Beyond the Stomach: Linking H. pylori Seropositivity to Insulin Resistance (TyG Index) in a Multi-Ethnic High-Risk Population from Southwest China

Beyond the Stomach: Linking H. pylori Seropositivity to Insulin Resistance (TyG Index) in a Multi-Ethnic High-Risk Population from Southwest China | 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 Beyond the Stomach: Linking H. pylori Seropositivity to Insulin Resistance (TyG Index) in a Multi-Ethnic High-Risk Population from Southwest China Guangpin Zeng, Linran Zeng, Yinrong Zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8617767/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background and Aims: Helicobacter pylori infection is associated with systemic inflammation and may contribute to insulin resistance. The triglyceride-glucose (TyG) index serves as a reliable surrogate marker of insulin resistance and is linked to cardiometabolic diseases. However, evidence regarding the association between H. pylori and the TyG index remains inconsistent, especially in multi-ethnic, high-risk populations from China’s southwestern border region. This study aimed to investigate the relationship between H. pylori infection and the TyG index in such a population. We sought to: (1) assess the independent association, (2) characterize dose–response patterns, and (3) identify metabolic modifiers through stratified analyses, in order to elucidate the extra‑gastric metabolic implications of H. pylori infection. Methods: This study analyzed data from 6,998 adults in a multi‑ethnic health‑screening cohort conducted in Wenshan, Southwest China (2020–2024). Helicobacter pylori infection was diagnosed using the ¹⁴C‑urea breath test (cut‑off > 50 DPM). The TyG index was calculated as ln[fasting triglycerides (mg/dL) × fasting glucose (mg/dL)/2]. Comprehensive biochemical and metabolic profiles were assessed. We performed univariable and multivariable regression adjusted for key metabolic confounders, examined dose–response relationships using restricted cubic splines, conducted stratified analyses to identify effect modifiers, and applied E‑value sensitivity analysis to evaluate robustness to unmeasured confounding. Results: Among the 6,998 participants from Wenshan, Southwest China, 2,736 (39.1%) were H. pylori -positive. The H. pylori -positive group exhibited a significantly higher TyG index compared to the negative group (8.8 ± 0.7 vs. 8.7 ± 0.7, p < 0.001). In adjusted models, a higher TyG index remained independently associated with H. pylori infection (OR = 1.14, 95% CI 1.06–1.21). Sensitivity analysis using the E‑value indicated that an unmeasured confounder (e.g., BMI) would need to be associated with both exposures by risk ratios of at least 1.54 to fully explain the observed association, supporting its robustness. Other independent risk factors included male sex, advanced age, dyslipidemia, and elevated absolute monocyte count. Conclusion: A higher TyG index is independently associated with an increased risk of H. pylori infection in this health-screening population from China's southwestern border. The TyG index may serve as a simple metabolic biomarker to identify high-risk individuals, aiding targeted screening in similar multi-ethnic groups. Our findings underscore the systemic inflammatory burden of H. pylori and suggest the TyG index as a simple tool for metabolic risk stratification in infected individuals. Helicobacter pylori Triglyceride-glucose index Insulin resistance Inflammation Monocytes Southwestern China Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. INTRODUCTION While Helicobacter pylori ( H. pylori ) infection is associated with systemic low-grade inflammation [ 1 ] and may contribute to metabolic dysregulation by impairing insulin sensitivity [ 2 ][ 3 ] , the precise metabolic pathways underlying this interplay in specific high-risk populations remain to be elucidated [ 4 ] . To quantify such metabolic disturbances, reliable and accessible biomarkers are essential. The triglyceride-glucose (TyG) index, calculated from fasting triglycerides and glucose levels, serves as a robust and validated surrogate marker of insulin resistance [ 5 ] . A higher TyG index is also a strong predictor for the development of major cardiometabolic conditions, including non-alcoholic fatty liver disease (NAFLD) [ 6 ][ 7 ] , subclinical atherosclerosis [ 8 – 10 ] , cerebrovascular disease [ 11 ] , and cardiovascular diseases [ 8 ][ 9 ][ 11 ] . Intriguingly, accumulating evidence suggests that H. pylori infection itself may be an independent risk factor for adverse metabolic profiles. This is supported by a systematic review linking it to a higher risk of metabolic syndrome and insulin resistance [ 12 ] . Observational studies further associate the infection with dyslipidemia [ 13 ][ 14 ] and with impaired endothelial function, a key event in atherosclerosis [ 15 ] . This raises the question of whether H. pylori infection influences systemic metabolism in a way that is detectable by the TyG index. Recent studies have begun to explore this direct link [ 16 ] , with some cohort studies further indicating that an elevated TyG index is associated with increased mortality and cardiovascular risk specifically in individuals with H. pylori infection [ 17 ][ 18 ] . Nonetheless, the overall evidence regarding the association between H. pylori seropositivity and the TyG index remains inconsistent [ 19 ] , and data are particularly scarce in multi-ethnic cohorts from high-risk regions such as the southwestern border of China. Given this significant gap in evidence, the present study aimed to investigate the association between H. pylori seropositivity and the TyG index in a multi-ethnic adult population from Southwest China. We sought to (1) assess their independent relationship; (2) characterize the dose-response patterns using linear and non-linear models; and (3) identify potential effect modifiers through stratified analyses. These findings are intended to enhance our understanding of the extra-gastric metabolic implications of H. pylori infection, especially in under-represented and ethnically diverse communities. 2. MATERIALS AND METHODS 2.1 Study population This study leveraged a large-scale, multi-ethnic health-screening cohort from Wenshan Prefecture, Yunnan Province, in Southwest China. Data were collected between January 2020 and December 2024. The study protocol was approved by the Institutional Review Board of the People's Hospital of Wenshan Prefecture (Approval No: 2025-006 (LW)-01), and the requirement for individual informed consent was waived for this retrospective analysis of anonymized routine health check-up data. Participant selection is outlined in Fig. 1 . From an initial pool, 6,998 adults were included in the final analysis. Inclusion required completion of a comprehensive examination, including abdominal ultrasound, complete blood count, infection screening, ¹⁴C-urea breath test, and a full biochemical panel (covering liver and renal function, and lipids). Key exclusion criteria were: habitual heavy alcohol consumption (≥ 210 g/week for men or ≥ 140 g/week for women); a history of hepatic or biliary tract disease; recent use of medications affecting H. pylori test results (e.g., proton-pump inhibitors, antibiotics, or bismuth); and any history of malignancy or psychiatric disorders. The TyG index was calculated for all participants. Based on ¹⁴C-urea breath test results, the cohort comprised 2,736 H. pylori -positive and 4,262 H. pylori -negative individuals, forming the final analytic sample for investigating the association between H. pylori infection and the TyG index. 2.2 Helicobacter pylori infection diagnosis The 14C-urea breath test (14C-UBT) is a well-validated, non-invasive standard for diagnosing active Helicobacter pylori infection [ 20 ][ 21 ] . In this study, we employed the HUBT-20A2 Helicobacter pylori Detector (HEADWAY), a single-sample liquid scintillation counter. The test was performed and results were interpreted strictly in accordance with the manufacturer’s protocol, wherein a measured value exceeding 50 DPM after background subtraction was defined as a positive result [ 22 ] . 2.3 Laboratory Testing Blood samples should be collected after the subject has fasted for at least 8 hours. The blood tests include assessments of age, gender, complete blood cell counts, and metabolic indicators, such as total cholesterol, triglycerides, LDL, HDL, blood glucose, and uric acid. Liver function tests include measurements of total protein, albumin, globulin, albumin-to-globulin ratio, alanine aminotransferase, gamma-glutamyl transferase, alkaline phosphatase, total bilirubin, direct bilirubin, indirect bilirubin, and serum bile acids. These tests are performed using the SIEMENS ADVIA 2400 Chemistry System, an automated biochemical analyzer that ensures precise measurements of these indicators. The AI KANG DR6660 -4 instrument is used to detect hepatitis B virus markers such as surface antigens and antibodies. Its automated process of identifying four distinct phases is called the 'immunofocusing instrument test.' 2.4 Definition of variables 2.4.1 Age grouping criteria: To explore potential non-linear or life-stage-specific variations in metabolic risk, participants’ age was analyzed both as a continuous variable and categorized into 12-year interval groups (spanning from 24 years old). This extended interval approach facilitates the examination of long-term metabolic trajectory patterns, and its conceptual framework has precedent in demographic and behavioral studies examining cyclical patterns in health outcomes. 2.4.2 TyG: Definition and Formula The triglyceride-glucose (TyG) index, calculated as ln[fasting triglycerides (mg/dL) × fasting glucose (mg/dL) / 2], is a well-validated surrogate marker for insulin resistance and cardiometabolic risk [ 23 – 25 ] . For the present analysis, a TyG index value ≥ 8.5 was used to define elevated insulin resistance, a threshold established in a hyperinsulinemic-euglycemic clamp validation study [ 26 ] . 2.4.3 Diagnostic criteria for abnormal liver function (liver damage): Elevated liver enzymes are characterized by alanine aminotransferase (ALT) levels ≥ 50 IU/L, aspartate aminotransferase (AST) levels ≥ 40 IU/L, or γ-glutamyl transferase (GGT) levels ≥ 60 IU/L. Elevated bilirubin is indicated by total bilirubin (TBIL) > 17.1 µmol/L or direct bilirubin (DBIL) > 6.8 µmol/L.Liver damage is indicated by any liver enzyme level exceeding the normal upper limit [ 27 ] . 2.4.4 Definitions of metabolic indicators: glucose metabolism, lipid metabolism, uric acid metabolism, and nitrogen metabolism: The diagnostic criteria for metabolic indicators were defined as follows. For glucose metabolism: normal fasting blood glucose (FBG) was defined as < 6.1 mmol/L, impaired fasting glucose (IFG) as FBG ≥ 6.1 and < 7.0 mmol/L, and diabetes mellitus (DM) as FBG ≥ 7.0 mmol/L [ 28 ][ 29 ] . For lipid metabolism (dyslipidemia), based on NCEP-ATP III and Chinese guidelines [ 30 ][ 31 ] , diagnosis required one or more of: triglycerides ≥ 2.26 mmol/L, LDL-C ≥ 4.14 mmol/L, HDL-C 420 µmol/L [ 32 ] . For nitrogen metabolism, standard laboratory reference intervals were used: blood urea nitrogen (BUN) 2.9–7.5 mmol/L and serum creatinine (Scr) 54–106 µmol/L [ 33 ] . 2.5 Statistical Analysis Statistical analyses were performed in R 4.3.2 (R Foundation for Statistical Computing, Vienna, Austria); SPSS 27.0 (IBM Corp., Armonk, NY) was used for data validation. Continuous variables were examined for normality (Shapiro-Wilk test and Q-Q plots). Normally distributed data are presented as mean ± SD and compared using the independent-sample t-test; non-normally distributed data are reported as median (IQR) and compared with the Mann-Whitney U test. Categorical variables are summarized as n (%) and analyzed by the χ² test or Fisher's exact test where appropriate. There were no missing data for any variable; complete-case analysis was therefore identical to the full sample. Univariate logistic regression was first applied to screen potential risk factors associated with H. pylori infection (variables with p < 0.10 were candidates for subsequent multivariable analysis). Notably, in light of the unavailability of height and weight data (and thus body mass index) in this health-screening dataset, our multivariable models incorporated a comprehensive panel of metabolic parameters that serve as strong surrogates and consequences of adiposity, including fasting plasma glucose, triglycerides, total cholesterol, LDL-C, HDL-C, and uric acid. This adjustment strategy partially captures the metabolic dysregulation associated with obesity, addressing a key potential source of unmeasured confounding. The linearity of continuous variables in the logit scale was verified using restricted cubic splines with 4 knots; no significant departure from linearity was found (p > 0.10). No interaction terms were included because preliminary analyses showed all interaction p > 0.10. To further quantify the robustness of our primary findings to potential unmeasured confounding (including by obesity), we conducted an E-value sensitivity analysis (see Sensitivity Analysis section). Finally, all statistical tests were two-tailed, and p < 0.05 was considered statistically significant. Our analytical approach, combined with the biological plausibility of the link between chronic H. pylori infection, systemic inflammation, and insulin resistance, aims to establish a robust association within this well-characterized cohort, independent of a single anthropometric measure. 