Causal role of peripheral immunity in gastroduodenal diseases: A Mendelian randomization study and meta-analysis

Causal role of peripheral immunity in gastroduodenal diseases: A Mendelian randomization study and meta-analysis | 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 Causal role of peripheral immunity in gastroduodenal diseases: A Mendelian randomization study and meta-analysis Ke Xie, Ling Li, Ding Zhang, Jing Li, Jin Feng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6612467/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 : Peripheral immune cells have important roles in upper digestive system diseases. In this meta-analysis, we explored causal relationships between three upper digestive system diseases and peripheral immune cells. Methods : Genetic summary statistics were collected from an open genome-wide association study database. Causal relationships between seven peripheral immune cell types and gastric ulcer, duodenal ulcer, and chronic gastritis conditions were evaluated using double-sample bidirectional Mendelian Randomization. Cochran's Q and Mendelian random-Egger regression tests were used to evaluate heterogeneity and pleiotropy. A meta-analysis improved the statistical efficiency of our results. Results : We observed a positive correlation between B cells, T cell maturation stages, and Tregs with the upper digestive system (odds ratio [OR]:1.0199, 95% confidence interval [CI]: 0.9604–1.0611; OR: 1.0006, 95% CI: 0.9997–1.0015; and OR: 1.0008, 95% CI: 1.0003–1.0014, respectively). By contrast cDCs, myeloid cells, and TBNKs were negatively correlated with the upper digestive system (OR: 0.9635, 95% CI: 0.8923–1.0403; OR: 0.9991, 95% CI: 0.9986–0.9997; and OR: 0.9740, 95% CI: 0.9199–1.0312, respectively). Conclusions : We observed cause-and-effect relationships between genetically predicted peripheral immune cells and upper digestive diseases. These findings suggest peripheral immune cell monitoring and improved guidelines for the risk management of upper digestive diseases. gastroduodenal diseases immunity Mendelian Randomization meta-analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Approximately one-fifth of the population experience ongoing symptoms associated with disturbed functional gastroduodenal regions, which include the stomach and the beginning of the small intestine [1]. These symptoms, which can significantly impact quality of life, often manifest without any detectable physical or organic abnormalities [2]. The conditions can range from acute to chronic and vary in severity. In this study, we focused on two common diseases, including chronic gastritis (CG) and peptic ulcer disease (PUD). CG manifests as persistent stomach wall inflammation, characterized by perennial afflictions that may generate grave complication-like peptic ulceration and perforation [3]. PUD relates to gastrointestinal wall rupture which extends to the submucosa and mainly localizes to the stomach and duodenum. The prevalence of PUD is estimated at 0.1%–0.19% in the general population and is higher among Asians [4]. Although CG and PUD etiologies have not yet been clearly elucidated, Helicobacter pylori infection, hypersecretory gastric acid, and widespread nonsteroidal anti-inflammatory drug use are the main underlying causes of pathogenesis [5, 6]. However, for those individuals with H. pylori infections, most are asymptomatic and the lifetime PUD prevalence is approximately 10% [7]. The risk of gastroenterological disease after H. pylori infection is associated with systemic immune responses and genetic predisposition [8]. H. pylori infection induces an immunological cascade and associated inflammation, recruiting host immune system cells such as dendritic cells (DCs), macrophages, neutrophils, and lymphocytes, but severity and distribution vary widely between individuals. Peripheral immunity has been shown to corelate with H. pylori infection and gastric diseases [9-11]. Bagheri et al. [12] reported that CD4+ T, T-bet+, and Th17 cells were increased in individuals with H. pylori, but remarkably higher in those with CG and PUD. Additionally, a notable diminution of regulatory B cell populations was reported in patients infected with H. pylori, suggesting potential alterations in the immunoregulatory mechanisms of these cells with host immune responses [11, 13]. However, results from epidemiological studies are often prone to confounder bias and reverse causation, while comprehensive large population studies on white cells in CG and PUD are lacking. Therefore, we performed two-sample Mendelian Randomization (MR) analysis to explore causal relationships between peripheral immunity, CG, and PUD, which is an emerging method of causal inference as distribution of genetic variants is analogues to random considering confounders. Methods Genome-wide association study (GWAS) summary data of exposure and outcomes Genetic exposure data came from a 3757-strong Sardinian cohort, with almost three times the number of immune cell traits and double the number of individuals to that previously studied [14]. Immune traits were analyzed by flow cytometry, including 118 absolute cell counts (ACs), 389 median fluorescence intensities (MFIs) of surface antigens, and 32 morphological parameters [14]. Gastric ulcer disease summary data were obtained from Biobank Japan and consisted of 79000 participants where phenotypes were defined by incorporating past medical histories and text-mining electronic medical records according to International Classification of Disease-10 code [15]. A CG and duodenal ulcer genetic association dataset was derived from a large European ethnicity meta-analysis from IEU OpenGWAS (ukb-b-6716 and ukb-b-4725) [16]. Table 1 Brief description of GWAS data included in MR. Exposure GWAS id Population Sample size Reference Peripheral immune cell counts ebi-a-GCST90001391 to ebi-a-GCST90002121 Sardinian 3757 8 Chronic gastritis ukb-b-6716 European 463010 10 Gastric ulcer ebi-a-GCST90018851 Japan 79000 9 Duodenal ulcer ukb-b-4725 European 462933 10 Statistical analysis Genetic variants were selected as IVs if they reached the GWAS p-value < 1 × 10−6, and were further clumped based on linkage disequilibrium (r2 = 