P125 MIG (CXCL9) and IL22 are key biomarkers that discriminates between paediatric IBD patients and non-IBD patients in a novel biomarker model
Bibliographic record
Abstract
Abstract Background The current gold standard for diagnosis of Inflammatory bowel disease (IBD) is based on clinical and endoscopic evaluation as well as histologic and radiologic evaluation. However, endoscopic evaluation can be uncomfortable and carry some risks, especially in the paediatric population that requires general anesthesia in order to perform the procedure. Our aim was to investigate whether serum biomarkers can differentiate between paediatric patients with and without IBD. Secondary aims were to determine whether there are biomarkers that can differentiate between Crohn's disease (CD) and Ulcerative colitis (UC), and whether we can predict progression to biologic treatment within one year from diagnosis using these biomarkers. Methods Paediatric patients undergoing a first diagnostic colonoscopy at British Columbia Children's Hospital (BCCH) between December 2019 and June 2022 were invited to participate in the study. A 2 ml blood sample was taken from each participant at the time of colonoscopy. Demographic and clinical data were collected from each patient. Blood samples were analyzed using the LegendplexTM flow cytometry kits investigating 12 different inflammatory cytokines and chemokines associated with IBD. Eleven of them were found most significant in an initial analysis performed on 100 patients using 50 biomarkers, and CCL7 was added due to significance in previous studies. A prediction model was built via a supervised learning method to determine how well we can predict the different groups. Results Two hundred and forty-six paediatric patients participated in the study. Median age was 13.03 years, 92 were females (37.4%) and 155 (63.01%) were diagnosed with IBD, with 103 (66.45%) diagnosed with Crohn's disease. The AUROC using SPLS-DA classification was 0.805. MIG and Il22 were found to be the key biomarkers to discriminate between pediatric IBD patients and non-IBD patients. In a random forest analysis MIG, Il8 and Il22 were found most important for discrimination. On a univariate analysis MIG and Il18 were found statistically significant as a predictor of CD (p=0.016 for both). CXCL1 was predictive to progression to biologic treatment within one year of diagnosis (p=0.039). However, the prediction model did not predict well the subclasses nor progression to biologic treatment. Conclusion Using serum biomarkers can predict the diagnosis of Paediatric IBD. MIG and Il22 were found to be the key biomarkers in the prediction model.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".