CD19 on CD20- immune cell causes aortic valve calcification by affecting levels of the plasma metabolite 1-(1-enyl-palmitoyl)-2-arachidonoyl-gpc (p-16:0/20:4): a Mendelian Randomization study

CD19 on CD20- immune cell causes aortic valve calcification by affecting levels of the plasma metabolite 1-(1-enyl-palmitoyl)-2-arachidonoyl-gpc (p-16:0/20:4): a Mendelian Randomization study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article CD19 on CD20- immune cell causes aortic valve calcification by affecting levels of the plasma metabolite 1-(1-enyl-palmitoyl)-2-arachidonoyl-gpc (p-16:0/20:4): a Mendelian Randomization study Qiang Zhou, Shuoshuo Yi, Sheng Liu, Wei Wei, Zhiming Zhou, Bin Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5410795/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 Immune cells play a significant role in the process of aortic valve calcification (AVC). However, the interactions between AVC and specific immune cell types have yet to be demonstrated. The aim of this study was to investigate the causal relationship between immune cells and AVC, as well as to determine the mediating role of potential plasma metabolites. Methods In this study, publicly available genome-wide association study (GWAS) summary statistics were employed to ascertain the correlation between 731 immune cells and 1400 plasma metabolites with AVC. Firstly, two-sample and reverse Mendelian Randomisation Mendelian Randomization (MR) analyses were conducted to ascertain the causal relationship between immune cells and AVC. Subsequently, a two-step MR analysis demonstrated that the relationship between immune cells and AVC was mediated by plasma metabolites. The robustness of the findings was confirmed by several sensitivity analyses. Results Our study indicate that 42 out of 731 immune cells were correlated with AVC. Among these, immune cell CD19 on CD20- demonstrated a positive correlation with AVC (OR_IVW = 1.0629, OR 95% CI = 1.0259–1.1012, P = 0.0007). Furthermore, immune cell CD19 on CD20- correlated with 47 metabolites, including a positive correlation with plasma 1-(1-alkenyl-palmitoyl)-2-propenoyl-GPC (P-16:0/20:4) levels (OR_IVW = 1.0535, OR 95% CI = 1.0079–1.1010, P = 0.0209). Additionally, 47 metabolites were found to be correlated with AVC. Notably, plasma 1-(1-enyl-palmitoyl)-2-arachidonoyl-GPC (P-16:0/20:4) levels exhibited a positive correlation with aortic calcification (OR_IVW = 1.0079, OR 95% CI = 1.0221–1.1383, P = 0.0058). Plasma 1-(1-enyl-palmitoyl)-2-arachidonoyl-GPC (P-16:0/20:4) levels acted as a mediator between CD19 on CD20- and AVC, with a mediation effect size of 0.0039, constituting 6.47% of the total effect. Conclusion The present study is based on a mediated MR analysis, which demonstrates that CD19 on CD20-immune cell cause AVC by affecting plasma 1-(1-enyl-palmitoyl)-2-arachidonoyl-GPC (p-16:0/20:4) levels. This provides a new perspective on the mechanism of the development of AVC and offers a potential therapeutic target for metabolic intervention. Immune cells Plasma metabolites Aortic valve calcification Mendelian randomisation CD19 on CD20- Plasma 1-(1-alkenyl-palmitoyl)-2-propenoyl-GPC (P-16:0/20:4) levels Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1 Introduction Aortic valve calcification (AVC) is a prevalent cardiovascular pathology, a chronic disease typified by the thickening of the aortic valve leaflets. Its pathophysiological mechanism encompasses a multitude of processes, including endothelial dysfunction, immune cell infiltration, myofibroblast and osteoclast differentiation, alterations in the extracellular matrix, oxidative stress, metabolic abnormalities, and genetic factors ( 1 – 3 ). Abnormal accumulation of extracellular matrix contributes to fibrosis and calcification of the valve, involving changes in collagen, elastin, and other matrix components. These alterations can affect the stability of the extracellular matrix and predispose to calcium deposition, which is a hallmark feature of AVC. As the disease progresses, AVC may reach a level of severity that results in the development of aortic valve stenosis (AVS). The severity of AVS can result in a range of symptoms, including shortness of breath, angina, and fainting, in patients. In cases of severe AVS, surgical intervention or transcatheter aortic valve replacement may be required, emphasizing the significance of elucidating the underlying pathophysiological mechanisms of AVC. Immune cells are of particular importance in the development of AVC ( 4 ), particularly those involved in inflammatory responses. Within the valve, 15% of cells originate from hematopoietic sources and contribute to the development of inflammation. This is achieved through the infiltration of the valve by macrophages, T lymphocytes, B lymphocytes, and innate immune cells ( 4 ). It can be reasonably deduced that aberrant immune system activation may serve to accelerate the progression of AVC. Conversely, immune modulation may prove an efficacious strategy for the treatment and prevention of this disease. Metabolites are small molecules that serve as intermediates or end products of metabolic reactions. The human blood metabolome provides a comprehensive readout of human physiology through the non-targeted assessment of hundreds of circulating small molecules that reflect the effects and interactions of genetics, lifestyle, environmental, medical, and microbial activity ( 5 ). The levels of these substances are influenced by a number of factors, including genetics, diet and lifestyle, the composition of the gut microbiota, and the presence of disease ( 6 ). A comprehensive examination of the molecular composition of human blood has the potential to facilitate the identification of novel pathways associated with disease, enhance risk prediction, and facilitate the implementation of stratified prevention and management strategies ( 7 ). Mendelian randomization (MR) employs genetic variation to construct instrumental variables for exposures and to estimate causal relationships between exposures and outcomes( 8 ). This study primarily employs MR mediation analysis to investigate the causal relationships among immune cell phenotypes, plasma metabolites, and AVC ( 9 ). 2 Methods 2.1 Study Design In this study, we conducted a two-sample two-way MR analysis to investigate the causal relationship between immune cells and AVC. Subsequently, we employed a two-step MR approach to assess the mediating effects of metabolites on the relationship between immune cells and AVC. The specific research design is illustrated in Fig. 1 . All data utilized in this study were obtained from previously published sources, each of which received approval from their respective Institutional Review Boards (IRBs). Consequently, there was no requirement to reapply for IRB approval. 2.2 Data Sources The data on the 731 immune cells utilized in this study were obtained from the GWAS catalog, with accession numbers ranging from GCST0001391 to GCST0002121 ( 10 ). AVC data is derived from a European population sample consisting of 95,662 individuals, as published in the GWAS catalog on March 18, 2024. The associated EFO ID is EFO_0005239, and further details can be found at the following link: https://www.ebi.ac.uk/gwas/efotraits/EFO_0005239 . This study analyzes 1,400 plasma metabolite data derived from GWAS, encompassing 1,091 individual metabolites and 309 metabolite ratios from a cohort of 8,299 individuals participating in the Canadian Longitudinal Study of Aging (CLSA) ( 11 ). 