Prevalence of Serologically Defined Celiac Disease in Patients With Irritable Bowel Syndrome in Asia: A Multicountry Hospital‐Based Study
Bibliographic record
Abstract
BACKGROUND AND AIM: Celiac disease (CeD) is not adequately recognized in Asia. We aimed to assess the prevalence of CeD in patients with irritable bowel syndrome (IBS) in six Asian countries and identify high-risk groups meriting screening. METHODS: Patients with IBS (Rome III) were recruited from Japan, Thailand, Indonesia, Malaysia, Singapore, and India. A two-step noninvasive strategy was used [positive IgA anti-tissue transglutaminase antibody (IgA anti-tTG-Ab) followed by confirmation with IgA deamidated gliadin-peptide antibodies (anti-DGP-Ab)]. Consenting patients with positive serology also underwent duodenal biopsies. Positivity for both IgA-anti-tTG-Ab and IgA-anti-DGP-Ab was labeled as serologically defined CeD. Important predictors of CeD were identified using the Boruta algorithm, and a nomogram for predicting CeD was constructed. RESULTS: 2546 patients with IBS were evaluated across 6 countries. Overall prevalence serologically defined CeD (positive for both IgA anti-tTG Ab and IgA anti-DGP antibodies) was 2.75% (n = 70; 95% CI, 2.11%-3.39%). Prevalence was highest in Malaysia (3.8%), India (3.75%), and Indonesia (3.61%) and lowest in Japan (0.1%). Duodenal biopsies were performed in 20 patients, and 14 of them showed villous abnormalities (modified Marsh grade 2 or more). Among IgA anti-tTG-Ab-positive patients (n = 204; 8.01% 95% CI, 6.96%-9.07%), 18 (0.71%), 21 (0.82%), and 165 (6.48%) exhibited anti-tTG-Ab titer more than 10-fold, 6-10-fold, and 1-5-fold above the upper limit of normal. We propose a nomogram to predict the risk of CeD in Asian patients with IBS based on country, hemoglobin, age, sex, and diarrhea. CONCLUSION: Overall prevalence of serologically defined CeD in Asian patients with IBS is 2.75% but differs across patient profiles. This study suggests the need for better awareness and further studies on the prevalence of CeD across Asia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".