0.01) and the genomic region (clump window, 1,000 kilobases). Inverse variance weighted (IVW), MR-Egger, weighted median, simple mode, and weighted mode approaches are the major MR analysis methods used to confirm robust results, with IVW used as the major random–effect model. Sensitivity analyses are pivotal in MR studies and provide robust causal inferences under weak assumptions. We used Cochran Q tests to assess heterogeneity. In addition, MR-Egger interception tests were used to identify and control bias due to horizontal pleiotropy, whereas Steiger directional tests were used to assess reverse causality. All analyses were performed using R statistical software (Version 4.3.1) with TwoSampleMR and forestploter (https://github.com/adayim/forestploter) packages. Results The causal effects of peripheral immunity on CG Causal immunity effects on CG risk are summarized (Figure 1). We observed that only CD33dim HLA DR+ CD11b− cells (myeloid cell panel) showed negative correlations with CG when we focused on absolute peripheral cell counts (odds ratio [OR]: 0.9991, 95% confidence interval [CI]: 0.9985–0.9997). But, MR-Egger and weighted median analyses showed a null causation. Additionally, terminally differentiated CD4−CD8−T cell %T cells (OR: 0.9993, 95% CI: 0.9987–0.9999) were negatively related to CG. However, CD127−CD8+T cell %T cells (OR: 1.0009, 95% CI: 1.0000–1.0018) and effector memory CD4+ T cell %T cells (OR: 1.0013, 95% CI: 1.0002–1.0024) were positively related to CG. Next, we explored causal cell surface antigen risk estimates on CG using MFIs. In a B cell panel, the results suggested that an increase of CD20, an atypical antigen expressed only on mature B cells, was associated with higher CG risk, including IgD−CD38−B cells (OR: 1.0007, 95% CI: 1.0000–1.0015), naive−mature B cells (OR: 1.0006, 95% CI: 1.0000–1.0013), switched memory B cells (OR: 1.0006, 95% CI: 1.0001–1.0013), and IgD+ B cells (OR: 1.0006, 95% CI: 1.0001–1.0012). In the monocyte panel, CD40 levels significantly affected CG incidence (OR: 1.0003, 95% CI: 1.0000–1.0006), containing CD14+CD16−monocytes (OR: 1.0003, 95% CI: 1.0000–1.0006) and CD14−CD16+ monocytes (OR: 1.0003, 95% CI: 1.0000–1.0006). High CD64 levels on monocytes also increased CG risk (OR: 1.0006, 95% CI: 1.0001–1.0011). Moreover, the OR for CD11c’s on granulocytes was 1.0008 (95% CI: 1.0002–1.0015), indicating a CG risk factor (Figure 1A, B). Heterogeneity, pleiotropy tests above showed no significance, indicating the results were robust. The Steiger method indicated no significant reverse causation. To further reveal correlations between immune cells and CG (Figure 1C–E), our meta-analysis showed that B cells, T cell maturation stages, and monocytes were positively correlated with CG (OR: 1.0007, 95% CI: 1.0003–1.0010; OR: 1.0002, 95% CI: 0.9984–1.0021; OR: and 1.0003, 95% CI: 1.0002–1.0005, respectively). Causal effects of peripheral immunity on GU MR analyses revealed that higher leukocyte cell counts decreased the risk of GU (OR: 0.9309, 95% CI: 0.8745–0.9908). In the T cell panel, CD4+T cell counts were negatively associated with GU (OR: 0.9147 95% CI: 0.8429–0.9925), however, the mature form, terminally differentiated CD4+T cells, were identified as a positive risk factor (OR: 1.0647, 95% CI: 1.0046–1.1284). Treg affect stomach ulcer crucially. High CD28-CD8dim T cell ACs may protect against ulcer diseases (OR: 0.9073 95% CI: 0.8234–0.9997). Also, resting CD4 regulatory T cell %CD4+ T cells (OR: 1.0326, 95% CI: 1.0001–1.0660) and CD39+ secreting CD4 regulatory T cell %CD4 regulatory T cells (OR: 1.0273, 95% CI: 1.0013–1.0541) were identified as promoters. In the B cell panel, high IgD−CD38dim B cell %lymphocytes (OR: 1.0799, 95% CI: 1.0197–1.1436) increased the risk of GU. With respect to other cell panels, granulocytic myeloid-derived suppressor cells with potent immunosuppressive activity were shown to promote GU (OR: 1.0713, 95% CI: 1.0041–1.1430), and CD86+ plasmacytoid DCs decreased the risk of GU (OR: 0.8739, 95% CI: 0.7993–0.9554). Causal immunity estimates on CG risk are summarized (Figure 2A, B). Cell surface antigen levels were also analyzed. Similar to CG results, CD20 expression on IgD+ CD24+ B cells (OR: 1.0886, 95% CI: 1.0082–1.1755), IgD− CD38dim B cells (OR: 1.0649, 95% CI: 1.0177–1.1143), and unswitched memory B cells (OR: 1.0900, 95% CI: 1.0055–1.1817) had causal effects on GU. However, CD19 effects on naive−mature B cells showed some protected from ulcers (OR: 0.8480, 95% CI: 0.7216–0.9966). In the T cell panel, CD25 on Tregs also showed causality for GU, including activated CD4 regulatory T cells (OR: 1.1438, 95% CI: 1.0080–1.2980), secreting CD4 regulatory T cells (OR: 1.1060, 95% CI: 1.0257–1.1926), and CD39+ secreting CD4 regulatory T cells (OR: 1.0867, 95% CI: 1.0090–1.1704). Moreover, high CD80 and CD123 levels on plasmacytoid DCs prevented GU incidences. In the myeloid cell panel, only CD14 and CD45 were detected in MR analysis, where they displayed reverse roles. Additionally, SSC-A on CD14+ monocytes showed some protection against GU (OR: 0.9575, 95% CI: 0.9184–0.9984) (Figure 2A, B). To further identify correlations between immune cells and GU (Figure 2C–G), our meta-analysis results showed that B cells, myeloid cells, and Tregs were positively correlated with GU (OR: 1.0675, 95% CI: 1.0364–1.0995; OR: 1.0006, 95% CI: 0.9972–1.0040; and OR: 1.0367, 95% CI: 0.9737–1.1038, respectively). By contrast cDCs, and TBNKs were negatively correlated with GU (OR: 0.9254, 95% CI: 0.9014–0.9501 and OR: 0.9438, 95% CI: 0.9142–0.9745, respectively). Causal effects of peripheral immunity on DU For DU, null causation was identified using peripheral immune cell counts. However, cell surface antigens occurred in GU diseases. CD4 on HLA DR+ CD4+ T cells appeared to prevent ulcers in the duodenum (OR: 0.9989, 95% CI: 0.9979–0.9999), while other antigens identified by MR promoted GU incidence, including BAFF−R on IgD+ CD24+ B cells (OR: 1.0006, 95% CI: 1.0000–1.0013), BAFF−R on IgD−CD27− B cells (OR: 1.0008, 95% CI: 1.0000–1.0017), CD20 on IgD−CD38dim B cells (OR: 1.0008, 95% CI: 1.0001–1.0015), CD25 on IgD+ CD38−unswitched memory B cells (OR: 1.0007, 95% CI: 1.0001–1.0013), SSC−A on T cells (OR: 1.0024, 95% CI: 1.0008–1.0039), CD28 on CD39+ activated CD4 regulatory T cells (OR: 1.0008, 95% CI: 1.0001–1.0014), and CD8 on Terminally Differentiated CD8+T cells (OR: 1.0007, 95% CI: 1.0000–1.0013) (Figure 3). To further identify correlations between immune cells and DU (Figure 3C, D), our meta-analysis showed that B cells and TBNKs were positively correlated with atrial fibrillation (OR: 1.0007, 95% CI: 1.0004–1.0011; OR: 1.0006, 95% CI: 0.9972–1.0040). Causal relationships between peripheral immune cells and gastroduodenal diseases We further explored causal associations between peripheral immune cells and cardiovascular disease. Heatmap analyses demonstrated causal relationships between immune cells and disease (Figure 4A). Additionally, the meta-analysis showed a positive correlation between B cells, T cell maturation stages, and Tregs and the upper digestive system (OR: 1.0199, 95% CI: 0.9604–1.0611; OR: 1.0006, 95% CI: 0.9997–1.0015; and OR: 1.0008, 95% CI: 1.0003–1.0014, respectively). By contrast, CDCs, myeloid cells, and TBNKs were negatively correlated with the upper digestive system (OR: 0.9635, 95% CI: 0.8923–1.0403; OR: 0.9991, 95% CI: 0.9986–0.9997; and OR: 0.9740, 95% CI: 0.9199–1.0312, respectively). Discussion To the best of our knowledge, this is the first MR study to explore causal relationships between peripheral immunity and gastroduodenal diseases. This is important given that related findings are often controversial. Emerging evidence now shows that peripheral immunity is related to gastroduodenal lesions; innate and adaptive immunity alterations promote CG and PUD, which may change with age [17, 18]. However, it was previously postulated that patients with DU and CG had no natural killer cell and T and B lymphocyte alterations when compared to healthy individuals [19]. Furthermore, comprehensive analyses are lacking on the roles of more white blood cells in gastroduodenal diseases in larger populations. MR is an emerging method used to examine causal inference, which overcomes confounding biases inherent in observational studies. Validated MR results are based on three assumptions: i) Single nucleotide polymorphisms are significantly correlated with an exposure trait, ii) the variant does not affect an outcome trait via confounding factors, and iii) the variant does not affect the outcome directly, only possibly indirectly via the exposure. GWAS data in our study came from different ethnicities, confirming the robustness of our MR analysis. Additionally, GWAS data related to peripheral immunity better characterized a large population and comprehensive immune traits when compared with other studies. Current H. pylori infection research highlights the complex interactions between the pathogen, immune responses, and gastroduodenal disease development. Recent studies have expanded on neutrophils and macrophage roles, and the resulting damage to gastric epithelial cells. These studies emphasize the importance of immune responses in the pathogenesis of H. pylori-related gastric diseases. Liu et al. provided evidence for the therapeutic potential of targeting specific pathways (e.g., mTOR) to reduce inflammation and oxidative stress, while Chen et al. suggested a broader perspective for immune cell and mediator roles (e.g., NO) in gastric disease [20, 21]. Together, these studies underscore the complexity of H. pylori infection and the potential for novel therapeutic approaches that go beyond traditional treatments and focus solely on acid suppression or bacterial eradication. Our research contributes to the theoretical understanding of Mendelian genetics in disease pathogenesis, particularly within a gastroduodenal disease context. The concept of monogenic inborn errors of immunity, as discussed by Casanova, is particularly relevant and suggests that similar mechanisms may be at play in gastroduodenal diseases. Research by Su et al. and Attauabi et al. highlighted the importance of considering a wide range of genetic and environmental factors when studying immune responses in disease [22, 23]. This is particularly relevant to our work as it underscores the significance of genetic factors in modulating immune responses in specific diseases. This aligns with our findings, suggesting that a multifactorial approach is crucial for understanding the genetic basis of peripheral immunity in gastroduodenal diseases. As for adaptive immunity, naive CD4+T cells are induced to differentiate towards Th1, Th2, Th17, and Treg phenotypes according to the local cytokine milieu [24]. H. pylori can also induce Treg cell differentiation. Tregs suppress inflammatory responses by regulating Th1/Th17 immune responses, which cause bacterial overgrowth and ulcers [25]. One study reported that Tregs were positively associated with H. pylori but negatively associated with PUD. Th1 and Th17 cells are also reported to increase during H. pylori infection and peptic ulcers [10, 26]. Collectively, white blood cells have various roles in the dynamic balance between inflammation and immunity when gastroduodenal diseases occur. However, how peripheral immune cell counts and cell surface antigens change during CG and PUD remain to be fully elucidated. Our study had several limitations. GWAS data represented summary‐level analyses and therefore, our study lacked subgroup analyses based on H. pylori infections and disease severity. Additionally, we could not set limits on CG and PUD disease stages, thus ignoring time changing. Conclusion We explored causal relationships between peripheral immune cells and cardiovascular diseases, which provide new ideas and insights for disease occurrence and management. Declarations Data Availability Statement The datasets presented in this study can be found in online repositories. Ethics Statement This article is a public database, so no additional ethics are required. Author contributions All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work. Funding This work was supported by Funding from Major projects of the Changzhou Health Commission (ZD202107). Disclosures All authors confirm that the content of this article has no conflicts of interest. More information is available in the supplementary files. Acknowledgments None References Stanghellini, V., et al., Gastroduodenal Disorders. Gastroenterology, 2016. 