Genetic evidence for causal effects of immune cell traits on risk for endometriosis: a bidirectional two-sample Mendelian Randomization Study

Genetic evidence for causal effects of immune cell traits on risk for endometriosis: a bidirectional two-sample Mendelian Randomization Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Genetic evidence for causal effects of immune cell traits on risk for endometriosis: a bidirectional two-sample Mendelian Randomization Study Ying Wang, Fenyong Sun, Han Wu, Chaoyan Yue, Qiuhong Man This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4103948/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract Previous studies have identified associations between immune cell traits and endometriosis, but the causality of these relationships remains uncertain. 731 immune cell signatures associated Single-nucleotide polymorphisms (SNPs) were extracted from a published genome-wide association study (GWAS) involving 472,174 individuals, while endometriosis data, including four stages and seven subtypes, were obtained from the FinnGen consortium. Four methods were used for Mendelian randomization. The causal effect of immune cell traits on endometriosis was explored after Bonferroni correction. Significant causal relationship included 92 immune cell traits distributed among B cell (28 cells), cDC (2 cells), Maturation stages of T cell (10 cells), Monocyte (12 cells), Myeloid cell (5 cells), TBNK (13 cells) and Treg panels (22 cells). In the reverse Mendelian randomization analysis, a one-unit increase in the log odds of endometriosis of the ovary risk corresponded to a decrease in the Absolute Count of CD4 + CD8 dim T cell by 0.10. This study represents the first comprehensive evaluation of the causal effects of immune cell traits on the risk/protection of different stages/subtypes of endometriosis. The findings highlight the complex and significant role of immune-derived factors in the pathogenesis of the disease. Biological sciences/Genetics Biological sciences/Immunology Health sciences/Diseases Health sciences/Medical research Health sciences/Pathogenesis Health sciences/Risk factors immune cell traits endometriosis inflammation Mendelian randomization causal relationship Figures Figure 1 Figure 2 Figure 3 Introduction Endometriosis, a gynecological disorder affecting approximately 10% of women of reproductive age, is often accompanied by severe pain and infertility 1 . This condition is characterized by the abnormal growth of endometrial tissue outside the uterus, manifesting in locations such as the pelvic cavity, ovaries, fallopian tubes, and even the intestines 2 . Despite extensive research, the exact cause of endometriosis remains elusive. Notably, women with endometriosis exhibit heightened markers of systemic inflammation 3 . Considering the observed association between immune cells and inflammation, there is a compelling rationale to explore the potential role of immune cell traits in the pathogenesis of endometriosis. Currently, a wealth of evidence indicates aberrant functions across various immune cell types in women with endometriosis. These include decreased T cell reactivity and natural killer (NK) cytotoxicity, polyclonal activation of B cells with increased antibody production, elevated numbers and activation of peritoneal macrophages, and alterations in inflammatory mediators 4 . Recent research findings also highlight regulatory T-cell (Treg) alterations in endometriosis, with an increased presence of highly active Tregs and macrophages in the peritoneal fluid of affected women. Notably, disruptions in the regulatory function of T cells and an imbalance between T helper cell types Th1 and Th2 have been reported in the endometria of women experiencing endometriosis-associated infertility 5 . Given the diverse array of immune cells, obtaining comprehensive results from cohort and case-control studies within a single research endeavor is challenging. Recognizing the importance of establishing a causal relationship between immune cell traits and the incidence of endometriosis, our study aims to overcome these limitations by employing Mendelian Randomization (MR) to investigate their causal relationship. By delving into the realm of immunology, this research seeks to elucidate the role of immune cell traits in the pathogenesis of endometriosis, with the ultimate goal of contributing to the development of novel therapeutic strategies. Results Exploration of the causal effect of immune cell traits on endometriosis risk via forward MR Figure 1 and 2 provide forest plots showing the estimates for each outcome using the IVW MR methods. After Bonferroni correction, 56 MFI, 13 AC, 19 RC, and 4 MP pairs of outcomes reached the level of significance ( P < 0.0125). Notably, a protective effect was identified for one immune cell trait against endometriosis (CD25 on CD39 + CD4 + T cells, OR = 0.97, 95% CI = 0.95 to 0.99, P = 0.011) while two immune cell traits were associated with an increased risk for endometriosis (CD28 on CD39 + activated CD4 regulatory T cell, OR = 1.03, 95% CI = 1.01 to 1.05, P = 0.011; CX3CR1 on monocyte, OR = 1.04, 95% CI = 1.01 to 1.08, P = 0.009). The causal relationship with immune cell traits varied when considering endometriosis stages and subtypes, with protective and risk associations detected. Seven immune cell traits demonstrated a protective effect against endometriosis ASRM stages 1 and 2, while two immune cell traits were identified as risk factors for endometriosis ASRM stages 1 and 2. However, only three immune cell traits exhibited a protective effect against endometriosis ASRM stages 3 and 4, while two immune cell traits were associated with an increased risk for endometriosis ASRM stages 3 and 4. Additionally, elevated levels of two immune cell traits and decreased levels of four immune cell traits were linked to an increased risk of deep endometriosis. When it comes to endometriosis diagnosis and infertility diagnosis occurring together, a decreased level of four immune cell traits could induce the risk of it. Figure 2 illustrates our analysis considering the seven subtypes of endometriosis as outcomes. The MR results indicated that increased levels of 12 immune cell traits and decreased levels of 5 immune cell traits could contribute to the risk of endometriosis of the fallopian tube. Furthermore, the causal analysis of morbidity risk outcomes for the remaining six subtypes is detailed as follows: endometriosis of intestine, increased levels of 3 immune cell traits and decreased levels of 6 immune cell traits; endometriosis of ovary, increased levels of 4 immune cell traits and decreased levels of 4 immune cell traits; endometriosis of pelvic peritoneum, increased levels of 3 immune cell traits and decreased levels of 6 immune cell traits; endometriosis of rectovaginal septum and vagina, increased levels of 3 immune cell traits and decreased levels of 4 immune cell traits; adenomyosis (endometriosis of uterus), increased levels of 8 immune cell traits and decreased levels of 3 immune cell traits; Unspecified/other endometriosis, increased levels of 2 immune cell traits and decreased levels of 3 immune cell traits. In summary, our analysis encompassed the exploration of the causal effect of immune cell traits on endometriosis risk, involving 92 immune cells distributed across various categories, including B cells (28 cells), cDC (2 cells), Maturation stages of T cells (10 cells), Monocytes (12 cells), Myeloid cells (5 cells), TBNK (13 cells), and Treg panels (22 cells). Exploration of the causal effect of endometriosis on immune cell traits via reverse MR To unravel the impact of endometriosis on the body's immune mechanisms, we conducted MR analysis to explore the causal effects of endometriosis on immune cells. Illustrated in Fig. 3 , endometriosis of the ovary demonstrated a significant association with a decrease in CD4 + CD8 dim T cell Absolute Count (Beta = -0.10, 95% CI = -0.18 to -0.03, P = 0.010). Additionally, we detected suggestive causal effects of endometriosis, including four stages and seven subtypes, on the levels of 13 immune cell traits, where endometriosis onset could increase levels of 2 immune cell traits and decrease levels of 11 immune cell traits. These 13 immune cell traits are distributed in B cell (2 cells), Maturation stages of T cell (1 cell), Monocyte (2 cell), TBNK (7 cells), and Treg panels (1 cell). Analysis of sensitivity, including heterogeneity and pleiotropy This study implemented various sensitivity analysis methods to ensure the robustness of our findings. Cochran’s Q-test was employed to assess heterogeneity between individual Single Nucleotide Polymorphism (SNP) estimates. As illustrated in Figs. 1 , 2 , and 3 , heterogeneity was observed in one group (HLA DR on CD14 + monocyte forward MR IVW to endometriosis ASRM stages 3,4; Q = 32.651, P = 0.037). However, no evidence of pleiotropy was found in any of the groups examined using the MR-Egger