Investigating the Relationship Between Gut Microbiota and Ischemic Stroke: Genetic Causality and Metabolite Mediation

Investigating the Relationship Between Gut Microbiota and Ischemic Stroke: Genetic Causality and Metabolite Mediation | 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 Investigating the Relationship Between Gut Microbiota and Ischemic Stroke: Genetic Causality and Metabolite Mediation Liya Ye, Gang Yu, Jing Shen, Hui Cai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4341342/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Background The gut microbiota(GM) plays a significant role in health and disease, with numerous studies confirming its association with various diseases. This study aims to evaluate the genetic causal relationship between GM and ischemic stroke (IS), along with exploring potential blood metabolite-mediated mechanisms. Methods Utilizing two-sample Mendelian Randomization (MR) and large-scale Genome-Wide Association Studies (GWAS) data, we investigate the association between GM and IS. Bayesian Weighted MR (BWMR) is employed for validation, and genetic correlations are assessed using Bivariate Linkage Disequilibrium Score Regression (LDSC) and Genetic Analysis Incorporating Pleiotropy and Annotation (GPA). Results Our analysis using Inverse Variance Weighted (IVW) method indicates that specific microbial groups, such as genus Ruminiclostridium and order Burkholderiales, are significantly associated with IS risk. Mediation analysis suggests that metabolites like Pyruvate, Arachidonate, and HDL-related lipoproteins may mediate this relationship. Multivariate MR analysis confirms the independence of these mediating effects. Furthermore, both LDSC and GPA analyses demonstrate significant genetic correlations between GM and IS. Conclusion Through the integration of various statistical methods and GWAS data, this study provides genetic evidence supporting the causal relationship between GM and IS, uncovering potential biological mediating mechanisms. These findings enhance our understanding of the GM's role in cardiovascular and cerebrovascular diseases, offering insights into preventive and treatment strategies. Gut Microbiota Mendelian Randomization Ischemic Stroke Genetic Causality Metabolite Mediation Analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Ischemic stroke (IS) is a major global health concern, contributing not only to mortality but also being a leading cause of long-term disability and significant socioeconomic burdens[ 1 – 3 ]. Recent research underscores the critical influence of the gut microbiota (GM) on health and cardiovascular conditions, particularly IS[ 4 – 6 ]. While initial evidence suggests a connection between GM dysbiosis and IS, the specific mechanisms and causal relationships remain inconsistent[ 7 , 8 ]. The GM influences stroke risk through various pathways, including host metabolism, immune responses, and vascular function. For instance, GM impact the production of metabolites such as short-chain fatty acids (SCFA) and trimethylamine N-oxide (TMAO), directly linked to stroke incidence[ 9 – 11 ]. However, observational studies have limitations, often overlooking key confounding factors, leading to biased results[ 12 ]. However, observational studies have limitations, often overlooking key confounding factors, leading to biased results[ 12 ]. Additionally, mediation analysis is susceptible to measurement errors, potentially underestimating mediation effects[ 13 , 14 ]. Mendelian Randomization (MR), which uses genetic variants as instrumental variables (IVs) to assess causal relationships between variables, offers an effective way to avoid confounding factors[ 15 ]. Since genetic variants are randomly assigned, they can act as proxies for long-term exposure or mediators and are not influenced by lifestyle factors or chronic diseases. Therefore, MR studies are robust against both measured and unmeasured confounding biases, often demonstrating resilience to non-differential measurement errors[ 16 , 17 ]. This method has been successfully applied in multiple fields to address reverse causation and confounding biases inherent in observational studies. Utilizing data from Genome-Wide Association Studies (GWAS), this study aims to explore the direct and indirect connections between GM and IS through bidirectional MR analysis and mediation analysis, particularly by mediating these relationships through the levels of blood metabolites. 2. Materials and Methods 2.1 Experimental Design and Data Sources In this MR cohort study, we utilized publicly available, ethically approved summary-level data, which did not require Institutional Review Board approval or informed consent. The study adheres to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) MR reporting guidelines. In this study, summary data from GWAS were used to identify genetic proxies for exposure and to investigate their associations with outcomes. Data were extracted between March 1, 2024, and April 30, 2024.(Fig. 1 ) The GWAS data for GM were obtained from the MiBioGen consortium ( https://mibiogen.gcc.rug.nl ), which includes 18,340 participants from 24 cohorts, with 78% of European ancestry. The MiBioGen consortium curated and analyzed participants' whole-genome genotypes and 16S fecal microbiomes. Genetic loci affecting relative abundance (microbiome quantitative trait loci) were identified using only taxa present in more than 10% of the samples, resulting in a total of 211 taxa: 131 genera, 35 families, 20 orders, 16 classes, and 9 phyla. The GWAS data for IS were derived from the study by Malik R et al.[ 18 ], which included 34,217 IS patients and 406,111 controls, all of European ancestry. The GWAS data for blood metabolites were obtained from various studies by So-Youn Shin[ 19 ], Mario Roederer[ 20 ], Johannes Kettunen[ 21 ], and others, comprising a total of 974 blood metabolite GWAS. 2.2 Data Extraction For the selection of MR instrumental variables, due to the significant individual variability and dynamic nature of GM in humans, we chose SNPs associated with GM with P-values less than 1×10^-5. Similarly, SNPs related to blood metabolites were selected under the same threshold (P < 5×10^-5). Then, within the 1000 Genomes reference panel, independent SNPs were clustered at an LD threshold of r^2 < 0.01, and SNPs with an F-statistic < 10 were excluded to avoid weak instrument bias. 2.3 Two-Sample MR Analysis We initially conducted bidirectional MR analysis to explore the causal relationships between GM and IS. Using the Inverse Variance Weighted (IVW) method as the primary approach, odds ratios (ORs) and 95% confidence intervals (CIs) were calculated; a Bonferroni-corrected FDR < 0.05 was considered to show a significant causal relationship, while P 0.05 was considered to nominally suggest a significant causal relationship. 2.4 BWMR Analysis To validate the results of the two-sample MR analysis, we utilized BWMR. Bayesian Weighted Mendelian Randomization (BWMR) is an efficient causal inference statistical method based on summary statistics. It provides estimations of model parameters and statistical inferences[ 22 ]. Due to the polygenic structure of complex traits/diseases and the prevalence of pleiotropy, MR has some limitations. To address these, BWMR emerged as a Bayesian weighted approach to causal inference. In the BWMR model, uncertainties due to polygenicity and weak effects are considered, and outlier detection through Bayesian weighting addresses violations of IV assumptions caused by pleiotropy. A Variational Expectation Maximization (VEM) algorithm was developed for more stable and efficient causal inference using BWMR[ 22 ]. Additionally, an exact closed-form solution was derived to correct often underestimated posterior covariances in variational inference. Primarily, calibrated BWMR was used to validate the two-sample MR findings[ 22 ]. 2.5 Genetic Correlation and Overlap We first used linkage disequilibrium score regression (LDSC) to assess the genome-wide genetic correlation between GM and IS for different trait pairs, using LD scores based on the European ancestry from the 1000 Genomes Project[ 23 ]. In LDSC analyses, we did not restrict the intercept; although sample overlap affects the intercept, it does not affect the slope. Thus, genetic correlations are not compromised by sample overlap, allowing consideration of residual confounding and indicating potential sample overlap between two GWAS studies[ 24 ]. We then used Genetic analysis incorporating Pleiotropy and Annotation (GPA) to explore the overall genetic overlap between traits. GPA integrates multiple GWAS datasets and functional annotations to identify associated signals[ 25 ]. GPA not only identifies many weak signals missed by traditional single-phenotype analysis but also reveals relationships between their genetic structures[ 25 ]. Here, we set the significance threshold to P < 0.05. 2.6 Mediation Analysis: GM-Blood Metabolites-IS We employed two mediation methods: Two-Step Mendelian Randomization (TSMR)[ 26 ]and Multivariable Mendelian Randomization (MVMR)[ 27 ], to dissect the direct and indirect effects of GM and blood metabolites on IS. In TSMR, we used 947 blood metabolite GWAS as mediators to analyze the mediators of the causal relationship between GM and IS. β0 − β1 × β2 serves as the direct effect of exposure on the outcome, where β0 measures the total effect of exposure on the outcome, β1 assesses the causal impact of exposure on the mediator, β2 represents the causal effect of the mediator on the outcome, and β1 × β2 indicates the mediated effect of exposure on the outcome, with mediation analysis based on IVW estimation. The proportion mediated can be calculated as “mediated effect / total effect” ([β1 × β2]/β0). Further, we used MVMR to validate TSMR results, to see if the mediators have an independent significant causal relationship with the outcome after excluding the influence of GM. 2.7 Sensitivity Analysis Additionally, we employed four MR methods with different pleiotropy assumptions (MR-Egger, Weighted Median, Simple Mode, and Weighted Mode) for sensitivity analysis. We used the MR-Egger method to assess horizontal pleiotropy, performing an unconstrained intercept weighted linear regression. The intercept represents the average pleiotropy of genetic variants (average direct effect of the variants on the outcome). If the intercept differs from zero (MR-Egger intercept P-value < 0.05), there is evidence of horizontal pleiotropy. We also used Cochran's Q test to assess heterogeneity (smaller P-values indicate higher heterogeneity, higher potential for directional pleiotropy), and leave-one-out analysis to detect SNP outliers. 