150(6): p. 1380-92. 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Zhang, S., et al., Human immune responses to H. pylori HLA Class II epitopes identified by immunoinformatic methods. PLoS One, 2014. 9(4): p. e94974. Liu, J., et al., Everolimus ameliorates Helicobacter pylori infection-induced inflammation in gastric epithelial cells. Bioengineered, 2022. 13(5): p. 11361-11372. Chen, Y., et al., Predicting response to immunotherapy in gastric cancer via multi-dimensional analyses of the tumour immune microenvironment. Nat Commun, 2022. 13(1): p. 4851. Su, Z., et al., Causality between Peripheral Immune Cell Counts and Membranous Nephropathy: A Bidirectional Mendelian Randomization Study. 2023: p. 2023.11.27.23299065. Attauabi, M., et al., Influence of Genetics, Immunity and the Microbiome on the Prognosis of Inflammatory Bowel Disease (IBD Prognosis Study): the protocol for a Copenhagen IBD Inception Cohort Study. 2022. 12(6): p. e055779. Zhu, J. and W.E. Paul, Peripheral CD4+ T-cell differentiation regulated by networks of cytokines and transcription factors. Immunol Rev, 2010. 238(1): p. 247-62. Bagheri, N., et al., Role of Regulatory T-cells in Different Clinical Expressions of Helicobacter pylori Infection. Arch Med Res, 2016. 47(4): p. 245-54. Bagheri, N., et al., Up-regulated Th17 cell function is associated with increased peptic ulcer disease in Helicobacter pylori-infection. Infect Genet Evol, 2018. 60: p. 117-125. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.xlsx Supplementary Table 1. Heterogeneity test. SupplementaryMaterial.xlsx Supplementary Table 2. Pleiotropy test. 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. 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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-6612467","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":456513633,"identity":"85af30e6-60bd-4535-899c-1861c08d54a7","order_by":0,"name":"Ke Xie","email":"","orcid":"","institution":"The Third Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Ke","middleName":"","lastName":"Xie","suffix":""},{"id":456513634,"identity":"54f0d9c8-8601-4949-b101-a0a998128d2c","order_by":1,"name":"Ling Li","email":"","orcid":"","institution":"The Third Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Ling","middleName":"","lastName":"Li","suffix":""},{"id":456513635,"identity":"ae9766f5-86cd-4d06-8b69-f1ea2b25f4e0","order_by":2,"name":"Ding Zhang","email":"","orcid":"","institution":"The Third Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Ding","middleName":"","lastName":"Zhang","suffix":""},{"id":456513636,"identity":"f22d7a55-d2cf-4d5a-988f-8e68d14dc288","order_by":3,"name":"Jing Li","email":"","orcid":"","institution":"The Third Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Li","suffix":""},{"id":456513637,"identity":"b5b22ea8-c0d4-4510-a703-99d98def4359","order_by":4,"name":"Jin Feng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABA0lEQVRIiWNgGAWjYBAC9gYgIWHAwMPP3pD4gIGBGSRogFcLzwGIFhnJngOPDYjXAgQ2BjcSn0kQp4W99/ALi4I7PAZnDqdV/myzlmdgb94mwVBzB7cWnnNpFhIGz3gkj7el3eZtSzds4DlWJsFw7BlOLfYSOWYGEgaHefjOnEm7zdh2mLEBKCLB2HAYty3ybyBaGG7kfyv82XbYvgEogl+LBI/xA5AWgRsJaQy8bYcTGyR4CGjhyTFjAGkBBnKyNM+59OQ2nrRii4RjeLSwnzH+LPHnsD0oKj/+KLO27Wc/vPHGhxrcWoCATVoChQsiEvBpAMbexw/4FYyCUTAKRsFIBwDP71O/qRtFqQAAAABJRU5ErkJggg==","orcid":"","institution":"The Third Affiliated Hospital of Soochow University","correspondingAuthor":true,"prefix":"","firstName":"Jin","middleName":"","lastName":"Feng","suffix":""}],"badges":[],"createdAt":"2025-05-07 13:38:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6612467/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6612467/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82870374,"identity":"cb500602-7385-4c94-acc0-669e8cf450df","added_by":"auto","created_at":"2025-05-16 08:42:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":193029,"visible":true,"origin":"","legend":"\u003cp\u003eMR analysis of peripheral immune cells and CG. (A and B) MR analysis results. (C) Meta-analysis of the B cell subgroup. (D) Meta-analysis of T cell maturation subgroup stages. (E) Meta-analysis of the monocyte cell subgroup.\u003c/p\u003e","description":"","filename":"Slide1.png","url":"https://assets-eu.researchsquare.com/files/rs-6612467/v1/b9bb2a3001efc9fd17b29b90.png"},{"id":82870376,"identity":"89f875d9-c407-4369-a59d-ecea80f810ac","added_by":"auto","created_at":"2025-05-16 08:42:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":270660,"visible":true,"origin":"","legend":"\u003cp\u003eMR analysis of peripheral immune cells and GU. (A and B). MR analysis results. (C) Meta-analysis of the B cell subgroup. (D) Meta-analysis of the cDC subgroup. (E) Meta-analysis of the myeloid cell subgroup. (F) Meta-analysis of the TBNK cell subgroup. (G) Meta-analysis of the Treg cell subgroup.\u003c/p\u003e","description":"","filename":"Slide2.png","url":"https://assets-eu.researchsquare.com/files/rs-6612467/v1/250d1f9df65c386f2e464c22.png"},{"id":82870378,"identity":"cce0c6d1-1483-44e8-9d5e-5020da3ede15","added_by":"auto","created_at":"2025-05-16 08:42:47","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":138610,"visible":true,"origin":"","legend":"\u003cp\u003eMR analysis of peripheral immune cells and DU. (A and B). MR analysis results. (C) Meta-analysis of the B cell subgroup. (D) Meta-analysis of the TBNK cell subgroup.\u003c/p\u003e","description":"","filename":"Slide3.png","url":"https://assets-eu.researchsquare.com/files/rs-6612467/v1/cdc6581993c65bed7320a06f.png"},{"id":82870377,"identity":"b53241ef-6a95-463e-9707-856a94f0513c","added_by":"auto","created_at":"2025-05-16 08:42:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":388015,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation analysis of peripheral immune cells and gastroduodenal diseases. (A) Heatmap showing peripheral immune cells and six gastroduodenal disease types. (B) Meta-analysis of associations between B cells and six cardiovascular disease types. (C) Meta-analysis of associations between cDCs and gastroduodenal diseases. (D) Meta-analysis of associations between T cell maturation stages and gastroduodenal diseases. (E) Meta-analysis of associations between myeloid cells and gastroduodenal diseases. (F) Meta-analysis of associations between TBNK cells and gastroduodenal diseases. (G) Meta-analysis of associations between Tregs and gastroduodenal diseases.