2.3 Selection of IVs We employed MR analysis to explore potential causal relationships between immune cells and AVC, utilizing genetic variation as an instrumental variable (IV). The validity of MR analysis hinges on three key assumptions: ( 1 ) the IV is not associated with any confounding variables; ( 2 ) the IV is strongly correlated with the exposure; and ( 3 ) the IV influences the results solely through the exposure ( 8 , 12 ). In the initial phase of the study, single nucleotide polymorphisms (SNPs) were selected from the genome-wide association study (GWAS) summary data for exposures that exhibited a genome-wide significant association (p < 5×10⁻⁸) with the traits, and these were used as instrumental variables (IVs). In instances where the number of IVs was limited, the significance threshold was relaxed to 5×10⁻⁵ to prevent the generation of inaccurate results due to insufficient SNPs. The selection of additional SNPs was conducted in accordance with the aforementioned threshold. Subsequently, linkage disequilibrium clumping was employed to exclude certain undesirable SNPs (r 2 10,000 kb). Ultimately, the exposure and outcome datasets were harmonized, and palindromic SNPs with allele frequencies approaching 0.5 were excluded ( 13 ). To ensure the strength of the genetic instruments for exposures, we calculated the F statistic using the following formula: F = (n - k − 1)/k×(R 2 /1 − R 2 ) ( 14 ), where R 2 represents the cumulative explained variance in the selected SNPs, N is the sample size, and k is the number of SNPs in the analysis. An F statistic exceeding 10 signifies sufficient strength to circumvent the issue of weak instrument bias in the two-sample model ( 15 ). 2.4 Statistical Analysis Analyses were conducted using R (version 4.2.0) along with the "Two Sample MR" package. We primarily employed the random-effects inverse variance weighted (IVW) analysis method to assess the causal relationship between the exposure and outcome ( 16 ). Additionally, we utilized auxiliary analysis methods, including the weighted median ( 17 ), MR-Egger ( 18 ), Simple mode, and Weighted mode ( 19 ). In the context of MR analysis, a p-value of < 0.05 was considered indicative of a significant causal relationship between the exposure and outcome. 2.5 Sensitivity Analysis For the sensitivity analysis, we employed three methods: the heterogeneity test, the horizontal pleiotropy test, and the leave-one-out method. Cochrane's Q test was utilized to assess heterogeneity, with a Q p-value < 0.05 considered indicative of heterogeneity ( 20 ). In the presence of heterogeneity, we conducted analyses using inverse variance weighting (IVW) with random effects. The statistical significance of the intercepts in MR-Egger regression indicates the presence of horizontal pleiotropy. Furthermore, we applied the global test of MR-PRESSO to evaluate the presence of pleiotropy in this study ( 21 ). To assess the impact of individual single nucleotide polymorphisms (SNPs) on causal associations, we performed a 'leave-one-out' analysis, sequentially removing each SNP ( 22 ). Additionally, scatter plots and funnel plots were generated to visualize the results. 2.6 Calculating Mediating Effects The mediating effect was calculated as beta12 = beta1*beta( 2 ); the proportion of mediating effect in the total effect: R = beta12/ beta_all*100%. After correction for confounders, the effect of exposure on outcome was considered to be a direct effect, and beta_dir = beta_all – beta12. 3 Results 3.1 Two-sample Mendelian analysis of the causal relationship between immune cells and AVC In this study, we examined 731 types of immune cells as the exposure variable and AVC as the outcome. Based on a significance threshold of P < 0.05, we identified 42 types of immune cells. For further details, please refer to Supplementary Table 1. The immune cells with the smallest P value, CD19 on CD20-, along with AVC, were selected for MR analysis. The results indicate a significant positive causal relationship between CD19 on CD20- and AVC, as evidenced by the following odds ratios: OR_IVW = 1.0629 (95% CI: 1.0259–1.1012, P = 0.0007); OR_MR Egger = 1.0597 (95% CI: 1.0086–1.1150, P = 0.0330); OR_Weighted median = 1.0640 (95% CI: 1.0072–1.1236, P = 0.2667); OR_Simple mode = 1.0963 (95% CI: 1.0085–1.1918, P = 0.0433); OR_Weighted mode = 1.0610 (95% CI: 1.0089–1.1159, P = 0.0321). For further details, please refer to Fig. 2 and Supplementary Table 2. The MR Egger intercept term is approximately zero, indicating the absence of horizontal pleiotropy (P = 0.8896), suggesting the absence of horizontal pleiotropy. Additionally, the MR-PRESSO results corroborated this finding, yielding no evidence of horizontal pleiotropy (P = 0.9290) (Fig. 3 and Supplementary Table 3). The leave-one-out analysis demonstrated that as SNPs were progressively removed, no individual SNP was identified that had a more substantial impact on the results(Fig. 4 ). Subsequently, we conducted a reverse MR analysis using plink1.9 software to screen the SNP data for AVC at a significance level of P < 5e-5 as the exposure variable. The results, which are detailed in the attachment, indicate that CD19 on CD20- was considered as the outcome. Our findings suggest that AVC does not significantly affect CD19 on CD20-, as evidenced by the following p-values: P_IVW = 0.2961; P_MR Egger = 0.2091; P_Weighted median = 0.5525; P_Simple mode = 0.1172; P_Weighted mode = 0.5370 (Fig. 5 ). 3.2 Two-sample Mendelian analysis of causal relationships CD19 on CD20- immune cell and metabolites MR analysis was conducted using CD19 on CD20- immune cell as exposures and 1,400 metabolites as outcomes. A total of 47 metabolites were identified with a significance threshold of P < 0.05; further details can be found in Supplementary Table 4. The metabolite with the smallest P value, identified as 1-(1-enyl-palmitoyl)-2-arachidonoyl-gpc (p-16:0/20:4) levels(GCST90200050), was selected as the outcome, while CD19 on CD20- was designated as the exposure. The results indicate a significant positive causal relationship between CD19 on CD20- and 1-(1-enyl-palmitoyl)-2-arachidonoyl-gpc (p-16:0/20:4) levels, with the following odds ratios and confidence intervals: OR_IVW = 1.0958 (95% CI: 1.0413–1.1531, P = 0.0004); OR_MR Egger = 1.0779 (95% CI: 1.0058–1.1551, P = 0.0495); OR_Weighted median = 1.0640 (95% CI: 1.0079–1.1870, P = 0.0317); OR_Simple mode = 1.0890 (95% CI: 0.9491–1.2497, P = 0.2410); and OR_Weighted mode = 1.0802 (95% CI: 1.0074–1.1584, P = 0.0448) (Fig. 6 ). The MR Egger intercept term was not significantly different from zero (P = 0.8444), indicating the absence of horizontal pleiotropy. Additionally, the MR-PRESSO results confirmed no evidence of horizontal pleiotropy (P = 0.9450) (Fig. 7 and Supplementary Table 3). The leave-one-out analysis demonstrated that, upon the gradual removal of included SNPs, no individual SNP exerted a greater influence on the results(Fig. 8 ). 3.3 Two-sample Mendelian analysis of the causal relationship between 47 metabolites and AVC A Mendelian Randomization (MR) analysis was conducted using 47 metabolites as exposures and AVC as the outcome. With a significance threshold of P < 0.05, the results indicated that two metabolites were significantly associated with AVC: Glycolithocholate sulfate levels (P = 0.0388) and 1-(1-enyl-palmitoyl)-2-arachidonoyl-gpc (p-16:0/20:4) levels (P = 0.0058) (Supplementary Table 5). The levels of 1-(1-enyl-palmitoyl)-2-arachidonoyl-gpc (p-16:0/20:4) were chosen as the exposure variable, while AVC was designated as the outcome. The results indicated that levels of 1-(1-enyl-palmitoyl)-2-arachidonoyl-gpc (p-16:0/20:4) significantly influences aortic valve calcification, with the following odds ratios: OR_IVW = 1.1087 (95% CI: 1.0221–1.1383, P = 0.0058); OR_MR Egger = 1.1769 (95% CI: 1.0876–1.2734, P < 0.001); OR_Weighted median = 1.1701 (95% CI: 1.1082–1.2355, P < 0.001); OR_Weighted mode = 1.1557 (95% CI: 1.0962–1.2184, P < 0.001); and OR_Simple mode = 1.0138 (95% CI: 0.8376–1.2271, P = 0.8888). For further details, please refer to Fig. 9 . 