Anti–GM-CSF autoantibodies promote a “pre-diseased” state in Crohn’s Disease
Bibliographic record
Abstract
Abstract Background & Aims Anti–GM-CSF autoantibodies (aGMAb) are detected in ileal Crohn’s Disease (CD) patients. Their induction and mode of action impacting homeostasis during, or prior to disease are not well understood. We aimed to investigate the underlying mechanisms leading to the induction of aGMAb, from functional orientation to recognized epitopes, for their impact on intestinal immune homeostasis and use as predictive biomarker for complicated CD. Methods Using longitudinally collected sera from active component US personnel, we characterize naturally occurring aGMAb in a subset of CD patients years before disease onset. We employed biochemical, cellular, and transcriptional analysis to uncover a mechanism that governs the impaired immune balance in CD years prior to diagnosis. Results Neutralizing aGMAb are specific to posttranslational glycosylations on GM-CSF, detectable years prior to diagnosis, and associated with complicated CD at presentation. Glycosylation and production of GM-CSF change in CD patients, altering myeloid homeostasis and destabilizing group 3 innate lymphoid cells. Perturbations in immune homeostasis precede the inflammation and are detectable in the non-inflamed CD mucosa of patients presenting with anti-GM-CSF autoantibodies. Conclusions Anti-GM-CSF autoantibodies predict the diagnosis of complicated CD, have unique epitopes, and impair myeloid cell homeostasis across the ILC3-GM-CSF-myeloid cell axis, altering intestinal immune homeostasis long before the diagnosis of disease.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".