Gluten is a Proinflammatory Inducer of Autoimmunity
Bibliographic record
Abstract
Gluten has multiple harmful effects that compromise human health, not only in gluten-dependent diseases but also in non-gluten-affected chronic inflammatory conditions. After consumption, the indigestible gluten peptides are modified by luminal microbial transglutaminase or transported through the gut epithelium to interact with the highly populated mucosal immune cells. As a disruptor of gut permeability, gluten peptides compromise tight junction integrity, allowing foreign immunogenic molecules to reach internal compartments. Gliadin peptides are distributed systemically to remote organs, where they encounter endogenous tissue transglutaminase. Following post-translational deamidation or transamidation, the peptides become immunogenic and pro-inflammatory, inducing organ dysfunction and pathology. Cross-reactivity and sequence homology between gluten/gliadin peptides and human epitopes may contribute to molecular mimicry in autoimmunity induction. A gluten-free diet can prevent these phenomena through various mechanisms. As proof of concept, gluten withdrawal alleviates disease activity in chronic inflammatory, metabolic, and autoimmune conditions, and even in neurodegeneration. We recommend combining the gluten-free and Mediterranean diets to leverage the advantages of both. Before recommending gluten withdrawal for non-gluten-dependent conditions, patients should be asked about gut symptomatology and screened for celiac-associated antibodies. The current list of gluten-induced diseases includes celiac disease, dermatitis herpetiformis, gluten ataxia, gluten allergy, and non-celiac gluten sensitivity. In view of gluten being a universal pro-inflammatory molecule, other non-celiac autoinflammatory and neurodegenerative conditions should be investigated for potential gluten avoidance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".