\u003c/p\u003e","description":"","filename":"Slide4.png","url":"https://assets-eu.researchsquare.com/files/rs-6612467/v1/67e47d1dbbd70b52c4fd6d4b.png"},{"id":86128044,"identity":"2985486a-f24e-4c76-97fd-2f24abf37a4c","added_by":"auto","created_at":"2025-07-07 06:02:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1180029,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6612467/v1/925862de-a215-4a82-99f3-ba77941f10ca.pdf"},{"id":82870693,"identity":"2ff26d7c-fe68-434a-843f-d14a676cdef3","added_by":"auto","created_at":"2025-05-16 08:50:47","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19815,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table 1. Heterogeneity test.\u003c/p\u003e","description":"","filename":"SupplementaryMaterial.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6612467/v1/b67e1aca0d676ff745ed8951.xlsx"},{"id":82870381,"identity":"214962f0-4864-4da2-8369-fcdaba5135d9","added_by":"auto","created_at":"2025-05-16 08:42:47","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":19815,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Table 2. Pleiotropy test.\u003c/p\u003e","description":"","filename":"SupplementaryMaterial.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6612467/v1/25d668b67e5260e9e4e378f8.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Causal role of peripheral immunity in gastroduodenal diseases: A Mendelian randomization study and meta-analysis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eApproximately one-fifth of the population experience ongoing symptoms associated with disturbed functional gastroduodenal regions, which include the stomach and the beginning of the small intestine [1]. These symptoms, which can significantly impact quality of life, often manifest without any detectable physical or organic abnormalities [2]. The conditions can range from acute to chronic and vary in severity. In this study, we focused on two common diseases, including chronic gastritis (CG) and peptic ulcer disease (PUD). CG manifests as persistent stomach wall inflammation, characterized by perennial afflictions that may generate grave complication-like peptic ulceration and perforation [3]. PUD relates to gastrointestinal wall rupture which extends to the submucosa and mainly localizes to the stomach and duodenum. The prevalence of PUD is estimated at 0.1%\u0026ndash;0.19% in the general population and is higher among Asians [4].\u003c/p\u003e\n\u003cp\u003eAlthough CG and PUD etiologies have not yet been clearly elucidated, Helicobacter pylori infection, hypersecretory gastric acid, and widespread nonsteroidal anti-inflammatory drug use are the main underlying causes of pathogenesis [5, 6]. However, for those individuals with H. pylori infections, most are asymptomatic and the lifetime PUD prevalence is approximately 10% [7]. The risk of gastroenterological disease after H. pylori infection is associated with systemic immune responses and genetic predisposition [8]. H. pylori infection induces an immunological cascade and associated inflammation, recruiting host immune system cells such as dendritic cells (DCs), macrophages, neutrophils, and lymphocytes, but severity and distribution vary widely between individuals. Peripheral immunity has been shown to corelate with H. pylori infection and gastric diseases [9-11]. Bagheri et al. [12] reported that CD4+ T, T-bet+, and Th17 cells were increased in individuals with H. pylori, but remarkably higher in those with CG and PUD. Additionally, a notable diminution of regulatory B cell populations was reported in patients infected with H. pylori, suggesting potential alterations in the immunoregulatory mechanisms of these cells with host immune responses [11, 13].\u003c/p\u003e\n\u003cp\u003eHowever, results from epidemiological studies are often prone to confounder bias and reverse causation, while comprehensive large population studies on white cells in CG and PUD are lacking. Therefore, we performed two-sample Mendelian Randomization (MR) analysis to explore causal relationships between peripheral immunity, CG, and PUD, which is an emerging method of causal inference as distribution of genetic variants is analogues to random considering confounders.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eGenome-wide association study (GWAS) summary data of exposure and outcomes\u003c/p\u003e\n\u003cp\u003eGenetic exposure data came from a 3757-strong Sardinian cohort, with almost three times the number of immune cell traits and double the number of individuals to that previously studied [14]. Immune traits were analyzed by flow cytometry, including 118 absolute cell counts (ACs), 389 median fluorescence intensities (MFIs) of surface antigens, and 32 morphological parameters [14].\u003c/p\u003e\n\u003cp\u003eGastric ulcer disease summary data were obtained from Biobank Japan and consisted of 79000 participants where phenotypes were defined by incorporating past medical histories and text-mining electronic medical records according to International Classification of Disease-10 code [15]. A CG and duodenal ulcer genetic association dataset was derived from a large European ethnicity meta-analysis from IEU OpenGWAS (ukb-b-6716 and ukb-b-4725) [16].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 Brief description of GWAS data included in MR.