P005 0190Molecular-based classification of ulcerative colitis and its dynamic
Bibliographic record
Abstract
Abstract Background A better characterization of inflammatory bowel disease is needed. We previously developed a molecular classification tool for Crohn’s disease, based on a combination of metagenomics (dysbiosis score - MDI), transcriptomics (barrier integrity score - BIS, autophagy score – ATS, unfolded protein response score - UPRS), serum proteomics (inflammation score - IPS) and genetics (polygenic risk score - GRS)1. We here investigated if this score could also apply for ulcerative colitis (UC) and how the individual components correlate with clinical outcomes. Methods For a total of 173 UC patients and 181 controls (CO), we collected faecal and blood samples, as well as colon biopsies (Table). Faecal microbiota 16S-sequencing, intestinal RNA-sequencing, and serum proteomics (OLINK inflammatory panel) were performed. Using proteomics and RNA expression data, IPS, ATS, UPRS and BIS were calculated. MDI was calculated as previously described1. Based on these findings, patients were categorized into quartiles, ranging from Q1 (the least dysfunctional state) to Q4 (the most dysfunctional state). The individual scores were correlated with time to first biological or biological switch, as well as correlations with C-reactive protein (CRP), faecal calprotectin (FC), and the Montreal classification. The therapeutic value of MDI score was studied in a small dietary intervention pilot study (n=8)2. Results None of the individual components of the score were associated with disease extent. Patients diagnosed at a later age had increased ATS and BIS levels. BIS and UPRS showed a positive correlation with disease duration (r=0.36, r=0.54 respectively, p<0.001) (Figure2). MDI (spearman r=0.27, r=0.44, r=0.46 respectively, all p<0.0001) and IPS (r=0.5, r=0.7, r=0.6 respectively, all p<0.0001) correlated with CRP and FC levels, and time to first biologic. UPRS, ATS, MDI, BIS, and IPS were considerably higher in patients who required a switch in biologic therapy (all p<0.001). Finally, we observed a decreasing trend in MDI in response to dietary modification (Figure3). Conclusion In UC patients, we created a multifaceted scoring system to molecularly describe disease by the degree of dysbiosis, dysregulation of the immune proteome, and intestinal barrier integrity. We also demonstrated the dynamic of MDI of this score in relation to dietary intervention. Therefore, molecular profiling of patients may represent a new, individualized strategy to the management of UC. References: 1. OP30 ECCO 2020, JCC 14(Supplement_1):S028-S0302. 2. P767 ECCO 2017, JCC 11(suppl_1):S473-S473.
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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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.034 | 0.003 |
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".