Serum C-X-C motif chemokine ligand 9, interleukin 8, and interleukin 22 as key biomarkers in pediatric inflammatory bowel disease
Bibliographic record
Abstract
BACKGROUND The diagnosis of inflammatory bowel disease (IBD) involves clinical, endoscopic, and radiologic evaluation. Endoscopic procedures, particularly in pediatrics, require general anesthesia and carry potential risks. AIM To investigate whether serum biomarkers can differentiate between pediatric patients with and without IBD. Secondary objectives included identifying biomarkers that distinguish Crohn’s disease (CD) from ulcerative colitis (UC) and assessing their predictive value for progression to biologic therapy. METHODS Pediatric patients undergoing diagnostic colonoscopy at British Columbia Children’s Hospital between December 2017 and June 2022 were enrolled. Blood samples were collected at colonoscopy, and demographic clinical data, laboratory, and histopathologic evaluation were obtained. An exploratory screen of 50 biomarkers was undertaken in a subset of patients (54 IBD, 41 controls) using LegendplexTM flow cytometry kits to identify candidates. A refined panel of 12 serum biomarkers was subsequently selected and a supervised learning model was developed to classify patients. RESULTS The study included 246 pediatric patients, who had a median age of 13.03 years and were 37.4% female (103 CD, 52 UC, 91 controls). In univariate analyses, C-X-C motif chemokine ligand 9 (CXCL9) was the only biomarker significantly elevated in IBD vs controls (P < 0.001). A multivariable model achieved an area under the receiver operating characteristic of 0.861 for distinguishing IBD from controls. Interleukin 8 (IL-8) emerged as a key biomarker alongside CXCL9 and IL-22 in the model. The random forest model identified CXCL9 with the greatest diagnostic accuracy (area under the curve [AUC] = 0.81), followed by IL8 and IL22 (AUC = 0.737 and 0.68, respectively). CXCL9 and IL-18 showed higher levels in CD (P = 0.016), whereas CXCL1 levels predicted progression to biologic therapy within 1 year (P = 0.039). However, the model did not effectively predict disease subclassification or progression to biologic therapy. CONCLUSION Serum biomarkers, particularly CXCL9, IL-8, and IL-22, can aid in the diagnosis of pediatric IBD. CXCL9 and IL18 were found to be significant predictors of CD, and CXCL1 differed between patients requiring biologic therapy vs those who did not.
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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 | 0.002 | 0.004 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".