2.8 Ethical Approval and Consent to Participate This study is based on publicly available data. Each study within the GWAS received approval from the relevant institutional review boards, and informed consent was obtained from participants, caregivers, legal guardians, or other proxies. 3. Results 3.1 Genetic Causal Relationships Between GM and IS We first assessed the causal impact of GM on IS, primarily using the IVW method. The genus Ruminiclostridium was found to increase the risk of IS (OR 1.090, 95% CI, 1.036–1.468; FDR = 0.043), while the order Burkholderiales was found to decrease the risk (OR 0.929, 95% CI, 0.887–0.973; FDR = 0.043) (Supplementary Table 1)(Fig. 2). In addition, seven other microbial groups showed suggestive significant causal relationships with IS (P 0.05), where class Betaproteobacteria, class Clostridia, family Porphyromonadaceae, genus Coprobacter, genus Lachnospira, and genus Slackia might reduce the risk, and genus Oscillibacter might increase the risk of IS(Supplementary Table 1)(Fig. 3). When assessing the causal impact of IS on GM, none of the above microbial groups showed a significant causal relationship (P > 0.05). Moreover, through sensitivity analysis, these results were considered reliable, showing no pleiotropy. Further, we validated the reliability of the above results using WBMR, which confirmed that the genus Coprobacter, genus Ruminiclostridium, and order Burkholderiales still showed significant causal relationships with IS (P values were 0.011, 0.005, and 0.006)(Supplementary Table 1)(Fig. 2). Lastly, through bivariate LDSC analysis, we found significant genetic correlations between IS and 11 microbial groups (genus Anaerostipes, genus Bacteroides, genus Lachnoclostridium, genus Lachnospiraceae, genus Subdoligranulum, order Bacteroidales, order Lactobacillales, family Bacteroidaceae, family Prevotellaceae, family Streptococcaceae, and class Bacteroidia; P-rg < 0.05). Specifically, genus Anaerostipes and genus Lachnoclostridium showed significant genetic overlaps with IS as per GPA analysis (P-GPA < 0.05).(Supplementary Table 2)(Table 1 ) Table 1 Genetic Correlation and Overlap LDSC GPA Trait1 Trait2 rg rg_se rg_p statistics pvalue genus Anaerostipes IS -0.369 0.151 0.014 5.200 0.023 genus Bacteroides IS 0.440 0.183 0.016 0.055 0.829 genus Lachnoclostridium IS 0.294 0.143 0.040 3.900 0.048 genus Lachnospiraceae IS -0.479 0.227 0.035 0.035 0.852 genus Subdoligranulum IS -0.491 0.243 0.044 0.286 0.593 order Bacteroidales IS 0.420 0.213 0.049 0.816 0.366 order Lactobacillales IS 0.394 0.189 0.037 0.032 0.858 family Bacteroidaceae IS 0.440 0.183 0.016 -0.011 1.000 family Prevotellaceae IS -0.575 0.254 0.024 2.107 0.147 family Streptococcaceae IS 0.312 0.158 0.048 1.147 0.284 class Bacteroidia IS 0.420 0.213 0.049 0.817 0.366 3.2 Mediation Analysis of Potential Blood Metabolites In the mediation MR analysis, it was found that genus Ruminiclostridium may increase the risk of IS by reducing Pyruvate levels (mediation effect 0.004, direct effect 0.082, mediation proportion 5%), and may increase the risk of IS by elevating levels of Arachidonate, Free cholesterol to total lipids ratio in large VLDL, Triglycerides in medium LDL, and Triglycerides to total lipids ratio in small HDL (mediation effects ranging from 0.002 to 0.006, direct effects from 0.080 to 0.085, mediation proportions from 2–7%). These mediation effects were then validated through MVMR, where in cases of Pyruvate and Arachidonate as mediators, no common SNPs were extracted or those extracted were excluded after harmonisation, indicating an independent causal relationship with the outcome. For Free cholesterol to total lipids ratio in large VLDL, Triglycerides in medium LDL, and Triglycerides to total lipids ratio in small HDL as mediators, after adjusting for GM influences, the mediation did not retain independent significant causal relationships (Supplementary Table 3)(Fig. 4 ). We also found that order Burkholderiales may decrease the risk of IS by increasing levels of Apolipoprotein A1, Citrate, Free cholesterol in HDL, Total lipids in HDL, Concentration of HDL particles, Phospholipids in HDL, Total lipids in medium HDL, Concentration of medium HDL particles, Phospholipids in medium HDL, and Total concentration of lipoprotein particles (mediation effect 0.004, direct effect 0.082, mediation proportion 5%; mediation effect − 0.001 to -0.002, direct effect − 0.071 to -0.072, mediation proportion 1.5–3.0%). Subsequently, we validated these mediation effects found in the TSMR using MVMR. Specifically, when Apolipoprotein A1, Total lipids in medium HDL, Concentration of medium HDL particles, Phospholipids in medium HDL, and Total concentration of lipoprotein particles served as mediators, the mediation effects remained independently significant even after adjusting for GM influence. Conversely, when Citrate, Free cholesterol in HDL, Total lipids in HDL, Concentration of HDL particles, and Phospholipids in HDL served as mediators, the mediation effects on outcomes were no longer independently significant after adjusting for GM influence (Supplementary Table 3)(Fig. 4 ). 4. Discussion This study, employing Mendelian Randomization (MR), provides genetic evidence of a causal relationship between the GM and IS. Our analysis indicates that specific microbial groups such as genus Ruminiclostridium increase the risk of IS, while order Burkholderiales appears to have a potential role in reducing the risk. Mediation analysis reveals potential mediating roles of metabolites such as Pyruvate and Arachidonate between genus Ruminiclostridium and IS. Metabolites such as Apolipoprotein A1, Total lipids in medium HDL, Concentration of medium HDL particles, Phospholipids in medium HDL, and Total concentration of lipoprotein particles may play significant mediating roles between order Burkholderiales and IS. This suggests that GM may directly influence stroke risk by regulating key metabolic pathways. These metabolites play crucial roles in energy metabolism and cellular signaling, and their aberrant expression may directly affect vascular function and inflammatory states, which are vital physiological processes in the development of stroke. Specifically, we found that genus Ruminiclostridium reduces Pyruvate levels, while Pyruvate plays a neuroprotective role in IS. Pyruvate is beneficial as it helps to clear harmful glutamate from the blood and brain, reducing excitotoxicity that may exacerbate stroke injury[ 28 ]. Studies using animal models have shown that administering Pyruvate can significantly lower glutamate levels, thereby reducing brain damage and improving recovery outcomes in IS[ 28 ]. Existing research indicates that Pyruvate plays a critical role in regulating inflammation and preventing oxidative stress, key mechanisms in IS injury. Pyruvate has been shown to support mitochondrial function and reduce oxidative damage in neuronal cells, potentially reducing the severity of IS outcomes. These findings support exploring Pyruvate as a therapeutic agent in clinical settings, to mitigate IS injury by protecting neurons from excitotoxicity and oxidative stress[ 29 , 30 ]. Moreover, our study demonstrates that genus Ruminiclostridium may lead to reduced levels of Pyruvate, thereby increasing the risk of IS. Additionally, we found that genus Ruminiclostridium increases Arachidonate levels, which is considered to be involved in inflammation processes and atherosclerosis, although its role in the risk of IS remains controversial[ 31 ]. In this study, we found that Arachidonate may increase the risk of IS. We also found that order Burkholderiales increases levels of metabolites such as Apolipoprotein A1, Total lipids in medium HDL, Concentration of medium HDL particles, Phospholipids in medium HDL, and Total concentration of lipoprotein particles. The relationship between Apolipoprotein A1 (ApoA1) and IS has been revealed in multiple studies, suggesting that ApoA1 may serve as a useful biomarker for predicting and monitoring the progression of IS. Higher levels of ApoA1 are generally associated with a lower risk of recurrent stroke and better prognosis in stroke patients. Studies have shown that ApoA1 levels are inversely related to the severity of intracranial atherosclerosis and can predict the recurrence of cerebrovascular events[ 32 – 34 ]The specific roles of Total lipids in medium HDL, Concentration of medium HDL particles, Phospholipids in medium HDL, and Total concentration of lipoprotein particles in relation to IS are not directly studied. However, given the established role of high-density lipoprotein (HDL) related markers in cardiovascular health, they might also be important in the context of stroke. High levels of HDL and its components are generally associated with better cardiovascular health, and due to their roles in lipid transport and protection against atherosclerosis, they might help reduce the risk of stroke and facilitate better recovery. Our study