Association of Body Roundness Index, A Body Shape Index, and Cardiometabolic Indices with Gastric Intestinal Metaplasia Severity Assessed by OLGA Staging

Association of Body Roundness Index, A Body Shape Index, and Cardiometabolic Indices with Gastric Intestinal Metaplasia Severity Assessed by OLGA Staging | 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 Association of Body Roundness Index, A Body Shape Index, and Cardiometabolic Indices with Gastric Intestinal Metaplasia Severity Assessed by OLGA Staging Mete Ucdal, Faruk Yazıcı, Evren Ekingen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8962909/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 10 You are reading this latest preprint version Abstract Background and Aim: Gastric intestinal metaplasia (IM) is a recognized precancerous lesion whose progression may be influenced by metabolic dysfunction. Novel anthropometric indices, including the Body Roundness Index (BRI) and A Body Shape Index (ABSI), have emerged as superior predictors of visceral adiposity in comparison to the conventional body mass index (BMI). However, the association of these indices with gastric precancerous lesions remains largely unexplored. This study aims to investigate the relationship between BRI, ABSI, BMI, and cardiometabolic indices (TyG, TyG-BMI) with the severity of IM, as measured by the Operative Link on Gastritis Assessment (OLGA) staging system. Methods: This cross-sectional study included 250 patients who underwent upper gastrointestinal endoscopy with gastric biopsy. IM was assessed histopathologically and graded using the OLGA staging system (stages 0–IV). Anthropometric indices (BMI, BRI, ABSI) and cardiometabolic indices (TyG, TyG-BMI) were computed from standardized measurements. Receiver operating characteristic (ROC) analysis, Spearman correlation, and multivariate logistic regression were performed. Results: Out of 250 patients (average age 53.6±18.9 years; 51.6% female), 154 (61.6%) were identified as IM-positive. All anthropometric and cardiometabolic indices were significantly elevated in IM-positive individuals (p<0.001). The Body Roundness Index (BRI) exhibited the strongest correlation with OLGA stage (ρ=0.448, p<0.001) and IM grade (ρ=0.493, p<0.001). In the prediction of advanced OLGA stages (≥stage III), the Triglyceride-Glucose (TyG) index demonstrated the highest discriminative capacity (AUC=0.848), followed by BRI (AUC=0.815) and waist circumference (AUC=0.809). In multivariate analysis, adjusting for age, sex, and Helicobacter pylori status, BRI (Odds Ratio=1.665, 95% Confidence Interval: 1.281–2.164, p<0.001), TyG (Odds Ratio=3.823, 95% Confidence Interval: 1.925–7.591, p<0.001), and their combination (BRI: Odds Ratio=1.469; TyG: Odds Ratio=2.654) persisted as independent predictors of intestinal metaplasia (IM) positivity. Tertile analysis demonstrated a dose–response relationship: the prevalence of IM increased from 39.8% (T1) to 82.1% (T3) for BRI and from 42.9% (T1) to 83.1% (T3) for TyG, with p-trend values less than 0.001 for both. Conclusions: BRI and TyG index are independently associated with gastric IM severity and demonstrate superior predictive performance compared to conventional BMI. These non-invasive, easily calculable indices may serve as practical tools for identifying individuals at increased risk of gastric precancerous lesions in clinical practice. Body Roundness Index A Body Shape Index triglyceride-glucose index intestinal metaplasia OLGA staging cardiometabolic risk visceral adiposity Figures Figure 1 Figure 2 Figure 3 Figure 4 1. INTRODUCTION Gastric cancer (GC) remains a leading cause of cancer-related mortality worldwide, accounting for approximately 660,000 deaths annually ( 1 ). The development of intestinal-type gastric adenocarcinoma follows the well-characterized Correa cascade, progressing from chronic gastritis through glandular atrophy, intestinal metaplasia (IM), dysplasia, and ultimately invasive carcinoma ( 2 ). Among these precancerous stages, IM represents a irreversible histological transformation in which the native gastric epithelium is replaced by an intestinal phenotype, conferring substantially elevated malignancy risk. The 2025 MAPS III guideline by the European Society of Gastrointestinal Endoscopy (ESGE) reinforces the necessity of systematic risk stratification and structured endoscopic surveillance for patients with advanced atrophic stages ( 3 ). Concordantly, the 2025 American College of Gastroenterology (ACG) Clinical Guideline emphasizes that IM carries an increased risk of progression to gastric adenocarcinoma ( 4 ). The Operative Link on Gastritis Assessment (OLGA) and its intestinal metaplasia-based counterpart (OLGIM) staging systems standardize histological risk evaluation by incorporating the topographic distribution and severity of atrophic changes across gastric biopsy sites. Prospective cohort data demonstrated that OLGA stages III–IV confer an increased risk of gastric cancer (pooled RR = 32.31; 95% CI: 9.14–114.21), thereby establishing these staging systems as reliable tools for identifying individuals at highest risk ( 5 ). Nevertheless, histopathological staging alone does not capture modifiable metabolic risk determinants that may modulate the progression of gastric precancerous lesions. Obesity and metabolic syndrome (MetS) have been implicated in gastric carcinogenesis through chronic low-grade inflammation, insulin resistance, and oxidative stress. Visceral adipose tissue secretes pro-inflammatory adipokines—including TNF-α, IL-6, and leptin—which perpetuate a chronic inflammatory microenvironment conducive to mucosal injury and metaplastic transformation ( 6 ). A large-scale Korean cohort study demonstrated that increasing BMI categories are associated with new-onset IM in a dose–response manner, independent of Helicobacter pylori status ( 7 ). Furthermore, insulin resistance promotes IGF-1/PI3K/Akt/mTOR signaling, accelerating cellular proliferation in gastric epithelial cells ( 8 ). Studies in bariatric surgery populations have confirmed that H. pylori -positive obese patients exhibit significantly higher IM prevalence with concomitant elevations in HOMA-IR ( 9 ). Despite its widespread use, BMI has well-documented limitations in characterizing visceral fat distribution ( 10 ). Novel anthropometric indices have been developed to overcome these shortcomings. The Body Roundness Index (BRI), integrating waist circumference and height to model the torso as an ellipse, provides superior sensitivity to visceral adiposity ( 11 ). A U-shaped association between body roundness index (BRI) and all-cause mortality was observed in a cohort of 32,995 US adults, and subsequent longitudinal studies from 2025–2026 have confirmed BRI as a multifaceted predictor of chronic disease mortality and cardiometabolic multimorbidity ( 12 – 14 ). A Body Shape Index (ABSI), which quantifies waist circumference independently of BMI, has been associated with overall and site-specific cancer incidence—including stomach cancer—and systematic review and meta-analytic evidence supports its utility in cancer risk prediction ( 15 , 16 ). The triglyceride–glucose (TyG) index, a cost-effective surrogate marker for insulin resistance, has emerged as a novel biomarker in gastric carcinogenesis. Kim et al. demonstrated a positive correlation between TyG index and precancerous conditions including atrophic gastritis and IM ( 17 ). Recent evidence from cohort studies and meta-analytic syntheses indicates that elevated triglyceride–glucose (TyG) index is associated with increased cancer incidence across several tumor types, and has been proposed as a prognostic biomarker in gastric cancer, although standardized cutoff values remain to be established( 18 ). Despite the expanding literature on novel adiposity and metabolic indices, no study to date has evaluated the association of BRI, ABSI, and TyG index with histopathological severity of gastric IM as determined by standardized OLGA staging. Therefore, the present study aimed to: ( 1 ) compare the discriminative performance of BRI, ABSI, BMI, TyG index, and TyG-BMI for predicting IM positivity and advanced OLGA stage; ( 2 ) evaluate independent associations through multivariate regression adjusted for age, sex, H. pylori infection, and smoking status; and ( 3 ) assess dose–response relationships through tertile-based analysis. 2. MATERIALS AND METHODS 2.1 Study Design and Population This cross-sectional study was conducted at the Department of Internal Medicine, Etimesgut Şehit Sait Ertürk State Hospital, Ankara, Turkey. Medical records of patients who underwent esophagogastroduodenoscopy ( 19 ) with gastric biopsy between January 2023 and December 2024 were retrospectively reviewed. The study protocol was approved by the Institutional Ethics Committee and conducted in accordance with the principles of the Declaration of Helsinki. Inclusion criteria were: ( 1 ) age ≥ 18 years; ( 2 ) availability of gastric biopsy specimens obtained according to the updated Sydney protocol (at least two biopsies from the antrum, two from the corpus, and one from the incisura angularis); ( 3 ) complete anthropometric measurements including height, weight, and waist circumference recorded within 30 days of endoscopy; and ( 4 ) fasting laboratory parameters including glucose, triglycerides, and HDL-cholesterol obtained within 30 days of endoscopy. Exclusion criteria included: ( 1 ) history of gastric surgery or malignancy; ( 2 ) active gastrointestinal bleeding at the time of endoscopy; ( 3 ) use of proton pump inhibitors for > 8 weeks prior to endoscopy; ( 4 ) pregnancy or lactation; and ( 5 ) incomplete biopsy specimens or laboratory data. A total of 250 patients fulfilling these criteria were included in the final analysis. The study design and patient selection process are illustrated in Fig. 1 . 2.2 Histopathological Assessment All gastric biopsy specimens were evaluated by an experienced gastrointestinal pathologist blinded to clinical and metabolic data. Histopathological assessment included evaluation of atrophy, intestinal metaplasia, and Helicobacter pylori colonization according to the updated Sydney classification. Atrophy and IM were graded as: 0 (absent), 1 (mild), 2 (moderate), and 3 (severe) for each biopsy location. OLGA staging was performed by integrating atrophy scores from antral and corporal compartments, yielding stages 0 through IV. Advanced OLGA was defined as stage ≥ III. In cases of diagnostic uncertainty, a second pathologist independently reviewed the specimens, and consensus was reached through discussion. 2.3 Anthropometric Measurements and Index Calculations Height was measured to the nearest 0.1 cm using a wall-mounted stadiometer with the patient in an upright position. Weight was measured to the nearest 0.1 kg using a calibrated digital scale with light clothing and no shoes. Waist circumference was measured at the midpoint between the lowest rib and the iliac crest at the end of gentle expiration using a non-stretchable tape measure. The following anthropometric and cardiometabolic indices were calculated: Body Mass Index (BMI) Weight (kg) / Height (m) 2 Body Roundness Index (BRI) 364.2 − 365.5 × √[1 − (WC / 2π) 2 / (0.5 × Height) 2 ] A Body Shape Index (ABSI) WC / (BMI 2/3 × Height 1/2 ) Triglyceride-Glucose Index (TyG) Ln [Triglycerides (mg/dL) × Fasting Glucose (mg/dL) / 2] TyG-BMI TyG × BMI (kg/m 2 ) ABSI z-scores (BSI-z) were calculated using age- and sex-specific reference values from the original publication by Krakauer and Krakauer. 2.4 Metabolic Syndrome Definition Metabolic syndrome was diagnosed according to the harmonized criteria jointly proposed by the International Diabetes Federation, National Heart, Lung, and Blood Institute, American Heart Association, World Heart Federation, International Atherosclerosis Society, and International Association for the Study of Obesity (2009) ( 20 ). The presence of ≥ 3 of the following criteria was required: ( 1 ) waist circumference ≥ 90 cm (men) or ≥ 80 cm (women); ( 2 ) triglycerides ≥ 150 mg/dL or pharmacological treatment; ( 3 ) HDL-cholesterol < 40 mg/dL (men) or < 50 mg/dL (women) or pharmacological treatment; ( 4 ) systolic blood pressure ≥ 130 mmHg or diastolic blood pressure ≥ 85 mmHg or antihypertensive treatment; ( 5 ) fasting plasma glucose ≥ 100 mg/dL or pharmacological treatment for hyperglycemia. The cardiometabolic score (0–5) represented the sum of individual metabolic syndrome components present. 2.5 Statistical Analysis Statistical analyses were performed using Python (version 3.12) with NumPy, SciPy, pandas, scikit-learn, and statsmodels libraries. Continuous variables were expressed as mean ± standard deviation ( 21 ) and compared using the Mann–Whitney U test (two groups) or Kruskal–Wallis H test (multiple groups) after assessing normality with the Shapiro–Wilk test. Categorical variables were presented as frequencies and percentages and compared using the chi-square test or Fisher exact test as appropriate. Spearman rank correlation coefficients (ρ) were calculated to evaluate the associations between anthropometric/cardiometabolic indices and histopathological outcomes (OLGA stage, IM grade, atrophy grade). Receiver operating characteristic (ROC) curve analysis was performed to assess the discriminative ability of each index for predicting IM positivity and advanced OLGA (≥ stage III). The area under the ROC curve ( 22 ) was calculated with 95% confidence intervals (CI), and optimal cutoff values were determined using the Youden index (J = sensitivity + specificity − 1). Binary logistic regression analysis was conducted to identify independent predictors of IM positivity. Univariate analysis was performed for each candidate variable, and those with p < 0.10 were entered into multivariate models. Five multivariate models were constructed: Model 1 (BMI-based), Model 2 (BRI-based), Model 3 (ABSI-based), Model 4 (TyG-based), and Model 5 (combined BRI + TyG), each adjusted for age, sex, H. pylori status, and smoking. Model performance was evaluated using the Akaike Information Criterion (AIC) and pseudo-R². Ordinal logistic regression (proportional odds model) was additionally used to evaluate associations with OLGA stage as an ordinal outcome. Dose–response relationships were assessed by dividing each index into tertiles and calculating the prevalence of IM positivity and advanced OLGA within each tertile category. The Cochran–Armitage trend test was used to evaluate the significance of linear trends across tertiles. A two-sided p-value < 0.05 was considered statistically significant for all analyses. 3. RESULTS 3.1 Patient Characteristics A total of 250 patients (129 female [51.6%], 121 male [48.4%]) with a mean age of 53.6 ± 18.9 years (range: 18–92) were enrolled in this cross-sectional study. Among these, 154 patients (61.6%) were classified as IM-positive based on histopathological evaluation of gastric biopsies obtained via the updated Sydney protocol, while 96 patients (38.4%) were IM-negative. Advanced OLGA staging (≥ stage III) was identified in 20 patients (8.0%). The distribution across OLGA stages was as follows: stage 0 (n = 108, 43.2%), stage I (n = 89, 35.6%), stage II (n = 33, 13.2%), stage III (n = 17, 6.8%), and stage IV (n = 3, 1.2%). Metabolic syndrome, defined according to the NCEP-ATP III criteria, was present in 93 patients (37.2%). Helicobacter pylori positivity was detected in 111 patients (44.4%), diabetes mellitus in 38 (15.2%), and hypertension in 75 (30.0%). The comprehensive demographic, clinical, and metabolic characteristics of the study population are presented in Table 1 . Table 1 Demographic, Clinical, and Metabolic Characteristics Stratified by Intestinal Metaplasia Status Variable Total (n = 250) IM (−) (n = 96) IM (+) (n = 154) p Age (years) 53.6 ± 18.9 41.7 ± 15.1 61.1 ± 17.2 < 0.001 Female, n (%) 129 (51.6) 53 (55.2) 76 (49.4) 0.441 Smoking, n (%) 99 (39.6) 26 (27.1) 73 (47.4) 0.002 BMI (kg/m²) 27.6 ± 6.8 25.1 ± 5.5 29.1 ± 7.1 < 0.001 WC (cm) 77.8 ± 12.9 71.4 ± 8.9 81.8 ± 13.4 < 0.001 BRI 2.87 ± 1.72 2.09 ± 1.03 3.36 ± 1.88 < 0.001 ABSI 0.066 ± 0.005 0.064 ± 0.005 0.068 ± 0.005 < 0.001 ABSI z-score 0.00 ± 1.00 –0.33 ± 1.03 0.21 ± 0.93 < 0.001 SBP (mmHg) 126.9 ± 16.4 119.6 ± 13.2 131.5 ± 16.7 < 0.001 FPG (mg/dL) 104.1 ± 23.5 94.4 ± 12.6 110.1 ± 26.5 < 0.001 HbA1c (%) 5.67 ± 0.78 5.38 ± 0.45 5.84 ± 0.88 < 0.001 TG (mg/dL) 161.9 ± 62.4 141.6 ± 57.0 174.5 ± 62.5 < 0.001 HDL-C (mg/dL) 42.9 ± 12.0 45.1 ± 12.6 41.5 ± 11.4 0.055 TyG index 8.93 ± 0.52 8.71 ± 0.49 9.07 ± 0.49 < 0.001 TyG-BMI 247.1 ± 68.1 219.4 ± 53.3 265.6 ± 72.9 < 0.001 MetS, n (%) 93 (37.2) 16 (16.7) 77 (50.0) < 0.001 H. pylori (+), n (%) 111 (44.4) 26 (27.1) 85 (55.2) < 0.001 DM, n (%) 38 (15.2) 2 (2.1) 36 (23.4) < 0.001 HT, n (%) 75 (30.0) 5 (5.2) 70 (45.5) < 0.001 3.2 Comparison of Cardiometabolic Indices Between IM-Positive and IM-Negative Groups Comparative analysis of anthropometric and cardiometabolic indices stratified by IM status revealed statistically significant differences across virtually all parameters (Table 1 ). IM-positive patients were significantly older than their IM-negative counterparts (61.1 ± 17.2 vs. 41.7 ± 15.1 years, p < 0.001) and demonstrated substantially higher BMI values (29.1 ± 7.1 vs. 25.1 ± 5.5 kg/m², p < 0.001). The Body Roundness Index showed a particularly striking difference between groups, with IM-positive patients exhibiting values 61% higher than IM-negative patients (3.36 ± 1.88 vs. 2.09 ± 1.03, p < 0.001), reflecting substantially greater central adiposity in the affected group. ABSI was also significantly elevated in IM-positive patients (0.068 ± 0.005 vs. 0.064 ± 0.005, p < 0.001), as was waist circumference (81.8 ± 13.4 vs. 71.4 ± 8.9 cm, p < 0.001). Among cardiometabolic parameters, the TyG index was significantly higher in IM-positive patients (9.07 ± 0.49 vs. 8.71 ± 0.49, p < 0.001), as was the TyG-BMI composite index (265.6 ± 72.9 vs. 219.4 ± 53.3, p < 0.001). Fasting plasma glucose (110.1 ± 26.5 vs. 94.4 ± 12.6 mg/dL, p < 0.001), triglycerides (174.5 ± 62.5 vs. 141.6 ± 57.0 mg/dL, p < 0.001), HbA1c (5.84 ± 0.88 vs. 5.38 ± 0.45%, p < 0.001), and systolic blood pressure (131.5 ± 16.7 vs. 119.6 ± 13.2 mmHg, p < 0.001) were all significantly elevated in the IM-positive group. HDL-cholesterol showed a trend toward lower values in IM-positive patients (41.5 ± 11.4 vs. 45.1 ± 12.6 mg/dL, p = 0.055) but did not reach statistical significance. The prevalence of metabolic syndrome was three-fold higher in IM-positive patients (50.0% vs. 16.7%, p < 0.001). Similarly, H. pylori positivity (55.2% vs. 27.1%, p < 0.001), diabetes mellitus (23.4% vs. 2.1%, p < 0.001), and hypertension (45.5% vs. 5.2%, p < 0.001) were all significantly more prevalent in the IM-positive group. Sex distribution did not differ significantly between groups (p = 0.441). 