EMerging BiomARKers in Inflammatory Bowel Disease (EMBARK) Study Identifies Fecal Calprotectin, Serum MMP9, and Serum IL-22 as a Novel Combination of Biomarkers for Crohn's Disease Activity: Role of Cross-Sectional Imaging
Bibliographic record
Abstract
OBJECTIVES: In Crohn's disease (CD), clinical symptoms correspond poorly to inflammatory disease activity. Biomarkers reflective of mucosal and bowel wall inflammation would be useful to monitor disease activity. The EMBARK study evaluated disease activity in patients with ulcerative colitis (UC) and CD, and used endoscopy with or without cross-sectional imaging for biomarker discovery. METHODS: UC (n=107) and CD (n=157) patients were characterized and underwent ileocolonoscopy (ICO). A subset of CD patients (n=66) also underwent computed tomography enterography (CTE). ICO and CTE were scored by a gastroenterologist and radiologist who incorporated findings of inflammation into a single score (ICO-CTE) for patients that underwent both procedures. Serum and fecal biomarkers were evaluated for association with the Mayo Clinic endoscopy score in UC patients and with ICO alone or ICO-CTE in CD patients. Individual biomarkers with a moderate degree of correlation (P≤0.3) were evaluated using multivariate analysis with model selection using a stepwise procedure. RESULTS: In UC, ordinal logistic regression using Mayo Clinic endoscopy subscore selected the combination of fecal calprotectin and serum matrix metalloproteinase 9 (MMP9; pseudo R(2)=0.353). In CD, we found that use of the ICO-CTE increased specificity of known biomarkers. Using ICO-CTE as the dependent variable for biomarker discovery, the selected biomarkers were the combination of fecal calprotectin, serum MMP9, and serum IL-22 (r=0.699). CONCLUSIONS: Incorporation of both ICO and CTE into a single measure increased biomarker performance in CD. Combinations of fecal calprotectin and serum MMP9 for UC, and combinations of fecal calprotectin, serum MMP9, and serum interleukin-22 in CD, demonstrated the strongest association with imaging/endoscopy-defined inflammation.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".