The effect of coagulation traits on the risk of retinal vein occlusion: a mendelian randomization study

The effect of coagulation traits on the risk of retinal vein occlusion: 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 Article The effect of coagulation traits on the risk of retinal vein occlusion: a mendelian randomization study Chaoyi Yuan, Chao He, Ling Zuo, Baoxing Liu, Hui Qi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4519232/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted 5 You are reading this latest preprint version Abstract Introduction Retinal vein occlusion (RVO) is the leading cause of vision loss due to an obstruction in the retinal venous system. While RVO is often linked to thrombotic tendencies and coagulation abnormalities, the exact role of coagulation traits in its development is not fully understood. This study aims to investigate the potential causal relationship between coagulation traits and the risk of RVO by analyzing publicly available genome-wide association study (GWAS) summary statistics. Materials and methods A two-sample Mendelian randomization (MR) analysis framework was employed to investigate the causal relationship between coagulation traits and the risk of RVO. Stringent quality control measures were applied to select appropriate instrumental variables strongly linked to exposure, such as coagulation factor III (FIII), coagulation factor V (FV), coagulation factor VIII (FVIII), coagulation factor XI (FXI), coagulation factor VII (FVII) and coagulation factor X (FX), as well as plasmin, platelet count, platelet crit (PCT), mean platelet volume (MPV), and platelet distribution width (PDW). The study utilized the FinnGen project RVO GWAS summary statistics cohort, consisting of 372 RVO cases and 182,573 controls. The analysis focused on 11 coagulation traits. Results The research suggests that genetically predicted plasma levels of FIII, FVII, MPV, and PCT may be potentially causative for reducing the risk of RVO, and that levels of FVIII may be potentially causative for increasing the risk of RVO. Conclusion Our MR analysis, utilizing GWAS data from a comprehensive population-based study, revealed a causal association between plasma levels of FFIII, FVII, FVIII, MPV, and PCT with the risk of RVO. Health sciences/Diseases Health sciences/Risk factors Mendelian randomization Retinal vein occlusion Coagulation factor Platelet parameter Causal association Risk Figures Figure 1 Figure 2 Figure 3 What is already known on this topic 1. The influence of different coagulation factors on RVO risk remains unclear. 2. Platelet parameter is associated with the risk of RVO. What this study adds 1. Our study is the first to report we first showed that higher levels of genetically predicted FVIII were significantly associated with an increased risk of RVO, whereas FIII, FVII, MPV and PCT were negatively associated. How this study might affect research, practice or policy 1. This extensive study has significantly enhanced our understanding of the role of the coagulation cascade in the pathogenesis of RVO. These findings could have important implications for the development of preventive and diagnostic strategies for RVO. 2. The findings robustly support a causal association between FIII, FVII, FVIII, MPV, and PCT, and the risk of RVO. It would likely be a potential target for RVO therapy. Introduction RVO represents a leading cause of severe vision impairment among retinal vascular disorders 1 , with a uniform sex distribution worldwide 2 . RVO frequently manifests abruptly, leading to painless vision loss in the affected eye. Vision impairment in these patients primarily results from macular edema, while additional vision-threatening complications include macular neovascularization and vitreous hemorrhages. RVO manifests in two types: central retinal vein occlusion (CRVO) originating from thrombosis within the central retinal vein (the primary outflow vessel of the eye), and branch retinal vein occlusion (BRVO) arising from thrombosis within a branch retinal vein 3 , 4 . The prevalence of CRVO varies between 0.1% and 0.4%, whereas branch retinal vein occlusion (BRVO) occurs in 0.6–1.2% of the population 2 , 5 , 6 . The exact cause of RVO is not fully understood, but research suggests that individuals with systemic arteriosclerotic vascular disease are at an increased risk of developing RVO 7 – 12 . Factors related to Virchow's triad, including vessel damage, stasis, and hypercoagulability, are commonly associated with RVO. Some studies indicate that thrombophilic factors may play a more significant role in RVO development in younger patients 13 – 15 , while others have conflicting opinions on the relevance of coagulation disorders as risk factors for RVO 16 , 17 . Various established risk factors, such as advanced age, hypertension, and hyperlipidemia, have been linked to the occurrence of RVO 18 – 26 . However, conducting observational studies like case-control or prospective longitudinal studies can be challenging due to practical limitations, as the presence of these risk factors can influence the assessment of RVO risk based on coagulation characteristics. Further research is needed to better understand the underlying mechanisms and risk factors associated with RVO to improve prevention, diagnosis, and treatment strategies for this vision-threatening condition. MR studies have become more common in research and use genetic variants linked to a risk factor as instrumental variables to evaluate causal effects or identify potential false associations that may result from reverse causation in disease outcomes observed in traditional observational studies 27 , 28 . While observational studies have explored the relationship between coagulation profiles and RVO, establishing a definitive causal link between these profiles and the condition remains challenging. The presence of confounding factors in traditional observational studies presents a significant obstacle in accurately determining the specific causal role of various coagulation characteristics in the risk of developing RVO. By utilizing MR studies, researchers may be able to overcome some of the limitations associated with observational studies and provide more robust evidence regarding the relationship between coagulation profiles and RVO. This approach can help clarify the causal pathways involved in the development of RVO and potentially inform more effective preventive and treatment strategies for this vision-threatening condition. MR can complement observational studies by applying genetic data to examine causal relationships between putative risk factors and disease 29 . To the best of our knowledge, MR studies on the relationship between coagulation traits and RVO have not been performed to date. In this study, we performed MR analyses to assess the relationship between genetically predicted coagulation traits and RVO risk. Materials and methods Study design In this study, GWAS summary-level data from the IEU Open GWAS Project database were utilized to explore the causal relationships between coagulation traits and the risk of RVO. The two-sample MR approach was employed to estimate these associations based on summary-level data. Ethical approval was obtained for all original studies used in the MR analysis, which was conducted using publicly available GWAS summary statistics. Three key assumptions are essential for conducting an MR analysis effectively. Firstly, genetic variants must be strongly correlated with the variables to serve as instrumental variables. Secondly, these genetic variants should be independent of any confounders between RVO and the variables being studied. Lastly, the genetic variants should impact the risk of RVO solely through the variables being investigated. The study design, as illustrated in Fig. 1 , involved the identification of seven coagulation factors and four platelet parameters using publicly available GWAS data. Subsequently, two-sample MR analyses were performed using summary-level GWAS data from independent FinnGen cohorts to assess the causal impact of coagulation traits on the susceptibility to RVO. Genetic instrumental variable selection To explore the causal relationships among coagulation factors, platelet parameters, and RVO, we utilized instrumental variables. We accessed the MR Base database ( http://www.mrbase.org/ ) 30 , which houses an extensive compilation of summary statistics from numerous GWASs. Our approach involved searching for GWASs of coagulation factors in European populations to identify genetic variants associated with these factors. From this curated list, we selected coagulation factor III (FIII), coagulation factor V (FV), coagulation factor VIII (FVIII), coagulation factor XI (FXI), coagulation factor VII (FVII), coagulation factor X (FX), plasmin, platelet count, platelet crit (PCT), mean platelet volume (MPV), and platelet distribution width (PDW) as the available genome-wide significant single nucleotide polymorphisms (SNPs) 31 – 36 ( Table S1 ). In order to select eligible instrumental variables for each coagulation indicator, we implemented a stringent quality control procedure. To avoid bias due to strong linkage disequilibrium (LD), we chose SNPs with genome-wide significance (P < 5e − 6) associated with specific coagulation metrics as candidate instrumental variables, with an r 2 critical value of 0.001 and a window size of 10 Mb; of which the r 2 value of 0.01 and a window size of 10 Mb were chosen for FVIII to serve only. Additionally, to assess the presence of weak instrumental variable bias, we calculated F-statistics to measure the strength of instrumental variables. F-statistics larger than 10 indicate a low possibility of weak instrumental variable bias 37 , 38 . ( Table S1 ). RVO outcome data To explore the causal relationships between coagulation factors, platelet parameters, and RVO, we utilized instrumental variables. By accessing the MR Base database ( http://www.mrbase.org/ ) 30 , which hosts comprehensive summary statistics from multiple GWASs, we searched for GWASs of coagulation factors in European populations to identify genetic variants associated with these factors. From this list, we identified FIII, FV, FVII, FXI, FVIII, FX, plasmin, platelet count, PCT, MPV, and PDW as significant genome-wide single nucleotide polymorphisms (SNPs) 31 – 36 . ( Table S1 ). To select eligible instrumental variables for each coagulation indicator, we implemented stringent quality control procedures. To avoid bias resulting from strong linkage disequilibrium (LD), we chose SNPs with genome-wide significance (P < 5e − 6) associated with specific coagulation metrics as candidate instrumental variables, with an r2 threshold of 0.001 and a window size of 10 Mb. Furthermore, F-statistics were computed to assess the robustness of the instrumental variables. Results indicated that F-statistics exceeding 10 suggest a minimal risk of weak instrumental variable bias 37 , 38 . (Table S1 ). Summary-level data pertaining to RVO were extracted from the FinnGen study, consisting of 372 RVO cases and 182,573 controls 39 . Mendelian randomization estimates We employed summary statistics, including β coefficients and standard errors, to separately estimate the causal relationships between seven coagulation factors and four platelet parameters with RVO, utilizing various MR methods. For the primary MR analysis evaluating the non-confounded causal association among coagulation factors, platelet parameters, and RVO, we utilized the inverse variance weighted approach. Figure 1 delineates the three pivotal assumptions essential for this method 40 . Initially conducted in the FinnGen cohorts, the MR analyses employed three distinct methods: inverse variance weighting (IVW), weighted mean (WM), and MR-Egger regression, each based on different assumptions. The IVW method was used as the primary statistical model. Heterogeneity in causal estimates among instrumental variables indicated a potential violation of the assumptions underlying MR analysis 41 . To scrutinize this heterogeneity, we used the Cochran's Q test and assessed it via both the causal estimates of the fixed-effects IVW method and MR-Egger regression. A P-value less than 0.05 was considered indicative of significant heterogeneity. MR Egger regression was utilized to evaluate potential pleiotropic effects of instrumental variables. The intercept term in MR-Egger regression may indicate directional horizontal pleiotropy in the causal estimates. Results Selection of instrumental variables We conducted a systematic compilation of genome-wide significant