The causal relationship between plasma thrombomodulin levels and Risk of Stroke: A Mendelian Randomization Study

The causal relationship between plasma thrombomodulin levels and Risk of Stroke: A Mendelian Randomization Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The causal relationship between plasma thrombomodulin levels and Risk of Stroke: A Mendelian Randomization Study Renjie Liu, Pengju Wang, Shaoning Guo, Jiahui Feng, Xuan Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4014670/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Evidence of the genetic interconnectedness between thrombomodulin levels (TM) and stroke is largely unclear. This study aims to explore the relationship between genetic predisposition to TM and stroke with a 2-sample, bidirectional Mendelian randomization (MR) method. Methods Genetic instruments for each TM and stroke-related phenotypes were derived from large-scale genome-wide association studies. The inverse variance weighted method was used in the primary analyses to obtain the causal estimates. Complementary sensitivity analyses were conducted to test the robustness of our results. Results Using univariable MR, we found evidence for each standard deviation (SD) increase in genetically predicted TM levels, the odds ratio (OR) was 1.092 (95% confidence interval (CI): 1.034–1.153; p = 0.002) for any stroke and 1.105 (95% CI: 1.035–1.180; p = 0.003) for IS. In multivariable MR, the association remained after accounting for body mass index (BMI), systolic blood pressure (SBP), type 2 diabetes (T2D), low-density lipoprotein (LDL) and smoking. Conclusions This MR estimate reveals robust evidence that higher genetically predicted circulating TM levels were associated with an increased risk of any stroke and IS. stroke thrombomodulin Mendelian Randomization Study Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Stroke is the second leading cause of death worldwide, remaining one of the most prevalent and devastating diseases affecting the global population 1 . According to a recent report from the Global Burden of Disease, the average lifetime risk of stroke in the United States increased from 22.8% in 1990 to 24.9% in 2016 2 . By 2030, stroke prevalence is expected to rise the most among Hispanic men, with direct costs of care increasing over 300% since 2012 3 . Moreover, an unexpectedly increasing trend in the incidence of stroke is seen among young adults 4 . To reduce stroke prevalence and its complications, much effort has been focused on prevention. However, despite advances, risk prediction remains imprecise with persistently high rates of stroke. As early as 2012, Ashley EA et al. proposed that the incorporation of genetics into risk prediction frameworks offers the opportunity to refine risks, toward the creation of earlier and tailored risk reduction strategies 5 . Therefore, it is necessary to place great emphasis on exploring potential biomarkers associated with stroke occurrence. Thrombomodulin (TM), a transmembrane glycoprotein mainly expressed by endothelial cells 6 , plays a crucial role in maintaining intravascular patency and blood fluidity due to its antithrombotic, anti-inflammatory, and cytoprotective properties. In healthy humans, the levels of TM are low (< 10 ng/mL), while high TM levels are common in patients suffering from various diseases 7 . In particular, the consistent elevation of TM levels during pathologies is now widely regarded as an important circulatory biomarker for endothelial cell dysfunction (ECD) and vascular risk assessment 8,9 . ECD underlies many cardiovascular diseases, including stroke, and may be a key initiating step in these diseases 10 . However, to date, there have been relatively few studies investigating the association between TM levels and stroke. Despite observational studies have examined circulating TM levels were elevated in ischemic stroke (IS) patients 11,12 , it remains unclear whether this relationship is causal and requires further elucidation. And observational study designs may be limited, additionally, by modest overall sample size, measurement error, and risk of residual confounding 13 . Thus, the casual relationship between TM levels and stroke remains controversial. Mendelian randomization (MR) is a powerful method that uses genetic variants as instrumental variables(IVs) to assess the causal effects of risk factors related to diseases, can overcome the limitations of observational studies, such as small sample sizes or reverse causation or biases caused by unmeasured confounding 14 . Here, we implemented bidirectional MR analysis to assess the association between serum TM levels with any stroke, IS, intracerebral hemorrhage (ICH), and subarachnoid hemorrhage (SAH). And then, we performed multivariate MR analysis to determine whether the observed association was driven by body mass index (BMI), systolic blood pressure (SBP), type 2 diabetes (T2D), low-density lipoprotein (LDL) and smoking. Methods Study design and data sources Our MR analysis was performed in accordance with the STROBE-MR guidelines. The study overview is presented in Fig. 1 . The valid IVs for the exposure should satisfy 3 key assumptions: ( 1 ) the genetic variants must be strongly associated with the exposure (relevance assumption), ( 2 ) the genetic variants must be independent of potential environmental confounding factors (independence assumption), and ( 3 ) the genetic instruments affect the outcome only via the risk factors (exclusion restriction) 15 . Data sources The data sources utilized in the present MR study are summarized in Supplementary Table S1 . Specifically, the summary-level genome-wide association study (GWAS) data of TM were extracted from the UK Biobank, encompassing a cohort of 21,758 individuals of European ancestry 16 . Data on any stroke and IS [including all IS, large artery stroke (LAS), small vessel stroke (SVS), and cardioembolic stroke (CES)] were obtained from the MEGASTROKE consortium GWAS, which included 446,696 individuals of European ancestry enrolled across 10 studies 17 . Genetic associations for ICH and SAH were obtained from summary statistics of GWAS comprising individuals of European ancestry, which included 456,348 individuals and 11,842,647 variants 16 . The effects of genetic variants on stroke risk factors (body mass index, low-density lipoprotein cholesterol level, systolic blood pressure, type 2 diabetes, and smoking) were obtained from the GWAS website ( http://gwas.mrcieu.ac.uk/datasets ), both sets of data were of European descent. Detailed information of genetic instruments used for each risk factors is shown in Supplementary Table S1 . Selection of Genetic Instruments We utilized single-nucleotide polymorphism (SNP) that were associated with the exposure at the standard genome-wide significance level ( P < 5×10 − 8 ) that were independent (r 2 10,000 kb) to minimize weak instrument bias. SNPs in linkage disequilibrium were clumped. If there are not enough SNPs met the genome-wide significance level, a lower threshold of P < 1×10 − 5 was used 18 . SNPs in linkage disequilibrium (LD) were clumped. Additionally, palindromic variants in which genetic ambiguity was present based on the comparative evaluation of the minor effect allele frequency in exposure and outcome traits were excluded from the analysis 19 . If SNPs for exposure were unavailable in the outcome data, we then identified proxy SNPs at the threshold of LD r 2 > 0.8 on SNiPA ( https://snipa.helmholtz-muenchen.de/snipa3/index.php ). SNPs absent in the outcomes with no appropriate proxies available were