Impact of Helicobacter pylori CagA on E-selectin Levels in Chronic Alcohol Consumers: A Cross-Sectional Study

Impact of Helicobacter pylori CagA on E-selectin Levels in Chronic Alcohol Consumers: A Cross-Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Impact of Helicobacter pylori CagA on E-selectin Levels in Chronic Alcohol Consumers: A Cross-Sectional Study Rui Guo, Wanxia Wang, Jing Jia, Chaojun Wei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5359270/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 Helicobacter pylori (H. pylori) infection is a major pathogen causing chronic gastritis, peptic ulcers, and gastric cancer. Its major virulence factor CagA and the endothelial adhesion molecule E-selectin play crucial roles in the development of gastrointestinal diseases. This study aimed to investigate the relationship between CagA levels and E-selectin levels in chronic alcohol consumers. Methods This cross-sectional study enrolled 112 chronic alcohol consumers. The exposure variable was CagA level, and the outcome variable was E-selectin level. Covariates included age, BMI, alcohol consumption history, daily alcohol intake, oxidative stress markers (MDA, SOD), and inflammatory factors (TNF, IL-10). Multivariable linear regression and piecewise linear regression were used to analyze the relationship between CagA and E-selectin, with subgroup analysis. Results CagA levels differed significantly across the high, middle, and low tertiles. CagA levels exhibited a nonlinear relationship with E-selectin levels, with a turning point at 140.8 CagA units, where the effect of CagA on E-selectin changed from positive to negative. Further stratified analysis revealed that in the high alcohol consumption group, CagA levels above 167.8 units had a significantly negative impact on E-selectin. Conclusion In chronic alcohol consumers, CagA levels and E-selectin levels have a complex nonlinear relationship, which is modulated by alcohol consumption history. CagA and E-selectin may serve as potential biomarkers for the prevention and management of related gastrointestinal diseases. Further multi-center prospective studies are needed to validate these findings. CagA E-selectin Helicobacter pylori Alcohol consumption Chronic disease Figures Figure 1 Figure 2 Figure 3 1. Introduction Helicobacter pylori (H. pylori) is a Gram-negative, spiral-shaped bacterium that colonizes the human gastric mucosa and is closely associated with chronic gastritis, peptic ulcers, and gastric cancer [ 1 , 2 ]. One of its major virulence factors, the cytotoxin-associated gene A (CagA), has been linked to increased pathogenicity[ 3 , 4 ]. CagA-positive strains can disrupt host cellular signaling pathways, promote abnormal cell proliferation, and inhibit apoptosis, playing a crucial role in the development of gastric cancer[ 5 , 6 ] .Therefore, the presence of CagA not only affects the pathogenicity of H. pylori but also provides important clues for the study of related diseases. E-selectin is closely related to H. pylori infection and plays a critical role in the pathogenesis of gastrointestinal diseases[ 7 , 8 ]. As an endothelial adhesion molecule, E-selectin is expressed on the surface of activated endothelial cells in response to pro-inflammatory cytokines triggered by H. pylori infection, such as interleukin-1 (IL-1) and tumor necrosis factor-alpha (TNF-α) [ 9 ].E-selectin not only mediates the adhesion and migration of leukocytes to sites of inflammation[ 10 ], but also directly activates macrophages and endothelial cells, inducing the release of inflammatory factors and chemokines, thereby exacerbating inflammatory responses and promoting tumor progression[ 11 ] .Consequently, the expression of E-selectin is closely related to the severity of gastrointestinal diseases caused by H. pylori infection, particularly in the context of chronic inflammation and tumor microenvironments. In chronic alcohol consumers, studies on CagA indicate that chronic drinking may lead to an increased prevalence of CagA-positive H. pylori strains, which is associated with the immunosuppressive effects of alcohol, potentially allowing CagA-positive strains to colonize more easily and cause more severe gastrointestinal diseases[ 12 ]. Concurrently, studies on E-selectin in chronic drinkers have also demonstrated that alcohol consumption may elevate E-selectin expression, thereby exacerbating inflammatory responses and promoting the progression of related diseases[ 13 ]. These findings suggest a potential association between CagA and E-selectin, particularly in chronic drinkers, where their interaction may intensify gastric inflammation and pathological changes. However, the relationship between H. pylori CagA and E-selectin levels in chronic drinkers remains inadequately studied, with existing research findings being inconsistent. Therefore, it is crucial to explore this relationship further. The purpose of this study is to investigate the association between H. pylori CagA and E-selectin levels in chronic alcohol consumers through a cross-sectional study design, aiming to reveal their roles in the pathogenesis of gastrointestinal diseases. This research may provide new insights and potential biomarkers for the prevention and treatment of related diseases 2. Methods 2.1 Data Source Data were sourced from the DATADRYAD website (www.datadryad.org), allowing users to freely access the original data. According to Dryad's terms of service, the data package should be cited as: Qu, Baoge et al. (2016), Data from: Effect of H. pylori infection on cytokine profiles and oxidative balance in subjects with chronic alcohol ingestion, Dataset：https://doi.org/10.5061/dryad.45ds3. 2.2 Study Population The study by Qu, Baoge, was a cross-sectional analysis conducted at Taishan Hospital in Shandong Province, China, from January 2012 to December 2013, involving 112 chronic alcohol consumers aged 30 to 60 years. Participants were recruited from routine health examinations and primary care services, with inclusion criteria comprising chronic alcohol consumption (daily intake >40g for men, >20g for women for over 5 years), known H. pylori infection status (either positive or negative), and age-matched controls without chronic alcohol consumption or H. pylori infection. Exclusion criteria included smoking, fever, infectious diseases, primary or secondary gastrointestinal diseases, liver and gallbladder diseases, cardiovascular, endocrine, neurological, renal, or hematological disorders, electrolyte and acid-base imbalances, and mental health disorders( see detailed flowchart in Fig.1). 2.3 Variables The exposure variable in this study was the presence of the CagA virulence factor of H. pylori. Fasting venous blood samples were collected at enrollment, and CagA antibodies were measured using enzyme-linked immunosorbent assay (ELISA) kits according to the manufacturer's instructions. The outcome variable was the serum level of E-selectin, measured using high-sensitivity human E-selectin ELISA kits, with blood samples collected after an overnight fast of at least 10 hours and processed according to the manufacturer's protocol, expressed in ng/mL. Laboratory personnel conducting the assays were blinded to participants' exposure status and clinical information to minimize measurement bias. Covariates included age, body mass index (BMI), duration of alcohol consumption, and daily alcohol intake, with age obtained via questionnaires. BMI was categorized based on standard classifications, and the duration of alcohol consumption was categorized based on the length of time, specifically divided into short-term and long-term drinking. Daily alcohol intake was based on self-reported consumption in grams per day. These covariates were selected for their potential confounding effects on E-selectin levels and H. pylori infection status. In statistical analyses, age was treated as a continuous variable, while BMI, duration of alcohol consumption, and daily intake were categorized based on clinical relevance and data distribution. For missing data, complete case analysis was performed if the proportion of missing values was less than 5%; otherwise, multiple imputation using the chained equations method was conducted to reduce bias associated with missingness. 2.4 Ethical Approval In the initial study, the authors indicated that the research was approved by the Clinical Research Ethics Committee of Taishan Hospital in Shandong Province and adhered to the principles outlined in the Declaration of Helsinki. Given that the database utilized for this study was publicly accessible, participant identities were anonymized, and the information was retrieved retrospectively. As a result, informed consent was not deemed necessary, as reported in other studies. 