polymorphisms (SNPs) were selected as instrumental variables (IVs) based on their genome-wide significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026ndash;8\u003c/sup\u003e). We ascertained the independence of SNPs after pruning for linkage disequilibrium (LD) (r2\u0026thinsp;\u0026lt;\u0026thinsp;0.001; distance\u0026thinsp;\u0026lt;\u0026thinsp;1000 kb). If a SNP was not included in the outcome dataset, a proxy SNP (R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.8) was used as a substitute. Ultimately, 317 SNPs were included in the analysis as instrumental variables. Summary statistics from the Medical Research Council Integrative Epidemiology Unit (MRC-IEU) consortium [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] were used as the data indicating SVT (GWAS ID \u0026ldquo;ukb-b-11748\u0026rdquo;), which included 463010 European individuals. The remaining genetic variants involved are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverview of genome-wide association studies used\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenetic variants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrait\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGWAS ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eConsortium\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNumbers of SNPs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation attainment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYears of schooling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIEU-A-1239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSSGAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e766,345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10,101,242\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupraventricular tachycardia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiagnoses - main ICD10: I47.1 Supraventricular tachycardia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eukb-b-11748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMRC-IEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e463,010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9,851,867\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBody mass index (BMI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eukb-b-19953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMRC-IEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e461.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9,851,867\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2DM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eType 2 diabetes, strict (exclude DM1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003efinn-b-E4_DM2_STRICT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFinngen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e212,351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16,380,434\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSystolic blood pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eieu-b-38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eInternational Consortium of Blood Pressure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e757,601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7,088,083\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking behaviour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmoking behaviour (cigarettes smoked per day)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eebi-a-GCST009968\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4,772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8,648,224\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStatistical method for MR analysis\u003c/h3\u003e\n\u003cp\u003eBecause MR analysis requires that the genetic variation be related to exposure, we assessed whether SNPs were independently associated with EA and whether each SNP was associated with the occurrence of SVT. Then, we combined these findings to assess a clear causal relationship between EA and the occurrence of SVT using MR analysis. We performed our analysis using two-sample MR[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], an approach for estimating the causal effect of one factor on another, in this case EA on SVT, using 184 SNPs as IVs to assess the causal relationship between EA and the development of SVT. We used inverse variance weighting (IVW) as the primary analysis method[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This approach leverages a random effects meta-analysis approach in which the Wald ratio estimates\u003c/p\u003e \u003cp\u003efor each SNP are combined into one causal estimate for each exposure[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]; this represents the most efficient combination of variable-specific rate estimates and accounts for the heterogeneity of causal estimates obtained from individual variables[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe performed multivariate MR analysis to investigate potential mediators of the relationship between EA and SVT[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Multivariate MR allows the use of genetic variations associated with primary and secondary exposures (mediating) as an analytical tool and can be used to decompose the total causal effect of exposure on outcomes into indirect effects through mediation and direct effects of exposure on outcomes that are not mediated. All multivariate MR analyses need to satisfy the following: (1) The genetic variation must be closely related to the exposure in the univariate MR analysis and must be closely related to at least one of the exposures in the multivariate MR analysis; (2) Genetic variants must not be associated with confounders of the associations between instruments of each exposure and SVT; and (3) The effect of genetic variation on SVT must be mediated by each exposure. As in the univariate MR analysis, we also used IVW as the primary multivariate MR analysis method[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe calculated the \u003cem\u003eF\u003c/em\u003e statistics of the 184 selected SNPs to detect strong IVs at a threshold of \u003cem\u003eF\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;10, as is typically recommended in MR analysis[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The \u003cem\u003eF\u003c/em\u003e statistic was calculated as F\u0026thinsp;=\u0026thinsp;R\u003csup\u003e2\u003c/sup\u003e(n\u0026thinsp;\u0026minus;\u0026thinsp;2) /(1\u0026thinsp;\u0026minus;\u0026thinsp;R\u003csup\u003e2\u003c/sup\u003e), where n represents the sample size and \u003cem\u003eR\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e represents the proportion of variation explained by the SNPs in the exposure[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo verify the robustness of the IVW results in univariate MR analysis, we employed several methods, namely, the weighted median, MR Egger, simple model, weighted mode and MR pleiotropy residual sum and outlier (MR-PRESSO) methods. We also used the MR-Egger intercept to test the magnitude of horizontal pleiotropy.