then discarded. Univariable MR analysis A bidirectional MR analysis was performed to determine the causal relationship between TM and stroke. We used the random-effects inverse-variance weighted (IVW) method as the primary analysis, which combines the Wald ratio estimates to obtain a consistent estimate of the causal effect of the exposure on the outcome. When there was no evidence of targeted pleiotropy in the selected IVs ( p for MR-Egger intercept > 0.05), the IVW approach was considered the most credible 20 . Moreover, F statistics were calculated to assess the strength of the selected genetic instruments in MR analysis. Values > 10 indicate valid genetic instruments. Additionally, other methods including MR-Egger regression 21 , weighted median 21 , simple mode 22 , and weighted mode 22 were used as sensitivity analyses to examine the robustness of MR effect estimates to potential invalid genetic variants. We formally performed the Cochran Q heterogeneity test to measure heterogeneity between variant-specific causal estimates for each instrument-outcome pair, which can offer a measure of potential violation of instrumental variable assumption and presence of confounding 23 . The MR-Egger test was conducted to find out whether the main assumptions of MR were violated due to directional pleiotropy 21 . P values for MR‒Egger < 0.05 indicates the presence of pleiotropic effects. We also inspected potential directional pleiotropy based on the asymmetry of the funnel plots. The Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) was performed to validate the results in the IVW model, which detected and corrected the effects of outliers, generating reliable causal estimates of heterogeneity 24 . Leave-one-out (LOO) analysis was also used to assess whether the causal effect was driven by an influential SNP via recalculating the MR estimates by leaving one instrument out at a time. A reverse MR analysis was performed using each stroke phenotype as an exposure and TM as an outcome to explore reverse causation between stroke phenotypes and TM. The selection of the genetic instruments and other analytic methods for this reverse MR analysis were identical to those of the primary analysis described earlier. Assessing the impact of sample overlap For the analysis with the exposure and outcome sample partially overlapped, we used an online tool ( https://sb452.shinyapps.io/overlap/ ) to assess the bias from sample overlap and the corresponding type 1 error rate 25 . Multivariate MR analysis According to the results of the search on the PhenoScanner website and possible confounding factors between exposures and outcomes, we performed the multivariate MR analysis to determine whether observed associations were driven by BMI, SBP, T2D, LDL and smoking. The SNPs of all risk factors used in the multivariable MR analysis were acquired by clumping to a LD threshold of r 2 < 0.001. Specifically, random-effects IVW was used in the multivariate MR analysis. Heterogeneity of IVW method was evaluated based on the Q-statistic, and pleiotropy was also appraised according to the intercept term derived from Egger regression. Statistical analyses Statistical analyses and data visualization were performed using R software, version 4.2.0 ( http://www.r-project.org ), with the TwoSampleMR and MR-PRESSO packages. Results Univariable MR Analysis Details of the SNPs included in the study are presented in Supplementary Table S2 . Figure 2 reports the univariable MR estimated of TM on stroke risks. The IVW estimate showed that each 1-SD increase in genetic liability to TM were significantly associated with the risk of any stroke (OR: 1.092, 95% CI: 1.034–1.153, P = 0.002) and IS (OR: 1.105, 95% CI: 1.035–1.180, P = 0.003). No obvious causal effect of genetically determined TM on ICH (OR 0.943, 95% CI: 0.525–1.694, P = 0.844), SAH (OR: 1.078, 95% CI: 0.835–1.392, P = 0.565), LAS (OR: 1.099, 95% CI: 0.944–1.280, P = 0.222), CES (OR: 1.103, 95% CI: 0.960–1.266, P = 0.165), and SVS (OR: 1.060, 95% CI: 0.922–1.219, P = 0.413). Besides, Cochran’s Q-test suggested that there was only little heterogeneity regarding the associations between TM and stroke phenotypes. And the MR-Egger intercepts were not statistically significant ( Supplementary Table S3 ). The scatter plots were displayed in Fig. 3 and Supplementary Figures S1 . The funnel plots were symmetrical ( Supplementary Figure S2 ) and the LOO method indicated that no SNP was substantially driving the association between TM and stroke risks (Fig. 3 and Supplementary Figure S3 ). In the reverse MR analyses, the primary analysis revealed no significant association of genetic liability to stroke and its phenotypes with TM ( Supplementary Table S4 ). When investigating the causal relationship between IS subtypes (LAS, CES, and SVS), ICH and SAH with TM, we used a lenient threshold of P < 1×10 − 5 for these analyses due to not enough of the SNPs reached the criteria for genome-wide association significance (P value). Sensitivity analysis indicated that no heterogeneity or pleiotropy was detected ( Supplementary Table S5 , and Supplementary Figure S4 - 6 ). Assessing the impact of sample overlap The bias and Type 1 error rate with sample overlap were < 0.005 and 0.05 respectively, demonstrating that our results were less likely affected by sample overlap bias ( Supplementary Table S6 ). Multivariate MR analysis In multivariate MR analysis, the causal effect of genetic liability for TM on stroke and IS remained after adjusting for BMI (stroke: IVW OR = 1.097, 95% CI = 1.039–1.158, P < 0.001; IS: IVW OR = 1.111, 95% CI = 1.046–1.181, P < 0.001), SBP(stroke: IVW OR = 1.100, 95% CI = 1.029–1.177, P = 0.005; IS: IVW OR = 1.116, 95% CI = 1.036–1.202, P = 0.004), LDL (stroke: IVW OR = 1.098, 95% CI = 1.011–1.192, P = 0.026; IS: IVW OR = 1.113, 95% CI = 1.021–1.215, P = 0.016), T2D (stroke: IVW OR = 1.086, 95% CI = 1.030–1.145, P = 0.002; IS: IVW OR = 1.110, 95% CI = 1.046–1.178, P < 0.001), and smoking (stroke: IVW OR = 1.082, 95% CI = 1.021–1.146, P = 0.007; IS: IVW OR = 1.094, 95% CI = 1.019–1.175, P = 0.013), and all risk factors (stroke: IVW OR = 1.104, 95% CI = 1.028–1.186, P = 0.006; IS: IVW OR = 1.123, 95% CI = 1.037–1.216, P = 0.004)(Fig. 4 ). Egger intercepts also indicated no horizontal pleiotropy ( Supplementary Table S5 ). Observing heterogeneity with a Cochran's Q test derived p value < 0.05, we deemed it acceptable as we used the random-effects IVW as the main result 26 ( Supplementary Table S5 ). Discussion The present MR study found further evidence for TM as the risk factors for stroke and IS. These results were consistent in multivariable MR adjusted for BMI, SBP, LDL, T2D, and smoking. In the reverse MR analyses, no evidence shown significant association of genetic liability to stroke and its phenotypes with TM. Previous clinical studies had suggested that circulating thrombomodulin levels were increased in IS patients, and increased plasma TM levels at baseline were associated with decreased risks of adverse clinical outcomes at 3 months after IS 27 . However, to date, there are few studies investigating the association between plasma TM levels and the risk of stroke. A population-based study about vascular injury biomarkers and stroke risk performed by Laura et al. found only a weak borderline significant trend between TM and overall stroke risk (hazard ratio:1.47 [0.99–2.19]; P = 0.05) 28 . Therefore, still little is known about associations between plasma TM levels and stroke risk. The current MR study extend the literature on the issue of TM in stroke in several