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eExposure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eGWAS id\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003ePopulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eSample size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003ePeripheral immune cell counts\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eebi-a-GCST90001391 to ebi-a-GCST90002121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eSardinian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e3757\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eChronic gastritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eukb-b-6716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e463010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eGastric ulcer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eebi-a-GCST90018851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eJapan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e79000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eDuodenal ulcer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eukb-b-4725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eEuropean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e462933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analysis\u003c/p\u003e\n\u003cp\u003eGenetic variants were selected as IVs if they reached the GWAS p-value \u0026lt; 1 \u0026times; 10\u0026minus;6, and were further clumped based on linkage disequilibrium (r2 = 0.01) and the genomic region (clump window, 1,000 kilobases). Inverse variance weighted (IVW), MR-Egger, weighted median, simple mode, and weighted mode approaches are the major MR analysis methods used to confirm robust results, with IVW used as the major random\u0026ndash;effect model.\u003c/p\u003e\n\u003cp\u003eSensitivity analyses are pivotal in MR studies and provide robust causal inferences under weak assumptions. We used Cochran Q tests to assess heterogeneity. In addition, MR-Egger interception tests were used to identify and control bias due to horizontal pleiotropy, whereas Steiger directional tests were used to assess reverse causality.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;All analyses were performed using R statistical software (Version 4.3.1) with TwoSampleMR and forestploter (https://github.com/adayim/forestploter) packages.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe causal effects of peripheral immunity on CG\u003c/p\u003e\n\u003cp\u003eCausal immunity effects on CG risk are summarized (Figure 1). We observed that only CD33dim HLA DR+ CD11b\u0026minus; cells (myeloid cell panel) showed negative correlations with CG when we focused on absolute peripheral cell counts (odds ratio [OR]: 0.9991, 95% confidence interval [CI]: 0.9985\u0026ndash;0.9997). But, MR-Egger and weighted median analyses showed a null causation. Additionally, terminally differentiated CD4\u0026minus;CD8\u0026minus;T cell %T cells (OR: 0.9993, 95% CI: 0.9987\u0026ndash;0.9999) were negatively related to CG. However, CD127\u0026minus;CD8+T cell %T cells (OR: 1.0009, 95% CI: 1.0000\u0026ndash;1.0018) and effector memory CD4+ T cell %T cells (OR: 1.0013, 95% CI: 1.0002\u0026ndash;1.0024) were positively related to CG.\u003c/p\u003e\n\u003cp\u003eNext, we explored causal cell surface antigen risk estimates on CG using MFIs. In a B cell panel, the results suggested that an increase of CD20, an atypical antigen expressed only on mature B cells, was associated with higher CG risk, including IgD\u0026minus;CD38\u0026minus;B cells (OR: 1.0007, 95% CI: 1.0000\u0026ndash;1.0015), naive\u0026minus;mature B cells (OR: 1.0006, 95% CI: 1.0000\u0026ndash;1.0013), switched memory B cells (OR: 1.0006, 95% CI: 1.0001\u0026ndash;1.0013), and IgD+ B cells (OR: 1.0006, 95% CI: 1.0001\u0026ndash;1.0012). In the monocyte panel, CD40 levels significantly affected CG incidence (OR: 1.0003, 95% CI: 1.0000\u0026ndash;1.0006), containing CD14+CD16\u0026minus;monocytes (OR: 1.0003, 95% CI: 1.0000\u0026ndash;1.0006) and CD14\u0026minus;CD16+ monocytes (OR: 1.0003, 95% CI: 1.0000\u0026ndash;1.0006). High CD64 levels on monocytes also increased CG risk (OR: 1.0006, 95% CI: 1.0001\u0026ndash;1.0011). Moreover, the OR for CD11c\u0026rsquo;s on granulocytes was 1.0008 (95% CI: 1.0002\u0026ndash;1.0015), indicating a CG risk factor (Figure 1A, B). Heterogeneity, pleiotropy tests above showed no significance, indicating the results were robust. The Steiger method indicated no significant reverse causation.\u003c/p\u003e\n\u003cp\u003eTo further reveal correlations between immune cells and CG (Figure 1C\u0026ndash;E), our meta-analysis showed that B cells, T cell maturation stages, and monocytes were positively correlated with CG (OR: 1.0007, 95% CI: 1.0003\u0026ndash;1.0010; OR: 1.0002, 95% CI: 0.9984\u0026ndash;1.0021; OR: and 1.0003, 95% CI: 1.0002\u0026ndash;1.0005, respectively).\u003c/p\u003e\n\u003cp\u003eCausal effects of peripheral immunity on GU\u003c/p\u003e\n\u003cp\u003eMR analyses revealed that higher leukocyte cell counts decreased the risk of GU (OR: 0.9309, 95% CI: 0.8745\u0026ndash;0.9908). In the T cell panel, CD4+T cell counts were negatively associated with GU (OR: 0.9147 95% CI: 0.8429\u0026ndash;0.9925), however, the mature form, terminally differentiated CD4+T cells, were identified as a positive risk factor (OR: 1.0647, 95% CI: 1.0046\u0026ndash;1.1284). Treg affect stomach ulcer crucially. High CD28-CD8dim T cell ACs may protect against ulcer diseases (OR: 0.9073 95% CI: 0.8234\u0026ndash;0.9997). Also, resting CD4 regulatory T cell %CD4+ T cells (OR: 1.0326, 95% CI: 1.0001\u0026ndash;1.0660) and CD39+ secreting CD4 regulatory T cell %CD4 regulatory T cells (OR: 1.0273, 95% CI: 1.0013\u0026ndash;1.0541) were identified as promoters. In the B cell panel, high IgD\u0026minus;CD38dim B cell %lymphocytes (OR: 1.0799, 95% CI: 1.0197\u0026ndash;1.1436) increased the risk of GU. With respect to other cell panels, granulocytic myeloid-derived suppressor cells with potent immunosuppressive activity were shown to promote GU (OR: 1.0713, 95% CI: 1.0041\u0026ndash;1.1430), and CD86+ plasmacytoid DCs decreased the risk of GU (OR: 0.8739, 95% CI: 0.7993\u0026ndash;0.9554). Causal immunity estimates on CG risk are summarized (Figure 2A, B).