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eHeterogeneity and sensitivity analysis\u003c/h2\u003e \u003cp\u003eIn addition to conducting analysis with several robust MR methods, we tested the heterogeneity between SNPs by using Cochran's Q statistic and funnel plots[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The funnel plot of the IV precisions vs the IV estimates should be a symmetric funnel, indicating that more precise estimates are less variable[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. We also performed a \u0026ldquo;leave-one-out\u0026rdquo; sensitivity analysis to examine whether the causal association was driven by a single SNP. Tests were considered statistically significant at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All MR analyses were conducted using R packages \u0026ldquo;TwoSampleMR\u0026rdquo;, \u0026ldquo;MRPRESSO\u0026rdquo; and \u0026ldquo;MVMR\u0026rdquo; in R software (version 4.3.2; the R Foundation for Statistical Computing, Vienna, Austria).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eValidity of Instrumental Variables\u003c/h2\u003e \u003cp\u003eWe included 184 SNPs that explained 1.3% (R2) of the EA variation as IVs for EA\u0026ndash;SVT causal estimations (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The strength of the genetic instruments denoted by the \u003cem\u003eF\u003c/em\u003e-statistic was thresholded at \u0026ge;\u0026thinsp;10 for all the EA variants (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMendelian Randomization\u003c/h2\u003e \u003cp\u003eThe IVW analysis results supported an inverse causative association between EA and SVT (β=\u0026minus;0.0018, SE\u0026thinsp;=\u0026thinsp;0.00066, p\u0026thinsp;=\u0026thinsp;0.0066). The results obtained from the MR-PRESSO approach also supported the inverse causative association between EA and SVT (β=\u0026minus;0.0018, SE\u0026thinsp;=\u0026thinsp;0.00065, p\u0026thinsp;=\u0026thinsp;0.0064) while the weighted median analysis results suggested a negative causative association between EA and SVT (β=\u0026minus;0.950, SE\u0026thinsp;=\u0026thinsp;0.355, p\u0026thinsp;=\u0026thinsp;0.008). MR-Egger regression analysis further revealed that directional pleiotropy does not seem to bias the results (intercept\u0026thinsp;=\u0026thinsp;3.5e\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e; p\u0026thinsp;=\u0026thinsp;0.301). However, the MR-Egger analysis showed no causative relationship between EA and SVT (β=\u0026minus;0.0045, SE\u0026thinsp;=\u0026thinsp;0.00265, p\u0026thinsp;=\u0026thinsp;0.094). The findings of the simple model (β=\u0026minus;0.0042, SE\u0026thinsp;=\u0026thinsp;0.00303, p\u0026thinsp;=\u0026thinsp;0.1645) and weighted model approaches (β=\u0026minus;0.0038, SE\u0026thinsp;=\u0026thinsp;0.00249, p\u0026thinsp;=\u0026thinsp;0.1296) also showed no causative relationship between EA and SVT. All details were shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverview of genome-wide association studies used\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMR method\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. of SNPs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR (95%CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInverse variance weighted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9982(0.9969\u0026ndash;0.9995)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMR Egger\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9955(0.9904\u0026ndash;1.0007)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeighted median\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9976(0.9956\u0026ndash;0.9996)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSimple mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9958(0.9899\u0026ndash;1.0017)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeighted mode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.1296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9962(0.9914\u0026ndash;1.0011)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMR-PRESSO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.0018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.9982(0.9969\u0026ndash;0.9995)\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\u003e \u003c/p\u003e \u003cp\u003eOverall, the findings of the IVW, weighted median and MR-PRESSO methods suggest a negative causal effect of EA on SVT risk, whereas the MR‒Egger, simple model and weighted model methods suggest a null causal effect. Nevertheless, the results of single SNP analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and the results of all methods tend show a negative correlation between EA and SVT (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). As a result, we conclude that the results of the MR analysis support a reverse causal relationship between EA and SVT.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe association between low EA and SVT risk may be mediated by increased cardiometabolic risk factors and unhealthy behaviours[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Previous MR analyses have shown that genetic susceptibility to EA is inversely associated with factors such as body mass index (BMI), type 2 diabetes mellitus (T2DM), systolic blood pressure (SBP) and smoking behaviour[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Using multivariate MR methods, we conducted a mediation analysis. Adjusting for the genetic correlation T2DM and smoking behaviour via multivariate MR analyses did not change the association between EA and SVT. Nevertheless, the effect sizes were slightly attenuated after correcting for the genetic liability. After adjusting for genetic association of BMI and SBP by multivariate MR analysis, the relationship between EA and AF was no longer significant. However, the effect