regression, encompassing both forward and reverse Mendelian Randomization. Discussion Our study thoroughly investigates the causal associations between immune cells and endometriosis, leveraging extensive publicly available genetic data. Utilizing a bidirectional two-sample Mendelian Randomization framework, our findings reveal a significant causal relationship involving 92 immune cells, distributed among B cell (28 cells), cDC (2 cells), Maturation stages of T cell (10 cells), Monocyte (12 cells), Myeloid cell (5 cells), TBNK (13 cells) and Treg panels (22 cells). In the reverse Mendelian randomization analysis, we identified significant or suggestive causal relationships involving 14 immune cells distributed among B cell (2 cells), Maturation stages of T cell (1 cell), Monocyte (2 cells), TBNK (8 cells) and Treg panels (1 cell). All these findings supported the hypothesis that the assessment of causal effects of immune cell traits significantly influences the risk and protection dynamics across different stages and subtypes of endometriosis. Several intriguing and common findings will be specifically covered and discussed below. Our findings indicated that for every 1-standard deviation (SD) increase in CX3CR1 on monocyte count, the risks of developing endometriosis, endometriosis ASRM stages 1,2, and endometriosis of pelvic peritoneum increased by 4%, 9%, and 10%, respectively. Endometriosis, a prevalent chronic condition in the female reproductive system, remains incompletely understood, with inflammation considered among the potential hypotheses 6 . CX3CR1, a cell surface receptor primarily expressed on monocytes, binds to its ligand CX3CL1 (also known as fractalkine) and plays a crucial role in regulating immune and inflammatory responses. Various functional aspects of the fractalkine-CX3CR1 axis have been identified, including the rapid capture and firm adhesion of immune cells, chemotaxis, and the crawling behavior of monocytes 7 . Our findings align with existing research and support the inflammatory hypothesis, suggesting that elevated CX3CR1 on monocytes is a risk factor for the development of endometriosis, specifically in endometriosis ASRM stages 1,2, and endometriosis of pelvic peritoneum. However, in the reverse Mendelian randomization analysis, a suggestive causal relationship indicated that a one-unit increase in the log odds of endometriosis risk could decrease the levels of CX3CR1 on monocytes by 0.13. Immunohistochemical experiments conducted by Hou X X et al. revealed significantly higher expression of fractalkine and CX3CR1 in ectopic endometrium compared to normal endometrium. The concentration of fractalkine in the peritoneal fluid of endometriosis patients was closely correlated with the severity of the condition 8 . It is hypothesized that the persistent enrichment of fractalkine and CX3CR1 in the patient's focal tissues may contribute to the relative decrease in CX3CR1 on blood monocytes. Considering the effectiveness of the fractalkine-CX3CR1 axis in treating various inflammatory diseases, such as rheumatoid arthritis 9 , chronic kidney disease 10 , and neuroinflammation 11 , it emerges as a potential new target for the treatment of endometriosis, particularly in endometriosis ASRM stages 1,2, and endometriosis of pelvic peritoneum. In our study, we identified several traits from B cells that exhibited significant causal relationships with endometriosis and its various subtypes. In the context of inflammation, the B-cell activating factor receptor (BAFF-R) on the surface of B cells binds to the B-cell activating factor (BAFF), produced by immune cells like macrophages and dendritic cells. This interaction promotes B-cell proliferation, survival, and antibody production by activating intracellular signaling pathways 12 . Elevated levels of several subpopulations representing B activation and memory status (e.g., BAFF-R on IgD + CD38 dim B cell, memory B cell, unswitched memory B cell, switched memory B cell, etc.) were identified as risk factors for the development of endometriosis in the fallopian tube. However, elevated subpopulations representing B activation status(e.g., BAFF-R on IgD + CD38 dim B cell, IgD + CD38 − B cell, etc.) were found to be protective factors against the development of endometriosis in the pelvic peritoneum. This observation suggests a diverse role of B cells in the pathogenesis of immunoinflammation across different subtypes of endometriosis, consistent with existing reports. L.G.C. Riccio et al. summarized findings from 22 selected studies related to B cells and endometriosis, revealing increased numbers and/or activation of B cells in most studies. However, some studies reported no difference, while others indicated a decreased number of B cells 13 . Further studies are needed to elucidate the specific role of B cells in the development of all endometriosis subtypes and to propose new therapeutic strategies. Moreover, our investigation highlighted that an elevated level of CD28 on CD39 + activated CD4 regulatory T cells poses a risk for endometriosis and several subtypes (ASRM stages 3 and 4, ovary). CD39 is an enzyme responsible for converting adenosine triphosphate into adenosine diphosphate and cyclic adenosine monophosphate, ultimately leading to the release of an immunosuppressive form of adenosine in the microenvironment 14 . The role of CD28 on D39 + activated CD4 + Treg cells is to maintain immune tolerance by regulating cellular activation and function. Several observations, including those in cancer research, suggest the pivotal role of CD39 in attenuating immune responses. 15,16 . For instance, Filip Ahlmanner et al. demonstrated that CD39 + Treg cells from cancer patients inhibit the transendothelial migration of conventional T cells 17 . These findings align with our study, where an increase in the absolute or relative number of CD28 on CD39 + activated CD4 regulatory T cells suppressed immunity and endothelial migration of T cells. This suppression, in turn, elevated the risk factors for endometriosis and its associated subtypes. In addition, reverse MR statistics uncovered that an increased risk of endometriosis and several subtypes (ASRM stages 3 and 4, ovary and pelvic peritoneal) corresponded to a decrease in the absolute and relative numbers of CD4 + CD8 dim T-cell subsets. These CD4 + CD8 dim T cells have demonstrated functionality, exhibiting preferential Th1-like activity by producing IL-2 and IFNγ. They also possess the ability to acquire cytotoxic activity, manifest an NK-T cell phenotype, and participate in antitumor activity 18 . The absolute counts of CD4 + CD8 dim T cells serve as an indicator of immune system function. In instances of immunodeficiency, such as HIV, a reduced count of CD4 + CD8 dim T cells may result in impaired immune function 19 . Conversely, certain conditions, including autoimmune diseases like β-thalassemia and non-autoimmune diseases like Alzheimer’s disease, are associated with elevated numbers of CD4 + CD8 dim T cells, leading to immune attacks 20,21 . Therefore, based on the findings presented here, it is hypothesized that the decrease in the relative and/or absolute number of this specific cell population, triggered by the heightened risk of developing endometriosis, might contribute to immune escape. This, in turn, could facilitate the continued growth of focal tissues and further progression of the disease. Our study boasts notable strengths, primarily the utilization of the MR method to counteract confounding bias, and robustness ensured through the application of four MR analysis techniques along with a comprehensive sensitivity analysis. Immune cell traits have emerged as significant risk/potential predictive factors for assessing the prevalence of endometriosis and its various subtypes. Certainly, our findings provide genetic support for the inflammatory hypothesis in endometriosis pathogenesis. However, it is essential to acknowledge certain limitations. Firstly, the study participants exclusively belong to European ancestry, emphasizing the need for validating causal associations in diverse populations. Despite detecting heterogeneity, the study did not identify outliers, indicating potential factors such as nonlinear relationships, sample size variations, measurement errors, or multiple pathogenic mechanisms influencing the results. The absence of identified outliers does not eliminate the possibility of others existing, which may represent distinct biological processes contributing to result heterogeneity 22 . Materials and methods Study design We conducted a two-sample Mendelian Randomization (MR) analysis to evaluate the causal relationship between 731 immune cell signatures categorized into 7 groups and endometriosis. MR utilizes genetic variation as proxies for risk factors, and thus, valid instrumental variables (IVs) must satisfy three crucial assumptions: ( 1 ) genetic variation is directly associated with the exposure; ( 