2.5 Statistical Analysis Descriptive statistics were used to summarize the baseline characteristics across the CAGA tertiles. Between-group comparisons were conducted using one-way ANOVA or chi-square tests, as appropriate. The relationship between CagA and E-selectin was explored through smoothing curve fitting and generalized additive models (GAM). Multiple linear regression models, adjusted for potential confounders, were employed to assess the association between CagA and E-selectin. Piecewise linear regression was utilized to identify potential threshold effects, with model comparisons conducted via likelihood ratio tests. A stratified curve fitting analysis examined the relationship across different levels of alcohol consumption. All analyses were performed using EmpowerStats software (www.empowerstats.com, X Mind, Inc., Boston, MA), with statistical significance set at p < 0.05. 3. Results 3.1 Description of the study groups In this study, a total of 142 participants were initially recruited, with 112 ultimately enrolled after screening. The baseline characteristics and key findings are delineated according to CagA tertiles (Low, Middle, High). A comprehensive summary of the baseline characteristics of the study population, stratified by CagA tertiles, is presented in Table 1. The mean ages of participants were consistent across the tertiles, and no significant differences were observed in BMI distribution or alcohol consumption history among the groups. Importantly, the analysis revealed significant associations between CagA levels and various parameters, including alcohol consumption, oxidative stress, and inflammatory markers. Daily alcohol intake exhibited a progressive increase across the tertiles. Additionally, oxidative stress markers, specifically malondialdehyde (MDA) and superoxide dismutase (SOD) levels, demonstrated significant elevation with increasing CagA levels. Inflammatory markers, such as tumor necrosis factor (TNF) and interleukin-10 (IL-10), also showed significant increases. Eelection levels were significantly higher in the high CagA tertile, with the following values recorded: Low CagA: 27.40 ± 16.91; Middle CagA: 25.69 ± 10.47; High CagA: 61.72 ± 40.09; p < 0.001. These results indicate a robust association between elevated CagA levels and increased alcohol consumption, oxidative stress, and inflammatory markers, underscoring the potential implications of CagA in the context of these health parameters. Table 1.Baseline Characteristics and Key Findings According to CagA Tertiles Parameter Low CagA Tertile Middle CagA Tertile High CagA Tertile p-value Age (years) 45.54 ± 7.08 47.72 ± 5.58 47.36 ± 5.45 0.259 BMI (kg/m²) 92.3% BMI = 24 97.3% BMI = 24 94.6% BMI = 24 0.535 Alcohol Drinking History (≥5 years) 76.9% 81.1% 78.4% 0.592 Daily Alcohol Consumption (ml/day) 53.41 ± 11.39 62.25 ± 16.44 74.69 ± 18.98 <0.001 MDA (μmol/L) 5.70 ± 6.05 5.39 ± 4.19 12.54 ± 9.88 <0.001 SOD (U/mL) 46.52 ± 43.41 49.31 ± 35.10 131.44 ± 85.39 <0.001 TNF (pg/mL) 167.61 ± 113.15 207.11 ± 74.49 499.46 ± 315.51 <0.001 IL-10 (pg/mL) 329.89 ± 134.20 376.02 ± 173.92 640.69 ± 355.69 <0.001 CagA 42.51 ± 7.38 59.57 ± 5.46 142.27 ± 63.31 <0.001 E-selectin 27.40 ± 16.91 25.69 ± 10.47 61.72 ± 40.09 <0.001 3.2 Univariate Analysis Reveals Key Factors Associated with E-selectin In our univariate analysis, we examined the relationships between various exposure factors and E-selectin, a key biomarker of inflammation and endothelial dysfunction. As illustrated in Table 2, the regression analysis revealed significant positive correlations between E-selectin levels and several factors, including CagA, daily alcohol consumption, oxidative stress markers such as MDA and SOD, as well as TNF and IL-10. In contrast, factors such as age, BMI, and alcohol drinking history did not demonstrate significant associations with E-selectin levels, indicating they may have a lesser influence in this context. Overall, our results emphasize the importance of monitoring various exposure factors, particularly CagA, alcohol consumption, oxidative stress, and inflammatory markers, in relation to E-selectin levels, as this can provide valuable insights for preventing and managing cardiovascular diseases and related complications. Table 2.Univariate Analysis of Factors Associated with E-selectin Levels Variable β (95% CI) p-value Age 0.07 (-0.88, 1.02) 0.8833 Body Mass Index (BMI) 10.59 (-17.27, 38.45) 0.4577 Alcohol Drinking History -2.55 (-16.82, 11.72) 0.7267 Daily Alcohol Consumption 0.52 (0.22, 0.83) 0.0011 CagA 0.36 (0.29, 0.43) <0.0001 MDA 2.52 (1.96, 3.09) <0.0001 SOD 0.31 (0.26, 0.37) <0.0001 TNF 0.09 (0.07, 0.10) <0.0001 IL10 0.05 (0.03, 0.07) <0.0001 3.3 Multivariate and Stratified Analysis of CagA's Impact on E-selectin Levels A multivariate regression analysis was conducted to assess the impact of CagA on E-selectin levels (Table 3), employing a progressive, stepwise approach.In the non-adjusted model, a 1 ng/L increase in CagA levels was associated with a 0.36 ng/L increase in E-selectin (β = 0.36, 95% CI: 0.29, 0.43, P < 0.0001), indicating a significant positive correlation.To address potential confounding factors, Adjusted Model I was created, incorporating age, recoded BMI, MDA, alcohol consumption history, SOD, TNF, and IL-10 as covariates. In this model, the effect size for CagA decreased to 0.18 but remained statistically significant (P = 0.0001), suggesting that the association persisted despite adjustments.Further refinement was achieved in Adjusted Model II, which included smoothed adjustments for the same covariates. This comprehensive model maintained the significant relationship between CagA and E-selectin, with an effect size of 0.13 (P = 0.0082). This finding reinforces the robustness of the CagA-E-selectin association, highlighting CagA's potential role in modulating inflammatory processes. In the stratified analysis of CagA, participants were divided into three groups based on their baseline CagA levels. As shown in Table 3, compared to the lowest CagA group, Tertile 2 exhibited a negative relationship with E-selectin in both the non-adjusted model and Adjusted Models I and II, however，this relationship was not statistically significant. In contrast, Tertile 3 demonstrated a significant effect size of 34.32 (95% CI: 22.50, 46.13) in the non-adjusted model, indicating a substantial increase in E-selectin levels compared to the lowest CagA group. Nevertheless, in Adjusted Models I and II, the increase in CagA was not statistically significantly correlated with E-selectin. These findings suggest that the relationship between CagA and E-selectin may not follow a linear pattern, but rather there may be a threshold or non-linear effect, particularly at higher CagA levels. Table 3. Multivariable Regression Analysis of CagA and E-selectin Levels Variable Non-adjusted(β (95%CI)) Adjust I(β (95% CI)) Adjust II(β (95% CI)) CagA 0.36 (0.29, 0.43) <0.0001 0.18 (0.09, 0.27) 0.0001 0.14 (0.05, 0.23) 0.0040 CagA Tertile Low CagA Tertile 0 0 0 Middle CagA Tertile -1.71 (-13.77, 10.34) 0.7810 -1.04 (-9.89, 7.80) 0.8174 -1.66 (-9.94, 6.62) 0.6951 High CagA Tertile 34.32 (22.50, 46.13) <0.0001 6.31 (-4.88, 17.50) 0.2721 4.75 (-7.21, 16.71) 0.4386 3.4 Nonlinear Association and Threshold Effect Analysis of CagA Levels and E-selectin The initial multivariable regression analysis indicated a nonlinear association between baseline CagA levels and E-selectin levels. To further investigate this complex relationship, we employed a two-piecewise linear regression model combined with a smoothing function and threshold effect analysis. The adjusted smoothed plots revealed an inverted U-shaped association between baseline CagA levels and E-selectin levels (Figure 2). To thoroughly characterize this intricate non-linear relationship, we conducted threshold effect analysis to identify the critical threshold between the variables. As shown in Table 4, the analysis confirmed a non-linear, inverted U-shaped relationship between CagA levels and E-selectin expression, pinpointing a turning point at a CagA level of 140.8 units. Below this threshold (CagA 140.8 units), the effect became significantly negative. The difference in effects between these two segments was statistically significant, reinforcing the presence of a threshold effect. The predicted E-selectin value at the threshold point (CagA = 140.8 units) was 68.73. Additionally, a likelihood ratio test demonstrated that the two-piecewise model was statistically superior to the simple linear model, highlighting the