3. RESULTS 3.1 Participant Demographics and Clinical Characteristics The study population exhibited distinct characteristics based on Helicobacter pylori status. Demographic features indicated a higher proportion of males in the H. pylori -positive group (40.6% vs. 59.4%, P < 0.001) and an older age (41.0 ± 12.0 years vs. 39.0 ± 12.0 years, P < 0.001). Metabolically, the positive group demonstrated significantly higher values for the TyG index (8.83 ± 0.73 vs. 8.76 ± 0.70, P < 0.001), fasting blood glucose (5.22 ± 1.29 vs. 5.12 ± 1.05 mmol/L, P < 0.001), triglycerides (2.17 ± 2.53 vs. 2.01 ± 1.95 mmol/L, P = 0.003), and total cholesterol (4.95 ± 0.97 vs. 4.82 ± 0.91 mmol/L, P 0.05). Regarding liver function, the positive group exhibited significantly lower levels of total bilirubin (12.7 ± 6.1 vs. 13.1 ± 6.0 µmol/L, P = 0.015) and indirect bilirubin (8.3 ± 4.7 vs. 8.7 ± 4.8 µmol/L, P < 0.001). Hematological analysis revealed significantly higher counts of white blood cells (6.52 ± 1.63 vs. 6.38 ± 1.60 × 10⁹/L, P < 0.001) and lymphocytes (2.19 ± 0.60 vs. 2.16 ± 0.60 × 10⁹/L, P = 0.036) in the positive group (Table 1 ). Table 1 Baseline Characteristics of the Study Population Stratified by Helicobacter pylori Status Characteristic H. pylori (-) (n = 4,262) H. pylori (+) (n = 2,736) P -value Demographics Sex, n (%) < 0.001 Male 3142 (59.4) 2145 (40.6) Female 1120 (65.5) 591 (34.5) Age, years 39.0 ± 12.0 41.0 ± 12.0 < 0.001 H. pylori Infection 14 C-UBT, dpm 17 ± 15 114 ± 61 < 0.001 Metabolic Parameters TyG index 8.76 ± 0.70 8.83 ± 0.73 < 0.001 Fasting plasma glucose, mmol/L 5.12 ± 1.05 5.22 ± 1.29 0.05 Total cholesterol, mmol/L 4.82 ± 0.91 4.95 ± 0.97 0.05 Albumin, g/L 48.2 ± 2.9 48.2 ± 2.9 > 0.05 Albumin-globulin ratio 1.8 ± 0.3 1.8 ± 0.3 > 0.05 Total bilirubin, µmol/L 13.1 ± 6.0 12.7 ± 6.1 0.015 Direct bilirubin, µmol/L 4.3 ± 1.8 4.4 ± 1.9 > 0.05 Indirect bilirubin, µmol/L 8.7 ± 4.8 8.3 ± 4.7 0.05 Alkaline phosphatase, U/L 76 ± 20 77 ± 22 0.003 γ-Glutamyl transferase, U/L 44 ± 56 47 ± 50 0.007 Alanine aminotransferase, U/L 27 ± 21 29 ± 53 > 0.05 Aspartate aminotransferase, U/L 23 ± 11 24 ± 30 0.044 Renal Function and Uric Acid Uric acid, µmol/L 386 ± 94 389 ± 93 > 0.05 Blood urea nitrogen, mmol/L 4.84 ± 1.25 4.89 ± 1.31 > 0.05 Creatinine, µmol/L 83.1 ± 17.6 83.0 ± 18.6 > 0.05 Blood Cell Counts Red blood cells, ×10¹²/L 5.20 ± 0.52 5.22 ± 0.51 > 0.05 Hemoglobin, g/L 158 ± 15 159 ± 15 > 0.05 White blood cells, ×10⁹/L 6.38 ± 1.60 6.52 ± 1.63 0.05 Absolute lymphocyte count, ×10⁹/L 2.16 ± 0.60 2.19 ± 0.60 0.036 Absolute monocyte count, ×10⁹/L 0.36 ± 0.12 0.37 ± 0.12 < 0.001 Absolute neutrophil count, ×10⁹/L 3.67 ± 1.22 3.77 ± 1.26 0.001 Note:Values are presented as n (%) or mean ± standard deviation. P values were calculated using the independent-samples t-test for continuous variables and Pearson's chi-square test for categorical variables. Abbreviations: as in the Abbreviations section. 3.2 Factors Associated with Helicobacter pylori Infection Univariate logistic regression analysis revealed that gender, age, TyG index, total bilirubin, indirect bilirubin, alkaline phosphatase, γ-glutamyltransferase, fasting blood glucose, low-density lipoprotein cholesterol, total cholesterol, triglycerides, white blood cell count, lymphocyte count, monocyte count, and neutrophil count were all significantly associated with Helicobacter pylori infection (P < 0.05). Specifically: For each 1-unit increase in TyG index, the infection risk rose by 14%; males had a 29% higher infection risk than females; for each additional year of age, the infection risk increased by 1%. The albumin/globulin ratio (A/G) showed a borderline significant association with infection risk (P = 0.054). Other variables (including total protein and albumin) were not significantly associated with Helicobacter pylori infection risk (Table 2 ). Table 2 Univariate Logistic Regression Analysis of Factors Associated with Helicobacter pylori Infection Variable OR (95% CI) p -value Demographics Sex (Male vs. Female) 1.29 (1.15–1.45) < 0.001 Age (per year) 1.01 (1.01–1.02) < 0.001 Metabolic Parameters TyG index 1.14 (1.06–1.21) < 0.001 FPG (mmol/L) 1.08 (1.03–1.12) < 0.001 LDL-C (mmol/L) 1.11 (1.04–1.18) 0.001 TC (mmol/L) 1.15 (1.09–1.21) < 0.001 TG (mmol/L) 1.03 (1.01–1.06) 0.004 HDL-C (mmol/L) 0.92 (0.79–1.06) 0.238 Liver Function Total protein (g/L) 1.00 (0.99–1.01) 0.536 Albumin (g/L) 0.99 (0.97–1.01) 0.265 Albumin-globulin ratio 0.86 (0.73–1.00) 0.054 Total bilirubin (µmol/L) 0.99 (0.98–1.00) 0.015 Direct bilirubin (µmol/L) 1.02 (1.00–1.05) 0.078 Indirect bilirubin (µmol/L) 0.98 (0.97–0.99) < 0.001 Total bile acid (µmol/L) 1.00 (0.99–1.00) 0.355 Alkaline phosphatase (U/L) 1.00 (1.00–1.01) 0.003 γ-Glutamyl transferase (U/L) 1.00 (1.00–1.00) 0.008 Alanine aminotransferase (U/L) 1.00 (1.00–1.00) 0.084 Aspartate aminotransferase (U/L) 1.00 (1.00–1.01) 0.07 Renal Function and Uric Acid Uric acid (µmol/L) 1.00 (1.00–1.00) 0.105 Blood urea nitrogen (mmol/L) 1.04 (1.00–1.08) 0.06 Creatinine (µmol/L) 1.00 (1.00–1.00) 0.738 Blood Cell Counts Red blood cells (×10¹²/L) 1.07 (0.98–1.17) 0.145 Hemoglobin (g/L) 1.00 (1.00–1.01) 0.065 White blood cells (×10⁹/L) 1.06 (1.03–1.09) < 0.001 Platelets (×10⁹/L) 1.00 (1.00–1.00) 0.626 Absolute lymphocyte count (×10⁹/L) 1.09 (1.01–1.18) 0.036 Absolute monocyte count (×10⁹/L) 2.16 (1.46–3.20) < 0.001 Absolute neutrophil count (×10⁹/L) 1.07 (1.03–1.11) 0.001 Notes:1.Abbreviations: as in the Abbreviations section.2.All continuous variables were included in the model as linear terms. The OR represents the change in odds per unit increase in the variable. 3.3 Univariate Stratified Analysis of Helicobacter pylori and TyG Index Based on the results of univariate logistic regression analysis in Table 2 , significant variables underwent further stratified analysis to explore their potential associations with Helicobacter pylori infection. Stratified analysis revealed that multiple factors were significantly associated with Helicobacter pylori infection (p < 0.05). Among demographic factors, males (OR = 1.29) and older age (≥ 60 years, OR = 1.95) demonstrated significantly elevated risks. Metabolic indicators showed significant positive correlations, including elevated TyG index (OR = 1.13), high fasting blood glucose (≥ 7.0 mmol/L, OR = 1.35), elevated total cholesterol (≥ 5.18 mmol/L, OR 1.31–1.32), elevated triglycerides (≥ 2.26 mmol/L, OR 1.16), and elevated low-density lipoprotein cholesterol (≥ 3.37 mmol/L, OR 1.15–1.22). Among liver function parameters, elevated γ-glutamyltransferase (≥ 60 U/L, OR 1.24) similarly demonstrated a significantly increased risk. Concurrently, elevated indirect bilirubin (≥ 13.7 µmol/L) showed a potential negative correlation trend (OR 0.87, p = 0.062). No statistically significant associations were observed for total bilirubin, alkaline phosphatase, specific lipid stratification, or any blood cell count stratification (all p ≥ 0.05) (Table 3 ). Table 3 Stratified Univariate Logistic Regression Analysis of Factors Associated with Helicobacter pylori Infection Variable & Stratum OR (95% CI) p -value Demographics Sex (Male vs. Female) 1.29 (1.15–1.45) < 0.001 Age, years 25–35 (vs. ≤24) 1.10 (0.79–1.54) 0.567 36–47 (vs. ≤24) 1.33 (0.95–1.86) 0.098 48–59 (vs. ≤24) 1.55 (1.10–2.18) 0.013 ≥ 60 (vs. ≤24) 1.95 (1.34–2.85) < 0.001 Metabolic Parameters TyG Index (≥ 8.5 vs. <8.5) 1.13 (1.03–1.25) 0.014 Fasting Blood Glucose, mmol/L 6.1–6.9 (vs. <6.1) 1.19 (0.91–1.55) 0.202 ≥ 7.0 (vs. <6.1) 1.35 (1.05–1.73) 0.019 Total Cholesterol, mmol/L 5.18–6.21 (vs. <5.18) 1.31 (1.17–1.46) < 0.001 ≥ 6.22 (vs. <5.18) 1.32 (1.10–1.59) 0.003 Triglycerides, mmol/L 1.7–2.25 (vs. <1.7) 1.01 (0.88–1.15) 0.928 ≥ 2.26 (vs. <1.7) 1.16 (1.03–1.29) 0.011 LDL-C, mmol/L 3.37–4.13 (vs. <3.37) 1.15 (1.02–1.29) 0.021 ≥ 4.14 (vs. <3.37) 1.22 (1.02–1.46) 0.027 Liver Function Total Bilirubin, µmol/L (≥ 17.1 vs. <17.1) 0.99 (0.88–1.12) 0.932 Indirect Bilirubin, µmol/L (≥ 13.7 vs. <13.7) 0.87 (0.75–1.01) 0.062 Alkaline Phosphatase, U/L (≥ 129 vs. <129) 1.31 (0.93–1.85) 0.118 γ-Glutamyl Transferase, U/L (≥ 60 vs. <60) 1.24 (1.11–1.40) < 0.001 Blood Cell Counts White Blood Cells, ×10⁹/L (≥ 11 vs. <11) 1.46 (0.95–2.24) 0.084 Absolute Neutrophil Count, ×10⁹/L (≥ 7.7 vs. <7.7) 1.30 (0.82–2.05) 0.267 Absolute Lymphocyte Count, ×10⁹/L (≥ 4.0 vs. <4.0) 0.98 (0.57–1.67) 0.938 Absolute Monocyte Count, ×10⁹/L (≥ 1.0 vs. <1.0) 0.93 (0.22–3.91) 0.926 Notes:1. Abbreviations: as in the Abbreviations section. 2.Continuous variables were dichotomized or categorized based on standard clinical reference values or distribution-derived cut-off points as specified in the Methods section. The reference group for each variable is indicated in parentheses. 3.4 Independent Validation: Robust Association of the TyG Index with Helicobacter pylori Infection in Multivariable Models Multivariable logistic regression revealed robust, statistically significant associations with Helicobacter pylori infection across four sequentially adjusted models (all p < 0.05). Male sex (OR = 1.29, 95% CI 1.15–1.45) and advancing age (per-year OR = 1.01, 95% CI 1.01–1.02) remained stable, independent risk factors in every model. Among liver-function tests, indirect bilirubin (OR = 0.98) and alkaline phosphatase (OR = 1.00) stayed significant in Models 2–4. After entry in Model 3, metabolic markers–including the TyG index (OR = 1.14), fasting plasma glucose (OR = 1.08), LDL-C (OR = 1.11), total cholesterol (OR = 1.15), and triglycerides (OR = 1.03) –showed consistent positive associations that persisted in Model 4. Hematologic indices added in the final model identified white-blood-cell count (OR = 1.06), absolute lymphocyte count (OR = 1.09), absolute monocyte count (OR = 2.16), and absolute neutrophil count (OR = 1.07) as independent predictors, with monocyte count displaying the strongest effect (OR > 2). Total protein, albumin, HDL–C, uric acid, creatinine, and platelet count were nonsignificant throughout (all p > 0.05). Thus, after stepwise adjustment for demographics, liver function, glucose–lipid metabolism, and inflammatory blood-cell parameters, the TyG index retained a stable, independent association with H. pylori infection (OR ≈ 1.14, p < 0.001) (Table 4 ). Table 4 Multivariable Logistic Regression Analysis of Factors Associated with Helicobacter pylori Infection Variable Model 1 (Demographics) Model 2 (+ Liver Function) Model 3 (+ Metabolic Parameters) Model 4 (+ Blood Cell Counts) OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) Demographics Sex (Male) 1.29 (1.15–1.45) 1.29 (1.15–1.45) 1.29 (1.15–1.45) 1.29 (1.15–1.45) Age (per year) 1.01 (1.01–1.02) 1.01 (1.01–1.02) 1.01 (1.01–1.02) 1.01 (1.01–1.02) Liver Function Total bilirubin (µmol/L) 0.99 (0.98-1.00) 0.99 (0.98-1.00) 0.99 (0.98-1.00) Indirect bilirubin (µmol/L) 0.98 (0.97–0.99) 0.98 (0.97–0.99) 0.98 (0.97–0.99) Alkaline phosphatase (U/L) 1.00 (1.00-1.01) 1.00 (1.00-1.01) 1.00 (1.00-1.01) γ-Glutamyl transferase (U/L) 1.00 (1.00–1.00) 1.00 (1.00–1.00) 1.00 (1.00–1.00) Metabolic Parameters TyG index 1.14 (1.06–1.21) 1.14 (1.06–1.21) Fasting plasma glucose (mmol/L) 1.08 (1.03–1.12) 1.08 (1.03–1.12) LDL-C (mmol/L) 1.11 (1.04–1.18) 1.11 (1.04–1.18) Total cholesterol (mmol/L) 1.15 (1.09–1.21) 1.15 (1.09–1.21) Triglycerides (mmol/L) 1.03 (1.01–1.06) 1.03 (1.01–1.06) Blood Cell Counts White blood cells (×10⁹/L) 1.06 (1.03–1.09) Absolute monocyte count (×10⁹/L) 2.16 (1.46–3.20) Absolute neutrophil count (×10⁹/L) 1.07 (1.03–1.11) Notes: Notes: 1. Abbreviations: as in the Abbreviations section. 2.All models were fitted using multivariable logistic regression. Data are presented as OR (95% CI). For continuous variables, the OR represents the change in the odds of infection per one-unit increase in the variable. 3.Model 1 was adjusted for demographics (sex, age). Model 2 was additionally adjusted for liver function parameters. Model 3 was additionally adjusted for metabolic parameters. Model 4 was additionally adjusted for blood cell counts. 4.The "—" symbol indicates that the variable was not included in that specific model. 5.Bolded OR values indicate a statistically significant association (p < 0.05). 