2 ) genetic variation is not linked to potential confounders between the exposure and outcome; and ( 3 ) genetic variation does not influence the outcome through pathways other than the exposure. Summary results used in the analysis were obtained from published studies, each having received approval from the respective institutional review boards, with no requirement for additional ethical approval. Data sources Immunity-wide GWAS Data: Summary statistics for each immune trait were sourced from the publicly available GWAS Catalog (accession numbers from GCST0001391 to GCST0002121) 23 . The dataset included 731 immune cell traits comprising absolute cell (AC) counts (n = 118), median fluorescence intensities (MFI) reflecting surface antigen levels (n = 389), morphological parameters [MP] (n = 32), and relative cell (RC) counts (n = 192). Specifically, MFI, AC, and RC features encompassed B cells, CDCs, mature stages of T cells, monocytes, myeloid cells, TBNK (T cells, B cells, natural killer cells), and Treg panels, while the MP feature included CDC and TBNK panels. The original GWAS on immune traits involved 3,757 European individuals with no overlapping cohorts. Approximately 22 million SNPs genotyped with high-density arrays were imputed using the Sardinian sequence-based reference panel, and associations were tested after adjusting for covariates (i.e., sex, age, and age2). GWAS Data for Endometriosis Aggregated data for endometriosis were obtained from the global research project FinnGen, Version R9. Detailed specifics are presented in Table 1 . Table 1 Aggregated GWAS Data for Endometriosis in Europe – FinnGen Version R9 FinnGen ncase ncontrol Endometriosis 15088 107,564 Endometriosis ASRM stages 1,2 5,769 205,101 Endometriosis ASRM stages 3,4 7,574 203,296 Deep endometriosis 2,856 203,296 Endometriosis diagnosis and infertility diagnosis occurring together 3,206 201,035 Endometriosis of fallopian tube 213 107,564 Endometriosis of intestine 436 107,564 Unspecified/other endometriosis 2,982 107,564 Endometriosis of ovary 5,867 107,564 Endometriosis of pelvic peritoneum 5,628 107,564 Endometriosis of rectovaginal septum and vagina 2,456 107,564 Adenomyosis (Endometriosis of uterus) 4,267 107,564 Selection of Instrumental Variables (IVs): Initially, genome-wide significant SNPs ( P < 5 × 10 –8 ) associated with immune cell signatures were identified as instrumental variables. Independence of these SNPs was ensured by excluding those in linkage disequilibrium (r 2 < 0.01, clumping window = 10,000 kb). Additionally, PhenoScanner was employed to query and eliminate SNPs associated with potential confounders, thus mitigating potential pleiotropic effects. Palindromic SNPs were carefully considered and removed. To assess the strength of the genetic instrumental variables, the F statistic was calculated, with an F-statistic ≥ 10 indicating a robust relationship between the instrumental variables and the exposure 24 . Statistical analysis To ensure data consistency, we harmonized the statistics of immune cell signatures and endometriosis before conducting the analysis. A two-sample Mendelian randomization approach was employed to estimate the direct impact of immune cell signatures on the risk of endometriosis. The analysis included the IVW model, MR Egger, weighted-median analysis, and Weighted Mode. The IVW model was the primary method, assessing the causative relationship between immune cell signatures and endometriosis 25 . Statistical significance was determined with Bonferroni-corrected P-values: P < 0.0125 (0.05/N, N = the number of testing methods) 26 . Results falling within the range of P < 0.0125 but < 0.05 were considered suggestive evidence of a causal association. To evaluate the sensitivity of individual SNPs in this Mendelian randomization study, Cochran's Q test (heterogeneity) was employed to scrutinize differences between various instrumental variables 22 . All data underwent meticulous analysis using R software (version 4.3.0), developed by the R Foundation for Statistical Computing in Vienna, Austria. Conclusion In summary, our bidirectional two-sample MR analysis furnishes compelling genetic evidence supporting the hypothesis that the assessment of causal effects of immune traits significantly influences the risk and protection dynamics across different stages and subtypes of endometriosis. These findings underscore the intricate and pivotal role played by immune-derived factors in the pathogenesis of the disease, laying a solid foundation for the exploration and development of innovative strategies for immunotherapy. Declarations Conflicts of interest 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 This study was supported by Shanghai Fourth People's Hospital, Tongji University (ID: SY-XKZT-2021-2002, sykyqd02101). Author Contribution Ying Wang: Investigation, Writing – review & editing, Conceptualization, Writing – original draft. Fenyong Sun: Resources, Supervision, Validation. Han Wu: Data curation, Formal Analysis, Software. Chaoyan Yue: Methodology, Data curation, Formal Analysis, Software, Supervision, Validation, Visualization, Writing – review & editing. Qiuhong Man: Supervision, Validation, Visualization, Writing – review & editing. Data availability statement The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author. References Shafrir, A. L. et al. Risk for and consequences of endometriosis: A critical epidemiologic review. Best practice & research. Clinical obstetrics & gynaecology 51, 1–15, doi: 10.1016/j.bpobgyn.2018.06.001 (2018). Zondervan, K. T. et al. Endometriosis. Nature reviews. Disease primers 4, 9, doi: 10.1038/s41572-018-0008-5 (2018). Mu, F. et al. A Prospective Study of Inflammatory Markers and Risk of Endometriosis. American journal of epidemiology 187, 515–522, doi: 10.1093/aje/kwx272 (2018). Riccio, L. et al. Immunology of endometriosis. Best practice & research. Clinical obstetrics & gynaecology 50, 39–49, doi: 10.1016/j.bpobgyn.2018.01.010 (2018). Szukiewicz, D. Epigenetic regulation and T-cell responses in endometriosis - something other than autoimmunity. Frontiers in immunology 13, 943839, doi: 10.3389/fimmu.2022.943839 (2022). Moghaddam, M. Z., Ansariniya, H., Seifati, S. M., Zare, F. & Fesahat, F. Immunopathogenesis of endometriosis: An overview of the role of innate and adaptive immune cells and their mediators. American journal of reproductive immunology (New York, N.Y.: 1989) 87, e13537, doi: 10.1111/aji.13537 (2022). Imai, T. & Yasuda, N. Therapeutic intervention of inflammatory/immune diseases by inhibition of the fractalkine (CX3CL1)-CX3CR1 pathway. Inflammation and regeneration 36, 9, doi: 10.1186/s41232-016-0017-2 (2016). Hou, X. X., Zhou, W. J., Wang, X. Q. & Li, D. J. Fractalkine/CX3CR1 is involved in the pathogenesis of endometriosis by regulating endometrial stromal cell proliferation and invasion. American journal of reproductive immunology (New York, N.Y. : 1989) 76, 318–325, doi: 10.1111/aji.12557 (2016). Nanki, T., Imai, T. & Kawai, S. Fractalkine/CX3CL1 in rheumatoid arthritis. Modern rheumatology 27, 392–397, doi: 10.1080/14397595.2016.1213481 (2017). Cormican, S. & Griffin, M. D. Fractalkine (CX3CL1) and Its Receptor CX3CR1: A Promising Therapeutic Target in Chronic Kidney Disease? Frontiers in immunology 12, 664202, doi: 10.3389/fimmu.2021.664202 (2021). Mendiola, A. S. et al. Defective fractalkine-CX3CR1 signaling aggravates neuroinflammation and affects recovery from cuprizone-induced demyelination. Journal of neurochemistry 162, 430–443, doi: 10.1111/jnc.15616 (2022). Pieper, K., Grimbacher, B. & Eibel, H. B-cell biology and development. The Journal of allergy and clinical immunology 131, 959–971, doi: 10.1016/j.jaci.2013.01.046 (2013). Riccio, L. G. C., Baracat, E. C., Chapron, C., Batteux, F. & Abrão, M. S. The role of the B lymphocytes in endometriosis: A systematic review. Journal of reproductive immunology 123, 29–34, doi: 10.1016/j.jri.2017.09.001 (2017). Timperi, E. & Barnaba, V. CD39 Regulation and Functions in T Cells. International journal of molecular sciences 22, doi: 10.3390/ijms22158068 (2021). Timperi, E. et al. Regulatory T cells with multiple suppressive and potentially pro-tumor activities accumulate in human colorectal cancer. Oncoimmunology 5, e1175800, doi: 10.1080/2162402x.2016.1175800 (2016). Kortekaas, K. E. et al. CD39 Identifies the CD4(+) Tumor-Specific T-cell Population in Human Cancer. Cancer immunology research 8, 1311–1321, doi: 10.1158/2326-6066.Cir-20-0270 (2020). Ahlmanner, F. et al. CD39(+) regulatory T cells accumulate in colon adenocarcinomas and display markers of increased suppressive function. Oncotarget 9, 36993–37007, doi: 10.18632/oncotarget.26435 (2018). Lambert, C., Ibrahim, M., Iobagiu, C. & Genin, C. Significance of unconventional peripheral CD4 + CD8dim T cell subsets. Journal of clinical immunology 25, 418–427, doi: 10.1007/s10875-005-5257-x (2005). Ding, Z. D. et al. Decreased CD4(+)CD8(low) T cells in early HIV infection