indicates that order Burkholderiales reduces the risk of IS by increasing these beneficial lipoprotein levels. Additionally, our findings were further validated for stability of the causal relationships through the BWMR method, showing that the relationships between genus Coprobacter, genus Ruminiclostridium, and order Burkholderiales with IS are consistent across multiple statistical models. This consistency underscores the reliability of our findings and also highlights potential therapeutic targets, which could aid in developing treatment strategies aimed at specific microbial groups to reduce the risk of stroke. Analyses of genetic correlation and overlap, such as those using LDSC and GPA, further revealed extensive genetic interactions between GM and IS. These analyses provide additional evidence supporting the role of GM in stroke, possibly by indirectly influencing the risk of stroke through effects on the host’s genetic background. However, although our study provides detailed insights into the causal relationship between GM and IS, it has some limitations. Firstly, our analysis relies on publicly available GWAS data, which may limit our complete assessment of the types and quantities of GM taxa. Furthermore, although our methods can reduce potential confounders and reverse causality, they cannot entirely eliminate non-genetic interferences. Lastly, as our study is based on European population data, it is difficult to avoid impacts caused by racial differences. Additionally, due to the considerable individual variability of GM, which can be influenced by medication or other lifestyle and dietary habits and diseases, and the more lenient threshold settings for instrumental variables compared to conventional MR analysis, there is a risk of result heterogeneity. Future research should validate these findings through more comprehensive datasets and diversified populations, and explore more complex biological mechanisms between GM and stroke. Overall, by integrating large-scale GWAS data and advanced statistical methods, this study strengthens the scientific understanding of the role of the gut microbiome in the pathophysiology of IS offering potential directions for future prevention and treatment strategies. 5. Conclusions In this study, we utilized Mendelian Randomization (MR) methods and Genome-Wide Association Studies (GWAS) data to explore the causal connections between the GM and IS. Our analysis identified specific microbial taxa such as genus Ruminiclostridium and order Burkholderiales as significantly associated with the risk of IS. Furthermore, our mediation analysis revealed that various metabolites including Pyruvate, Arachidonate, and those related to high-density lipoprotein (HDL) may act as mediators between GM and IS. These findings not only enhance our understanding of the role of GM in cardiovascular and cerebrovascular diseases but also provide potential targets for developing future preventive and therapeutic strategies. Declarations Author Contributions: . Conceptualization, Liya YE, Gang Yu, Jing Shen and Hui Cai; methodology, Liya YE, Gang Yu, Jing Shen and Hui Cai; software, Liya YE, Gang Yu, Jing Shen and Hui Cai; validation, Liya YE, Gang Yu, Jing Shen and Hui Cai; formal analysis, Liya YE, Gang Yu, Jing Shen and Hui Cai; investigation, Liya YE, Gang Yu, Jing Shen and Hui Cai; resources, Liya YE, Gang Yu, Jing Shen and Hui Cai; data curation, Liya YE, Gang Yu, Jing Shen and Hui Cai; writing—original draft preparation, Liya YE, Gang Yu, Jing Shen and Hui Cai; writing—review and editing, Liya YE, Gang Yu, Jing Shen and Hui Cai; visualization, Liya YE, Gang Yu, Jing Shen and Hui Cai; supervision, Liya YE, Gang Yu, Jing Shen and Hui Cai; project administration, Liya YE, Gang Yu, Jing Shen and Hui Cai; funding acquisition, NA. All authors have read and agreed to the published version of the manuscript. Funding: NA. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The GWAS data for GM were obtained from the MiBioGen consortium (https://mibiogen.gcc.rug.nl), which includes 18,340 participants from 24 cohorts, with 78% of European ancestry. The MiBioGen consortium curated and analyzed participants' whole-genome genotypes and 16S fecal microbiomes. Genetic loci affecting relative abundance (microbiome quantitative trait loci) were identified using only taxa present in more than 10% of the samples, resulting in a total of 211 taxa: 131 genera, 35 families, 20 orders, 16 classes, and 9 phyla. The GWAS data for IS were derived from the study by Malik R et al.[18], which included 34,217 IS patients and 406,111 controls, all of European ancestry. The GWAS data for blood metabolites were obtained from various studies by So-Youn Shin[19], Mario Roederer[20], Johannes Kettunen[21], and others, comprising a total of 974 blood metabolite GWAS. Acknowledgments: Not applicable. Conflicts of Interest: The authors declare no conflicts of interest. References Rochmah TN, Rahmawati IT, Dahlui M, Budiarto W, Bilqis N. Economic Burden of Stroke Disease: A Systematic Review. Int J Environ Res Public Health. 2021;18(14). 10.3390/ijerph18147552 . Mukherjee D, Patil CG. Epidemiology and the global burden of stroke. World Neurosurg. 2011;76(6 Suppl):S85–90. 10.1016/j.wneu.2011.07.023 . Global regional, national burden of stroke and its risk factors. 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol. 2021;20(10):795–820. 10.1016/s1474-4422(21)00252-0 . Rahman MM, Islam F, Or-Rashid MH, Mamun AA, Rahaman MS, Islam MM, et al. The Gut Microbiota (Microbiome) in Cardiovascular Disease and Its Therapeutic Regulation. Front Cell Infect Microbiol. 2022;12:903570. 10.3389/fcimb.2022.903570 . Witkowski M, Weeks TL, Hazen SL. Gut Microbiota and Cardiovascular Disease. Circ Res. 2020;127(4):553–70. 10.1161/circresaha.120.316242 . Tang WHW, Li DY, Hazen SL. Dietary metabolism, the gut microbiome, and heart failure. Nat Rev Cardiol. 2019;16(3):137–54. 10.1038/s41569-018-0108-7 . Tang WH, Kitai T, Hazen SL. Gut Microbiota in Cardiovascular Health and Disease. Circ Res. 2017;120(7):1183–96. 10.1161/circresaha.117.309715 . Wang Q, Dai H, Hou T, Hou Y, Wang T, Lin H, et al. Dissecting Causal Relationships Between Gut Microbiota, Blood Metabolites, and Stroke: A Mendelian Randomization Study. J Stroke. 2023;25(3):350–60. 10.5853/jos.2023.00381 . Avendaño-Ortiz J, Lorente-Ros Á, Briones-Figueroa A, Morán-Alvarez P, García-Fernández A, Garrote-Corral S, et al. Serological short-chain fatty acid and trimethylamine N-oxide microbial metabolite imbalances in young adults with acute myocardial infarction. Heliyon. 2023;9(10):e20854. 10.1016/j.heliyon.2023.e20854 . Chou PS, Yang IH, Kuo CM, Wu MN, Lin TC, Fong YO, et al. The Prognostic Biomarkers of Plasma Trimethylamine N-Oxide and Short-Chain Fatty Acids for Recanalization Therapy in Acute Ischemic Stroke. Int J Mol Sci. 2023;24(13). 10.3390/ijms241310796 . Yamashiro K, Kurita N, Urabe T, Hattori N. Role of the Gut Microbiota in Stroke Pathogenesis and Potential Therapeutic Implications. Ann Nutr Metab. 2021;77(2):36–44. 10.1159/000516398 . McHale P, Maudsley G, Pennington A, Schlüter DK, Barr B, Paranjothy S, et al. Mediators of socioeconomic inequalities in preterm birth: a systematic review. BMC Public Health. 2022;22(1):1134. 10.1186/s12889-022-13438-9 . Sanderson E, Glymour MM, Holmes MV, Kang H, Morrison J, Munafò MR, et al. Mendelian randomization. Nat Rev Methods Primers. 2022;2. 10.1038/s43586-021-00092-5 . Blakely T, McKenzie S, Carter K. Misclassification of the mediator matters when estimating indirect effects. J Epidemiol Community Health. 2013;67(5):458–66. 10.1136/jech-2012-201813 . Burgess S, Bowden J, Fall T, Ingelsson E, Thompson SG. Sensitivity Analyses for Robust Causal Inference from Mendelian Randomization Analyses with Multiple Genetic Variants. Epidemiology. 2017;28(1):30–42. 10.1097/ede.0000000000000559 . Rogne T, Gill D, Liew Z, Shi X, Stensrud VH, Nilsen TIL, et al. Mediating Factors in the Association of Maternal Educational Level With Pregnancy Outcomes: A Mendelian Randomization Study. JAMA Netw Open. 2024;7(1):e2351166. 10.1001/jamanetworkopen.2023.51166 . Relton CL, Davey Smith G. Two-step epigenetic Mendelian randomization: a strategy for establishing the causal role of epigenetic processes in pathways to disease. Int J Epidemiol. 2012;41(1):161–76. 10.1093/ije/dyr233 . Malik R, Chauhan G, Traylor M, Sargurupremraj M, Okada Y, Mishra A, et al. Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes. Nat Genet. 2018;50(4):524–37. 10.1038/s41588-018-0058-3 . Shin SY, Fauman EB, Petersen AK, Krumsiek J, Santos R, Huang J, et al. An atlas of genetic influences on human blood metabolites. Nat Genet. 2014;46(6):543–50. 10.1038/ng.2982 . Roederer M, Quaye L, Mangino M, Beddall MH, Mahnke Y, Chattopadhyay P, et al. The genetic architecture of the human immune system: a bioresource for autoimmunity and disease pathogenesis. Cell. 2015;161(2):387–403. 10.1016/j.cell.2015.02.046 . Kettunen J, Demirkan A, Würtz P, Draisma HH, Haller T, Rawal R, et al. Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA. Nat Commun. 2016;7:11122. 10.1038/ncomms11122 . Zhao J, Ming J, Hu X, Chen G, Liu J, Yang C. Bayesian weighted Mendelian randomization for causal inference based on summary statistics. Bioinformatics. 2020;36(5):1501–8. 10.1093/bioinformatics/btz749 . Bulik-Sullivan BK, Loh PR, Finucane HK, Ripke S, Yang J, Patterson N, et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet. 2015;47(3):291–5. 10.1038/ng.3211 . Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, Korbel JO, et al. A global reference for human genetic variation. Nature. 2015;526(7571):68–74. 10.1038/nature15393 . Chung D, Yang C, Li C, Gelernter J, Zhao H. GPA: a statistical approach to prioritizing GWAS results by integrating pleiotropy and annotation. PLoS Genet. 2014;10(11):e1004787. 