SNPs associated with seven coagulation factors (FIII, FV, FVIII, FXI, FVII, FX, and plasmin) and four platelet parameters (platelet count, PCT, MPV, and PDW) from various GWAS results via extensive literature review to investigate their potential causal effects on the risk of RVO 31 – 36 (Table S1 ) . These indicators were categorized into five distinct groups: platelet parameters (platelet count, PCT, MPV, and PDW), coagulation intrinsic pathway (FXI, FVIII), extrinsic pathway (FVII, FIII), common pathways (FV, FX), and fibrin clot dissociation (plasmin). To assess the strength of each instrumental variable, we calculated F-statistics for each instrument-exposure association. In our study, the F-statistics notably exceeded 10, indicating robustness and strength of these SNPs as instrumental variables (Table S1 ). This rigorous selection process ensured that the instrumental variables used in our analysis were reliable and suitable for assessing causal relationships between coagulation factors, platelet parameters, and the risk of RVO, Causal effects of coagulation indicator on retinal vein occlusion Utilizing GWAS summary statistics retrieved from the FinnGen study involving 372 cases and 182,573 controls, we conducted MR analyses to assess the causal impacts of seven coagulation factors and four platelet parameters on the risk of RVO. The resulting MR estimates obtained from various methods are presented in Table S2 . Notably, the IVW method provided substantial evidence supporting a causal link between coagulation traits and the occurrence of RVO. Our findings indicate that genetically predicted levels of FIII and FVII are associated with a reduced risk of RVO [FIII: (Inverse variance weighted: OR = 0.577, 95% CI: 0.346–0.962, P = 3.3513×10 − 2 Fig. 2 , Table S2 , Figure S1 ; FVII: (Inverse variance weighted: OR = 0.742, 95% CI: 0.568–0.969, P = 2.84×10 − 2) Fig. 2 , Table S2 , Figure S1 ]. Additionally, we observed that genetically predicted MPV and PCT levels are causally linked to a decreased risk of RVO [MPV: (IVW: OR = 0.783, 95% CI: 0.621–0.987, P = 3.848×10 − 2; MR Egger: OR = 0.686, 95% CI: 0.475–0.989, P = 4.401×10 − 2) Fig. 3 , Table S2 , Figure S1 ]; PCT: (IVW: OR = 0.740, 95% CI: 0.560–0.977, P = 3.342×10 − 2; WM: OR = 0.590, 95% CI: 0.358–0.972, P = 3.821×10 − 2) Fig. 3 , Table S2 , Figure S1 ]. Intriguingly, our investigation identified a positive causal relationship between genetically predicted FVIII levels and RVO [IVW: OR = 1.308, 95% CI: 1.067–1.604, P = 9.787×10 − 3) Fig. 2 , Table S2 , Figure S1 ]. Nevertheless, no significant causal effect on RVO was observed for other coagulation factors (FV, FX, FXI, fibrinolytic enzymes) or platelet parameters (platelet count, PDW) (Fig. 2 , Fig. 3 , Table S2 , Figure S1 .). Additionally, the absence of significant horizontal pleiotropy or heterogeneity among coagulation factors and platelet parameters in our analyses strengthens the validity and reliability of the MR estimates obtained ( Table S2 ). Leave-one-out analyses were conducted to identify any potential outliers, and Cochran's Q test indicated no influential individual SNP driving the IVW point estimate or causing heterogeneity between IVW estimates based on individual variants ( Table S3, Figure S2 ). Both the funnel plot and MR Egger regression test showed no evidence of asymmetry, suggesting the absence of directional horizontal pleiotropy that could introduce bias to the MR analyses (Figure S3, Table S2 ). These results collectively contribute to our understanding of the causal relationships between coagulation traits and the risk of RVO. Discussion Using summary statistics from GWAS of European ancestry, we examined the causal effects of seven coagulation factors and four platelet parameters on RVO risk by applying a unified MR framework for GWAS data analysis. Genetically forecasted plasma levels of FIII, FVII, MPV, and PCT demonstrated inverse correlations with RVO, while genetically anticipated plasma FVIII levels displayed a positive causal association with RVO. RVO is a partial or complete blockage of a retinal vein in which the formation of a blood clot causes the retinal venous system to narrow and impedes venous return from the retinal circulation. The exact pathogenesis of RVO remains unclear. The condition may be due to a combination of three systemic changes known as Virchow’s triad: hemodynamic changes (venous stasis), degenerative changes of the vessel wall and blood hypercoagulability 42 . It has been shown to be associated with thrombotic events due to systemic changes, especially hypercoagulable states 3 , 41 . The central retinal vein exits the eye at the optic disc and proceeds into the optic nerve. Within the optic nerve, it traverses through the lamina cribrosa, a connective tissue structure that offers support to the vein and the axons as they penetrate the sclera. Postmortem examination of eyes with CRVO revealed the presence of fresh or recanalized thrombus near the lamina cribrosa, where the vein typically narrows and blood flow intensifies 3 . BRVOs were observed at sites where arteries cross over veins, particularly those with an artery passing over a vein 43 . In cases of BRVO, postmortem analysis showed thrombus formation within a retinal vein compressed by a thickened-walled retinal artery crossing over the vein 4 . Narrowing or irregularity of the vessels leads to turbulent flow and causes stress on endothelial cells 44 , 45 . Thrombophilia, arising from specific coagulation factor activation, has been implicated in the pathogenesis of thrombosis 46 . Our study notably demonstrates a substantial correlation between genetically projected levels of FIII and FVII and a reduced RVO risk, representing the pioneering identification of a causal nexus between FIII, FVII, and RVO. The activation of the exogenous coagulation pathway begins with FIII, which is released to form a TF-Ca²⁺-FVII complex with FVII in the bloodstream thereby generating a cascade reaction to produce plasminogen activator, which generates thrombin. However, due to the action of procoagulants and anticoagulants, thrombin production by the FIII pathway becomes a threshold-limited process and the small amount of thrombin in the initial phase is insufficient to maintain the coagulation process 47 . Despite the presence of a small amount of activated FVII in the blood, the coagulation process is similarly not initiated without the release of tissue factor. However, Hunter et al.'s prospective study, encompassing 18 central RVO and 22 branch RVO cases, did not discern a significant correlation between FVII and RVO 48 . Nonetheless, our findings significantly contribute to understanding the potential association between coagulation factors and RVO risk. High coagulation factor VIII activity is an independent risk factor for thromboembolism 49 . Our findings are consistent with the observations in Glueck et al 's research, indicating elevated coagulation factor VIII levels in RVO or CRVO patients compared to controls 50 , 51 . Moreover, Faude et al. corroborated the relationship between FVIII and RVO occurrence in their study involving 62 CRVO patients and 107 healthy individuals, with over 53.2% of CRVO patients exhibiting Factor VIII activity exceeding 150% (> 150 IU/dl) 52 . These collective studies underscore the relevance of coagulation factors, particularly FIII, FVII, and FVIII, in the pathogenesis and risk assessment of RVO. The findings regarding MPV in our study imply a possible adverse causal link with RVO, contradicting some observational studies and meta-analyses 53 – 57 . Historically, studies employing traditional observational methods have generated inconsistent outcomes concerning the association between MPV and RVO. For instance, prior investigations have indicated a correlation between elevated MPV and RVO 53 , 58 . Sahin et al.'s retrospective analysis involving 193 RVO patients and 83 healthy controls revealed significantly higher MPV values in the RVO patient group 54 . Similarly, Yilmaz and Yilmaz reported a similar trend in another retrospective study 58 . They suggested that larger platelets have greater haemostatic reactivity. However, Ornek et al. did not observe any association between increased MPV values and RVO occurrence 56 . They concluded that the key factor in the development of RVO appears to be changes in the neighboring arteries rather than systemic haematological abnormalities. Moreover, within the cohort displaying clinical features of RVO, it was observed that patients diagnosed with BRVO had a lower MPV value compared to those suffering from central RVO and a control group 56 . These studies exhibited considerable heterogeneity in terms of ethnicity, study design, participant numbers, participant characteristics, RVO identification, RVO prevalence, and statistical methodologies, potentially contributing to the discrepancies in findings. Past research has consistently identified age, hypertension, diabetes mellitus, and dyslipidemia as traditional risk factors for RVO 58 – 62 . Some researchers postulate a greater significance of thrombophilia in younger patients 63 – 65 . Study confounders could potentially bias the causal association between MPV and RVO. The inclusion of a small patient cohort might have influenced statistical significance. Notably, patient body mass index was not documented and considered, despite its potential impact on MPV values. Considering that most previous studies were traditional retrospective studies, the use of new research methods is particularly important for these clinical questions. Dual-sample MR analysis was applied to explore the risk factors for RVO. The exact mechanisms underlying the increased risk of low MPV and RVO have not been clarified. In in vitro experiments, smaller platelets secreted more P-selectin, which plays an important role in surface adhesion for thrombus formation 66 . Smaller platelets with lower MPV were more likely to form thrombi than larger platelets 67 . Notably, this result was observed in cancer patients. Therefore, the applicability of this finding to non-cancer patients remains to be confirmed. There are fewer studies and more basic research is needed in the future to explore the specific mechanisms. Furthermore, in the absence of a specific rationale for conducting a thorough hemostatic examination in patients with RVO, additional studies are required to elucidate the potential involvement of platelet parameters in the pathophysiology of RVO. Our investigation has unveiled a association between PCT and RVO, a finding uncommon in current literature. There might exist a plausible negative causal link between platelet crit and RVO. Besides MPV, other indicators of platelet morphology, specifically PCT, could potentially wield substantial influence in vascular ailments, including atherosclerosis and thrombosis 68 , 69 . However, findings from Yilmaz et al.'s study revealed no discernible difference in PCT levels between RVO patients and healthy controls 58 . Further comprehensive research is essential to fully fathom the underlying mechanisms and mediators that dictate the relationship between PCT levels and RVO. To our knowledge, this is the first study to evaluate MR studies that have assessed how different coagulation characteristics affect the risk of RVO, and the first to provide pooled estimates of the causal effects of FIII, FVII, FVIII, MPV and PCT on the risk of RVO. In addition, previous observational studies provided conflicting correlations and biological assumptions. By providing causality estimates, our study eliminates the true extent of the biological effects of relevant confounding risk factors on RVO. Some potential limitation of our study should be mentioned. Firstly, in studies using sample-bank cohorts 31 – 36 , causal effects represent only selected European populations. These were chosen primarily to avoid population stratification. However, these findings may not be representative of more general or non-European populations. Secondly, although we utilized the largest available sample size and the most recent GWAS dataset for MR analysis, it's essential to acknowledge that our study had a comparatively smaller sample size and event count compared to population-based observational studies. In addition, the inclusion of different studies using overlapping cohorts (especially databases), while unavoidable, inevitably reduces the effective sample size and statistical power of our analyses. Thirdly, we did not evaluate genetically predicted coagulation factors and platelet parameters in relation to CRVO and BRVO because of the