3.4 Intermediary Analysis A mediation analysis of metabolites was conducted to ascertain whether the impact of immune cells on aortic valve calcification was mediated by them. The results demonstrated that the total effect was beta_all = 0.0610, while the mediated effect was beta1*beta2 = 0.0039. The proportion of the mediated effect was thus R = 6.47%. The direct effect was calculated as beta_dir = beta_all-beta1*beta2, resulting in a value of 0.0571. The findings indicated that 1-(1-enyl-palmitoyl)-2-arachidonoyl-GPC (p-16:0/20:4) levels played a mediating role in CD19 on CD20- with AVC. 4. Discussion The present study is based on mediator MR analysis, and our findings demonstrate a causal relationship between CD19 on CD20-, p-16:0/20:4, and AVC. Furthermore, the mediator analysis emphasizes the mediating role of p-16:0/20:4 in the causal relationship between CD19 on CD20- and AVC. To the best of our knowledge, this is the inaugural MR mediation analysis to investigate the causal relationship between immune cell phenotype, metabolites, and AVC. This may offer novel insights into the mechanisms of AVC development and potential metabolic intervention targets for therapy. AVC disease is among the most prevalent heart valve diseases globally [23, 24]. With an aging population, its prevalence is steadily increasing [25], and AVS is increasingly recognized as a significant public health concern. Notably, the 2-year mortality rate for severely affected patients is 50% [26]. The pathophysiological mechanisms underlying AVS involve a fibrocalcification process characterized by myofibroblast activation, osteoblast transformation, lipoprotein deposition, and inflammation [27–29]. Given these characteristics, it is not surprising that immune cell infiltration plays a crucial role in the formation of AVS, and the involvement of immune cells in its development is only beginning to be understood. Over the past decade, the literature on immune signaling and cellular alterations in AVS has expanded rapidly. In our study, we identified 42 immune cell phenotypes that are causally related to AVC. Additionally, we focused on CD19 in CD20-negative immune cell. CD19 on CD20 is a marker that belongs to the immune B lymphocyte family. B lymphocytes are adaptive immune cells primarily responsible for antibody production and may differentiate into plasmablasts following antigen activation. Previous studies have demonstrated that the number of B cells in the valve correlates with the severity of valve calcification ( 30 , 31 ), with an increase in B cell count associated with greater valve calcification and transvalvular pressure gradients( 32 ). It has been hypothesized that the accumulation of B cells and their subsequent interactions with macrophages may contribute to the progressive thickening and calcification of the valve. This is supported by evidence of colocalization and known bidirectional interactions, which suggest a pro-inflammatory role in antigen presentation and the generation of pro-inflammatory markers, creating a progressive cycle that links B cells to valvular pathogenesis( 31 ). Studies have demonstrated that glycerophosphocholine (GPC) metabolites influence cardiovascular disease through mechanisms involving oxidative stress and inflammatory responses ( 33 ). Additionally, prior research ( 34 – 37 ) indicates that GPC metabolites enhance the prediction of outcomes across various clinical conditions. Our findings reveal that elevated plasma p-16:0/20:4 levels are associated with an increased risk of AVC, thereby offering new insights into our understanding of this condition. In this study, we employed Two Steps Mediation Regression (TSMR) to investigate the causal relationship between immune cells and AVC. Our findings indicate a significant positive correlation: as CD19 on CD20- level increase, the risk of AVC also rises. Conversely, reverse TSMR results revealed that AVC does not lead to an increase in CD19 on CD20- level. Notably, the causal relationship between plasma p-16:0/20:4 metabolite levels and AVC has received limited attention in the literature. To address this gap, we utilized TSMR to analyze the causal relationship between p-16:0/20:4 and AVC, and we conducted mediation analysis to elucidate the role of p-16:0/20:4 in the relationship between CD19 on CD20- and AVC. Our mediation analysis demonstrated a robust and consistent effect between valve calcification and the variables studied, with sensitivity analyses confirming the reliability of these findings. 5 Study strengths and limitations MR is the principal strength of this study, utilizing single nucleotide polymorphisms as instrumental variables to analyze the relationship between exposure and outcome. In comparison to randomized controlled trials (RCTs), MR mitigates bias introduced by confounding factors and prevents the influence of reverse causality. We employed two-sample MR to investigate linear associations between exposures and outcomes, as well as mediation analysis to explore potential nonlinear associations. However, our study is subject to several limitations. First, the data were derived from European populations, which may introduce bias into our findings. Second, the relationship between immune cells and AVC is influenced by numerous factors, and our study cannot entirely eliminate the impact of confounding factors. Third, the data utilized were sourced from public databases, limiting our ability to conduct subgroup analyses on specific factors such as gender and age. 6 Conclusions In conclusion, through MR analysis, this study provides genetic evidence for a causal relationship between CD19 on CD20- immune cell and AVC. Specifically, CD19 on CD20- immune cell contributes to AVC by influencing plasma levels of 1-(1-enyl-palmitoyl)-2-acryloyl-GPC (P-16:0/20:4). These findings may offer new insights into the mechanisms underlying the occurrence and progression of AVC, as well as identify novel metabolic intervention targets for treatment. Abbreviations AVC aortic valve calcification GWAS Genome-wide association studie IVs Instrumental variables OR Odds ratio CI Confdence interval IVW Inverse variance weighted MR analysis Mendelian randomization analysis MVMR Multivariate Mendelian randomization SNPs Single-nucleotide polymorphisms MR-PRESSO MR Pleiotropy Residual Sum and Outlier Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The datasets analysed during the current study are available in the GWAS catalog database (https://www.ebi.ac.uk/gwas/). and the IEU Open GWAS repository(https://gwas.mrcieu.ac.uk/). Competing interests The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by two projects of Medical Science and Technology Tackling Programme of Henan Province (242102310224) and Science and Technology Innovation of Zhengzhou City (2024YLZDJH226). Author contributions QZ,SY: Methodology,Writing-original draft. SL:Datacuration,Writing–original draft. ZZ:Writing– original draft. WW:Writing–original draft.BY: Funding acquisition, Supervision, Validation,Writing – review & editing Acknowledgments We appreciate all the volunteers who participated in this study. We are grateful to the MiBioGen consortium and Open GWAS for providing GWAS summary statistics. References Moncla LM, Briend M, Bosse Y, Mathieu P: Calcific aortic valve disease: mechanisms, prevention and treatment. NAT REV CARDIOL 2023, 20(8):546-559. Tanase DM, Valasciuc E, Gosav EM, Floria M, Costea CF, Dima N, Tudorancea I, Maranduca MA, Serban IL: Contribution of Oxidative Stress (OS) in Calcific Aortic Valve Disease (CAVD): From Pathophysiology to Therapeutic Targets. 