Serum Protein Biomarkers of Fibrosis Aid in Risk Stratification of Future Stricturing Complications in Pediatric Crohn's Disease
Bibliographic record
Abstract
OBJECTIVES: Avoiding fibrostenotic complications is of paramount concern in the management of Crohn's disease (CD). We sought to investigate the association of candidate biomarkers of fibrosis collected at diagnosis with the future development of fibrostenotic CD. METHODS: Using the Risk Stratification and Identification of Immunogenetic and Microbial Markers of Rapid Disease Progression in Children with Crohn's Disease cohort, a multicenter prospective observational pediatric inception cohort, subjects with an inflammatory phenotype (B1) at diagnosis who later converted to a stricturing phenotype (B2) within 3 years were compared with those who remained B1. Serum collected at diagnosis underwent both parallel reaction monitoring-targeted proteomic analysis and conventional enzyme-linked immunosorbent assay for 10 candidate biomarkers of intestinal fibrosis. Cox proportional hazard regression was used for multivariable analysis of time-dependent outcomes. RESULTS: In 116 subjects 58 subjects with verified B1 phenotype at diagnosis who later converted to B2 disease were compared with 58 subjects who remained B1 over 3 years of follow-up. Extracellular matrix protein 1 (ECM1) levels in the upper quartile (hazard ratio [HR] 3.43, 95% confidence limit [CL] 1.33, 8.42) were associated with future fibrostenotic disease. ASCA IgA (HR 4.99, 95% CL 1.50, 16.68) and CBir levels (HR 5.19, 95% CL 1.83, 14.74) were also associated with future intestinal fibrostenosis, although ECM1 continued to demonstrate independent association with conversion to B2 even with adjustment for serologies in multivariable analysis (HR 5.33, 95% CL 1.29, 22.13). CONCLUSIONS: ECM1 and other biomarkers of fibrosis may aid in determining the risk of uncomplicated inflammatory disease converting to B2 stricturing phenotypes in children with CD. Prospective validation studies to verify test performance and optimize clinical utilization are needed before clinical implementation.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".