6. Sensitivity analysis using the E-value suggests that the observed association between the TyG index and H. pylori infection (OR = 1.14) is reasonably robust to potential unmeasured confounding. The E-value for the point estimate is 1.54, and for the lower confidence limit (1.06) is 1.22. See the Results text (Section 3.5) for details. The results of the multivariable logistic regression analyses are detailed in Table 4. To better visualize the effect sizes and precision of the estimates from the fully adjusted model (Model 4), a forest plot is provided below (Fig 2). As visually summarized in the forest plot, the TyG index, alongside male sex, advanced age, an adverse lipid profile, and elevated inflammatory blood cell counts (particularly absolute monocyte count), remained independently associated with H. pylori infection after comprehensive adjustment. 3.5 Sensitivity Analysis for Unmeasured Confounding Sensitivity analysis using the E-value [34] was performed on the primary association observed in the fully adjusted Model 4 (Table 4), where the TyG index was significantly associated with H. pylori infection (OR = 1.14, 95% CI 1.06–1.21). The calculated E-value for the point estimate (OR = 1.14) was 1.54, and the E-value for the lower bound of the 95% confidence interval (OR = 1.06) was 1.22. This indicates that to fully nullify the observed association, an unmeasured confounder (e.g., BMI) would need to be associated with both a higher TyG index and H. pylori infection by risk ratios of at least 1.54-fold each, above and beyond the comprehensive set of covariates already adjusted for in our model (Fig 3-5). 4. DISCUSSION 4.1. Summary of Key Findings This study, conducted in a health-screening population from a multi-ethnic border region of China, reveals two key findings: first, a significant independent association between Helicobacter pylori infection and an elevated triglyceride-glucose (TyG) index, a surrogate marker of insulin resistance; and second, a strikingly strong association with elevated absolute monocyte count—a direct hematological indicator of systemic inflammation. After rigorous adjustment for a comprehensive set of demographic, hepatic, metabolic, and hematologic variables, and despite the lack of direct anthropometric data, these associations remained robust, as supported by quantitative sensitivity analysis. 4.2. Relationship of TyG Index with Existing Evidence After comprehensive adjustment for demographic characteristics, liver function parameters, metabolic profiles, and inflammatory blood cell counts, the TyG index remained robustly associated with H. pylori infection status (OR ≈ 1.14, P < 0.001). Our findings are consistent with a growing body of evidence linking H. pylori infection to systemic metabolic disorders, including diabetes [35-37] and insulin resistance [12][38] . Chronic H. pylori infection can induce low-grade systemic inflammation and oxidative stress, which are key pathophysiological mechanisms underlying insulin resistance and metabolic dysregulation [39][40] . The observed association between H. pylori infection and elevated levels of fasting plasma glucose, total cholesterol, triglycerides, and LDL-C in our study further supports its potential role in promoting an adverse metabolic phenotype. The TyG index, integrating both triglyceride and glucose levels, effectively captures this dysmetabolic state and has been validated as a marker of insulin resistance and a predictor of incident cardiovascular diseases [5][41][42] . Our findings extend this concept by establishing a specific association with Helicobacter pylori infection in a previously understudied multi-ethnic region. 4.3. Independent Risk Factors and Potential Inflammatory Pathway Notably, our study also identified several other independent factors associated with H. pylori infection. Consistent with epidemiological profiles across diverse populations, male gender [43][44] and advancing age [44][35] were confirmed as demographic risk factors. Among metabolic parameters, adverse lipid profiles (elevated TC, TG, LDL-C) and elevated fasting glucose were significantly associated with infection, reinforcing the metabolic interplay. The most pronounced association was between H. pylori infection and an elevated absolute monocyte count (OR > 2.0). Monocytes are circulating precursors of tissue macrophages and key mediators of innate immunity and chronic low-grade inflammation. Chronic gastric infection by H. pylori is known to induce a persistent local and systemic inflammatory response [15] . We hypothesize that this state of chronic immune activation leads to the mobilization and priming of monocytes. Once recruited into metabolic tissues such as visceral fat or liver, these activated monocytes/macrophages secrete pro-inflammatory cytokines (e.g., TNF-α, IL-6), which are well-established drivers of insulin resistance and hepatic dyslipidemia [40] . Therefore, the elevated monocyte count may represent a critical cellular link connecting persistent H. pylori infection to the systemic metabolic disturbances reflected by the TyG index. This pathway provides a plausible biological mechanism extending beyond the stomach, positioning H. pylori as a potential contributor to the inflammatory underpinnings of cardiometabolic diseases. Conversely, indirect bilirubin showed a modest inverse association, which might hint at a potential protective role. Bilirubin is known for its antioxidant and anti-inflammatory properties [6] , which could theoretically mitigate the establishment or persistence of infection, though this warrants further investigation. 4.4. Limitations and Strengths Our study has several limitations. First, the cross-sectional design precludes causal inference. Second, the lack of anthropometric data for BMI calculation is a notable constraint. However, we actively addressed this by: (i) adjusting for a comprehensive panel of metabolic covariates that are strong surrogates and consequences of adiposity; and (ii) performing a quantitative E-value sensitivity analysis, which suggested that the observed association between the TyG index and H. pylori is reasonably robust to potential confounding by unmeasured factors like obesity. Third, while the cohort was drawn from a defined regional population, which may affect generalizability, it provides a unique representation of an understudied multi-ethnic border community. Finally, the absence of detailed ethnic classification data prevents exploration of potential inter‑ethnic differences—an important area for future research. Strengths of this study include the large sample size, use of the ¹⁴C‑urea breath test for active H. pylori diagnosis, extensive laboratory phenotyping, and the application of rigorous statistical methods including sensitivity analysis to assess result stability. Most importantly, we highlight monocytosis as a novel and potent hematological correlate of H. pylori infection, offering a fresh perspective on its extra‑gastric inflammatory burden. 5. Conclusion In this health-screening population from China's southwestern border region, our study reveals a significant and independent association between Helicobacter pylori infection and an elevated triglyceride-glucose (TyG) index, a reliable surrogate marker of insulin resistance. This association remained robust after comprehensive adjustment for demographic, hepatic, metabolic, and hematologic variables. To our knowledge, this is the first study to identify the TyG index as a central metabolic link between H. pylori infection and an adverse profile of blood glucose and lipids in a multi-ethnic border population. This suggests that H. pylori infection may detrimentally impact cardiometabolic health by exacerbating insulin resistance. Consequently, the TyG index, owing to its simplicity and low cost, could serve as a practical clinical indicator. Our findings provide a new rationale for integrating metabolic screening into the management of H. pylori infection and for implementing comprehensive prevention strategies against metabolic syndrome in regions with high infection rates. ABBREVIATIONS The abbreviations used in this article are as follows:TC, total cholesterol; TG, triglycerides; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; FPG, fasting plasma glucose; 14 C-UBT, 14 C-urea breath test; TyG, triglyceride-glucose; OR, odds ratio; CI, confidence interval. Declarations ETHICS APPROVAL AND CONSENT TO PARTICIPATE This study was conducted in accordance with the principles of the Declaration of Helsinki. The study protocol was reviewed and approved by the Institutional Review Board / Ethics Committee of the People's Hospital of Wenshan Prefecture (Approval No: 2025-006 (LW) -01). The requirement for informed consent was waived by the same ethics committee due to the retrospective nature of the study and the use of anonymized data from a health-screening program. CONSENT FOR PUBLICATION Not applicable. The requirement for individual informed consent for publication was waived by the Institutional Review Board of the People's Hospital of Wenshan Prefecture because this study involved retrospective analysis of anonymized data. AVAILABILITY OF DATA AND MATERIALS The datasets generated and/or analysed during the current study are available in the figshare repository, under the citation: Zeng, Guangpin (2025). SWBorder_HP_TyG_Analysis_Database. figshare. Dataset. https://doi.org/10.6084/m9.figshare.30671456 COMPETING INTERESTS The authors declare no conflicts of interest. FUNDING This study was funded by the Wenshan Prefecture Hongju Yang Expert Workstation. AUTHORS' CONTRIBUTIONS GZ conceived the study, designed the protocol, oversaw data collection, and drafted the manuscript. LZ performed the statistical analysis, contributed to data interpretation, and critically revised the manuscript. YZ contributed to data curation, literature review, and manuscript preparation. 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Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 23 Feb, 2026 Reviews received at journal 19 Feb, 2026 Reviews received at journal 17 Feb, 2026 Reviewers agreed at journal 02 Feb, 2026 Reviewers agreed at journal 28 Jan, 2026 Reviewers agreed at journal 28 Jan, 2026 Reviewers invited by journal 23 Jan, 2026 Editor assigned by journal 17 Jan, 2026 Submission checks completed at journal 17 Jan, 2026 First submitted to journal 16 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8617767","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":580061733,"identity":"a89eb9c7-bf19-4b89-a1c0-7435dbc69e46","order_by":0,"name":"Guangpin 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selection.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8617767/v1/8ae11fc9560c2df309996f2a.png"},{"id":101397825,"identity":"be37ef73-c6ab-42ca-b38e-5c9dfb7548f6","added_by":"auto","created_at":"2026-01-29 09:37:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":254226,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical Representation of the Multivariable Models\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8617767/v1/c4f81446296eb72b176918c0.png"},{"id":101363193,"identity":"d1fe5604-335e-4df7-b00f-aba7cefaad0b","added_by":"auto","created_at":"2026-01-29 00:34:37","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":555749,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of TyG index and monocyte count by \u003cem\u003eH. pylori\u003c/em\u003e status, and their relationship\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8617767/v1/2252cae3dfb6e25a1713305a.png"},{"id":101363195,"identity":"31d6fc4e-dc4f-47d9-ad55-b2ed15a0b872","added_by":"auto","created_at":"2026-01-29 00:34:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":245648,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction between TyG index and sex on the risk of \u003cem\u003eH. pylori\u003c/em\u003e infection\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8617767/v1/d1fb20c125484e68d6b2a47c.png"},{"id":101363197,"identity":"dbb084ee-8fa0-4ab6-9c84-4d0acc6f5326","added_by":"auto","created_at":"2026-01-29 00:34:37","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":168858,"visible":true,"origin":"","legend":"\u003cp\u003eMediation analysis of the relationship between \u003cem\u003eH. pylori\u003c/em\u003e infection and TyG index through monocyte count\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8617767/v1/b9f075e7c93a8e3551d7e5c3.png"},{"id":101751625,"identity":"83302b69-0665-4513-b846-9f2ac7368859","added_by":"auto","created_at":"2026-02-03 10:21:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3193595,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8617767/v1/f1568db3-17ab-4710-bd5c-cc16d9ad1dc9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Beyond the Stomach: Linking H. pylori Seropositivity to Insulin Resistance (TyG