ways. Firstly, in the context of the MR framework, our study provided compelling evidence of the causal relationship between TM and an increased risk of any stroke, as well as IS. In particular, evidence for causal inference was supported by the consistent direction and magnitude of effect estimates across various MR methods, including IVW, MR-Egger regression, weighted median, simple mode, and weighted mode. These results suggest that measuring serum TM levels could serve as a valuable screening tool for predicting the passible occurrence of any stroke and IS. Endothelial cells play an important role in the prevention of intravascular thrombus formation 29 , and ECD is critically involved in development of atherosclerosis and stroke 30,31 . The anticoagulant properties of the endothelium are mediated primarily by TM, a multidomain type-1 transmembrane glycoprotein constitutively expressed on the luminal surface of endothelial cells 32 . Binding of thrombin to the high affinity TM receptor transforms its procoagulant activity into an anticoagulant potential, by activating protein C 33 . TM detaches from the cell membrane surface and releases when the vascular endothelial cells are damaged; thus, the plasma TM levels are increased. However, at this time, the binding between thrombin and TM weakens, making it impossible to activate protein C. Eventually, the anticoagulant effect of body fluids is weakened, resulting in a procoagulant effect that increase the risk of stroke. Secondly, we found no causal evidence that the occurrence of IS, or indeed any other type of stroke, leads to increased serum TM levels. Although, there have been observational studies showing that circulating thrombomodulin levels were elevated in IS patients, as is known, results from such studies are limited by small sample size, residual confounding and reverse causality 13 . Alternatively, we prefer to believe that the reason why circulating TM levels were increased in IS patients may be due to the fact that TM has already been released into the blood due to the occurrence of ECD before the onset of IS. Nevertheless, further studies are needed to support this hypothesis. The present study had several strengths. It was the first MR study with a comprehensive assessment of TM in relation to stroke risk. Compared to previous observational studies, MR analysis could effectively reduce potential bias including confounders and reverse causation, thus enhancing the causal inference. Another strength is that the bidirectional analysis guaranteed the inference of causality between TM and stroke in both directions, avoiding misleading causal effect 14 . In addition, we performed multivariate MR analysis to determine whether the observed association was driven by confounding factors. Our study has several limitations that need to be considered. Firstly, the study only included a single population, and the representativeness of the results remains to be further verified in the whole population. Secondly, it is possible that our findings might have been affected by weak instrument bias, which is influenced by the selection of the genetic instrument through the relatively lenient threshold of P = 1 × 10 − 5 for IS phenotypes (including LAS, CEA, and SVS), although the F statistics did not indicate that our instruments were weak. Conclusions This MR estimate reveals robust evidence that higher genetically predicted circulating TM levels were associated with an increased risk of any stroke and IS. These findings may provide new insight into the mechanisms underlying the association between TM and stroke, and may have indications for clinicians to pay more attention to that measuring serum TM levels could serve as a valuable screening tool for predicting the passible occurrence of any stroke and IS. Declarations Ethics approval This study used publicly available de-identified data from participant studies that were approved by an ethical standards committee with respect to human experimentation. No separate ethical approval was required in this study. Consent to participate All data in the MR study were obtained from published GWAS studies, and the participants had signed informed consent in the published original GWAS study. Consent to publication All data in the MR study were obtained from published GWAS studies, and the participants had signed informed consent in the published original GWAS study. Availability of data and materials All data generated or analyzed during this study are included in this article and its additional materials. Competing interests The authors declare no competing interests. Funding This study was supported by the Jilin Provincial Medical and Health Talents Project (JLSWSRCZX2023-18). Authors’ contributions Xuan Chen and Renjie Liu designed and conceived the study. Pengju Wang contributed to data acquisition. Shaoning Guo supervised data entry and integrity. Jiahui Feng analyzed data and prepared the figures. Renjie Liu wrote the manuscript. All authors reviewed and approved the final draft. Acknowledgements We sincerely thank the original GWASs and the related consortiums for sharing and managing the summary statistics. 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Additional Declarations No competing interests reported. Supplementary Files FigureS1.jpg Supplementary Figure S1. Scatter plots for univariable MR estimates genetic risk of TM on LAS(A), CE (B), SVS(C), ICH(D), and SAH(E). TM, plasma thrombomodulin levels; LAS, large artery stroke; SVS, small vessel stroke; CES, cardioembolic stroke; ICH, intracerebral hemorrhage; SAH, subarachnoid hemorrhage. FigureS2.jpg Supplementary Figure S2. Funnel plots of univariable Mendelian randomization estimates genetic risk of TM on any stroke(A), IS(B), LAS(C), CE (D), SVS(E), ICH(F) and SAH(G). TM, plasma thrombomodulin levels; IS, ischemic stroke; LAS, large artery stroke; SVS, small vessel stroke; CES, cardioembolic stroke; ICH, intracerebral hemorrhage; SAH, subarachnoid hemorrhage. FigureS3.jpg Supplementary Figure S3. Leave-one-out analysis for TM on LAS(A), CE (B), SVS(C), ICH(D), and SAH (E). TM, plasma thrombomodulin levels; LAS, large artery stroke; SVS, small vessel stroke; CES, cardioembolic stroke; ICH, intracerebral hemorrhage; SAH, subarachnoid hemorrhage. FigureS4.jpg Supplementary Figure S4. Scatter plots for univariable MR estimates genetic risk of any stroke(A), IS(B), LAS(C), CE (D), SVS(E), ICH(F) and SAH(G) on TM. TM, plasma thrombomodulin levels; IS, ischemic stroke; LAS, large artery stroke; SVS, small vessel stroke; CES, cardioembolic stroke; ICH, intracerebral hemorrhage; SAH, subarachnoid hemorrhage. FigureS5.jpg Supplementary Figure S5. Funnel plots of univariable MR estimates genetic risk of any stroke(A), IS(B), LAS(C), CE (D), SVS(E), ICH(F) and SAH(G) on TM. TM, plasma thrombomodulin levels; IS, ischemic stroke; LAS, large artery stroke; SVS, small vessel stroke; CES, cardioembolic stroke; ICH, intracerebral hemorrhage; SAH, subarachnoid hemorrhage. FigureS6.jpg Supplementary Figure S6. Leave-one-out analysis for any stroke(A), IS(B), LAS(C), CE (D), SVS(E), ICH(F) and SAH(G) on TM. TM, plasma thrombomodulin levels; IS, ischemic stroke; LAS, large artery stroke; SVS, small vessel stroke; CES, cardioembolic stroke; ICH, intracerebral hemorrhage; SAH, subarachnoid hemorrhage. STROBEMRchecklist.docx SupplementarytableS16.