are associated with rapid disease progression. Cytokine 125, 154801, doi: 10.1016/j.cyto.2019.154801 (2020). Noulsri, E., Lerdwana, S., Fucharoen, S. & Pattanapanyasat, K. Phenotypic characterization of circulating CD4/CD8 T-lymphocytes in β-thalassemia patients. Asian Pacific journal of allergy and immunology 32, 261–269, doi: 10.12932/ap0426.32.3.2014 (2014). Liao, J., Zhang, Y., Tang, Z., Liu, P. & He, L. Causal relationships between peripheral immune cells and Alzheimer's disease: a two-sample Mendelian randomization study. Neurological sciences: official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology, doi: 10.1007/s10072-024-07324-y (2024). Bowden, J., Davey Smith, G., Haycock, P. C. & Burgess, S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genetic epidemiology 40, 304–314, doi: 10.1002/gepi.21965 (2016). Orrù, V. et al. Complex genetic signatures in immune cells underlie autoimmunity and inform therapy. Nature genetics 52, 1036–1045, doi: 10.1038/s41588-020-0684-4 (2020). Burgess, S. & Thompson, S. G. Avoiding bias from weak instruments in Mendelian randomization studies. International journal of epidemiology 40, 755–764, doi: 10.1093/ije/dyr036 (2011). Bowden, J., Davey Smith, G. & Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. International journal of epidemiology 44, 512–525, doi: 10.1093/ije/dyv080 (2015). Wu, F., Huang, Y., Hu, J. & Shao, Z. Mendelian randomization study of inflammatory bowel disease and bone mineral density. BMC medicine 18, 312, doi: 10.1186/s12916-020-01778-5 (2020). Additional Declarations No competing interests reported. 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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-4103948","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":285082864,"identity":"5c1dd1ed-c4fe-437f-a61e-53147d3eaf61","order_by":0,"name":"Ying Wang","email":"","orcid":"","institution":"Shanghai Fourth People’s Hospital, Tongji University","correspondingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Wang","suffix":""},{"id":285082865,"identity":"972a86c8-5582-4956-93a5-2ce798150f7d","order_by":1,"name":"Fenyong Sun","email":"","orcid":"","institution":"Shanghai Tenth People’s Hospital, Tongji University, China","correspondingAuthor":false,"prefix":"","firstName":"Fenyong","middleName":"","lastName":"Sun","suffix":""},{"id":285082866,"identity":"20c7a175-8709-4a8c-9a54-89a24cefe4db","order_by":2,"name":"Han Wu","email":"","orcid":"","institution":"Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Han","middleName":"","lastName":"Wu","suffix":""},{"id":285082867,"identity":"8b250d39-0e17-4263-8861-2ce7bb8511a1","order_by":3,"name":"Chaoyan Yue","email":"","orcid":"","institution":"Obstetrics and Gynecology Hospital of Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Chaoyan","middleName":"","lastName":"Yue","suffix":""},{"id":285082868,"identity":"89638d65-f456-4579-adbf-2f75d4e763cc","order_by":4,"name":"Qiuhong Man","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIiWNgGAWjYBACgwNAIoEBRLIfOPynQkJOnpAWS4QWnsQHPGcsjA0bCGixPwCmwKSxAW9bRSKEjQeYHT978MbDtjty5vwL0iQk50kkMDYwP3x0A5+WM3nJFoltz4wtZzw8JmG4TSKPnYHN2DgHn5YDOWYSiW2HEzfcOJAmkbhNopixgYdNGp8Wg/Nv4FrMJA7OkUhsOEBIyw2YLecbjA0bG4jS8sbYIuHcYWODGzyJjxmOSRgbNhPwi8H5HMObP8oOyxmcP37gMENNnZw8e/PDx/i0gIAEhEyAcpkJKEdo4T9AhNJRMApGwSgYkQAAWHNXoge7JlwAAAAASUVORK5CYII=","orcid":"","institution":"Shanghai Fourth People’s Hospital, Tongji University","correspondingAuthor":true,"prefix":"","firstName":"Qiuhong","middleName":"","lastName":"Man","suffix":""}],"badges":[],"createdAt":"2024-03-15 01:14:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4103948/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4103948/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53757270,"identity":"8e02f70c-35aa-49c6-b7c8-cdd6443c05d1","added_by":"auto","created_at":"2024-03-29 19:06:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1652445,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot presenting significant estimates as odds ratios for endometriosis and its four stages per increase1-SD immune cell traits count. The results are displayed using the Inverse Variance Weighted (IVW) method.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4103948/v1/f42c20e2fcac0e1eb02c60f9.png"},{"id":53754811,"identity":"68072ea7-0565-4aa9-8284-5b5628181eaa","added_by":"auto","created_at":"2024-03-29 18:58:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1872528,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot presenting significant estimates as odds ratios for endometriosis and its seven subtypes per increase1-SD immune cell traits count. The results are displayed using the IVW method.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4103948/v1/02dc192e484c3fb7c0467629.png"},{"id":53757285,"identity":"a3ef0f74-4f8f-4106-8479-22d0f733c556","added_by":"auto","created_at":"2024-03-29 19:06:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":987014,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot presenting significant/suggestive estimates as Beta for immune cell traits per increase unit in the risk of endometriosis (including its four stages and seven subtypes). The results are presented using the IVW method.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4103948/v1/f585da5c81d332c3af4a8448.png"},{"id":53758471,"identity":"d4acbfeb-b65e-4b25-ba09-02e78c4cdd2e","added_by":"auto","created_at":"2024-03-29 19:14:46","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1283268,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4103948/v1/13603d16-21cc-400d-95c8-75e87c259188.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genetic evidence for causal effects of immune cell traits on risk for endometriosis: a bidirectional two-sample Mendelian Randomization Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEndometriosis, a gynecological disorder affecting approximately 10% of women of reproductive age, is often accompanied by severe pain and infertility \u003csup\u003e1\u003c/sup\u003e. This condition is characterized by the abnormal growth of endometrial tissue outside the uterus, manifesting in locations such as the pelvic cavity, ovaries, fallopian tubes, and even the intestines \u003csup\u003e2\u003c/sup\u003e. Despite extensive research, the exact cause of endometriosis remains elusive. Notably, women with endometriosis exhibit heightened markers of systemic inflammation \u003csup\u003e3\u003c/sup\u003e. Considering the observed association between immune cells and inflammation, there is a compelling rationale to explore the potential role of immune cell traits in the pathogenesis of endometriosis.\u003c/p\u003e \u003cp\u003eCurrently, a wealth of evidence indicates aberrant functions across various immune cell types in women with endometriosis. These include decreased T cell reactivity and natural killer (NK) cytotoxicity, polyclonal activation of B cells with increased antibody production, elevated numbers and activation of peritoneal macrophages, and alterations in inflammatory mediators\u003csup\u003e4\u003c/sup\u003e. Recent research findings also highlight regulatory T-cell (Treg) alterations in endometriosis, with an increased presence of highly active Tregs and macrophages in the peritoneal fluid of affected women. Notably, disruptions in the regulatory function of T cells and an imbalance between T helper cell types Th1 and Th2 have been reported in the endometria of women experiencing endometriosis-associated infertility\u003csup\u003e5\u003c/sup\u003e. Given the diverse array of immune cells, obtaining comprehensive results from cohort and case-control studies within a single research endeavor is challenging. Recognizing the importance of establishing a causal relationship between immune cell traits and the incidence of endometriosis, our study aims to overcome these limitations by employing Mendelian Randomization (MR) to investigate their causal relationship. By delving into the realm of immunology, this research seeks to elucidate the role of immune cell traits in the pathogenesis of endometriosis, with the ultimate goal of contributing to the development of novel therapeutic strategies.