Index) in a Multi-Ethnic High-Risk Population from Southwest China","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eWhile \u003cem\u003eHelicobacter pylori\u003c/em\u003e (\u003cem\u003eH. pylori\u003c/em\u003e) infection is associated with systemic low-grade inflammation\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e and may contribute to metabolic dysregulation by impairing insulin sensitivity\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e][\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e, the precise metabolic pathways underlying this interplay in specific high-risk populations remain to be elucidated \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. To quantify such metabolic disturbances, reliable and accessible biomarkers are essential. The triglyceride-glucose (TyG) index, calculated from fasting triglycerides and glucose levels, serves as a robust and validated surrogate marker of insulin resistance\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. A higher TyG index is also a strong predictor for the development of major cardiometabolic conditions, including non-alcoholic fatty liver disease (NAFLD)\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e][\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e, subclinical atherosclerosis\u003csup\u003e[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e, cerebrovascular disease\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e, and cardiovascular diseases\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e][\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e][\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIntriguingly, accumulating evidence suggests that \u003cem\u003eH. pylori\u003c/em\u003e infection itself may be an independent risk factor for adverse metabolic profiles. This is supported by a systematic review linking it to a higher risk of metabolic syndrome and insulin resistance \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. Observational studies further associate the infection with dyslipidemia\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e][\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e and with impaired endothelial function, a key event in atherosclerosis \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. This raises the question of whether \u003cem\u003eH. pylori\u003c/em\u003e infection influences systemic metabolism in a way that is detectable by the TyG index. Recent studies have begun to explore this direct link\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e, with some cohort studies further indicating that an elevated TyG index is associated with increased mortality and cardiovascular risk specifically in individuals with \u003cem\u003eH. pylori\u003c/em\u003e infection\u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e][\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Nonetheless, the overall evidence regarding the association between \u003cem\u003eH. pylori\u003c/em\u003e seropositivity and the TyG index remains inconsistent \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e, and data are particularly scarce in multi-ethnic cohorts from high-risk regions such as the southwestern border of China.\u003c/p\u003e \u003cp\u003eGiven this significant gap in evidence, the present study aimed to investigate the association between \u003cem\u003eH. pylori\u003c/em\u003e seropositivity and the TyG index in a multi-ethnic adult population from Southwest China. We sought to (1) assess their independent relationship; (2) characterize the dose-response patterns using linear and non-linear models; and (3) identify potential effect modifiers through stratified analyses. These findings are intended to enhance our understanding of the extra-gastric metabolic implications of \u003cem\u003eH. pylori\u003c/em\u003e infection, especially in under-represented and ethnically diverse communities.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study population\u003c/h2\u003e \u003cp\u003eThis study leveraged a large-scale, multi-ethnic health-screening cohort from Wenshan Prefecture, Yunnan Province, in Southwest China. Data were collected between January 2020 and December 2024. The study protocol was approved by the Institutional Review Board of the People's Hospital of Wenshan Prefecture (Approval No: 2025-006 (LW)-01), and the requirement for individual informed consent was waived for this retrospective analysis of anonymized routine health check-up data.\u003c/p\u003e \u003cp\u003eParticipant selection is outlined in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. From an initial pool, 6,998 adults were included in the final analysis. Inclusion required completion of a comprehensive examination, including abdominal ultrasound, complete blood count, infection screening, \u0026sup1;⁴C-urea breath test, and a full biochemical panel (covering liver and renal function, and lipids). Key exclusion criteria were: habitual heavy alcohol consumption (\u0026ge;\u0026thinsp;210 g/week for men or \u0026ge;\u0026thinsp;140 g/week for women); a history of hepatic or biliary tract disease; recent use of medications affecting \u003cem\u003eH. pylori\u003c/em\u003e test results (e.g., proton-pump inhibitors, antibiotics, or bismuth); and any history of malignancy or psychiatric disorders. The TyG index was calculated for all participants. Based on \u0026sup1;⁴C-urea breath test results, the cohort comprised 2,736 \u003cem\u003eH. pylori\u003c/em\u003e-positive and 4,262 \u003cem\u003eH. pylori\u003c/em\u003e-negative individuals, forming the final analytic sample for investigating the association between \u003cem\u003eH. pylori\u003c/em\u003e infection and the TyG index.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 \u003cem\u003eHelicobacter pylori\u003c/em\u003e infection diagnosis\u003c/h2\u003e \u003cp\u003eThe 14C-urea breath test (14C-UBT) is a well-validated, non-invasive standard for diagnosing active \u003cem\u003eHelicobacter pylori\u003c/em\u003e infection\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e][\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. In this study, we employed the HUBT-20A2 \u003cem\u003eHelicobacter pylori\u003c/em\u003e Detector (HEADWAY), a single-sample liquid scintillation counter. The test was performed and results were interpreted strictly in accordance with the manufacturer\u0026rsquo;s protocol, wherein a measured value exceeding 50 DPM after background subtraction was defined as a positive result \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Laboratory Testing\u003c/h2\u003e \u003cp\u003eBlood samples should be collected after the subject has fasted for at least 8 hours. The blood tests include assessments of age, gender, complete blood cell counts, and metabolic indicators, such as total cholesterol, triglycerides, LDL, HDL, blood glucose, and uric acid. Liver function tests include measurements of total protein, albumin, globulin, albumin-to-globulin ratio, alanine aminotransferase, gamma-glutamyl transferase, alkaline phosphatase, total bilirubin, direct bilirubin, indirect bilirubin, and serum bile acids. These tests are performed using the SIEMENS ADVIA 2400 Chemistry System, an automated biochemical analyzer that ensures precise measurements of these indicators. The AI KANG DR6660 -4 instrument is used to detect hepatitis B virus markers such as surface antigens and antibodies. Its automated process of identifying four distinct phases is called the 'immunofocusing instrument test.'\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Definition of variables\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Age grouping criteria:\u003c/h2\u003e \u003cp\u003eTo explore potential non-linear or life-stage-specific variations in metabolic risk, participants\u0026rsquo; age was analyzed both as a continuous variable and categorized into 12-year interval groups (spanning from 24 years old). This extended interval approach facilitates the examination of long-term metabolic trajectory patterns, and its conceptual framework has precedent in demographic and behavioral studies examining cyclical patterns in health outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 TyG: Definition and Formula\u003c/h2\u003e \u003cp\u003eThe triglyceride-glucose (TyG) index, calculated as ln[fasting triglycerides (mg/dL) \u0026times; fasting glucose (mg/dL) / 2], is a well-validated surrogate marker for insulin resistance and cardiometabolic risk \u003csup\u003e[\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. For the present analysis, a TyG index value\u0026thinsp;\u0026ge;\u0026thinsp;8.5 was used to define elevated insulin resistance, a threshold established in a hyperinsulinemic-euglycemic clamp validation study\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.4.3 Diagnostic criteria for abnormal liver function (liver damage):\u003c/h2\u003e \u003cp\u003eElevated liver enzymes are characterized by alanine aminotransferase (ALT) levels\u0026thinsp;\u0026ge;\u0026thinsp;50 IU/L, aspartate aminotransferase (AST) levels\u0026thinsp;\u0026ge;\u0026thinsp;40 IU/L, or γ-glutamyl transferase (GGT) levels\u0026thinsp;\u0026ge;\u0026thinsp;60 IU/L. Elevated bilirubin is indicated by total bilirubin (TBIL)\u0026thinsp;\u0026gt;\u0026thinsp;17.1 \u0026micro;mol/L or direct bilirubin (DBIL)\u0026thinsp;\u0026gt;\u0026thinsp;6.8 \u0026micro;mol/L.Liver damage is indicated by any liver enzyme level exceeding the normal upper limit\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.4.4 Definitions of metabolic indicators: glucose metabolism, lipid metabolism, uric acid metabolism, and nitrogen metabolism:\u003c/h2\u003e \u003cp\u003eThe diagnostic criteria for metabolic indicators were defined as follows. For glucose metabolism: normal fasting blood glucose (FBG) was defined as \u0026lt;\u0026thinsp;6.1 mmol/L, impaired fasting glucose (IFG) as FBG\u0026thinsp;\u0026ge;\u0026thinsp;6.1 and \u0026lt;\u0026thinsp;7.0 mmol/L, and diabetes mellitus (DM) as FBG\u0026thinsp;\u0026ge;\u0026thinsp;7.0 mmol/L\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e][\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. For lipid metabolism (dyslipidemia), based on NCEP-ATP III and Chinese guidelines \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e][\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e, diagnosis required one or more of: triglycerides\u0026thinsp;\u0026ge;\u0026thinsp;2.26 mmol/L, LDL-C\u0026thinsp;\u0026ge;\u0026thinsp;4.14 mmol/L, HDL-C\u0026thinsp;\u0026lt;\u0026thinsp;1.04 mmol/L, or total cholesterol\u0026thinsp;\u0026ge;\u0026thinsp;6.22 mmol/L. Hyperuricemia was defined as a fasting serum uric acid level\u0026thinsp;\u0026gt;\u0026thinsp;420 \u0026micro;mol/L\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. For nitrogen metabolism, standard laboratory reference intervals were used: blood urea nitrogen (BUN) 2.9\u0026ndash;7.5 mmol/L and serum creatinine (Scr) 54\u0026ndash;106 \u0026micro;mol/L \u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed in R 4.3.2 (R Foundation for Statistical Computing, Vienna, Austria); SPSS 27.0 (IBM Corp., Armonk, NY) was used for data validation. Continuous variables were examined for normality (Shapiro-Wilk test and Q-Q plots). Normally distributed data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD and compared using the independent-sample t-test; non-normally distributed data are reported as median (IQR) and compared with the Mann-Whitney U test. Categorical variables are summarized as n (%) and analyzed by the χ\u0026sup2; test or Fisher's exact test where appropriate. There were no missing data for any variable; complete-case analysis was therefore identical to the full sample.\u003c/p\u003e \u003cp\u003eUnivariate logistic regression was first applied to screen potential risk factors associated with \u003cem\u003eH. pylori\u003c/em\u003e infection (variables with p\u0026thinsp;\u0026lt;\u0026thinsp;0.10 were candidates for subsequent multivariable analysis). Notably, in light of the unavailability of height and weight data (and thus body mass index) in this health-screening dataset, our multivariable models incorporated a comprehensive panel of metabolic parameters that serve as strong surrogates and consequences of adiposity, including fasting plasma glucose, triglycerides, total cholesterol, LDL-C, HDL-C, and uric acid. This adjustment strategy partially captures the metabolic dysregulation associated with obesity, addressing a key potential source of unmeasured confounding. The linearity of continuous variables in the logit scale was verified using restricted cubic splines with 4 knots; no significant departure from linearity was found (p\u0026thinsp;\u0026gt;\u0026thinsp;0.10). No interaction terms were included because preliminary analyses showed all interaction p\u0026thinsp;\u0026gt;\u0026thinsp;0.10.