Immune response and barrier dysfunction-related proteomic signatures in preclinical phase of Crohn’s disease highlight earliest events of pathogenesis
Bibliographic record
Abstract
OBJECTIVE: The measure of serum proteome in the preclinical state of Crohn's disease (CD) may provide insight into biological pathways involved in CD pathogenesis. We aimed to assess associations of serum proteins with future CD onset and with other biomarkers predicting CD risk in a healthy at-risk cohort. DESIGN: In a nested case-control study within the Crohn's and Colitis Canada Genetics Environment Microbial Project (CCC-GEM) cohort, which prospectively follows healthy first-degree relatives (FDRs), subjects who developed CD (n=71) were matched with four FDRs remaining healthy (n=284). Using samples at recruitment, serum protein profiles using the Olink Proximity Extension Assay platform was assessed for association with future development of CD and with other baseline biomarkers as follows: serum antimicrobial antibodies (AS: positive antibody sum) (Prometheus); faecal calprotectin (FCP); gut barrier function using the fractional excretion of lactulose-to-mannitol ratio (LMR) assay. RESULTS: We identified 25 of 446 serum proteins significantly associated with future development of CD. C-X-C motif chemokine 9 (CXCL9) had the highest OR with future risk of CD (OR=2.07 per SD, 95% CI 1.58 to 2.73, q=7.9e-5), whereas matrix extracellular phosphoglycoprotein had the lowest OR (OR 0.44, 95% CI 0.29 to 0.66, q=0.02). Notably, CXCL9 was the only analyte significantly associated with all other CD-risk biomarkers with consistent direction of effect (FCP: OR=2.21; LMR: OR=1.67; AS: OR=1.59) (q<0.05 for all). CONCLUSION: We identified serum proteomic signatures associated with future CD development, reflecting potential early biological processes of immune and barrier dysfunction.
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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) | 0.000 | 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 | 0.000 |
| 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".