10.1371/journal.pgen.1004787 . Jin Q, Ren F, Dai D, Sun N, Qian Y, Song P. The causality between intestinal flora and allergic diseases: Insights from a bi-directional two-sample Mendelian randomization analysis. Front Immunol. 2023;14:1121273. 10.3389/fimmu.2023.1121273 . Zhou W, Liu G, Hung RJ, Haycock PC, Aldrich MC, Andrew AS, et al. Causal relationships between body mass index, smoking and lung cancer: Univariable and multivariable Mendelian randomization. Int J Cancer. 2021;148(5):1077–86. 10.1002/ijc.33292 . Kaplan-Arabaci O, Acari A, Ciftci P, Gozuacik D. Glutamate Scavenging as a Neuroreparative Strategy in Ischemic Stroke. Front Pharmacol. 2022;13:866738. 10.3389/fphar.2022.866738 . Takahashi S. Neuroprotective Function of High Glycolytic Activity in Astrocytes: Common Roles in Stroke and Neurodegenerative Diseases. Int J Mol Sci. 2021;22(12). 10.3390/ijms22126568 . Narne P, Pandey V, Phanithi PB. Interplay between mitochondrial metabolism and oxidative stress in ischemic stroke: An epigenetic connection. Mol Cell Neurosci. 2017;82:176–94. 10.1016/j.mcn.2017.05.008 . Marklund M, Wu JHY, Imamura F, Del Gobbo LC, Fretts A, de Goede J, et al. Biomarkers of Dietary Omega-6 Fatty Acids and Incident Cardiovascular Disease and Mortality. Circulation. 2019;139(21):2422–36. 10.1161/circulationaha.118.038908 . Yuan S, Tang B, Zheng J, Larsson SC. Circulating Lipoprotein Lipids, Apolipoproteins and Ischemic Stroke. Ann Neurol. 2020;88(6):1229–36. 10.1002/ana.25916 . Martín-Campos JM, Cárcel-Márquez J, Llucià-Carol L, Lledós M, Cullell N, Muiño E, et al. Causal role of lipid metabolome on the risk of ischemic stroke, its etiological subtypes, and long-term outcome: A Mendelian randomization study. Atherosclerosis. 2023;386:117382. 10.1016/j.atherosclerosis.2023.117382 . Lee M, Lim JS, Kim Y, Park SH, Lee SH, Kim C, et al. High ApoB/ApoA-I Ratio Predicts Post-Stroke Cognitive Impairment in Acute Ischemic Stroke Patients with Large Artery Atherosclerosis. Nutrients. 2023;15(21). 10.3390/nu15214670 . Additional Declarations No competing interests reported. Supplementary Files Supplementarytable.xls Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 24 Jun, 2024 Submission checks completed at journal 24 Jun, 2024 First submitted to journal 29 Apr, 2024 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-4341342","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":318243602,"identity":"75f96246-be4d-4e53-b675-c80af2ce7f4b","order_by":0,"name":"Liya Ye","email":"","orcid":"","institution":"The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Liya","middleName":"","lastName":"Ye","suffix":""},{"id":318243603,"identity":"7f7cbd05-751f-4949-84b5-a15b55cc2990","order_by":1,"name":"Gang Yu","email":"","orcid":"","institution":"The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Gang","middleName":"","lastName":"Yu","suffix":""},{"id":318243604,"identity":"f88bcc68-86c0-42c2-93d6-f47b7cf60432","order_by":2,"name":"Jing Shen","email":"","orcid":"","institution":"The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Shen","suffix":""},{"id":318243605,"identity":"a53856aa-2d2d-4acb-8380-015c382fc08e","order_by":3,"name":"Hui Cai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYDCCAyDCgIGBjb+x4cAHIJuNnVgt/BKHDx6cAdLCTJQWIJBsSEs+zANiEdLCdyP52WOeArs8gwNnDA7b/Nomz8fMwPjhYw5uLZI30swNZxgkFxsc7jE4nNt327CNmYFZcuY23FoMbiSYSXwwYE7cALIlt+c2I1ALGzMvXi3p3yQSDOqBWnIMDlv23LYnQksOyJbDiTMb0hIOM/y4nUhQi+SZN2WSMwyOJ/ZLHD5wsLfhdnIbM2MzXr/wHU/fJs3zpzqxjb+x+cOPP7dt57c3H/zwEY8WVMDYBiYbiFUPAn9IUTwKRsEoGAUjBQAAnQlaDHx7PE4AAAAASUVORK5CYII=","orcid":"","institution":"The Affiliated Jiangsu Shengze Hospital of Nanjing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Hui","middleName":"","lastName":"Cai","suffix":""}],"badges":[],"createdAt":"2024-04-29 08:26:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4341342/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4341342/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60345932,"identity":"f01afbf6-793f-4437-9c3d-3b02e2fe0294","added_by":"auto","created_at":"2024-07-15 19:51:55","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":755072,"visible":true,"origin":"","legend":"\u003cp\u003eflow chart\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4341342/v1/32064ec46a5878c34995bd7a.jpg"},{"id":60345548,"identity":"9e89ba2e-e11c-41f9-979f-a34ff8d50c14","added_by":"auto","created_at":"2024-07-15 19:43:54","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1493661,"visible":true,"origin":"","legend":"\u003cp\u003egenus Ruminiclostridium was found to increase the risk of IS; order Burkholderiales was found to decrease the risk of IS\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4341342/v1/a2a9d7a448e8826126e14067.jpg"},{"id":60346210,"identity":"08ae00f9-6500-410d-9a74-28e1c9623580","added_by":"auto","created_at":"2024-07-15 19:59:54","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":922658,"visible":true,"origin":"","legend":"\u003cp\u003eseven other microbial groups showed suggestive significant causal relationships with IS\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4341342/v1/9f993e6a92fa20bf42103813.jpg"},{"id":60345931,"identity":"b83aeec1-8a54-4dfe-8a0b-a9bfe9221e6b","added_by":"auto","created_at":"2024-07-15 19:51:54","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1577369,"visible":true,"origin":"","legend":"\u003cp\u003eMediation Analysis: GM-Blood Metabolites-IS\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4341342/v1/05dd96c31784946287f05434.jpg"},{"id":60346653,"identity":"112f7c8c-d3a2-4500-9ea7-3f85bbe9dbf0","added_by":"auto","created_at":"2024-07-15 20:08:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5240136,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4341342/v1/b40efbd8-4e48-4d75-8957-4a26e7ea25aa.pdf"},{"id":60345552,"identity":"50fe977b-da69-403b-96f3-9df32d94c69f","added_by":"auto","created_at":"2024-07-15 19:43:55","extension":"xls","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":41984,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable.xls","url":"https://assets-eu.researchsquare.com/files/rs-4341342/v1/93d1618bc0a0821f537d1e15.xls"}],"financialInterests":"No competing interests reported.","formattedTitle":"Investigating the Relationship Between Gut Microbiota and Ischemic Stroke: Genetic Causality and Metabolite Mediation","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIschemic stroke (IS) is a major global health concern, contributing not only to mortality but also being a leading cause of long-term disability and significant socioeconomic burdens[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Recent research underscores the critical influence of the gut microbiota (GM) on health and cardiovascular conditions, particularly IS[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. While initial evidence suggests a connection between GM dysbiosis and IS, the specific mechanisms and causal relationships remain inconsistent[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe GM influences stroke risk through various pathways, including host metabolism, immune responses, and vascular function. For instance, GM impact the production of metabolites such as short-chain fatty acids (SCFA) and trimethylamine N-oxide (TMAO), directly linked to stroke incidence[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, observational studies have limitations, often overlooking key confounding factors, leading to biased results[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, observational studies have limitations, often overlooking key confounding factors, leading to biased results[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Additionally, mediation analysis is susceptible to measurement errors, potentially underestimating mediation effects[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMendelian Randomization (MR), which uses genetic variants as instrumental variables (IVs) to assess causal relationships between variables, offers an effective way to avoid confounding factors[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Since genetic variants are randomly assigned, they can act as proxies for long-term exposure or mediators and are not influenced by lifestyle factors or chronic diseases. Therefore, MR studies are robust against both measured and unmeasured confounding biases, often demonstrating resilience to non-differential measurement errors[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. This method has been successfully applied in multiple fields to address reverse causation and confounding biases inherent in observational studies.\u003c/p\u003e \u003cp\u003eUtilizing data from Genome-Wide Association Studies (GWAS), this study aims to explore the direct and indirect connections between GM and IS through bidirectional MR analysis and mediation analysis, particularly by mediating these relationships through the levels of blood metabolites.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Experimental Design and Data Sources\u003c/h2\u003e \u003cp\u003eIn this MR cohort study, we utilized publicly available, ethically approved summary-level data, which did not require Institutional Review Board approval or informed consent. The study adheres to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) MR reporting guidelines. In this study, summary data from GWAS were used to identify genetic proxies for exposure and to investigate their associations with outcomes. Data were extracted between March 1, 2024, and April 30, 2024.