lack of pooled data on CRVO and the inability to further subdivide MR analyses by other factors such as disease staging, further clinical trials are needed to confirm our findings in order to obtain partially consistent evidence of an association between certain coagulation characteristics and RVO or RVO of varying severity. In addition, our magnetic resonance study demonstrated a causal relationship between genetically predicted coagulation characteristics and RVO; however, it is important to note that the results of the magnetic resonance analyses were based on genetic evidence only. Platelet play a pivotal role in thrombo-occlusive disorders, with MPV serving as a vital indicator of platelet size and activity. Larger platelets exhibit heightened reactivity, increased thromboxane A2 production, elevated expression of glycoprotein Ib and glycoprotein IIb/IIIa receptors, and a greater propensity for aggregation 70 , 71 . The etiology of hypercoagulable states in RVO remains uncertain, lacking robust validation for extensive screening of thrombotic and coagulopathic conditions in most patients. However, assessing underlying coagulopathies might be warranted when routine tests for common cardiovascular risk factors yield negative results. These investigations should delve into the effects of various coagulation factors and platelet function on RVO, utilizing MR studies with more extensive sample sizes. Furthermore, conducting RVO MR studies among GWAS cohorts of diverse ethnicities will elucidate how distinct genetic compositions and diverse environments from inter-ethnic diversity influence the causal implications of specific coagulation profile-related risk factors in RVO development. Conclusions This study represents the pioneering use of MR to investigate the causal link between coagulation traits and RVO susceptibility specifically in a European population. The robust findings support a causal relationship between FIII, FVII, FVIII, MPV, PCT, and RVO risk. This comprehensive exploration significantly enhances our understanding of the coagulation cascade's involvement in RVO pathogenesis, potentially informing preventive and diagnostic strategies for RVO. Abbreviations GWAS Genome-wide association study IVW Inverse variance weighting SNP Single nucleotide polymorphism WM Weighted mean MR Mendelian randomization OR Odds ratio MPV mean platelet volume PDW platelet distribution width PCT platelet crit RVO retinal vein occlusion Declarations Ethics statement This MR study, which exclusively utilized publicly available summary statistics, was exempt from ethical approval. Patient and Public Involvement It was not appropriate or possible to involve patients or the public in the design, or conduct, or reporting, or dissemination plans of our research. Conflict of interest The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Author details 1、Department of Ophthalmology, The Second Hospital of Jilin University, Changchun, 130041, Jilin Province, China 2、Department of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qingdao University, Qingdao 266555, Shandong Province, China Funding Not applicable. Author Contribution Y. C.Y，Q .H and H. C performed conceived and designed the study. Q. H supervised the study and data analysis. Y.C.Y performed the data analysis with help from Q.H, Z.L, H. C and L.B.X. Q. H and Z. L and H.C supervised and revised the manuscript; all authors gave final approval for the submitted version of the manuscript. Data Availability We analyzed publicly available datasets which can be found here: The IEU Open GWAS Project database (https://gwas.mrcieu.ac.uk/). References Scott, I. U., Campochiaro, P. A., Newman, N. J. & Biousse, V. Retinal vascular occlusions. Lancet 396, 1927–1940 (2020). Rogers, S. et al. The prevalence of retinal vein occlusion: pooled data from population studies from the United States, Europe, Asia, and Australia. Ophthalmology 117, 313–319.e1 (2010). Green, W. R., Chan, C. C., Hutchins, G. M. & Terry, J. M. Central retinal vein occlusion: a prospective histopathologic study of 29 eyes in 28 cases. Trans Am Ophthalmol Soc 79, 371–422 (1981). Frangieh, G. T., Green, W. R., Barraquer-Somers, E. & Finkelstein, D. Histopathologic study of nine branch retinal vein occlusions. Arch Ophthalmol 100, 1132–1140 (1982). Klein, R., Moss, S. E., Meuer, S. M. & Klein, B. E. K. The 15-year cumulative incidence of retinal vein occlusion: the Beaver Dam Eye Study. Arch Ophthalmol 126, 513–518 (2008). Prevalence and associations of retinal vein occlusion in Australia. The Blue Mountains Eye Study - PubMed. https://pubmed.ncbi.nlm.nih.gov/8859084/ . Mitchell, P., Smith, W. & Chang, A. Prevalence and associations of retinal vein occlusion in Australia. The Blue Mountains Eye Study. Arch Ophthalmol 114, 1243–1247 (1996). Wong, T. Y. et al. Cardiovascular risk factors for retinal vein occlusion and arteriolar emboli: the Atherosclerosis Risk in Communities & Cardiovascular Health studies. Ophthalmology 112, 540–547 (2005). Elman, M. J., Bhatt, A. K., Quinlan, P. M. & Enger, C. The risk for systemic vascular diseases and mortality in patients with central retinal vein occlusion. Ophthalmology 97, 1543–1548 (1990). Rath, E. Z., Frank, R. N., Shin, D. H. & Kim, C. Risk factors for retinal vein occlusions. A case-control study. Ophthalmology 99, 509–514 (1992). Cardiovascular and thrombophilic risk factors for central retinal vein occlusion - PubMed. https://pubmed.ncbi.nlm.nih.gov/12020623/ . Janssen, M. C. H., den Heijer, M., Cruysberg, J. R. M., Wollersheim, H. & Bredie, S. J. H. Retinal vein occlusion: a form of venous thrombosis or a complication of atherosclerosis? A meta-analysis of thrombophilic factors. Thromb Haemost 93, 1021–1026 (2005). Bertram, B., Remky, A., Arend, O., Wolf, S. & Reim, M. Protein C, protein S, and antithrombin III in acute ocular occlusive diseases. Ger J Ophthalmol 4, 332–335 (1995). Laboratory evaluation of hypercoagulable states in patients with central retinal vein occlusion who are less than 56 years of age - PubMed. https://pubmed.ncbi.nlm.nih.gov/11772591/ . Activated protein C resistance and anticoagulant proteins in young adults with central retinal vein occlusion - PubMed. https://pubmed.ncbi.nlm.nih.gov/10634554/ . Cruciani, F. et al. MTHFR C677T mutation, factor II G20210A mutation and factor V Leiden as risks factor for youth retinal vein occlusion. Clin Ter 154, 299–303 (2003). Ciardella, A. P. et al. Factor V Leiden, activated protein C resistance, and retinal vein occlusion. Retina 18, 308–315 (1998). Prevalence and risk factors of retinal vein occlusion: the Gutenberg Health Study - PubMed. https://pubmed.ncbi.nlm.nih.gov/25894549/ . Kuhli-Hattenbach, C., Miesbach, W., Lüchtenberg, M., Kohnen, T. & Hattenbach, L.-O. Elevated lipoprotein (a) levels are an independent risk factor for retinal vein occlusion. Acta Ophthalmol 95, 140–145 (2017). Hypertension and multiple cardiovascular risk factors increase the risk for retinal vein occlusions: results from the Gutenberg Retinal Vein Occlusion Study - PubMed. https://pubmed.ncbi.nlm.nih.gov/31145709/ . Retinal Vein Occlusion is Associated with Low Blood High-Density Lipoprotein Cholesterol: A Nationwide Cohort Study - PubMed. https://pubmed.ncbi.nlm.nih.gov/30959001/ . Napal Lecumberri, J. J. et al. Lipid profile and serum folate, vitamin B12 and homocysteine levels in patients with retinal vein occlusion. Clin Investig Arterioscler 33, 169–174 (2021). A review of risk factors for retinal vein occlusions - PubMed. https://pubmed.ncbi.nlm.nih.gov/35972726/ . Risk factors for central retinal vein occlusion. The Eye Disease Case-Control Study Group. Arch Ophthalmol 114, 545–554 (1996). Risk factors for branch retinal vein occlusion. The Eye Disease Case-control Study Group - PubMed. https://pubmed.ncbi.nlm.nih.gov/8357052/ . Systemic disorders associated with retinal vascular occlusion - PubMed. https://pubmed.ncbi.nlm.nih.gov/11141642/ . Using published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors - PubMed. https://pubmed.ncbi.nlm.nih.gov/25773750/ . Interpreting findings from Mendelian randomization using the MR-Egger method - PubMed. https://pubmed.ncbi.nlm.nih.gov/28527048/ . Burgess, S., Foley, C. N. & Zuber, V. Inferring Causal Relationships Between Risk Factors and Outcomes from Genome-Wide Association Study Data. Annu Rev Genomics Hum Genet 19, 303–327 (2018). Hemani, G. et al. The MR-Base platform supports systematic causal inference across the human phenome. Elife 7, e34408 (2018). Pietzner, M. et al. Genetic architecture of host proteins involved in SARS-CoV-2 infection. Nat Commun 11, 6397 (2020). Folkersen, L. et al. Mapping of 79 loci for 83 plasma protein biomarkers in cardiovascular disease. PLoS Genet 13, e1006706 (2017). Chen, M.-H. et al. Trans-ethnic and Ancestry-Specific Blood-Cell Genetics in 746,667 Individuals from 5 Global Populations. Cell 182, 1198–1213.e14 (2020). Vuckovic, D. et al. The Polygenic and Monogenic Basis of Blood Traits and Diseases. Cell 182, 1214–1231.e11 (2020). Vuckovic, D. et al. The Polygenic and Monogenic Basis of Blood Traits and Diseases. Cell 182, 1214–1231.e11 (2020). Whole-exome imputation within UK Biobank powers rare coding variant association and fine-mapping analyses - PubMed. https://pubmed.ncbi.nlm.nih.gov/34226706/ . Georgakis, M. K. et al. Genetically Determined Levels of Circulating Cytokines and Risk of Stroke. Circulation 139, 256–268 (2019). An approximation to the F distribution using the chi-square distribution - ScienceDirect. https://www.sciencedirect.com/science/article/pii/S0167947301000974 . FinnGen: Unique genetic insights from combining isolated population and national health register data | medRxiv. https://www.medrxiv.org/content/ 10.1101/2022.03.03.22271360v1 . An Overview of Methods and Exemplars of the Use of Mendelian Randomisation in Nutritional Research - PubMed. https://pubmed.ncbi.nlm.nih.gov/36014914/ . A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization - PubMed. https://pubmed.ncbi.nlm.nih.gov/28114746/ . Jw, Y., P, L., Ty, W., J, B. & A, J. Retinal vein occlusion: an approach to diagnosis, systemic risk factors and management. Internal medicine journal 38, (2008). Zhao, J., Sastry, S. M., Sperduto, R. D., Chew, E. Y. & Remaley, N. A. Arteriovenous crossing patterns in branch retinal vein occlusion. The Eye Disease Case-Control Study Group. Ophthalmology 100, 423–428 (1993). Taylor, A. W., Sehu, W., Williamson, T. H. & Lee, W. R. Morphometric assessment of the central retinal artery and vein in the optic nerve head. Can J Ophthalmol 28, 320–324 (1993). Williamson, T. H. A ‘throttle’ mechanism in the central retinal vein in the region of the lamina cribrosa. Br J Ophthalmol 91, 1190–1193 (2007). Retinal vein occlusion: pathophysiology and treatment options - PubMed. https://pubmed.ncbi.nlm.nih.gov/20689798/ . Mann, K. G., van’t Veer, C., Cawthern, K. & Butenas, S. The role of the tissue factor pathway in initiation of coagulation. Blood Coagul Fibrinolysis 9 Suppl 1, S3-7 (1998). Connors, J. M. Thrombophilia Testing and Venous Thrombosis. N Engl J Med 377, 1177–1187 (2017). O’Donnell, J. et al. High prevalence of elevated factor VIII levels in patients referred for thrombophilia screening: role of increased synthesis and relationship to the acute phase reaction. Thromb Haemost 77, 825–828 (1997). Glueck, C. J., Hutchins, R. K., Jurantee, J., Khan, Z. & Wang, P. Thrombophilia and retinal vascular occlusion. Clin Ophthalmol 6, 1377–1384 (2012). Glueck, C. J., Wang, P., Bell, H., Rangaraj, V. & Goldenberg, N. Associations of thrombophilia, hypofibrinolysis, and retinal vein occlusion. Clin Appl Thromb Hemost 11, 375–389 (2005). Faude, F., Faude, S., Siegemund, A. & Wiedemann, P. [Factor VIII activity in patients with central retinal vein occlusion in comparison to patients with a history of pelvic and lower limb venous thrombosis and a healthy control group]. Klin Monbl Augenheilkd 221, 862–866 (2004). Onder, H. I. et al. Relation between platelet indices and branch retinal vein occlusion in hypertensive patients. Indian J Ophthalmol 61, 160–162 (2013). Sahin, A. et al. The mean platelet volume in patients with retinal vein occlusion. J Ophthalmol 2013, 236371 (2013). Bawankar, P., Samant, P., Lahane, T., Parekh, R. & Lahane, S. Mean platelet volume and central retinal vein occlusion in hypertensive patients. Can J Ophthalmol 54, 275–279 (2019). Ornek, N., Ogurel, T., Ornek, K. & Onaran, Z. Mean platelet volume in retinal vein occlusion. Eur Rev Med Pharmacol Sci 18, 2778–2782 (2014). Pinna, A. et al. Mean Platelet Volume, Red Cell Distribution Width, and Complete Blood Cell Count Indices in Retinal Vein Occlusions. Ophthalmic Epidemiol 28, 39–47 (2021). Yilmaz, T. & Yilmaz, A. Altered platelet morphological parameters in patients with retinal vein occlusion. Eur Rev Med Pharmacol Sci 20, 1934–1939 (2016). Rogers, S. et al. The prevalence of retinal vein occlusion: pooled data from population studies from the United States, Europe, Asia, and Australia. Ophthalmology 117, 313–319.e1 (2010). Song, P., Xu, Y., Zha, M., Zhang, Y. & Rudan, I. Global epidemiology of retinal vein occlusion: a systematic review and meta-analysis of prevalence, incidence, and risk factors. J Glob Health 9, 010427 (2019). Chang, Y.