of mediation was not statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eHeterogeneity and sensitivity test\u003c/h2\u003e \u003cp\u003eCochran's Q test showed that there was no evidence of heterogeneity between the estimates of IVs based on individual variables. There is no evidence of asymmetry in funnel plots and MR-Egger regression analysis in this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The results of the \u0026ldquo;leave one out\u0026rdquo; analysis showed that no single SNP drives IVW point estimation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe attainment of many years of education has been considered a protective factor against cardiovascular events. However, it is not known whether EA has a causative relationship with the occurrence of SVT. The objective of this study was to explore whether there was a causal relationship between education and SVT using MR analysis. We used six distinct estimation methods (IVW, weighted median, simple model, weighted model, MR-Egger regression test and MR-PRESSO) for the MR analyses. The results of the IVW, weighted median analysis and MR-PRESSO methods suggested a negative causative association between EA and SVT. Considering the advantage of the weighted median method compared to the MR-Egger method, all methods above showed a negative trend. Common cardiovascular risk factors did not mediate the association between EA and SVT. Therefore, our study provides supportive evidence of an inverse causative association of years of education with SVT.\u003c/p\u003e \u003cp\u003eThe association between education and cardiovascular disease has been reported by several previous studies[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Studies have shown that in low-income countries, low education is associated with a higher risk of cardiovascular disease and death than in higher-income countries[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. A previous MR analysis also demonstrated a negative causal relationship between education and cardiovascular disease[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Similar to our results, they identified a negative causal relationship between education and atrial fibrillation[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Education influences a variety of conditions from childhood via exposure to community-level factors (such as living or working in a healthier environment) and better access to health and social resources[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Our findings support that the completion of more years of education is related to a reduced risk of SVT. However, the potential biological pathway between EA and SVT remains unknown.\u003c/p\u003e \u003cp\u003eMR minimizes the possibility of bias inherent to observational studies[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, genetic variants may be associated with more than one genetic phenotype, resulting in pleiotropy[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Since pleiotropy may bias the causal estimation of MR, we also used sensitivity analysis to verify the validity of the MR results. In the sensitivity analysis, the weighted median estimator and MR-Egger regression gave opposite results. However, MR-Egger regression has less precision and power, while the weighted median estimator provides valid estimates even if 50% of the SNPs are not valid instruments[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]; thus, the estimation results of the weighted median method may be more accurate than those of the MR-Egger analysis. Meanwhile, the results of the weighted median method were consistent with those of the IVW and MR-PRESSO results. MR-PRESSO methods detect outlier SNPS of potential level multipotency and assess whether exclusion of outlier SNPS affects the causal estimate[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Moreover, in all sensitivity analyses, the results tended to show a negative relationship between EA and SVT.\u003c/p\u003e \u003cp\u003eThere are several limitations of this study. First, the population involved in this study only consisted of individuals with European ancestry. As ethnicity may affect causality, more studies involving different populations are required to verify our conclusions. Second, EA in this study was determined by the number of years of education, which may not be completely representative of an individual\u0026rsquo;s EA.\u003c/p\u003e \u003cp\u003eImportantly, without social and political reforms, it will be difficult to achieve the goal of direct intervention in education. Based on the results of this study, it is reasonable to conclude that increased screening for SVT among people with low EA would reduce its public health burden.\u003c/p\u003e \u003cp\u003eIn conclusion, MR analysis suggests a potential negative causal association between EA and the occurrence of SVT, ultimately indicating that education may serve as a protective factor in the pathogenesis of SVT. Further research may build upon these findings to identify potential mechanisms underlying the impact of EA on the risk of SVT. The results of this study have significant implications for policymakers, as they identify potential strategies for reducing health education inequalities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eAll participants signed an informed consent document.\u0026nbsp;Our study used only publicly available data. Ethical endorsement can be found in the original publication.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eAll authors have consented for publication.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe data supporting the findings of this study are openly available in IEU OpenGWAS (https://gwas.mrcieu.ac.uk/), with reference numbers ieu-a-1239, ukb-b-11748, ukb-b-19953, and ieu-b-38; Finngen (https://www.finngen.fi/fi), with reference number E4_DM2_STRICT; and PubMed (doi: 10.1038/s41380-020-0702-z).\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eAll authors report that they have no conflicts of interest to disclose.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was not supported by grants.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026apos; contributions\u003c/h2\u003e\n\u003cp\u003eRuochen.Xu wrote the manuscript text, prepared all figures and did the main statistical work. Zhuen Zhong helped do the statistical work. Qiushi Chen provided thoughts support. All authors reviewed the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eWe thank the IEU OpenGWAS, UK Biobank, SSGAC and FinnGen consortiums for providing aggregated statistics for the analysis.