(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe GWAS data for GM were obtained from the MiBioGen consortium (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://mibiogen.gcc.rug.nl\u003c/span\u003e\u003cspan address=\"https://mibiogen.gcc.rug.nl\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which includes 18,340 participants from 24 cohorts, with 78% of European ancestry. The MiBioGen consortium curated and analyzed participants' whole-genome genotypes and 16S fecal microbiomes. Genetic loci affecting relative abundance (microbiome quantitative trait loci) were identified using only taxa present in more than 10% of the samples, resulting in a total of 211 taxa: 131 genera, 35 families, 20 orders, 16 classes, and 9 phyla. The GWAS data for IS were derived from the study by Malik R et al.[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], which included 34,217 IS patients and 406,111 controls, all of European ancestry. The GWAS data for blood metabolites were obtained from various studies by So-Youn Shin[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], Mario Roederer[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], Johannes Kettunen[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and others, comprising a total of 974 blood metabolite GWAS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data Extraction\u003c/h2\u003e \u003cp\u003eFor the selection of MR instrumental variables, due to the significant individual variability and dynamic nature of GM in humans, we chose SNPs associated with GM with P-values less than 1\u0026times;10^-5. Similarly, SNPs related to blood metabolites were selected under the same threshold (P\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10^-5). Then, within the 1000 Genomes reference panel, independent SNPs were clustered at an LD threshold of r^2\u0026thinsp;\u0026lt;\u0026thinsp;0.01, and SNPs with an F-statistic\u0026thinsp;\u0026lt;\u0026thinsp;10 were excluded to avoid weak instrument bias.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Two-Sample MR Analysis\u003c/h2\u003e \u003cp\u003eWe initially conducted bidirectional MR analysis to explore the causal relationships between GM and IS. Using the Inverse Variance Weighted (IVW) method as the primary approach, odds ratios (ORs) and 95% confidence intervals (CIs) were calculated; a Bonferroni-corrected FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to show a significant causal relationship, while P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 but FDR\u0026thinsp;\u0026gt;\u0026thinsp;0.05 was considered to nominally suggest a significant causal relationship.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 BWMR Analysis\u003c/h2\u003e \u003cp\u003eTo validate the results of the two-sample MR analysis, we utilized BWMR. Bayesian Weighted Mendelian Randomization (BWMR) is an efficient causal inference statistical method based on summary statistics. It provides estimations of model parameters and statistical inferences[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Due to the polygenic structure of complex traits/diseases and the prevalence of pleiotropy, MR has some limitations. To address these, BWMR emerged as a Bayesian weighted approach to causal inference. In the BWMR model, uncertainties due to polygenicity and weak effects are considered, and outlier detection through Bayesian weighting addresses violations of IV assumptions caused by pleiotropy. A Variational Expectation Maximization (VEM) algorithm was developed for more stable and efficient causal inference using BWMR[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Additionally, an exact closed-form solution was derived to correct often underestimated posterior covariances in variational inference. Primarily, calibrated BWMR was used to validate the two-sample MR findings[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Genetic Correlation and Overlap\u003c/h2\u003e \u003cp\u003eWe first used linkage disequilibrium score regression (LDSC) to assess the genome-wide genetic correlation between GM and IS for different trait pairs, using LD scores based on the European ancestry from the 1000 Genomes Project[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In LDSC analyses, we did not restrict the intercept; although sample overlap affects the intercept, it does not affect the slope. Thus, genetic correlations are not compromised by sample overlap, allowing consideration of residual confounding and indicating potential sample overlap between two GWAS studies[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. We then used Genetic analysis incorporating Pleiotropy and Annotation (GPA) to explore the overall genetic overlap between traits. GPA integrates multiple GWAS datasets and functional annotations to identify associated signals[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. GPA not only identifies many weak signals missed by traditional single-phenotype analysis but also reveals relationships between their genetic structures[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Here, we set the significance threshold to P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Mediation Analysis: GM-Blood Metabolites-IS\u003c/h2\u003e \u003cp\u003eWe employed two mediation methods: Two-Step Mendelian Randomization (TSMR)[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]and Multivariable Mendelian Randomization (MVMR)[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], to dissect the direct and indirect effects of GM and blood metabolites on IS. In TSMR, we used 947 blood metabolite GWAS as mediators to analyze the mediators of the causal relationship between GM and IS. β0\u0026thinsp;\u0026minus;\u0026thinsp;β1\u0026thinsp;\u0026times;\u0026thinsp;β2 serves as the direct effect of exposure on the outcome, where β0 measures the total effect of exposure on the outcome, β1 assesses the causal impact of exposure on the mediator, β2 represents the causal effect of the mediator on the outcome, and β1\u0026thinsp;\u0026times;\u0026thinsp;β2 indicates the mediated effect of exposure on the outcome, with mediation analysis based on IVW estimation. The proportion mediated can be calculated as \u0026ldquo;mediated effect / total effect\u0026rdquo; ([β1\u0026thinsp;\u0026times;\u0026thinsp;β2]/β0).\u003c/p\u003e \u003cp\u003eFurther, we used MVMR to validate TSMR results, to see if the mediators have an independent significant causal relationship with the outcome after excluding the influence of GM.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Sensitivity Analysis\u003c/h2\u003e \u003cp\u003eAdditionally, we employed four MR methods with different pleiotropy assumptions (MR-Egger, Weighted Median, Simple Mode, and Weighted Mode) for sensitivity analysis. We used the MR-Egger method to assess horizontal pleiotropy, performing an unconstrained intercept weighted linear regression. The intercept represents the average pleiotropy of genetic variants (average direct effect of the variants on the outcome). If the intercept differs from zero (MR-Egger intercept P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05), there is evidence of horizontal pleiotropy. We also used Cochran's Q test to assess heterogeneity (smaller P-values indicate higher heterogeneity, higher potential for directional pleiotropy), and leave-one-out analysis to detect SNP outliers.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003e2.8 Ethical Approval and Consent to Participate\u003c/strong\u003e \u003cp\u003eThis study is based on publicly available data. Each study within the GWAS received approval from the relevant institutional review boards, and informed consent was obtained from participants, caregivers, legal guardians, or other proxies.\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Genetic Causal Relationships Between GM and IS\u003c/h2\u003e \u003cp\u003eWe first assessed the causal impact of GM on IS, primarily using the IVW method. The genus Ruminiclostridium was found to increase the risk of IS (OR 1.090, 95% CI, 1.036\u0026ndash;1.468; FDR\u0026thinsp;=\u0026thinsp;0.043), while the order Burkholderiales was found to decrease the risk (OR 0.929, 95% CI, 0.887\u0026ndash;0.973; FDR\u0026thinsp;=\u0026thinsp;0.043) (Supplementary Table\u0026nbsp;1)(Fig.\u0026nbsp;2). In addition, seven other microbial groups showed suggestive significant causal relationships with IS (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and FDR\u0026thinsp;\u0026gt;\u0026thinsp;0.05), where class Betaproteobacteria, class Clostridia, family Porphyromonadaceae, genus Coprobacter, genus Lachnospira, and genus Slackia might reduce the risk, and genus Oscillibacter might increase the risk of IS(Supplementary Table\u0026nbsp;1)(Fig.\u0026nbsp;3).\u003c/p\u003e \u003cp\u003eWhen assessing the causal impact of IS on GM, none of the above microbial groups showed a significant causal relationship (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Moreover, through sensitivity analysis, these results were considered reliable, showing no pleiotropy.\u003c/p\u003e \u003cp\u003eFurther, we validated the reliability of the above results using WBMR, which confirmed that the genus Coprobacter, genus Ruminiclostridium, and order Burkholderiales still showed significant causal relationships with IS (P values were 0.011, 0.005, and 0.006)(Supplementary Table\u0026nbsp;1)(Fig.\u0026nbsp;2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eLastly, through bivariate LDSC analysis, we found significant genetic correlations between IS and 11 microbial groups (genus Anaerostipes, genus Bacteroides, genus Lachnoclostridium, genus Lachnospiraceae, genus Subdoligranulum, order Bacteroidales, order Lactobacillales, family Bacteroidaceae, family Prevotellaceae, family Streptococcaceae, and class Bacteroidia; P-rg\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Specifically, genus Anaerostipes and genus Lachnoclostridium showed significant genetic overlaps with IS as per GPA analysis (P-GPA\u0026thinsp;\u0026lt;\u0026thinsp;0.05).