importance of considering non-linear relationships. Table 4.Threshold Effect Analysis Results for CagA Levels and E-selectin Model β (95% CI) P-value Model I（Linear analysis） -0.04 (-0.12, 0.04) 0.2768 Model II (Two-piecewise regression） Turning point 140.8 Turning point -0.32 (-0.53, -0.11) 0.0029 Predicted Value at Threshold 68.73 (58.64, 78.82) Log-Likelihood Ratio Test 0.003 Adjusted Variables: BMI, Age, Daily Alcohol Consumption, Alcohol Drinking History, MDA, SOD, TNF, IL-10. Given that our study population consisted of chronic alcohol consumers, we stratified the analysis based on years of alcohol consumption to assess its impact on the relationship between CagA and E-selectin. The smooth curve fitting analysis visually represented this relationship, categorizing data points into two groups: low alcohol consumption (red dots) and high alcohol consumption (blue circles)(Fig 3). In the low alcohol consumption group, E-selectin levels remained relatively stable across varying CagA levels, indicating a minimal effect of CagA on E-selectin expression. In contrast, the high alcohol consumption group exhibited a significant increase in E-selectin levels as CagA levels rose, particularly at lower CagA concentrations. This suggests a more pronounced influence of CagA on E-selectin expression among individuals with a history of high alcohol consumption. Threshold effects analysis also identified a clear threshold at 167.8 CagA units in this group, where CagA showed a positive but non-significant effect below this threshold and a significantly negative impact above it. The predicted E-selectin values were 61.04 ng/mL for the low alcohol group and 93.95 ng/mL for the high alcohol group(Table 5). These findings underscore the intricate, non-linear relationship between CagA and E-selectin, with distinct effects shaped by both CagA levels and alcohol consumption history. The integration of threshold effect analysis and smooth curve fitting provides a robust framework for understanding the complex associations between the exposure factor and the outcome variable. Table 5. Threshold Effect Analysis of CagA Levels on E-selectin with Alcohol Drinking History as a Modifier Alchol.Drinking.History Low (β (95% CI)P-value) High(β (95% CI)P-value) Total(β (95% CI)P-value) Model I（Linear analysis） 0.05 (-0.04, 0.15) 0.2572 -0.35 (-0.53, -0.16) 0.0020 -0.02 (-0.11, 0.06) 0.6288 Model II (Two-piecewise regression） Turning point 124 167.8 140 Turning point -0.10 (-0.33, 0.13) 0.4045 -1.41 (-1.93, -0.89) 0.0001 -0.39 (-0.60, -0.18) 0.0005 Predicted Value at Threshold 61.04 (49.51, 72.57) 93.95 (73.26, 114.64) 68.64 (58.54, 78.73) Log-Likelihood Ratio Test 0.135 <0.001 <0.001 Adjusted Variables: Age, BMI, Daily Alcohol Consumption, MDA, SOD, TNF, IL-10. Discussion This cross-sectional study explored the relationship between the Helicobacter pylori CagA virulence factor and the endothelial function marker E-selectin levels, revealing a significant non-linear relationship between CagA levels and E-selectin. Specifically, when CagA levels exceeded 140.8 units, the effect on E-selectin became negative, while below this threshold, it did not reach statistical significance. This finding emphasizes the potential role of CagA among chronic alcohol consumers, particularly when considering alcohol consumption history. Compared to existing literature, our findings align with those of Yousef Rasmi et al. who also reported a positive correlation between CagA and E-selectin[14].However, our results indicate that this positive relationship exists only within a specific threshold range. Beyond this threshold, the relationship becomes inversely proportional, suggesting that elevated CagA levels may lead to decreased E-selectin levels. This finding underscores the importance of considering threshold effects when examining the relationship between CagA and E-selectin. Although our sample size of 112 participants is slightly smaller, we employed more sophisticated statistical models, such as generalized additive models and piecewise linear regression, allowing for a more precise identification of threshold effects. Additionally, we specifically highlighted the role of alcohol consumption history as a moderating factor, revealing that the impact of CagA on E-selectin is particularly pronounced in populations with high alcohol consumption[15].Mechanistically, alcohol may enhance the pathogenicity of CagA by affecting immune responses and inflammatory pathways, thereby impacting inflammatory cytokines expression[16, 17]. Consequently, variations in alcohol consumption history could be a critical factor in explaining discrepancies in results.In summary, while our findings align with certain aspects of existing literature regarding the relationship between CagA and E-selectin, they also highlight the complexity of this relationship, particularly concerning threshold effects. The findings of this study offer important implications for clinical practice. First, individuals who are CagA-positive and engage in heavy drinking may face a higher risk of endothelial dysfunction[18], so clinicians should closely monitor this high-risk population and develop targeted prevention and management strategies. Second, monitoring CagA levels and E-selectin levels may become potential biomarkers for assessing and preventing related diseases[19, 20]. Future research could further explore the interactions between CagA and other inflammatory factors and oxidative stress markers to comprehensively elucidate the complex regulatory mechanisms of CagA in the occurrence and development of gastrointestinal diseases. Additionally, prospective cohort studies targeting individuals with varying CagA levels and alcohol consumption status could provide more targeted prevention and management strategies for clinical practice. Despite its strengths, our study has several limitations. As a single-center, cross-sectional study conducted in China, the generalizability of our findings to other populations or ethnicities may be limited. The observational nature of the study precludes the establishment of causal relationships between CagA and E-selectin levels. Our exclusion criteria, which omitted individuals with various comorbidities such as liver diseases and mental disorders, may limit the applicability of our findings to these subpopulations. Additionally, while we adjusted for several measured confounders, unmeasured factors could still influence the observed associations. The study's focus on chronic alcohol consumers aged 30-60 years may not fully represent the spectrum of H. pylori infections in the general population. Lastly, the lack of longitudinal data prevents us from assessing the temporal dynamics of CagA's impact on E-selectin levels. Future multi-center, longitudinal studies with diverse populations are needed to validate and extend our findings. Declarations Ethics approval and consent to participate This study was approved by the Clinical Research Ethics Committee of Taishan Hospital (Shandong Province) and conducted in accordance with the Declaration of Helsinki. The requirement for informed consent was waived due to the retrospective nature of the study, use of publicly accessible database and anonymized participant information. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Acknowledgments The authors would like to express their gratitude to the Dryad digital repository for their valuable contributions to the availability of the data used in this research. Funding This study was supported by the Lanzhou Science and Technology Plan Projects (Grant No. 2022-ZD-46) and the Research Project of Gansu Provincial Hospital (Grant No. 21GSSYC-1). Author contributions RG and CJW conceived and designed the study. RG and JJ were responsible for data acquisition. RG conducted the data analysis and drafted the manuscript. WXW and CJW provided critical revisions to the manuscript. All authors have read and approved the final version of the manuscript. References M.J. Sanaei, H. Shirzad, A. Soltani, M. Abdollahpour-Alitappeh, M.H. Shafigh, G. Rahimian, Y. Mirzaei, N. 