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4014670","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":279854428,"identity":"14ed3712-82cd-49fa-b33f-af8e0dabce1f","order_by":0,"name":"Renjie Liu","email":"","orcid":"","institution":"The First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Renjie","middleName":"","lastName":"Liu","suffix":""},{"id":279854430,"identity":"56afd3e0-e9ae-40bc-8fcb-57876146dade","order_by":1,"name":"Pengju Wang","email":"","orcid":"","institution":"The First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Pengju","middleName":"","lastName":"Wang","suffix":""},{"id":279854431,"identity":"afb46f21-2290-4527-810d-5e4376af1fdf","order_by":2,"name":"Shaoning Guo","email":"","orcid":"","institution":"The First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Shaoning","middleName":"","lastName":"Guo","suffix":""},{"id":279854432,"identity":"96980b68-d57d-4f74-9e12-41db52a4b8d3","order_by":3,"name":"Jiahui Feng","email":"","orcid":"","institution":"The First Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Jiahui","middleName":"","lastName":"Feng","suffix":""},{"id":279854433,"identity":"551d1e00-fffb-4f70-a674-e5fc760b8bfd","order_by":4,"name":"Xuan Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIie3OPQrCMBiA4YRCXQJd41C8QkSog39XSSjoIrg6CgVHZ6UeIqtbJFCXHkDRIVMnZydBE0Hc0rgJ5l2SQB6+DwCf7xfD70tjwYQ+4MKdIEG/JZgCNxLlWaHgUra6TaUkAv2Yi6BS1iGXYkJAKdu7nFJNxh0uwi6xEYKnCQZzCfn5RSTjAoXYTmY3DIgc8ZMw5OFCpqGZwvgRGCLqCT6O9WLlJOUlpfstSTsbGSZWEq3TCsNlb8APJVPX+TBeHbLKSkzB/XUgqvc0z7r/nxrC/a/P5/P9VU/8YErcxMs0OwAAAABJRU5ErkJggg==","orcid":"","institution":"The First Hospital of Jilin University","correspondingAuthor":true,"prefix":"","firstName":"Xuan","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-03-05 00:47:58","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4014670/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4014670/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52986901,"identity":"d144c70e-4657-4b6f-8b03-35831ac525f3","added_by":"auto","created_at":"2024-03-19 11:15:23","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2495290,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConceptual framework for the Mendelian randomization analysis of the causal association of TM and stroke. \u003c/strong\u003eThe design is under the following three basic criteria.: Ⅰ relevance: (1) the genetic variants must be strongly associated with the exposure (relevance assumption), (2) the genetic variants must be independent of potential environmental confounding factors (independence assumption), and (3) the genetic instruments affect the outcome only via the risk factors (exclusion restriction).\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4014670/v1/8fee2fef5791e7f717a458f1.jpg"},{"id":52986902,"identity":"32578247-4cbe-48d0-ac0b-af5d9ad4f529","added_by":"auto","created_at":"2024-03-19 11:15:23","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1152900,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociations of TM with stroke in univariable Mendelian randomization analysis. \u003c/strong\u003eTM, plasma thrombomodulin levels; IS, ischemic stroke; LAS, large artery stroke; SVS, small vessel stroke; CES, cardioembolic stroke; ICH, intracerebral hemorrhage; SAH, subarachnoid hemorrhage.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4014670/v1/79c3919a6e22f9388d384d79.jpg"},{"id":52986906,"identity":"e107a89f-ed20-41c3-bcb8-023a8a7d5d3c","added_by":"auto","created_at":"2024-03-19 11:15:23","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1668787,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScatter plots and leave-one-out analysis of univariable MR estimates genetic risk of TM on any stroke and IS. \u003c/strong\u003e(A) Scatter plot for TM on any stroke and IS. The slope of each line corresponds to the estimated MR effect from different methods. Each dot represents an instrumental single-nucleotide polymorphism (SNP). The x-axis represents the genetic association with the exposure; the y-axis represents the genetic association with risk of the outcome. The slope of each line represents the causal estimate of an exposure on corresponding outcome per method. (B) Leave-one-out analysis for TM on any stroke and IS.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4014670/v1/6d17a645addc80180025c707.jpg"},{"id":52987741,"identity":"0c0565e6-97ac-4f85-ae95-c3338b85ec2e","added_by":"auto","created_at":"2024-03-19 11:23:24","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1032314,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociations of TM with stroke in multivariable Mendelian randomization analysis. \u003c/strong\u003eTM, plasma thrombomodulin levels; IS, ischemic stroke; BMI, body mass index; SBP, systolic blood pressure; T2D, type 2 diabetes; LDL, low density lipoprotein.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4014670/v1/0ff40032043ad6344803e831.jpg"},{"id":59062688,"identity":"f6d4d623-ef85-45d2-bafc-53e2932ec2d8","added_by":"auto","created_at":"2024-06-26 01:56:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7007462,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4014670/v1/16853a81-47ce-4407-b90d-0891488906c8.pdf"},{"id":52986904,"identity":"571fc231-723e-49be-9ff1-35d40f188543","added_by":"auto","created_at":"2024-03-19 11:15:23","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2151713,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure S1.\u003c/strong\u003e Scatter plots for univariable MR estimates genetic risk of TM on LAS(A), CE (B), SVS(C), ICH(D), and SAH(E). TM, plasma thrombomodulin levels; LAS, large artery stroke; SVS, small vessel stroke; CES, cardioembolic stroke; ICH, intracerebral hemorrhage; SAH, subarachnoid hemorrhage.\u003c/p\u003e","description":"","filename":"FigureS1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4014670/v1/3d62a77b10e53924e00c2219.jpg"},{"id":52986903,"identity":"8e032d1e-5b35-443a-86a9-a80315980105","added_by":"auto","created_at":"2024-03-19 11:15:23","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1343900,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure S2. \u003c/strong\u003eFunnel plots of univariable Mendelian randomization estimates genetic risk of TM on any stroke(A), IS(B), LAS(C), CE (D), SVS(E), ICH(F) and SAH(G). TM, plasma thrombomodulin levels; IS, ischemic stroke; LAS, large artery stroke; SVS, small vessel stroke; CES, cardioembolic stroke; ICH, intracerebral hemorrhage; SAH, subarachnoid hemorrhage.\u003c/p\u003e","description":"","filename":"FigureS2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4014670/v1/def837d4b9e85a6d5420c29a.jpg"},{"id":52986910,"identity":"dfb47beb-d28b-4ed9-8f3a-dfb6b3350fbb","added_by":"auto","created_at":"2024-03-19 11:15:24","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1878620,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure S3.\u003c/strong\u003e Leave-one-out analysis for TM on LAS(A), CE (B), SVS(C), ICH(D), and SAH (E). TM, plasma thrombomodulin levels; LAS, large artery stroke; SVS, small vessel stroke; CES, cardioembolic stroke; ICH, intracerebral hemorrhage; SAH, subarachnoid hemorrhage.\u003c/p\u003e","description":"","filename":"FigureS3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4014670/v1/198512a421aa7e882b765323.jpg"},{"id":52986913,"identity":"5829ffbe-c685-462f-a788-d45008068f7b","added_by":"auto","created_at":"2024-03-19 11:15:24","extension":"jpg","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":2417351,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure S4.\u003c/strong\u003e Scatter plots for univariable MR estimates genetic risk of any stroke(A), IS(B), LAS(C), CE (D), SVS(E), ICH(F) and SAH(G) on TM. TM, plasma thrombomodulin levels; IS, ischemic stroke; LAS, large artery stroke; SVS, small vessel stroke; CES, cardioembolic stroke; ICH, intracerebral hemorrhage; SAH, subarachnoid hemorrhage.