(Supplementary Table\u0026nbsp;2)(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\u003eGenetic Correlation and Overlap\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLDSC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGPA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTrait1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrait2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003erg\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003erg_se\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003erg_p\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003estatistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003epvalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egenus Anaerostipes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egenus Bacteroides\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egenus Lachnoclostridium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egenus Lachnospiraceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.852\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egenus Subdoligranulum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.491\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.593\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eorder Bacteroidales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eorder Lactobacillales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efamily Bacteroidaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efamily Prevotellaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.147\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003efamily Streptococcaceae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.284\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eclass Bacteroidia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.420\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.366\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 \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Mediation Analysis of Potential Blood Metabolites\u003c/h2\u003e \u003cp\u003eIn the mediation MR analysis, it was found that genus Ruminiclostridium may increase the risk of IS by reducing Pyruvate levels (mediation effect 0.004, direct effect 0.082, mediation proportion 5%), and may increase the risk of IS by elevating levels of Arachidonate, Free cholesterol to total lipids ratio in large VLDL, Triglycerides in medium LDL, and Triglycerides to total lipids ratio in small HDL (mediation effects ranging from 0.002 to 0.006, direct effects from 0.080 to 0.085, mediation proportions from 2\u0026ndash;7%). These mediation effects were then validated through MVMR, where in cases of Pyruvate and Arachidonate as mediators, no common SNPs were extracted or those extracted were excluded after harmonisation, indicating an independent causal relationship with the outcome. For Free cholesterol to total lipids ratio in large VLDL, Triglycerides in medium LDL, and Triglycerides to total lipids ratio in small HDL as mediators, after adjusting for GM influences, the mediation did not retain independent significant causal relationships (Supplementary Table\u0026nbsp;3)(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe also found that order Burkholderiales may decrease the risk of IS by increasing levels of Apolipoprotein A1, Citrate, Free cholesterol in HDL, Total lipids in HDL, Concentration of HDL particles, Phospholipids in HDL, Total lipids in medium HDL, Concentration of medium HDL particles, Phospholipids in medium HDL, and Total concentration of lipoprotein particles (mediation effect 0.004, direct effect 0.082, mediation proportion 5%; mediation effect \u0026minus;\u0026thinsp;0.001 to -0.002, direct effect \u0026minus;\u0026thinsp;0.071 to -0.072, mediation proportion 1.5\u0026ndash;3.0%). Subsequently, we validated these mediation effects found in the TSMR using MVMR. Specifically, when Apolipoprotein A1, Total lipids in medium HDL, Concentration of medium HDL particles, Phospholipids in medium HDL, and Total concentration of lipoprotein particles served as mediators, the mediation effects remained independently significant even after adjusting for GM influence. Conversely, when Citrate, Free cholesterol in HDL, Total lipids in HDL, Concentration of HDL particles, and Phospholipids in HDL served as mediators, the mediation effects on outcomes were no longer independently significant after adjusting for GM influence (Supplementary Table\u0026nbsp;3)(Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study, employing Mendelian Randomization (MR), provides genetic evidence of a causal relationship between the GM and IS. Our analysis indicates that specific microbial groups such as genus Ruminiclostridium increase the risk of IS, while order Burkholderiales appears to have a potential role in reducing the risk. Mediation analysis reveals potential mediating roles of metabolites such as Pyruvate and Arachidonate between genus Ruminiclostridium and IS. Metabolites such as Apolipoprotein A1, Total lipids in medium HDL, Concentration of medium HDL particles, Phospholipids in medium HDL, and Total concentration of lipoprotein particles may play significant mediating roles between order Burkholderiales and IS. This suggests that GM may directly influence stroke risk by regulating key metabolic pathways. These metabolites play crucial roles in energy metabolism and cellular signaling, and their aberrant expression may directly affect vascular function and inflammatory states, which are vital physiological processes in the development of stroke.\u003c/p\u003e \u003cp\u003eSpecifically, we found that genus Ruminiclostridium reduces Pyruvate levels, while Pyruvate plays a neuroprotective role in IS. Pyruvate is beneficial as it helps to clear harmful glutamate from the blood and brain, reducing excitotoxicity that may exacerbate stroke injury[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Studies using animal models have shown that administering Pyruvate can significantly lower glutamate levels, thereby reducing brain damage and improving recovery outcomes in IS[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Existing research indicates that Pyruvate plays a critical role in regulating inflammation and preventing oxidative stress, key mechanisms in IS injury. Pyruvate has been shown to support mitochondrial function and reduce oxidative damage in neuronal cells, potentially reducing the severity of IS outcomes. These findings support exploring Pyruvate as a therapeutic agent in clinical settings, to mitigate IS injury by protecting neurons from excitotoxicity and oxidative stress[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Moreover, our study demonstrates that genus Ruminiclostridium may lead to reduced levels of Pyruvate, thereby increasing the risk of IS. Additionally, we found that genus Ruminiclostridium increases Arachidonate levels, which is considered to be involved in inflammation processes and atherosclerosis, although its role in the risk of IS remains controversial[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In this study, we found that Arachidonate may increase the risk of IS.\u003c/p\u003e \u003cp\u003eWe also found that order Burkholderiales increases levels of metabolites such as Apolipoprotein A1, Total lipids in medium HDL, Concentration of medium HDL particles, Phospholipids in medium HDL, and Total concentration of lipoprotein particles. The relationship between Apolipoprotein A1 (ApoA1) and IS has been revealed in multiple studies, suggesting that ApoA1 may serve as a useful biomarker for predicting and monitoring the progression of IS. Higher levels of ApoA1 are generally associated with a lower risk of recurrent stroke and better prognosis in stroke patients. Studies have shown that ApoA1 levels are inversely related to the severity of intracranial atherosclerosis and can predict the recurrence of cerebrovascular events[\u003cspan additionalcitationids=\"CR33\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]The specific roles of Total lipids in medium HDL, Concentration of medium HDL particles, Phospholipids in medium HDL, and Total concentration of lipoprotein particles in relation to IS are not directly studied. However, given the established role of high-density lipoprotein (HDL) related markers in cardiovascular health, they might also be important in the context of stroke. High levels of HDL and its components are generally associated with better cardiovascular health, and due to their roles in lipid transport and protection against atherosclerosis, they might help reduce the risk of stroke and facilitate better recovery. Our study indicates that order Burkholderiales reduces the risk of IS by increasing these beneficial lipoprotein levels.\u003c/p\u003e \u003cp\u003eAdditionally, our findings were further validated for stability of the causal relationships through the BWMR method, showing that the relationships between genus Coprobacter, genus Ruminiclostridium, and order Burkholderiales with IS are consistent across multiple statistical models. This consistency underscores the reliability of our findings and also highlights potential therapeutic targets, which could aid in developing treatment strategies aimed at specific microbial groups to reduce the risk of stroke. Analyses of genetic correlation and overlap, such as those using LDSC and GPA, further revealed extensive genetic interactions between GM and IS. These analyses provide additional evidence supporting the role of GM in stroke, possibly by indirectly influencing the risk of stroke through effects on the host\u0026rsquo;s genetic background.