3.3 Progressive Distribution of Indices Across OLGA Stages All anthropometric and cardiometabolic indices demonstrated a progressive, monotonic increase across OLGA stages, as illustrated in Fig. 2 . Statistically significant differences were confirmed across all five OLGA stages for each index: BMI (H = 29.9, p < 0.001), BRI (H = 56.5, p < 0.001), ABSI (H = 33.1, p < 0.001), TyG index (H = 53.9, p < 0.001), TyG-BMI (H = 40.0, p < 0.001), and waist circumference (H = 57.0, p < 0.001). Among all indices, BRI and waist circumference yielded the highest test statistics, indicating the most pronounced separation across histopathological stages. The magnitude of change across the staging spectrum was clinically remarkable. Mean BRI increased by 136% from OLGA stage 0 (2.24 ± 1.17) to the combined stage III–IV group (5.03 ± 2.42). The TyG index demonstrated a parallel trajectory, rising from 8.76 ± 0.50 at stage 0 to 9.54 ± 0.40 at advanced stages, representing a 0.78-unit increase that exceeded one standard deviation of the overall population distribution. BMI increased from 26.0 ± 5.9 kg/m² at stage 0 to 34.3 ± 8.7 kg/m² at advanced stages, while waist circumference rose from 73.5 ± 10.5 cm to 95.8 ± 14.2 cm. These findings demonstrate a consistent gradient of metabolic burden paralleling the severity of gastric mucosal pathology. 3.4 Correlation Between Cardiometabolic Indices and Histopathological Outcomes Correlation analysis (Fig. 3 ) revealed a clear hierarchy of association strengths between cardiometabolic indices and histopathological outcomes. BRI exhibited the strongest correlations with both IM grade (ρ = 0.493, p < 0.001) and OLGA stage (ρ = 0.448, p < 0.001), closely followed by waist circumference (ρ = 0.491 and 0.460, respectively). The TyG index demonstrated robust correlations with IM grade (ρ = 0.428, p < 0.001) and OLGA stage (ρ = 0.413, p < 0.001), outperforming BMI in both measures (IM grade: ρ = 0.360; OLGA stage: ρ = 0.303; both p < 0.001). ABSI showed weaker but still statistically significant correlations with OLGA stage (ρ = 0.362, p < 0.001) and IM grade (ρ = 0.310, p < 0.001). Notably, all indices demonstrated significant positive correlations with atrophy grade, with ABSI exhibiting a relatively stronger association (ρ = 0.252, p < 0.001) in proportion to its overall correlation pattern, suggesting that ABSI may preferentially capture atrophic rather than metaplastic changes. The TyG-BMI composite index did not demonstrate superior correlations compared to either TyG or BMI individually, suggesting limited additive value from combining metabolic and anthropometric information within a linear correlation framework. 3.5 Discriminative Performance of Cardiometabolic Indices ROC analysis was performed for two clinically relevant endpoints: IM positivity and advanced OLGA stage (≥ stage III). The complete results are summarized in Fig. 1 and Table 2 . Table 2 Receiver Operating Characteristic Analysis for Predicting IM Positivity and Advanced OLGA Stage (≥ III) IM Positivity Advanced OLGA (≥ Stage III) Index AUC Cutoff Se / Sp AUC Cutoff Se Sp BMI 0.671 29.65 0.455 / 0.875 0.755 32.49 0.650 0.839 BRI 0.736 2.409 0.662 / 0.708 0.815 3.335 0.800 0.748 ABSI 0.651 0.066 0.636 / 0.635 0.714 0.069 0.650 0.713 TyG 0.705 8.954 0.688 / 0.656 0.848 9.305 0.750 0.822 TyG-BMI 0.689 268.1 0.435 / 0.885 0.791 267.2 0.800 0.717 WC 0.740 78.3 0.610 / 0.792 0.809 90.3 0.650 0.887 For predicting IM positivity (Fig. 4 A), waist circumference achieved the highest area under the curve (AUC = 0.740; 95% CI: 0.677–0.803), closely followed by BRI (AUC = 0.736; 95% CI: 0.673–0.799). The TyG index (AUC = 0.705), TyG-BMI (AUC = 0.689), BMI (AUC = 0.671), and ABSI (AUC = 0.651) demonstrated progressively lower discriminative abilities. At the optimal cutoff of 2.409, BRI yielded a sensitivity of 66.2% and specificity of 70.8% for IM detection. For predicting advanced OLGA (≥ stage III) (Fig. 4 B), the TyG index achieved the highest discriminative performance (AUC = 0.848; 95% CI: 0.773–0.923), followed by BRI (AUC = 0.815), waist circumference (AUC = 0.809), TyG-BMI (AUC = 0.791), BMI (AUC = 0.755), and ABSI (AUC = 0.714). The superior performance of TyG for advanced OLGA detection—compared to its relatively lower ranking for IM positivity—suggests that metabolic derangement as captured by insulin resistance may be particularly relevant in distinguishing higher-risk histological stages. BRI demonstrated a balanced diagnostic profile at its optimal cutoff of 3.335 (sensitivity: 80.0%, specificity: 74.8%), while TyG at its cutoff of 9.305 provided 75.0% sensitivity and 82.2% specificity. Notably, waist circumference at cutoff 90.3 cm achieved the highest specificity (88.7%) among all indices for advanced OLGA detection. 3.6 Independent Predictors of Intestinal Metaplasia In univariate analysis, all anthropometric and cardiometabolic indices, along with age, smoking, diabetes mellitus, hypertension, dyslipidemia, metabolic syndrome, and H. pylori were identified as significant predictors of IM positivity (all p < 0.01). Sex was not significantly associated with IM status (p = 0.368). Five multivariate models were constructed, each adjusted for age, sex, H. pylori status, and smoking (Table 3 ). Among single-index models, Model 2 (BRI) demonstrated the best fit, with BRI emerging as an independent predictor of IM positivity (OR = 1.665; 95% CI: 1.281–2.164; p < 0.001; AIC = 235.1; pseudo-R² = 0.330). This indicates that each unit increase in BRI was associated with a 66.5% increase in the odds of IM positivity after controlling for confounders. Model 4 (TyG) showed comparable predictive performance (OR = 3.823; 95% CI: 1.925–7.591; p < 0.001; AIC = 236.5; pseudo-R² = 0.326), suggesting that each unit increase in the TyG index was associated with nearly four-fold higher odds of IM. Table 3 Multivariate Logistic Regression Models for Predicting Intestinal Metaplasia Positivity Model Key Index OR (95% CI) p AIC Pseudo-R² Model 1 BMI 1.101 (1.043–1.163) < 0.001 239.7 0.316 Model 2 BRI 1.665 (1.281–2.164) < 0.001 235.1 0.330 Model 3 ABSI — 0.262 251.8 0.280 Model 4 TyG 3.823 (1.925–7.591) < 0.001 236.5 0.326 Model 5 BRI + TyG 1.469 / 2.654 0.006 / 0.009 233.8 0.334 Notably, Model 5 , which simultaneously incorporated both BRI and TyG, achieved the lowest AIC (233.8) and highest pseudo-R² (0.334) among all models, with both indices retaining independent statistical significance (BRI: OR = 1.469; 95% CI: 1.116–1.934; p = 0.006; TyG: OR = 2.654; 95% CI: 1.275–5.525; p = 0.009). The additive improvement in model fit by incorporating both indices suggests that BRI and TyG capture complementary dimensions of metabolic risk—visceral adiposity and insulin resistance, respectively—both of which independently contribute to gastric mucosal pathology. Model 1 (BMI) was inferior to the BRI-based model (AIC = 239.7; pseudo-R² = 0.316), while Model 3 (ABSI) lost statistical significance after adjustment for age (p = 0.262; AIC = 251.8), indicating substantial confounding by age-related changes in body composition. 3.7 Ordinal Association and Dose–Response Gradient When OLGA stage was treated as an ordinal outcome, significant associations were confirmed with BRI (OR = 1.557; 95% CI: 1.318–1.840; p < 0.001), TyG index (OR = 5.388; 95% CI: 2.978–9.747; p < 0.001), and BMI (OR = 1.101; 95% CI: 1.054–1.149; p 0.05), supporting the validity of this ordinal approach. Tertile-based analysis (Fig. 4 ) demonstrated striking dose–response relationships for both BRI and TyG index. BRI tertile-stratified IM prevalence increased progressively from T1 (39.8%) through T2 (62.7%) to T3 (82.1%; p for trend < 0.001), representing an absolute increase of 42.3 percentage points across the BRI distribution. Advanced OLGA prevalence showed an even more dramatic gradient, rising from 2.4% in the lowest tertile to 19.0% in the highest tertile (p for trend < 0.001)—corresponding to an approximately eight-fold relative increase. Mean OLGA stage also increased monotonically across BRI tertiles from 0.48 (T1) to 0.87 (T2) to 1.39 (T3). A parallel gradient was observed for TyG index tertiles: IM prevalence increased from 42.9% (T1) to 59.0% (T2) to 83.1% (T3; p for trend < 0.001), while advanced OLGA prevalence rose from 1.2% to 2.4% to 20.5% (p for trend < 0.001). The consistency of these dose–response patterns across two mechanistically distinct indices—one reflecting visceral adiposity (BRI) and the other insulin resistance (TyG)—reinforces the biological plausibility of the cardiometabolic–gastric mucosal pathology axis. 3.8 Metabolic Syndrome and Gastric Pathology Patients with metabolic syndrome exhibited significantly more severe gastric mucosal pathology across all histopathological parameters. Mean OLGA stage was nearly three-fold higher in MetS-positive compared to MetS-negative patients (1.48 ± 1.09 vs. 0.51 ± 0.66, p < 0.001). IM grade (1.82 ± 1.09 vs. 0.82 ± 0.99, p < 0.001) and atrophy grade (1.59 ± 1.04 vs. 0.96 ± 0.91, p < 0.001) were also markedly elevated in the MetS group. Advanced OLGA (≥ stage III) was strongly associated with metabolic syndrome prevalence (17.2% vs. 2.5%; χ² = 23.54, p < 0.001). A composite cardiometabolic score incorporating individual MetS components demonstrated a significant linear relationship with all histopathological outcomes, further supporting the concept that cumulative metabolic burden drives gastric mucosal damage in a graded fashion. 4. DISCUSSION The present study investigated the associations of novel anthropometric indices—Body Roundness Index (BRI) and A Body Shape Index (ABSI)—alongside cardiometabolic markers (TyG index, TyG-BMI) with gastric intestinal metaplasia severity assessed by OLGA staging. The principal findings are as follows: ( 1 ) among all indices evaluated, BRI exhibited the strongest correlation with both OLGA stage (ρ = 0.448) and IM grade (ρ = 0.493); ( 2 ) the TyG index demonstrated the highest discriminative performance for advanced OLGA stages (AUC = 0.848), followed by BRI (AUC = 0.815); ( 3 ) both BRI and TyG retained independent predictive significance in multivariate models adjusted for age, sex, Helicobacter pylori status, and smoking, with their combined model yielding the lowest AIC (233.8); and ( 4 ) a consistent dose–response gradient was observed across tertiles of both indices, with IM prevalence increasing from approximately 40% in the lowest tertile to over 82% in the highest tertile. Collectively, these results indicate a graded association between visceral adiposity, insulin resistance, and gastric precancerous lesion severity. The finding that BRI outperformed BMI in predicting gastric mucosal pathology is consistent with the established understanding that visceral adiposity, rather than overall body mass, is more closely related to obesity-associated disease risk. BRI integrates waist circumference and height to model the torso as an ellipse, thereby providing greater sensitivity to central fat distribution than BMI, which does not differentiate lean mass from adipose tissue or characterize fat topography ( 11 , 23 ). In a 2025 cross-sectional analysis of 264 healthy multiethnic adults that included MRI measures of visceral adipose tissue and hyperinsulinemic-euglycemic clamp assessments, the Body Roundness Index (BRI) demonstrated strong correlations with visceral adiposity (r = 0.72) and with insulin sensitivity index (r = − 0.51), confirming its biological relevance as an anthropometric indicator of metabolically active abdominal fat ( 23 ). In our cohort, mean BRI increased by 136% from OLGA stage 0 to advanced stages (III–IV), compared to a 32% increase for BMI across the same range. This disparity suggests that BRI captures the visceral adipose-associated inflammatory burden—mediated by TNF-α, IL-6, and leptin—more effectively than BMI, which may account for its stronger correlation with histopathological severity ( 24 ). Recent longitudinal data further support the relevance of BRI in oncological risk assessment. Recent longitudinal data further support the relevance of the Body Roundness Index in oncological risk assessment; in three national aging cohorts (n = 33 624), elevated BRI demonstrated a threshold association with overall cancer incidence that persisted irrespective of cardiometabolic disease status ( 25 ). Analysis of the UK Biobank data revealed that participants demonstrating a consistently high Body Roundness Index (BRI) trajectory over time exhibited an increased risk of incident cancer. For instance, middle-aged men with persistent elevated BRI had a hazard ratio of 1.46 for cancer occurrence when compared to individuals in the low-stable trajectory group, thereby emphasizing the prognostic significance of enduring central adiposity patterns in relation to cancer risk ( 26 ). A large Chinese prospective cohort study documented significant associations between Body Roundness Index and mortality risk across multiple chronic diseases, indicating that elevated BRI is predictive of increased long-term death risk beyond traditional adiposity measures ( 27 ). The present finding that BRI correlated most strongly with gastric IM severity extends these observations to gastric precancerous lesions, suggesting potential utility as a non-invasive screening parameter for gastric mucosal pathology risk stratification. The TyG index demonstrated the highest discriminative capacity for advanced OLGA stages (AUC = 0.848), indicating that insulin resistance, as captured by this composite marker, may play a role in distinguishing higher-risk histological stages. This observation is consistent with the Korean health checkup cohort study (n = 127,564) by Kim et al., which reported progressive associations between TyG index quartiles and precancerous conditions (Q4 vs. Q1: OR = 1.656) as well as gastric cancer (Q4 vs. Q1: OR = 2.363) ( 28 ). Our identified cutoff of 9.305 for advanced OLGA detection is lower than the gastric cancer cutoff of 9.73 reported by Kim et al., which is expected given that the present study targeted an earlier point in the Correa cascade. The TyG index demonstrated acceptable discriminatory performance in predicting cancer occurrence, with individuals having cancer showing a mean TyG difference of 0.34 units compared with controls and a summary ROC AUC of 0.72, indicating its potential utility as a metabolic marker in cancer risk assessment ( 29 ).Elevated TyG index has been consistently linked with higher cancer incidence and mortality across cohort studies, a relationship that has been attributed to insulin resistance-mediated activation of oncogenic signaling pathways such as PI3K/Akt/mTOR and chronic inflammation via NF-κB ( 30 ). The combined BRI–TyG model (Model 5: AIC = 233.8; pseudo-R² = 0.334) yielded a lower AIC than either index alone, suggesting additive predictive value. This finding can be interpreted in terms of the mechanistically distinct pathways captured by each index. BRI reflects visceral adiposity and the pro-inflammatory changes associated with excess abdominal fat, including dysregulated adipokine secretion, increased macrophage infiltration, and oxidative stress—mechanisms that have been implicated in chronic low-grade inflammation linked to obesity-related tissue injury and metabolic dysfunction( 31 ). The TyG index captures