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003eExploration of the causal effect of immune cell traits on endometriosis risk via forward MR\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e provide forest plots showing the estimates for each outcome using the IVW MR methods. After Bonferroni correction, 56 MFI, 13 AC, 19 RC, and 4 MP pairs of outcomes reached the level of significance (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0125). Notably, a protective effect was identified for one immune cell trait against endometriosis (CD25 on CD39\u003csup\u003e+\u003c/sup\u003eCD4\u003csup\u003e+\u003c/sup\u003e T cells, OR\u0026thinsp;=\u0026thinsp;0.97, 95% CI\u0026thinsp;=\u0026thinsp;0.95 to 0.99, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011) while two immune cell traits were associated with an increased risk for endometriosis (CD28 on CD39\u003csup\u003e+\u003c/sup\u003e activated CD4 regulatory T cell, OR\u0026thinsp;=\u0026thinsp;1.03, 95% CI\u0026thinsp;=\u0026thinsp;1.01 to 1.05, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.011; CX3CR1 on monocyte, OR\u0026thinsp;=\u0026thinsp;1.04, 95% CI\u0026thinsp;=\u0026thinsp;1.01 to 1.08, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009). The causal relationship with immune cell traits varied when considering endometriosis stages and subtypes, with protective and risk associations detected. Seven immune cell traits demonstrated a protective effect against endometriosis ASRM stages 1 and 2, while two immune cell traits were identified as risk factors for endometriosis ASRM stages 1 and 2. However, only three immune cell traits exhibited a protective effect against endometriosis ASRM stages 3 and 4, while two immune cell traits were associated with an increased risk for endometriosis ASRM stages 3 and 4. Additionally, elevated levels of two immune cell traits and decreased levels of four immune cell traits were linked to an increased risk of deep endometriosis. When it comes to endometriosis diagnosis and infertility diagnosis occurring together, a decreased level of four immune cell traits could induce the risk of it.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates our analysis considering the seven subtypes of endometriosis as outcomes. The MR results indicated that increased levels of 12 immune cell traits and decreased levels of 5 immune cell traits could contribute to the risk of endometriosis of the fallopian tube. Furthermore, the causal analysis of morbidity risk outcomes for the remaining six subtypes is detailed as follows: endometriosis of intestine, increased levels of 3 immune cell traits and decreased levels of 6 immune cell traits; endometriosis of ovary, increased levels of 4 immune cell traits and decreased levels of 4 immune cell traits; endometriosis of pelvic peritoneum, increased levels of 3 immune cell traits and decreased levels of 6 immune cell traits; endometriosis of rectovaginal septum and vagina, increased levels of 3 immune cell traits and decreased levels of 4 immune cell traits; adenomyosis (endometriosis of uterus), increased levels of 8 immune cell traits and decreased levels of 3 immune cell traits; Unspecified/other endometriosis, increased levels of 2 immune cell traits and decreased levels of 3 immune cell traits. In summary, our analysis encompassed the exploration of the causal effect of immune cell traits on endometriosis risk, involving 92 immune cells distributed across various categories, including B cells (28 cells), cDC (2 cells), Maturation stages of T cells (10 cells), Monocytes (12 cells), Myeloid cells (5 cells), TBNK (13 cells), and Treg panels (22 cells).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eExploration of the causal effect of endometriosis on immune cell traits via reverse MR\u003c/h2\u003e \u003cp\u003eTo unravel the impact of endometriosis on the body's immune mechanisms, we conducted MR analysis to explore the causal effects of endometriosis on immune cells. Illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, endometriosis of the ovary demonstrated a significant association with a decrease in CD4\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003edim\u003c/sup\u003e T cell Absolute Count (Beta = -0.10, 95% CI = -0.18 to -0.03, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010). Additionally, we detected suggestive causal effects of endometriosis, including four stages and seven subtypes, on the levels of 13 immune cell traits, where endometriosis onset could increase levels of 2 immune cell traits and decrease levels of 11 immune cell traits. These 13 immune cell traits are distributed in B cell (2 cells), Maturation stages of T cell (1 cell), Monocyte (2 cell), TBNK (7 cells), and Treg panels (1 cell).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of sensitivity, including heterogeneity and pleiotropy\u003c/h2\u003e \u003cp\u003eThis study implemented various sensitivity analysis methods to ensure the robustness of our findings. Cochran\u0026rsquo;s Q-test was employed to assess heterogeneity between individual Single Nucleotide Polymorphism (SNP) estimates. As illustrated in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, heterogeneity was observed in one group (HLA DR on CD14\u003csup\u003e+\u003c/sup\u003e monocyte forward MR IVW to endometriosis ASRM stages 3,4; Q\u0026thinsp;=\u0026thinsp;32.651, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.037). However, no evidence of pleiotropy was found in any of the groups examined using the MR-Egger regression, encompassing both forward and reverse Mendelian Randomization.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study thoroughly investigates the causal associations between immune cells and endometriosis, leveraging extensive publicly available genetic data. Utilizing a bidirectional two-sample Mendelian Randomization framework, our findings reveal a significant causal relationship involving 92 immune cells, distributed among B cell (28 cells), cDC (2 cells), Maturation stages of T cell (10 cells), Monocyte (12 cells), Myeloid cell (5 cells), TBNK (13 cells) and Treg panels (22 cells). In the reverse Mendelian randomization analysis, we identified significant or suggestive causal relationships involving 14 immune cells distributed among B cell (2 cells), Maturation stages of T cell (1 cell), Monocyte (2 cells), TBNK (8 cells) and Treg panels (1 cell). All these findings supported the hypothesis that the assessment of causal effects of immune cell traits significantly influences the risk and protection dynamics across different stages and subtypes of endometriosis. Several intriguing and common findings will be specifically covered and discussed below.\u003c/p\u003e \u003cp\u003eOur findings indicated that for every 1-standard deviation (SD) increase in CX3CR1 on monocyte count, the risks of developing endometriosis, endometriosis ASRM stages 1,2, and endometriosis of pelvic peritoneum increased by 4%, 9%, and 10%, respectively. Endometriosis, a prevalent chronic condition in the female reproductive system, remains incompletely understood, with inflammation considered among the potential hypotheses\u003csup\u003e6\u003c/sup\u003e. CX3CR1, a cell surface receptor primarily expressed on monocytes, binds to its ligand CX3CL1 (also known as fractalkine) and plays a crucial role in regulating immune and inflammatory responses. Various functional aspects of the fractalkine-CX3CR1 axis have been identified, including the rapid capture and firm adhesion of immune cells, chemotaxis, and the crawling behavior of monocytes\u003csup\u003e7\u003c/sup\u003e. Our findings align with existing research and support the inflammatory hypothesis, suggesting that elevated CX3CR1 on monocytes is a risk factor for the development of endometriosis, specifically in endometriosis ASRM stages 1,2, and endometriosis of pelvic peritoneum. However, in the reverse Mendelian randomization analysis, a suggestive causal relationship indicated that a one-unit increase in the log odds of endometriosis risk could decrease the levels of CX3CR1 on monocytes by 0.13. Immunohistochemical experiments conducted by Hou X X et al. revealed significantly higher expression of fractalkine and CX3CR1 in ectopic endometrium compared to normal endometrium. The concentration of fractalkine in the peritoneal fluid of endometriosis patients was closely correlated with the severity of the condition\u003csup\u003e8\u003c/sup\u003e. It is hypothesized that the persistent enrichment of fractalkine and CX3CR1 in the patient's focal tissues may contribute to the relative decrease in CX3CR1 on blood monocytes. Considering the effectiveness of the fractalkine-CX3CR1 axis in treating various inflammatory diseases, such as rheumatoid arthritis \u003csup\u003e9\u003c/sup\u003e, chronic kidney disease\u003csup\u003e10\u003c/sup\u003e, and neuroinflammation\u003csup\u003e11\u003c/sup\u003e, it emerges as a potential new target for the treatment of endometriosis, particularly in endometriosis ASRM stages 1,2, and endometriosis of pelvic peritoneum.