Exploring the Causality Between Mean Platelet Volume and Sepsis: A Two-Sample Mendelian Randomization Study.

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Xueshu Yu, Wen Xu, Xiangyuan Ruan, Luwei Xu, Yincai Ye This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3924061/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: The potential relationship between Mean Platelet Volume (MPV) and the progression of sepsis has been proposed; however, the nature of this association, whether it entails causation or a subsequent outcome, remains a topic of debate. The primary objective of this study is to evaluate the impact of MPV on sepsis using a bidirectional Mendelian randomization framework. Methods: Genetic associations related to sepsis were derived from the most extensive and current genome-wide association study (GWAS) available, encompassing 11,643 cases and 474,841 controls, serving as the dataset for outcomes. Additionally, genetic associations linked to Mean Platelet Volume (MPV) were drawn from another GWAS, constituting the dataset for exposure. Estimates were computed through inverse-variance weighting, supplemented by sensitivity analyses using MR-Egger, weighted median, simple mode, and weighted mode. Moreover, Cochran's Q test and “leave-one-out” analysis were also taken into account. Results: The results obtained from the inverse-variance weighting (IVW) analysis failed to provide evidence in support of a causal relationship between MPV and sepsis (β = -0.119, SE = 0.280, P = 0.671). Consistent estimates were derived from the MR analyses conducted using the IVW, MR-Egger, weighted median, simple mode, and weighted mode methods. Examination of heterogeneity through Cochran's Q test revealed no indications of variability among IV estimates derived from individual genetic variants. Additionally, the outcomes of the "leave-one-out" analysis demonstrated that no individual single nucleotide polymorphism (SNP) exerted undue influence on the IVW estimate. Conclusion: The outcomes of the MR analysis did not substantiate a causal connection between MPV and sepsis. Mean Platelet Volume Sepsis Mendelian randomization Figures Figure 1 Figure 2 Background Sepsis represents a significant global healthcare challenge, affecting millions of individuals annually and resulting in mortality rates ranging from one in three to one in six among those afflicted[ 1 ]. Timely recognition within the initial hours following sepsis onset has been shown to enhance patient outcomes[ 2 ]. In consideration of this, there is a notable interest in biomarkers that have the potential to be employed in emergency departments or critical care units, playing a crucial role in the effective diagnosis of sepsis[ 3 ]. However, there is currently an absence of biomarkers that have undergone comprehensive validation and can offer the essential diagnostic accuracy to aid clinicians during this vital timeframe[ 4 ]. Platelets are discernible elements in the bloodstream. Once activated, they unleash a myriad of cytokines and inflammatory mediators, critically contributing to the initiation of inflammation and the regulation of immune responses[ 5 ]. The mean platelet volume (MPV), which characterizes the mean size of individual platelets, assumes significance as a gauge of platelet proportions, operational dynamics, and reactivity. MPV can be easily integrated into daily medical activities, without extra costs. An escalating array of recent studies has emphasized the mean platelet volume (MPV) as a predictive indicator for sepsis[ 6 – 10 ]. And Selma Ates’s study showed that MPV (venous blood sampling was performed within the first 12 hours after first presentation,) was a biomarker for the diagnosis of sepsis and the cut-off was 8.915, sensitivity 71%, and specificity 63.9% in sepsis patients[ 8 ]. Meanwhile, the specific combinations of MPV and C-reactive protein can improve the power to distinguish patients with bloodstream infection caused by different pathogens[ 11 ]. Yet, its thorough clinical significance remains to be fully illuminated, and its diagnostic application has been confined[ 12 ]. Given that a considerable proportion of earlier reports stem from traditional observational research, the outcomes were inevitably influenced by confounding variables and reverse causation[ 13 ]. In this regard, Mendelian randomization (MR) has emerged as a novel approach extensively employed for causal inference[ 14 ], yet its application to ascertain the diagnostic potential of MPV for sepsis remains unexplored. The evidence of causality presented by the MR analyses has been positioned at the intersection of randomized controlled trials (RCTs) and traditional epidemiological investigations[ 15 ]. In this current study, our objective was to explore a potential causal connection between MPV and the susceptibility to sepsis through the application of MR. Methods Study design and assumption MR analyses employ genetic instrumental variables (IVs) to assess the causal connection between the exposure and the outcome. MR analyses rest upon three assumptions: Firstly, IVs were significantly correlated with interest exposure. Secondly, IVs remain independent of the confounders affecting the exposure-outcome relationship. Thirdly, IVs must only influence the outcome through the exposure of interest[ 15 ]. If all these assumptions hold true, MR analyses can deduce a causality that is immune to unmeasured confounding and reverse causality. Single-nucleotide polymorphisms (SNPs) encapsulate the genetic variability among individuals, expected to be randomly distributed within the population, thus serving as robust estimators of lifelong effects. These SNPs were harnessed as instrumental variables in the MR analysis. Data source Genetic linkages encompassing gene-exposure and gene-outcome data were procured from two separate repositories of genome-wide association studies (GWAS) accessible through the catalog ( https://www.ebi.ac.uk/gwas/ ). These datasets were drawn from distinct samples that do not overlap, enabling an inquiry into the potential causal interplay between the genetic susceptibility to sepsis and the Mean Platelet Volume. Given that the original GWAS had previously secured appropriate ethical and institutional review board approval, no further ethical endorsement was required for this study. Summary statistics datasets of MPV GWASs as an exposure, which from the European population comprised 66,867 participants ( https://gwas.mrcieu.ac.uk/datasets/ieu-a-1006/ ). The outcome dataset summary statistics of sepsis were extracted from a GWAS conducted in UK Biobank participants (European: n = 11,643 cases and 474,841 controls, https://gwas.mrcieu.ac.uk/datasets/ieu-b-4980/ ). Genetic instruments Independent SNPs with genome-wide significance with sepsis were used as instruments. The selection threshold was set as p-value < 5×10 − 8 , linkage disequilibrium r 2 < 0.001, as previous study[ 16 ]. A threshold of F 0.3 were also removed. Statistical analysis for MR The principal analysis for the Mendelian randomization (MR) investigation employed the inverse-variance weighted (IVW) technique[ 18 ]. The IVW method is an estimate in an ideal state. It assumes that all genetic variations are effective tool indicators for effective analysis, and it has the ability to detect causality. However, the IVW method specifically requires that the genetic variation is only passed through the SNP of the known confounding factors in this study, but there are still many unknown confounding factors that will lead to bias in the estimation of the pleiotropic effect value of the gene. Therefore, we used four other methods to test the reliability and stability of the results. The MR-Egger regression[ 19 ], the weighted median estimator (WME)[ 20 ], the weighted mode[ 21 ] and the simple mode followed by MR analysis. If the above five different MR models produced similar estimates of the causal effect, we considered the causal relationship between MPV and sepsis to be stable and reliable. The results obtained from the Mendelian randomization (MR) analysis were portrayed in the form of odds ratios (OR) and corresponding confidence intervals (CI), displayed visually using a forest plot and scatter plot. Additionally, we assessed the statistical power of our MR outcomes employing Brion's methodology. ( https://shiny.cnsgenomics.com/mRnd/ ). Sensitivity test We assessed heterogeneities between SNPs using Cochran's Q statistics[ 22 ]. Meanwhile, the MR-Egger sensitivity test was applied to analyze the direct pleiotropy effect to ensure that IVs satisfy these assumptions[ 23 ]. We also performed a “leave-one-out” analysis to investigate the possibility that the causal association was driven by a single SNP. We conducted an additional reverse Mendelian randomization (MR) analysis to investigate the potential for reverse causality. A significant finding in the reverse MR analysis would suggest a reverse causal relationship from sepsis (treated as the exposure) to MPV (considered as the outcome). The methodology employed in the reverse MR analysis remained consistent with the approach used in the preceding MR analysis. All the MR analyses were conducted with R (version 4.1.3). R packages “TwoSampleMR 0.5.7” were utilized. Results This study is reported according to the STROBE guideline for cohort studies[ 24 ]. According to the selection criteria of IVs, a total of 23 SNPs were used as IVs, details about the selected instrumental variables are shown in sTable 1. MR Analyses The IVW method showed no evidence to support a causal association between MPV and sepsis (β =-0.119, SE = 0.280, P = 0.671; Table 1). Figure 1 shows forest plots of the estimates for each outcome using the different MR methods. Funnel and radial plots are provided in Supplementary File (sFigure.1 and sFigure.2). The MR estimates determined using the IVW, MR-Egger, weighted median, simple mode, and weighted mode were consistent (Table 1). Sensitivity test Cochran's Q test indicated no evidence of heterogeneity between IV estimates based on the individual variants (Table 2). In order to investigate the potential disproportionate influence of any single SNP in the MPV instrument on the aggregate outcomes, IVW analyses were recomputed with the omission of individual SNPs, performed sequentially. The outcomes derived from a "leave-one-out" analysis underscored the absence of any solitary SNP wielding a significant sway over the IVW point estimate (Fig. 2 ). The reverse-direction Mendelian randomization (MR) IVW analysis assesses the link between Mean Platelet Volume (MPV) and sepsis. Furthermore, the outcomes of the MR analysis also did not provide evidence in favor of a causal relationship between sepsis and MPV (sTable.2). Discussion In this study, we did not find evidence that MPV was causally associated with risk of sepsis. Meanwhile, we observed no evidence of causal association for sepsis with MPV. It is known that MPV is genetically determined. Genome wide association studies identified genes and gene regions that influence thrombopoiesis by encoding kinases, membrane proteins, transcription factors, signaling proteins, proteins involved in megakaryocyte development and platelet production. Regulation of these genes may be responsible for higher MPVs observed in sepsis[ 25 , 26 ]. Previous observational studies have shown sepsis to be associated with sepsis [ 6 – 9 ]. However, it remains unclear whether MPV has a causal relationship with sepsis, as it may be a result of bias or confounding factors inherent to observational studies, including reverse causation, small study numbers and sizes, single center study and selection biases observational studies may be subject to confounding and cannot always distinguish symptoms from causes. Thus, to clarify this association, we evaluated causality using five different estimation methods (MR-Egger, weighted median, simple mode, and weighted mode) for MR analyses. Our study findings did not indicate that the associations between MPV and sepsis and do not support a causal inverse association between MPV and sepsis. The result was different from those of previous studies (Includes adults and neonatal sepsis)[ 6 – 10 , 27 ]. As we mentioned before, the relationship between MPV and sepsis is influenced by various factors, and most studies had small sample sizes, with inherent biases in retrospective research. Therefore, MPV could be used as an indicator for the early diagnosis of sepsis should be interpreted with caution. In our study, MR analysis was performed to determine the causal association between MPV and sepsis, thus excluding the interference of confounding factors and reversing causation on causal inference. The SNP biomarker and SNP schizophrenia estimates were obtained in mostly European studies, thus minimizing the possibility of population stratification bias. Meanwhile, we conducted a wide range of suggested sensitivity analyses[ 15 ], which supported that our findings were not meaningfully