the metabolic component of insulin resistance, which has been linked to gastric carcinogenesis through hyperinsulinemia-driven IGF-1/PI3K/Akt/mTOR signaling, promoting cellular proliferation and inhibiting apoptosis ( 32 ). Persistent insulin resistance, as evidenced by elevated TyG index values, may promote malignant transformation in precancerous lesions through the activation of oncogenic signaling pathways such as PI3K/Akt/mTOR and through chronic inflammation mediated by NF-κB. Additionally, a higher metabolic reserve associated with insulin resistance could potentially influence treatment tolerance and survival outcomes in patients with advanced cancer patients ( 33 ). The present data suggest that these two pathophysiological dimensions—visceral inflammation and metabolic dysregulation—contribute to gastric mucosal pathology through parallel but complementary mechanisms, and their combined assessment provides improved risk stratification compared to either index individually. In contrast to BRI and TyG, ABSI demonstrated weaker discriminative performance and lost statistical significance in multivariate analysis after age adjustment (p = 0.262).This attenuation is consistent with the known age-dependency of ABSI, which quantifies waist circumference independently of BMI but is influenced by age-related changes in body composition and sarcopenia ( 34 ). Nevertheless, ABSI exhibited a proportionally stronger association with atrophy grade relative to its overall correlation pattern, which may indicate a preferential relationship with atrophic rather than metaplastic changes. UK Biobank data have shown that ABSI is positively associated with gastric cardia cancer risk in men (HR = 1.31; 95% CI: 1.07–1.61 per SD increment), and that the combination of high ABSI and elevated BMI further increases stomach cancer risk ( 35 ). In an analysis of National Health and Nutrition Examination Survey (NHANES) data from 1999–2018, higher ABSI levels were positively associated with colorectal cancer risk, with individuals in the highest ABSI quartile exhibiting greater odds of CRC compared with those in the lowest quartile (OR = 1.88; 95% CI, 1.19–2.96; p = 0.006), and this relationship was more pronounced in adults under 60 years of age ( 36 ). The loss of ABSI’s independent effect in our multivariate models may be attributable to confounding by age, as the IM-positive group was significantly older (61.1 vs. 41.7 years), and age-related redistribution of body composition may obscure ABSI’s independent contribution. The dose–response relationships observed across BRI and TyG tertiles are consistent with a graded biological gradient linking metabolic burden to gastric mucosal pathology. Advanced OLGA prevalence increased approximately eight-fold across BRI tertiles (2.4% to 19.0%) and approximately seventeen-fold across TyG tertiles (1.2% to 20.5%). These gradients parallel the observations of Kim et al. in a Korean cohort (n = 142,832), where increasing BMI categories were associated with incident IM in a dose–response fashion, independently of H. pylori status ( 28 ). The present results extend this finding by demonstrating that the gradient is more pronounced when visceral adiposity (BRI) and insulin resistance (TyG) are measured directly rather than approximated by BMI. Additionally, the three-fold difference in mean OLGA stage between metabolic syndrome-positive and -negative patients (1.48 vs. 0.51) supports the concept that cumulative metabolic burden is associated with gastric mucosal damage in a graded manner, consistent with mechanistic links between obesity-driven inflammation—including adipokine imbalance, oxidative stress, and gut microbiome dysbiosis—and increased cancer susceptibility ( 24 ). Several biological mechanisms may account for the observed associations. First, visceral adipose tissue—preferentially captured by BRI—secretes pro-inflammatory cytokines (TNF-α, IL-6) and adipokines (leptin, resistin) that enter systemic circulation and may reach the gastric mucosa, potentially promoting chronic inflammation conducive to epithelial injury and metaplastic transformation ( 37 ). Second, insulin resistance—reflected by the TyG index—may promote gastric carcinogenesis through hyperinsulinemia-mediated upregulation of the IGF-1 axis, which activates PI3K/Akt/mTOR signaling and promotes cellular proliferation while suppressing apoptosis ( 38 ). Dysregulation of the IGF signaling axis and downstream PI3K/AKT/mTOR pathway, including increased phosphorylation of key effectors such as AKT and activation of mTORC1, has been observed in gastric cancer and is implicated in promoting tumor cell proliferation, survival, and metabolic reprogramming ( 38 ). Third, hypertriglyceridemia generates lipotoxic intermediates (ceramides, diacylglycerols) that may induce endoplasmic reticulum stress and mitochondrial dysfunction in gastric epithelial cells( 39 ). Fourth, the hyperglycemic component of the TyG index may contribute through formation of advanced glycation end-products (AGEs), which bind to RAGE receptors on gastric epithelial cells and activate NF-κB-dependent inflammatory cascades; a recent comparative tissue analysis demonstrated increased RAGE and HMGB-1 expression in gastric cancer tissue of diabetic patients ( 40 ). The complementary nature of these pathways may explain why the combined BRI–TyG model provided improved predictive performance. The present findings have several potential clinical implications. First, both BRI and TyG index are calculable from routine clinical measurements (waist circumference, height, fasting glucose, triglycerides) without additional cost, making them feasible for use in primary care and gastroenterology settings. The identified BRI cutoff of 3.335 for advanced OLGA detection (sensitivity 80.0%, specificity 74.8%) and TyG cutoff of 9.305 (sensitivity 75.0%, specificity 82.2%) may inform clinical decision-making. Second, the 2025 MAPS III guideline by ESGE and the 2025 ACG Clinical Guideline both emphasize individualized risk stratification in gastric precancerous conditions ( 3 , 4 ). The incorporation of BRI and TyG into existing surveillance algorithms could potentially improve the identification of individuals who may benefit from closer endoscopic follow-up. Third, given the modifiable nature of both visceral adiposity and insulin resistance, these findings raise the hypothesis that lifestyle interventions targeting weight reduction and metabolic optimization could attenuate gastric precancerous lesion progression, although prospective interventional studies are required to test this hypothesis. Several limitations should be acknowledged. First, the cross-sectional design precludes determination of temporal causality between elevated cardiometabolic indices and IM development or progression. Prospective cohort studies with serial histopathological assessments are needed to establish whether changes in BRI or TyG precede IM progression. Second, the sample size (n = 250) and single-center setting may limit generalizability to populations with different ethnic compositions, dietary habits, and H. pylori prevalence. Third, serum adipokines (leptin, adiponectin), inflammatory cytokines (IL-6, TNF-α), and insulin levels were not directly measured, which would have strengthened mechanistic interpretation. Fourth, the advanced OLGA subgroup (n = 20, 8.0%) was limited in size, which may have reduced statistical power for subgroup analyses and widened confidence intervals for ROC analysis at this endpoint. Fifth, despite adjustment for age, sex, H. pylori status, and smoking, residual confounding from unmeasured variables—including dietary patterns, physical activity, genetic predisposition, and pharmacological treatments—cannot be excluded. Sixth, although OLGA staging is standardized, interobserver variability in histopathological interpretation remains a potential source of bias, although consensus review by a second pathologist was employed to mitigate this concern. Future research should address these limitations through several approaches. Multicenter prospective cohorts with longitudinal BRI and TyG monitoring could determine whether dynamic changes in these indices predict IM progression or regression following metabolic interventions. Integration of serum biomarkers (adipokines, inflammatory cytokines, insulin, IGF-1) with anthropometric indices may facilitate the development of composite risk scores with improved predictive accuracy. Additionally, the potential role of artificial intelligence in combining metabolic risk assessment with endoscopic grading systems such as EGGIM warrants investigation ( 41 ).Artificial intelligence–assisted endoscopic systems have demonstrated high diagnostic accuracy in detecting gastric intestinal metaplasia and other precancerous lesions, with performance metrics that are comparable to those of expert endoscopists, highlighting their potential to standardize mucosal assessment and enhance risk stratification during upper gastrointestinal endoscopy ( 42 ). Interventional studies examining whether targeted weight loss, metabolic syndrome treatment, or pharmacological modulation of insulin resistance can attenuate gastric precancerous lesion severity would provide evidence for clinical translation of these findings. 5. CONCLUSION This study demonstrates that the BRI and TyG index are independently associated with the severity of gastric intestinal metaplasia and advanced OLGA staging, exhibiting superior predictive performance compared to conventional BMI. The combined BRI-TyG model offers the highest discriminative ability, indicating complementary pathophysiological mechanisms involving visceral adiposity and insulin resistance in gastric carcinogenesis. These non-invasive, easily calculable indices may serve as practical tools for identifying individuals at increased risk of gastric precancerous lesions. Future prospective, multicenter studies with larger sample sizes are warranted to validate these findings and to assess the potential of metabolic intervention in modifying gastric cancer risk. Declarations Ethics Approval and Consent to Participate This study was approved by the Non-Interventional Clinical Research Ethics Committee of Yenimahalle Training and Research Hospital (Approval Date: October 15, 2025) and conducted in accordance with the principles of the Declaration of Helsinki (2013 revision). Written informed consent was obtained from all participants prior to enrollment. Consent for Publication Not applicable. Availability of Data and Materials The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Competing Interests The authors declare that they have no competing interests. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors' Contributions M.U. conceived and designed the study, collected and curated clinical data, performed all statistical analyses, generated all figures and tables, interpreted the results, and wrote the original manuscript draft. F.Y. contributed to patient enrollment, endoscopic procedures, and surgical data acquisition, and critically reviewed the manuscript for intellectual content. E.E. contributed to data collection, clinical data verification, and critically reviewed the manuscript for intellectual content. All authors read and approved the final version of the manuscript. Acknowledgements Not applicable. Clinical trial number Not applicable References Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209–49. Li W, Zhang T. Precancerous pathways to gastric cancer: a review of experimental animal models recapitulating the correa cascade. Front Cell Dev Biology. 2025;Volume 13–2025. Dinis-Ribeiro M, Libânio D, Uchima H, Spaander MCW, Bornschein J, Matysiak-Budnik T, et al. Management of epithelial precancerous conditions and early neoplasia of the stomach (MAPS III): European Society of Gastrointestinal Endoscopy (ESGE), European Helicobacter and Microbiota Study Group (EHMSG) and European Society of Pathology (ESP) Guideline update 2025. Endoscopy. 2025;57(5):504–54. Morgan DR, Corral JE, Li D, Montgomery EA, Riquelme A, Kim JJ, et al. ACG Clinical Guideline: Diagnosis and Management of Gastric Premalignant Conditions. Am J Gastroenterol. 2025;120(4):709–37. Benites-Goñi H, Cabrera-Hinojosa D, Latorre G, Hernandez AV, Uchima H, Riquelme A. OLGA and OLGIM staging systems on the risk assessment of gastric cancer: a systematic review and meta–analysis of prospective cohorts. Th Adv Gastroenterol. 2025;18:17562848251325461. Varra FN, Varras M, Varra VK, Theodosis-Nobelos P. Molecular and pathophysiological relationship between obesity and chronic inflammation in the manifestation of metabolic dysfunctions and their inflammation–mediating treatment options (Review). Mol Med Rep. 2024;29(6). Bae J-M. Body Mass Index and Risk of Gastric Cancer in Asian Adults: A Meta-Epidemiological Meta-Analysis of Population-Based Cohort Studies. Cancer Res Treat. 2020;52(2):369–73. Ceylaner B, Sahin F, Yildiz A, Tastekin D, Gok AF, Baykal AT. IGF Pathway Components as Potential Biomarkers in Gastric Cancer. Int J Mol Sci [Internet]. 2025;26(22):10880. Nasif WA, Hasan Mukhtar M, El-Moursy Ali AS, Nour Eldein MM, Almaimani RA, Ashgar SS. Body mass index is associated with Helicobacter pylori infection and increased oxidative DNA damage in an obese population. J Int Med Res. 2022;50(2):03000605221076975. Blundell JE, Dulloo AG, Salvador J, Frühbeck G. Beyond BMI–phenotyping the obesities. Obes Facts. 2014;7(5):322–8. de Luis D, Muñoz M, Izaola O, Lopez Gomez JJ, Rico D, Primo D. Body Roundness Index (BRI) Predicts Metabolic Syndrome in Postmenopausal Women with Obesity Better than Insulin Resistance. Diabetology. 2025;6(7):60. Zhang X, Ma N, Lin Q, Chen K, Zheng F, Wu J, et al. Body Roundness Index and All-Cause Mortality Among US Adults. JAMA Netw Open. 2024;7(6):e2415051–e. Kunutsor SK, Bhattacharjee A, Jae SY, Laukkanen JA. Body roundness index and cardiometabolic multimorbidity: Findings from the English Longitudinal Study of Ageing. Nutrition, Metabolism and Cardiovascular Diseases. 2025:104475. Cai X, Liao Y, Yang X, Liang Y, Ma J, Liu R, et al. Body Roundness Index Associated With Cardiometabolic Multimorbidity and Mortality: A Multistate Model. Obes (Silver Spring). 2025;33(12):2377–86. Liu H, Fu H, Wang Z, Yao Z, Wang Q, Sui A et al. The predictive value of a body shape index as a novel obesity metric for cancer risk: a systematic review and meta-analysis. Front Nutr. 2025;Volume 12–2025. Parra-Soto S, Malcomson FC, Ho FK, Pell JP, Sharp L, Mathers JC, et al. Associations of A Body Shape Index (ABSI) with Cancer Incidence, All-Cause, and at 23 Sites-Findings from the UK Biobank Prospective Cohort Study. Cancer Epidemiol Biomarkers Prev. 2022;31(2):315–24. Kim YM, Kim J-H, Park JS, Baik SJ, Chun J, Youn YH, et al. Association between triglyceride-glucose index and gastric carcinogenesis: a health checkup cohort study. Gastric Cancer. 2022;25(1):33–41. Eskazan T, Saribas S, Kocazeybek B. Prognostic value of the triglyceride-glucose index in gastric cancer. World J Gastroenterol. 2025;31(17):104651. Erdem H, Baymakova M, Alkan S, Letaief A, Yahia WB, Dayyab F, et al. Classical fever of unknown origin in 21 countries with different economic development: an international ID-IRI study. Eur J Clin Microbiol Infect Dis. 2023;42(4):387–98. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation. 2009;120(16):1640-5. Petersdorf RG, Beeson PB. Fever of unexplained origin: report on 100 cases. Med (Baltim). 1961;40:1–30. Littlejohns TJ, Henley WE, Lang IA, Annweiler C, Beauchet O, Chaves PH, et al. Vitamin D and the risk of dementia and Alzheimer disease. Neurology. 2014;83(10):920–8. Eng PC, Teo AE, Leow MKS, Tai ES, Khoo CM. Body roundness index (BRI) and obesity-related anthropometrics: Relationship to visceral adiposity, insulin sensitivity index and cardiometabolic risk. Diabetes Obes Metab. 2025;27(10):5554–65. Lamabadusuriya DA, Jayasena H, Bopitiya AK, De Silva AD, Jayasekera P. Obesity-driven inflammation and cancer risk: A comprehensive review. Semin Cancer Biol. 2025;114:256–66. Luo P, Lian Y. Association Between Body Roundness Index and Cancer Risk, With Further Stratification by Cardiometabolic Disease Status: Findings From Three National Longitudinal Cohorts. Cancer Med. 2025;14(21):e71324. Chen Y, Wang Y, Zheng X, Liu T, Liu C, Lin S, et al. Body Roundness Index Trajectories and the Risk of Cancer: A Cohort Study. Cancer Med. 2024;13(23):e70447. Sun X, Deng Q, Li Z, Lin C, Song G, Chen S, et al. Body roundness index and mortality risk in chronic diseases: a national prospective longitudinal study in China. Aging Clin Exp Res. 2025;38(1):5. Kim YM, Kim JH, Park JS, Baik SJ, Chun J, Youn YH, et al. Association between triglyceride-glucose index and gastric carcinogenesis: a health checkup cohort study. Gastric Cancer. 2022;25(1):33–41. Chen IW, Wang WT, Wu JY, Yu CH, Chang YJ, Hung KC. Diagnostic performance of the triglyceride-glucose index in predicting occurrence of cancer: a meta-analysis. Front Oncol. 