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Supplementary Files SupplementaryTable2CD19onCD20immunecellsandaorticvalvecalcificationForwardMendelianrandomisationresults.csv SupplementaryTable3.Sensitivityanalysisresults.docx SupplementaryTable4CD19onCD20immunecellasexposuresand1400metabolitesasoutcomesMendelianrandomisationresults.csv SupplementaryTable547metabolitesasexposuresandAVCastheoutcome.csv SupplementaryTable1731immunecellsandaorticvalvecalcificationForwardMendelianrandomisationresults.csv Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-5410795","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":377129310,"identity":"33c009cf-574b-44bd-89e2-52ada0d1ee5c","order_by":0,"name":"Qiang Zhou","email":"","orcid":"","institution":"Zhengzhou Seventh People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Qiang","middleName":"","lastName":"Zhou","suffix":""},{"id":377129311,"identity":"a124d287-67f5-4cf5-91d5-b2ed9e2aa6f8","order_by":1,"name":"Shuoshuo Yi","email":"","orcid":"","institution":"Xinxiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shuoshuo","middleName":"","lastName":"Yi","suffix":""},{"id":377129312,"identity":"51569190-52d4-400a-8854-9a96ad894823","order_by":2,"name":"Sheng Liu","email":"","orcid":"","institution":"Xinxiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Sheng","middleName":"","lastName":"Liu","suffix":""},{"id":377129313,"identity":"eddef424-cd7c-48bb-979d-3c22c7e2fe3e","order_by":3,"name":"Wei Wei","email":"","orcid":"","institution":"Xinxiang Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Wei","suffix":""},{"id":377129314,"identity":"e6442566-b6d7-4979-8b55-95a5309cc7e5","order_by":4,"name":"Zhiming Zhou","email":"","orcid":"","institution":"Zhengzhou Seventh People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhiming","middleName":"","lastName":"Zhou","suffix":""},{"id":377129315,"identity":"a5f01823-39aa-42d4-90f4-87c71691a98b","order_by":5,"name":"Bin Yang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA10lEQVRIiWNgGAWjYJACZgYDBh5+9uYDBz78IEGLjGTPscSDM3uI1sLAYGMww8f4MAcbEcoNjp89/Lqg4A6PgQTPh8MMPAzy/GIHCGg5k5dmPcPgGY+5dO+GwwUWDIYzZycQ0HIgx8yYx+Awj+WcsxsOz+BhSDC4TUjL+TcQLQY3ch4c5mEjRsuNHOPHUC0MxGmRvPHGjHkGUAswkA2AgSxB2C9853OMPxf8OWwPjMrHHz78sJHnlyagReEAA5sEEl8Cp0o4kG9gYP5AWNkoGAWjYBSMaAAAOr1Jh2vD5fsAAAAASUVORK5CYII=","orcid":"","institution":"Zhengzhou Seventh People’s Hospital","correspondingAuthor":true,"prefix":"","firstName":"Bin","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2024-11-07 14:53:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5410795/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5410795/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":70182126,"identity":"a6ce79f5-d8eb-4d61-b740-12717640869a","added_by":"auto","created_at":"2024-11-29 08:47:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":37446,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of Bidirectional Two-Sample Mendelian Randomization and mediation Analysis.\u003c/p\u003e","description":"","filename":"Binder11.png","url":"https://assets-eu.researchsquare.com/files/rs-5410795/v1/5975b5aa37acf9a85d10428e.png"},{"id":70181661,"identity":"78f98ad0-dbbc-471f-9262-daa7587208b9","added_by":"auto","created_at":"2024-11-29 08:39:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":21881,"visible":true,"origin":"","legend":"\u003cp\u003eCD19 on CD20- immune cell as exposure, aortic valve calcification as outcome, MR results forest plot\u003c/p\u003e","description":"","filename":"Binder12.png","url":"https://assets-eu.researchsquare.com/files/rs-5410795/v1/7e9ecd5300d6c0282423b5c3.png"},{"id":70181674,"identity":"9463e38d-cb51-4576-bb27-44a9f6b6e9bc","added_by":"auto","created_at":"2024-11-29 08:39:57","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":68841,"visible":true,"origin":"","legend":"\u003cp\u003eCD19 on CD20- immune cell as exposure, aortic valve calcification as outcome, MR results scatter plot, Scatterplot showing regression line showing no significant shift.\u003c/p\u003e","description":"","filename":"Binder13.png","url":"https://assets-eu.researchsquare.com/files/rs-5410795/v1/3d2e33a24c96cbac87a34de5.png"},{"id":70184700,"identity":"b861c116-d317-440e-b5c4-cc12f7537857","added_by":"auto","created_at":"2024-11-29 09:11:57","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":57327,"visible":true,"origin":"","legend":"\u003cp\u003eCD19 on CD20- immune cell as exposure, aortic valve calcification as outcome, leave-one-out sensitivity analysis.\u003c/p\u003e","description":"","filename":"Binder14.png","url":"https://assets-eu.researchsquare.com/files/rs-5410795/v1/7398c788959e11ac267c6ed1.png"},{"id":70181665,"identity":"4c491bb3-580c-4d77-8450-51fe18ffa7f4","added_by":"auto","created_at":"2024-11-29 08:39:57","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":23264,"visible":true,"origin":"","legend":"\u003cp\u003eCD19 on CD20- immune cell as outcome, aortic valve calcification as exposure, MR results forest plot.\u003c/p\u003e","description":"","filename":"Binder15.png","url":"https://assets-eu.researchsquare.com/files/rs-5410795/v1/11e37045696f82090a0c9365.png"},{"id":70181662,"identity":"3b46160a-021e-492e-9e25-56163a2c864b","added_by":"auto","created_at":"2024-11-29 08:39:57","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":23527,"visible":true,"origin":"","legend":"\u003cp\u003eCD19 on CD20- immune cell as exposure, 1-(1-enyl-palmitoyl)-2-arachidonoyl-gpc (p-16:0/20:4)as outcome, MR results forest plot\u003c/p\u003e","description":"","filename":"Binder16.png","url":"https://assets-eu.researchsquare.com/files/rs-5410795/v1/d37e36211a5746bcd5056dfa.png"},{"id":70183416,"identity":"3ccde039-6a15-4abf-87ac-8ebe44558f01","added_by":"auto","created_at":"2024-11-29 09:03:57","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":68260,"visible":true,"origin":"","legend":"\u003cp\u003eCD19 on CD20- immune cell as exposure, 1-(1-enyl-palmitoyl)-2-arachidonoyl-gpc (p-16:0/20:4) levels as outcome, MR results scatter plot, Scatterplot showing regression line showing no significant shift.\u003c/p\u003e","description":"","filename":"Binder17.png","url":"https://assets-eu.researchsquare.com/files/rs-5410795/v1/0e79ab95f65d5aef107a92ca.png"},{"id":70183418,"identity":"9149c83f-259c-472b-a94f-dcca6a76001c","added_by":"auto","created_at":"2024-11-29 09:03:57","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":66728,"visible":true,"origin":"","legend":"\u003cp\u003eCD19 on CD20- immune cell as exposure, 1-(1-enyl-palmitoyl)-2-arachidonoyl-gpc (p-16:0/20:4) levels as outcome, leave-one-out sensitivity analysis.\u003c/p\u003e","description":"","filename":"Binder18.png","url":"https://assets-eu.researchsquare.com/files/rs-5410795/v1/6c94df153cc2dc2cb8e8131a.png"},{"id":70182132,"identity":"a89565ea-0a3d-4b22-a8db-17eaf96b5497","added_by":"auto","created_at":"2024-11-29 08:47:57","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":23969,"visible":true,"origin":"","legend":"\u003cp\u003eaortic valve calcification as outcome, 1-(1-enyl-palmitoyl)-2-arachidonoyl-gpc (p-16:0/20:4) levels as exposure, MR results forest plot.\u003c/p\u003e","description":"","filename":"Binder19.png","url":"https://assets-eu.researchsquare.com/files/rs-5410795/v1/d9bd248fd3d14bdb360e5305.png"},{"id":70347136,"identity":"a62a4003-2b13-410d-8741-7e8744d668b9","added_by":"auto","created_at":"2024-12-02 11:17:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":854213,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5410795/v1/7547fdd4-0ad9-4357-a28d-0c5266a2d331.pdf"},{"id":70183177,"identity":"ee10e95e-ad56-47af-b1a2-f40d44d3c97e","added_by":"auto","created_at":"2024-11-29 08:55:57","extension":"csv","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":369,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2CD19onCD20immunecellsandaorticvalvecalcificationForwardMendelianrandomisationresults.csv","url":"https://assets-eu.researchsquare.com/files/rs-5410795/v1/be0e4f01ac19af292badafb5.csv"},{"id":70182125,"identity":"cf19d37e-7881-4ddb-8db7-a126f846a83b","added_by":"auto","created_at":"2024-11-29 08:47:57","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":12464,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable3.Sensitivityanalysisresults.docx","url":"https://assets-eu.researchsquare.com/files/rs-5410795/v1/04a2932ed3dbdf351b57b047.docx"},{"id":70181667,"identity":"9fba9537-15d1-4dd4-aba7-7d3a7624199c","added_by":"auto","created_at":"2024-11-29 08:39:57","extension":"csv","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":2126,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable4CD19onCD20immunecellasexposuresand1400metabolitesasoutcomesMendelianrandomisationresults.csv","url":"https://assets-eu.researchsquare.com/files/rs-5410795/v1/5acdcdec5876c2c8b8c04fdd.csv"},{"id":70181672,"identity":"177a358d-40b2-4f60-9faf-06df5bd92988","added_by":"auto","created_at":"2024-11-29 