P788 Microbiota related disease activity and distribution in subgroups of inflammatory bowel disease
Bibliographic record
Abstract
Background: Knowledge about a patients' microbiota profiles might give useful information in diagnosing, early relapse prediction and to distinguish responders from non-responders to treatment. Fecal calprotectin (FCal) is used as a marker in diagnosis and follow-up of patients with inflammatory bowel diseases (IBD). The IBD-Character project aims to analyse fecal microbiota profiles, microbial diversity and concentration of FCal in treatment naive newly diagnosed IBD patients, symptomatic non-IBD patients and healthy controls. Methods: Patients were diagnosed according to international criteria, including endoscopic and histopathologic assessment. Patients with ulcerative colitis (UC) and Crohn's disease (CD) were classified based on anatomic distribution of inflammation according to the Montreal classification. Stool samples were stored at −80°C before microbiota 16S rRNA analysis (GA-map™ Dysbiosis Test) (1) of dysbiosis defined as non, mild or severe, and analysis of specific microbial taxa. High FCal (fCAL® ELISA, Bühlmann laboratories AG) was defined as >100 μg/g. Stool samples collected within 369 days prior to and within 14 days after diagnosis (= onset of treatment), and with no antibiotic treatment last two months, were included. Results: Data on dysbiosis, bacteria profiles and FCal were available in 41 CD, 58 UC, 8 IBD-U patients, and 129 symptomatic non-IBD and 45 healthy controls. There was a relationship between FCal and dysbiosis in UC patients (p=0.0249, ANCOVA), which was not the case for CD and the control groups. Univariate analysis of the bacterial profiles among the Montreal classified subgroups identified bacteria that could differentiate between one or more of the subgroups, see Table 1. Increasing UC severity consistently yielded lower bacteria abundance, e.g. Bifidobacterium. For CD patients no significant relationship was found, however the strongest nonsignificant bacteria; Bifidobacterium showed increased abundance. (Kruskal-Wallis Rank Sum test, Benjamini & Hochberg p-value adjustment). Table 1 Conclusions: We have identified a relationship between gut microbiota profiles and UC diagnosed patients, and showed that specific bacteria profiles are able to stratify subgroups of UC. Relationship between FCal and dysbiosis was significant in the UC group only. The data demonstrate a diagnostic potential for a microbiota test in IBD [1]. References: [1] Casen et al. (2015), Deviations in human gut microbiota: a novel diagnostic test for determining dysbiosis in patients with IBS or IBD, Aliment Pharmacol Ther 42: 71–83
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".