\u003c/p\u003e\n\u003cp\u003eCell surface antigen levels were also analyzed. Similar to CG results, CD20 expression on IgD+ CD24+ B cells (OR: 1.0886, 95% CI: 1.0082\u0026ndash;1.1755), IgD\u0026minus; CD38dim B cells (OR: 1.0649, 95% CI: 1.0177\u0026ndash;1.1143), and unswitched memory B cells (OR: 1.0900, 95% CI: 1.0055\u0026ndash;1.1817) had causal effects on GU. However, CD19 effects on naive\u0026minus;mature B cells showed some protected from ulcers (OR: 0.8480, 95% CI: 0.7216\u0026ndash;0.9966). In the T cell panel, CD25 on Tregs also showed causality for GU, including activated CD4 regulatory T cells (OR: 1.1438, 95% CI: 1.0080\u0026ndash;1.2980), secreting CD4 regulatory T cells (OR: 1.1060, 95% CI: 1.0257\u0026ndash;1.1926), and CD39+ secreting CD4 regulatory T cells (OR: 1.0867, 95% CI: 1.0090\u0026ndash;1.1704). Moreover, high CD80 and CD123 levels on plasmacytoid DCs prevented GU incidences. In the myeloid cell panel, only CD14 and CD45 were detected in MR analysis, where they displayed reverse roles. Additionally, SSC-A on CD14+ monocytes showed some protection against GU (OR: 0.9575, 95% CI: 0.9184\u0026ndash;0.9984) (Figure 2A, B).\u003c/p\u003e\n\u003cp\u003eTo further identify correlations between immune cells and GU (Figure 2C\u0026ndash;G), our meta-analysis results showed that B cells, myeloid cells, and Tregs were positively correlated with GU (OR: 1.0675, 95% CI: 1.0364\u0026ndash;1.0995; OR: 1.0006, 95% CI: 0.9972\u0026ndash;1.0040; and OR: 1.0367, 95% CI: 0.9737\u0026ndash;1.1038, respectively). By contrast cDCs, and TBNKs were negatively correlated with GU (OR: 0.9254, 95% CI: 0.9014\u0026ndash;0.9501 and OR: 0.9438, 95% CI: 0.9142\u0026ndash;0.9745, respectively).\u003c/p\u003e\n\u003cp\u003eCausal effects of peripheral immunity on DU\u003c/p\u003e\n\u003cp\u003eFor DU, null causation was identified using peripheral immune cell counts. However, cell surface antigens occurred in GU diseases. CD4 on HLA DR+ CD4+ T cells appeared to prevent ulcers in the duodenum (OR: 0.9989, 95% CI: 0.9979\u0026ndash;0.9999), while other antigens identified by MR promoted GU incidence, including BAFF\u0026minus;R on IgD+ CD24+ B cells (OR: 1.0006, 95% CI: 1.0000\u0026ndash;1.0013), BAFF\u0026minus;R on IgD\u0026minus;CD27\u0026minus; B cells (OR: 1.0008, 95% CI: 1.0000\u0026ndash;1.0017), CD20 on IgD\u0026minus;CD38dim B cells (OR: 1.0008, 95% CI: 1.0001\u0026ndash;1.0015), CD25 on IgD+ CD38\u0026minus;unswitched memory B cells (OR: 1.0007, 95% CI: 1.0001\u0026ndash;1.0013), SSC\u0026minus;A on T cells (OR: 1.0024, 95% CI: 1.0008\u0026ndash;1.0039), CD28 on CD39+ activated CD4 regulatory T cells (OR: 1.0008, 95% CI: 1.0001\u0026ndash;1.0014), and CD8 on Terminally Differentiated CD8+T cells (OR: 1.0007, 95% CI: 1.0000\u0026ndash;1.0013) (Figure 3).\u003c/p\u003e\n\u003cp\u003eTo further identify correlations between immune cells and DU (Figure 3C, D), our meta-analysis showed that B cells and TBNKs were positively correlated with atrial fibrillation (OR: 1.0007, 95% CI: 1.0004\u0026ndash;1.0011; OR: 1.0006, 95% CI: 0.9972\u0026ndash;1.0040).\u003c/p\u003e\n\u003cp\u003eCausal relationships between peripheral immune cells and gastroduodenal diseases\u003c/p\u003e\n\u003cp\u003eWe further explored causal associations between peripheral immune cells and cardiovascular disease. Heatmap analyses demonstrated causal relationships between immune cells and disease (Figure 4A). Additionally, the meta-analysis showed a positive correlation between B cells, T cell maturation stages, and Tregs and the upper digestive system (OR: 1.0199, 95% CI: 0.9604\u0026ndash;1.0611; OR: 1.0006, 95% CI: 0.9997\u0026ndash;1.0015; and OR: 1.0008, 95% CI: 1.0003\u0026ndash;1.0014, respectively). By contrast, CDCs, myeloid cells, and TBNKs were negatively correlated with the upper digestive system (OR: 0.9635, 95% CI: 0.8923\u0026ndash;1.0403; OR: 0.9991, 95% CI: 0.9986\u0026ndash;0.9997; and OR: 0.9740, 95% CI: 0.9199\u0026ndash;1.0312, respectively).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo the best of our knowledge, this is the first MR study to explore causal relationships between peripheral immunity and gastroduodenal diseases. This is important given that related findings are often controversial. Emerging evidence now shows that peripheral immunity is related to gastroduodenal lesions; innate and adaptive immunity alterations promote CG and PUD, which may change with age [17, 18]. However, it was previously postulated that patients with DU and CG had no natural killer cell and T and B lymphocyte alterations when compared to healthy individuals [19]. Furthermore, comprehensive analyses are lacking on the roles of more white blood cells in gastroduodenal diseases in larger populations.\u003c/p\u003e\n\u003cp\u003eMR is an emerging method used to examine causal inference, which overcomes confounding biases inherent in observational studies. Validated MR results are based on three assumptions: i) Single nucleotide polymorphisms are significantly correlated with an exposure trait, ii) the variant does not affect an outcome trait via confounding factors, and iii) the variant does not affect the outcome directly, only possibly indirectly via the exposure. GWAS data in our study came from different ethnicities, confirming the robustness of our MR analysis. Additionally, GWAS data related to peripheral immunity better characterized a large population and comprehensive immune traits when compared with other studies.\u003c/p\u003e\n\u003cp\u003eCurrent H. pylori infection research highlights the complex interactions between the pathogen, immune responses, and gastroduodenal disease development. Recent studies have expanded on neutrophils and macrophage roles, and the resulting damage to gastric epithelial cells. These studies emphasize the importance of immune responses in the pathogenesis of H. pylori-related gastric diseases. Liu et al. provided evidence for the therapeutic potential of targeting specific pathways (e.g., mTOR) to reduce inflammation and oxidative stress, while Chen et al. suggested a broader perspective for immune cell and mediator roles (e.g., NO) in gastric disease [20, 21]. Together, these studies underscore the complexity of H. pylori infection and the potential for novel therapeutic approaches that go beyond traditional treatments and focus solely on acid suppression or bacterial eradication.\u003c/p\u003e\n\u003cp\u003eOur research contributes to the theoretical understanding of Mendelian genetics in disease pathogenesis, particularly within a gastroduodenal disease context. The concept of monogenic inborn errors of immunity, as discussed by Casanova, is particularly relevant and suggests that similar mechanisms may be at play in gastroduodenal diseases.