\u003c/p\u003e","description":"","filename":"FigureS4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4014670/v1/8cb8b90261f649c344167d1b.jpg"},{"id":52986912,"identity":"f1c68ea1-215e-4fcd-bb0e-b2bf2d4f9f77","added_by":"auto","created_at":"2024-03-19 11:15:24","extension":"jpg","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1840739,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure S5.\u003c/strong\u003e Funnel plots of univariable MR estimates genetic risk of any stroke(A), IS(B), LAS(C), CE (D), SVS(E), ICH(F) and SAH(G) on TM. TM, plasma thrombomodulin levels; IS, ischemic stroke; LAS, large artery stroke; SVS, small vessel stroke; CES, cardioembolic stroke; ICH, intracerebral hemorrhage; SAH, subarachnoid hemorrhage.\u003c/p\u003e","description":"","filename":"FigureS5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4014670/v1/cb4a95127ea36b18dd3a7096.jpg"},{"id":52986907,"identity":"1b0ffbbc-3db0-4bd6-8e03-e66e6a39779e","added_by":"auto","created_at":"2024-03-19 11:15:24","extension":"jpg","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":3163392,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure S6.\u003c/strong\u003e Leave-one-out analysis for any stroke(A), IS(B), LAS(C), CE (D), SVS(E), ICH(F) and SAH(G) on TM. TM, plasma thrombomodulin levels; IS, ischemic stroke; LAS, large artery stroke; SVS, small vessel stroke; CES, cardioembolic stroke; ICH, intracerebral hemorrhage; SAH, subarachnoid hemorrhage.\u003c/p\u003e","description":"","filename":"FigureS6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4014670/v1/5cfe1a0271dd433d9c9e6927.jpg"},{"id":52988433,"identity":"49bfa889-9cdc-40f0-9c6f-8546bc01d2c4","added_by":"auto","created_at":"2024-03-19 11:31:24","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":28911,"visible":true,"origin":"","legend":"","description":"","filename":"STROBEMRchecklist.docx","url":"https://assets-eu.researchsquare.com/files/rs-4014670/v1/3a880d312444eee5a419f0a4.docx"},{"id":52986911,"identity":"1b86254a-fecd-4179-8593-da0fa8d1a07d","added_by":"auto","created_at":"2024-03-19 11:15:24","extension":"xlsx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":39424,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarytableS16.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4014670/v1/dc9983235ba247d91678e5f7.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The causal relationship between plasma thrombomodulin levels and Risk of Stroke: A Mendelian Randomization Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eStroke is the second leading cause of death worldwide, remaining one of the most prevalent and devastating diseases affecting the global population \u003csup\u003e1\u003c/sup\u003e. According to a recent report from the Global Burden of Disease, the average lifetime risk of stroke in the United States increased from 22.8% in 1990 to 24.9% in 2016 \u003csup\u003e2\u003c/sup\u003e. By 2030, stroke prevalence is expected to rise the most among Hispanic men, with direct costs of care increasing over 300% since 2012\u003csup\u003e3\u003c/sup\u003e. Moreover, an unexpectedly increasing trend in the incidence of stroke is seen among young adults\u003csup\u003e4\u003c/sup\u003e. To reduce stroke prevalence and its complications, much effort has been focused on prevention. However, despite advances, risk prediction remains imprecise with persistently high rates of stroke. As early as 2012, Ashley EA et al. proposed that the incorporation of genetics into risk prediction frameworks offers the opportunity to refine risks, toward the creation of earlier and tailored risk reduction strategies \u003csup\u003e5\u003c/sup\u003e. Therefore, it is necessary to place great emphasis on exploring potential biomarkers associated with stroke occurrence.\u003c/p\u003e \u003cp\u003eThrombomodulin (TM), a transmembrane glycoprotein mainly expressed by endothelial cells\u003csup\u003e6\u003c/sup\u003e, plays a crucial role in maintaining intravascular patency and blood fluidity due to its antithrombotic, anti-inflammatory, and cytoprotective properties. In healthy humans, the levels of TM are low (\u0026lt;\u0026thinsp;10 ng/mL), while high TM levels are common in patients suffering from various diseases\u003csup\u003e7\u003c/sup\u003e. In particular, the consistent elevation of TM levels during pathologies is now widely regarded as an important circulatory biomarker for endothelial cell dysfunction (ECD) and vascular risk assessment \u003csup\u003e8,9\u003c/sup\u003e. ECD underlies many cardiovascular diseases, including stroke, and may be a key initiating step in these diseases\u003csup\u003e10\u003c/sup\u003e. However, to date, there have been relatively few studies investigating the association between TM levels and stroke. Despite observational studies have examined circulating TM levels were elevated in ischemic stroke (IS) patients\u003csup\u003e11,12\u003c/sup\u003e, it remains unclear whether this relationship is causal and requires further elucidation. And observational study designs may be limited, additionally, by modest overall sample size, measurement error, and risk of residual confounding\u003csup\u003e13\u003c/sup\u003e. Thus, the casual relationship between TM levels and stroke remains controversial.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) is a powerful method that uses genetic variants as instrumental variables(IVs) to assess the causal effects of risk factors related to diseases, can overcome the limitations of observational studies, such as small sample sizes or reverse causation or biases caused by unmeasured confounding\u003csup\u003e14\u003c/sup\u003e. Here, we implemented bidirectional MR analysis to assess the association between serum TM levels with any stroke, IS, intracerebral hemorrhage (ICH), and subarachnoid hemorrhage (SAH). And then, we performed multivariate MR analysis to determine whether the observed association was driven by body mass index (BMI), systolic blood pressure (SBP), type 2 diabetes (T2D), low-density lipoprotein (LDL) and smoking.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003ch2\u003eStudy design and data sources\u003c/h2\u003e\n\u003cp\u003eOur MR analysis was performed in accordance with the STROBE-MR guidelines. The study overview is presented in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e. The valid IVs for the exposure should satisfy 3 key assumptions: (\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e) the genetic variants must be strongly associated with the exposure (relevance assumption), (\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e) the genetic variants must be independent of potential environmental confounding factors (independence assumption), and (\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e) the genetic instruments affect the outcome only via the risk factors (exclusion restriction) \u003csup\u003e15\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003ch2\u003eData sources\u003c/h2\u003e\n\u003cp\u003eThe data sources utilized in the present MR study are summarized in \u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/strong\u003e Specifically, the summary-level genome-wide association study (GWAS) data of TM were extracted from the UK Biobank, encompassing a cohort of 21,758 individuals of European ancestry \u003csup\u003e16\u003c/sup\u003e. Data on any stroke and IS [including all IS, large artery stroke (LAS), small vessel stroke (SVS), and cardioembolic stroke (CES)] were obtained from the MEGASTROKE