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Redmond, Alcohol and vascular endothelial function: Biphasic effect highlights the importance of dose, Alcohol, clinical & experimental research, 47 (2023) 1467-1477.DOI: 10.1111/acer.15138 V. Barbier, J. Erbani, C. Fiveash, J.M. Davies, J. Tay, M.R. Tallack, J. Lowe, J.L. Magnani, D.R. Pattabiraman, A.C. Perkins, J. Lisle, J.E.J. Rasko, J.P. Levesque, I.G. Winkler, Endothelial E-selectin inhibition improves acute myeloid leukaemia therapy by disrupting vascular niche-mediated chemoresistance, Nature communications, 11 (2020) 2042.DOI: 10.1038/s41467-020-15817-5 S. Shiota, K. Murakami, T. Okimoto, M. Kodama, Y. Yamaoka, Serum Helicobacter pylori CagA antibody titer as a useful marker for advanced inflammation in the stomach in Japan, Journal of gastroenterology and hepatology, 29 (2014) 67-73.DOI: 10.1111/jgh.12359 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-5359270","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":372799417,"identity":"c6671a38-3fcb-44c6-821d-3a8e7a706de4","order_by":0,"name":"Rui Guo","email":"","orcid":"","institution":"NHC key laboratory of Diagnosis and Therapy of Gastrointestinal Tumor, Gansu Provincial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Guo","suffix":""},{"id":372799418,"identity":"0cb6daf9-f8ec-4aaf-94d2-4e32cfc9e58c","order_by":1,"name":"Wanxia Wang","email":"","orcid":"","institution":"NHC key laboratory of Diagnosis and Therapy of Gastrointestinal Tumor, Gansu Provincial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wanxia","middleName":"","lastName":"Wang","suffix":""},{"id":372799419,"identity":"45241946-7112-4efa-a49e-1cb8677437cf","order_by":2,"name":"Jing Jia","email":"","orcid":"","institution":"NHC key laboratory of Diagnosis and Therapy of Gastrointestinal Tumor, Gansu Provincial Hospital","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Jia","suffix":""},{"id":372799420,"identity":"fa8b3ece-687a-4d15-abc3-23bbc8138a9d","order_by":3,"name":"Chaojun Wei","email":"data:image/png;base64,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","orcid":"","institution":"NHC key laboratory of Diagnosis and Therapy of Gastrointestinal Tumor, Gansu Provincial Hospital","correspondingAuthor":true,"prefix":"","firstName":"Chaojun","middleName":"","lastName":"Wei","suffix":""}],"badges":[],"createdAt":"2024-10-30 07:38:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5359270/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5359270/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68932161,"identity":"41179290-2543-4a62-a866-db4dcae69636","added_by":"auto","created_at":"2024-11-13 15:45:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":276138,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy design and participant flow\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-5359270/v1/344346eb71fce91221c00e62.png"},{"id":68932159,"identity":"4d31a884-9494-42d9-888d-2e01789dd619","added_by":"auto","created_at":"2024-11-13 15:45:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":8992,"visible":true,"origin":"","legend":"\u003cp\u003eSmoothed Curve Fitting Analysis of E-selectin Levels by CagA Concentration\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-5359270/v1/4c42195e9919f71f5d5c00bf.png"},{"id":68932162,"identity":"2e233755-1d83-4f07-915a-f5965c4f3aee","added_by":"auto","created_at":"2024-11-13 15:45:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":8570,"visible":true,"origin":"","legend":"\u003cp\u003eThe Relationship between CagA and E-selectin : Impact Analysis of Alcohol Drinking History\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-5359270/v1/cf348ffee5c876eb61c4d6c8.png"},{"id":103199975,"identity":"820d2b48-ef18-48a3-bf3e-93791887e62e","added_by":"auto","created_at":"2026-02-23 05:40:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":859225,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5359270/v1/33b8028d-70f2-418c-b157-499b0b2b01ea.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of Helicobacter pylori CagA on E-selectin Levels in Chronic Alcohol Consumers: A Cross-Sectional Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eHelicobacter pylori (H. pylori) is a Gram-negative, spiral-shaped bacterium that colonizes the human gastric mucosa and is closely associated with chronic gastritis, peptic ulcers, and gastric cancer [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. One of its major virulence factors, the cytotoxin-associated gene A (CagA), has been linked to increased pathogenicity[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. CagA-positive strains can disrupt host cellular signaling pathways, promote abnormal cell proliferation, and inhibit apoptosis, playing a crucial role in the development of gastric cancer[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] .Therefore, the presence of CagA not only affects the pathogenicity of H. pylori but also provides important clues for the study of related diseases.\u003c/p\u003e \u003cp\u003eE-selectin is closely related to H. pylori infection and plays a critical role in the pathogenesis of gastrointestinal diseases[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. As an endothelial adhesion molecule, E-selectin is expressed on the surface of activated endothelial cells in response to pro-inflammatory cytokines triggered by H. pylori infection, such as interleukin-1 (IL-1) and tumor necrosis factor-alpha (TNF-α) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].E-selectin not only mediates the adhesion and migration of leukocytes to sites of inflammation[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], but also directly activates macrophages and endothelial cells, inducing the release of inflammatory factors and chemokines, thereby exacerbating inflammatory responses and promoting tumor progression[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] .Consequently, the expression of E-selectin is closely related to the severity of gastrointestinal diseases caused by H. pylori infection, particularly in the context of chronic inflammation and tumor microenvironments.\u003c/p\u003e \u003cp\u003eIn chronic alcohol consumers, studies on CagA indicate that chronic drinking may lead to an increased prevalence of CagA-positive H. pylori strains, which is associated with the immunosuppressive effects of alcohol, potentially allowing CagA-positive strains to colonize more easily and cause more severe gastrointestinal diseases[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Concurrently, studies on E-selectin in chronic drinkers have also demonstrated that alcohol consumption may elevate E-selectin expression, thereby exacerbating inflammatory responses and promoting the progression of related diseases[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. These findings suggest a potential association between CagA and E-selectin, particularly in chronic drinkers, where their interaction may intensify gastric inflammation and pathological changes. However, the relationship between H. pylori CagA and E-selectin levels in chronic drinkers remains inadequately studied, with existing research findings being inconsistent. Therefore, it is crucial to explore this relationship further.\u003c/p\u003e \u003cp\u003eThe purpose of this study is to investigate the association between H. pylori CagA and E-selectin levels in chronic alcohol consumers through a cross-sectional study design, aiming to reveal their roles in the pathogenesis of gastrointestinal diseases. This research may provide new insights and potential biomarkers for the prevention and treatment of related diseases\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Data Source\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData were sourced from the DATADRYAD website (www.datadryad.org), allowing users to freely access the original data. According to Dryad\u0026apos;s terms of service, the data package should be cited as: Qu, Baoge et al. (2016), Data from: Effect of H. pylori infection on cytokine profiles and oxidative balance in subjects with chronic alcohol ingestion, Dataset：https://doi.org/10.5061/dryad.45ds3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Study Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study by Qu, Baoge, was a cross-sectional analysis conducted at Taishan Hospital in Shandong Province, China, from January 2012 to December 2013, involving 112 chronic alcohol consumers aged 30 to 60 years. Participants were recruited from routine health examinations and primary care services, with inclusion criteria comprising chronic alcohol consumption (daily intake \u0026gt;40g for men, \u0026gt;20g for women for over 5 years), known H. pylori infection status (either positive or negative), and age-matched controls without chronic alcohol consumption or H. pylori infection. Exclusion criteria included smoking, fever, infectious diseases, primary or secondary gastrointestinal diseases, liver and gallbladder diseases, cardiovascular, endocrine, neurological, renal, or hematological disorders, electrolyte and acid-base imbalances, and mental health disorders( see detailed flowchart in Fig.1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe exposure