08:39:57","extension":"csv","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":89,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable547metabolitesasexposuresandAVCastheoutcome.csv","url":"https://assets-eu.researchsquare.com/files/rs-5410795/v1/007232b1cd0141bd4d446899.csv"},{"id":70182129,"identity":"ecd9d365-9144-4115-b16b-9ee42d6155a9","added_by":"auto","created_at":"2024-11-29 08:47:57","extension":"csv","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1352,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1731immunecellsandaorticvalvecalcificationForwardMendelianrandomisationresults.csv","url":"https://assets-eu.researchsquare.com/files/rs-5410795/v1/f951c477a1510462c204e979.csv"}],"financialInterests":"No competing interests reported.","formattedTitle":"CD19 on CD20- immune cell causes aortic valve calcification by affecting levels of the plasma metabolite 1-(1-enyl-palmitoyl)-2-arachidonoyl-gpc (p-16:0/20:4): a Mendelian Randomization study","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eAortic valve calcification (AVC) is a prevalent cardiovascular pathology, a chronic disease typified by the thickening of the aortic valve leaflets. Its pathophysiological mechanism encompasses a multitude of processes, including endothelial dysfunction, immune cell infiltration, myofibroblast and osteoclast differentiation, alterations in the extracellular matrix, oxidative stress, metabolic abnormalities, and genetic factors (\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Abnormal accumulation of extracellular matrix contributes to fibrosis and calcification of the valve, involving changes in collagen, elastin, and other matrix components. These alterations can affect the stability of the extracellular matrix and predispose to calcium deposition, which is a hallmark feature of AVC. As the disease progresses, AVC may reach a level of severity that results in the development of aortic valve stenosis (AVS). The severity of AVS can result in a range of symptoms, including shortness of breath, angina, and fainting, in patients. In cases of severe AVS, surgical intervention or transcatheter aortic valve replacement may be required, emphasizing the significance of elucidating the underlying pathophysiological mechanisms of AVC.\u003c/p\u003e \u003cp\u003eImmune cells are of particular importance in the development of AVC (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), particularly those involved in inflammatory responses. Within the valve, 15% of cells originate from hematopoietic sources and contribute to the development of inflammation. This is achieved through the infiltration of the valve by macrophages, T lymphocytes, B lymphocytes, and innate immune cells (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). It can be reasonably deduced that aberrant immune system activation may serve to accelerate the progression of AVC. Conversely, immune modulation may prove an efficacious strategy for the treatment and prevention of this disease.\u003c/p\u003e \u003cp\u003eMetabolites are small molecules that serve as intermediates or end products of metabolic reactions. The human blood metabolome provides a comprehensive readout of human physiology through the non-targeted assessment of hundreds of circulating small molecules that reflect the effects and interactions of genetics, lifestyle, environmental, medical, and microbial activity (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). The levels of these substances are influenced by a number of factors, including genetics, diet and lifestyle, the composition of the gut microbiota, and the presence of disease (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). A comprehensive examination of the molecular composition of human blood has the potential to facilitate the identification of novel pathways associated with disease, enhance risk prediction, and facilitate the implementation of stratified prevention and management strategies (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) employs genetic variation to construct instrumental variables for exposures and to estimate causal relationships between exposures and outcomes(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). This study primarily employs MR mediation analysis to investigate the causal relationships among immune cell phenotypes, plasma metabolites, and AVC (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design\u003c/h2\u003e \u003cp\u003eIn this study, we conducted a two-sample two-way MR analysis to investigate the causal relationship between immune cells and AVC. Subsequently, we employed a two-step MR approach to assess the mediating effects of metabolites on the relationship between immune cells and AVC. The specific research design is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. All data utilized in this study were obtained from previously published sources, each of which received approval from their respective Institutional Review Boards (IRBs). Consequently, there was no requirement to reapply for IRB approval.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data Sources\u003c/h2\u003e \u003cp\u003eThe data on the 731 immune cells utilized in this study were obtained from the GWAS catalog, with accession numbers ranging from GCST0001391 to GCST0002121 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAVC data is derived from a European population sample consisting of 95,662 individuals, as published in the GWAS catalog on March 18, 2024. The associated EFO ID is EFO_0005239, and further details can be found at the following link: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ebi.ac.uk/gwas/efotraits/EFO_0005239\u003c/span\u003e\u003cspan address=\"https://www.ebi.ac.uk/gwas/efotraits/EFO_0005239\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThis study analyzes 1,400 plasma metabolite data derived from GWAS, encompassing 1,091 individual metabolites and 309 metabolite ratios from a cohort of 8,299 individuals participating in the Canadian Longitudinal Study of Aging (CLSA) (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Selection of IVs\u003c/h2\u003e \u003cp\u003eWe employed MR analysis to explore potential causal relationships between immune cells and AVC, utilizing genetic variation as an instrumental variable (IV). The validity of MR analysis hinges on three key assumptions: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) the IV is not associated with any confounding variables; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) the IV is strongly correlated with the exposure; and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) the IV influences the results solely through the exposure (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the initial phase of the study, single nucleotide polymorphisms (SNPs) were selected from the genome-wide association study (GWAS) summary data for exposures that exhibited a genome-wide significant association (p\u0026thinsp;\u0026lt;\u0026thinsp;5\u0026times;10⁻⁸) with the traits, and these were used as instrumental variables (IVs). In instances where the number of IVs was limited, the significance threshold was relaxed to 5\u0026times;10⁻⁵ to prevent the generation of inaccurate results due to insufficient SNPs. The selection of additional SNPs was conducted in accordance with the aforementioned threshold. Subsequently, linkage disequilibrium clumping was employed to exclude certain undesirable SNPs (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, window size\u0026thinsp;\u0026gt;\u0026thinsp;10,000 kb). Ultimately, the exposure and outcome datasets were harmonized, and palindromic SNPs with allele frequencies approaching 0.5 were excluded (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo ensure the strength of the genetic instruments for exposures, we calculated the F statistic using the following formula: F = (n - k\u0026thinsp;\u0026minus;\u0026thinsp;1)/k\u0026times;(R\u003csup\u003e2\u003c/sup\u003e/1\u0026thinsp;\u0026minus;\u0026thinsp;R\u003csup\u003e2\u003c/sup\u003e) (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), where R\u003csup\u003e2\u003c/sup\u003e represents the cumulative explained variance in the selected SNPs, N is the sample size, and k is the number of SNPs in the analysis. An F statistic exceeding 10 signifies sufficient strength to circumvent the issue of weak instrument bias in the two-sample model (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical Analysis\u003c/h2\u003e \u003cp\u003eAnalyses were conducted using R (version 4.2.0) along with the \"Two Sample MR\" package. We primarily employed the random-effects inverse variance weighted (IVW) analysis method to assess the causal relationship between the exposure and outcome (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Additionally, we utilized auxiliary analysis methods, including the weighted median (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), MR-Egger (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), Simple mode, and Weighted mode (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). In the context of MR analysis, a p-value of \u0026lt;\u0026thinsp;0.05 was considered indicative of a significant causal relationship between the exposure and outcome.