-S., Ho, C.-H., Chu, C.-C., Wang, J.-J. & Jan, R.-L. Risk of retinal vein occlusion in patients with diabetes mellitus: A retrospective cohort study. Diabetes Res Clin Pract 171, 108607 (2021). Pacella, F. et al. Impact of cardiovascular risk factors on incidence and severity of Retinal Vein Occlusion. Clin Ter 171, e534–e538 (2020). Arsène, S. et al. Increased prevalence of factor V Leiden in patients with retinal vein occlusion and under 60 years of age. Thromb Haemost 94, 101–106 (2005). Prevalence of factor V Leiden in young adults with retinal vein occlusion - PubMed. https://pubmed.ncbi.nlm.nih.gov/9031476/ . High prevalence of resistance to APC in young patients with retinal vein occlusion - PubMed. https://pubmed.ncbi.nlm.nih.gov/11935272/ . Handtke, S. et al. Role of Platelet Size Revisited-Function and Protein Composition of Large and Small Platelets. Thromb Haemost 119, 407–420 (2019). Ferroni, P. et al. Evaluation of mean platelet volume as a predictive marker for cancer-associated venous thromboembolism during chemotherapy. Haematologica 99, 1638–1644 (2014). Davì, G. & Patrono, C. Platelet activation and atherothrombosis. N Engl J Med 357, 2482–2494 (2007). Evaluation of mean platelet volume in patients with type 2 diabetes mellitus and blood glucose regulation: a marker for atherosclerosis? - PubMed. https://pubmed.ncbi.nlm.nih.gov/24955167/ . Giles, H., Smith, R. E. & Martin, J. F. Platelet glycoprotein IIb-IIIa and size are increased in acute myocardial infarction. Eur J Clin Invest 24, 69–72 (1994). Haver, V. M. & Gear, A. R. Functional fractionation of platelets. J Lab Clin Med 97, 187–204 (1981). Additional Declarations No competing interests reported. Supplementary Files TABLES1S3.xlsx SupplementaryFigureS1S3.pdf STROBEMRchecklistfillable.docx Cite Share Download PDF Status: Published Journal Publication published 24 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 31 Jul, 2024 Editor assigned by journal 30 Jul, 2024 Editor invited by journal 11 Jul, 2024 Submission checks completed at journal 10 Jul, 2024 First submitted to journal 03 Jun, 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. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4519232","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":334387583,"identity":"ec418fc9-3d6e-434f-83aa-5ebe5b1b83eb","order_by":0,"name":"Chaoyi Yuan","email":"","orcid":"","institution":"The Second Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Chaoyi","middleName":"","lastName":"Yuan","suffix":""},{"id":334387584,"identity":"492908ea-01bb-4aae-b131-0c733e65737a","order_by":1,"name":"Chao He","email":"","orcid":"","institution":"The Affiliated Hospital of Qingdao University","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"He","suffix":""},{"id":334387585,"identity":"03fce604-f1dc-4ba7-8afa-b4e966f1fba3","order_by":2,"name":"Ling Zuo","email":"","orcid":"","institution":"The Second Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Ling","middleName":"","lastName":"Zuo","suffix":""},{"id":334387586,"identity":"db914bb4-7ed9-4ef0-a99b-a1e0035d7f6e","order_by":3,"name":"Baoxing Liu","email":"","orcid":"","institution":"The Second Hospital of Jilin University","correspondingAuthor":false,"prefix":"","firstName":"Baoxing","middleName":"","lastName":"Liu","suffix":""},{"id":334387587,"identity":"5e015673-7e67-4415-986f-e1a70d16de5d","order_by":4,"name":"Hui Qi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtElEQVRIiWNgGAWjYDACZh4GhoQKNhkQW4IELWfYeEjQwgBUzNjGQIIWvuO8xyQezuPjMTjAfPA2D4NdHkEtkof50iQSt7EBtbAlW/MwJBcT1GJwmMcMqoXHTJqH4UBiA3Fa5oC08H8jRUsD2BY24rRIHuYxtkg4xsYjeZjN2HKOQTJhLXznzxje/FFzTI7vePPDG28q7AhrYTgAJo8B4xTsToLq4VpqiFE6CkbBKBgFIxUAALs9MvX3gyGlAAAAAElFTkSuQmCC","orcid":"","institution":"The Second Hospital of Jilin University","correspondingAuthor":true,"prefix":"","firstName":"Hui","middleName":"","lastName":"Qi","suffix":""}],"badges":[],"createdAt":"2024-06-03 04:37:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4519232/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4519232/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-87648-7","type":"published","date":"2025-01-24T15:58:12+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":62159249,"identity":"0c09478c-04c4-4457-8f17-3dc59c7630fa","added_by":"auto","created_at":"2024-08-09 21:40:24","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1475341,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOverall study design.\u003c/strong\u003e A flowchart illustrates the sequential process of conducting the MR analysis in this study.\u003c/p\u003e","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4519232/v1/5a7363c414fe6474b3f15fbb.png"},{"id":62159247,"identity":"d44d431c-1018-4cde-833a-55bce367d89c","added_by":"auto","created_at":"2024-08-09 21:40:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":451330,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMR analysis results for the effect of 7 coagulation factors on RVO. \u003c/strong\u003eA: Forest plots displaying the causal impact of 7 coagulation factors on RVO in the FinnGen cohort of European descent. Horizontal bars indicate the 95% confidence intervals (CI).\u003c/p\u003e","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4519232/v1/253d868f2290f6d894b980f2.png"},{"id":62158490,"identity":"fd0d3b8d-f97c-4cf2-ac4f-d3def18806b2","added_by":"auto","created_at":"2024-08-09 21:32:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":477718,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMR analysis results for the effect of 4 platelet parameters on RVO. \u003c/strong\u003eA: Forest plots displaying the causal impact of 4 platelet parameters on RVO in the FinnGen cohort of European descent. Horizontal bars indicate the 95% confidence intervals (CI).\u003c/p\u003e","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4519232/v1/3d7349e1ad07c6c8edd26fde.png"},{"id":74858479,"identity":"337dda33-d181-471d-a745-b1d320d8683d","added_by":"auto","created_at":"2025-01-27 16:10:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1220189,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4519232/v1/795d1ec6-b02d-4a91-9d69-f181eaeb23ba.pdf"},{"id":62158485,"identity":"7ead62e9-81b9-4639-a401-d9795c0cd1e5","added_by":"auto","created_at":"2024-08-09 21:32:23","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":328561,"visible":true,"origin":"","legend":"","description":"","filename":"TABLES1S3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4519232/v1/a2a81d85b380ced40fd98d4d.xlsx"},{"id":62158489,"identity":"9b1ff845-8596-4933-a685-faacf23ca615","added_by":"auto","created_at":"2024-08-09 21:32:24","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":672794,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS1S3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4519232/v1/ed441c5ab69df2186bc2c5c0.pdf"},{"id":62159394,"identity":"2544a927-49ee-4cdf-9b0c-c240ec57e325","added_by":"auto","created_at":"2024-08-09 21:48:23","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":56316,"visible":true,"origin":"","legend":"","description":"","filename":"STROBEMRchecklistfillable.docx","url":"https://assets-eu.researchsquare.com/files/rs-4519232/v1/7a12a6c26b6e4c6a07990232.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The effect of coagulation traits on the risk of retinal vein occlusion: a mendelian randomization study","fulltext":[{"header":"What is already known on this topic","content":"\u003cp\u003e1. The influence of different coagulation factors on RVO risk remains unclear.\u003c/p\u003e\n\u003cp\u003e2. Platelet parameter is associated with the risk of RVO.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWhat this study adds\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. Our study is the first to report we first showed that higher levels of genetically predicted FVIII were significantly associated with an increased risk of RVO, whereas FIII, FVII, MPV and PCT were negatively associated.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHow this study might affect research, practice or policy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. This extensive study has significantly enhanced our understanding of the role of the coagulation cascade in the pathogenesis of RVO. These findings could have important implications for the development of preventive and diagnostic strategies for RVO.\u003c/p\u003e\n\u003cp\u003e2. The findings robustly support a causal association between FIII, FVII, FVIII, MPV, and PCT, and the risk of RVO. It would likely be a potential target for RVO therapy.\u003c/p\u003e"},{"header":"Introduction","content":"\u003cp\u003eRVO represents a leading cause of severe vision impairment among retinal vascular disorders\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, with a uniform sex distribution worldwide\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. RVO frequently manifests abruptly, leading to painless vision loss in the affected eye. Vision impairment in these patients primarily results from macular edema, while additional vision-threatening complications include macular neovascularization and vitreous hemorrhages. RVO manifests in two types: central retinal vein occlusion (CRVO) originating from thrombosis within the central retinal vein (the primary outflow vessel of the eye), and branch retinal vein occlusion (BRVO) arising from thrombosis within a branch retinal vein\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. The prevalence of CRVO varies between 0.1% and 0.4%, whereas branch retinal vein occlusion (BRVO) occurs in 0.6\u0026ndash;1.2% of the population\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe exact cause of RVO is not fully understood, but research suggests that individuals with systemic arteriosclerotic vascular disease are at an increased risk of developing RVO\u003csup\u003e\u003cspan additionalcitationids=\"CR8 CR9 CR10 CR11\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Factors related to Virchow's triad, including vessel damage, stasis, and hypercoagulability, are commonly associated with RVO. Some studies indicate that thrombophilic factors may play a more significant role in RVO development in younger patients\u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e, while others have conflicting opinions on the relevance of coagulation disorders as risk factors for RVO\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Various established risk factors, such as advanced age, hypertension, and hyperlipidemia, have been linked to the occurrence of RVO\u003csup\u003e\u003cspan additionalcitationids=\"CR19 CR20 CR21 CR22 CR23 CR24 CR25\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. However, conducting observational studies like case-control or prospective longitudinal studies can be challenging due to practical limitations, as the presence of these risk factors can influence the assessment of RVO risk based on coagulation characteristics. Further research is needed to better understand the underlying mechanisms and risk factors associated with RVO to improve prevention, diagnosis, and treatment strategies for this vision-threatening condition.\u003c/p\u003e \u003cp\u003eMR studies have become more common in research and use genetic variants linked to a risk factor as instrumental variables to evaluate causal effects or identify potential false associations that may result from reverse causation in disease outcomes observed in traditional observational studies\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. While observational studies have explored the relationship between coagulation profiles and RVO, establishing a definitive causal link between these profiles and the condition remains challenging. The presence of confounding factors in traditional observational studies presents a significant obstacle in accurately determining the specific causal role of various coagulation characteristics in the risk of developing RVO. By utilizing MR studies, researchers may be able to overcome some of the limitations associated with observational studies and provide more robust evidence regarding the relationship between coagulation profiles and RVO. This approach can help clarify the causal pathways involved in the development of RVO and potentially inform more effective preventive and treatment strategies for this vision-threatening condition.