\u003c/p\u003e \u003cp\u003eIn our study, we identified several traits from B cells that exhibited significant causal relationships with endometriosis and its various subtypes. In the context of inflammation, the B-cell activating factor receptor (BAFF-R) on the surface of B cells binds to the B-cell activating factor (BAFF), produced by immune cells like macrophages and dendritic cells. This interaction promotes B-cell proliferation, survival, and antibody production by activating intracellular signaling pathways\u003csup\u003e12\u003c/sup\u003e. Elevated levels of several subpopulations representing B activation and memory status (e.g., BAFF-R on IgD\u003csup\u003e+\u003c/sup\u003e CD38\u003csup\u003edim\u003c/sup\u003e B cell, memory B cell, unswitched memory B cell, switched memory B cell, etc.) were identified as risk factors for the development of endometriosis in the fallopian tube. However, elevated subpopulations representing B activation status(e.g., BAFF-R on IgD\u003csup\u003e+\u003c/sup\u003e CD38\u003csup\u003edim\u003c/sup\u003e B cell, IgD\u003csup\u003e+\u003c/sup\u003e CD38\u003csup\u003e\u0026minus;\u003c/sup\u003e B cell, etc.) were found to be protective factors against the development of endometriosis in the pelvic peritoneum. This observation suggests a diverse role of B cells in the pathogenesis of immunoinflammation across different subtypes of endometriosis, consistent with existing reports. L.G.C. Riccio et al. summarized findings from 22 selected studies related to B cells and endometriosis, revealing increased numbers and/or activation of B cells in most studies. However, some studies reported no difference, while others indicated a decreased number of B cells\u003csup\u003e13\u003c/sup\u003e. Further studies are needed to elucidate the specific role of B cells in the development of all endometriosis subtypes and to propose new therapeutic strategies.\u003c/p\u003e \u003cp\u003eMoreover, our investigation highlighted that an elevated level of CD28 on CD39\u003csup\u003e+\u003c/sup\u003e activated CD4 regulatory T cells poses a risk for endometriosis and several subtypes (ASRM stages 3 and 4, ovary). CD39 is an enzyme responsible for converting adenosine triphosphate into adenosine diphosphate and cyclic adenosine monophosphate, ultimately leading to the release of an immunosuppressive form of adenosine in the microenvironment\u003csup\u003e14\u003c/sup\u003e. The role of CD28 on D39\u003csup\u003e+\u003c/sup\u003e activated CD4\u003csup\u003e+\u003c/sup\u003e Treg cells is to maintain immune tolerance by regulating cellular activation and function. Several observations, including those in cancer research, suggest the pivotal role of CD39 in attenuating immune responses.\u003csup\u003e15,16\u003c/sup\u003e. For instance, Filip Ahlmanner et al. demonstrated that CD39\u003csup\u003e+\u003c/sup\u003e Treg cells from cancer patients inhibit the transendothelial migration of conventional T cells \u003csup\u003e17\u003c/sup\u003e. These findings align with our study, where an increase in the absolute or relative number of CD28 on CD39\u003csup\u003e+\u003c/sup\u003e activated CD4 regulatory T cells suppressed immunity and endothelial migration of T cells. This suppression, in turn, elevated the risk factors for endometriosis and its associated subtypes.\u003c/p\u003e \u003cp\u003eIn addition, reverse MR statistics uncovered that an increased risk of endometriosis and several subtypes (ASRM stages 3 and 4, ovary and pelvic peritoneal) corresponded to a decrease in the absolute and relative numbers of CD4\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003edim\u003c/sup\u003e T-cell subsets. These CD4\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003edim\u003c/sup\u003e T cells have demonstrated functionality, exhibiting preferential Th1-like activity by producing IL-2 and IFNγ. They also possess the ability to acquire cytotoxic activity, manifest an NK-T cell phenotype, and participate in antitumor activity\u003csup\u003e18\u003c/sup\u003e. The absolute counts of CD4\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003edim\u003c/sup\u003e T cells serve as an indicator of immune system function. In instances of immunodeficiency, such as HIV, a reduced count of CD4\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003edim\u003c/sup\u003e T cells may result in impaired immune function\u003csup\u003e19\u003c/sup\u003e. Conversely, certain conditions, including autoimmune diseases like β-thalassemia and non-autoimmune diseases like Alzheimer\u0026rsquo;s disease, are associated with elevated numbers of CD4\u003csup\u003e+\u003c/sup\u003eCD8\u003csup\u003edim\u003c/sup\u003e T cells, leading to immune attacks\u003csup\u003e20,21\u003c/sup\u003e. Therefore, based on the findings presented here, it is hypothesized that the decrease in the relative and/or absolute number of this specific cell population, triggered by the heightened risk of developing endometriosis, might contribute to immune escape. This, in turn, could facilitate the continued growth of focal tissues and further progression of the disease.\u003c/p\u003e \u003cp\u003eOur study boasts notable strengths, primarily the utilization of the MR method to counteract confounding bias, and robustness ensured through the application of four MR analysis techniques along with a comprehensive sensitivity analysis. Immune cell traits have emerged as significant risk/potential predictive factors for assessing the prevalence of endometriosis and its various subtypes. Certainly, our findings provide genetic support for the inflammatory hypothesis in endometriosis pathogenesis. However, it is essential to acknowledge certain limitations. Firstly, the study participants exclusively belong to European ancestry, emphasizing the need for validating causal associations in diverse populations. Despite detecting heterogeneity, the study did not identify outliers, indicating potential factors such as nonlinear relationships, sample size variations, measurement errors, or multiple pathogenic mechanisms influencing the results. The absence of identified outliers does not eliminate the possibility of others existing, which may represent distinct biological processes contributing to result heterogeneity\u003csup\u003e22\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eWe conducted a two-sample Mendelian Randomization (MR) analysis to evaluate the causal relationship between 731 immune cell signatures categorized into 7 groups and endometriosis. MR utilizes genetic variation as proxies for risk factors, and thus, valid instrumental variables (IVs) must satisfy three crucial assumptions: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) genetic variation is directly associated with the exposure; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) genetic variation is not linked to potential confounders between the exposure and outcome; and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) genetic variation does not influence the outcome through pathways other than the exposure. Summary results used in the analysis were obtained from published studies, each having received approval from the respective institutional review boards, with no requirement for additional ethical approval.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eData sources\u003c/h2\u003e \u003cp\u003eImmunity-wide GWAS Data:\u003c/p\u003e \u003cp\u003eSummary statistics for each immune trait were sourced from the publicly available GWAS Catalog (accession numbers from GCST0001391 to GCST0002121)\u003csup\u003e23\u003c/sup\u003e. The dataset included 731 immune cell traits comprising absolute cell (AC) counts (n\u0026thinsp;=\u0026thinsp;118), median fluorescence intensities (MFI) reflecting surface antigen levels (n\u0026thinsp;=\u0026thinsp;389), morphological parameters [MP] (n\u0026thinsp;=\u0026thinsp;32), and relative cell (RC) counts (n\u0026thinsp;=\u0026thinsp;192). Specifically, MFI, AC, and RC features encompassed B cells, CDCs, mature stages of T cells, monocytes, myeloid cells, TBNK (T cells, B cells, natural killer cells), and Treg panels, while the MP feature included CDC and TBNK panels. The original GWAS on immune traits involved 3,757 European individuals with no overlapping cohorts. Approximately 22\u0026nbsp;million SNPs genotyped with high-density arrays were imputed using the Sardinian sequence-based reference panel, and associations were tested after adjusting for covariates (i.e., sex, age, and age2).