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBrugada J, et al. 2019 ESC Guidelines for the management of patients with supraventricular tachycardiaThe Task Force for the management of patients with supraventricular tachycardia of the European Society of Cardiology (ESC). Eur Heart J. 2020;41(5):655\u0026ndash;720.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKotadia ID, Williams SE, O'Neill M. Supraventricular tachycardia: An overview of diagnosis and management. Clin Med (Lond). 2020;20(1):43\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePorter MJ, et al. 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Int J Epidemiol. 2015;44(2):484\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgess S, Labrecque JA. Mendelian randomization with a binary exposure variable: interpretation and presentation of causal estimates. Eur J Epidemiol. 2018;33(10):947\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHemani G et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife, 2018. 7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarter AR, et al. Understanding the consequences of education inequality on cardiovascular disease: mendelian randomisation study. BMJ. 2019;365:l1855.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee JJ, et al. Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. 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Physical activity and risks of breast and colorectal cancer: a Mendelian randomisation analysis. Nat Commun. 2020;11(1):597.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEgger M, Smith GD, Phillips AN. Meta-analysis: principles and procedures. BMJ. 1997;315(7121):1533\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgess S, et al. Sensitivity Analyses for Robust Causal Inference from Mendelian Randomization Analyses with Multiple Genetic Variants. Epidemiology. 2017;28(1):30\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchultz WM, et al. Socioeconomic Status and Cardiovascular Outcomes: Challenges and Interventions. Circulation. 2018;137(20):2166\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavies NM et al. Multivariable two-sample Mendelian randomization estimates of the effects of intelligence and education on health. Elife, 2019. 8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYusuf S, et al. Modifiable risk factors, cardiovascular disease, and mortality in 155 722 individuals from 21 high-income, middle-income, and low-income countries (PURE): a prospective cohort study. Lancet. 2020;395(10226):795\u0026ndash;808.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetrelli A, et al. Education inequalities in cardiovascular and coronary heart disease in Italy and the role of behavioral and biological risk factors. Nutr Metab Cardiovasc Dis. 2022;32(4):918\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Y, Liu C, Liu Q. Education and Atrial Fibrillation: Mendelian Randomization Study. Glob Heart. 2022;17(1):22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBae SC, Lee YH. Causal association between body mass index and risk of rheumatoid arthritis: A Mendelian randomization study. Eur J Clin Invest. 2019;49(4):e13076.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThompson JR, et al. Mendelian randomization incorporating uncertainty about pleiotropy. Stat Med. 2017;36(29):4627\u0026ndash;45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBowden J, et al. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol. 2016;40(4):304\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerbanck M, et al. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50(5):693\u0026ndash;8.\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":"education, supraventricular tachycardia, mendelian randomization analysis","lastPublishedDoi":"10.21203/rs.3.rs-4084844/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4084844/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eObjective\u003c/strong\u003e\u003c/em\u003e: The objective of this study was to explore whether there was a causal relationship between educational attainment (EA) and supraventricular tachycardia (SVT) using Mendelian randomization (MR) analysis.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMethod\u003c/strong\u003e\u003c/em\u003e: A two‐sample Mendelian randomization (MR) analysis using the inverse‐\u003c/p\u003e\n\u003cp\u003evariance weighted (IVW), weighted median, MR‐Egger regression, simple model, weighted mode and MR pleiotropy residual sum and outlier (MR-PRESSO) methods were performed. A mediation analysis using multivariate MR methods was also conducted. We used the publicly available summary statistics data sets of genome‐ wide association studies (GWAS) meta‐analyses for EA in individuals of European descent (n = 766 345; SSGAC consortium) as the exposure and a GWAS for Diagnoses - main ICD10: I47.1 SVT from the individuals included in the UK Biobank (total n = 463 010; case = 1306, control = 461704) as the outcome.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/em\u003e: The IVW analysis results supported an inverse causative association between EA and SVT (β=−0.0018, SE=0.00066, p=0.0066), which was consistent with the results of weighted median, as well as MR-PRESSO. Common cardiovascular risk factors such as body mass index (BMI), type 2 diabetes mellitus (T2DM), systolic blood pressure (SBP) and smoking behaviour did not mediate the association between EA and SVT.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/em\u003e: The results of MR analysis suggest a potential negative causal association between EA and the occurrence of SVT.\u003c/p\u003e","manuscriptTitle":"Educational Attainment and Supraventricular Tachycardia: A Mendelian Randomization Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-21 18:00:20","doi":"10.21203/rs.3.rs-4084844/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":"5857d259-1e4c-4a26-b6b3-5b9fd34f6615","owner":[],"postedDate":"March 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-01T10:12:14+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-21 18:00:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4084844","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4084844","identity":"rs-4084844","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}