Body mass index — the universal clinical shorthand for obesity risk — may be the wrong number to measure when assessing a patient's risk of precancerous stomach changes. A prospective cross-sectional study published in the Journal of Clinical Gastroenterology on August 6, 2026, found that two simpler, more targeted indices — the Body Roundness Index (BRI) and the Triglyceride-Glucose Index (TyG) — independently predicted both the presence and histologic severity of gastric intestinal metaplasia (GIM), and did so more accurately than BMI across every key outcome measure.[1]
The implications matter for oncology and gastroenterology trial teams: GIM is a well-characterised precancerous condition on the Correa cascade to gastric adenocarcinoma, and identifying which patients are progressing to higher-risk stages — particularly advanced OLGA stage III or IV — determines who should receive endoscopic surveillance. If BRI and TyG can stratify that risk from routine blood tests and a waist measurement, they could become low-cost eligibility or stratification variables in future gastric cancer prevention trials.
The Gastric Cancer Precursor Pathway
Gastric cancer — the fifth most common cancer globally and the fourth leading cause of cancer mortality — rarely arises in a healthy stomach. The dominant pathway to intestinal-type gastric adenocarcinoma follows the Correa cascade: a stepwise progression from normal mucosa through chronic gastritis, gastric atrophy, intestinal metaplasia, and dysplasia, before invasive carcinoma develops.
Gastric intestinal metaplasia (GIM) is the stage at which gastric mucosa is replaced by intestine-like epithelium — a metaplastic adaptation that carries meaningful cancer risk, particularly in patients who progress to incomplete subtype or extend the metaplasia to the corpus. The OLGA (Operative Link on Gastritis Assessment) system classifies this severity on a 0-IV scale using the combination of atrophy and metaplasia scores from standardised biopsy sites: advanced disease is defined as stage ≥III, which carries substantially higher cancer risk and triggers a recommendation for more intensive endoscopic surveillance in European guidelines (MAPS II).
The challenge is identifying which patients with GIM are at advanced stages without relying solely on endoscopy and biopsy — procedures that are resource-intensive and not suitable for large-scale population screening. This study asked whether simple anthropometric and metabolic indices — calculated from measurements already routinely available — could fill that gap.
Study Design and Population
Study at a Glance
All 250 patients underwent esophagogastroduodenoscopy (EGD) with gastric biopsy according to the updated Sydney protocol — at least two biopsies from the antrum, two from the corpus, and one from the incisura angularis. An experienced GI pathologist blinded to clinical and metabolic data scored intestinal metaplasia and atrophy at each site on a 0–3 scale. OLGA staging integrated these scores to yield a final stage 0–IV, with advanced disease defined as stage ≥III.
Simultaneously, five indices were calculated from standardised measurements: BMI (weight/height²); BRI (Body Roundness Index, derived from height and waist circumference); ABSI (A Body Shape Index, also incorporating height, weight, and waist); TyG (Triglyceride-Glucose Index: ln[TG × fasting glucose / 2]); and TyG-BMI (TyG × BMI). The primary question was which index best predicted the presence and severity of GIM and the OLGA stage.
Understanding the Markers: What BRI and TyG Actually Measure
BMI's limitations as a metabolic risk marker are well-established: it cannot distinguish fat mass from muscle mass, and critically, it cannot distinguish visceral (intra-abdominal) fat from subcutaneous fat. Visceral adiposity — fat deposited around the organs — is the biologically active compartment: it secretes pro-inflammatory adipokines, drives hepatic insulin resistance, and promotes systemic low-grade inflammation, all mechanisms implicated in the progression of premalignant mucosal change.
What it measures: Visceral adiposity using waist circumference and height. BRI estimates the cross-sectional area of the torso relative to body height, better approximating fat distribution around the abdominal organs than BMI.
Calculation: Uses height (m) and waist circumference (cm). Higher BRI = more central adiposity.
Key result:
r = 0.493Spearman correlation with IM grade (P <.001) — strongest of all indices AUC = 0.815For predicting advanced OLGA ≥III (vs BMI 0.755)What it measures: Insulin resistance, using fasting triglycerides and fasting glucose. TyG serves as a surrogate for the hyperinsulinaemia and metabolic dysfunction that characterises insulin-resistant states — computable from a standard fasting blood panel.
