Anti-integrin αvβ6 autoantibodies are a novel predictive biomarker in ulcerative colitis
Bibliographic record
Abstract
Abstract Background and Aims Better biomarkers for prediction of ulcerative colitis (UC) development and prognostication are needed. Anti-integrin αvβ6 autoantibodies (anti-αvβ6) have been described in UC patients. Here, we tested for the presence of anti-αvβ6 antibodies in the pre-clinical phase of UC and studied their association with disease-related outcomes after diagnosis. Methods Anti-αvβ6 were measured in 4 longitudinal serum samples collected from 82 subjects who later developed UC and 82 matched controls from a Department of Defense pre-clinical cohort (PREDICTS). In a distinct, external validation cohort (GEM), we tested 12 pre-UC subjects and 49 matched controls. Further, anti-αvβ6 were measured in 2 incident UC cohorts (COMPASS n=55 and OSCCAR n=104) and associations between anti-αvβ6 and UC-related outcomes were defined using Cox proportional-hazards model. Results Anti-αvβ6 were significantly higher among individuals who developed UC compared to controls up to 10 years before diagnosis in PREDICTS. The anti-αvβ6 seropositivity was 12.2% 10 years before diagnosis and increased to 52.4% at the time of diagnosis in subjects who developed UC compared with 2.7% in controls across the 4 timepoints. Anti-αvβ6 predicted UC development with an AUC of at least 0.8 up to 10 years before diagnosis. The presence of anti-αvβ6 in pre-clinical UC samples was validated in the GEM cohort. Finally, high anti-αvβ6 was associated with a composite of adverse UC-outcomes including hospitalization, disease extension, colectomy, systemic steroid use and/or escalation to biologic therapy in recently diagnosed UC. Conclusion Anti-integrin αvβ6 auto-antibodies precede the clinical diagnosis of UC by up to 10 years and are associated with adverse UC-related outcomes.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".