affected by pleiotropy. Horizontal pleiotropy was detected and excluded by using the MR-Egger regression intercept term tests. Furthermore, a two-sample MR design was adopted and non-overlapping exposure and outcome summary-level data were used to avoid bias. Therefore, previously reported inverse associations between MPV and occurrence of sepsis may be the result of bias or confounding factors inherent to observational studies. However, it is important to acknowledge that our study does have certain limitations. Firstly, the utilization of summary-level statistics impedes our ability to conduct analyses stratified by covariates that were adjusted for in the original GWAS. Secondly, it's worth noting that our findings were derived from European populations, which might not necessarily generalize to other ethnic groups. Thirdly, due to our reliance on summary-level data for two-sample MR analyses, the feasibility of conducting analyses within specific subgroups was constrained. Moreover, while mendelian randomization methods offer distinct advantages compared to conventional meta-analysis techniques, they still hinge on model assumptions and experimental design. Lastly, it's important to acknowledge that genetic variations could potentially exert varying impacts on the connection between exposure and outcome across different age cohorts or temporal junctures. Mendelian randomization might encounter challenges in capturing these dynamic causal effects. Conclusion The MR analysis results do not support a causal inverse association between sepsis and sepsis. Further, well designed epidemiological using more variants that explain a larger proportion of MPV and sepsis are warranted to confirm or rule out causality. Abbreviations corresponding confidence intervals CI inverse-variance weighting IVW genome-wide association study GWAS Instrumental variables IVs Mean Platelet Volume MPV Mendelian randomization MR odds ratios OR single nucleotide polymorphism SNP weighted median estimator WME Declarations Ethics approval and consent to participate original GWAS had previously secured appropriate ethical and institutional review board approval, no further ethical endorsement was required for this study. Consent for publication original GWAS had previously secured appropriate ethical and institutional review board approval, thus individual patient informed consent was not required. Availability of data and material The data can be downloaded from the website https://www.ebi.ac.uk/gwas/. The other datasets generated and/or analysed during the current study are publicly available and included in this published article and its supplementary information files. Competing interests The authors declare that they have no competing interests. Funding Not applicable Authors' contributions Xueshu Yu designed this study, analysed data, and drafted the manuscript; Wen Xu designed this study, collected data, drafted, and revised the manuscript; Xiangyuan Ruan and Luwei Xu compiled and analysed the data; Yincai Ye designed and supervised this study. All authors read and approved the final manuscript. Acknowledgements Not applicable References Fleischmann-Struzek C, Mellhammar L, Rose N, Cassini A, Rudd KE, Schlattmann P, Allegranzi B, Reinhart K. Incidence and mortality of hospital- and ICU-treated sepsis: results from an updated and expanded systematic review and meta-analysis. Intensive Care Med. 2020;46(8):1552–62. Evans L, Rhodes A, Alhazzani W, Antonelli M, Coopersmith CM, French C, Machado FR, McIntyre L, Ostermann M, Prescott HC, et al. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021. Intensive Care Med. 2021;47(11):1181–247. Teggert A, Datta H, Ali Z. Biomarkers for Point-of-Care Diagnosis of Sepsis. Micromachines (Basel) 2020, 11(3). Shetty A, Macdonald SP, Keijzers G, Williams JM, Tang B, de Groot B, Thompson K, Fraser JF, Finfer S, Bellomo R, et al. Review article: Sepsis in the emergency department - Part 2: Investigations and monitoring. Emerg Med Australas. 2018;30(1):4–12. Koupenova M, Clancy L, Corkrey HA, Freedman JE. Circulating Platelets as Mediators of Immunity, Inflammation, and Thrombosis. Circ Res. 2018;122(2):337–51. Aydemir H, Piskin N, Akduman D, Kokturk F, Aktas E. Platelet and mean platelet volume kinetics in adult patients with sepsis. Platelets. 2015;26(4):331–5. Liu JP, Wang YM, Zhou J. Platelet parameters aid identification of adult-onset Still's disease from sepsis. Neth J Med. 2019;77(8):274–9. Ates S, Oksuz H, Dogu B, Bozkus F, Ucmak H, Yanıt F. Can mean platelet volume and mean platelet volume/platelet count ratio be used as a diagnostic marker for sepsis and systemic inflammatory response syndrome? Saudi Med J. 2015;36(10):1186–90. Dursun A, Ozsoylu S, Akyildiz BN. Neutrophil-to-lymphocyte ratio and mean platelet volume can be useful markers to predict sepsis in children. Pak J Med Sci. 2018;34(4):918–22. Catal F, Tayman C, Tonbul A, Akça H, Kara S, Tatli MM, Oztekin O, Bilici M. Mean platelet volume (MPV) may simply predict the severity of sepsis in preterm infants. Clin Lab. 2014;60(7):1193–200. Tang W, Zhang W, Li X, Cheng J, Liu Z, Zhou Q, Guan S. Hematological parameters in patients with bloodstream infection: A retrospective observational study. J Infect Dev Ctries. 2020;14(11):1264–73. Korniluk A, Koper-Lenkiewicz OM, Kamińska J, Kemona H, Dymicka-Piekarska V. Mean Platelet Volume (MPV): New Perspectives for an Old Marker in the Course and Prognosis of Inflammatory Conditions. Mediators Inflamm. 2019;2019:9213074. Burgess S, Swanson SA, Labrecque JA. Are Mendelian randomization investigations immune from bias due to reverse causation? Eur J Epidemiol. 2021;36(3):253–7. Sekula P, Del Greco MF, Pattaro C, Köttgen A. Mendelian Randomization as an Approach to Assess Causality Using Observational Data. J Am Soc Nephrol. 2016;27(11):3253–65. Davies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018;362:k601. Schaid DJ, Chen W, Larson NB. From genome-wide associations to candidate causal variants by statistical fine-mapping. Nat Rev Genet. 2018;19(8):491–504. Burgess S, Thompson SG. Bias in causal estimates from Mendelian randomization studies with weak instruments. Stat Med. 2011;30(11):1312–23. Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37(7):658–65. Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44(2):512–25. Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol. 2016;40(4):304–14. Hartwig FP, Davey Smith G, Bowden J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol. 2017;46(6):1985–98. Greco MF, Minelli C, Sheehan NA, Thompson JR. Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome. Stat Med. 2015;34(21):2926–40. Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50(5):693–8. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet. 2007;370(9596):1453–7. Shameer K, Denny JC, Ding K, Jouni H, Crosslin DR, de Andrade M, Chute CG, Peissig P, Pacheco JA, Li R, et al. A genome- and phenome-wide association study to identify genetic variants influencing platelet count and volume and their pleiotropic effects. Hum Genet. 2014;133(1):95–109. Middleton EA, Rowley JW, Campbell RA, Grissom CK, Brown SM, Beesley SJ, Schwertz H, Kosaka Y, Manne BK, Krauel K, et al. Sepsis alters the transcriptional and translational landscape of human and murine platelets. Blood. 2019;134(12):911–23. Wang J, Wang Z, Zhang M, Lou Z, Deng J, Li Q. Diagnostic value of mean platelet volume for neonatal sepsis: A systematic review and meta-analysis. Med (Baltim). 2020;99(32):e21649. Tables Table.1 MR analysis of MPV (exposure) with sepsis outcomes. Method number of SNPs β Standard error P value MR Egger 23 -0.681 0.639 0.3 Weighted median 23 -0.131 0.398 0.74 Inverse variance weighted 23 -0.119 0.28 0.67 Simple mode 23 -0.035 0.654 0.96 Weighted mode 23 -0.127 0.463 0.79 Table.2 The results of Cochran's Q test and the MR-Egger regression intercept test. Method Cochran Q statistic Heterogeneity P value MR Egger 20.94927102 0.462 Inverse variance weighted 21.90606698 0.466 Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-3924061","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273520453,"identity":"404d523f-e044-4fa7-98d3-1f426047d382","order_by":0,"name":"Xueshu Yu","email":"","orcid":"","institution":"The First Affiliated Hospital of Wenzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xueshu","middleName":"","lastName":"Yu","suffix":""},{"id":273520454,"identity":"37bd8ed4-dae1-4471-9742-390560f9c927","order_by":1,"name":"Wen Xu","email":"","orcid":"","institution":"The Second Affiliated Hospital of Zhejiang 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2","display":"","copyAsset":false,"role":"figure","size":25128,"visible":true,"origin":"","legend":"\u003cp\u003e“Leave‐one‐out” analysis to investigate whether the causal association was driven by a unique single nucleotide polymorphism.\u003c/p\u003e","description":"","filename":"Fig.24.png","url":"https://assets-eu.researchsquare.com/files/rs-3924061/v1/68d7e2323c51216706ac8c43.png"},{"id":72198066,"identity":"92de0baa-0f05-4aa3-b4ac-6be8fd1c788b","added_by":"auto","created_at":"2024-12-23 15:17:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":443937,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3924061/v1/14d64865-04a9-48c5-9454-1ed2aa74a049.pdf"},{"id":51392538,"identity":"bed43787-4062-442f-90e0-ce42cbe8c2b8","added_by":"auto","created_at":"2024-02-20 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Timely recognition within the initial hours following sepsis onset has been shown to enhance patient outcomes[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In consideration of this, there is a notable interest in biomarkers that have the potential to be employed in emergency departments or critical care units, playing a crucial role in the effective diagnosis of sepsis[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, there is currently an absence of biomarkers that have undergone comprehensive validation and can offer the essential diagnostic accuracy to aid clinicians during this vital timeframe[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Platelets are discernible elements in the bloodstream. Once activated, they unleash a myriad of cytokines and inflammatory mediators, critically contributing to the initiation of inflammation and the regulation of immune responses[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The mean platelet volume (MPV), which characterizes the mean size of individual platelets, assumes significance as a gauge of platelet proportions, operational dynamics, and reactivity. MPV can be easily integrated into daily medical activities, without extra costs. An escalating array of recent studies has emphasized the mean platelet volume (MPV) as a predictive indicator for sepsis[\u003cspan additionalcitationids=\"CR7 CR8 CR9\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. And Selma Ates\u0026rsquo;s study showed that MPV (venous blood sampling was performed within the first 12 hours after first presentation,) was a biomarker for the diagnosis of sepsis and the cut-off was 8.915, sensitivity 71%, and specificity 63.9% in sepsis patients[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Meanwhile, the specific combinations of MPV and C-reactive protein can improve the power to distinguish patients with bloodstream infection caused by different pathogens[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Yet, its thorough clinical significance remains to be fully illuminated, and its diagnostic application has been confined[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Given that a considerable proportion of earlier reports stem from traditional observational research, the outcomes were inevitably influenced by confounding variables and reverse causation[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In this regard, Mendelian randomization (MR) has emerged as a novel approach extensively employed for causal inference[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], yet its application to ascertain the diagnostic potential of MPV for sepsis remains unexplored. The evidence of causality presented by the MR analyses has been positioned at the intersection of randomized controlled trials (RCTs) and traditional epidemiological investigations[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this current study, our objective was to explore a potential causal connection between MPV and the susceptibility to sepsis through the application of MR.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy design and assumption\u003c/p\u003e \u003cp\u003eMR analyses employ genetic instrumental variables (IVs) to assess the causal connection between the exposure and the outcome. MR analyses rest upon three assumptions: Firstly, IVs were significantly correlated with interest exposure. Secondly, IVs remain independent of the confounders affecting the exposure-outcome relationship. Thirdly, IVs must only influence the outcome through the exposure of interest[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. If all these assumptions hold true, MR analyses can deduce a causality that is immune to unmeasured confounding and reverse causality. Single-nucleotide polymorphisms (SNPs) encapsulate the genetic variability among individuals, expected to be randomly distributed within the population, thus serving as robust estimators of lifelong effects. These SNPs were harnessed as instrumental variables in the MR analysis.