Calculation: ln [triglycerides (mg/dL) × fasting glucose (mg/dL) / 2]
Key result:
AUC = 0.848Highest of all indices for advanced OLGA ≥III (95% CI 0.773–0.923) OR = 3.823Multivariate-adjusted OR for IM positivity (P <.001)What it measures: Overall body mass relative to height. Does not distinguish fat distribution, muscle mass, or visceral vs. subcutaneous fat. Remains the most widely used clinical obesity metric.
Calculation: Weight (kg) / height² (m²)
Key result:
r = 0.360Spearman correlation with IM grade — lower than both BRI and TyG AUC = 0.755For predicting advanced OLGA ≥III — lowest of the three main comparatorsKey Findings
1. Correlation with OLGA stage and IM severity
BRI showed the strongest correlation with the histologic intestinal metaplasia grade (Spearman r = 0.493, P <.001) — and with the OLGA stage (Spearman r = 0.448, P <.001). TyG also outperformed BMI on both measures. BMI's Spearman coefficients of 0.360 (IM grade) and 0.303 (OLGA stage) were materially lower — a difference that compounds when predicting the clinically important advanced stages.
| Index | Correlation with OLGA stage | Correlation with IM grade | vs BMI |
|---|---|---|---|
| BRI | r = 0.448 (P <.001) | r = 0.493 (P <.001) | Outperforms BMI on both |
| TyG | Outperforms BMI | Outperforms BMI | Outperforms BMI on both |
| BMI | r = 0.303 (P <.001) | r = 0.360 (P <.001) | Reference comparator |
2. Predictive accuracy for advanced OLGA stage (≥III)
The most clinically consequential finding is the AUC comparison for predicting advanced precancerous disease (OLGA stage ≥III). TyG achieved an AUC of 0.848 (95% CI 0.773–0.923), followed by BRI at 0.815. BMI reached 0.755. A difference of 0.09 AUC units between TyG and BMI is clinically meaningful — at a population screening scale, it represents a substantial difference in correctly identified high-risk patients.[1]
Both TyG and BRI outperformed BMI. TyG showed the highest discriminative accuracy overall.
3. Independent prediction in multivariate analysis
After adjusting for age, sex, Helicobacter pylori infection status, and smoking — the four most established confounders for GIM risk — both BRI (OR 1.665, P <.001) and TyG (OR 3.823, P <.001) remained independently significant predictors of intestinal metaplasia positivity. This independence is critical: it means the metabolic signal captured by BRI and TyG is not simply proxy-ing for H. pylori infection or smoking behaviour. Visceral adiposity and insulin resistance appear to contribute to GIM risk through separate biological pathways.
Both independently predicted intestinal metaplasia positivity — confirming the metabolic signal is not confounded by H. pylori status.
4. Dose-response across tertiles — a gradient, not a threshold
When patients were divided into tertiles by BRI and TyG level, the prevalence of intestinal metaplasia showed a clear, monotonic dose-response gradient: approximately 40% in the lowest tertile rising to more than 80% in the highest tertile for both indices (P for trend <.001 for both).[1] This gradient pattern — where risk increases progressively with higher index values rather than crossing a single threshold — is characteristic of a true biological relationship rather than statistical artefact, and mirrors the dose-response relationships documented between metabolic syndrome and colorectal cancer risk.
A graded dose-response relationship supports biological plausibility, not threshold effect.
Why Visceral Fat and Insulin Resistance Might Drive Gastric Metaplasia
The mechanistic link between visceral adiposity, insulin resistance, and gastric premalignant change is plausible via at least three overlapping pathways, though this study's cross-sectional design could not directly test them:
- Adipokine dysregulation: Visceral fat secretes pro-inflammatory adipokines (leptin, resistin, IL-6, TNF-α) and reduced anti-inflammatory adiponectin. Chronic leptin excess promotes epithelial proliferation and suppresses apoptosis in gastric mucosa, creating conditions permissive to metaplasia. Adiponectin suppression removes a key anti-inflammatory brake.
- Hyperinsulinaemia and IGF-1 signalling: Insulin resistance drives compensatory hyperinsulinaemia, which in turn elevates circulating IGF-1. IGF-1 activates the PI3K/Akt/mTOR pathway in gastric epithelial cells, promoting proliferation and reducing programmed cell death — effects that facilitate the stepwise progression of metaplasia.