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Sensitivity Analysis\u003c/h2\u003e \u003cp\u003eFor the sensitivity analysis, we employed three methods: the heterogeneity test, the horizontal pleiotropy test, and the leave-one-out method. Cochrane's Q test was utilized to assess heterogeneity, with a Q p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered indicative of heterogeneity (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). In the presence of heterogeneity, we conducted analyses using inverse variance weighting (IVW) with random effects. The statistical significance of the intercepts in MR-Egger regression indicates the presence of horizontal pleiotropy. Furthermore, we applied the global test of MR-PRESSO to evaluate the presence of pleiotropy in this study (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). To assess the impact of individual single nucleotide polymorphisms (SNPs) on causal associations, we performed a 'leave-one-out' analysis, sequentially removing each SNP (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Additionally, scatter plots and funnel plots were generated to visualize the results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Calculating Mediating Effects\u003c/h2\u003e \u003cp\u003eThe mediating effect was calculated as beta12\u0026thinsp;=\u0026thinsp;beta1*beta(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e); the proportion of mediating effect in the total effect: R\u0026thinsp;=\u0026thinsp;beta12/ beta_all*100%. After correction for confounders, the effect of exposure on outcome was considered to be a direct effect, and beta_dir\u0026thinsp;=\u0026thinsp;beta_all \u0026ndash; beta12.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Two-sample Mendelian analysis of the causal relationship between immune cells and AVC\u003c/h2\u003e \u003cp\u003eIn this study, we examined 731 types of immune cells as the exposure variable and AVC as the outcome. Based on a significance threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, we identified 42 types of immune cells. For further details, please refer to Supplementary Table\u0026nbsp;1. The immune cells with the smallest P value, CD19 on CD20-, along with AVC, were selected for MR analysis. The results indicate a significant positive causal relationship between CD19 on CD20- and AVC, as evidenced by the following odds ratios: OR_IVW\u0026thinsp;=\u0026thinsp;1.0629 (95% CI: 1.0259\u0026ndash;1.1012, P\u0026thinsp;=\u0026thinsp;0.0007); OR_MR Egger\u0026thinsp;=\u0026thinsp;1.0597 (95% CI: 1.0086\u0026ndash;1.1150, P\u0026thinsp;=\u0026thinsp;0.0330); OR_Weighted median\u0026thinsp;=\u0026thinsp;1.0640 (95% CI: 1.0072\u0026ndash;1.1236, P\u0026thinsp;=\u0026thinsp;0.2667); OR_Simple mode\u0026thinsp;=\u0026thinsp;1.0963 (95% CI: 1.0085\u0026ndash;1.1918, P\u0026thinsp;=\u0026thinsp;0.0433); OR_Weighted mode\u0026thinsp;=\u0026thinsp;1.0610 (95% CI: 1.0089\u0026ndash;1.1159, P\u0026thinsp;=\u0026thinsp;0.0321). For further details, please refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Supplementary Table\u0026nbsp;2. The MR Egger intercept term is approximately zero, indicating the absence of horizontal pleiotropy (P\u0026thinsp;=\u0026thinsp;0.8896), suggesting the absence of horizontal pleiotropy. Additionally, the MR-PRESSO results corroborated this finding, yielding no evidence of horizontal pleiotropy (P\u0026thinsp;=\u0026thinsp;0.9290) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Supplementary Table\u0026nbsp;3). The leave-one-out analysis demonstrated that as SNPs were progressively removed, no individual SNP was identified that had a more substantial impact on the results(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSubsequently, we conducted a reverse MR analysis using plink1.9 software to screen the SNP data for AVC at a significance level of P\u0026thinsp;\u0026lt;\u0026thinsp;5e-5 as the exposure variable. The results, which are detailed in the attachment, indicate that CD19 on CD20- was considered as the outcome. Our findings suggest that AVC does not significantly affect CD19 on CD20-, as evidenced by the following p-values: P_IVW\u0026thinsp;=\u0026thinsp;0.2961; P_MR Egger\u0026thinsp;=\u0026thinsp;0.2091; P_Weighted median\u0026thinsp;=\u0026thinsp;0.5525; P_Simple mode\u0026thinsp;=\u0026thinsp;0.1172; P_Weighted mode\u0026thinsp;=\u0026thinsp;0.5370 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Two-sample Mendelian analysis of causal relationships CD19 on CD20- immune cell and metabolites\u003c/h2\u003e \u003cp\u003eMR analysis was conducted using CD19 on CD20- immune cell as exposures and 1,400 metabolites as outcomes. A total of 47 metabolites were identified with a significance threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; further details can be found in Supplementary Table\u0026nbsp;4. The metabolite with the smallest P value, identified as 1-(1-enyl-palmitoyl)-2-arachidonoyl-gpc (p-16:0/20:4) levels(GCST90200050), was selected as the outcome, while CD19 on CD20- was designated as the exposure. The results indicate a significant positive causal relationship between CD19 on CD20- and 1-(1-enyl-palmitoyl)-2-arachidonoyl-gpc (p-16:0/20:4) levels, with the following odds ratios and confidence intervals: OR_IVW\u0026thinsp;=\u0026thinsp;1.0958 (95% CI: 1.0413\u0026ndash;1.1531, P\u0026thinsp;=\u0026thinsp;0.0004); OR_MR Egger\u0026thinsp;=\u0026thinsp;1.0779 (95% CI: 1.0058\u0026ndash;1.1551, P\u0026thinsp;=\u0026thinsp;0.0495); OR_Weighted median\u0026thinsp;=\u0026thinsp;1.0640 (95% CI: 1.0079\u0026ndash;1.1870, P\u0026thinsp;=\u0026thinsp;0.0317); OR_Simple mode\u0026thinsp;=\u0026thinsp;1.0890 (95% CI: 0.9491\u0026ndash;1.2497, P\u0026thinsp;=\u0026thinsp;0.2410); and OR_Weighted mode\u0026thinsp;=\u0026thinsp;1.0802 (95% CI: 1.0074\u0026ndash;1.1584, P\u0026thinsp;=\u0026thinsp;0.0448) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The MR Egger intercept term was not significantly different from zero (P\u0026thinsp;=\u0026thinsp;0.8444), indicating the absence of horizontal pleiotropy. Additionally, the MR-PRESSO results confirmed no evidence of horizontal pleiotropy (P\u0026thinsp;=\u0026thinsp;0.9450) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and Supplementary Table\u0026nbsp;3). The leave-one-out analysis demonstrated that, upon the gradual removal of included SNPs, no individual SNP exerted a greater influence on the results(Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Two-sample Mendelian analysis of the causal relationship between 47 metabolites and AVC\u003c/h2\u003e \u003cp\u003eA Mendelian Randomization (MR) analysis was conducted using 47 metabolites as exposures and AVC as the outcome. With a significance threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, the results indicated that two metabolites were significantly associated with AVC: Glycolithocholate sulfate levels (P\u0026thinsp;=\u0026thinsp;0.0388) and 1-(1-enyl-palmitoyl)-2-arachidonoyl-gpc (p-16:0/20:4) levels (P\u0026thinsp;=\u0026thinsp;0.0058) (Supplementary Table\u0026nbsp;5).