consortium GWAS, which included 446,696 individuals of European ancestry enrolled across 10 studies \u003csup\u003e17\u003c/sup\u003e. Genetic associations for ICH and SAH were obtained from summary statistics of GWAS comprising individuals of European ancestry, which included 456,348 individuals and 11,842,647 variants \u003csup\u003e16\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe effects of genetic variants on stroke risk factors (body mass index, low-density lipoprotein cholesterol level, systolic blood pressure, type 2 diabetes, and smoking) were obtained from the GWAS website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://gwas.mrcieu.ac.uk/datasets\u003c/span\u003e\u003c/span\u003e), both sets of data were of European descent. Detailed information of genetic instruments used for each risk factors is shown in \u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n\u003ch2\u003eSelection of Genetic Instruments\u003c/h2\u003e\n\u003cp\u003eWe utilized single-nucleotide polymorphism (SNP) that were associated with the exposure at the standard genome-wide significance level (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) that were independent (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and distance\u0026thinsp;\u0026gt;\u0026thinsp;10,000 kb) to minimize weak instrument bias. SNPs in linkage disequilibrium were clumped. If there are not enough SNPs met the genome-wide significance level, a lower threshold of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e was used\u003csup\u003e18\u003c/sup\u003e. SNPs in linkage disequilibrium (LD) were clumped. Additionally, palindromic variants in which genetic ambiguity was present based on the comparative evaluation of the minor effect allele frequency in exposure and outcome traits were excluded from the analysis\u003csup\u003e19\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eIf SNPs for exposure were unavailable in the outcome data, we then identified proxy SNPs at the threshold of LD r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.8 on SNiPA (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://snipa.helmholtz-muenchen.de/snipa3/index.php\u003c/span\u003e\u003c/span\u003e). SNPs absent in the outcomes with no appropriate proxies available were then discarded.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n\u003ch2\u003eUnivariable MR analysis\u003c/h2\u003e\n\u003cp\u003eA bidirectional MR analysis was performed to determine the causal relationship between TM and stroke. We used the random-effects inverse-variance weighted (IVW) method as the primary analysis, which combines the Wald ratio estimates to obtain a consistent estimate of the causal effect of the exposure on the outcome. When there was no evidence of targeted pleiotropy in the selected IVs (\u003cem\u003ep\u003c/em\u003e for MR-Egger intercept\u0026thinsp;\u0026gt;\u0026thinsp;0.05), the IVW approach was considered the most credible\u003csup\u003e20\u003c/sup\u003e. Moreover, F statistics were calculated to assess the strength of the selected genetic instruments in MR analysis. Values\u0026thinsp;\u0026gt;\u0026thinsp;10 indicate valid genetic instruments. Additionally, other methods including MR-Egger regression\u003csup\u003e21\u003c/sup\u003e, weighted median\u003csup\u003e21\u003c/sup\u003e, simple mode\u003csup\u003e22\u003c/sup\u003e, and weighted mode\u003csup\u003e22\u003c/sup\u003e were used as sensitivity analyses to examine the robustness of MR effect estimates to potential invalid genetic variants. We formally performed the Cochran Q heterogeneity test to measure heterogeneity between variant-specific causal estimates for each instrument-outcome pair, which can offer a measure of potential violation of instrumental variable assumption and presence of confounding\u003csup\u003e23\u003c/sup\u003e. The MR-Egger test was conducted to find out whether the main assumptions\u0026nbsp;of MR were violated due to directional pleiotropy\u003csup\u003e21\u003c/sup\u003e. \u003cem\u003eP\u003c/em\u003e values for MR‒Egger\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicates the presence of pleiotropic effects. We also inspected potential directional pleiotropy based on the asymmetry of the funnel plots. The Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) was performed to validate the results in the IVW model, which detected and corrected the effects of outliers, generating reliable causal estimates of heterogeneity \u003csup\u003e24\u003c/sup\u003e. Leave-one-out (LOO) analysis was also used to assess whether the causal effect was driven by an influential SNP via recalculating the MR estimates by leaving one instrument out at a time.\u003c/p\u003e\n\u003cp\u003eA reverse MR analysis was performed using each stroke phenotype as an exposure and TM as an outcome to explore reverse causation between stroke phenotypes and TM. The selection of the genetic instruments and other analytic methods for this reverse MR analysis were identical to those of the primary analysis described earlier.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003ch2\u003eAssessing the impact of sample overlap\u003c/h2\u003e\n\u003cp\u003eFor the analysis with the exposure and outcome sample partially overlapped, we used an online tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://sb452.shinyapps.io/overlap/\u003c/span\u003e\u003c/span\u003e) to assess the bias from sample overlap and the corresponding type 1 error rate \u003csup\u003e25\u003c/sup\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n\u003ch2\u003eMultivariate MR analysis\u003c/h2\u003e\n\u003cp\u003eAccording to the results of the search on the PhenoScanner website and possible confounding factors between exposures and outcomes, we performed the multivariate MR analysis to determine whether observed associations were driven by BMI, SBP, T2D, LDL and smoking. The SNPs of all risk factors used in the multivariable MR analysis were acquired by clumping to a LD threshold of r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001. Specifically, random-effects IVW\u0026nbsp;was used in the multivariate MR analysis. Heterogeneity of IVW method was evaluated based on the Q-statistic, and pleiotropy was also appraised according to the intercept term derived from Egger regression.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n\u003ch2\u003eStatistical analyses\u003c/h2\u003e\n\u003cp\u003eStatistical analyses and data visualization were performed using R software, version 4.2.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.r-project.org\u003c/span\u003e\u003c/span\u003e), with the TwoSampleMR and MR-PRESSO packages.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n\u003ch2\u003eUnivariable MR Analysis\u003c/h2\u003e\n\u003cp\u003eDetails of the SNPs included in the study are presented in \u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/strong\u003e. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e reports the univariable MR estimated of TM on stroke risks. The IVW estimate showed that each 1-SD increase in genetic liability to TM were significantly associated with the risk of any stroke (OR: 1.092, 95% CI: 1.034\u0026ndash;1.153, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) and IS (OR: 1.105, 95% CI: 1.035\u0026ndash;1.180, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003). No obvious causal effect of genetically determined TM on ICH (OR 0.943, 95% CI: 0.525\u0026ndash;1.694, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.844), SAH (OR: 1.078, 95% CI: 0.835\u0026ndash;1.392, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.565), LAS (OR: 1.099, 95% CI: 0.944\u0026ndash;1.280, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.222), CES (OR: 1.103, 95% CI: 0.960\u0026ndash;1.266, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.165), and SVS (OR: 1.060, 95% CI: 0.922\u0026ndash;1.219, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.413). Besides, Cochran\u0026rsquo;s Q-test suggested that there was only little heterogeneity regarding the associations between TM and stroke phenotypes. And the MR-Egger intercepts were not statistically significant (\u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/strong\u003e). The scatter plots were displayed in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cstrong\u003eSupplementary Figures \u003cspan class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/strong\u003e. The funnel plots were symmetrical (\u003cstrong\u003eSupplementary Figure \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/strong\u003e) and the LOO method indicated that no SNP was substantially driving the association between TM and stroke risks (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cstrong\u003eSupplementary Figure \u003cspan class=\"InternalRef\"\u003eS3\u003c/span\u003e\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eIn the reverse MR analyses, the primary analysis revealed no significant association of genetic liability to stroke and its phenotypes with TM (\u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS4\u003c/span\u003e\u003c/strong\u003e). When investigating the causal relationship between IS subtypes (LAS, CES, and SVS), ICH and SAH with TM, we used a lenient threshold of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e for these analyses due to not enough of the SNPs reached the criteria for genome-wide association significance (P value). Sensitivity analysis indicated that no heterogeneity or pleiotropy was detected (\u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS5\u003c/span\u003e\u003c/strong\u003e, and \u003cstrong\u003eSupplementary Figure \u003cspan class=\"InternalRef\"\u003eS4\u003c/span\u003e-\u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e\u003c/strong\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003ch2\u003eAssessing the impact of sample overlap\u003c/h2\u003e\n\u003cp\u003eThe bias and Type 1 error rate with sample overlap were \u0026lt;\u0026thinsp;0.005 and 0.05 respectively, demonstrating that our results were less likely affected by sample overlap bias (\u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS6\u003c/span\u003e\u003c/strong\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003ch2\u003eMultivariate MR analysis\u003c/h2\u003e\n\u003cp\u003eIn multivariate MR analysis, the causal effect of genetic liability for TM on stroke and IS remained after adjusting for BMI (stroke: IVW OR\u0026thinsp;=\u0026thinsp;1.097, 95% CI\u0026thinsp;=\u0026thinsp;1.039\u0026ndash;1.158, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; IS: IVW OR\u0026thinsp;=\u0026thinsp;1.111, 95% CI\u0026thinsp;=\u0026thinsp;1.046\u0026ndash;1.181, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), SBP(stroke: IVW OR\u0026thinsp;=\u0026thinsp;1.100, 95% CI\u0026thinsp;=\u0026thinsp;1.029\u0026ndash;1.177, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005; IS: IVW OR\u0026thinsp;=\u0026thinsp;1.116, 95% CI\u0026thinsp;=\u0026thinsp;1.036\u0026ndash;1.202, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), LDL (stroke: IVW OR\u0026thinsp;=\u0026thinsp;1.098, 95% CI\u0026thinsp;=\u0026thinsp;1.011\u0026ndash;1.192, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026; IS: IVW OR\u0026thinsp;=\u0026thinsp;1.113, 95% CI\u0026thinsp;=\u0026thinsp;1.021\u0026ndash;1.215, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.016), T2D (stroke: IVW OR\u0026thinsp;=\u0026thinsp;1.086, 95% CI\u0026thinsp;=\u0026thinsp;1.030\u0026ndash;1.145, P\u0026thinsp;=\u0026thinsp;0.002; IS: IVW OR\u0026thinsp;=\u0026thinsp;1.110, 95% CI\u0026thinsp;=\u0026thinsp;1.046\u0026ndash;1.178, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and smoking (stroke: IVW OR\u0026thinsp;=\u0026thinsp;1.082, 95% CI\u0026thinsp;=\u0026thinsp;1.021\u0026ndash;1.146, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007; IS: IVW OR\u0026thinsp;=\u0026thinsp;1.094, 95% CI\u0026thinsp;=\u0026thinsp;1.019\u0026ndash;1.175, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013), and all risk factors (stroke: IVW OR\u0026thinsp;=\u0026thinsp;1.104, 95% CI\u0026thinsp;=\u0026thinsp;1.028\u0026ndash;1.186, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006; IS: IVW OR\u0026thinsp;=\u0026thinsp;1.123, 95% CI\u0026thinsp;=\u0026thinsp;1.037\u0026ndash;1.216, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004)(Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Egger intercepts also indicated no horizontal pleiotropy (\u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS5\u003c/span\u003e\u003c/strong\u003e). Observing heterogeneity with a Cochran's Q test derived \u003cem\u003ep\u003c/em\u003e value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, we deemed it acceptable as we used the random-effects IVW as the main result\u003csup\u003e26\u003c/sup\u003e (\u003cstrong\u003eSupplementary Table \u003cspan class=\"InternalRef\"\u003eS5\u003c/span\u003e\u003c/strong\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe present MR study found further evidence for TM as the risk factors for stroke and IS. These results were consistent in multivariable MR adjusted for BMI, SBP, LDL, T2D, and smoking. In the reverse MR analyses, no evidence shown significant association of genetic liability to stroke and its phenotypes with TM.\u003c/p\u003e \u003cp\u003ePrevious clinical studies had suggested that circulating thrombomodulin levels were increased in IS patients, and increased plasma TM levels at baseline were associated with decreased risks of adverse clinical outcomes at 3 months after IS \u003csup\u003e27\u003c/sup\u003e. However, to date, there are few studies investigating the association between plasma TM levels and the risk of stroke. A population-based study about vascular injury biomarkers and stroke risk performed by Laura et al. found only a weak borderline significant trend between TM and overall stroke risk (hazard ratio:1.47 [0.99\u0026ndash;2.19]; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05) \u003csup\u003e28\u003c/sup\u003e. Therefore, still little is known about associations between plasma TM levels and stroke risk.