2025;15:1532253. Peng S, Wang C, Liu Q, Luo Q. The association of triglyceride-glucose index with cancer incidence and mortality: a systematic review and meta-analysis of cohort studies. Front Endocrinol (Lausanne). 2025;16:1682062. Savulescu-Fiedler I, Mihalcea R, Dragosloveanu S, Scheau C, Baz RO, Caruntu A, et al. Interplay between Obes Inflamm Life [Internet]. 2024;14(7):856. p.]. Crasan I-M, Tanase M, Delia CE, Gradisteanu-Pircalabioru G, Cimpean A, Ionica E. Metaflammation’s Role in Systemic Dysfunction in Obesity: A Comprehensive Review. Int J Mol Sci [Internet]. 2025;26(21):10445. Zhao CF, Liu XL, Wu NB, Xu ZF. Triglyceride-glucose index as a prognostic indicator in advanced gastric cancer: Insights and future research. World J Gastroenterol. 2025;31(17):104794. Shang H, Deng Z, Lian Y, Ai Y, Li G. A body shape index and sarcopenia risk in middle-aged and older adults: evidence from the China Health and Retirement Longitudinal Study. Eur J Med Res. 2025;31(1):94. Christakoudi S, Tsilidis KK, Evangelou E, Riboli E. A Body Shape Index (ABSI), hip index, and risk of cancer in the UK Biobank cohort. Cancer Med. 2021;10(16):5614–28. Liu H, Kang J, Liu W, Shen Y. Association between a body shape index and colorectal cancer in US population: a cross-sectional study based on NHANES. Front Nutr. 2025;12:1535655. Kawai T, Autieri MV, Scalia R. Adipose tissue inflammation and metabolic dysfunction in obesity. Am J Physiol Cell Physiol. 2021;320(3):C375–91. Jiang M, Zhang K, Zhang Z, Zeng X, Huang Z, Qin P et al. PI3K/AKT/mTOR Axis in Cancer: From Pathogenesis to Treatment. MedComm (2020). 2025;6(8):e70295. Law BA, Liao X, Moore KS, Southard A, Roddy P, Ji R, et al. Lipotoxic very-long-chain ceramides cause mitochondrial dysfunction, oxidative stress, and cell death in cardiomyocytes. Faseb j. 2018;32(3):1403–16. Zhou Y, Liu SX, Zhou YN, Wang J, Ji R. Research on the relationship between RAGE and its ligand HMGB1, and prognosis and pathogenesis of gastric cancer with diabetes mellitus. Eur Rev Med Pharmacol Sci. 2021;25(3):1339–50. Almeida E, Martins ML, Marques D, Delas R, Almeida T, Chaves J, et al. Artificial intelligence for endoscopic grading of gastric intestinal metaplasia: advancing risk stratification for gastric cancer. Endoscopy. 2025;57(11):1254–60. Gong EJ, Woo J, Lee JJ, Bang CS. Role of artificial intelligence in gastric diseases. World J Gastroenterol. 2025;31(37):111327. Additional Declarations No competing interests reported. Supplementary Files GA.png Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 02 Apr, 2026 Reviews received at journal 01 Apr, 2026 Reviewers agreed at journal 27 Mar, 2026 Reviews received at journal 22 Mar, 2026 Reviewers agreed at journal 21 Mar, 2026 Reviewers invited by journal 20 Mar, 2026 Editor invited by journal 20 Mar, 2026 Editor assigned by journal 05 Mar, 2026 Submission checks completed at journal 05 Mar, 2026 First submitted to journal 24 Feb, 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8962909","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":611319700,"identity":"e30ad5bd-f1aa-4439-9254-a5e746c75da4","order_by":0,"name":"Mete Ucdal","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIiWNgGAWjYPACZgY+9h5kATb8qsEEG88ZEMNAAq6Fh6AWiRwitei29x98zFNjLc8m+fbghx8Vf+r4pZsPMHwoO8xgL30AqxazM4eZjXmOpRu2SeclS/acMZCQnHMsgXHGucMMPHwJ2LXcSGaT5m04zNgmnWMgzdhmIGFwI8eAmbcNqAWHy8zuP2b/DdRi3yZ5xvg34z8DCfsb+R+Y/+LTcoOZjRmoJbFNgsdMmrEBaAswHJgZ8Wk5k2wMdH16chtPjpllzzFjyRl3jhkc7DmXzgMJdSxajh98+OFNjbVtP/sZ4xs/auT4+Wc3P3zwo8xaDjVu8QJgzBxgwBeTWLWMglEwCkbBKEAGABXmVILfQJ1vAAAAAElFTkSuQmCC","orcid":"","institution":"Etimesgut Asker Hastanesi","correspondingAuthor":true,"prefix":"","firstName":"Mete","middleName":"","lastName":"Ucdal","suffix":""},{"id":611319702,"identity":"4486924d-1f5b-4f0c-9ffc-b96c4842567c","order_by":1,"name":"Faruk Yazıcı","email":"","orcid":"","institution":"Etimesgut Asker Hastanesi","correspondingAuthor":false,"prefix":"","firstName":"Faruk","middleName":"","lastName":"Yazıcı","suffix":""},{"id":611319704,"identity":"76cf5d61-d3f1-4b82-ad07-f7886a9a0e30","order_by":2,"name":"Evren Ekingen","email":"","orcid":"","institution":"Etimesgut Asker Hastanesi","correspondingAuthor":false,"prefix":"","firstName":"Evren","middleName":"","lastName":"Ekingen","suffix":""}],"badges":[],"createdAt":"2026-02-25 04:09:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8962909/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8962909/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105432991,"identity":"97b6fabf-3657-4705-8a91-2491f8cf2bcc","added_by":"auto","created_at":"2026-03-26 02:57:32","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":97437,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy Flowchart.\u003c/strong\u003e Flowchart illustrating patient enrollment, inclusion/exclusion criteria, data collection framework, histopathological stratification, and statistical analysis pipeline. Of 487 screened patients, 250 met inclusion criteria and were enrolled. Patients were stratified into IM-positive (n = 154, 61.6%) and IM-negative (n = 96, 38.4%) groups. The comprehensive analytical framework including comparative, correlation, discriminative, predictive, ordinal, dose–response, and metabolic syndrome analyses is depicted.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8962909/v1/5b1454637938ee19af7b234a.jpg"},{"id":105432994,"identity":"4fd36278-9656-4fa3-b90f-b33d5c55ed9f","added_by":"auto","created_at":"2026-03-26 02:57:32","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":42401,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of Anthropometric and Cardiometabolic Indices Across OLGA Stages (0–IV). Box plots displaying median, interquartile range, and outliers for BMI, BRI, ABSI, TyG index, TyG-BMI, and waist circumference across OLGA stages 0–IV. All indices demonstrated statistically significant progressive increases across stages (p \u0026lt; 0.001 for all). BRI (H = 56.5) and waist circumference (H = 57.0) showed the highest test statistics, indicating the most pronounced separation across stages.\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8962909/v1/13543bb25aa7b3c21d6aa962.jpg"},{"id":105432995,"identity":"b9b9d64a-2cc4-43af-a8bb-6a4bee2b15e3","added_by":"auto","created_at":"2026-03-26 02:57:32","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":44756,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation Matrix Between Anthropometric, Cardiometabolic, and Histopathological Variables. Heat map displaying Spearman correlation coefficients (ρ). Color intensity reflects the strength of correlation, ranging from weak (light) to strong (dark). BRI demonstrates the strongest correlations with OLGA stage (ρ = 0.448) and IM grade (ρ = 0.493), followed by waist circumference (ρ = 0.460 and 0.491, respectively). All displayed correlations are statistically significant (p \u0026lt; 0.001). Gold stars (★) indicate ρ ≥ 0.48.\u003c/p\u003e","description":"","filename":"Picture4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8962909/v1/76c8d76a7140dab4b3d3aff6.jpg"},{"id":105432992,"identity":"974f19fb-641b-4a02-bff0-d04dddb996ff","added_by":"auto","created_at":"2026-03-26 02:57:32","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":38905,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver Operating Characteristic (ROC) Curves for Anthropometric and Cardiometabolic Indices. (A) ROC curves for predicting intestinal metaplasia positivity. Waist circumference (AUC = 0.740) and BRI (AUC = 0.736) demonstrate the highest discriminative ability, followed by TyG index (AUC = 0.705). (B) ROC curves for predicting advanced OLGA stage (≥III). TyG index (AUC = 0.848) and BRI (AUC = 0.815) achieve superior performance compared to conventional BMI (AUC = 0.755). The diagonal dashed line represents the reference line (AUC = 0.500).\u003c/p\u003e","description":"","filename":"Picture5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8962909/v1/ee2498c8ea5eab4cf55fb1aa.jpg"},{"id":105570137,"identity":"4600aba4-198f-4a57-a592-7c71f5c2048e","added_by":"auto","created_at":"2026-03-27 13:14:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1483808,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8962909/v1/a42151af-270a-4732-abe0-489414fabfce.pdf"},{"id":105566972,"identity":"bb066422-9d7f-4997-8f5d-7035c7cf6721","added_by":"auto","created_at":"2026-03-27 12:57:53","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":106453,"visible":true,"origin":"","legend":"","description":"","filename":"GA.png","url":"https://assets-eu.researchsquare.com/files/rs-8962909/v1/678279e480404dc7d6879f6e.png"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association of Body Roundness Index, A Body Shape Index, and Cardiometabolic Indices with Gastric Intestinal Metaplasia Severity Assessed by OLGA Staging","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eGastric cancer (GC) remains a leading cause of cancer-related mortality worldwide, accounting for approximately 660,000 deaths annually (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The development of intestinal-type gastric adenocarcinoma follows the well-characterized Correa cascade, progressing from chronic gastritis through glandular atrophy, intestinal metaplasia (IM), dysplasia, and ultimately invasive carcinoma (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Among these precancerous stages, IM represents a irreversible histological transformation in which the native gastric epithelium is replaced by an intestinal phenotype, conferring substantially elevated malignancy risk. The 2025 MAPS III guideline by the European Society of Gastrointestinal Endoscopy (ESGE) reinforces the necessity of systematic risk stratification and structured endoscopic surveillance for patients with advanced atrophic stages (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Concordantly, the 2025 American College of Gastroenterology (ACG) Clinical Guideline emphasizes that IM carries an increased risk of progression to gastric adenocarcinoma (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Operative Link on Gastritis Assessment (OLGA) and its intestinal metaplasia-based counterpart (OLGIM) staging systems standardize histological risk evaluation by incorporating the topographic distribution and severity of atrophic changes across gastric biopsy sites. Prospective cohort data demonstrated that OLGA stages III\u0026ndash;IV confer an increased risk of gastric cancer (pooled RR\u0026thinsp;=\u0026thinsp;32.31; 95% CI: 9.14\u0026ndash;114.21), thereby establishing these staging systems as reliable tools for identifying individuals at highest risk (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Nevertheless, histopathological staging alone does not capture modifiable metabolic risk determinants that may modulate the progression of gastric precancerous lesions.\u003c/p\u003e \u003cp\u003eObesity and metabolic syndrome (MetS) have been implicated in gastric carcinogenesis through chronic low-grade inflammation, insulin resistance, and oxidative stress. Visceral adipose tissue secretes pro-inflammatory adipokines\u0026mdash;including TNF-α, IL-6, and leptin\u0026mdash;which perpetuate a chronic inflammatory microenvironment conducive to mucosal injury and metaplastic transformation (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). A large-scale Korean cohort study demonstrated that increasing BMI categories are associated with new-onset IM in a dose\u0026ndash;response manner, independent of \u003cem\u003eHelicobacter pylori\u003c/em\u003e status (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Furthermore, insulin resistance promotes IGF-1/PI3K/Akt/mTOR signaling, accelerating cellular proliferation in gastric epithelial cells (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Studies in bariatric surgery populations have confirmed that \u003cem\u003eH. pylori\u003c/em\u003e-positive obese patients exhibit significantly higher IM prevalence with concomitant elevations in HOMA-IR (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite its widespread use, BMI has well-documented limitations in characterizing visceral fat distribution (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Novel anthropometric indices have been developed to overcome these shortcomings. The Body Roundness Index (BRI), integrating waist circumference and height to model the torso as an ellipse, provides superior sensitivity to visceral adiposity (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). A U-shaped association between body roundness index (BRI) and all-cause mortality was observed in a cohort of 32,995 US adults, and subsequent longitudinal studies from 2025\u0026ndash;2026 have confirmed BRI as a multifaceted predictor of chronic disease mortality and cardiometabolic multimorbidity (\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). A Body Shape Index (ABSI), which quantifies waist circumference independently of BMI, has been associated with overall and site-specific cancer incidence\u0026mdash;including stomach cancer\u0026mdash;and systematic review and meta-analytic evidence supports its utility in cancer risk prediction (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe triglyceride\u0026ndash;glucose (TyG) index, a cost-effective surrogate marker for insulin resistance, has emerged as a novel biomarker in gastric carcinogenesis. Kim et al. demonstrated a positive correlation between TyG index and precancerous conditions including atrophic gastritis and IM (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Recent evidence from cohort studies and meta-analytic syntheses indicates that elevated triglyceride\u0026ndash;glucose (TyG) index is associated with increased cancer incidence across several tumor types, and has been proposed as a prognostic biomarker in gastric cancer, although standardized cutoff values remain to be established(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the expanding literature on novel adiposity and metabolic indices, no study to date has evaluated the association of BRI, ABSI, and TyG index with histopathological severity of gastric IM as determined by standardized OLGA staging. Therefore, the present study aimed to: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) compare the discriminative performance of BRI, ABSI, BMI, TyG index, and TyG-BMI for predicting IM positivity and advanced OLGA stage; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) evaluate independent associations through multivariate regression adjusted for age, sex, \u003cem\u003eH. pylori\u003c/em\u003e infection, and smoking status; and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) assess dose\u0026ndash;response relationships through tertile-based analysis.\u003c/p\u003e"},{"header":"2. MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design and Population\u003c/h2\u003e \u003cp\u003eThis cross-sectional study was conducted at the Department of Internal Medicine, Etimesgut Şehit Sait Ert\u0026uuml;rk State Hospital, Ankara, Turkey. Medical records of patients who underwent esophagogastroduodenoscopy (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) with gastric biopsy between January 2023 and December 2024 were retrospectively reviewed. The study protocol was approved by the Institutional Ethics Committee and conducted in accordance with the principles of the Declaration of Helsinki.\u003c/p\u003e \u003cp\u003eInclusion criteria were: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) availability of gastric biopsy specimens obtained according to the updated Sydney protocol (at least two biopsies from the antrum, two from the corpus, and one from the incisura angularis); (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) complete anthropometric measurements including height, weight, and waist circumference recorded within 30 days of endoscopy; and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) fasting laboratory parameters including glucose, triglycerides, and HDL-cholesterol obtained within 30 days of endoscopy. Exclusion criteria included: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) history of gastric surgery or malignancy; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) active gastrointestinal bleeding at the time of endoscopy; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) use of proton pump inhibitors for \u0026gt;\u0026thinsp;8 weeks prior to endoscopy; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) pregnancy or lactation; and (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) incomplete biopsy specimens or laboratory data. A total of 250 patients fulfilling these criteria were included in the final analysis. The study design and patient selection process are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Histopathological Assessment\u003c/h2\u003e \u003cp\u003eAll gastric biopsy specimens were evaluated by an experienced gastrointestinal pathologist blinded to clinical and metabolic data. Histopathological assessment included evaluation of atrophy, intestinal metaplasia, and Helicobacter pylori colonization according to the updated Sydney classification. Atrophy and IM were graded as: 0 (absent), 1 (mild), 2 (moderate), and 3 (severe) for each biopsy location. OLGA staging was performed by integrating atrophy scores from antral and corporal compartments, yielding stages 0 through IV. Advanced OLGA was defined as stage\u0026thinsp;\u0026ge;\u0026thinsp;III. In cases of diagnostic uncertainty, a second pathologist independently reviewed the specimens, and consensus was reached through discussion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Anthropometric Measurements and Index Calculations\u003c/h2\u003e \u003cp\u003eHeight was measured to the nearest 0.1 cm using a wall-mounted stadiometer with the patient in an upright position. Weight was measured to the nearest 0.1 kg using a calibrated digital scale with light clothing and no shoes. Waist circumference was measured at the midpoint between the lowest rib and the iliac crest at the end of gentle expiration using a non-stretchable tape measure.