\u003c/p\u003e \u003cp\u003eThe levels of 1-(1-enyl-palmitoyl)-2-arachidonoyl-gpc (p-16:0/20:4) were chosen as the exposure variable, while AVC was designated as the outcome. The results indicated that levels of 1-(1-enyl-palmitoyl)-2-arachidonoyl-gpc (p-16:0/20:4) significantly influences aortic valve calcification, with the following odds ratios: OR_IVW\u0026thinsp;=\u0026thinsp;1.1087 (95% CI: 1.0221\u0026ndash;1.1383, P\u0026thinsp;=\u0026thinsp;0.0058); OR_MR Egger\u0026thinsp;=\u0026thinsp;1.1769 (95% CI: 1.0876\u0026ndash;1.2734, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001); OR_Weighted median\u0026thinsp;=\u0026thinsp;1.1701 (95% CI: 1.1082\u0026ndash;1.2355, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001); OR_Weighted mode\u0026thinsp;=\u0026thinsp;1.1557 (95% CI: 1.0962\u0026ndash;1.2184, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001); and OR_Simple mode\u0026thinsp;=\u0026thinsp;1.0138 (95% CI: 0.8376\u0026ndash;1.2271, P\u0026thinsp;=\u0026thinsp;0.8888). For further details, please refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Intermediary Analysis\u003c/h2\u003e \u003cp\u003eA mediation analysis of metabolites was conducted to ascertain whether the impact of immune cells on aortic valve calcification was mediated by them. The results demonstrated that the total effect was beta_all\u0026thinsp;=\u0026thinsp;0.0610, while the mediated effect was beta1*beta2\u0026thinsp;=\u0026thinsp;0.0039. The proportion of the mediated effect was thus R\u0026thinsp;=\u0026thinsp;6.47%. The direct effect was calculated as beta_dir\u0026thinsp;=\u0026thinsp;beta_all-beta1*beta2, resulting in a value of 0.0571. The findings indicated that 1-(1-enyl-palmitoyl)-2-arachidonoyl-GPC (p-16:0/20:4) levels played a mediating role in CD19 on CD20- with AVC.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe present study is based on mediator MR analysis, and our findings demonstrate a causal relationship between CD19 on CD20-, p-16:0/20:4, and AVC. Furthermore, the mediator analysis emphasizes the mediating role of p-16:0/20:4 in the causal relationship between CD19 on CD20- and AVC. To the best of our knowledge, this is the inaugural MR mediation analysis to investigate the causal relationship between immune cell phenotype, metabolites, and AVC. This may offer novel insights into the mechanisms of AVC development and potential metabolic intervention targets for therapy.\u003c/p\u003e \u003cp\u003eAVC disease is among the most prevalent heart valve diseases globally [23, 24]. With an aging population, its prevalence is steadily increasing [25], and AVS is increasingly recognized as a significant public health concern. Notably, the 2-year mortality rate for severely affected patients is 50% [26]. The pathophysiological mechanisms underlying AVS involve a fibrocalcification process characterized by myofibroblast activation, osteoblast transformation, lipoprotein deposition, and inflammation [27\u0026ndash;29]. Given these characteristics, it is not surprising that immune cell infiltration plays a crucial role in the formation of AVS, and the involvement of immune cells in its development is only beginning to be understood. Over the past decade, the literature on immune signaling and cellular alterations in AVS has expanded rapidly. In our study, we identified 42 immune cell phenotypes that are causally related to AVC. Additionally, we focused on CD19 in CD20-negative immune cell.\u003c/p\u003e \u003cp\u003eCD19 on CD20 is a marker that belongs to the immune B lymphocyte family. B lymphocytes are adaptive immune cells primarily responsible for antibody production and may differentiate into plasmablasts following antigen activation. Previous studies have demonstrated that the number of B cells in the valve correlates with the severity of valve calcification (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), with an increase in B cell count associated with greater valve calcification and transvalvular pressure gradients(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). It has been hypothesized that the accumulation of B cells and their subsequent interactions with macrophages may contribute to the progressive thickening and calcification of the valve. This is supported by evidence of colocalization and known bidirectional interactions, which suggest a pro-inflammatory role in antigen presentation and the generation of pro-inflammatory markers, creating a progressive cycle that links B cells to valvular pathogenesis(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStudies have demonstrated that glycerophosphocholine (GPC) metabolites influence cardiovascular disease through mechanisms involving oxidative stress and inflammatory responses (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Additionally, prior research (\u003cspan additionalcitationids=\"CR35 CR36\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) indicates that GPC metabolites enhance the prediction of outcomes across various clinical conditions. Our findings reveal that elevated plasma p-16:0/20:4 levels are associated with an increased risk of AVC, thereby offering new insights into our understanding of this condition.\u003c/p\u003e \u003cp\u003eIn this study, we employed Two Steps Mediation Regression (TSMR) to investigate the causal relationship between immune cells and AVC. Our findings indicate a significant positive correlation: as CD19 on CD20- level increase, the risk of AVC also rises. Conversely, reverse TSMR results revealed that AVC does not lead to an increase in CD19 on CD20- level. Notably, the causal relationship between plasma p-16:0/20:4 metabolite levels and AVC has received limited attention in the literature. To address this gap, we utilized TSMR to analyze the causal relationship between p-16:0/20:4 and AVC, and we conducted mediation analysis to elucidate the role of p-16:0/20:4 in the relationship between CD19 on CD20- and AVC. Our mediation analysis demonstrated a robust and consistent effect between valve calcification and the variables studied, with sensitivity analyses confirming the reliability of these findings.\u003c/p\u003e"},{"header":"5 Study strengths and limitations","content":"\u003cp\u003eMR is the principal strength of this study, utilizing single nucleotide polymorphisms as instrumental variables to analyze the relationship between exposure and outcome. In comparison to randomized controlled trials (RCTs), MR mitigates bias introduced by confounding factors and prevents the influence of reverse causality. We employed two-sample MR to investigate linear associations between exposures and outcomes, as well as mediation analysis to explore potential nonlinear associations. However, our study is subject to several limitations. First, the data were derived from European populations, which may introduce bias into our findings. Second, the relationship between immune cells and AVC is influenced by numerous factors, and our study cannot entirely eliminate the impact of confounding factors. Third, the data utilized were sourced from public databases, limiting our ability to conduct subgroup analyses on specific factors such as gender and age.\u003c/p\u003e"},{"header":"6 Conclusions","content":"\u003cp\u003eIn conclusion, through MR analysis, this study provides genetic evidence for a causal relationship between CD19 on CD20- immune cell and AVC. Specifically, CD19 on CD20- immune cell contributes to AVC by influencing plasma levels of 1-(1-enyl-palmitoyl)-2-acryloyl-GPC (P-16:0/20:4). These findings may offer new insights into the mechanisms underlying the occurrence and progression of AVC, as well as identify novel metabolic intervention targets for treatment.