\u003c/p\u003e \u003cp\u003eHowever, although our study provides detailed insights into the causal relationship between GM and IS, it has some limitations. Firstly, our analysis relies on publicly available GWAS data, which may limit our complete assessment of the types and quantities of GM taxa. Furthermore, although our methods can reduce potential confounders and reverse causality, they cannot entirely eliminate non-genetic interferences. Lastly, as our study is based on European population data, it is difficult to avoid impacts caused by racial differences. Additionally, due to the considerable individual variability of GM, which can be influenced by medication or other lifestyle and dietary habits and diseases, and the more lenient threshold settings for instrumental variables compared to conventional MR analysis, there is a risk of result heterogeneity.\u003c/p\u003e \u003cp\u003eFuture research should validate these findings through more comprehensive datasets and diversified populations, and explore more complex biological mechanisms between GM and stroke. Overall, by integrating large-scale GWAS data and advanced statistical methods, this study strengthens the scientific understanding of the role of the gut microbiome in the pathophysiology of IS offering potential directions for future prevention and treatment strategies.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn this study, we utilized Mendelian Randomization (MR) methods and Genome-Wide Association Studies (GWAS) data to explore the causal connections between the GM and IS. Our analysis identified specific microbial taxa such as genus Ruminiclostridium and order Burkholderiales as significantly associated with the risk of IS. Furthermore, our mediation analysis revealed that various metabolites including Pyruvate, Arachidonate, and those related to high-density lipoprotein (HDL) may act as mediators between GM and IS. These findings not only enhance our understanding of the role of GM in cardiovascular and cerebrovascular diseases but also provide potential targets for developing future preventive and therapeutic strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e. Conceptualization, Liya YE, Gang Yu, Jing Shen and Hui Cai; methodology, Liya YE, Gang Yu, Jing Shen and Hui Cai; software, Liya YE, Gang Yu, Jing Shen and Hui Cai; validation, Liya YE, Gang Yu, Jing Shen and Hui Cai; formal analysis, Liya YE, Gang Yu, Jing Shen and Hui Cai; investigation, Liya YE, Gang Yu, Jing Shen and Hui Cai; resources, Liya YE, Gang Yu, Jing Shen and Hui Cai; data curation, Liya YE, Gang Yu, Jing Shen and Hui Cai; writing\u0026mdash;original draft preparation, Liya YE, Gang Yu, Jing Shen and Hui Cai; writing\u0026mdash;review and editing, Liya YE, Gang Yu, Jing Shen and Hui Cai; visualization, Liya YE, Gang Yu, Jing Shen and Hui Cai; supervision, Liya YE, Gang Yu, Jing Shen and Hui Cai; project administration, Liya YE, Gang Yu, Jing Shen and Hui Cai; funding acquisition, NA. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e NA.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e The GWAS data for GM were obtained from the MiBioGen consortium (https://mibiogen.gcc.rug.nl), which includes 18,340 participants from 24 cohorts, with 78% of European ancestry. The MiBioGen consortium curated and analyzed participants\u0026apos; whole-genome genotypes and 16S fecal microbiomes. Genetic loci affecting relative abundance (microbiome quantitative trait loci) were identified using only taxa present in more than 10% of the samples, resulting in a total of 211 taxa: 131 genera, 35 families, 20 orders, 16 classes, and 9 phyla. The GWAS data for IS were derived from the study by Malik R et al.[18], which included 34,217 IS patients and 406,111 controls, all of European ancestry. The GWAS data for blood metabolites were obtained from various studies by So-Youn Shin[19], Mario Roederer[20], Johannes Kettunen[21], and others, comprising a total of 974 blood metabolite GWAS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The authors declare no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRochmah TN, Rahmawati IT, Dahlui M, Budiarto W, Bilqis N. Economic Burden of Stroke Disease: A Systematic Review. Int J Environ Res Public Health. 2021;18(14). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijerph18147552\u003c/span\u003e\u003cspan address=\"10.3390/ijerph18147552\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMukherjee D, Patil CG. Epidemiology and the global burden of stroke. World Neurosurg. 2011;76(6 Suppl):S85\u0026ndash;90. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.wneu.2011.07.023\u003c/span\u003e\u003cspan address=\"10.1016/j.wneu.2011.07.023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlobal regional, national burden of stroke and its risk factors. 1990\u0026ndash;2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol. 2021;20(10):795\u0026ndash;820. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/s1474-4422(21)00252-0\u003c/span\u003e\u003cspan address=\"10.1016/s1474-4422(21)00252-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRahman MM, Islam F, Or-Rashid MH, Mamun AA, Rahaman MS, Islam MM, et al. The Gut Microbiota (Microbiome) in Cardiovascular Disease and Its Therapeutic Regulation. Front Cell Infect Microbiol. 2022;12:903570. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fcimb.2022.903570\u003c/span\u003e\u003cspan address=\"10.3389/fcimb.2022.903570\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWitkowski M, Weeks TL, Hazen SL. Gut Microbiota and Cardiovascular Disease. Circ Res. 2020;127(4):553\u0026ndash;70. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/circresaha.120.316242\u003c/span\u003e\u003cspan address=\"10.1161/circresaha.120.316242\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang WHW, Li DY, Hazen SL. Dietary metabolism, the gut microbiome, and heart failure. Nat Rev Cardiol. 2019;16(3):137\u0026ndash;54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41569-018-0108-7\u003c/span\u003e\u003cspan address=\"10.1038/s41569-018-0108-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang WH, Kitai T, Hazen SL. Gut Microbiota in Cardiovascular Health and Disease. Circ Res. 2017;120(7):1183\u0026ndash;96. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/circresaha.117.309715\u003c/span\u003e\u003cspan address=\"10.1161/circresaha.117.309715\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Q, Dai H, Hou T, Hou Y, Wang T, Lin H, et al. Dissecting Causal Relationships Between Gut Microbiota, Blood Metabolites, and Stroke: A Mendelian Randomization Study. J Stroke. 2023;25(3):350\u0026ndash;60. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5853/jos.2023.00381\u003c/span\u003e\u003cspan address=\"10.5853/jos.2023.00381\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAvenda\u0026ntilde;o-Ortiz J, Lorente-Ros \u0026Aacute;, Briones-Figueroa A, Mor\u0026aacute;n-Alvarez P, Garc\u0026iacute;a-Fern\u0026aacute;ndez A, Garrote-Corral S, et al. Serological short-chain fatty acid and trimethylamine N-oxide microbial metabolite imbalances in young adults with acute myocardial infarction. Heliyon. 2023;9(10):e20854. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.heliyon.2023.e20854\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2023.e20854\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChou PS, Yang IH, Kuo CM, Wu MN, Lin TC, Fong YO, et al. The Prognostic Biomarkers of Plasma Trimethylamine N-Oxide and Short-Chain Fatty Acids for Recanalization Therapy in Acute Ischemic Stroke. Int J Mol Sci. 2023;24(13). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms241310796\u003c/span\u003e\u003cspan address=\"10.3390/ijms241310796\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamashiro K, Kurita N, Urabe T, Hattori N. Role of the Gut Microbiota in Stroke Pathogenesis and Potential Therapeutic Implications. Ann Nutr Metab. 2021;77(2):36\u0026ndash;44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1159/000516398\u003c/span\u003e\u003cspan address=\"10.1159/000516398\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcHale P, Maudsley G, Pennington A, Schl\u0026uuml;ter DK, Barr B, Paranjothy S, et al. Mediators of socioeconomic inequalities in preterm birth: a systematic review. BMC Public Health. 2022;22(1):1134. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12889-022-13438-9\u003c/span\u003e\u003cspan address=\"10.1186/s12889-022-13438-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanderson E, Glymour MM, Holmes MV, Kang H, Morrison J, Munaf\u0026ograve; MR, et al. Mendelian randomization. Nat Rev Methods Primers. 2022;2. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s43586-021-00092-5\u003c/span\u003e\u003cspan address=\"10.1038/s43586-021-00092-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlakely T, McKenzie S, Carter K. Misclassification of the mediator matters when estimating indirect effects. J Epidemiol Community Health. 2013;67(5):458\u0026ndash;66. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/jech-2012-201813\u003c/span\u003e\u003cspan address=\"10.1136/jech-2012-201813\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgess S, Bowden J, Fall T, Ingelsson E, Thompson SG. Sensitivity Analyses for Robust Causal Inference from Mendelian Randomization Analyses with Multiple Genetic Variants. Epidemiology. 2017;28(1):30\u0026ndash;42. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/ede.0000000000000559\u003c/span\u003e\u003cspan address=\"10.1097/ede.0000000000000559\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRogne T, Gill D, Liew Z, Shi X, Stensrud VH, Nilsen TIL, et al. Mediating Factors in the Association of Maternal Educational Level With Pregnancy Outcomes: A Mendelian Randomization Study. JAMA Netw Open. 2024;7(1):e2351166. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jamanetworkopen.2023.51166\u003c/span\u003e\u003cspan address=\"10.1001/jamanetworkopen.2023.51166\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRelton CL, Davey Smith G. Two-step epigenetic Mendelian randomization: a strategy for establishing the causal role of epigenetic processes in pathways to disease. Int J Epidemiol. 