\u003c/p\u003e\n\u003cp\u003eResearch by Su et al. and Attauabi et al. highlighted the importance of considering a wide range of genetic and environmental factors when studying immune responses in disease [22, 23]. This is particularly relevant to our work as it underscores the significance of genetic factors in modulating immune responses in specific diseases. This aligns with our findings, suggesting that a multifactorial approach is crucial for understanding the genetic basis of peripheral immunity in gastroduodenal diseases. As for adaptive immunity, naive CD4+T cells are induced to differentiate towards Th1, Th2, Th17, and Treg phenotypes according to the local cytokine milieu [24]. H. pylori can also induce Treg cell differentiation. Tregs suppress inflammatory responses by regulating Th1/Th17 immune responses, which cause bacterial overgrowth and ulcers [25]. One study reported that Tregs were positively associated with H. pylori but negatively associated with PUD. Th1 and Th17 cells are also reported to increase during H. pylori infection and peptic ulcers [10, 26]. Collectively, white blood cells have various roles in the dynamic balance between inflammation and immunity when gastroduodenal diseases occur. However, how peripheral immune cell counts and cell surface antigens change during CG and PUD remain to be fully elucidated.\u003c/p\u003e\n\u003cp\u003eOur study had several limitations. GWAS data represented summary‐level analyses and therefore, our study lacked subgroup analyses based on H. pylori infections and disease severity. Additionally, we could not set limits on CG and PUD disease stages, thus ignoring time changing.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe explored causal relationships between peripheral immune cells and cardiovascular diseases, which provide new ideas and insights for disease occurrence and management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets presented in this study can be found in online repositories.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article is a public database, so no additional ethics are required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Funding from Major projects of the Changzhou Health Commission (ZD202107).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors confirm that the content of this article has no conflicts of interest. More information is available in the supplementary files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eStanghellini, V., et al., Gastroduodenal Disorders. Gastroenterology, 2016. 150(6): p. 1380-92.\u003c/li\u003e\n\u003cli\u003eZuzek, R., et al., Prevalence of Histological Gastritis in a Community Population and Association with Epigastric Pain. Dig Dis Sci, 2023.\u003c/li\u003e\n\u003cli\u003eMassironi, S., et al., The changing face of chronic autoimmune atrophic gastritis: an updated comprehensive perspective. Autoimmun Rev, 2019. 18(3): p. 215-222.\u003c/li\u003e\n\u003cli\u003eWang, R., et al., Global, regional, and national burden of 10 digestive diseases in 204 countries and territories from 1990 to 2019. 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Paul, Peripheral CD4+ T-cell differentiation regulated by networks of cytokines and transcription factors. Immunol Rev, 2010. 238(1): p. 247-62.\u003c/li\u003e\n\u003cli\u003eBagheri, N., et al., Role of Regulatory T-cells in Different Clinical Expressions of Helicobacter pylori Infection. Arch Med Res, 2016. 47(4): p. 245-54.\u003c/li\u003e\n\u003cli\u003eBagheri, N., et al., Up-regulated Th17 cell function is associated with increased peptic ulcer disease in Helicobacter pylori-infection. Infect Genet Evol, 2018. 60: p. 117-125.\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":"gastroduodenal diseases, immunity, Mendelian Randomization, meta-analysis","lastPublishedDoi":"10.21203/rs.3.rs-6612467/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6612467/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Peripheral immune cells have important roles in upper digestive system diseases. In this meta-analysis, we explored causal relationships between three upper digestive system diseases and peripheral immune cells.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: Genetic summary statistics were collected from an open genome-wide association study database. Causal relationships between seven peripheral immune cell types and gastric ulcer, duodenal ulcer, and chronic gastritis conditions were evaluated using double-sample bidirectional Mendelian Randomization. Cochran's Q and Mendelian random-Egger regression tests were used to evaluate heterogeneity and pleiotropy. A meta-analysis improved the statistical efficiency of our results.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: We observed a positive correlation between B cells, T cell maturation stages, and Tregs with the upper digestive system (odds ratio [OR]:1.0199, 95% confidence interval [CI]: 0.9604–1.0611; OR: 1.0006, 95% CI: 0.9997–1.0015; and OR: 1.0008, 95% CI: 1.0003–1.0014, respectively). By contrast cDCs, myeloid cells, and TBNKs were negatively correlated with the upper digestive system (OR: 0.9635, 95% CI: 0.8923–1.0403; OR: 0.9991, 95% CI: 0.9986–0.9997; and OR: 0.9740, 95% CI: 0.9199–1.0312, respectively).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: We observed cause-and-effect relationships between genetically predicted peripheral immune cells and upper digestive diseases. These findings suggest peripheral immune cell monitoring and improved guidelines for the risk management of upper digestive diseases.\u003c/p\u003e","manuscriptTitle":"Causal role of peripheral immunity in gastroduodenal diseases: A Mendelian randomization study and meta-analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-16 08:42:42","doi":"10.21203/rs.3.rs-6612467/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":"2e94c065-a9c9-485a-b04e-e4ff643c7f85","owner":[],"postedDate":"May 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-07-07T05:53:55+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-16 08:42:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6612467","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6612467","identity":"rs-6612467","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}