\u003c/p\u003e \u003cp\u003eTo further quantify the robustness of our primary findings to potential unmeasured confounding (including by obesity), we conducted an E-value sensitivity analysis (see Sensitivity Analysis section). Finally, all statistical tests were two-tailed, and p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Our analytical approach, combined with the biological plausibility of the link between chronic \u003cem\u003eH. pylori\u003c/em\u003e infection, systemic inflammation, and insulin resistance, aims to establish a robust association within this well-characterized cohort, independent of a single anthropometric measure.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Participant Demographics and Clinical Characteristics\u003c/h2\u003e \u003cp\u003eThe study population exhibited distinct characteristics based on \u003cem\u003eHelicobacter pylori\u003c/em\u003e status. Demographic features indicated a higher proportion of males in the \u003cem\u003eH. pylori\u003c/em\u003e-positive group (40.6% vs. 59.4%, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and an older age (41.0\u0026thinsp;\u0026plusmn;\u0026thinsp;12.0 years vs. 39.0\u0026thinsp;\u0026plusmn;\u0026thinsp;12.0 years, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Metabolically, the positive group demonstrated significantly higher values for the TyG index (8.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73 vs. 8.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), fasting blood glucose (5.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29 vs. 5.12\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05 mmol/L, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), triglycerides (2.17\u0026thinsp;\u0026plusmn;\u0026thinsp;2.53 vs. 2.01\u0026thinsp;\u0026plusmn;\u0026thinsp;1.95 mmol/L, P\u0026thinsp;=\u0026thinsp;0.003), and total cholesterol (4.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97 vs. 4.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91 mmol/L, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while HDL-C showed no significant difference (1.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33 vs. 1.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33 mmol/L, P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Regarding liver function, the positive group exhibited significantly lower levels of total bilirubin (12.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1 vs. 13.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0 \u0026micro;mol/L, P\u0026thinsp;=\u0026thinsp;0.015) and indirect bilirubin (8.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7 vs. 8.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8 \u0026micro;mol/L, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Hematological analysis revealed significantly higher counts of white blood cells (6.52\u0026thinsp;\u0026plusmn;\u0026thinsp;1.63 vs. 6.38\u0026thinsp;\u0026plusmn;\u0026thinsp;1.60 \u0026times; 10⁹/L, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and lymphocytes (2.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60 vs. 2.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60 \u0026times; 10⁹/L, P\u0026thinsp;=\u0026thinsp;0.036) in the positive group (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics of the Study Population Stratified by \u003cem\u003eHelicobacter pylori\u003c/em\u003e Status\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eH. pylori\u003c/em\u003e (-)\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;4,262)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eH. pylori\u003c/em\u003e (+)\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2,736)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDemographics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex, n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3142 (59.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2145 (40.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1120 (65.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e591 (34.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39.0\u0026thinsp;\u0026plusmn;\u0026thinsp;12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.0\u0026thinsp;\u0026plusmn;\u0026thinsp;12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH. pylori\u003c/b\u003e \u003cb\u003eInfection\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003csup\u003e14\u003c/sup\u003eC-UBT, dpm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u0026thinsp;\u0026plusmn;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114\u0026thinsp;\u0026plusmn;\u0026thinsp;61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMetabolic Parameters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting plasma glucose, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.12\u0026thinsp;\u0026plusmn;\u0026thinsp;1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.28\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.01\u0026thinsp;\u0026plusmn;\u0026thinsp;1.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.17\u0026thinsp;\u0026plusmn;\u0026thinsp;2.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLiver Function\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal protein, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin-globulin ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal bilirubin, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.7\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect bilirubin, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.3\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect bilirubin, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal bile acid, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.2\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlkaline phosphatase, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76\u0026thinsp;\u0026plusmn;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77\u0026thinsp;\u0026plusmn;\u0026thinsp;22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eγ-Glutamyl transferase, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44\u0026thinsp;\u0026plusmn;\u0026thinsp;56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47\u0026thinsp;\u0026plusmn;\u0026thinsp;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlanine aminotransferase, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u0026thinsp;\u0026plusmn;\u0026thinsp;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29\u0026thinsp;\u0026plusmn;\u0026thinsp;53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspartate aminotransferase, U/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u0026thinsp;\u0026plusmn;\u0026thinsp;11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u0026thinsp;\u0026plusmn;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRenal Function and Uric Acid\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e386\u0026thinsp;\u0026plusmn;\u0026thinsp;94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e389\u0026thinsp;\u0026plusmn;\u0026thinsp;93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood urea nitrogen, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.84\u0026thinsp;\u0026plusmn;\u0026thinsp;1.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.89\u0026thinsp;\u0026plusmn;\u0026thinsp;1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine, \u0026micro;mol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e83.1\u0026thinsp;\u0026plusmn;\u0026thinsp;17.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.0\u0026thinsp;\u0026plusmn;\u0026thinsp;18.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlood Cell Counts\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed blood cells, \u0026times;10\u0026sup1;\u0026sup2;/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin, g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e158\u0026thinsp;\u0026plusmn;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e159\u0026thinsp;\u0026plusmn;\u0026thinsp;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood cells, \u0026times;10⁹/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.38\u0026thinsp;\u0026plusmn;\u0026thinsp;1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.52\u0026thinsp;\u0026plusmn;\u0026thinsp;1.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets, \u0026times;10⁹/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e238\u0026thinsp;\u0026plusmn;\u0026thinsp;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e238\u0026thinsp;\u0026plusmn;\u0026thinsp;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsolute lymphocyte count, \u0026times;10⁹/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.16\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsolute monocyte count, \u0026times;10⁹/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsolute neutrophil count, \u0026times;10⁹/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.67\u0026thinsp;\u0026plusmn;\u0026thinsp;1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.77\u0026thinsp;\u0026plusmn;\u0026thinsp;1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote:Values are presented as n (%) or mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation. P values were calculated using the independent-samples t-test for continuous variables and Pearson's chi-square test for categorical variables. Abbreviations: as in the Abbreviations section.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Factors Associated with \u003cem\u003eHelicobacter pylori\u003c/em\u003e Infection\u003c/h2\u003e \u003cp\u003eUnivariate logistic regression analysis revealed that gender, age, TyG index, total bilirubin, indirect bilirubin, alkaline phosphatase, γ-glutamyltransferase, fasting blood glucose, low-density lipoprotein cholesterol, total cholesterol, triglycerides, white blood cell count, lymphocyte count, monocyte count, and neutrophil count were all significantly associated with \u003cem\u003eHelicobacter pylori\u003c/em\u003e infection (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Specifically: For each 1-unit increase in TyG index, the infection risk rose by 14%; males had a 29% higher infection risk than females; for each additional year of age, the infection risk increased by 1%. The albumin/globulin ratio (A/G) showed a borderline significant association with infection risk (P\u0026thinsp;=\u0026thinsp;0.054). Other variables (including total protein and albumin) were not significantly associated with \u003cem\u003eHelicobacter pylori\u003c/em\u003e infection risk (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate Logistic Regression Analysis of Factors Associated with \u003cem\u003eHelicobacter pylori\u003c/em\u003e Infection\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDemographics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (Male vs. Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.29 (1.15\u0026ndash;1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (per year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (1.01\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMetabolic Parameters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.14 (1.06\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFPG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.08 (1.03\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.11 (1.04\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.15 (1.09\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.03 (1.01\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.92 (0.79\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLiver Function\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal protein (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.99\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.536\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.97\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.265\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlbumin-globulin ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.86 (0.73\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal bilirubin (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.98\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect bilirubin (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.02 (1.00\u0026ndash;1.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect bilirubin (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.97\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal bile acid (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.99\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.355\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlkaline phosphatase (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eγ-Glutamyl transferase (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlanine aminotransferase (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAspartate aminotransferase (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRenal Function and Uric Acid\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUric acid (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood urea nitrogen (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.04 (1.00\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlood Cell Counts\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRed blood cells (\u0026times;10\u0026sup1;\u0026sup2;/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.07 (0.98\u0026ndash;1.