\u003c/p\u003e \u003cp\u003eThe following anthropometric and cardiometabolic indices were calculated:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eBody Mass Index (BMI)\u003c/strong\u003e \u003cp\u003eWeight (kg) / Height (m)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eBody Roundness Index (BRI)\u003c/strong\u003e \u003cp\u003e364.2\u0026thinsp;\u0026minus;\u0026thinsp;365.5 \u0026times; \u0026radic;[1 \u0026minus; (WC / 2π)\u003csup\u003e2\u003c/sup\u003e / (0.5 \u0026times; Height)\u003csup\u003e2\u003c/sup\u003e]\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eA Body Shape Index (ABSI)\u003c/strong\u003e \u003cp\u003eWC / (BMI\u003csup\u003e2/3\u003c/sup\u003e \u0026times; Height\u003csup\u003e1/2\u003c/sup\u003e)\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eTriglyceride-Glucose Index (TyG)\u003c/strong\u003e \u003cp\u003eLn [Triglycerides (mg/dL) \u0026times; Fasting Glucose (mg/dL) / 2]\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eTyG-BMI\u003c/strong\u003e \u003cp\u003eTyG \u0026times; BMI (kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/p\u003e \u003cp\u003eABSI z-scores (BSI-z) were calculated using age- and sex-specific reference values from the original publication by Krakauer and Krakauer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Metabolic Syndrome Definition\u003c/h2\u003e \u003cp\u003eMetabolic syndrome was diagnosed according to the harmonized criteria jointly proposed by the International Diabetes Federation, National Heart, Lung, and Blood Institute, American Heart Association, World Heart Federation, International Atherosclerosis Society, and International Association for the Study of Obesity (2009) (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The presence of \u0026ge;\u0026thinsp;3 of the following criteria was required: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) waist circumference\u0026thinsp;\u0026ge;\u0026thinsp;90 cm (men) or \u0026ge;\u0026thinsp;80 cm (women); (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) triglycerides\u0026thinsp;\u0026ge;\u0026thinsp;150 mg/dL or pharmacological treatment; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) HDL-cholesterol\u0026thinsp;\u0026lt;\u0026thinsp;40 mg/dL (men) or \u0026lt;\u0026thinsp;50 mg/dL (women) or pharmacological treatment; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;130 mmHg or diastolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;85 mmHg or antihypertensive treatment; (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) fasting plasma glucose\u0026thinsp;\u0026ge;\u0026thinsp;100 mg/dL or pharmacological treatment for hyperglycemia. The cardiometabolic score (0\u0026ndash;5) represented the sum of individual metabolic syndrome components present.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were performed using Python (version 3.12) with NumPy, SciPy, pandas, scikit-learn, and statsmodels libraries. Continuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e) and compared using the Mann\u0026ndash;Whitney U test (two groups) or Kruskal\u0026ndash;Wallis H test (multiple groups) after assessing normality with the Shapiro\u0026ndash;Wilk test. Categorical variables were presented as frequencies and percentages and compared using the chi-square test or Fisher exact test as appropriate.\u003c/p\u003e \u003cp\u003eSpearman rank correlation coefficients (ρ) were calculated to evaluate the associations between anthropometric/cardiometabolic indices and histopathological outcomes (OLGA stage, IM grade, atrophy grade). Receiver operating characteristic (ROC) curve analysis was performed to assess the discriminative ability of each index for predicting IM positivity and advanced OLGA (\u0026ge;\u0026thinsp;stage III). The area under the ROC curve (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) was calculated with 95% confidence intervals (CI), and optimal cutoff values were determined using the Youden index (J\u0026thinsp;=\u0026thinsp;sensitivity\u0026thinsp;+\u0026thinsp;specificity\u0026thinsp;\u0026minus;\u0026thinsp;1).\u003c/p\u003e \u003cp\u003eBinary logistic regression analysis was conducted to identify independent predictors of IM positivity. Univariate analysis was performed for each candidate variable, and those with p\u0026thinsp;\u0026lt;\u0026thinsp;0.10 were entered into multivariate models. Five multivariate models were constructed: Model 1 (BMI-based), Model 2 (BRI-based), Model 3 (ABSI-based), Model 4 (TyG-based), and Model 5 (combined BRI\u0026thinsp;+\u0026thinsp;TyG), each adjusted for age, sex, H. pylori status, and smoking. Model performance was evaluated using the Akaike Information Criterion (AIC) and pseudo-R\u0026sup2;. Ordinal logistic regression (proportional odds model) was additionally used to evaluate associations with OLGA stage as an ordinal outcome.\u003c/p\u003e \u003cp\u003eDose\u0026ndash;response relationships were assessed by dividing each index into tertiles and calculating the prevalence of IM positivity and advanced OLGA within each tertile category. The Cochran\u0026ndash;Armitage trend test was used to evaluate the significance of linear trends across tertiles. A two-sided p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant for all analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. RESULTS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Patient Characteristics\u003c/h2\u003e \u003cp\u003eA total of 250 patients (129 female [51.6%], 121 male [48.4%]) with a mean age of 53.6\u0026thinsp;\u0026plusmn;\u0026thinsp;18.9 years (range: 18\u0026ndash;92) were enrolled in this cross-sectional study. Among these, 154 patients (61.6%) were classified as IM-positive based on histopathological evaluation of gastric biopsies obtained via the updated Sydney protocol, while 96 patients (38.4%) were IM-negative. Advanced OLGA staging (\u0026ge;\u0026thinsp;stage III) was identified in 20 patients (8.0%). The distribution across OLGA stages was as follows: stage 0 (n\u0026thinsp;=\u0026thinsp;108, 43.2%), stage I (n\u0026thinsp;=\u0026thinsp;89, 35.6%), stage II (n\u0026thinsp;=\u0026thinsp;33, 13.2%), stage III (n\u0026thinsp;=\u0026thinsp;17, 6.8%), and stage IV (n\u0026thinsp;=\u0026thinsp;3, 1.2%). Metabolic syndrome, defined according to the NCEP-ATP III criteria, was present in 93 patients (37.2%). \u003cem\u003eHelicobacter pylori\u003c/em\u003e positivity was detected in 111 patients (44.4%), diabetes mellitus in 38 (15.2%), and hypertension in 75 (30.0%). The comprehensive demographic, clinical, and metabolic characteristics of the study population are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic, Clinical, and Metabolic Characteristics Stratified by Intestinal Metaplasia Status\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=\"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=\"left\" 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\"\u003e \u003cp\u003e Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;250)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIM (\u0026minus;) (n\u0026thinsp;=\u0026thinsp;96)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIM (+) (n\u0026thinsp;=\u0026thinsp;154)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\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\u003eAge (years)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53.6\u0026thinsp;\u0026plusmn;\u0026thinsp;18.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.7\u0026thinsp;\u0026plusmn;\u0026thinsp;15.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.1\u0026thinsp;\u0026plusmn;\u0026thinsp;17.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFemale, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129 (51.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53 (55.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76 (49.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.441\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99 (39.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (27.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73 (47.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBMI (kg/m\u0026sup2;)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWC (cm)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.8\u0026thinsp;\u0026plusmn;\u0026thinsp;12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.8\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBRI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.87\u0026thinsp;\u0026plusmn;\u0026thinsp;1.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.09\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eABSI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.066\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.064\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.068\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eABSI z-score\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.00\u0026thinsp;\u0026plusmn;\u0026thinsp;1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026ndash;0.33\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.21\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSBP (mmHg)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126.9\u0026thinsp;\u0026plusmn;\u0026thinsp;16.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e119.6\u0026thinsp;\u0026plusmn;\u0026thinsp;13.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131.5\u0026thinsp;\u0026plusmn;\u0026thinsp;16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFPG (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e104.1\u0026thinsp;\u0026plusmn;\u0026thinsp;23.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e94.4\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e110.1\u0026thinsp;\u0026plusmn;\u0026thinsp;26.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHbA1c (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTG (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e161.9\u0026thinsp;\u0026plusmn;\u0026thinsp;62.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e141.6\u0026thinsp;\u0026plusmn;\u0026thinsp;57.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e174.5\u0026thinsp;\u0026plusmn;\u0026thinsp;62.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHDL-C (mg/dL)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.9\u0026thinsp;\u0026plusmn;\u0026thinsp;12.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.5\u0026thinsp;\u0026plusmn;\u0026thinsp;11.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTyG index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.93\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTyG-BMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e247.1\u0026thinsp;\u0026plusmn;\u0026thinsp;68.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e219.4\u0026thinsp;\u0026plusmn;\u0026thinsp;53.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e265.6\u0026thinsp;\u0026plusmn;\u0026thinsp;72.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMetS, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93 (37.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (16.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e77 (50.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eH. pylori (+), n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111 (44.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26 (27.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85 (55.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDM, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38 (15.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36 (23.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHT, n (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75 (30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (5.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e70 (45.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Comparison of Cardiometabolic Indices Between IM-Positive and IM-Negative Groups\u003c/h2\u003e \u003cp\u003eComparative analysis of anthropometric and cardiometabolic indices stratified by IM status revealed statistically significant differences across virtually all parameters (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). IM-positive patients were significantly older than their IM-negative counterparts (61.1\u0026thinsp;\u0026plusmn;\u0026thinsp;17.2 vs. 41.7\u0026thinsp;\u0026plusmn;\u0026thinsp;15.1 years, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and demonstrated substantially higher BMI values (29.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.1 vs. 25.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.5 kg/m\u0026sup2;, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The Body Roundness Index showed a particularly striking difference between groups, with IM-positive patients exhibiting values 61% higher than IM-negative patients (3.36\u0026thinsp;\u0026plusmn;\u0026thinsp;1.88 vs. 2.09\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), reflecting substantially greater central adiposity in the affected group. ABSI was also significantly elevated in IM-positive patients (0.068\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005 vs. 0.064\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as was waist circumference (81.8\u0026thinsp;\u0026plusmn;\u0026thinsp;13.4 vs. 71.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.9 cm, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eAmong cardiometabolic parameters, the TyG index was significantly higher in IM-positive patients (9.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49 vs. 8.71\u0026thinsp;\u0026plusmn;\u0026thinsp;0.49, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as was the TyG-BMI composite index (265.6\u0026thinsp;\u0026plusmn;\u0026thinsp;72.9 vs. 219.4\u0026thinsp;\u0026plusmn;\u0026thinsp;53.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Fasting plasma glucose (110.1\u0026thinsp;\u0026plusmn;\u0026thinsp;26.5 vs. 94.4\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6 mg/dL, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), triglycerides (174.5\u0026thinsp;\u0026plusmn;\u0026thinsp;62.5 vs. 141.6\u0026thinsp;\u0026plusmn;\u0026thinsp;57.0 mg/dL, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), HbA1c (5.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88 vs. 5.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and systolic blood pressure (131.5\u0026thinsp;\u0026plusmn;\u0026thinsp;16.7 vs. 119.6\u0026thinsp;\u0026plusmn;\u0026thinsp;13.2 mmHg, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were all significantly elevated in the IM-positive group. HDL-cholesterol showed a trend toward lower values in IM-positive patients (41.5\u0026thinsp;\u0026plusmn;\u0026thinsp;11.4 vs. 45.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6 mg/dL, p\u0026thinsp;=\u0026thinsp;0.055) but did not reach statistical significance. The prevalence of metabolic syndrome was three-fold higher in IM-positive patients (50.0% vs. 16.7%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, \u003cem\u003eH. pylori\u003c/em\u003e positivity (55.2% vs. 27.1%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), diabetes mellitus (23.4% vs. 2.1%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and hypertension (45.5% vs. 5.2%, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were all significantly more prevalent in the IM-positive group. Sex distribution did not differ significantly between groups (p\u0026thinsp;=\u0026thinsp;0.441).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Progressive Distribution of Indices Across OLGA Stages\u003c/h2\u003e \u003cp\u003eAll anthropometric and cardiometabolic indices demonstrated a progressive, monotonic increase across OLGA stages, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Statistically significant differences were confirmed across all five OLGA stages for each index: BMI (H\u0026thinsp;=\u0026thinsp;29.9, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), BRI (H\u0026thinsp;=\u0026thinsp;56.5, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), ABSI (H\u0026thinsp;=\u0026thinsp;33.1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), TyG index (H\u0026thinsp;=\u0026thinsp;53.9, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), TyG-BMI (H\u0026thinsp;=\u0026thinsp;40.0, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and waist circumference (H\u0026thinsp;=\u0026thinsp;57.0, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among all indices, BRI and waist circumference yielded the highest test statistics, indicating the most pronounced separation across histopathological stages.