\u003c/p\u003e \u003cp\u003eMR can complement observational studies by applying genetic data to examine causal relationships between putative risk factors and disease\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. To the best of our knowledge, MR studies on the relationship between coagulation traits and RVO have not been performed to date. In this study, we performed MR analyses to assess the relationship between genetically predicted coagulation traits and RVO risk.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eIn this study, GWAS summary-level data from the IEU Open GWAS Project database were utilized to explore the causal relationships between coagulation traits and the risk of RVO. The two-sample MR approach was employed to estimate these associations based on summary-level data. Ethical approval was obtained for all original studies used in the MR analysis, which was conducted using publicly available GWAS summary statistics. Three key assumptions are essential for conducting an MR analysis effectively. Firstly, genetic variants must be strongly correlated with the variables to serve as instrumental variables. Secondly, these genetic variants should be independent of any confounders between RVO and the variables being studied. Lastly, the genetic variants should impact the risk of RVO solely through the variables being investigated. The study design, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, involved the identification of seven coagulation factors and four platelet parameters using publicly available GWAS data. Subsequently, two-sample MR analyses were performed using summary-level GWAS data from independent FinnGen cohorts to assess the causal impact of coagulation traits on the susceptibility to RVO.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eGenetic instrumental variable selection\u003c/h2\u003e \u003cp\u003eTo explore the causal relationships among coagulation factors, platelet parameters, and RVO, we utilized instrumental variables. We accessed the MR Base database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.mrbase.org/\u003c/span\u003e\u003cspan address=\"http://www.mrbase.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e30\u003c/sup\u003e, which houses an extensive compilation of summary statistics from numerous GWASs. Our approach involved searching for GWASs of coagulation factors in European populations to identify genetic variants associated with these factors. From this curated list, we selected coagulation factor III (FIII), coagulation factor V (FV), coagulation factor VIII (FVIII), coagulation factor XI (FXI), coagulation factor VII (FVII), coagulation factor X (FX), plasmin, platelet count, platelet crit (PCT), mean platelet volume (MPV), and platelet distribution width (PDW) as the available genome-wide significant single nucleotide polymorphisms (SNPs)\u003csup\u003e\u003cspan additionalcitationids=\"CR32 CR33 CR34 CR35\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). In order to select eligible instrumental variables for each coagulation indicator, we implemented a stringent quality control procedure. To avoid bias due to strong linkage disequilibrium (LD), we chose SNPs with genome-wide significance (P\u0026thinsp;\u0026lt;\u0026thinsp;5e \u0026minus;\u0026thinsp;6) associated with specific coagulation metrics as candidate instrumental variables, with an r\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e critical value of 0.001 and a window size of 10 Mb; of which the r\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e value of 0.01 and a window size of 10 Mb were chosen for FVIII to serve only. Additionally, to assess the presence of weak instrumental variable bias, we calculated F-statistics to measure the strength of instrumental variables. F-statistics larger than 10 indicate a low possibility of weak instrumental variable bias\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eRVO outcome data\u003c/h2\u003e \u003cp\u003eTo explore the causal relationships between coagulation factors, platelet parameters, and RVO, we utilized instrumental variables. By accessing the MR Base database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.mrbase.org/\u003c/span\u003e\u003cspan address=\"http://www.mrbase.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e30\u003c/sup\u003e, which hosts comprehensive summary statistics from multiple GWASs, we searched for GWASs of coagulation factors in European populations to identify genetic variants associated with these factors. From this list, we identified FIII, FV, FVII, FXI, FVIII, FX, plasmin, platelet count, PCT, MPV, and PDW as significant genome-wide single nucleotide polymorphisms (SNPs)\u003csup\u003e\u003cspan additionalcitationids=\"CR32 CR33 CR34 CR35\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. (\u003cb\u003eTable \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e). To select eligible instrumental variables for each coagulation indicator, we implemented stringent quality control procedures. To avoid bias resulting from strong linkage disequilibrium (LD), we chose SNPs with genome-wide significance (P\u0026thinsp;\u0026lt;\u0026thinsp;5e \u0026minus;\u0026thinsp;6) associated with specific coagulation metrics as candidate instrumental variables, with an r2 threshold of 0.001 and a window size of 10 Mb. Furthermore, F-statistics were computed to assess the robustness of the instrumental variables. Results indicated that F-statistics exceeding 10 suggest a minimal risk of weak instrumental variable bias\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e,\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e.\u003cb\u003e(Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003eSummary-level data pertaining to RVO were extracted from the FinnGen study, consisting of 372 RVO cases and 182,573 controls\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMendelian randomization estimates\u003c/h2\u003e \u003cp\u003eWe employed summary statistics, including β coefficients and standard errors, to separately estimate the causal relationships between seven coagulation factors and four platelet parameters with RVO, utilizing various MR methods. For the primary MR analysis evaluating the non-confounded causal association among coagulation factors, platelet parameters, and RVO, we utilized the inverse variance weighted approach. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e delineates the three pivotal assumptions essential for this method\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Initially conducted in the FinnGen cohorts, the MR analyses employed three distinct methods: inverse variance weighting (IVW), weighted mean (WM), and MR-Egger regression, each based on different assumptions. The IVW method was used as the primary statistical model. Heterogeneity in causal estimates among instrumental variables indicated a potential violation of the assumptions underlying MR analysis\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. To scrutinize this heterogeneity, we used the Cochran's Q test and assessed it via both the causal estimates of the fixed-effects IVW method and MR-Egger regression. A P-value less than 0.05 was considered indicative of significant heterogeneity. MR Egger regression was utilized to evaluate potential pleiotropic effects of instrumental variables. The intercept term in MR-Egger regression may indicate directional horizontal pleiotropy in the causal estimates.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSelection of instrumental variables\u003c/h2\u003e \u003cp\u003eWe conducted a systematic compilation of genome-wide significant SNPs associated with seven coagulation factors (FIII, FV, FVIII, FXI, FVII, FX, and plasmin) and four platelet parameters (platelet count, PCT, MPV, and PDW) from various GWAS results via extensive literature review to investigate their potential causal effects on the risk of RVO\u003csup\u003e\u003cspan additionalcitationids=\"CR32 CR33 CR34 CR35\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e \u003cb\u003e(Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e)\u003c/b\u003e. These indicators were categorized into five distinct groups: platelet parameters (platelet count, PCT, MPV, and PDW), coagulation intrinsic pathway (FXI, FVIII), extrinsic pathway (FVII, FIII), common pathways (FV, FX), and fibrin clot dissociation (plasmin). To assess the strength of each instrumental variable, we calculated F-statistics for each instrument-exposure association. In our study, the F-statistics notably exceeded 10, indicating robustness and strength of these SNPs as instrumental variables \u003cb\u003e(Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/b\u003e This rigorous selection process ensured that the instrumental variables used in our analysis were reliable and suitable for assessing causal relationships between coagulation factors, platelet parameters, and the risk of RVO,\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eCausal effects of coagulation indicator on retinal vein occlusion\u003c/h2\u003e \u003cp\u003eUtilizing GWAS summary statistics retrieved from the FinnGen study involving 372 cases and 182,573 controls, we conducted MR analyses to assess the causal impacts of seven coagulation factors and four platelet parameters on the risk of RVO. The resulting MR estimates obtained from various methods are presented in \u003cb\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e. Notably, the IVW method provided substantial evidence supporting a causal link between coagulation traits and the occurrence of RVO. Our findings indicate that genetically predicted levels of FIII and FVII are associated with a reduced risk of RVO [FIII: (Inverse variance weighted: OR\u0026thinsp;=\u0026thinsp;0.577, 95% CI: 0.346\u0026ndash;0.962, P\u0026thinsp;=\u0026thinsp;3.3513\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;2 Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cb\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e; FVII: (Inverse variance weighted: OR\u0026thinsp;=\u0026thinsp;0.742, 95% CI: 0.568\u0026ndash;0.969, P\u0026thinsp;=\u0026thinsp;2.84\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;2) Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cb\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e]. Additionally, we observed that genetically predicted MPV and PCT levels are causally linked to a decreased risk of RVO [MPV: (IVW: OR\u0026thinsp;=\u0026thinsp;0.783, 95% CI: 0.621\u0026ndash;0.987, P\u0026thinsp;=\u0026thinsp;3.848\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;2; MR Egger: OR\u0026thinsp;=\u0026thinsp;0.686, 95% CI: 0.475\u0026ndash;0.989, P\u0026thinsp;=\u0026thinsp;4.401\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;2) Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cb\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e]; PCT: (IVW: OR\u0026thinsp;=\u0026thinsp;0.740, 95% CI: 0.560\u0026ndash;0.977, P\u0026thinsp;=\u0026thinsp;3.342\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;2; WM: OR\u0026thinsp;=\u0026thinsp;0.590, 95% CI: 0.358\u0026ndash;0.972, P\u0026thinsp;=\u0026thinsp;3.821\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;2) Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cb\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e]. Intriguingly, our investigation identified a positive causal relationship between genetically predicted FVIII levels and RVO [IVW: OR\u0026thinsp;=\u0026thinsp;1.308, 95% CI: 1.067\u0026ndash;1.604, P\u0026thinsp;=\u0026thinsp;9.787\u0026times;10\u0026thinsp;\u0026minus;\u0026thinsp;3) Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cb\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e]. Nevertheless, no significant causal effect on RVO was observed for other coagulation factors (FV, FX, FXI, fibrinolytic enzymes) or platelet parameters (platelet count, PDW) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cb\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e.). Additionally, the absence of significant horizontal pleiotropy or heterogeneity among coagulation factors and platelet parameters in our analyses strengthens the validity and reliability of the MR estimates obtained (\u003cb\u003eTable \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e). Leave-one-out analyses were conducted to identify any potential outliers, and Cochran's Q test indicated no influential individual SNP driving the IVW point estimate or causing heterogeneity between IVW estimates based on individual variants (\u003cb\u003eTable S3, Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e). Both the funnel plot and MR Egger regression test showed no evidence of asymmetry, suggesting the absence of directional horizontal pleiotropy that could introduce bias to the MR analyses \u003cb\u003e(Figure S3, Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003c/b\u003e). These results collectively contribute to our understanding of the causal relationships between coagulation traits and the risk of RVO.