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood cells (\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.06 (1.03\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets (\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.626\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsolute lymphocyte count (\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.09 (1.01\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsolute monocyte count (\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.16 (1.46\u0026ndash;3.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsolute neutrophil count (\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.07 (1.03\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNotes:1.Abbreviations: as in the Abbreviations section.2.All continuous variables were included in the model as linear terms. The OR represents the change in odds per unit increase in the variable.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Univariate Stratified Analysis of \u003cem\u003eHelicobacter pylori\u003c/em\u003e and TyG Index\u003c/h2\u003e \u003cp\u003eBased on the results of univariate logistic regression analysis in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, significant variables underwent further stratified analysis to explore their potential associations with \u003cem\u003eHelicobacter pylori\u003c/em\u003e infection. Stratified analysis revealed that multiple factors were significantly associated with \u003cem\u003eHelicobacter pylori\u003c/em\u003e infection (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Among demographic factors, males (OR\u0026thinsp;=\u0026thinsp;1.29) and older age (\u0026ge;\u0026thinsp;60 years, OR\u0026thinsp;=\u0026thinsp;1.95) demonstrated significantly elevated risks. Metabolic indicators showed significant positive correlations, including elevated TyG index (OR\u0026thinsp;=\u0026thinsp;1.13), high fasting blood glucose (\u0026ge;\u0026thinsp;7.0 mmol/L, OR\u0026thinsp;=\u0026thinsp;1.35), elevated total cholesterol (\u0026ge;\u0026thinsp;5.18 mmol/L, OR 1.31\u0026ndash;1.32), elevated triglycerides (\u0026ge;\u0026thinsp;2.26 mmol/L, OR 1.16), and elevated low-density lipoprotein cholesterol (\u0026ge;\u0026thinsp;3.37 mmol/L, OR 1.15\u0026ndash;1.22). Among liver function parameters, elevated γ-glutamyltransferase (\u0026ge;\u0026thinsp;60 U/L, OR 1.24) similarly demonstrated a significantly increased risk. Concurrently, elevated indirect bilirubin (\u0026ge;\u0026thinsp;13.7 \u0026micro;mol/L) showed a potential negative correlation trend (OR 0.87, p\u0026thinsp;=\u0026thinsp;0.062). No statistically significant associations were observed for total bilirubin, alkaline phosphatase, specific lipid stratification, or any blood cell count stratification (all p\u0026thinsp;\u0026ge;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStratified Univariate Logistic Regression Analysis of Factors Associated with \u003cem\u003eHelicobacter pylori\u003c/em\u003e Infection\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable \u0026amp; Stratum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDemographics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (Male vs. Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.29 (1.15\u0026ndash;1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, years\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u0026ndash;35 (vs. \u0026le;24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.10 (0.79\u0026ndash;1.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.567\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u0026ndash;47 (vs. \u0026le;24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.33 (0.95\u0026ndash;1.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e48\u0026ndash;59 (vs. \u0026le;24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.55 (1.10\u0026ndash;2.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60 (vs. \u0026le;24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.95 (1.34\u0026ndash;2.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMetabolic Parameters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG Index (\u0026ge;\u0026thinsp;8.5 vs. \u0026lt;8.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.13 (1.03\u0026ndash;1.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting Blood Glucose, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.1\u0026ndash;6.9 (vs. \u0026lt;6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.19 (0.91\u0026ndash;1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;7.0 (vs. \u0026lt;6.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.35 (1.05\u0026ndash;1.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Cholesterol, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.18\u0026ndash;6.21 (vs. \u0026lt;5.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.31 (1.17\u0026ndash;1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;6.22 (vs. \u0026lt;5.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.32 (1.10\u0026ndash;1.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.7\u0026ndash;2.25 (vs. \u0026lt;1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (0.88\u0026ndash;1.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.928\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2.26 (vs. \u0026lt;1.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.16 (1.03\u0026ndash;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C, mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.37\u0026ndash;4.13 (vs. \u0026lt;3.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.15 (1.02\u0026ndash;1.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;4.14 (vs. \u0026lt;3.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.22 (1.02\u0026ndash;1.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLiver Function\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Bilirubin, \u0026micro;mol/L (\u0026ge;\u0026thinsp;17.1 vs. \u0026lt;17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.99 (0.88\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.932\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect Bilirubin, \u0026micro;mol/L (\u0026ge;\u0026thinsp;13.7 vs. \u0026lt;13.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.87 (0.75\u0026ndash;1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlkaline Phosphatase, U/L (\u0026ge;\u0026thinsp;129 vs. \u0026lt;129)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.31 (0.93\u0026ndash;1.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eγ-Glutamyl Transferase, U/L (\u0026ge;\u0026thinsp;60 vs. \u0026lt;60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.24 (1.11\u0026ndash;1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlood Cell Counts\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite Blood Cells, \u0026times;10⁹/L (\u0026ge;\u0026thinsp;11 vs. \u0026lt;11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.46 (0.95\u0026ndash;2.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.084\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsolute Neutrophil Count, \u0026times;10⁹/L (\u0026ge;\u0026thinsp;7.7 vs. \u0026lt;7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.30 (0.82\u0026ndash;2.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsolute Lymphocyte Count, \u0026times;10⁹/L (\u0026ge;\u0026thinsp;4.0 vs. \u0026lt;4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.98 (0.57\u0026ndash;1.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.938\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsolute Monocyte Count, \u0026times;10⁹/L (\u0026ge;\u0026thinsp;1.0 vs. \u0026lt;1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93 (0.22\u0026ndash;3.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.926\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eNotes:1. Abbreviations: as in the Abbreviations section. 2.Continuous variables were dichotomized or categorized based on standard clinical reference values or distribution-derived cut-off points as specified in the Methods section. The reference group for each variable is indicated in parentheses.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Independent Validation: Robust Association of the TyG Index with \u003cem\u003eHelicobacter pylori\u003c/em\u003e Infection in Multivariable Models\u003c/h2\u003e \u003cp\u003eMultivariable logistic regression revealed robust, statistically significant associations with \u003cem\u003eHelicobacter pylori\u003c/em\u003e infection across four sequentially adjusted models (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Male sex (OR\u0026thinsp;=\u0026thinsp;1.29, 95% CI 1.15\u0026ndash;1.45) and advancing age (per-year OR\u0026thinsp;=\u0026thinsp;1.01, 95% CI 1.01\u0026ndash;1.02) remained stable, independent risk factors in every model. Among liver-function tests, indirect bilirubin (OR\u0026thinsp;=\u0026thinsp;0.98) and alkaline phosphatase (OR\u0026thinsp;=\u0026thinsp;1.00) stayed significant in Models 2\u0026ndash;4. After entry in Model 3, metabolic markers\u0026ndash;including the TyG index (OR\u0026thinsp;=\u0026thinsp;1.14), fasting plasma glucose (OR\u0026thinsp;=\u0026thinsp;1.08), LDL-C (OR\u0026thinsp;=\u0026thinsp;1.11), total cholesterol (OR\u0026thinsp;=\u0026thinsp;1.15), and triglycerides (OR\u0026thinsp;=\u0026thinsp;1.03) \u0026ndash;showed consistent positive associations that persisted in Model 4. Hematologic indices added in the final model identified white-blood-cell count (OR\u0026thinsp;=\u0026thinsp;1.06), absolute lymphocyte count (OR\u0026thinsp;=\u0026thinsp;1.09), absolute monocyte count (OR\u0026thinsp;=\u0026thinsp;2.16), and absolute neutrophil count (OR\u0026thinsp;=\u0026thinsp;1.07) as independent predictors, with monocyte count displaying the strongest effect (OR\u0026thinsp;\u0026gt;\u0026thinsp;2). Total protein, albumin, HDL\u0026ndash;C, uric acid, creatinine, and platelet count were nonsignificant throughout (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Thus, after stepwise adjustment for demographics, liver function, glucose\u0026ndash;lipid metabolism, and inflammatory blood-cell parameters, the TyG index retained a stable, independent association with \u003cem\u003eH. pylori\u003c/em\u003e infection (OR\u0026thinsp;\u0026asymp;\u0026thinsp;1.14, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable Logistic Regression Analysis of Factors Associated with \u003cem\u003eHelicobacter pylori\u003c/em\u003e Infection\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003cp\u003e(Demographics)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003cp\u003e(+\u0026thinsp;Liver Function)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003cp\u003e(+\u0026thinsp;Metabolic Parameters)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 4\u003c/p\u003e \u003cp\u003e(+\u0026thinsp;Blood Cell Counts)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.29 (1.15\u0026ndash;1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.29 (1.15\u0026ndash;1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.29 (1.15\u0026ndash;1.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.29 (1.15\u0026ndash;1.45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (per year)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.01 (1.01\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.01 (1.01\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.01 (1.01\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.01 (1.01\u0026ndash;1.02)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLiver Function\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal bilirubin (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99 (0.98-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99 (0.98-1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99 (0.98-1.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect bilirubin (\u0026micro;mol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98 (0.97\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.98 (0.97\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.98 (0.97\u0026ndash;0.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlkaline phosphatase (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00 (1.00-1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00 (1.00-1.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00 (1.00-1.