variable in this study was the presence of the CagA virulence factor of H. pylori. Fasting venous blood samples were collected at enrollment, and CagA antibodies were measured using enzyme-linked immunosorbent assay (ELISA) kits according to the manufacturer\u0026apos;s instructions. The outcome variable was the serum level of E-selectin, measured using high-sensitivity human E-selectin ELISA kits, with blood samples collected after an overnight fast of at least 10 hours and processed according to the manufacturer\u0026apos;s protocol, expressed in ng/mL. Laboratory personnel conducting the assays were blinded to participants\u0026apos; exposure status and clinical information to minimize measurement bias. Covariates included age, body mass index (BMI), duration of alcohol consumption, and daily alcohol intake, with age obtained via questionnaires. BMI was categorized based on standard classifications, and the duration of alcohol consumption was categorized based on the length of time, specifically divided into short-term and long-term drinking. Daily alcohol intake was based on self-reported consumption in grams per day. These covariates were selected for their potential confounding effects on E-selectin levels and H. pylori infection status. In statistical analyses, age was treated as a continuous variable, while BMI, duration of alcohol consumption, and daily intake were categorized based on clinical relevance and data distribution. For missing data, complete case analysis was performed if the proportion of missing values was less than 5%; otherwise, multiple imputation using the chained equations method was conducted to reduce bias associated with missingness.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4\u003c/strong\u003e \u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the initial study, the authors indicated that the research was approved by the Clinical Research Ethics Committee of Taishan Hospital in Shandong Province and adhered to the principles outlined in the Declaration of Helsinki. Given that the database utilized for this study was publicly accessible, participant identities were anonymized, and the information was retrieved retrospectively. As a result, informed consent was not deemed necessary, as reported in other studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive statistics were used to summarize the baseline characteristics across the CAGA tertiles. Between-group comparisons were conducted using one-way ANOVA or chi-square tests, as appropriate. The relationship between CagA and E-selectin was explored through smoothing curve fitting and generalized additive models (GAM). Multiple linear regression models, adjusted for potential confounders, were employed to assess the association between CagA and E-selectin. Piecewise linear regression was utilized to identify potential threshold effects, with model comparisons conducted via likelihood ratio tests. A stratified curve fitting analysis examined the relationship across different levels of alcohol consumption. All analyses were performed using EmpowerStats software (www.empowerstats.com, X Mind, Inc., Boston, MA), with statistical significance set at p \u0026lt; 0.05.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Description of the study groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, a total of 142 participants were initially recruited, with 112 ultimately enrolled after screening. The baseline characteristics and key findings are delineated according to CagA tertiles (Low, Middle, High). A comprehensive summary of the baseline characteristics of the study population, stratified by CagA tertiles, is presented in Table 1. The mean ages of participants were consistent across the tertiles, and no significant differences were observed in BMI distribution or alcohol consumption history among the groups.\u003c/p\u003e\n\u003cp\u003eImportantly, the analysis revealed significant associations between CagA levels and various parameters, including alcohol consumption, oxidative stress, and inflammatory markers. Daily alcohol intake exhibited a progressive increase across the tertiles. Additionally, oxidative stress markers, specifically malondialdehyde (MDA) and superoxide dismutase (SOD) levels, demonstrated significant elevation with increasing CagA levels. Inflammatory markers, such as tumor necrosis factor (TNF) and interleukin-10 (IL-10), also showed significant increases.\u003c/p\u003e\n\u003cp\u003eEelection levels were significantly higher in the high CagA tertile, with the following values recorded: Low CagA: 27.40 \u0026plusmn; 16.91; Middle CagA: 25.69 \u0026plusmn; 10.47; High CagA: 61.72 \u0026plusmn; 40.09; p \u0026lt; 0.001. These results indicate a robust association between elevated CagA levels and increased alcohol consumption, oxidative stress, and inflammatory markers, underscoring the potential implications of CagA in the context of these health parameters.\u003c/p\u003e\n\u003cp\u003eTable 1.Baseline Characteristics and Key Findings According to CagA Tertiles\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"610\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eParameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLow CagA Tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMiddle CagA Tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh CagA Tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e45.54 \u0026plusmn; 7.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e47.72 \u0026plusmn; 5.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e47.36 \u0026plusmn; 5.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.259\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e92.3% BMI = 24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e97.3% BMI = 24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e94.6% BMI = 24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAlcohol Drinking History (\u0026ge;5 years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e76.9%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e81.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e78.4%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.592\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDaily Alcohol Consumption (ml/day)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e53.41 \u0026plusmn; 11.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e62.25 \u0026plusmn; 16.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e74.69 \u0026plusmn; 18.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMDA (\u0026mu;mol/L)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.70 \u0026plusmn; 6.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e5.39 \u0026plusmn; 4.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e12.54 \u0026plusmn; 9.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSOD (U/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e46.52 \u0026plusmn; 43.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e49.31 \u0026plusmn; 35.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e131.44 \u0026plusmn; 85.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTNF (pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e167.61 \u0026plusmn; 113.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e207.11 \u0026plusmn; 74.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e499.46 \u0026plusmn; 315.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIL-10 (pg/mL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e329.89 \u0026plusmn; 134.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e376.02 \u0026plusmn; 173.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e640.69 \u0026plusmn; 355.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCagA\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e42.51 \u0026plusmn; 7.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e59.57 \u0026plusmn; 5.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e142.27 \u0026plusmn; 63.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eE-selectin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e27.40 \u0026plusmn; 16.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e25.69 \u0026plusmn; 10.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e61.72 \u0026plusmn; 40.