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe magnitude of change across the staging spectrum was clinically remarkable. Mean BRI increased by 136% from OLGA stage 0 (2.24\u0026thinsp;\u0026plusmn;\u0026thinsp;1.17) to the combined stage III\u0026ndash;IV group (5.03\u0026thinsp;\u0026plusmn;\u0026thinsp;2.42). The TyG index demonstrated a parallel trajectory, rising from 8.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50 at stage 0 to 9.54\u0026thinsp;\u0026plusmn;\u0026thinsp;0.40 at advanced stages, representing a 0.78-unit increase that exceeded one standard deviation of the overall population distribution. BMI increased from 26.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9 kg/m\u0026sup2; at stage 0 to 34.3\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7 kg/m\u0026sup2; at advanced stages, while waist circumference rose from 73.5\u0026thinsp;\u0026plusmn;\u0026thinsp;10.5 cm to 95.8\u0026thinsp;\u0026plusmn;\u0026thinsp;14.2 cm. These findings demonstrate a consistent gradient of metabolic burden paralleling the severity of gastric mucosal pathology.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Correlation Between Cardiometabolic Indices and Histopathological Outcomes\u003c/h2\u003e \u003cp\u003eCorrelation analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) revealed a clear hierarchy of association strengths between cardiometabolic indices and histopathological outcomes. BRI exhibited the strongest correlations with both IM grade (ρ\u0026thinsp;=\u0026thinsp;0.493, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and OLGA stage (ρ\u0026thinsp;=\u0026thinsp;0.448, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), closely followed by waist circumference (ρ\u0026thinsp;=\u0026thinsp;0.491 and 0.460, respectively). The TyG index demonstrated robust correlations with IM grade (ρ\u0026thinsp;=\u0026thinsp;0.428, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and OLGA stage (ρ\u0026thinsp;=\u0026thinsp;0.413, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), outperforming BMI in both measures (IM grade: ρ\u0026thinsp;=\u0026thinsp;0.360; OLGA stage: ρ\u0026thinsp;=\u0026thinsp;0.303; both p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eABSI showed weaker but still statistically significant correlations with OLGA stage (ρ\u0026thinsp;=\u0026thinsp;0.362, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and IM grade (ρ\u0026thinsp;=\u0026thinsp;0.310, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Notably, all indices demonstrated significant positive correlations with atrophy grade, with ABSI exhibiting a relatively stronger association (ρ\u0026thinsp;=\u0026thinsp;0.252, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in proportion to its overall correlation pattern, suggesting that ABSI may preferentially capture atrophic rather than metaplastic changes. The TyG-BMI composite index did not demonstrate superior correlations compared to either TyG or BMI individually, suggesting limited additive value from combining metabolic and anthropometric information within a linear correlation framework.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Discriminative Performance of Cardiometabolic Indices\u003c/h2\u003e \u003cp\u003eROC analysis was performed for two clinically relevant endpoints: IM positivity and advanced OLGA stage (\u0026ge;\u0026thinsp;stage III). The complete results are summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and 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\u003eReceiver Operating Characteristic Analysis for Predicting IM Positivity and Advanced OLGA Stage (\u0026ge;\u0026thinsp;III) \u003cb\u003eIM Positivity Advanced OLGA (\u0026ge;\u0026thinsp;Stage III)\u003c/b\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCutoff\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSe / Sp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAUC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCutoff\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSp\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\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.455 / 0.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e32.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.839\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBRI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.736\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e2.409\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.662 / 0.708\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.815\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e3.335\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.800\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.748\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eABSI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.636 / 0.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.714\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.713\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTyG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.705\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e8.954\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.688 / 0.656\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.848\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e9.305\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.750\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.822\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTyG-BMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e268.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.435 / 0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e267.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e78.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.610 / 0.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e90.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.887\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor predicting IM positivity (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA), waist circumference achieved the highest area under the curve (AUC\u0026thinsp;=\u0026thinsp;0.740; 95% CI: 0.677\u0026ndash;0.803), closely followed by BRI (AUC\u0026thinsp;=\u0026thinsp;0.736; 95% CI: 0.673\u0026ndash;0.799). The TyG index (AUC\u0026thinsp;=\u0026thinsp;0.705), TyG-BMI (AUC\u0026thinsp;=\u0026thinsp;0.689), BMI (AUC\u0026thinsp;=\u0026thinsp;0.671), and ABSI (AUC\u0026thinsp;=\u0026thinsp;0.651) demonstrated progressively lower discriminative abilities. At the optimal cutoff of 2.409, BRI yielded a sensitivity of 66.2% and specificity of 70.8% for IM detection.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor predicting advanced OLGA (\u0026ge;\u0026thinsp;stage III) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB), the TyG index achieved the highest discriminative performance (AUC\u0026thinsp;=\u0026thinsp;0.848; 95% CI: 0.773\u0026ndash;0.923), followed by BRI (AUC\u0026thinsp;=\u0026thinsp;0.815), waist circumference (AUC\u0026thinsp;=\u0026thinsp;0.809), TyG-BMI (AUC\u0026thinsp;=\u0026thinsp;0.791), BMI (AUC\u0026thinsp;=\u0026thinsp;0.755), and ABSI (AUC\u0026thinsp;=\u0026thinsp;0.714). The superior performance of TyG for advanced OLGA detection\u0026mdash;compared to its relatively lower ranking for IM positivity\u0026mdash;suggests that metabolic derangement as captured by insulin resistance may be particularly relevant in distinguishing higher-risk histological stages. BRI demonstrated a balanced diagnostic profile at its optimal cutoff of 3.335 (sensitivity: 80.0%, specificity: 74.8%), while TyG at its cutoff of 9.305 provided 75.0% sensitivity and 82.2% specificity. Notably, waist circumference at cutoff 90.3 cm achieved the highest specificity (88.7%) among all indices for advanced OLGA detection.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Independent Predictors of Intestinal Metaplasia\u003c/h2\u003e \u003cp\u003eIn univariate analysis, all anthropometric and cardiometabolic indices, along with age, smoking, diabetes mellitus, hypertension, dyslipidemia, metabolic syndrome, and \u003cem\u003eH. pylori\u003c/em\u003e were identified as significant predictors of IM positivity (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Sex was not significantly associated with IM status (p\u0026thinsp;=\u0026thinsp;0.368).\u003c/p\u003e \u003cp\u003eFive multivariate models were constructed, each adjusted for age, sex, \u003cem\u003eH. pylori\u003c/em\u003e status, and smoking (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Among single-index models, \u003cb\u003eModel 2 (BRI)\u003c/b\u003e demonstrated the best fit, with BRI emerging as an independent predictor of IM positivity (OR\u0026thinsp;=\u0026thinsp;1.665; 95% CI: 1.281\u0026ndash;2.164; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; AIC\u0026thinsp;=\u0026thinsp;235.1; pseudo-R\u0026sup2; = 0.330). This indicates that each unit increase in BRI was associated with a 66.5% increase in the odds of IM positivity after controlling for confounders. \u003cb\u003eModel 4 (TyG)\u003c/b\u003e showed comparable predictive performance (OR\u0026thinsp;=\u0026thinsp;3.823; 95% CI: 1.925\u0026ndash;7.591; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; AIC\u0026thinsp;=\u0026thinsp;236.5; pseudo-R\u0026sup2; = 0.326), suggesting that each unit increase in the TyG index was associated with nearly four-fold higher odds of IM.\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\u003eMultivariate Logistic Regression Models for Predicting Intestinal Metaplasia Positivity\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKey Index\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\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePseudo-R\u0026sup2;\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\u003eModel 1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBMI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.101 (1.043\u0026ndash;1.163)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e239.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.316\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBRI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.665 (1.281\u0026ndash;2.164)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e235.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.330\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eABSI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e251.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.280\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eTyG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.823 (1.925\u0026ndash;7.591)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e236.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.326\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel 5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBRI\u0026thinsp;+\u0026thinsp;TyG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.469 / 2.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.006 / 0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e233.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.334\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNotably, \u003cb\u003eModel 5\u003c/b\u003e, which simultaneously incorporated both BRI and TyG, achieved the lowest AIC (233.8) and highest pseudo-R\u0026sup2; (0.334) among all models, with both indices retaining independent statistical significance (BRI: OR\u0026thinsp;=\u0026thinsp;1.469; 95% CI: 1.116\u0026ndash;1.934; p\u0026thinsp;=\u0026thinsp;0.006; TyG: OR\u0026thinsp;=\u0026thinsp;2.654; 95% CI: 1.275\u0026ndash;5.525; p\u0026thinsp;=\u0026thinsp;0.009). The additive improvement in model fit by incorporating both indices suggests that BRI and TyG capture complementary dimensions of metabolic risk\u0026mdash;visceral adiposity and insulin resistance, respectively\u0026mdash;both of which independently contribute to gastric mucosal pathology. \u003cb\u003eModel 1 (BMI)\u003c/b\u003e was inferior to the BRI-based model (AIC\u0026thinsp;=\u0026thinsp;239.7; pseudo-R\u0026sup2; = 0.316), while \u003cb\u003eModel 3 (ABSI)\u003c/b\u003e lost statistical significance after adjustment for age (p\u0026thinsp;=\u0026thinsp;0.262; AIC\u0026thinsp;=\u0026thinsp;251.8), indicating substantial confounding by age-related changes in body composition.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Ordinal Association and Dose\u0026ndash;Response Gradient\u003c/h2\u003e \u003cp\u003eWhen OLGA stage was treated as an ordinal outcome, significant associations were confirmed with BRI (OR\u0026thinsp;=\u0026thinsp;1.557; 95% CI: 1.318\u0026ndash;1.840; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), TyG index (OR\u0026thinsp;=\u0026thinsp;5.388; 95% CI: 2.978\u0026ndash;9.747; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and BMI (OR\u0026thinsp;=\u0026thinsp;1.101; 95% CI: 1.054\u0026ndash;1.149; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) after adjustment for age, sex, and \u003cem\u003eH. pylori\u003c/em\u003e status. The proportional odds assumption was satisfied for all models (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), supporting the validity of this ordinal approach.\u003c/p\u003e \u003cp\u003eTertile-based analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) demonstrated striking dose\u0026ndash;response relationships for both BRI and TyG index. BRI tertile-stratified IM prevalence increased progressively from T1 (39.8%) through T2 (62.7%) to T3 (82.1%; p for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001), representing an absolute increase of 42.3 percentage points across the BRI distribution. Advanced OLGA prevalence showed an even more dramatic gradient, rising from 2.4% in the lowest tertile to 19.0% in the highest tertile (p for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u0026mdash;corresponding to an approximately eight-fold relative increase. Mean OLGA stage also increased monotonically across BRI tertiles from 0.48 (T1) to 0.87 (T2) to 1.39 (T3).\u003c/p\u003e \u003cp\u003eA parallel gradient was observed for TyG index tertiles: IM prevalence increased from 42.9% (T1) to 59.0% (T2) to 83.1% (T3; p for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while advanced OLGA prevalence rose from 1.2% to 2.4% to 20.5% (p for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The consistency of these dose\u0026ndash;response patterns across two mechanistically distinct indices\u0026mdash;one reflecting visceral adiposity (BRI) and the other insulin resistance (TyG)\u0026mdash;reinforces the biological plausibility of the cardiometabolic\u0026ndash;gastric mucosal pathology axis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.8 Metabolic Syndrome and Gastric Pathology\u003c/h2\u003e \u003cp\u003ePatients with metabolic syndrome exhibited significantly more severe gastric mucosal pathology across all histopathological parameters. Mean OLGA stage was nearly three-fold higher in MetS-positive compared to MetS-negative patients (1.48\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09 vs. 0.51\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). IM grade (1.82\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09 vs. 0.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and atrophy grade (1.59\u0026thinsp;\u0026plusmn;\u0026thinsp;1.04 vs. 0.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) were also markedly elevated in the MetS group. Advanced OLGA (\u0026ge;\u0026thinsp;stage III) was strongly associated with metabolic syndrome prevalence (17.2% vs. 2.5%; χ\u0026sup2; = 23.54, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A composite cardiometabolic score incorporating individual MetS components demonstrated a significant linear relationship with all histopathological outcomes, further supporting the concept that cumulative metabolic burden drives gastric mucosal damage in a graded fashion.