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source\u003c/h2\u003e \u003cp\u003eGenetic linkages encompassing gene-exposure and gene-outcome data were procured from two separate repositories of genome-wide association studies (GWAS) accessible through the catalog (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/gwas/\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/gwas/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). These datasets were drawn from distinct samples that do not overlap, enabling an inquiry into the potential causal interplay between the genetic susceptibility to sepsis and the Mean Platelet Volume. Given that the original GWAS had previously secured appropriate ethical and institutional review board approval, no further ethical endorsement was required for this study.\u003c/p\u003e \u003cp\u003eSummary statistics datasets of MPV GWASs as an exposure, which from the European population comprised 66,867 participants (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/datasets/ieu-a-1006/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/datasets/ieu-a-1006/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The outcome dataset summary statistics of sepsis were extracted from a GWAS conducted in UK Biobank participants (European: n\u0026thinsp;=\u0026thinsp;11,643 cases and 474,841 controls, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/datasets/ieu-b-4980/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/datasets/ieu-b-4980/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eGenetic instruments\u003c/h2\u003e \u003cp\u003eIndependent SNPs with genome-wide significance with sepsis were used as instruments. The selection threshold was set as p-value\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e, linkage disequilibrium r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001, as previous study[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. A threshold of F\u0026thinsp;\u0026lt;\u0026thinsp;10 was used to define a \u0026ldquo;weak IV\u0026rdquo;[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], which were removed. Meanwhile, instruments that failed to harmonize with outcome parameters, or the palindromic instruments with minor allele frequency\u0026thinsp;\u0026gt;\u0026thinsp;0.3 were also removed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis for MR\u003c/h2\u003e \u003cp\u003eThe principal analysis for the Mendelian randomization (MR) investigation employed the inverse-variance weighted (IVW) technique[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The IVW method is an estimate in an ideal state. It assumes that all genetic variations are effective tool indicators for effective analysis, and it has the ability to detect causality. However, the IVW method specifically requires that the genetic variation is only passed through the SNP of the known confounding factors in this study, but there are still many unknown confounding factors that will lead to bias in the estimation of the pleiotropic effect value of the gene. Therefore, we used four other methods to test the reliability and stability of the results. The MR-Egger regression[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], the weighted median estimator (WME)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], the weighted mode[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and the simple mode followed by MR analysis. If the above five different MR models produced similar estimates of the causal effect, we considered the causal relationship between MPV and sepsis to be stable and reliable.\u003c/p\u003e \u003cp\u003eThe results obtained from the Mendelian randomization (MR) analysis were portrayed in the form of odds ratios (OR) and corresponding confidence intervals (CI), displayed visually using a forest plot and scatter plot. Additionally, we assessed the statistical power of our MR outcomes employing Brion's methodology. (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://shiny.cnsgenomics.com/mRnd/\u003c/span\u003e\u003cspan address=\"https://shiny.cnsgenomics.com/mRnd/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity test\u003c/h2\u003e \u003cp\u003eWe assessed heterogeneities between SNPs using Cochran's Q statistics[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Meanwhile, the MR-Egger sensitivity test was applied to analyze the direct pleiotropy effect to ensure that IVs satisfy these assumptions[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. We also performed a \u0026ldquo;leave-one-out\u0026rdquo; analysis to investigate the possibility that the causal association was driven by a single SNP.\u003c/p\u003e \u003cp\u003eWe conducted an additional reverse Mendelian randomization (MR) analysis to investigate the potential for reverse causality. A significant finding in the reverse MR analysis would suggest a reverse causal relationship from sepsis (treated as the exposure) to MPV (considered as the outcome). The methodology employed in the reverse MR analysis remained consistent with the approach used in the preceding MR analysis.\u003c/p\u003e \u003cp\u003eAll the MR analyses were conducted with R (version 4.1.3). R packages \u0026ldquo;TwoSampleMR 0.5.7\u0026rdquo; were utilized.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThis study is reported according to the STROBE guideline for cohort studies[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. According to the selection criteria of IVs, a total of 23 SNPs were used as IVs, details about the selected instrumental variables are shown in sTable 1.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMR Analyses\u003c/h2\u003e \u003cp\u003eThe IVW method showed no evidence to support a causal association between MPV and sepsis (β =-0.119, SE\u0026thinsp;=\u0026thinsp;0.280, P\u0026thinsp;=\u0026thinsp;0.671; Table\u0026nbsp;1). Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows forest plots of the estimates for each outcome using the different MR methods. Funnel and radial plots are provided in Supplementary File (sFigure.1 and sFigure.2).