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Univariate Analysis Reveals Key Factors Associated with\u0026nbsp;\u003c/strong\u003eE-selectin\u003c/p\u003e\n\u003cp\u003eIn our univariate analysis, we examined the relationships between various exposure factors and E-selectin, a key biomarker of inflammation and endothelial dysfunction. As illustrated in Table 2, the regression analysis revealed significant positive correlations between E-selectin levels and several factors, including CagA, daily alcohol consumption, oxidative stress markers such as MDA and SOD, as well as TNF and IL-10. In contrast, factors such as age, BMI, and alcohol drinking history did not demonstrate significant associations with E-selectin levels, indicating they may have a lesser influence in this context. Overall, our results emphasize the importance of monitoring various exposure factors, particularly CagA, alcohol consumption, oxidative stress, and inflammatory markers, in relation to E-selectin levels, as this can provide valuable insights for preventing and managing cardiovascular diseases and related complications.\u003c/p\u003e\n\u003cp\u003eTable 2.Univariate Analysis of Factors Associated with E-selectin Levels\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"577\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.07 (-0.88, 1.02)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.8833\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBody Mass Index (BMI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e10.59 (-17.27, 38.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.4577\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAlcohol Drinking History\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-2.55 (-16.82, 11.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.7267\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eDaily Alcohol Consumption\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.52 (0.22, 0.83)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.0011\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eCagA\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e0.36 (0.29, 0.43)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.0001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMDA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e2.52 (1.96, 3.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.0001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSOD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.31 (0.26, 0.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.0001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTNF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.09 (0.07, 0.10)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIL10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.05 (0.03, 0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026lt;0.0001\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Multivariate and Stratified Analysis of CagA\u0026apos;s Impact on E-selectin Levels\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA multivariate regression analysis was conducted to assess the impact of CagA on E-selectin levels (Table 3), employing a progressive, stepwise approach.In the non-adjusted model, a 1 ng/L increase in CagA levels was associated with a 0.36 ng/L increase in E-selectin (\u0026beta; = 0.36, 95% CI: 0.29, 0.43, P \u0026lt; 0.0001), indicating a significant positive correlation.To address potential confounding factors, Adjusted Model I was created, incorporating age, recoded BMI, MDA, alcohol consumption history, SOD, TNF, and IL-10 as covariates. In this model, the effect size for CagA decreased to 0.18 but remained statistically significant (P = 0.0001), suggesting that the association persisted despite adjustments.Further refinement was achieved in Adjusted Model II, which included smoothed adjustments for the same covariates. This comprehensive model maintained the significant relationship between CagA and E-selectin, with an effect size of 0.13 (P = 0.0082). This finding reinforces the robustness of the CagA-E-selectin association, highlighting CagA\u0026apos;s potential role in modulating inflammatory processes.\u003c/p\u003e\n\u003cp\u003eIn the stratified analysis of CagA, participants were divided into three groups based on their baseline CagA levels. As shown in Table 3, compared to the lowest CagA group, Tertile 2 exhibited a negative relationship with E-selectin in both the non-adjusted model and Adjusted Models I and II, however，this relationship was not statistically significant. In contrast, Tertile 3 demonstrated a significant effect size of 34.32 (95% CI: 22.50, 46.13) in the non-adjusted model, indicating a substantial increase in E-selectin levels compared to the lowest CagA group. Nevertheless, in Adjusted Models I and II, the increase in CagA was not statistically significantly correlated with E-selectin. These findings suggest that the relationship between CagA and E-selectin may not follow a linear pattern, but rather there may be a threshold or non-linear effect, particularly at higher CagA levels.\u003c/p\u003e\n\u003cp\u003eTable 3. Multivariable Regression Analysis of CagA and E-selectin Levels\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eNon-adjusted(\u0026beta; (95%CI))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAdjust I(\u0026beta; (95% CI))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAdjust II(\u0026beta; (95% CI))\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCagA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.36 (0.29, 0.43) \u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.18 (0.09, 0.27) 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.14 (0.05, 0.23) 0.0040\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCagA Tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLow CagA Tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMiddle CagA Tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.71 (-13.77, 10.34) 0.7810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.04 (-9.89, 7.80) 0.8174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e-1.66 (-9.94, 6.62) 0.6951\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHigh CagA Tertile\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e34.32 (22.50, 46.13) \u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e6.31 (-4.88, 17.50) 0.2721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e4.75 (-7.21, 16.71) 0.4386\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Nonlinear Association and Threshold Effect Analysis of CagA Levels and E-selectin\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe initial multivariable regression analysis indicated a nonlinear association between baseline CagA levels and E-selectin levels. To further investigate this complex relationship, we employed a two-piecewise linear regression model combined with a smoothing function and threshold effect analysis. The adjusted smoothed plots revealed an inverted U-shaped association between baseline CagA levels and E-selectin levels (Figure 2). To thoroughly characterize this intricate non-linear relationship, we conducted threshold effect analysis to identify the critical threshold between the variables. As shown in Table 4, the analysis confirmed a non-linear, inverted U-shaped relationship between CagA levels and E-selectin expression, pinpointing a turning point at a CagA level of 140.8 units. Below this threshold (CagA \u0026lt; 140.8 units), the effect of CagA on E-selectin was positive but not statistically significant. Conversely, above the threshold (CagA \u0026gt; 140.8 units), the effect became significantly negative. The difference in effects between these two segments was statistically significant, reinforcing the presence of a threshold effect. The predicted E-selectin value at the threshold point (CagA = 140.8 units) was 68.73. Additionally, a likelihood ratio test demonstrated that the two-piecewise model was statistically superior to the simple linear model, highlighting the importance of considering non-linear relationships.\u003c/p\u003e\n\u003cp\u003eTable 4.Threshold Effect Analysis Results for CagA Levels and E-selectin\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026beta; (95% CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eP-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eModel I（Linear analysis）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e-0.04 (-0.12, 0.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.2768\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 568px;\"\u003e\n \u003cp\u003eModel II \u0026nbsp;(Two-piecewise regression）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003eTurning point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 379px;\"\u003e\n \u003cp\u003e140.