Between Celiac Disease and Irritable Bowel Syndrome: The “No Man's Land” of Gluten Sensitivity
Bibliographic record
Abstract
The repertoire of gastrointestinal (GI) symptoms is finite; however, the etiologies and mechanisms underlying symptom generation and perception are diverse and, in many cases, unknown. This review examines the clinical and experimental evidence exploring the putative relationship between gluten sensitivity (GS) and the generation of GI symptoms. It explores the hypothesis that, in a proportion of patients, GS causes functional bowel disorder (FBD)-like symptoms. We propose a model for investigating and understanding the induction of GI symptoms and dysfunction by gluten in FBD and organic disease. We hypothesize that, even in the absence of fully developed celiac disease, gluten can induce symptoms similar to FBD. We discuss the hypothesis that GS and post-infectious irritable bowel syndrome (IBS) provide two triggers that can explain at least part of the spectrum that constitutes IBS, further advancing an understanding of the role of mucosal responses to luminal factors in FBDs. We propose that the animal model of GS in human leukocyte antigen (HLA)- DQ8 mice allows investigation of mucosal pathophysiological changes that occur before the onset of full-blown inflammation in a GS host. A better understanding of how gluten can cause symptoms in sensitive individuals will illuminate the interaction between host genotype, diet, and intestinal microbiota in generating one of the most common GI conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".