- Chronic mucosal inflammation: Visceral adiposity promotes systemic low-grade inflammatory states that potentiate H. pylori-induced mucosal damage and accelerate the pace of atrophy and metaplasia progression in infected patients — explaining why the metabolic signal persists after statistical adjustment for H. pylori status.
As the authors noted: "The finding that BRI outperformed BMI in identifying gastric pathology is consistent with the established principle that visceral adiposity, rather than overall body mass, is more closely related to obesity-associated disease risk."[1]
Limitations — What This Study Cannot Tell Us
Study Limitations (authors' own)
- Cross-sectional design: Cannot establish temporal sequence — the study cannot determine whether elevated cardiometabolic indices caused intestinal metaplasia, followed its development, or merely co-exist in a shared metabolic phenotype. A prospective cohort with baseline metabolic measurement and longitudinal endoscopic follow-up would be required to confirm causality.
- Single-centre, single-country: 250 patients from one hospital in Ankara, Turkey. Gastric cancer risk, H. pylori prevalence, and dietary patterns differ substantially by region. Results may not generalise to North American or other Western populations with lower baseline H. pylori burden.
- Biomarker gaps: Serum adipokines (leptin, adiponectin), inflammatory cytokines (IL-6, TNF-α, CRP), and fasting insulin levels were not directly measured — limiting mechanistic inference about which biological pathway mediates the observed associations.
What This Means for Clinical Practice and Trial Design
- BRI and TyG are calculable from routine data (waist circumference, height, fasting triglycerides, fasting glucose) — adding them to pre-endoscopy assessments costs nothing if the blood draw is already planned.
- TyG's AUC of 0.848 for advanced OLGA stage approaches the threshold at which a biomarker becomes useful for endoscopic triage — patients in the highest TyG tertile had more than double the IM prevalence of those in the lowest tertile.
- The independent multivariate effect (OR 3.823 for TyG) suggests that H. pylori eradication alone is insufficient for metabolically dysregulated patients — metabolic intervention may need to be part of any gastric cancer prevention strategy in obese, insulin-resistant individuals.
- For clinical trial design: BRI and TyG are candidate stratification variables for gastric cancer prevention trials. Including them as pre-specified baseline covariates in H. pylori eradication trials or dietary intervention trials could reveal differential treatment effect by metabolic phenotype.
- The dose-response gradient (40% → 80% IM across tertiles) supports BRI and TyG as continuous risk biomarkers, not binary thresholds — suggesting that risk modelling tools incorporating them could outperform simple cut-off-based guidelines.
- Caveat: Cross-sectional causality cannot be confirmed. Validation in a prospective cohort — ideally across multiple geographic and ethnic populations — is required before these indices should influence clinical guidelines.
References
- Ucdal M et al. "Simple Markers Identify Precancerous Gastric Lesions Better Than BMI." Journal of Clinical Gastroenterology. Published August 6, 2026. doi:10.1097/MCG.0000000000002416 Primary Study
- Rugge M et al. "OLGA staging for gastritis: a tutorial." Digestive and Liver Disease. 2008. Landmark paper establishing the OLGA staging system used in this study. Methods Reference
- Marcos-Pinto R et al. "Management of epithelial precancerous conditions and lesions in the stomach (MAPS II)." Endoscopy. 2019;51(4):365–388. European guideline establishing OLGA ≥III as the threshold for intensive surveillance. Guidelines
- Sung H et al. "Global Cancer Statistics 2020." CA: A Cancer Journal for Clinicians. 2021;71(3):209–249. Gastric cancer ranked 5th in incidence and 4th in cancer mortality globally. doi:10.3322/caac.21660 Epidemiology
- Abbasi F et al. "The TyG index is a simple measure of insulin resistance." J Investig Med. 2016. Validation of TyG as an insulin resistance surrogate against hyperinsulinaemic-euglycaemic clamp. Methods Reference
Disclaimer: This article is a clinical analysis for informational and educational purposes. It does not constitute medical advice. Patients and clinicians should consult current clinical guidelines and qualified specialists before making screening, diagnostic, or therapeutic decisions based on this or any single study. The original study was conducted at a single centre with 250 participants and has not yet been externally validated.