\u003c/p\u003e \u003cp\u003eGWAS Data for Endometriosis\u003c/p\u003e \u003cp\u003eAggregated data for endometriosis were obtained from the global research project FinnGen, Version R9. Detailed specifics are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAggregated GWAS Data for Endometriosis in Europe \u0026ndash; FinnGen Version R9\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFinnGen\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003encase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003encontrol\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometriosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e107,564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometriosis ASRM stages 1,2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e205,101\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometriosis ASRM stages 3,4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7,574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e203,296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDeep endometriosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,856\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e203,296\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometriosis diagnosis and infertility diagnosis occurring together\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3,206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e201,035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometriosis of fallopian tube\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e107,564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometriosis of intestine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e107,564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnspecified/other endometriosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e107,564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometriosis of ovary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e107,564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometriosis of pelvic peritoneum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,628\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e107,564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndometriosis of rectovaginal septum and vagina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,456\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e107,564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdenomyosis (Endometriosis of uterus)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4,267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e107,564\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSelection of Instrumental Variables (IVs):\u003c/h2\u003e \u003cp\u003eInitially, genome-wide significant SNPs (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026ndash;8\u003c/sup\u003e) associated with immune cell signatures were identified as instrumental variables. Independence of these SNPs was ensured by excluding those in linkage disequilibrium (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, clumping window\u0026thinsp;=\u0026thinsp;10,000 kb). Additionally, PhenoScanner was employed to query and eliminate SNPs associated with potential confounders, thus mitigating potential pleiotropic effects. Palindromic SNPs were carefully considered and removed. To assess the strength of the genetic instrumental variables, the F statistic was calculated, with an F-statistic\u0026thinsp;\u0026ge;\u0026thinsp;10 indicating a robust relationship between the instrumental variables and the exposure\u003csup\u003e24\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eTo ensure data consistency, we harmonized the statistics of immune cell signatures and endometriosis before conducting the analysis. A two-sample Mendelian randomization approach was employed to estimate the direct impact of immune cell signatures on the risk of endometriosis. The analysis included the IVW model, MR Egger, weighted-median analysis, and Weighted Mode. The IVW model was the primary method, assessing the causative relationship between immune cell signatures and endometriosis \u003csup\u003e25\u003c/sup\u003e. Statistical significance was determined with Bonferroni-corrected P-values: \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0125 (0.05/N, N\u0026thinsp;=\u0026thinsp;the number of testing methods)\u003csup\u003e26\u003c/sup\u003e. Results falling within the range of \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0125 but \u0026lt;\u0026thinsp;0.05 were considered suggestive evidence of a causal association. To evaluate the sensitivity of individual SNPs in this Mendelian randomization study, Cochran's Q test (heterogeneity) was employed to scrutinize differences between various instrumental variables \u003csup\u003e22\u003c/sup\u003e. All data underwent meticulous analysis using R software (version 4.3.0), developed by the R Foundation for Statistical Computing in Vienna, Austria.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn summary, our bidirectional two-sample MR analysis furnishes compelling genetic evidence supporting the hypothesis that the assessment of causal effects of immune traits significantly influences the risk and protection dynamics across different stages and subtypes of endometriosis. These findings underscore the intricate and pivotal role played by immune-derived factors in the pathogenesis of the disease, laying a solid foundation for the exploration and development of innovative strategies for immunotherapy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of interest\u003c/h2\u003e \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.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis study was supported by Shanghai Fourth People's Hospital, Tongji University (ID: SY-XKZT-2021-2002, sykyqd02101).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYing Wang: Investigation, Writing \u0026ndash; review \u0026amp; editing, Conceptualization, Writing \u0026ndash; original draft. Fenyong Sun: Resources, Supervision, Validation. Han Wu: Data curation, Formal Analysis, Software. Chaoyan Yue: Methodology, Data curation, Formal Analysis, Software, Supervision, Validation, Visualization, Writing \u0026ndash; review \u0026amp; editing. Qiuhong Man: Supervision, Validation, Visualization, Writing \u0026ndash; review \u0026amp; editing.\u003c/p\u003e\u003ch2\u003eData availability statement\u003c/h2\u003e \u003cp\u003eThe original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eShafrir, A. L. \u003cem\u003eet al.\u003c/em\u003e Risk for and consequences of endometriosis: A critical epidemiologic review. Best practice \u0026amp; research. Clinical obstetrics \u0026amp; gynaecology 51, 1\u0026ndash;15, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.bpobgyn.2018.06.001\u003c/span\u003e\u003cspan address=\"10.1016/j.bpobgyn.2018.06.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZondervan, K. T. \u003cem\u003eet al.\u003c/em\u003e Endometriosis. Nature reviews. Disease primers 4, 9, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41572-018-0008-5\u003c/span\u003e\u003cspan address=\"10.1038/s41572-018-0008-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMu, F. \u003cem\u003eet al.\u003c/em\u003e A Prospective Study of Inflammatory Markers and Risk of Endometriosis. American journal of epidemiology 187, 515\u0026ndash;522, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/aje/kwx272\u003c/span\u003e\u003cspan address=\"10.1093/aje/kwx272\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiccio, L. \u003cem\u003eet al.\u003c/em\u003e Immunology of endometriosis. Best practice \u0026amp; research. Clinical obstetrics \u0026amp; gynaecology 50, 39\u0026ndash;49, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.bpobgyn.2018.01.010\u003c/span\u003e\u003cspan address=\"10.1016/j.bpobgyn.2018.01.010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSzukiewicz, D. Epigenetic regulation and T-cell responses in endometriosis - something other than autoimmunity. Frontiers in immunology 13, 943839, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2022.943839\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2022.943839\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoghaddam, M. Z., Ansariniya, H., Seifati, S. M., Zare, F. \u0026amp; Fesahat, F. Immunopathogenesis of endometriosis: An overview of the role of innate and adaptive immune cells and their mediators. American journal of reproductive immunology (New York, N.Y.: 1989) 87, e13537, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/aji.13537\u003c/span\u003e\u003cspan address=\"10.1111/aji.13537\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eImai, T. \u0026amp; Yasuda, N. Therapeutic intervention of inflammatory/immune diseases by inhibition of the fractalkine (CX3CL1)-CX3CR1 pathway. Inflammation and regeneration 36, 9, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s41232-016-0017-2\u003c/span\u003e\u003cspan address=\"10.1186/s41232-016-0017-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHou, X. X., Zhou, W. J., Wang, X. Q. \u0026amp; Li, D. J. Fractalkine/CX3CR1 is involved in the pathogenesis of endometriosis by regulating endometrial stromal cell proliferation and invasion. \u003cem\u003eAmerican journal of reproductive immunology (New York, N.Y.\u003c/em\u003e: 1989) 76, 318\u0026ndash;325, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/aji.12557\u003c/span\u003e\u003cspan address=\"10.1111/aji.12557\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNanki, T., Imai, T. \u0026amp; Kawai, S. Fractalkine/CX3CL1 in rheumatoid arthritis. Modern rheumatology 27, 392\u0026ndash;397, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/14397595.2016.1213481\u003c/span\u003e\u003cspan address=\"10.1080/14397595.2016.1213481\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCormican, S. \u0026amp; Griffin, M. D. Fractalkine (CX3CL1) and Its Receptor CX3CR1: A Promising Therapeutic Target in Chronic Kidney Disease? Frontiers in immunology 12, 664202, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fimmu.2021.664202\u003c/span\u003e\u003cspan address=\"10.3389/fimmu.2021.664202\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMendiola, A. S. \u003cem\u003eet al.