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eUsing summary statistics from GWAS of European ancestry, we examined the causal effects of seven coagulation factors and four platelet parameters on RVO risk by applying a unified MR framework for GWAS data analysis. Genetically forecasted plasma levels of FIII, FVII, MPV, and PCT demonstrated inverse correlations with RVO, while genetically anticipated plasma FVIII levels displayed a positive causal association with RVO.\u003c/p\u003e \u003cp\u003eRVO is a partial or complete blockage of a retinal vein in which the formation of a blood clot causes the retinal venous system to narrow and impedes venous return from the retinal circulation. The exact pathogenesis of RVO remains unclear. The condition may be due to a combination of three systemic changes known as Virchow\u0026rsquo;s triad: hemodynamic changes (venous stasis), degenerative changes of the vessel wall and blood hypercoagulability\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. It has been shown to be associated with thrombotic events due to systemic changes, especially hypercoagulable states\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. The central retinal vein exits the eye at the optic disc and proceeds into the optic nerve. Within the optic nerve, it traverses through the lamina cribrosa, a connective tissue structure that offers support to the vein and the axons as they penetrate the sclera. Postmortem examination of eyes with CRVO revealed the presence of fresh or recanalized thrombus near the lamina cribrosa, where the vein typically narrows and blood flow intensifies\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. BRVOs were observed at sites where arteries cross over veins, particularly those with an artery passing over a vein\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. In cases of BRVO, postmortem analysis showed thrombus formation within a retinal vein compressed by a thickened-walled retinal artery crossing over the vein\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Narrowing or irregularity of the vessels leads to turbulent flow and causes stress on endothelial cells\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThrombophilia, arising from specific coagulation factor activation, has been implicated in the pathogenesis of thrombosis\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. Our study notably demonstrates a substantial correlation between genetically projected levels of FIII and FVII and a reduced RVO risk, representing the pioneering identification of a causal nexus between FIII, FVII, and RVO. The activation of the exogenous coagulation pathway begins with FIII, which is released to form a TF-Ca\u0026sup2;⁺-FVII complex with FVII in the bloodstream thereby generating a cascade reaction to produce plasminogen activator, which generates thrombin. However, due to the action of procoagulants and anticoagulants, thrombin production by the FIII pathway becomes a threshold-limited process and the small amount of thrombin in the initial phase is insufficient to maintain the coagulation process\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Despite the presence of a small amount of activated FVII in the blood, the coagulation process is similarly not initiated without the release of tissue factor. However, Hunter et al.'s prospective study, encompassing 18 central RVO and 22 branch RVO cases, did not discern a significant correlation between FVII and RVO\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Nonetheless, our findings significantly contribute to understanding the potential association between coagulation factors and RVO risk.\u003c/p\u003e \u003cp\u003eHigh coagulation factor VIII activity is an independent risk factor for thromboembolism\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e. Our findings are consistent with the observations in Glueck et al 's research, indicating elevated coagulation factor VIII levels in RVO or CRVO patients compared to controls\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. Moreover, Faude et al. corroborated the relationship between FVIII and RVO occurrence in their study involving 62 CRVO patients and 107 healthy individuals, with over 53.2% of CRVO patients exhibiting Factor VIII activity exceeding 150% (\u0026gt;\u0026thinsp;150 IU/dl) \u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. These collective studies underscore the relevance of coagulation factors, particularly FIII, FVII, and FVIII, in the pathogenesis and risk assessment of RVO.\u003c/p\u003e \u003cp\u003eThe findings regarding MPV in our study imply a possible adverse causal link with RVO, contradicting some observational studies and meta-analyses\u003csup\u003e\u003cspan additionalcitationids=\"CR54 CR55 CR56\" citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Historically, studies employing traditional observational methods have generated inconsistent outcomes concerning the association between MPV and RVO. For instance, prior investigations have indicated a correlation between elevated MPV and RVO\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e,\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Sahin et al.'s retrospective analysis involving 193 RVO patients and 83 healthy controls revealed significantly higher MPV values in the RVO patient group\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Similarly, Yilmaz and Yilmaz reported a similar trend in another retrospective study\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. They suggested that larger platelets have greater haemostatic reactivity. However, Ornek et al. did not observe any association between increased MPV values and RVO occurrence\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. They concluded that the key factor in the development of RVO appears to be changes in the neighboring arteries rather than systemic haematological abnormalities. Moreover, within the cohort displaying clinical features of RVO, it was observed that patients diagnosed with BRVO had a lower MPV value compared to those suffering from central RVO and a control group\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. These studies exhibited considerable heterogeneity in terms of ethnicity, study design, participant numbers, participant characteristics, RVO identification, RVO prevalence, and statistical methodologies, potentially contributing to the discrepancies in findings. Past research has consistently identified age, hypertension, diabetes mellitus, and dyslipidemia as traditional risk factors for RVO\u003csup\u003e\u003cspan additionalcitationids=\"CR59 CR60 CR61\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. Some researchers postulate a greater significance of thrombophilia in younger patients\u003csup\u003e\u003cspan additionalcitationids=\"CR64\" citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e. Study confounders could potentially bias the causal association between MPV and RVO. The inclusion of a small patient cohort might have influenced statistical significance. Notably, patient body mass index was not documented and considered, despite its potential impact on MPV values. Considering that most previous studies were traditional retrospective studies, the use of new research methods is particularly important for these clinical questions. Dual-sample MR analysis was applied to explore the risk factors for RVO. The exact mechanisms underlying the increased risk of low MPV and RVO have not been clarified. In in vitro experiments, smaller platelets secreted more P-selectin, which plays an important role in surface adhesion for thrombus formation\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. Smaller platelets with lower MPV were more likely to form thrombi than larger platelets\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e. Notably, this result was observed in cancer patients. Therefore, the applicability of this finding to non-cancer patients remains to be confirmed. There are fewer studies and more basic research is needed in the future to explore the specific mechanisms. Furthermore, in the absence of a specific rationale for conducting a thorough hemostatic examination in patients with RVO, additional studies are required to elucidate the potential involvement of platelet parameters in the pathophysiology of RVO.\u003c/p\u003e \u003cp\u003eOur investigation has unveiled a association between PCT and RVO, a finding uncommon in current literature. There might exist a plausible negative causal link between platelet crit and RVO. Besides MPV, other indicators of platelet morphology, specifically PCT, could potentially wield substantial influence in vascular ailments, including atherosclerosis and thrombosis\u003csup\u003e\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e,\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u003c/sup\u003e. However, findings from Yilmaz et al.'s study revealed no discernible difference in PCT levels between RVO patients and healthy controls\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Further comprehensive research is essential to fully fathom the underlying mechanisms and mediators that dictate the relationship between PCT levels and RVO.\u003c/p\u003e \u003cp\u003eTo our knowledge, this is the first study to evaluate MR studies that have assessed how different coagulation characteristics affect the risk of RVO, and the first to provide pooled estimates of the causal effects of FIII, FVII, FVIII, MPV and PCT on the risk of RVO. In addition, previous observational studies provided conflicting correlations and biological assumptions. By providing causality estimates, our study eliminates the true extent of the biological effects of relevant confounding risk factors on RVO.\u003c/p\u003e \u003cp\u003eSome potential limitation of our study should be mentioned. Firstly, in studies using sample-bank cohorts\u003csup\u003e\u003cspan additionalcitationids=\"CR32 CR33 CR34 CR35\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, causal effects represent only selected European populations. These were chosen primarily to avoid population stratification. However, these findings may not be representative of more general or non-European populations. Secondly, although we utilized the largest available sample size and the most recent GWAS dataset for MR analysis, it's essential to acknowledge that our study had a comparatively smaller sample size and event count compared to population-based observational studies. In addition, the inclusion of different studies using overlapping cohorts (especially databases), while unavoidable, inevitably reduces the effective sample size and statistical power of our analyses. Thirdly, we did not evaluate genetically predicted coagulation factors and platelet parameters in relation to CRVO and BRVO because of the lack of pooled data on CRVO and the inability to further subdivide MR analyses by other factors such as disease staging, further clinical trials are needed to confirm our findings in order to obtain partially consistent evidence of an association between certain coagulation characteristics and RVO or RVO of varying severity. In addition, our magnetic resonance study demonstrated a causal relationship between genetically predicted coagulation characteristics and RVO; however, it is important to note that the results of the magnetic resonance analyses were based on genetic evidence only.