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026lt; Turning point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.12 (-0.02, 0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.0863\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026gt; Turning point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e-0.32 (-0.53, -0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e0.0029\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003ePredicted Value at Threshold\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e68.73 (58.64, 78.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003eLog-Likelihood Ratio Test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAdjusted Variables: BMI, Age, Daily Alcohol Consumption, Alcohol Drinking History, MDA, SOD, TNF, IL-10.\u003c/p\u003e\n\u003cp\u003eGiven that our study population consisted of chronic alcohol consumers, we stratified the analysis based on years of alcohol consumption to assess its impact on the relationship between CagA and E-selectin. The smooth curve fitting analysis visually represented this relationship, categorizing data points into two groups: low alcohol consumption (red dots) and high alcohol consumption (blue circles)(Fig 3). In the low alcohol consumption group, E-selectin levels remained relatively stable across varying CagA levels, indicating a minimal effect of CagA on E-selectin expression. In contrast, the high alcohol consumption group exhibited a significant increase in E-selectin levels as CagA levels rose, particularly at lower CagA concentrations. This suggests a more pronounced influence of CagA on E-selectin expression among individuals with a history of high alcohol consumption. Threshold effects analysis also identified a clear threshold at 167.8 CagA units in this group, where CagA showed a positive but non-significant effect below this threshold and a significantly negative impact above it. The predicted E-selectin values were 61.04 ng/mL for the low alcohol group and 93.95 ng/mL for the high alcohol group(Table 5).\u003c/p\u003e\n\u003cp\u003eThese findings underscore the intricate, non-linear relationship between CagA and E-selectin, with distinct effects shaped by both CagA levels and alcohol consumption history. The integration of threshold effect analysis and smooth curve fitting provides a robust framework for understanding the complex associations between the exposure factor and the outcome variable.\u003c/p\u003e\n\u003cp\u003eTable 5. Threshold Effect Analysis of CagA Levels on E-selectin with Alcohol Drinking History as a Modifier\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"710\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 26.4789%;\"\u003e\n \u003cp\u003eAlchol.Drinking.History\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6761%;\"\u003e\n \u003cp\u003eLow (\u0026beta; (95% CI)P-value)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.507%;\"\u003e\n \u003cp\u003eHigh(\u0026beta; (95% CI)P-value)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.338%;\"\u003e\n \u003cp\u003eTotal(\u0026beta; (95% CI)P-value)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4789%;\"\u003e\n \u003cp\u003eModel I（Linear analysis）\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6761%;\"\u003e\n \u003cp\u003e0.05 (-0.04, 0.15) 0.2572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.507%;\"\u003e\n \u003cp\u003e-0.35 (-0.53, -0.16) 0.0020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.338%;\"\u003e\n \u003cp\u003e-0.02 (-0.11, 0.06) 0.6288\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003eModel II \u0026nbsp;(Two-piecewise regression）\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4789%;\"\u003e\n \u003cp\u003eTurning point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6761%;\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.507%;\"\u003e\n \u003cp\u003e167.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 26.338%;\"\u003e\n \u003cp\u003e140\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4789%;\"\u003e\n \u003cp\u003e\u0026lt; Turning point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6761%;\"\u003e\n \u003cp\u003e0.17 (-0.01, 0.35) 0.0744\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.507%;\"\u003e\n \u003cp\u003e0.31(-0.02, 0.65) 0.0898\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.338%;\"\u003e\n \u003cp\u003e0.20 (0.05, 0.34) 0.0078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4789%;\"\u003e\n \u003cp\u003e\u0026gt; Turning point\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6761%;\"\u003e\n \u003cp\u003e-0.10 (-0.33, 0.13) 0.4045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.507%;\"\u003e\n \u003cp\u003e-1.41 (-1.93, -0.89) 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.338%;\"\u003e\n \u003cp\u003e-0.39 (-0.60, -0.18) 0.0005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4789%;\"\u003e\n \u003cp\u003ePredicted Value at Threshold\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6761%;\"\u003e\n \u003cp\u003e61.04 (49.51, 72.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.507%;\"\u003e\n \u003cp\u003e93.95 (73.26, 114.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.338%;\"\u003e\n \u003cp\u003e68.64 (58.54, 78.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.4789%;\"\u003e\n \u003cp\u003eLog-Likelihood Ratio Test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22.6761%;\"\u003e\n \u003cp\u003e0.135 \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 24.507%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 26.338%;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAdjusted Variables: Age, BMI, Daily Alcohol Consumption, MDA, SOD, TNF, IL-10.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis cross-sectional study explored the relationship between the Helicobacter pylori CagA virulence factor and the endothelial function marker E-selectin levels, revealing a significant non-linear relationship between CagA levels and E-selectin. Specifically, when CagA levels exceeded 140.8 units, the effect on E-selectin became negative, while below this threshold, it did not reach statistical significance. This finding emphasizes the potential role of CagA among chronic alcohol consumers, particularly when considering alcohol consumption history.\u003c/p\u003e\n\u003cp\u003eCompared to existing literature, our findings align with those of Yousef Rasmi et al. who also reported a positive correlation between CagA and E-selectin[14].However, our results indicate that this positive relationship exists only within a specific threshold range. Beyond this threshold, the relationship becomes inversely proportional, suggesting that elevated CagA levels may lead to decreased E-selectin levels. This finding underscores the importance of considering threshold effects when examining the relationship between CagA and E-selectin. Although our sample size of 112 participants is slightly smaller, we employed more sophisticated statistical models, such as generalized additive models and piecewise linear regression, allowing for a more precise identification of threshold effects. Additionally, we specifically highlighted the role of alcohol consumption history as a moderating factor, revealing that the impact of CagA on E-selectin is particularly pronounced in populations with high alcohol consumption[15].Mechanistically, alcohol may enhance the pathogenicity of CagA by affecting immune responses and inflammatory pathways, thereby impacting inflammatory cytokines expression[16, 17]. Consequently, variations in alcohol consumption history could be a critical factor in explaining discrepancies in results.In summary, while our findings align with certain aspects of existing literature regarding the relationship between CagA and E-selectin, they also highlight the complexity of this relationship, particularly concerning threshold effects.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe findings of this study offer important implications for clinical practice. First, individuals who are CagA-positive and engage in heavy drinking may face a higher risk of endothelial dysfunction[18], so clinicians should closely monitor this high-risk population and develop targeted prevention and management strategies. Second, monitoring CagA levels and E-selectin levels may become potential biomarkers for assessing and preventing related diseases[19, 20]. Future research could further explore the interactions between CagA and other inflammatory factors and oxidative stress markers to comprehensively elucidate the complex regulatory mechanisms of CagA in the occurrence and development of gastrointestinal diseases. Additionally, prospective cohort studies targeting individuals with varying CagA levels and alcohol consumption status could provide more targeted prevention and management strategies for clinical practice.