\u003c/em\u003e Defective fractalkine-CX3CR1 signaling aggravates neuroinflammation and affects recovery from cuprizone-induced demyelination. Journal of neurochemistry 162, 430\u0026ndash;443, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/jnc.15616\u003c/span\u003e\u003cspan address=\"10.1111/jnc.15616\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePieper, K., Grimbacher, B. \u0026amp; Eibel, H. B-cell biology and development. The Journal of allergy and clinical immunology 131, 959\u0026ndash;971, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jaci.2013.01.046\u003c/span\u003e\u003cspan address=\"10.1016/j.jaci.2013.01.046\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRiccio, L. G. C., Baracat, E. C., Chapron, C., Batteux, F. \u0026amp; Abr\u0026atilde;o, M. S. The role of the B lymphocytes in endometriosis: A systematic review. Journal of reproductive immunology 123, 29\u0026ndash;34, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jri.2017.09.001\u003c/span\u003e\u003cspan address=\"10.1016/j.jri.2017.09.001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTimperi, E. \u0026amp; Barnaba, V. CD39 Regulation and Functions in T Cells. International journal of molecular sciences 22, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijms22158068\u003c/span\u003e\u003cspan address=\"10.3390/ijms22158068\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTimperi, E. \u003cem\u003eet al.\u003c/em\u003e Regulatory T cells with multiple suppressive and potentially pro-tumor activities accumulate in human colorectal cancer. Oncoimmunology 5, e1175800, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1080/2162402x.2016.1175800\u003c/span\u003e\u003cspan address=\"10.1080/2162402x.2016.1175800\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKortekaas, K. E. \u003cem\u003eet al.\u003c/em\u003e CD39 Identifies the CD4(+) Tumor-Specific T-cell Population in Human Cancer. Cancer immunology research 8, 1311\u0026ndash;1321, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1158/2326-6066.Cir-20-0270\u003c/span\u003e\u003cspan address=\"10.1158/2326-6066.Cir-20-0270\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhlmanner, F. \u003cem\u003eet al.\u003c/em\u003e CD39(+) regulatory T cells accumulate in colon adenocarcinomas and display markers of increased suppressive function. Oncotarget 9, 36993\u0026ndash;37007, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.18632/oncotarget.26435\u003c/span\u003e\u003cspan address=\"10.18632/oncotarget.26435\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLambert, C., Ibrahim, M., Iobagiu, C. \u0026amp; Genin, C. Significance of unconventional peripheral CD4\u0026thinsp;+\u0026thinsp;CD8dim T cell subsets. Journal of clinical immunology 25, 418\u0026ndash;427, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10875-005-5257-x\u003c/span\u003e\u003cspan address=\"10.1007/s10875-005-5257-x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDing, Z. D. \u003cem\u003eet al.\u003c/em\u003e Decreased CD4(+)CD8(low) T cells in early HIV infection are associated with rapid disease progression. Cytokine 125, 154801, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.cyto.2019.154801\u003c/span\u003e\u003cspan address=\"10.1016/j.cyto.2019.154801\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNoulsri, E., Lerdwana, S., Fucharoen, S. \u0026amp; Pattanapanyasat, K. Phenotypic characterization of circulating CD4/CD8 T-lymphocytes in β-thalassemia patients. Asian Pacific journal of allergy and immunology 32, 261\u0026ndash;269, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.12932/ap0426.32.3.2014\u003c/span\u003e\u003cspan address=\"10.12932/ap0426.32.3.2014\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiao, J., Zhang, Y., Tang, Z., Liu, P. \u0026amp; He, L. Causal relationships between peripheral immune cells and Alzheimer's disease: a two-sample Mendelian randomization study. Neurological sciences: official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s10072-024-07324-y\u003c/span\u003e\u003cspan address=\"10.1007/s10072-024-07324-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBowden, J., Davey Smith, G., Haycock, P. C. \u0026amp; Burgess, S. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. Genetic epidemiology 40, 304\u0026ndash;314, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/gepi.21965\u003c/span\u003e\u003cspan address=\"10.1002/gepi.21965\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrr\u0026ugrave;, V. \u003cem\u003eet al.\u003c/em\u003e Complex genetic signatures in immune cells underlie autoimmunity and inform therapy. Nature genetics 52, 1036\u0026ndash;1045, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1038/s41588-020-0684-4\u003c/span\u003e\u003cspan address=\"10.1038/s41588-020-0684-4\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgess, S. \u0026amp; Thompson, S. G. Avoiding bias from weak instruments in Mendelian randomization studies. International journal of epidemiology 40, 755\u0026ndash;764, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ije/dyr036\u003c/span\u003e\u003cspan address=\"10.1093/ije/dyr036\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBowden, J., Davey Smith, G. \u0026amp; Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. International journal of epidemiology 44, 512\u0026ndash;525, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/ije/dyv080\u003c/span\u003e\u003cspan address=\"10.1093/ije/dyv080\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, F., Huang, Y., Hu, J. \u0026amp; Shao, Z. Mendelian randomization study of inflammatory bowel disease and bone mineral density. BMC medicine 18, 312, doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12916-020-01778-5\u003c/span\u003e\u003cspan address=\"10.1186/s12916-020-01778-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"immune cell traits, endometriosis, inflammation, Mendelian randomization, causal relationship","lastPublishedDoi":"10.21203/rs.3.rs-4103948/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4103948/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePrevious studies have identified associations between immune cell traits and endometriosis, but the causality of these relationships remains uncertain. 731 immune cell signatures associated Single-nucleotide polymorphisms (SNPs) were extracted from a published genome-wide association study (GWAS) involving 472,174 individuals, while endometriosis data, including four stages and seven subtypes, were obtained from the FinnGen consortium. Four methods were used for Mendelian randomization. The causal effect of immune cell traits on endometriosis was explored after Bonferroni correction. Significant causal relationship included 92 immune cell traits distributed among B cell (28 cells), cDC (2 cells), Maturation stages of T cell (10 cells), Monocyte (12 cells), Myeloid cell (5 cells), TBNK (13 cells) and Treg panels (22 cells). In the reverse Mendelian randomization analysis, a one-unit increase in the log odds of endometriosis of the ovary risk corresponded to a decrease in the Absolute Count of CD4\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003edim\u003c/sup\u003e T cell by 0.10. This study represents the first comprehensive evaluation of the causal effects of immune cell traits on the risk/protection of different stages/subtypes of endometriosis. The findings highlight the complex and significant role of immune-derived factors in the pathogenesis of the disease.\u003c/p\u003e","manuscriptTitle":"Genetic evidence for causal effects of immune cell traits on risk for endometriosis: a bidirectional two-sample Mendelian Randomization Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-29 18:58:41","doi":"10.21203/rs.3.rs-4103948/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorAssigned","content":"","date":"2024-05-02T14:43:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-03-27T03:08:26+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-03-27T03:06:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-03-15T01:07:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e0f249b8-25b9-4275-9344-468114fdec8e","owner":[],"postedDate":"March 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":30014274,"name":"Biological sciences/Genetics"},{"id":30014275,"name":"Biological sciences/Immunology"},{"id":30014276,"name":"Health sciences/Diseases"},{"id":30014277,"name":"Health sciences/Medical research"},{"id":30014278,"name":"Health sciences/Pathogenesis"},{"id":30014279,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2024-03-29T18:58:41+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-29 18:58:41","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4103948","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4103948","identity":"rs-4103948","version":["v1"]},"buildId":"B-jG_2CBjPDmsCi4Wdhf-","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}