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe MR estimates determined using the IVW, MR-Egger, weighted median, simple mode, and weighted mode were consistent (Table\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity test\u003c/h2\u003e \u003cp\u003eCochran's Q test indicated no evidence of heterogeneity between IV estimates based on the individual variants (Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eIn order to investigate the potential disproportionate influence of any single SNP in the MPV instrument on the aggregate outcomes, IVW analyses were recomputed with the omission of individual SNPs, performed sequentially. The outcomes derived from a \"leave-one-out\" analysis underscored the absence of any solitary SNP wielding a significant sway over the IVW point estimate (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe reverse-direction Mendelian randomization (MR) IVW analysis assesses the link between Mean Platelet Volume (MPV) and sepsis. Furthermore, the outcomes of the MR analysis also did not provide evidence in favor of a causal relationship between sepsis and MPV (sTable.2).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we did not find evidence that MPV was causally associated with risk of sepsis. Meanwhile, we observed no evidence of causal association for sepsis with MPV.\u003c/p\u003e \u003cp\u003eIt is known that MPV is genetically determined. Genome wide association studies identified genes and gene regions that influence thrombopoiesis by encoding kinases, membrane proteins, transcription factors, signaling proteins, proteins involved in megakaryocyte development and platelet production. Regulation of these genes may be responsible for higher MPVs observed in sepsis[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrevious observational studies have shown sepsis to be associated with sepsis [\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, it remains unclear whether MPV has a causal relationship with sepsis, as it may be a result of bias or confounding factors inherent to observational studies, including reverse causation, small study numbers and sizes, single center study and selection biases observational studies may be subject to confounding and cannot always distinguish symptoms from causes. Thus, to clarify this association, we evaluated causality using five different estimation methods (MR-Egger, weighted median, simple mode, and weighted mode) for MR analyses.\u003c/p\u003e \u003cp\u003eOur study findings did not indicate that the associations between MPV and sepsis and do not support a causal inverse association between MPV and sepsis. The result was different from those of previous studies (Includes adults and neonatal sepsis)[\u003cspan additionalcitationids=\"CR7 CR8 CR9\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. As we mentioned before, the relationship between MPV and sepsis is influenced by various factors, and most studies had small sample sizes, with inherent biases in retrospective research. Therefore, MPV could be used as an indicator for the early diagnosis of sepsis should be interpreted with caution.\u003c/p\u003e \u003cp\u003eIn our study, MR analysis was performed to determine the causal association between MPV and sepsis, thus excluding the interference of confounding factors and reversing causation on causal inference. The SNP biomarker and SNP schizophrenia estimates were obtained in mostly European studies, thus minimizing the possibility of population stratification bias. Meanwhile, we conducted a wide range of suggested sensitivity analyses[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], which supported that our findings were not meaningfully affected by pleiotropy. Horizontal pleiotropy was detected and excluded by using the MR-Egger regression intercept term tests. Furthermore, a two-sample MR design was adopted and non-overlapping exposure and outcome summary-level data were used to avoid bias. Therefore, previously reported inverse associations between MPV and occurrence of sepsis may be the result of bias or confounding factors inherent to observational studies.\u003c/p\u003e \u003cp\u003eHowever, it is important to acknowledge that our study does have certain limitations. Firstly, the utilization of summary-level statistics impedes our ability to conduct analyses stratified by covariates that were adjusted for in the original GWAS. Secondly, it's worth noting that our findings were derived from European populations, which might not necessarily generalize to other ethnic groups. Thirdly, due to our reliance on summary-level data for two-sample MR analyses, the feasibility of conducting analyses within specific subgroups was constrained. Moreover, while mendelian randomization methods offer distinct advantages compared to conventional meta-analysis techniques, they still hinge on model assumptions and experimental design. Lastly, it's important to acknowledge that genetic variations could potentially exert varying impacts on the connection between exposure and outcome across different age cohorts or temporal junctures. Mendelian randomization might encounter challenges in capturing these dynamic causal effects.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe MR analysis results do not support a causal inverse association between sepsis and sepsis. Further, well designed epidemiological using more variants that explain a larger proportion of MPV and sepsis are warranted to confirm or rule out causality.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"301\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"81.72757475083057%\"\u003e\n \u003cp\u003ecorresponding confidence intervals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.272425249169434%\"\u003e\n \u003cp\u003eCI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"81.72757475083057%\"\u003e\n \u003cp\u003einverse-variance weighting\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.272425249169434%\"\u003e\n \u003cp\u003eIVW\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"81.72757475083057%\"\u003e\n \u003cp\u003egenome-wide association study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.272425249169434%\"\u003e\n \u003cp\u003eGWAS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"81.72757475083057%\"\u003e\n \u003cp\u003eInstrumental variables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.272425249169434%\"\u003e\n \u003cp\u003eIVs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"81.72757475083057%\"\u003e\n \u003cp\u003eMean Platelet Volume\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.272425249169434%\"\u003e\n \u003cp\u003eMPV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"81.72757475083057%\"\u003e\n \u003cp\u003eMendelian randomization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.272425249169434%\"\u003e\n \u003cp\u003eMR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"81.72757475083057%\"\u003e\n \u003cp\u003eodds ratios\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.272425249169434%\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"81.72757475083057%\"\u003e\n \u003cp\u003esingle nucleotide polymorphism\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.272425249169434%\"\u003e\n \u003cp\u003eSNP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"81.72757475083057%\"\u003e\n \u003cp\u003eweighted median estimator\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.272425249169434%\"\u003e\n \u003cp\u003eWME\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eoriginal GWAS had previously secured appropriate ethical and institutional review board approval, no further ethical endorsement was required for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eoriginal GWAS had previously secured appropriate ethical and institutional review board approval, thus individual patient informed consent was not required.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data can be downloaded from the website\u0026nbsp;https://www.ebi.ac.uk/gwas/. The other datasets generated and/or analysed during the current study are publicly available and included in this published article and its supplementary information files.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXueshu Yu designed this study, analysed data, and drafted the manuscript; Wen Xu designed this study, collected data, drafted, and revised the manuscript; Xiangyuan Ruan and Luwei Xu compiled and analysed the data; Yincai Ye designed and supervised this study. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eFleischmann-Struzek C, Mellhammar L, Rose N, Cassini A, Rudd KE, Schlattmann P, Allegranzi B, Reinhart K. Incidence and mortality of hospital- and ICU-treated sepsis: results from an updated and expanded systematic review and meta-analysis. Intensive Care Med. 2020;46(8):1552\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEvans L, Rhodes A, Alhazzani W, Antonelli M, Coopersmith CM, French C, Machado FR, McIntyre L, Ostermann M, Prescott HC, et al. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021. Intensive Care Med. 2021;47(11):1181\u0026ndash;247.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeggert A, Datta H, Ali Z. 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Clin Lab. 2014;60(7):1193\u0026ndash;200.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang W, Zhang W, Li X, Cheng J, Liu Z, Zhou Q, Guan S. Hematological parameters in patients with bloodstream infection: A retrospective observational study. J Infect Dev Ctries. 2020;14(11):1264\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKorniluk A, Koper-Lenkiewicz OM, Kamińska J, Kemona H, Dymicka-Piekarska V. Mean Platelet Volume (MPV): New Perspectives for an Old Marker in the Course and Prognosis of Inflammatory Conditions. Mediators Inflamm. 2019;2019:9213074.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgess S, Swanson SA, Labrecque JA. Are Mendelian randomization investigations immune from bias due to reverse causation? Eur J Epidemiol. 2021;36(3):253\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSekula P, Del Greco MF, Pattaro C, K\u0026ouml;ttgen A. Mendelian Randomization as an Approach to Assess Causality Using Observational Data. J Am Soc Nephrol. 2016;27(11):3253\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavies NM, Holmes MV, Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018;362:k601.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSchaid DJ, Chen W, Larson NB. From genome-wide associations to candidate causal variants by statistical fine-mapping. Nat Rev Genet. 2018;19(8):491\u0026ndash;504.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgess S, Thompson SG. Bias in causal estimates from Mendelian randomization studies with weak instruments. Stat Med. 2011;30(11):1312\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet Epidemiol. 2013;37(7):658\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44(2):512\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBowden J, Davey Smith G, Haycock PC, Burgess S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genet Epidemiol. 2016;40(4):304\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHartwig FP, Davey Smith G, Bowden J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol. 2017;46(6):1985\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGreco MF, Minelli C, Sheehan NA, Thompson JR. Detecting pleiotropy in Mendelian randomisation studies with summary data and a continuous outcome. Stat Med. 2015;34(21):2926\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVerbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases. Nat Genet. 2018;50(5):693\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evon Elm E, Altman DG, Egger M, Pocock SJ, G\u0026oslash;tzsche PC, Vandenbroucke JP. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet. 2007;370(9596):1453\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShameer K, Denny JC, Ding K, Jouni H, Crosslin DR, de Andrade M, Chute CG, Peissig P, Pacheco JA, Li R, et al. A genome- and phenome-wide association study to identify genetic variants influencing platelet count and volume and their pleiotropic effects. Hum Genet. 2014;133(1):95\u0026ndash;109.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMiddleton EA, Rowley JW, Campbell RA, Grissom CK, Brown SM, Beesley SJ, Schwertz H, Kosaka Y, Manne BK, Krauel K, et al. Sepsis alters the transcriptional and translational landscape of human and murine platelets. Blood. 2019;134(12):911\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J, Wang Z, Zhang M, Lou Z, Deng J, Li Q. Diagnostic value of mean platelet volume for neonatal sepsis: A systematic review and meta-analysis. Med (Baltim). 2020;99(32):e21649.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable.1 MR analysis of MPV (exposure) with sepsis outcomes.