Faecal microbiota signatures of IBD and their relation to diagnosis, disease phenotype, inflammation, treatment escalation and anti-TNF response in a European Multicentre Study (IBD-Character)
Bibliographic record
Abstract
Method We examined faecal samples, using the GA-map™ Dysbiosis Test, to associate gut microbiota composition with Crohn’s disease (CD) and ulcerative colitis (UC) and to identify markers for future biomarker identification. We conducted a prospective case-control study (EU-ref. no. 305676) in an inception cohort of 324 individuals (64 CD, 84 UC, 116 symptomatic non-IBD controls and 44 healthy controls) across five European centres and examined 54 predetermined bacterial markers. We categorized patients according to the Montreal Classification and calculated the dysbiosis index (DI). Non-parametric tests were used to compare groups and the Bonferroni correction to adjust for multiple comparisons. Results The fluorescent signals (FSSs) for Firmicutes and Eubacterium hallii were lower in inflammatory bowel disease (IBD) vs. symptomatic controls (p<.05). FSS for Firmicutes, Lachnospiraceae, Eubacterium hallii and Ruminococcus albus/bromii were lower, whereas the signal for Bacteroides Fragilis was higher in UC vs. symptomatic controls (p<.05). FSS was higher for Bifidobacterium spp., Eubacterium hallii, Actinobacteria and Firmicutes among patients with ulcerative proctitis, compared to extensive colitis (p<.05). In CD, we observed no association with disease location. The DI correlated with faecal-calprotectin in both CD and in UC (p<.001). In terms of treatment escalation and anti-TNF response, differences were observed for some bacterial markers, but none of these associations were statistically significant. Conclusion Our data reveal that the GA-map™ Dysbiosis Test holds the potential to characterize the faecal microbiota composition and to assess the degree of dysbiosis in new-onset IBD. On the other hand, our results cannot demonstrate any proven diagnostic or predictive value of this method to support clinical decision making.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".