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eγ-Glutamyl transferase (U/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00 (1.00\u0026ndash;1.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMetabolic Parameters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyG index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.14 (1.06\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.14 (1.06\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting plasma glucose (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.08 (1.03\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.08 (1.03\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.11 (1.04\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.11 (1.04\u0026ndash;1.18)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal cholesterol (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.15 (1.09\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.15 (1.09\u0026ndash;1.21)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides (mmol/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.03 (1.01\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.03 (1.01\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlood Cell Counts\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite blood cells (\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.06 (1.03\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsolute monocyte count (\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.16 (1.46\u0026ndash;3.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsolute neutrophil count (\u0026times;10⁹/L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.07 (1.03\u0026ndash;1.11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes:\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\u003cp\u003eNotes:\u003c/p\u003e\n\u003cp\u003e1. Abbreviations: as in the Abbreviations section.\u003c/p\u003e\n\u003cp\u003e2.All models were fitted using multivariable logistic regression. Data are presented as OR (95% CI). For continuous variables, the OR represents the change in the odds of infection per one-unit increase in the variable.\u003c/p\u003e\n\u003cp\u003e3.Model 1 was adjusted for demographics (sex, age). Model 2 was additionally adjusted for liver function parameters. Model 3 was additionally adjusted for metabolic parameters. Model 4 was additionally adjusted for blood cell counts.\u003c/p\u003e\n\u003cp\u003e4.The \u0026quot;\u0026mdash;\u0026quot; symbol indicates that the variable was not included in that specific model.\u003c/p\u003e\n\u003cp\u003e5.Bolded OR values indicate a statistically significant association (p \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003e6. Sensitivity analysis using the E-value suggests that the observed association between the TyG index and \u003cem\u003eH. pylori\u003c/em\u003e infection (OR = 1.14) is reasonably robust to potential unmeasured confounding. The E-value for the point estimate is 1.54, and for the lower confidence limit (1.06) is 1.22. See the Results text (Section 3.5) for details.\u003c/p\u003e\n\u003cp\u003eThe results of the multivariable logistic regression analyses are detailed in Table 4. To better visualize the effect sizes and precision of the estimates from the fully adjusted model (Model 4), a forest plot is provided below (Fig 2).\u003c/p\u003e\n\u003cp\u003eAs visually summarized in the forest plot, the TyG index, alongside male sex, advanced age, an adverse lipid profile, and elevated inflammatory blood cell counts (particularly absolute monocyte count), remained independently associated with \u003cem\u003eH. pylori\u003c/em\u003e infection after comprehensive adjustment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Sensitivity Analysis for Unmeasured Confounding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSensitivity analysis using the E-value\u003csup\u003e[34]\u003c/sup\u003e was performed on the primary association observed in the fully adjusted Model 4 (Table 4), where the TyG index was significantly associated with \u003cem\u003eH. pylori\u003c/em\u003e infection (OR = 1.14, 95% CI 1.06\u0026ndash;1.21). The calculated E-value for the point estimate (OR = 1.14) was 1.54, and the E-value for the lower bound of the 95% confidence interval (OR = 1.06) was 1.22. This indicates that to fully nullify the observed association, an unmeasured confounder (e.g., BMI) would need to be associated with both a higher TyG index and \u003cem\u003eH. pylori\u003c/em\u003e infection by risk ratios of at least 1.54-fold each, above and beyond the comprehensive set of covariates already adjusted for in our model (Fig 3-5).\u003c/p\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003e\u003cstrong\u003e4.1. Summary of Key Findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study, conducted in a health-screening population from a multi-ethnic border region of China, reveals two key findings: first, a significant independent association between \u003cem\u003eHelicobacter pylori\u003c/em\u003e infection and an elevated triglyceride-glucose (TyG) index, a surrogate marker of insulin resistance; and second, a strikingly strong association with elevated absolute monocyte count\u0026mdash;a direct hematological indicator of systemic inflammation. After rigorous adjustment for a comprehensive set of demographic, hepatic, metabolic, and hematologic variables, and despite the lack of direct anthropometric data, these associations remained robust, as supported by quantitative sensitivity analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2. Relationship of TyG Index with Existing Evidence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter comprehensive adjustment for demographic characteristics, liver function parameters, metabolic profiles, and inflammatory blood cell counts, the TyG index remained robustly associated with \u003cem\u003eH. pylori\u003c/em\u003e infection status (OR \u0026asymp; 1.14, P \u0026lt; 0.001). Our findings are consistent with a growing body of evidence linking \u003cem\u003eH. pylori\u003c/em\u003e infection to systemic metabolic disorders, including diabetes\u003csup\u003e[35-37]\u003c/sup\u003e and insulin resistance \u003csup\u003e[12][38]\u003c/sup\u003e. Chronic \u003cem\u003eH. pylori\u003c/em\u003e infection can induce low-grade systemic inflammation and oxidative stress, which are key pathophysiological mechanisms underlying insulin resistance and metabolic dysregulation\u003csup\u003e[39][40]\u003c/sup\u003e. The observed association between \u003cem\u003eH. pylori\u003c/em\u003e infection and elevated levels of fasting plasma glucose, total cholesterol, triglycerides, and LDL-C in our study further supports its potential role in promoting an adverse metabolic phenotype. The TyG index, integrating both triglyceride and glucose levels, effectively captures this dysmetabolic state and has been validated as a marker of insulin resistance and a predictor of incident cardiovascular diseases\u003csup\u003e[5][41][42]\u003c/sup\u003e. Our findings extend this concept by establishing a specific association with \u003cem\u003eHelicobacter pylori\u003c/em\u003e infection in a previously understudied multi-ethnic region.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.3. Independent Risk Factors and Potential Inflammatory Pathway\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNotably, our study also identified several other independent factors associated with \u003cem\u003eH. pylori\u003c/em\u003e infection. Consistent with epidemiological profiles across diverse populations, male gender \u003csup\u003e[43][44]\u003c/sup\u003e and advancing age\u003csup\u003e[44][35]\u003c/sup\u003e were confirmed as demographic risk factors. Among metabolic parameters, adverse lipid profiles (elevated TC, TG, LDL-C) and elevated fasting glucose were significantly associated with infection, reinforcing the metabolic interplay.\u003c/p\u003e\n\u003cp\u003eThe most pronounced association was between \u003cem\u003eH. pylori\u003c/em\u003e infection and an elevated absolute monocyte count (OR \u0026gt; 2.0). Monocytes are circulating precursors of tissue macrophages and key mediators of innate immunity and chronic low-grade inflammation. Chronic gastric infection by \u003cem\u003eH. pylori\u003c/em\u003e is known to induce a persistent local and systemic inflammatory response\u003csup\u003e\u0026nbsp;[15]\u003c/sup\u003e. We hypothesize that this state of chronic immune activation leads to the mobilization and priming of monocytes. Once recruited into metabolic tissues such as visceral fat or liver, these activated monocytes/macrophages secrete pro-inflammatory cytokines (e.g., TNF-\u0026alpha;, IL-6), which are well-established drivers of insulin resistance and hepatic dyslipidemia\u003csup\u003e\u0026nbsp;[40]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTherefore, the elevated monocyte count may represent a critical cellular link connecting persistent \u003cem\u003eH. pylori\u003c/em\u003e infection to the systemic metabolic disturbances reflected by the TyG index. This pathway provides a plausible biological mechanism extending beyond the stomach, positioning \u003cem\u003eH. pylori\u003c/em\u003e as a potential contributor to the inflammatory underpinnings of cardiometabolic diseases.\u003c/p\u003e\n\u003cp\u003eConversely, indirect bilirubin showed a modest inverse association, which might hint at a potential protective role. Bilirubin is known for its antioxidant and anti-inflammatory properties\u003csup\u003e\u0026nbsp;[6]\u003c/sup\u003e, which could theoretically mitigate the establishment or persistence of infection, though this warrants further investigation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.4. Limitations and Strengths\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study has several limitations. First, the cross-sectional design precludes causal inference. Second, the lack of anthropometric data for BMI calculation is a notable constraint. However, we actively addressed this by: (i) adjusting for a comprehensive panel of metabolic covariates that are strong surrogates and consequences of adiposity; and (ii) performing a quantitative E-value sensitivity analysis, which suggested that the observed association between the TyG index and \u003cem\u003eH. pylori\u003c/em\u003e is reasonably robust to potential confounding by unmeasured factors like obesity. Third, while the cohort was drawn from a defined regional population, which may affect generalizability, it provides a unique representation of an understudied multi-ethnic border community. Finally, the absence of detailed ethnic classification data prevents exploration of potential inter‑ethnic differences\u0026mdash;an important area for future research.\u003c/p\u003e\n\u003cp\u003eStrengths of this study include the large sample size, use of the \u0026sup1;⁴C‑urea breath test for active \u003cem\u003eH. pylori\u003c/em\u003e diagnosis, extensive laboratory phenotyping, and the application of rigorous statistical methods including sensitivity analysis to assess result stability. Most importantly, we highlight monocytosis as a novel and potent hematological correlate of \u003cem\u003eH. pylori\u003c/em\u003e infection, offering a fresh perspective on its extra‑gastric inflammatory burden.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn this health-screening population from China\u0026apos;s southwestern border region, our study reveals a significant and independent association between \u003cem\u003eHelicobacter pylori\u003c/em\u003e infection and an elevated triglyceride-glucose (TyG) index, a reliable surrogate marker of insulin resistance. This association remained robust after comprehensive adjustment for demographic, hepatic, metabolic, and hematologic variables. To our knowledge, this is the first study to identify the TyG index as a central metabolic link between \u003cem\u003eH. pylori\u003c/em\u003e infection and an adverse profile of blood glucose and lipids in a multi-ethnic border population. This suggests that \u003cem\u003eH. pylori\u003c/em\u003e infection may detrimentally impact cardiometabolic health by exacerbating insulin resistance. Consequently, the TyG index, owing to its simplicity and low cost, could serve as a practical clinical indicator. Our findings provide a new rationale for integrating metabolic screening into the management of \u003cem\u003eH. pylori\u003c/em\u003e infection and for implementing comprehensive prevention strategies against metabolic syndrome in regions with high infection rates.\u003c/p\u003e"},{"header":"ABBREVIATIONS","content":"\u003cp\u003eThe abbreviations used in this article are as follows:TC, total cholesterol; TG, triglycerides; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol; FPG, fasting plasma glucose; \u003csup\u003e14\u003c/sup\u003eC-UBT, \u003csup\u003e14\u003c/sup\u003eC-urea breath test; TyG, triglyceride-glucose; OR, odds ratio; CI, confidence interval.