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eThe present study investigated the associations of novel anthropometric indices\u0026mdash;Body Roundness Index (BRI) and A Body Shape Index (ABSI)\u0026mdash;alongside cardiometabolic markers (TyG index, TyG-BMI) with gastric intestinal metaplasia severity assessed by OLGA staging. The principal findings are as follows: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) among all indices evaluated, BRI exhibited the strongest correlation with both OLGA stage (ρ\u0026thinsp;=\u0026thinsp;0.448) and IM grade (ρ\u0026thinsp;=\u0026thinsp;0.493); (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) the TyG index demonstrated the highest discriminative performance for advanced OLGA stages (AUC\u0026thinsp;=\u0026thinsp;0.848), followed by BRI (AUC\u0026thinsp;=\u0026thinsp;0.815); (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) both BRI and TyG retained independent predictive significance in multivariate models adjusted for age, sex, \u003cem\u003eHelicobacter pylori\u003c/em\u003e status, and smoking, with their combined model yielding the lowest AIC (233.8); and (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) a consistent dose\u0026ndash;response gradient was observed across tertiles of both indices, with IM prevalence increasing from approximately 40% in the lowest tertile to over 82% in the highest tertile. Collectively, these results indicate a graded association between visceral adiposity, insulin resistance, and gastric precancerous lesion severity.\u003c/p\u003e \u003cp\u003eThe finding that BRI outperformed BMI in predicting gastric mucosal pathology is consistent with the established understanding that visceral adiposity, rather than overall body mass, is more closely related to obesity-associated disease risk. BRI integrates waist circumference and height to model the torso as an ellipse, thereby providing greater sensitivity to central fat distribution than BMI, which does not differentiate lean mass from adipose tissue or characterize fat topography (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). In a 2025 cross-sectional analysis of 264 healthy multiethnic adults that included MRI measures of visceral adipose tissue and hyperinsulinemic-euglycemic clamp assessments, the Body Roundness Index (BRI) demonstrated strong correlations with visceral adiposity (r\u0026thinsp;=\u0026thinsp;0.72) and with insulin sensitivity index (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.51), confirming its biological relevance as an anthropometric indicator of metabolically active abdominal fat (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). In our cohort, mean BRI increased by 136% from OLGA stage 0 to advanced stages (III\u0026ndash;IV), compared to a 32% increase for BMI across the same range. This disparity suggests that BRI captures the visceral adipose-associated inflammatory burden\u0026mdash;mediated by TNF-α, IL-6, and leptin\u0026mdash;more effectively than BMI, which may account for its stronger correlation with histopathological severity (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent longitudinal data further support the relevance of BRI in oncological risk assessment. Recent longitudinal data further support the relevance of the Body Roundness Index in oncological risk assessment; in three national aging cohorts (n\u0026thinsp;=\u0026thinsp;33 624), elevated BRI demonstrated a threshold association with overall cancer incidence that persisted irrespective of cardiometabolic disease status (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAnalysis of the UK Biobank data revealed that participants demonstrating a consistently high Body Roundness Index (BRI) trajectory over time exhibited an increased risk of incident cancer. For instance, middle-aged men with persistent elevated BRI had a hazard ratio of 1.46 for cancer occurrence when compared to individuals in the low-stable trajectory group, thereby emphasizing the prognostic significance of enduring central adiposity patterns in relation to cancer risk (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). A large Chinese prospective cohort study documented significant associations between Body Roundness Index and mortality risk across multiple chronic diseases, indicating that elevated BRI is predictive of increased long-term death risk beyond traditional adiposity measures (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). The present finding that BRI correlated most strongly with gastric IM severity extends these observations to gastric precancerous lesions, suggesting potential utility as a non-invasive screening parameter for gastric mucosal pathology risk stratification.\u003c/p\u003e \u003cp\u003eThe TyG index demonstrated the highest discriminative capacity for advanced OLGA stages (AUC\u0026thinsp;=\u0026thinsp;0.848), indicating that insulin resistance, as captured by this composite marker, may play a role in distinguishing higher-risk histological stages. This observation is consistent with the Korean health checkup cohort study (n\u0026thinsp;=\u0026thinsp;127,564) by Kim et al., which reported progressive associations between TyG index quartiles and precancerous conditions (Q4 vs. Q1: OR\u0026thinsp;=\u0026thinsp;1.656) as well as gastric cancer (Q4 vs. Q1: OR\u0026thinsp;=\u0026thinsp;2.363) (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). Our identified cutoff of 9.305 for advanced OLGA detection is lower than the gastric cancer cutoff of 9.73 reported by Kim et al., which is expected given that the present study targeted an earlier point in the Correa cascade. The TyG index demonstrated acceptable discriminatory performance in predicting cancer occurrence, with individuals having cancer showing a mean TyG difference of 0.34 units compared with controls and a summary ROC AUC of 0.72, indicating its potential utility as a metabolic marker in cancer risk assessment (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).Elevated TyG index has been consistently linked with higher cancer incidence and mortality across cohort studies, a relationship that has been attributed to insulin resistance-mediated activation of oncogenic signaling pathways such as PI3K/Akt/mTOR and chronic inflammation via NF-κB (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). The combined BRI\u0026ndash;TyG model (Model 5: AIC\u0026thinsp;=\u0026thinsp;233.8; pseudo-R\u0026sup2; = 0.334) yielded a lower AIC than either index alone, suggesting additive predictive value. This finding can be interpreted in terms of the mechanistically distinct pathways captured by each index. BRI reflects visceral adiposity and the pro-inflammatory changes associated with excess abdominal fat, including dysregulated adipokine secretion, increased macrophage infiltration, and oxidative stress\u0026mdash;mechanisms that have been implicated in chronic low-grade inflammation linked to obesity-related tissue injury and metabolic dysfunction(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). The TyG index captures the metabolic component of insulin resistance, which has been linked to gastric carcinogenesis through hyperinsulinemia-driven IGF-1/PI3K/Akt/mTOR signaling, promoting cellular proliferation and inhibiting apoptosis (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Persistent insulin resistance, as evidenced by elevated TyG index values, may promote malignant transformation in precancerous lesions through the activation of oncogenic signaling pathways such as PI3K/Akt/mTOR and through chronic inflammation mediated by NF-κB. Additionally, a higher metabolic reserve associated with insulin resistance could potentially influence treatment tolerance and survival outcomes in patients with advanced cancer patients (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). The present data suggest that these two pathophysiological dimensions\u0026mdash;visceral inflammation and metabolic dysregulation\u0026mdash;contribute to gastric mucosal pathology through parallel but complementary mechanisms, and their combined assessment provides improved risk stratification compared to either index individually.\u003c/p\u003e \u003cp\u003eIn contrast to BRI and TyG, ABSI demonstrated weaker discriminative performance and lost statistical significance in multivariate analysis after age adjustment (p\u0026thinsp;=\u0026thinsp;0.262).This attenuation is consistent with the known age-dependency of ABSI, which quantifies waist circumference independently of BMI but is influenced by age-related changes in body composition and sarcopenia (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Nevertheless, ABSI exhibited a proportionally stronger association with atrophy grade relative to its overall correlation pattern, which may indicate a preferential relationship with atrophic rather than metaplastic changes. UK Biobank data have shown that ABSI is positively associated with gastric cardia cancer risk in men (HR\u0026thinsp;=\u0026thinsp;1.31; 95% CI: 1.07\u0026ndash;1.61 per SD increment), and that the combination of high ABSI and elevated BMI further increases stomach cancer risk (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). In an analysis of National Health and Nutrition Examination Survey (NHANES) data from 1999\u0026ndash;2018, higher ABSI levels were positively associated with colorectal cancer risk, with individuals in the highest ABSI quartile exhibiting greater odds of CRC compared with those in the lowest quartile (OR\u0026thinsp;=\u0026thinsp;1.88; 95% CI, 1.19\u0026ndash;2.96; p\u0026thinsp;=\u0026thinsp;0.006), and this relationship was more pronounced in adults under 60 years of age (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). The loss of ABSI\u0026rsquo;s independent effect in our multivariate models may be attributable to confounding by age, as the IM-positive group was significantly older (61.1 vs. 41.7 years), and age-related redistribution of body composition may obscure ABSI\u0026rsquo;s independent contribution.\u003c/p\u003e \u003cp\u003eThe dose\u0026ndash;response relationships observed across BRI and TyG tertiles are consistent with a graded biological gradient linking metabolic burden to gastric mucosal pathology. Advanced OLGA prevalence increased approximately eight-fold across BRI tertiles (2.4% to 19.0%) and approximately seventeen-fold across TyG tertiles (1.2% to 20.5%). These gradients parallel the observations of Kim et al. in a Korean cohort (n\u0026thinsp;=\u0026thinsp;142,832), where increasing BMI categories were associated with incident IM in a dose\u0026ndash;response fashion, independently of \u003cem\u003eH. pylori\u003c/em\u003e status (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). The present results extend this finding by demonstrating that the gradient is more pronounced when visceral adiposity (BRI) and insulin resistance (TyG) are measured directly rather than approximated by BMI. Additionally, the three-fold difference in mean OLGA stage between metabolic syndrome-positive and -negative patients (1.48 vs. 0.51) supports the concept that cumulative metabolic burden is associated with gastric mucosal damage in a graded manner, consistent with mechanistic links between obesity-driven inflammation\u0026mdash;including adipokine imbalance, oxidative stress, and gut microbiome dysbiosis\u0026mdash;and increased cancer susceptibility (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral biological mechanisms may account for the observed associations. First, visceral adipose tissue\u0026mdash;preferentially captured by BRI\u0026mdash;secretes pro-inflammatory cytokines (TNF-α, IL-6) and adipokines (leptin, resistin) that enter systemic circulation and may reach the gastric mucosa, potentially promoting chronic inflammation conducive to epithelial injury and metaplastic transformation (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Second, insulin resistance\u0026mdash;reflected by the TyG index\u0026mdash;may promote gastric carcinogenesis through hyperinsulinemia-mediated upregulation of the IGF-1 axis, which activates PI3K/Akt/mTOR signaling and promotes cellular proliferation while suppressing apoptosis (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Dysregulation of the IGF signaling axis and downstream PI3K/AKT/mTOR pathway, including increased phosphorylation of key effectors such as AKT and activation of mTORC1, has been observed in gastric cancer and is implicated in promoting tumor cell proliferation, survival, and metabolic reprogramming (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Third, hypertriglyceridemia generates lipotoxic intermediates (ceramides, diacylglycerols) that may induce endoplasmic reticulum stress and mitochondrial dysfunction in gastric epithelial cells(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Fourth, the hyperglycemic component of the TyG index may contribute through formation of advanced glycation end-products (AGEs), which bind to RAGE receptors on gastric epithelial cells and activate NF-κB-dependent inflammatory cascades; a recent comparative tissue analysis demonstrated increased RAGE and HMGB-1 expression in gastric cancer tissue of diabetic patients (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). The complementary nature of these pathways may explain why the combined BRI\u0026ndash;TyG model provided improved predictive performance.\u003c/p\u003e \u003cp\u003eThe present findings have several potential clinical implications. First, both BRI and TyG index are calculable from routine clinical measurements (waist circumference, height, fasting glucose, triglycerides) without additional cost, making them feasible for use in primary care and gastroenterology settings. The identified BRI cutoff of 3.335 for advanced OLGA detection (sensitivity 80.0%, specificity 74.8%) and TyG cutoff of 9.305 (sensitivity 75.0%, specificity 82.2%) may inform clinical decision-making. Second, the 2025 MAPS III guideline by ESGE and the 2025 ACG Clinical Guideline both emphasize individualized risk stratification in gastric precancerous conditions (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The incorporation of BRI and TyG into existing surveillance algorithms could potentially improve the identification of individuals who may benefit from closer endoscopic follow-up. Third, given the modifiable nature of both visceral adiposity and insulin resistance, these findings raise the hypothesis that lifestyle interventions targeting weight reduction and metabolic optimization could attenuate gastric precancerous lesion progression, although prospective interventional studies are required to test this hypothesis.\u003c/p\u003e \u003cp\u003eSeveral limitations should be acknowledged. First, the cross-sectional design precludes determination of temporal causality between elevated cardiometabolic indices and IM development or progression. Prospective cohort studies with serial histopathological assessments are needed to establish whether changes in BRI or TyG precede IM progression. Second, the sample size (n\u0026thinsp;=\u0026thinsp;250) and single-center setting may limit generalizability to populations with different ethnic compositions, dietary habits, and \u003cem\u003eH. pylori\u003c/em\u003e prevalence. Third, serum adipokines (leptin, adiponectin), inflammatory cytokines (IL-6, TNF-α), and insulin levels were not directly measured, which would have strengthened mechanistic interpretation. Fourth, the advanced OLGA subgroup (n\u0026thinsp;=\u0026thinsp;20, 8.0%) was limited in size, which may have reduced statistical power for subgroup analyses and widened confidence intervals for ROC analysis at this endpoint. Fifth, despite adjustment for age, sex, \u003cem\u003eH. pylori\u003c/em\u003e status, and smoking, residual confounding from unmeasured variables\u0026mdash;including dietary patterns, physical activity, genetic predisposition, and pharmacological treatments\u0026mdash;cannot be excluded. Sixth, although OLGA staging is standardized, interobserver variability in histopathological interpretation remains a potential source of bias, although consensus review by a second pathologist was employed to mitigate this concern.\u003c/p\u003e \u003cp\u003eFuture research should address these limitations through several approaches. Multicenter prospective cohorts with longitudinal BRI and TyG monitoring could determine whether dynamic changes in these indices predict IM progression or regression following metabolic interventions. Integration of serum biomarkers (adipokines, inflammatory cytokines, insulin, IGF-1) with anthropometric indices may facilitate the development of composite risk scores with improved predictive accuracy. Additionally, the potential role of artificial intelligence in combining metabolic risk assessment with endoscopic grading systems such as EGGIM warrants investigation (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e).Artificial intelligence\u0026ndash;assisted endoscopic systems have demonstrated high diagnostic accuracy in detecting gastric intestinal metaplasia and other precancerous lesions, with performance metrics that are comparable to those of expert endoscopists, highlighting their potential to standardize mucosal assessment and enhance risk stratification during upper gastrointestinal endoscopy (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Interventional studies examining whether targeted weight loss, metabolic syndrome treatment, or pharmacological modulation of insulin resistance can attenuate gastric precancerous lesion severity would provide evidence for clinical translation of these findings.