2012;41(1):161\u0026ndash;76. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ije/dyr233\u003c/span\u003e\u003cspan address=\"10.1093/ije/dyr233\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalik R, Chauhan G, Traylor M, Sargurupremraj M, Okada Y, Mishra A, et al. Multiancestry genome-wide association study of 520,000 subjects identifies 32 loci associated with stroke and stroke subtypes. Nat Genet. 2018;50(4):524\u0026ndash;37. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41588-018-0058-3\u003c/span\u003e\u003cspan address=\"10.1038/s41588-018-0058-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShin SY, Fauman EB, Petersen AK, Krumsiek J, Santos R, Huang J, et al. An atlas of genetic influences on human blood metabolites. Nat Genet. 2014;46(6):543\u0026ndash;50. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/ng.2982\u003c/span\u003e\u003cspan address=\"10.1038/ng.2982\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRoederer M, Quaye L, Mangino M, Beddall MH, Mahnke Y, Chattopadhyay P, et al. The genetic architecture of the human immune system: a bioresource for autoimmunity and disease pathogenesis. Cell. 2015;161(2):387\u0026ndash;403. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cell.2015.02.046\u003c/span\u003e\u003cspan address=\"10.1016/j.cell.2015.02.046\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKettunen J, Demirkan A, W\u0026uuml;rtz P, Draisma HH, Haller T, Rawal R, et al. Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA. Nat Commun. 2016;7:11122. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/ncomms11122\u003c/span\u003e\u003cspan address=\"10.1038/ncomms11122\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao J, Ming J, Hu X, Chen G, Liu J, Yang C. Bayesian weighted Mendelian randomization for causal inference based on summary statistics. Bioinformatics. 2020;36(5):1501\u0026ndash;8. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/bioinformatics/btz749\u003c/span\u003e\u003cspan address=\"10.1093/bioinformatics/btz749\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBulik-Sullivan BK, Loh PR, Finucane HK, Ripke S, Yang J, Patterson N, et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet. 2015;47(3):291\u0026ndash;5. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/ng.3211\u003c/span\u003e\u003cspan address=\"10.1038/ng.3211\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAuton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, Korbel JO, et al. A global reference for human genetic variation. Nature. 2015;526(7571):68\u0026ndash;74. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/nature15393\u003c/span\u003e\u003cspan address=\"10.1038/nature15393\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChung D, Yang C, Li C, Gelernter J, Zhao H. GPA: a statistical approach to prioritizing GWAS results by integrating pleiotropy and annotation. PLoS Genet. 2014;10(11):e1004787. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pgen.1004787\u003c/span\u003e\u003cspan address=\"10.1371/journal.pgen.1004787\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJin Q, Ren F, Dai D, Sun N, Qian Y, Song P. The causality between intestinal flora and allergic diseases: Insights from a bi-directional two-sample Mendelian randomization analysis. Front Immunol. 2023;14:1121273. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2023.1121273\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2023.1121273\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou W, Liu G, Hung RJ, Haycock PC, Aldrich MC, Andrew AS, et al. Causal relationships between body mass index, smoking and lung cancer: Univariable and multivariable Mendelian randomization. Int J Cancer. 2021;148(5):1077\u0026ndash;86. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/ijc.33292\u003c/span\u003e\u003cspan address=\"10.1002/ijc.33292\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKaplan-Arabaci O, Acari A, Ciftci P, Gozuacik D. Glutamate Scavenging as a Neuroreparative Strategy in Ischemic Stroke. Front Pharmacol. 2022;13:866738. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fphar.2022.866738\u003c/span\u003e\u003cspan address=\"10.3389/fphar.2022.866738\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTakahashi S. Neuroprotective Function of High Glycolytic Activity in Astrocytes: Common Roles in Stroke and Neurodegenerative Diseases. Int J Mol Sci. 2021;22(12). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms22126568\u003c/span\u003e\u003cspan address=\"10.3390/ijms22126568\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNarne P, Pandey V, Phanithi PB. Interplay between mitochondrial metabolism and oxidative stress in ischemic stroke: An epigenetic connection. Mol Cell Neurosci. 2017;82:176\u0026ndash;94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.mcn.2017.05.008\u003c/span\u003e\u003cspan address=\"10.1016/j.mcn.2017.05.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarklund M, Wu JHY, Imamura F, Del Gobbo LC, Fretts A, de Goede J, et al. Biomarkers of Dietary Omega-6 Fatty Acids and Incident Cardiovascular Disease and Mortality. Circulation. 2019;139(21):2422\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1161/circulationaha.118.038908\u003c/span\u003e\u003cspan address=\"10.1161/circulationaha.118.038908\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan S, Tang B, Zheng J, Larsson SC. Circulating Lipoprotein Lipids, Apolipoproteins and Ischemic Stroke. Ann Neurol. 2020;88(6):1229\u0026ndash;36. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/ana.25916\u003c/span\u003e\u003cspan address=\"10.1002/ana.25916\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMart\u0026iacute;n-Campos JM, C\u0026aacute;rcel-M\u0026aacute;rquez J, Lluci\u0026agrave;-Carol L, Lled\u0026oacute;s M, Cullell N, Mui\u0026ntilde;o E, et al. Causal role of lipid metabolome on the risk of ischemic stroke, its etiological subtypes, and long-term outcome: A Mendelian randomization study. Atherosclerosis. 2023;386:117382. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.atherosclerosis.2023.117382\u003c/span\u003e\u003cspan address=\"10.1016/j.atherosclerosis.2023.117382\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee M, Lim JS, Kim Y, Park SH, Lee SH, Kim C, et al. High ApoB/ApoA-I Ratio Predicts Post-Stroke Cognitive Impairment in Acute Ischemic Stroke Patients with Large Artery Atherosclerosis. Nutrients. 2023;15(21). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/nu15214670\u003c/span\u003e\u003cspan address=\"10.3390/nu15214670\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Gut Microbiota, Mendelian Randomization, Ischemic Stroke, Genetic Causality, Metabolite Mediation Analysis","lastPublishedDoi":"10.21203/rs.3.rs-4341342/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4341342/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe gut microbiota(GM) plays a significant role in health and disease, with numerous studies confirming its association with various diseases. This study aims to evaluate the genetic causal relationship between GM and ischemic stroke (IS), along with exploring potential blood metabolite-mediated mechanisms.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUtilizing two-sample Mendelian Randomization (MR) and large-scale Genome-Wide Association Studies (GWAS) data, we investigate the association between GM and IS. Bayesian Weighted MR (BWMR) is employed for validation, and genetic correlations are assessed using Bivariate Linkage Disequilibrium Score Regression (LDSC) and Genetic Analysis Incorporating Pleiotropy and Annotation (GPA).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur analysis using Inverse Variance Weighted (IVW) method indicates that specific microbial groups, such as genus Ruminiclostridium and order Burkholderiales, are significantly associated with IS risk. Mediation analysis suggests that metabolites like Pyruvate, Arachidonate, and HDL-related lipoproteins may mediate this relationship. Multivariate MR analysis confirms the independence of these mediating effects. Furthermore, both LDSC and GPA analyses demonstrate significant genetic correlations between GM and IS.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThrough the integration of various statistical methods and GWAS data, this study provides genetic evidence supporting the causal relationship between GM and IS, uncovering potential biological mediating mechanisms. These findings enhance our understanding of the GM's role in cardiovascular and cerebrovascular diseases, offering insights into preventive and treatment strategies.\u003c/p\u003e","manuscriptTitle":"Investigating the Relationship Between Gut Microbiota and Ischemic Stroke: Genetic Causality and Metabolite Mediation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-15 19:43:50","doi":"10.21203/rs.3.rs-4341342/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2024-06-24T10:34:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-06-24T10:33:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Microbiology","date":"2024-04-29T08:14:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-microbiology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mcro","sideBox":"Learn more about [BMC Microbiology](http://bmcmicrobiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/mcro","title":"BMC Microbiology","twitterHandle":"#bmcmicrobiology","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2e343407-185c-44b3-95b7-60f91fdf598d","owner":[],"postedDate":"July 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-07-15T19:43:50+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-15 19:43:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4341342","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4341342","identity":"rs-4341342","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}