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAVC aortic valve calcification\u003c/p\u003e\n\u003cp\u003eGWAS Genome-wide association studie \u003c/p\u003e\n\u003cp\u003eIVs Instrumental variables\u003c/p\u003e\n\u003cp\u003eOR Odds ratio\u003c/p\u003e\n\u003cp\u003eCI Confdence interval\u003c/p\u003e\n\u003cp\u003eIVW Inverse variance weighted\u003c/p\u003e\n\u003cp\u003eMR analysis Mendelian randomization analysis\u003c/p\u003e\n\u003cp\u003eMVMR Multivariate Mendelian randomization\u003c/p\u003e\n\u003cp\u003eSNPs Single-nucleotide polymorphisms\u003c/p\u003e\n\u003cp\u003eMR-PRESSO MR Pleiotropy Residual Sum and Outlier\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analysed during the current study are available in the GWAS \u0026nbsp;catalog database (https://www.ebi.ac.uk/gwas/). and the IEU Open GWAS \u0026nbsp;repository(https://gwas.mrcieu.ac.uk/).\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 the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author(s) declare financial support was received for the research, authorship, and/or publication of this article.\u0026nbsp;This work was supported by two projects of Medical Science and Technology Tackling Programme of Henan Province (242102310224) and Science and Technology Innovation of Zhengzhou City (2024YLZDJH226).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQZ,SY: Methodology,Writing-original draft. SL:Datacuration,Writing\u0026ndash;original draft. ZZ:Writing\u0026ndash; original draft. WW:Writing\u0026ndash;original draft.BY: Funding acquisition, Supervision, Validation,Writing \u0026ndash; review \u0026amp; editing\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe appreciate all the volunteers who participated in this study. We are grateful to the MiBioGen consortium and Open GWAS for providing GWAS summary statistics.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMoncla LM, Briend M, Bosse Y, Mathieu P: Calcific aortic valve disease: mechanisms, prevention and treatment. 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NEW ENGL J MED 2014, 371(8):744-756.\u003c/li\u003e\n\u003cli\u003eRaddatz MA, Madhur MS, Merryman WD: Adaptive immune cells in calcific aortic valve disease. AM J PHYSIOL-HEART C 2019, 317(1):H141-H155.\u003c/li\u003e\n\u003cli\u003eRajamannan NM, Evans FJ, Aikawa E, Grande-Allen KJ, Demer LL, Heistad DD, Simmons CA, Masters KS, Mathieu P, O\u0026apos;Brien KD et al: Calcific aortic valve disease: not simply a degenerative process: A review and agenda for research from the National Heart and Lung and Blood Institute Aortic Stenosis Working Group. Executive summary: Calcific aortic valve disease-2011 update. CIRCULATION 2011, 124(16):1783-1791.\u003c/li\u003e\n\u003cli\u003eRajamannan NM, Moura L: The Lipid Hypothesis in Calcific Aortic Valve Disease: The Role of the Multi-Ethnic Study of Atherosclerosis. ARTERIOSCL THROM VAS 2016, 36(5):774-776.\u003c/li\u003e\n\u003cli\u003eSteiner I, Krbal L, Rozkos T, Harrer J, Laco J: Calcific aortic valve stenosis: Immunohistochemical analysis of inflammatory infiltrate. PATHOL RES PRACT 2012, 208(4):231-234.\u003c/li\u003e\n\u003cli\u003eNatorska J, Marek G, Sadowski J, Undas A: Presence of B cells within aortic valves in patients with aortic stenosis: Relation to severity of the disease. J CARDIOL 2016, 67(1):80-85.\u003c/li\u003e\n\u003cli\u003eMazur P, Mielimonka A, Natorska J, Wypasek E, Gaweda B, Sobczyk D, Kapusta P, Malinowski KP, Kapelak B: Lymphocyte and monocyte subpopulations in severe aortic stenosis at the time of surgical intervention. CARDIOVASC PATHOL 2018, 35:1-7.\u003c/li\u003e\n\u003cli\u003eLee G, Choi S, Chang J, Choi D, Son JS, Kim K, Kim SM, Jeong S, Park SM: Association of L-alpha Glycerylphosphorylcholine With Subsequent Stroke Risk After 10 Years. JAMA NETW OPEN 2021, 4(11):e2136008.\u003c/li\u003e\n\u003cli\u003eStegemann C, Pechlaner R, Willeit P, Langley SR, Mangino M, Mayr U, Menni C, Moayyeri A, Santer P, Rungger G et al: Lipidomics profiling and risk of cardiovascular disease in the prospective population-based Bruneck study. CIRCULATION 2014, 129(18):1821-1831.\u003c/li\u003e\n\u003cli\u003eCheng M, Bhujwalla ZM, Glunde K: Targeting Phospholipid Metabolism in Cancer. FRONT ONCOL 2016, 6:266.\u003c/li\u003e\n\u003cli\u003evan der Kemp WJ, Stehouwer BL, Runge JH, Wijnen JP, Nederveen AJ, Luijten PR, Klomp DW: Glycerophosphocholine and Glycerophosphoethanolamine Are Not the Main Sources of the In Vivo (31)P MRS Phosphodiester Signals from Healthy Fibroglandular Breast Tissue at 7 T. FRONT ONCOL 2016, 6:29.\u003c/li\u003e\n\u003cli\u003ePoupore N, Chosed R, Arce S, Rainer R, Goodwin RL, Nathaniel TI: Metabolomic Profiles of Men and Women Ischemic Stroke Patients. DIAGNOSTICS 2021, 11(10).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Immune cells, Plasma metabolites, Aortic valve calcification, Mendelian randomisation, CD19 on CD20-, Plasma 1-(1-alkenyl-palmitoyl)-2-propenoyl-GPC (P-16:0/20:4) levels","lastPublishedDoi":"10.21203/rs.3.rs-5410795/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5410795/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eImmune cells play a significant role in the process of aortic valve calcification (AVC). However, the interactions between AVC and specific immune cell types have yet to be demonstrated. The aim of this study was to investigate the causal relationship between immune cells and AVC, as well as to determine the mediating role of potential plasma metabolites.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this study, publicly available genome-wide association study (GWAS) summary statistics were employed to ascertain the correlation between 731 immune cells and 1400 plasma metabolites with AVC. Firstly, two-sample and reverse Mendelian Randomisation Mendelian Randomization (MR) analyses were conducted to ascertain the causal relationship between immune cells and AVC. Subsequently, a two-step MR analysis demonstrated that the relationship between immune cells and AVC was mediated by plasma metabolites. The robustness of the findings was confirmed by several sensitivity analyses.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur study indicate that 42 out of 731 immune cells were correlated with AVC. Among these, immune cell CD19 on CD20- demonstrated a positive correlation with AVC (OR_IVW\u0026thinsp;=\u0026thinsp;1.0629, OR 95% CI\u0026thinsp;=\u0026thinsp;1.0259\u0026ndash;1.1012, P\u0026thinsp;=\u0026thinsp;0.0007). Furthermore, immune cell CD19 on CD20- correlated with 47 metabolites, including a positive correlation with plasma 1-(1-alkenyl-palmitoyl)-2-propenoyl-GPC (P-16:0/20:4) levels (OR_IVW\u0026thinsp;=\u0026thinsp;1.0535, OR 95% CI\u0026thinsp;=\u0026thinsp;1.0079\u0026ndash;1.1010, P\u0026thinsp;=\u0026thinsp;0.0209). Additionally, 47 metabolites were found to be correlated with AVC. Notably, plasma 1-(1-enyl-palmitoyl)-2-arachidonoyl-GPC (P-16:0/20:4) levels exhibited a positive correlation with aortic calcification (OR_IVW\u0026thinsp;=\u0026thinsp;1.0079, OR 95% CI\u0026thinsp;=\u0026thinsp;1.0221\u0026ndash;1.1383, P\u0026thinsp;=\u0026thinsp;0.0058). Plasma 1-(1-enyl-palmitoyl)-2-arachidonoyl-GPC (P-16:0/20:4) levels acted as a mediator between CD19 on CD20- and AVC, with a mediation effect size of 0.0039, constituting 6.47% of the total effect.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe present study is based on a mediated MR analysis, which demonstrates that CD19 on CD20-immune cell cause AVC by affecting plasma 1-(1-enyl-palmitoyl)-2-arachidonoyl-GPC (p-16:0/20:4) levels. This provides a new perspective on the mechanism of the development of AVC and offers a potential therapeutic target for metabolic intervention.\u003c/p\u003e","manuscriptTitle":"CD19 on CD20- immune cell causes aortic valve calcification by affecting levels of the plasma metabolite 1-(1-enyl-palmitoyl)-2-arachidonoyl-gpc (p-16:0/20:4): a Mendelian Randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-29 08:39:52","doi":"10.21203/rs.3.rs-5410795/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":"52e09b97-3a9e-4d2c-9366-e8403b8bc438","owner":[],"postedDate":"November 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-02T11:09:06+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-29 08:39:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5410795","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5410795","identity":"rs-5410795","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}