\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 conducted in accordance with the principles of the Declaration of Helsinki. The study protocol was reviewed and approved by the Institutional Review Board / Ethics Committee of the People\u0026apos;s Hospital of Wenshan Prefecture (Approval No: 2025-006 (LW) -01). The requirement for informed consent was waived by the same ethics committee due to the retrospective nature of the study and the use of anonymized data from a health-screening program.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCONSENT FOR PUBLICATION\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. The requirement for individual informed consent for publication was waived by the Institutional Review Board of the People\u0026apos;s Hospital of Wenshan Prefecture because this study involved retrospective analysis of anonymized data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAVAILABILITY OF DATA AND MATERIALS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available in the figshare repository, under the citation: Zeng, Guangpin (2025). SWBorder_HP_TyG_Analysis_Database. figshare. Dataset. https://doi.org/10.6084/m9.figshare.30671456\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCOMPETING INTERESTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFUNDING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Wenshan Prefecture Hongju Yang Expert Workstation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAUTHORS\u0026apos; CONTRIBUTIONS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGZ\u003c/strong\u003e conceived the study, designed the protocol, oversaw data collection, and drafted the manuscript. \u003cstrong\u003eLZ\u003c/strong\u003e performed the statistical analysis, contributed to data interpretation, and critically revised the manuscript. \u003cstrong\u003eYZ\u003c/strong\u003e contributed to data curation, literature review, and manuscript preparation. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eACKNOWLEDGEMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank Hongju Yang for his helpful discussions and methodological support.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWijarnpreecha K, Thongprayoon C, Panjawatanan P, Manatsathit W, Jaruvongvanich V, Ungprasert P. \u003cem\u003eHelicobacter pylori\u003c/em\u003e and Risk of Nonalcoholic Fatty Liver Disease: A Systematic Review and Meta-analysis. Journal of Clinical Gastroenterology. 2018;52(5):386-391. doi:10.1097/MCG.0000000000000784\u003c/li\u003e\n\u003cli\u003eQiu J, Yu Y, Liu D, et al. Association between non-insulin-based insulin resistance surrogate makers and \u003cem\u003eHelicobacter pylori\u003c/em\u003e infection: a population-based study. 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Published online August 8, 2022:gutjnl-2022-327745. doi:10.1136/gutjnl-2022-327745\u003c/li\u003e\n\u003cli\u003eCorrigendum: 2022 Chinese national clinical practice guideline on \u003cem\u003eHelicobacter pylori\u003c/em\u003e eradication treatment. Chin Med J (Engl). 2024;137(9):1068. doi:10.1097/CM9.0000000000003134\u003c/li\u003e\n\u003cli\u003eJin JL, Sun D, Cao YX, et al. Triglyceride glucose and haemoglobin glycation index for predicting outcomes in diabetes patients with new-onset, stable coronary artery disease: a nested case-control study. Ann Med. 2018;50(7):576-586. doi:10.1080/07853890.2018.1523549\u003c/li\u003e\n\u003cli\u003eLiu X, Tan Z, Huang Y, et al. Relationship between the triglyceride-glucose index and risk of cardiovascular diseases and mortality in the general population: a systematic review and meta-analysis. Cardiovasc Diabetol. 2022;21(1):124. doi:10.1186/s12933-022-01546-0\u003c/li\u003e\n\u003cli\u003eLi C, Zhang Z, Luo X, et al. 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St. Louis, MO: Elsevier Saunders, 2006, 2448 pp., $229.00, hardcover. ISBN 0-7216-0189-8. Clinical Chemistry. 2006;52(6):1214-1214. doi:10.1373/clinchem.2005.062638\u003c/li\u003e\n\u003cli\u003eVanderWeele TJ, Ding P. Sensitivity Analysis in Observational Research: Introducing the E-Value. Ann Intern Med. 2017;167(4):268-274. doi:10.7326/M16-2607\u003c/li\u003e\n\u003cli\u003eWan Z, Song L, Hu L, et al. \u003cem\u003eHelicobacter pylori\u003c/em\u003e infection is associated with diabetes among Chinese adults. J of Diabetes Invest. 2020;11(1):199-205. doi:10.1111/jdi.13102\u003c/li\u003e\n\u003cli\u003eKouitcheu Mabeku LB, Noundjeu Ngamga ML, Leundji H. \u003cem\u003eHelicobacter pylori\u003c/em\u003e infection, a risk factor for Type 2 diabetes mellitus: a hospital-based cross-sectional study among dyspeptic patients in Douala-Cameroon. Sci Rep. 2020;10(1):12141. doi:10.1038/s41598-020-69208-3\u003c/li\u003e\n\u003cli\u003eJeon CY, Haan MN, Cheng C, et al. \u003cem\u003eHelicobacter pylori\u003c/em\u003e Infection Is Associated With an Increased Rate of Diabetes. Diabetes Care. 2012;35(3):520-525. doi:10.2337/dc11-1043\u003c/li\u003e\n\u003cli\u003eAslan M. Insulin resistance in H pylori infection and its association with oxidative stress. WJG. 2006;12(42):6865. doi:10.3748/wjg.v12.i42.6865\u003c/li\u003e\n\u003cli\u003eHardbower DM, Peek RM, Wilson KT. At the Bench: \u003cem\u003eHelicobacter pylori\u003c/em\u003e , dysregulated host responses, DNA damage, and gastric cancer. Journal of Leukocyte Biology. 2014;96(2):201-212. doi:10.1189/jlb.4BT0214-099R\u003c/li\u003e\n\u003cli\u003eCheng D dan, He C, Ai H hui, Huang Y, Lu N hua. The Possible Role of \u003cem\u003eHelicobacter pylori\u003c/em\u003e Infection in Non-alcoholic Fatty Liver Disease. Front Microbiol. 2017;8:743. doi:10.3389/fmicb.2017.00743\u003c/li\u003e\n\u003cli\u003eXiong S, Chen Q, Long Y, et al. Association of the triglyceride\u0026ndash;glucose index with coronary artery disease complexity in patients with acute coronary syndrome. Cardiovasc Diabetol. 2023;22(1):56. doi:10.1186/s12933-023-01780-0\u003c/li\u003e\n\u003cli\u003eDing H, Zhu J, Tian Y, et al. Relationship between the triglyceride\u0026ndash;glucose index and coronary artery calcification in asymptomatic, non-diabetic patients undergoing maintenance hemodialysis. Renal Failure. 2023;45(1):2200849. doi:10.1080/0886022X.2023.2200849\u003c/li\u003e\n\u003cli\u003eMyint T. Prevalence of \u003cem\u003eHelicobacter pylori\u003c/em\u003e infection and atrophic gastritis in patients with dyspeptic symptoms in Myanmar. WJG. 2015;21(2):629. doi:10.3748/wjg.v21.i2.629\u003c/li\u003e\n\u003cli\u003eWu Y, Zeng H, Zhang M, et al. Sex-Specific Risk Factors Associated with \u003cem\u003eHelicobacter pylori\u003c/em\u003e Infection Among Individuals Undergoing Health Examinations in China. IJGM. 2022;Volume 15:5861-5868. doi:10.2147/IJGM.S367142\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"gut-pathogens","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gutp","sideBox":"Learn more about [Gut Pathogens](http://gutpathogens.biomedcentral.com/)","snPcode":"13099","submissionUrl":"https://submission.nature.com/new-submission/13099/3","title":"Gut Pathogens","twitterHandle":"@GutPathogens","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Helicobacter pylori, Triglyceride-glucose index, Insulin resistance, Inflammation, Monocytes, Southwestern China","lastPublishedDoi":"10.21203/rs.3.rs-8617767/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8617767/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground and Aims:\u003c/p\u003e \u003cp\u003e \u003cem\u003eHelicobacter pylori\u003c/em\u003e infection is associated with systemic inflammation and may contribute to insulin resistance. The triglyceride-glucose (TyG) index serves as a reliable surrogate marker of insulin resistance and is linked to cardiometabolic diseases. However, evidence regarding the association between \u003cem\u003eH. pylori\u003c/em\u003e and the TyG index remains inconsistent, especially in multi-ethnic, high-risk populations from China\u0026rsquo;s southwestern border region. This study aimed to investigate the relationship between \u003cem\u003eH. pylori\u003c/em\u003e infection and the TyG index in such a population. We sought to: (1) assess the independent association, (2) characterize dose\u0026ndash;response patterns, and (3) identify metabolic modifiers through stratified analyses, in order to elucidate the extra‑gastric metabolic implications of \u003cem\u003eH. pylori\u003c/em\u003e infection.\u003c/p\u003e \u003cp\u003eMethods:\u003c/p\u003e \u003cp\u003eThis study analyzed data from 6,998 adults in a multi‑ethnic health‑screening cohort conducted in Wenshan, Southwest China (2020\u0026ndash;2024). \u003cem\u003eHelicobacter pylori\u003c/em\u003e infection was diagnosed using the \u0026sup1;⁴C‑urea breath test (cut‑off \u0026gt;\u0026thinsp;50 DPM). The TyG index was calculated as ln[fasting triglycerides (mg/dL) \u0026times; fasting glucose (mg/dL)/2]. Comprehensive biochemical and metabolic profiles were assessed. We performed univariable and multivariable regression adjusted for key metabolic confounders, examined dose\u0026ndash;response relationships using restricted cubic splines, conducted stratified analyses to identify effect modifiers, and applied E‑value sensitivity analysis to evaluate robustness to unmeasured confounding.\u003c/p\u003e \u003cp\u003eResults:\u003c/p\u003e \u003cp\u003eAmong the 6,998 participants from Wenshan, Southwest China, 2,736 (39.1%) were \u003cem\u003eH. pylori\u003c/em\u003e-positive. The \u003cem\u003eH. pylori\u003c/em\u003e-positive group exhibited a significantly higher TyG index compared to the negative group (8.8\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7 vs. 8.7\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In adjusted models, a higher TyG index remained independently associated with \u003cem\u003eH. pylori\u003c/em\u003e infection (OR\u0026thinsp;=\u0026thinsp;1.14, 95% CI 1.06\u0026ndash;1.21). Sensitivity analysis using the E‑value indicated that an unmeasured confounder (e.g., BMI) would need to be associated with both exposures by risk ratios of at least 1.54 to fully explain the observed association, supporting its robustness. Other independent risk factors included male sex, advanced age, dyslipidemia, and elevated absolute monocyte count.\u003c/p\u003e \u003cp\u003eConclusion:\u003c/p\u003e \u003cp\u003eA higher TyG index is independently associated with an increased risk of \u003cem\u003eH. pylori\u003c/em\u003e infection in this health-screening population from China's southwestern border. The TyG index may serve as a simple metabolic biomarker to identify high-risk individuals, aiding targeted screening in similar multi-ethnic groups. Our findings underscore the systemic inflammatory burden of \u003cem\u003eH. pylori\u003c/em\u003e and suggest the TyG index as a simple tool for metabolic risk stratification in infected individuals.\u003c/p\u003e","manuscriptTitle":"Beyond the Stomach: Linking H. pylori Seropositivity to Insulin Resistance (TyG Index) in a Multi-Ethnic High-Risk Population from Southwest China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-29 00:34:32","doi":"10.21203/rs.3.rs-8617767/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-23T05:52:55+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-19T09:29:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-17T20:54:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"66896490752627365437998697555847686765","date":"2026-02-02T05:58:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"66895805376885780335863341816178549756","date":"2026-01-28T15:19:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"191533854361346407519132269062815069099","date":"2026-01-28T11:19:45+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-23T07:32:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-17T05:51:01+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-17T05:50:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Gut Pathogens","date":"2026-01-16T10:14:05+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"gut-pathogens","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gutp","sideBox":"Learn more about [Gut Pathogens](http://gutpathogens.biomedcentral.com/)","snPcode":"13099","submissionUrl":"https://submission.nature.com/new-submission/13099/3","title":"Gut Pathogens","twitterHandle":"@GutPathogens","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"177a790a-9b77-4487-8b77-75ff21ecaabe","owner":[],"postedDate":"January 29th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-11T01:53:56+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-29 00:34:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8617767","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8617767","identity":"rs-8617767","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}