\u003c/p\u003e\n\u003cp\u003eDespite its strengths, our study has several limitations. As a single-center, cross-sectional study conducted in China, the generalizability of our findings to other populations or ethnicities may be limited. The observational nature of the study precludes the establishment of causal relationships between CagA and E-selectin levels. Our exclusion criteria, which omitted individuals with various comorbidities such as liver diseases and mental disorders, may limit the applicability of our findings to these subpopulations. Additionally, while we adjusted for several measured confounders, unmeasured factors could still influence the observed associations. The study's focus on chronic alcohol consumers aged 30-60 years may not fully represent the spectrum of H. pylori infections in the general population. Lastly, the lack of longitudinal data prevents us from assessing the temporal dynamics of CagA's impact on E-selectin levels. Future multi-center, longitudinal studies with diverse populations are needed to validate and extend our findings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Clinical Research Ethics Committee of Taishan Hospital (Shandong Province) and conducted in accordance with the Declaration of Helsinki. The requirement for informed consent was waived due to the retrospective nature of the study, use of publicly accessible database and anonymized participant information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to express their gratitude to the Dryad digital repository for their valuable contributions to the availability of the data used in this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Lanzhou Science and Technology Plan Projects (Grant No. 2022-ZD-46) and the Research Project of Gansu Provincial Hospital (Grant No. 21GSSYC-1).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRG and CJW conceived and designed the study. RG and JJ were responsible for data acquisition. RG conducted the data analysis and drafted the manuscript. WXW and CJW provided critical revisions to the manuscript. All authors have read and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eM.J. Sanaei, H. Shirzad, A. Soltani, M. Abdollahpour-Alitappeh, M.H. Shafigh, G. Rahimian, Y. Mirzaei, N. Bagheri, Up-regulated CCL18, CCL28 and CXCL13 Expression is Associated with the Risk of Gastritis and Peptic Ulcer Disease in Helicobacter Pylori infection, The American journal of the medical sciences, (2021) 43-54. DOI: 10.1016/j.amjms.2020.07.030\u003c/li\u003e\n\u003cli\u003eZ. Wang, Y. Hu, R. Fei, W. Han, X. Wang, D. Chen, S. She, Tracking the Helicobacter pylori Epidemic in Adults and Children in China, Helicobacter, 29 (2024) e13139.DOI: 10.1111/hel.13139\u003c/li\u003e\n\u003cli\u003eY.S. Teng, W.Y. Chen, Z.B. Yan, Y.P. Lv, Y.G. Liu, F.Y. Mao, Y.L. Zhao, L.S. Peng, P. Cheng, M.B. Duan, W. Chen, Y. Wang, P. Luo, Q.M. Zou, J. Chen, Y. Zhuang, L-Plastin Promotes Gastric Cancer Growth and Metastasis in a Helicobacter pylori cagA-ERK-SP1-Dependent Manner, Molecular cancer research : MCR, 19 (2021) 968-978. DOI: 10.1158/1541-7786.MCR-20-0936\u003c/li\u003e\n\u003cli\u003eA. Takahashi-Kanemitsu, C.T. Knight, M. Hatakeyama, Molecular anatomy and pathogenic actions of Helicobacter pylori CagA that underpin gastric carcinogenesis, Cellular \u0026amp; molecular immunology, 17 (2020) 50-63. DOI: 10.1038/s41423-019-0339-5\u003c/li\u003e\n\u003cli\u003eF.Y. Mao, Y.P. Lv, C.J. Hao, Y.S. Teng, Y.G. Liu, P. Cheng, S.M. Yang, W. Chen, T. Liu, Q.M. Zou, R. Xie, J.Y. Xu, Y. Zhuang, Helicobacter pylori-Induced Rev-erb\u0026alpha; Fosters Gastric Bacteria Colonization by Impairing Host Innate and Adaptive Defense, Cellular and molecular gastroenterology and hepatology, 12 (2021).DOI: 10.1016/j.jcmgh.2021.02.013\u003c/li\u003e\n\u003cli\u003eY. Peng, X. Lei, Q. Yang, G. Zhang, S. He, M. Wang, R. Ling, B. Zheng, J. He, X. Chen, F. Li, Q. Zhou, L. Zhao, G. Ye, G. Li, Helicobacter pylori CagA-mediated ether lipid biosynthesis promotes ferroptosis susceptibility in gastric cancer, Experimental \u0026amp; molecular medicine, 56 (2024) 441-452.DOI: 10.1038/s12276-024-01167-5\u003c/li\u003e\n\u003cli\u003eH. Svensson, M. Hansson, J. Kilhamn, S. Backert, M. Quiding-J\u0026auml;rbrink, Selective upregulation of endothelial E-selectin in response to Helicobacter pylori-induced gastritis, Infection and immunity, 77 (2009) 3109-3116.DOI: 10.1128/IAI.01460-08\u003c/li\u003e\n\u003cli\u003eW. Yang, Y. Lv, T. Ma, N. Wang, P. Chen, Q. Liu, H. Yan, Exploring the association between inflammatory biomarkers and gastric cancer development: A two-sample mendelian randomization analysis, Medicine, 103 (2024) e36458.DOI: 10.1097/MD.0000000000036458\u003c/li\u003e\n\u003cli\u003eJ. Zhang, S. Huang, Z. Zhu, A. Gatt, J. Liu, E-selectin in vascular pathophysiology, Frontiers in immunology, 15 (2024) 1401399.DOI: 10.3389/fimmu.2024.1401399\u003c/li\u003e\n\u003cli\u003eJ.M. Peterson, T.A. Smith, E.P. Rock, J.L. Magnani, Selectins in Biology and Human Disease: Opportunity in E-selectin Antagonism, Cureus, 16 (2024) e61996.DOI: 10.7759/cureus.61996\u003c/li\u003e\n\u003cli\u003eS.A. Kang, C.A. Blache, S. Bajana, N. Hasan, M. Kamal, Y. Morita, V. Gupta, B. Tsolmon, K.S. Suh, D.G. Gorenstein, W. Razaq, H. Rui, T. Tanaka, The effect of soluble E-selectin on tumor progression and metastasis, BMC cancer, 16 (2016) 331.DOI: 10.1186/s12885-016-2366-2\u003c/li\u003e\n\u003cli\u003eB. Qu, X. Han, G. Ren, Y. Jia, Y. Liu, J. Su, Z. Wang, Y. Wang, H. Wang, J. Pan, L.L. Liu, W.J. Hu, Influence of H. pylori CagA Coupled with Alcohol Consumption on Cytokine Profiles in Men, Medicine, 95 (2016) e2721.DOI: 10.1097/MD.0000000000002721\u003c/li\u003e\n\u003cli\u003eA. Bertola, O. Park, B. 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Yamaoka, Serum Helicobacter pylori CagA antibody titer as a useful marker for advanced inflammation in the stomach in Japan, Journal of gastroenterology and hepatology, 29 (2014) 67-73.DOI: 10.1111/jgh.12359\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":"CagA, E-selectin, Helicobacter pylori, Alcohol consumption, Chronic disease","lastPublishedDoi":"10.21203/rs.3.rs-5359270/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5359270/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHelicobacter pylori (H. pylori) infection is a major pathogen causing chronic gastritis, peptic ulcers, and gastric cancer. Its major virulence factor CagA and the endothelial adhesion molecule E-selectin play crucial roles in the development of gastrointestinal diseases. This study aimed to investigate the relationship between CagA levels and E-selectin levels in chronic alcohol consumers.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis cross-sectional study enrolled 112 chronic alcohol consumers. The exposure variable was CagA level, and the outcome variable was E-selectin level. Covariates included age, BMI, alcohol consumption history, daily alcohol intake, oxidative stress markers (MDA, SOD), and inflammatory factors (TNF, IL-10). Multivariable linear regression and piecewise linear regression were used to analyze the relationship between CagA and E-selectin, with subgroup analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eCagA levels differed significantly across the high, middle, and low tertiles. CagA levels exhibited a nonlinear relationship with E-selectin levels, with a turning point at 140.8 CagA units, where the effect of CagA on E-selectin changed from positive to negative. Further stratified analysis revealed that in the high alcohol consumption group, CagA levels above 167.8 units had a significantly negative impact on E-selectin.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eIn chronic alcohol consumers, CagA levels and E-selectin levels have a complex nonlinear relationship, which is modulated by alcohol consumption history. CagA and E-selectin may serve as potential biomarkers for the prevention and management of related gastrointestinal diseases. Further multi-center prospective studies are needed to validate these findings.\u003c/p\u003e","manuscriptTitle":"Impact of Helicobacter pylori CagA on E-selectin Levels in Chronic Alcohol Consumers: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-13 15:45:17","doi":"10.21203/rs.3.rs-5359270/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":"21942b1e-362b-4572-9930-431c0ee62464","owner":[],"postedDate":"November 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-23T05:39:14+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-13 15:45:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5359270","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5359270","identity":"rs-5359270","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}