\u003c/p\u003e \u003cp\u003eThe current MR study extend the literature on the issue of TM in stroke in several ways. Firstly, in the context of the MR framework, our study provided compelling evidence of the causal relationship between TM and an increased risk of any stroke, as well as IS. In particular, evidence for causal inference was supported by the consistent direction and magnitude of effect estimates across various MR methods, including IVW, MR-Egger regression, weighted median, simple mode, and weighted mode. These results suggest that measuring serum TM levels could serve as a valuable screening tool for predicting the passible occurrence of any stroke and IS. Endothelial cells play an important role in the prevention of intravascular thrombus formation\u003csup\u003e29\u003c/sup\u003e, and ECD is critically involved in development of atherosclerosis and stroke\u003csup\u003e30,31\u003c/sup\u003e. The anticoagulant properties of the endothelium are mediated primarily by TM, a multidomain type-1 transmembrane glycoprotein constitutively expressed on the luminal surface of endothelial cells\u003csup\u003e32\u003c/sup\u003e. Binding of thrombin to the high affinity TM receptor transforms its procoagulant activity into an anticoagulant potential, by activating protein C\u003csup\u003e33\u003c/sup\u003e. TM detaches from the cell membrane surface and releases when the vascular endothelial cells are damaged; thus, the plasma TM levels are increased. However, at this time, the binding between thrombin and TM weakens, making it impossible to activate protein C. Eventually, the anticoagulant effect of body fluids is weakened, resulting in a procoagulant effect that increase the risk of stroke. Secondly, we found no causal evidence that the occurrence of IS, or indeed any other type of stroke, leads to increased serum TM levels. Although, there have been observational studies showing that circulating thrombomodulin levels were elevated in IS patients, as is known, results from such studies are limited by small sample size, residual confounding and reverse causality\u003csup\u003e13\u003c/sup\u003e. Alternatively, we prefer to believe that the reason why circulating TM levels were increased in IS patients may be due to the fact that TM has already been released into the blood due to the occurrence of ECD before the onset of IS. Nevertheless, further studies are needed to support this hypothesis.\u003c/p\u003e \u003cp\u003eThe present study had several strengths. It was the first MR study with a comprehensive assessment of TM in relation to stroke risk. Compared to previous observational studies, MR analysis could effectively reduce potential bias including confounders and reverse causation, thus enhancing the causal inference. Another strength is that the bidirectional analysis guaranteed the inference of causality between TM and stroke in both directions, avoiding misleading causal effect\u003csup\u003e14\u003c/sup\u003e. In addition, we performed multivariate MR analysis to determine whether the observed association was driven by confounding factors.\u003c/p\u003e \u003cp\u003eOur study has several limitations that need to be considered. Firstly, the study only included a single population, and the representativeness of the results remains to be further verified in the whole population. Secondly, it is possible that our findings might have been affected by weak instrument bias, which is influenced by the selection of the genetic instrument through the relatively lenient threshold of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e for IS phenotypes (including LAS, CEA, and SVS), although the F statistics did not indicate that our instruments were weak.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis MR estimate reveals robust evidence that higher genetically predicted circulating TM levels were associated with an increased risk of any stroke and IS. These findings may provide new insight into the mechanisms underlying the association between TM and stroke, and may have indications for clinicians to pay more attention to that measuring serum TM levels could serve as a valuable screening tool for predicting the passible occurrence of any stroke and IS.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study used publicly available de-identified data from participant studies that were approved by an ethical standards committee with respect to human experimentation. No separate ethical approval was required in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data in the MR study were obtained from published GWAS studies, and the participants had signed informed consent in the published original GWAS study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data in the MR study were obtained from published GWAS studies, and the participants had signed informed consent in the published original GWAS study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this article and its additional materials.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Jilin Provincial Medical and Health Talents Project (JLSWSRCZX2023-18).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXuan Chen and Renjie Liu designed and conceived the study. Pengju Wang contributed to data acquisition. Shaoning Guo supervised data entry and integrity. Jiahui Feng analyzed data and prepared the figures. Renjie Liu wrote the manuscript. All authors reviewed and approved the final draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe sincerely thank the original GWASs and the related consortiums for sharing and managing the summary statistics.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eGlobal, regional, and national age-sex specific mortality for 264 causes of death, 1980-2016: a systematic analysis for the Global Burden of Disease Study 2016. \u003cem\u003eLancet (London, England)\u003c/em\u003e \u003cstrong\u003e390\u003c/strong\u003e, 1151-1210, doi:10.1016/s0140-6736(17)32152-9 (2017).\u003c/li\u003e\n\u003cli\u003eGorelick, P. B. 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Positive antiphospholipid antibodies: observation or treatment? \u003cem\u003eJournal of thrombosis and thrombolysis\u003c/em\u003e \u003cstrong\u003e56\u003c/strong\u003e, 301-314, doi:10.1007/s11239-023-02834-6 (2023).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"stroke, thrombomodulin, Mendelian Randomization Study","lastPublishedDoi":"10.21203/rs.3.rs-4014670/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4014670/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eEvidence of the genetic interconnectedness between thrombomodulin levels (TM) and stroke is largely unclear. This study aims to explore the relationship between genetic predisposition to TM and stroke with a 2-sample, bidirectional Mendelian randomization (MR) method.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eGenetic instruments for each TM and stroke-related phenotypes were derived from large-scale genome-wide association studies. The inverse variance weighted method was used in the primary analyses to obtain the causal estimates. Complementary sensitivity analyses were conducted to test the robustness of our results.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eUsing univariable MR, we found evidence for each standard deviation (SD) increase in genetically predicted TM levels, the odds ratio (OR) was 1.092 (95% confidence interval (CI): 1.034\u0026ndash;1.153; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) for any stroke and 1.105 (95% CI: 1.035\u0026ndash;1.180; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) for IS. In multivariable MR, the association remained after accounting for body mass index (BMI), systolic blood pressure (SBP), type 2 diabetes (T2D), low-density lipoprotein (LDL) and smoking.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis MR estimate reveals robust evidence that higher genetically predicted circulating TM levels were associated with an increased risk of any stroke and IS.\u003c/p\u003e","manuscriptTitle":"The causal relationship between plasma thrombomodulin levels and Risk of Stroke: A Mendelian Randomization Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-19 11:15:18","doi":"10.21203/rs.3.rs-4014670/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c944f95b-02ba-47b8-ab36-d01333de0b67","owner":[],"postedDate":"March 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-26T01:48:30+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-19 11:15:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4014670","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4014670","identity":"rs-4014670","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}