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"593\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.08768971332209%\"\u003e\n \u003cp\u003eMethod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.06745362563238%\"\u003e\n \u003cp\u003enumber of SNPs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.141652613827993%\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.561551433389546%\"\u003e\n \u003cp\u003eStandard error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.141652613827993%\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.08768971332209%\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.06745362563238%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.141652613827993%\"\u003e\n \u003cp\u003e-0.681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.561551433389546%\"\u003e\n \u003cp\u003e0.639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.141652613827993%\"\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.08768971332209%\"\u003e\n \u003cp\u003eWeighted median\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.06745362563238%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.141652613827993%\"\u003e\n \u003cp\u003e-0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.561551433389546%\"\u003e\n \u003cp\u003e0.398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.141652613827993%\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.08768971332209%\"\u003e\n \u003cp\u003eInverse variance weighted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.06745362563238%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.141652613827993%\"\u003e\n \u003cp\u003e-0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.561551433389546%\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.141652613827993%\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.08768971332209%\"\u003e\n \u003cp\u003eSimple mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.06745362563238%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.141652613827993%\"\u003e\n \u003cp\u003e-0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.561551433389546%\"\u003e\n \u003cp\u003e0.654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.141652613827993%\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.08768971332209%\"\u003e\n \u003cp\u003eWeighted mode\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.06745362563238%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.141652613827993%\"\u003e\n \u003cp\u003e-0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.561551433389546%\"\u003e\n \u003cp\u003e0.463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.141652613827993%\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable.2 The results of Cochran\u0026apos;s Q test and the MR-Egger regression intercept test.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"480\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"54.375%\"\u003e\n \u003cp\u003eMethod\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.041666666666668%\"\u003e\n \u003cp\u003eCochran Q\u003cbr\u003e\u0026nbsp;statistic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.583333333333332%\"\u003e\n \u003cp\u003eHeterogeneity\u0026nbsp;\u003cbr\u003e\u0026nbsp;P value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"54.375%\"\u003e\n \u003cp\u003eMR Egger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.041666666666668%\"\u003e\n \u003cp\u003e20.94927102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.583333333333332%\"\u003e\n \u003cp\u003e0.462\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"54.375%\"\u003e\n \u003cp\u003eInverse variance weighted\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.041666666666668%\"\u003e\n \u003cp\u003e21.90606698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.583333333333332%\"\u003e\n \u003cp\u003e0.466\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mean Platelet Volume, Sepsis, Mendelian randomization","lastPublishedDoi":"10.21203/rs.3.rs-3924061/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3924061/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe potential relationship between Mean Platelet Volume (MPV) and the progression of sepsis has been proposed; however, the nature of this association, whether it entails causation or a subsequent outcome, remains a topic of debate. The primary objective of this study is to evaluate the impact of MPV on sepsis using a bidirectional Mendelian randomization framework.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGenetic associations related to sepsis were derived from the most extensive and current genome-wide association study (GWAS) available, encompassing 11,643 cases and 474,841 controls, serving as the dataset for outcomes. Additionally, genetic associations linked to Mean Platelet Volume (MPV) were drawn from another GWAS, constituting the dataset for exposure. Estimates were computed through inverse-variance weighting, supplemented by sensitivity analyses using MR-Egger, weighted median, simple mode, and weighted mode. Moreover, Cochran's Q test and “leave-one-out” analysis were also taken into account.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results obtained from the inverse-variance weighting (IVW) analysis failed to provide evidence in support of a causal relationship between MPV and sepsis (β = -0.119, SE = 0.280, P = 0.671). Consistent estimates were derived from the MR analyses conducted using the IVW, MR-Egger, weighted median, simple mode, and weighted mode methods. Examination of heterogeneity through Cochran's Q test revealed no indications of variability among IV estimates derived from individual genetic variants. Additionally, the outcomes of the \"leave-one-out\" analysis demonstrated that no individual single nucleotide polymorphism (SNP) exerted undue influence on the IVW estimate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe outcomes of the MR analysis did not substantiate a causal connection between MPV and sepsis.\u003c/p\u003e","manuscriptTitle":"Exploring the Causality Between Mean Platelet Volume and Sepsis: A Two-Sample Mendelian Randomization Study.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-20 18:42:43","doi":"10.21203/rs.3.rs-3924061/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a6cb035e-6e95-46b6-9656-24d67191d686","owner":[],"postedDate":"February 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-23T15:08:48+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-20 18:42:43","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3924061","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3924061","identity":"rs-3924061","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}