\u003c/p\u003e \u003cp\u003ePlatelet play a pivotal role in thrombo-occlusive disorders, with MPV serving as a vital indicator of platelet size and activity. Larger platelets exhibit heightened reactivity, increased thromboxane A2 production, elevated expression of glycoprotein Ib and glycoprotein IIb/IIIa receptors, and a greater propensity for aggregation\u003csup\u003e\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e,\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e\u003c/sup\u003e. The etiology of hypercoagulable states in RVO remains uncertain, lacking robust validation for extensive screening of thrombotic and coagulopathic conditions in most patients. However, assessing underlying coagulopathies might be warranted when routine tests for common cardiovascular risk factors yield negative results. These investigations should delve into the effects of various coagulation factors and platelet function on RVO, utilizing MR studies with more extensive sample sizes. Furthermore, conducting RVO MR studies among GWAS cohorts of diverse ethnicities will elucidate how distinct genetic compositions and diverse environments from inter-ethnic diversity influence the causal implications of specific coagulation profile-related risk factors in RVO development.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study represents the pioneering use of MR to investigate the causal link between coagulation traits and RVO susceptibility specifically in a European population. The robust findings support a causal relationship between FIII, FVII, FVIII, MPV, PCT, and RVO risk. This comprehensive exploration significantly enhances our understanding of the coagulation cascade's involvement in RVO pathogenesis, potentially informing preventive and diagnostic strategies for RVO.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eGWAS Genome-wide association study\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIVW Inverse variance weighting\u003c/p\u003e\n\u003cp\u003eSNP Single nucleotide polymorphism\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWM Weighted mean\u003c/p\u003e\n\u003cp\u003eMR Mendelian randomization\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOR Odds ratio\u003c/p\u003e\n\u003cp\u003eMPV mean platelet volume\u003c/p\u003e\n\u003cp\u003ePDW platelet distribution width\u003c/p\u003e\n\u003cp\u003ePCT platelet crit\u003c/p\u003e\n\u003cp\u003eRVO retinal vein occlusion\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthics statement\u003c/h2\u003e \u003cp\u003eThis MR study, which exclusively utilized publicly available summary statistics, was exempt from ethical approval.\u003c/p\u003e \u003ch2\u003ePatient and Public Involvement\u003c/strong\u003e \u003cp\u003eIt was not appropriate or possible to involve patients or the public in the design, or conduct, or reporting, or dissemination plans of our research.\u003c/p\u003e \u003cp\u003e \u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003eThe author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eAuthor details\u003c/h2\u003e \u003cp\u003e1、Department of Ophthalmology, The Second Hospital of Jilin University, Changchun, 130041, Jilin Province, China\u003c/p\u003e \u003cp\u003e2、Department of Hepatobiliary and Pancreatic Surgery, The Affiliated Hospital of Qingdao University, Qingdao 266555, Shandong Province, China\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eY. C.Y，Q .H and H. C performed conceived and designed the study. Q. H supervised the study and data analysis. Y.C.Y performed the data analysis with help from Q.H, Z.L, H. C and L.B.X. Q. H and Z. L and H.C supervised and revised the manuscript; all authors gave final approval for the submitted version of the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eWe analyzed publicly available datasets which can be found here: The IEU Open GWAS Project database (https://gwas.mrcieu.ac.uk/).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eScott, I. U., Campochiaro, P. A., Newman, N. J. \u0026amp; Biousse, V. Retinal vascular occlusions. Lancet 396, 1927\u0026ndash;1940 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRogers, S. \u003cem\u003eet al.\u003c/em\u003e The prevalence of retinal vein occlusion: pooled data from population studies from the United States, Europe, Asia, and Australia. Ophthalmology 117, 313\u0026ndash;319.e1 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreen, W. R., Chan, C. C., Hutchins, G. M. \u0026amp; Terry, J. M. Central retinal vein occlusion: a prospective histopathologic study of 29 eyes in 28 cases. Trans Am Ophthalmol Soc 79, 371\u0026ndash;422 (1981).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrangieh, G. T., Green, W. R., Barraquer-Somers, E. \u0026amp; Finkelstein, D. Histopathologic study of nine branch retinal vein occlusions. Arch Ophthalmol 100, 1132\u0026ndash;1140 (1982).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlein, R., Moss, S. E., Meuer, S. M. \u0026amp; Klein, B. E. K. The 15-year cumulative incidence of retinal vein occlusion: the Beaver Dam Eye Study. Arch Ophthalmol 126, 513\u0026ndash;518 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrevalence and associations of retinal vein occlusion in Australia. The Blue Mountains Eye Study - PubMed. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/8859084/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/8859084/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMitchell, P., Smith, W. \u0026amp; Chang, A. Prevalence and associations of retinal vein occlusion in Australia. The Blue Mountains Eye Study. Arch Ophthalmol 114, 1243\u0026ndash;1247 (1996).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWong, T. Y. \u003cem\u003eet al.\u003c/em\u003e Cardiovascular risk factors for retinal vein occlusion and arteriolar emboli: the Atherosclerosis Risk in Communities \u0026amp; Cardiovascular Health studies. Ophthalmology 112, 540\u0026ndash;547 (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElman, M. J., Bhatt, A. K., Quinlan, P. M. \u0026amp; Enger, C. The risk for systemic vascular diseases and mortality in patients with central retinal vein occlusion. Ophthalmology 97, 1543\u0026ndash;1548 (1990).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRath, E. Z., Frank, R. N., Shin, D. H. \u0026amp; Kim, C. Risk factors for retinal vein occlusions. A case-control study. Ophthalmology 99, 509\u0026ndash;514 (1992).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCardiovascular and thrombophilic risk factors for central retinal vein occlusion - PubMed. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/12020623/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/12020623/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJanssen, M. C. H., den Heijer, M., Cruysberg, J. R. M., Wollersheim, H. \u0026amp; Bredie, S. J. H. Retinal vein occlusion: a form of venous thrombosis or a complication of atherosclerosis? A meta-analysis of thrombophilic factors. Thromb Haemost 93, 1021\u0026ndash;1026 (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBertram, B., Remky, A., Arend, O., Wolf, S. \u0026amp; Reim, M. Protein C, protein S, and antithrombin III in acute ocular occlusive diseases. Ger J Ophthalmol 4, 332\u0026ndash;335 (1995).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaboratory evaluation of hypercoagulable states in patients with central retinal vein occlusion who are less than 56 years of age - PubMed. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/11772591/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/11772591/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eActivated protein C resistance and anticoagulant proteins in young adults with central retinal vein occlusion - PubMed. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/10634554/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/10634554/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCruciani, F. \u003cem\u003eet al.\u003c/em\u003e MTHFR C677T mutation, factor II G20210A mutation and factor V Leiden as risks factor for youth retinal vein occlusion. Clin Ter 154, 299\u0026ndash;303 (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCiardella, A. P. \u003cem\u003eet al.\u003c/em\u003e Factor V Leiden, activated protein C resistance, and retinal vein occlusion. Retina 18, 308\u0026ndash;315 (1998).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrevalence and risk factors of retinal vein occlusion: the Gutenberg Health Study - PubMed. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/25894549/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/25894549/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuhli-Hattenbach, C., Miesbach, W., L\u0026uuml;chtenberg, M., Kohnen, T. \u0026amp; Hattenbach, L.-O. Elevated lipoprotein (a) levels are an independent risk factor for retinal vein occlusion. Acta Ophthalmol 95, 140\u0026ndash;145 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHypertension and multiple cardiovascular risk factors increase the risk for retinal vein occlusions: results from the Gutenberg Retinal Vein Occlusion Study - PubMed. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/31145709/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/31145709/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRetinal Vein Occlusion is Associated with Low Blood High-Density Lipoprotein Cholesterol: A Nationwide Cohort Study - PubMed. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/30959001/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/30959001/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNapal Lecumberri, J. J. \u003cem\u003eet al.\u003c/em\u003e Lipid profile and serum folate, vitamin B12 and homocysteine levels in patients with retinal vein occlusion. Clin Investig Arterioscler 33, 169\u0026ndash;174 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA review of risk factors for retinal vein occlusions - PubMed. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/35972726/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/35972726/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRisk factors for central retinal vein occlusion. The Eye Disease Case-Control Study Group. Arch Ophthalmol 114, 545\u0026ndash;554 (1996).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRisk factors for branch retinal vein occlusion. The Eye Disease Case-control Study Group - PubMed. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/8357052/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/8357052/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSystemic disorders associated with retinal vascular occlusion - PubMed. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/11141642/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/11141642/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUsing published data in Mendelian randomization: a blueprint for efficient identification of causal risk factors - PubMed. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/25773750/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/25773750/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eInterpreting findings from Mendelian randomization using the MR-Egger method - PubMed. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/28527048/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/28527048/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgess, S., Foley, C. N. \u0026amp; Zuber, V. Inferring Causal Relationships Between Risk Factors and Outcomes from Genome-Wide Association Study Data. Annu Rev Genomics Hum Genet 19, 303\u0026ndash;327 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHemani, G. \u003cem\u003eet al.\u003c/em\u003e The MR-Base platform supports systematic causal inference across the human phenome. Elife 7, e34408 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePietzner, M. \u003cem\u003eet al.\u003c/em\u003e Genetic architecture of host proteins involved in SARS-CoV-2 infection. Nat Commun 11, 6397 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFolkersen, L. \u003cem\u003eet al.\u003c/em\u003e Mapping of 79 loci for 83 plasma protein biomarkers in cardiovascular disease. PLoS Genet 13, e1006706 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, M.-H. \u003cem\u003eet al.\u003c/em\u003e Trans-ethnic and Ancestry-Specific Blood-Cell Genetics in 746,667 Individuals from 5 Global Populations. Cell 182, 1198\u0026ndash;1213.e14 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVuckovic, D. \u003cem\u003eet al.\u003c/em\u003e The Polygenic and Monogenic Basis of Blood Traits and Diseases. Cell 182, 1214\u0026ndash;1231.e11 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVuckovic, D. \u003cem\u003eet al.\u003c/em\u003e The Polygenic and Monogenic Basis of Blood Traits and Diseases. Cell 182, 1214\u0026ndash;1231.e11 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhole-exome imputation within UK Biobank powers rare coding variant association and fine-mapping analyses - PubMed. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/34226706/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/34226706/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeorgakis, M. K. \u003cem\u003eet al.\u003c/em\u003e Genetically Determined Levels of Circulating Cytokines and Risk of Stroke. Circulation 139, 256\u0026ndash;268 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAn approximation to the F distribution using the chi-square distribution - ScienceDirect. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.sciencedirect.com/science/article/pii/S0167947301000974\u003c/span\u003e\u003cspan address=\"https://www.sciencedirect.com/science/article/pii/S0167947301000974\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFinnGen: Unique genetic insights from combining isolated population and national health register data | medRxiv. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.medrxiv.org/content/\u003c/span\u003e\u003cspan address=\"https://www.medrxiv.org/content/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1101/2022.03.03.22271360v1\u003c/span\u003e\u003cspan address=\"10.1101/2022.03.03.22271360v1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAn Overview of Methods and Exemplars of the Use of Mendelian Randomisation in Nutritional Research - PubMed. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/36014914/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/36014914/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eA framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization - PubMed. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/28114746/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/28114746/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJw, Y., P, L., Ty, W., J, B. \u0026amp; A, J. Retinal vein occlusion: an approach to diagnosis, systemic risk factors and management. Internal medicine journal 38, (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao, J., Sastry, S. M., Sperduto, R. D., Chew, E. Y. \u0026amp; Remaley, N. A. Arteriovenous crossing patterns in branch retinal vein occlusion. The Eye Disease Case-Control Study Group. Ophthalmology 100, 423\u0026ndash;428 (1993).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaylor, A. W., Sehu, W., Williamson, T. H. \u0026amp; Lee, W. R. Morphometric assessment of the central retinal artery and vein in the optic nerve head. Can J Ophthalmol 28, 320\u0026ndash;324 (1993).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilliamson, T. H. A \u0026lsquo;throttle\u0026rsquo; mechanism in the central retinal vein in the region of the lamina cribrosa. Br J Ophthalmol 91, 1190\u0026ndash;1193 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRetinal vein occlusion: pathophysiology and treatment options - PubMed. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/20689798/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/20689798/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMann, K. G., van\u0026rsquo;t Veer, C., Cawthern, K. \u0026amp; Butenas, S. The role of the tissue factor pathway in initiation of coagulation. Blood Coagul Fibrinolysis 9 Suppl 1, S3-7 (1998).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eConnors, J. M. Thrombophilia Testing and Venous Thrombosis. N Engl J Med 377, 1177\u0026ndash;1187 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO\u0026rsquo;Donnell, J. \u003cem\u003eet al.\u003c/em\u003e High prevalence of elevated factor VIII levels in patients referred for thrombophilia screening: role of increased synthesis and relationship to the acute phase reaction. Thromb Haemost 77, 825\u0026ndash;828 (1997).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlueck, C. J., Hutchins, R. K., Jurantee, J., Khan, Z. \u0026amp; Wang, P. Thrombophilia and retinal vascular occlusion. Clin Ophthalmol 6, 1377\u0026ndash;1384 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlueck, C. J., Wang, P., Bell, H., Rangaraj, V. \u0026amp; Goldenberg, N. Associations of thrombophilia, hypofibrinolysis, and retinal vein occlusion. Clin Appl Thromb Hemost 11, 375\u0026ndash;389 (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFaude, F., Faude, S., Siegemund, A. \u0026amp; Wiedemann, P. [Factor VIII activity in patients with central retinal vein occlusion in comparison to patients with a history of pelvic and lower limb venous thrombosis and a healthy control group]. Klin Monbl Augenheilkd 221, 862\u0026ndash;866 (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOnder, H. I. \u003cem\u003eet al.\u003c/em\u003e Relation between platelet indices and branch retinal vein occlusion in hypertensive patients. Indian J Ophthalmol 61, 160\u0026ndash;162 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSahin, A. \u003cem\u003eet al.\u003c/em\u003e The mean platelet volume in patients with retinal vein occlusion. \u003cem\u003eJ Ophthalmol\u003c/em\u003e 2013, 236371 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBawankar, P., Samant, P., Lahane, T., Parekh, R. \u0026amp; Lahane, S. Mean platelet volume and central retinal vein occlusion in hypertensive patients. Can J Ophthalmol 54, 275\u0026ndash;279 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrnek, N., Ogurel, T., Ornek, K. \u0026amp; Onaran, Z. Mean platelet volume in retinal vein occlusion. Eur Rev Med Pharmacol Sci 18, 2778\u0026ndash;2782 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePinna, A. \u003cem\u003eet al.\u003c/em\u003e Mean Platelet Volume, Red Cell Distribution Width, and Complete Blood Cell Count Indices in Retinal Vein Occlusions. Ophthalmic Epidemiol 28, 39\u0026ndash;47 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYilmaz, T. \u0026amp; Yilmaz, A. Altered platelet morphological parameters in patients with retinal vein occlusion. Eur Rev Med Pharmacol Sci 20, 1934\u0026ndash;1939 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRogers, S. \u003cem\u003eet al.\u003c/em\u003e The prevalence of retinal vein occlusion: pooled data from population studies from the United States, Europe, Asia, and Australia. Ophthalmology 117, 313\u0026ndash;319.e1 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong, P., Xu, Y., Zha, M., Zhang, Y. \u0026amp; Rudan, I. Global epidemiology of retinal vein occlusion: a systematic review and meta-analysis of prevalence, incidence, and risk factors. J Glob Health 9, 010427 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang, Y.-S., Ho, C.-H., Chu, C.-C., Wang, J.-J. \u0026amp; Jan, R.-L. Risk of retinal vein occlusion in patients with diabetes mellitus: A retrospective cohort study. Diabetes Res Clin Pract 171, 108607 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePacella, F. \u003cem\u003eet al.\u003c/em\u003e Impact of cardiovascular risk factors on incidence and severity of Retinal Vein Occlusion. Clin Ter 171, e534\u0026ndash;e538 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArs\u0026egrave;ne, S. \u003cem\u003eet al.\u003c/em\u003e Increased prevalence of factor V Leiden in patients with retinal vein occlusion and under 60 years of age. Thromb Haemost 94, 101\u0026ndash;106 (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrevalence of factor V Leiden in young adults with retinal vein occlusion - PubMed. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/9031476/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/9031476/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHigh prevalence of resistance to APC in young patients with retinal vein occlusion - PubMed. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/11935272/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/11935272/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHandtke, S. \u003cem\u003eet al.\u003c/em\u003e Role of Platelet Size Revisited-Function and Protein Composition of Large and Small Platelets. Thromb Haemost 119, 407\u0026ndash;420 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFerroni, P. \u003cem\u003eet al.\u003c/em\u003e Evaluation of mean platelet volume as a predictive marker for cancer-associated venous thromboembolism during chemotherapy. Haematologica 99, 1638\u0026ndash;1644 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDav\u0026igrave;, G. \u0026amp; Patrono, C. Platelet activation and atherothrombosis. N Engl J Med 357, 2482\u0026ndash;2494 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEvaluation of mean platelet volume in patients with type 2 diabetes mellitus and blood glucose regulation: a marker for atherosclerosis? - PubMed. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/24955167/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/24955167/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGiles, H., Smith, R. E. \u0026amp; Martin, J. F. Platelet glycoprotein IIb-IIIa and size are increased in acute myocardial infarction. Eur J Clin Invest 24, 69\u0026ndash;72 (1994).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHaver, V. M. \u0026amp; Gear, A. R. Functional fractionation of platelets. J Lab Clin Med 97, 187\u0026ndash;204 (1981).\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":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Mendelian randomization, Retinal vein occlusion, Coagulation factor, Platelet parameter, Causal association, Risk","lastPublishedDoi":"10.21203/rs.3.rs-4519232/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4519232/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction \u003c/strong\u003eRetinal vein occlusion (RVO) is the leading cause of vision loss due to an obstruction in the retinal venous system. While RVO is often linked to thrombotic tendencies and coagulation abnormalities, the exact role of coagulation traits in its development is not fully understood. This study aims to investigate the potential causal relationship between coagulation traits and the risk of RVO by analyzing publicly available genome-wide association study (GWAS) summary statistics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterials and methods\u003c/strong\u003e A two-sample Mendelian randomization (MR) analysis framework was employed to investigate the causal relationship between coagulation traits and the risk of RVO. Stringent quality control measures were applied to select appropriate instrumental variables strongly linked to exposure, such as coagulation factor III (FIII), coagulation factor V (FV), coagulation factor VIII (FVIII), coagulation factor XI (FXI), coagulation factor VII (FVII) and coagulation factor X (FX), as well as plasmin, platelet count, platelet crit (PCT), mean platelet volume (MPV), and platelet distribution width (PDW). The study utilized the FinnGen project RVO GWAS summary statistics cohort, consisting of 372 RVO cases and 182,573 controls. The analysis focused on 11 coagulation traits.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e The research suggests that genetically predicted plasma levels of FIII, FVII, MPV, and PCT may be potentially causative for reducing the risk of RVO, and that levels of FVIII may be potentially causative for increasing the risk of RVO.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion \u003c/strong\u003eOur MR analysis, utilizing GWAS data from a comprehensive population-based study, revealed a causal association between plasma levels of FFIII, FVII, FVIII, MPV, and PCT with the risk of RVO.\u003c/p\u003e","manuscriptTitle":"The effect of coagulation traits on the risk of retinal vein occlusion: a mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-09 21:32:19","doi":"10.21203/rs.3.rs-4519232/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-07-31T17:32:16+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-07-30T22:52:05+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-07-11T15:22:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-07-10T06:10:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-06-03T04:36:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fba9d176-94d3-4d3b-bcc1-66f95456c410","owner":[],"postedDate":"August 9th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":35412238,"name":"Health sciences/Diseases"},{"id":35412239,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-01-27T16:04:27+00:00","versionOfRecord":{"articleIdentity":"rs-4519232","link":"https://doi.org/10.1038/s41598-025-87648-7","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-01-24 15:58:12","publishedOnDateReadable":"January 24th, 2025"},"versionCreatedAt":"2024-08-09 21:32:19","video":"","vorDoi":"10.1038/s41598-025-87648-7","vorDoiUrl":"https://doi.org/10.1038/s41598-025-87648-7","workflowStages":[]},"version":"v1","identity":"rs-4519232","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4519232","identity":"rs-4519232","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}