\u003c/p\u003e"},{"header":"5. CONCLUSION","content":"\u003cp\u003eThis study demonstrates that the BRI and TyG index are independently associated with the severity of gastric intestinal metaplasia and advanced OLGA staging, exhibiting superior predictive performance compared to conventional BMI. The combined BRI-TyG model offers the highest discriminative ability, indicating complementary pathophysiological mechanisms involving visceral adiposity and insulin resistance in gastric carcinogenesis. These non-invasive, easily calculable indices may serve as practical tools for identifying individuals at increased risk of gastric precancerous lesions. Future prospective, multicenter studies with larger sample sizes are warranted to validate these findings and to assess the potential of metabolic intervention in modifying gastric cancer risk.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e This study was approved by the Non-Interventional Clinical Research Ethics Committee of Yenimahalle Training and Research Hospital (Approval Date: October 15, 2025) and conducted in accordance with the principles of the Declaration of Helsinki (2013 revision). Written informed consent was obtained from all participants prior to enrollment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e The authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; Contributions\u003c/strong\u003e M.U. conceived and designed the study, collected and curated clinical data, performed all statistical analyses, generated all figures and tables, interpreted the results, and wrote the original manuscript draft. F.Y. contributed to patient enrollment, endoscopic procedures, and surgical data acquisition, and critically reviewed the manuscript for intellectual content. E.E. contributed to data collection, clinical data verification, and critically reviewed the manuscript for intellectual content. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e Not applicable\u003cstrong\u003e\u003cbr clear=\"all\"\u003e\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi W, Zhang T. Precancerous pathways to gastric cancer: a review of experimental animal models recapitulating the correa cascade. Front Cell Dev Biology. 2025;Volume 13\u0026ndash;2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDinis-Ribeiro M, Lib\u0026acirc;nio D, Uchima H, Spaander MCW, Bornschein J, Matysiak-Budnik T, et al. Management of epithelial precancerous conditions and early neoplasia of the stomach (MAPS III): European Society of Gastrointestinal Endoscopy (ESGE), European Helicobacter and Microbiota Study Group (EHMSG) and European Society of Pathology (ESP) Guideline update 2025. Endoscopy. 2025;57(5):504\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorgan DR, Corral JE, Li D, Montgomery EA, Riquelme A, Kim JJ, et al. ACG Clinical Guideline: Diagnosis and Management of Gastric Premalignant Conditions. Am J Gastroenterol. 2025;120(4):709\u0026ndash;37.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenites-Go\u0026ntilde;i H, Cabrera-Hinojosa D, Latorre G, Hernandez AV, Uchima H, Riquelme A. OLGA and OLGIM staging systems on the risk assessment of gastric cancer: a systematic review and meta\u0026ndash;analysis of prospective cohorts. Th Adv Gastroenterol. 2025;18:17562848251325461.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVarra FN, Varras M, Varra VK, Theodosis-Nobelos P. Molecular and pathophysiological relationship between obesity and chronic inflammation in the manifestation of metabolic dysfunctions and their inflammation\u0026ndash;mediating treatment options (Review). Mol Med Rep. 2024;29(6).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBae J-M. Body Mass Index and Risk of Gastric Cancer in Asian Adults: A Meta-Epidemiological Meta-Analysis of Population-Based Cohort Studies. Cancer Res Treat. 2020;52(2):369\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCeylaner B, Sahin F, Yildiz A, Tastekin D, Gok AF, Baykal AT. IGF Pathway Components as Potential Biomarkers in Gastric Cancer. Int J Mol Sci [Internet]. 2025;26(22):10880.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNasif WA, Hasan Mukhtar M, El-Moursy Ali AS, Nour Eldein MM, Almaimani RA, Ashgar SS. Body mass index is associated with Helicobacter pylori infection and increased oxidative DNA damage in an obese population. J Int Med Res. 2022;50(2):03000605221076975.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlundell JE, Dulloo AG, Salvador J, Fr\u0026uuml;hbeck G. Beyond BMI\u0026ndash;phenotyping the obesities. Obes Facts. 2014;7(5):322\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Luis D, Mu\u0026ntilde;oz M, Izaola O, Lopez Gomez JJ, Rico D, Primo D. Body Roundness Index (BRI) Predicts Metabolic Syndrome in Postmenopausal Women with Obesity Better than Insulin Resistance. Diabetology. 2025;6(7):60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang X, Ma N, Lin Q, Chen K, Zheng F, Wu J, et al. Body Roundness Index and All-Cause Mortality Among US Adults. JAMA Netw Open. 2024;7(6):e2415051\u0026ndash;e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKunutsor SK, Bhattacharjee A, Jae SY, Laukkanen JA. Body roundness index and cardiometabolic multimorbidity: Findings from the English Longitudinal Study of Ageing. Nutrition, Metabolism and Cardiovascular Diseases. 2025:104475.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCai X, Liao Y, Yang X, Liang Y, Ma J, Liu R, et al. Body Roundness Index Associated With Cardiometabolic Multimorbidity and Mortality: A Multistate Model. Obes (Silver Spring). 2025;33(12):2377\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu H, Fu H, Wang Z, Yao Z, Wang Q, Sui A et al. The predictive value of a body shape index as a novel obesity metric for cancer risk: a systematic review and meta-analysis. Front Nutr. 2025;Volume 12\u0026ndash;2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParra-Soto S, Malcomson FC, Ho FK, Pell JP, Sharp L, Mathers JC, et al. Associations of A Body Shape Index (ABSI) with Cancer Incidence, All-Cause, and at 23 Sites-Findings from the UK Biobank Prospective Cohort Study. Cancer Epidemiol Biomarkers Prev. 2022;31(2):315\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim YM, Kim J-H, Park JS, Baik SJ, Chun J, Youn YH, et al. Association between triglyceride-glucose index and gastric carcinogenesis: a health checkup cohort study. Gastric Cancer. 2022;25(1):33\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEskazan T, Saribas S, Kocazeybek B. Prognostic value of the triglyceride-glucose index in gastric cancer. World J Gastroenterol. 2025;31(17):104651.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eErdem H, Baymakova M, Alkan S, Letaief A, Yahia WB, Dayyab F, et al. Classical fever of unknown origin in 21 countries with different economic development: an international ID-IRI study. Eur J Clin Microbiol Infect Dis. 2023;42(4):387\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation. 2009;120(16):1640-5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetersdorf RG, Beeson PB. Fever of unexplained origin: report on 100 cases. Med (Baltim). 1961;40:1\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLittlejohns TJ, Henley WE, Lang IA, Annweiler C, Beauchet O, Chaves PH, et al. Vitamin D and the risk of dementia and Alzheimer disease. Neurology. 2014;83(10):920\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEng PC, Teo AE, Leow MKS, Tai ES, Khoo CM. Body roundness index (BRI) and obesity-related anthropometrics: Relationship to visceral adiposity, insulin sensitivity index and cardiometabolic risk. Diabetes Obes Metab. 2025;27(10):5554\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLamabadusuriya DA, Jayasena H, Bopitiya AK, De Silva AD, Jayasekera P. Obesity-driven inflammation and cancer risk: A comprehensive review. Semin Cancer Biol. 2025;114:256\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo P, Lian Y. Association Between Body Roundness Index and Cancer Risk, With Further Stratification by Cardiometabolic Disease Status: Findings From Three National Longitudinal Cohorts. Cancer Med. 2025;14(21):e71324.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen Y, Wang Y, Zheng X, Liu T, Liu C, Lin S, et al. Body Roundness Index Trajectories and the Risk of Cancer: A Cohort Study. Cancer Med. 2024;13(23):e70447.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun X, Deng Q, Li Z, Lin C, Song G, Chen S, et al. Body roundness index and mortality risk in chronic diseases: a national prospective longitudinal study in China. Aging Clin Exp Res. 2025;38(1):5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim YM, Kim JH, Park JS, Baik SJ, Chun J, Youn YH, et al. Association between triglyceride-glucose index and gastric carcinogenesis: a health checkup cohort study. Gastric Cancer. 2022;25(1):33\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen IW, Wang WT, Wu JY, Yu CH, Chang YJ, Hung KC. Diagnostic performance of the triglyceride-glucose index in predicting occurrence of cancer: a meta-analysis. Front Oncol. 2025;15:1532253.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng S, Wang C, Liu Q, Luo Q. The association of triglyceride-glucose index with cancer incidence and mortality: a systematic review and meta-analysis of cohort studies. Front Endocrinol (Lausanne). 2025;16:1682062.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSavulescu-Fiedler I, Mihalcea R, Dragosloveanu S, Scheau C, Baz RO, Caruntu A, et al. Interplay between Obes Inflamm Life [Internet]. 2024;14(7):856. p.].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrasan I-M, Tanase M, Delia CE, Gradisteanu-Pircalabioru G, Cimpean A, Ionica E. Metaflammation\u0026rsquo;s Role in Systemic Dysfunction in Obesity: A Comprehensive Review. Int J Mol Sci [Internet]. 2025;26(21):10445.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao CF, Liu XL, Wu NB, Xu ZF. Triglyceride-glucose index as a prognostic indicator in advanced gastric cancer: Insights and future research. World J Gastroenterol. 2025;31(17):104794.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShang H, Deng Z, Lian Y, Ai Y, Li G. A body shape index and sarcopenia risk in middle-aged and older adults: evidence from the China Health and Retirement Longitudinal Study. Eur J Med Res. 2025;31(1):94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChristakoudi S, Tsilidis KK, Evangelou E, Riboli E. A Body Shape Index (ABSI), hip index, and risk of cancer in the UK Biobank cohort. Cancer Med. 2021;10(16):5614\u0026ndash;28.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu H, Kang J, Liu W, Shen Y. Association between a body shape index and colorectal cancer in US population: a cross-sectional study based on NHANES. Front Nutr. 2025;12:1535655.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKawai T, Autieri MV, Scalia R. Adipose tissue inflammation and metabolic dysfunction in obesity. Am J Physiol Cell Physiol. 2021;320(3):C375\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang M, Zhang K, Zhang Z, Zeng X, Huang Z, Qin P et al. PI3K/AKT/mTOR Axis in Cancer: From Pathogenesis to Treatment. MedComm (2020). 2025;6(8):e70295.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLaw BA, Liao X, Moore KS, Southard A, Roddy P, Ji R, et al. Lipotoxic very-long-chain ceramides cause mitochondrial dysfunction, oxidative stress, and cell death in cardiomyocytes. Faseb j. 2018;32(3):1403\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou Y, Liu SX, Zhou YN, Wang J, Ji R. Research on the relationship between RAGE and its ligand HMGB1, and prognosis and pathogenesis of gastric cancer with diabetes mellitus. Eur Rev Med Pharmacol Sci. 2021;25(3):1339\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlmeida E, Martins ML, Marques D, Delas R, Almeida T, Chaves J, et al. Artificial intelligence for endoscopic grading of gastric intestinal metaplasia: advancing risk stratification for gastric cancer. Endoscopy. 2025;57(11):1254\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGong EJ, Woo J, Lee JJ, Bang CS. Role of artificial intelligence in gastric diseases. World J Gastroenterol. 2025;31(37):111327.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Medicine](https://link.springer.com/journal/44337)","snPcode":"44337","submissionUrl":"https://submission.springernature.com/new-submission/44337/3","title":"Discover Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Body Roundness Index, A Body Shape Index, triglyceride-glucose index, intestinal metaplasia, OLGA staging, cardiometabolic risk, visceral adiposity","lastPublishedDoi":"10.21203/rs.3.rs-8962909/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8962909/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground and Aim: \u003c/strong\u003eGastric intestinal metaplasia (IM) is a recognized precancerous lesion whose progression may be influenced by metabolic dysfunction. Novel anthropometric indices, including the Body Roundness Index (BRI) and A Body Shape Index (ABSI), have emerged as superior predictors of visceral adiposity in comparison to the conventional body mass index (BMI). However, the association of these indices with gastric precancerous lesions remains largely unexplored. This study aims to investigate the relationship between BRI, ABSI, BMI, and cardiometabolic indices (TyG, TyG-BMI) with the severity of IM, as measured by the Operative Link on Gastritis Assessment (OLGA) staging system.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eThis cross-sectional study included 250 patients who underwent upper gastrointestinal endoscopy with gastric biopsy. IM was assessed histopathologically and graded using the OLGA staging system (stages 0–IV). Anthropometric indices (BMI, BRI, ABSI) and cardiometabolic indices (TyG, TyG-BMI) were computed from standardized measurements. Receiver operating characteristic (ROC) analysis, Spearman correlation, and multivariate logistic regression were performed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eOut of 250 patients (average age 53.6±18.9 years; 51.6% female), 154 (61.6%) were identified as IM-positive. All anthropometric and cardiometabolic indices were significantly elevated in IM-positive individuals (p\u0026lt;0.001). The Body Roundness Index (BRI) exhibited the strongest correlation with OLGA stage (ρ=0.448, p\u0026lt;0.001) and IM grade (ρ=0.493, p\u0026lt;0.001). In the prediction of advanced OLGA stages (≥stage III), the Triglyceride-Glucose (TyG) index demonstrated the highest discriminative capacity (AUC=0.848), followed by BRI (AUC=0.815) and waist circumference (AUC=0.809).\u003c/p\u003e\n\u003cp\u003eIn multivariate analysis, adjusting for age, sex, and Helicobacter pylori status, BRI (Odds Ratio=1.665, 95% Confidence Interval: 1.281–2.164, p\u0026lt;0.001), TyG (Odds Ratio=3.823, 95% Confidence Interval: 1.925–7.591, p\u0026lt;0.001), and their combination (BRI: Odds Ratio=1.469; TyG: Odds Ratio=2.654) persisted as independent predictors of intestinal metaplasia (IM) positivity. Tertile analysis demonstrated a dose–response relationship: the prevalence of IM increased from 39.8% (T1) to 82.1% (T3) for BRI and from 42.9% (T1) to 83.1% (T3) for TyG, with p-trend values less than 0.001 for both.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eBRI and TyG index are independently associated with gastric IM severity and demonstrate superior predictive performance compared to conventional BMI. These non-invasive, easily calculable indices may serve as practical tools for identifying individuals at increased risk of gastric precancerous lesions in clinical practice.\u003c/p\u003e","manuscriptTitle":"Association of Body Roundness Index, A Body Shape Index, and Cardiometabolic Indices with Gastric Intestinal Metaplasia Severity Assessed by OLGA Staging","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-26 02:57:27","doi":"10.21203/rs.3.rs-8962909/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-02T10:48:36+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-01T12:25:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"78897670715236449363569897070810338244","date":"2026-03-27T18:35:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-23T03:15:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"93614482985160636091174650758756021639","date":"2026-03-22T00:08:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-20T10:00:25+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-20T07:08:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-05T07:08:09+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-05T07:01:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Medicine","date":"2026-02-25T04:05:41+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Medicine](https://link.springer.com/journal/44337)","snPcode":"44337","submissionUrl":"https://submission.springernature.com/new-submission/44337/3","title":"Discover Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8cb25e62-1527-468d-a9f9-2738563b945e","owner":[],"postedDate":"March 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-05-13T12:55:27+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-26 02:57:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8962909","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8962909","identity":"rs-8962909","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}