P1338 Defining the preclinical changes of microbial composition, gut barrier function, and subclinical inflammatory markers associated with future risk of ulcerative colitis
Bibliographic record
Abstract
Abstract Background Ulcerative colitis (UC) is a chronic inflammatory condition of the rectum and colon with a complex aetiology involving genetic, immune, and microbial factors. Less is known about the pre-disease phase of UC compared to Crohn’s disease (CD). Investigating the preclinical biomarkers that precede the development of UC may provide insights into its pathogenesis. Methods Participants were recruited as part of the CCC-GEM project, a prospective cohort study following healthy first-degree relatives (FDR) of patients with CD. Baseline gut inflammation was assessed using faecal calprotectin (FCP), with a cut-off of 100ug/g, while baseline gut permeability was assessed by the fractional urinary excretion ratio of lactulose-to-mannitol (LMR). Baseline faecal microbiome composition was analyzed using 16s rRNA sequences, and functional pathways were inferred using PICRUSt2. Cox proportional hazards models were used to assess the association of baseline variables with UC onset, adjusting for age, sex, and family clustering. Significance was determined using p-values and false discovery rate-adjusted q-values. Results Among 3,596 FDRs, 16 developed UC during a median follow-up of 6.88 years. FDRs with elevated baseline FCP had a 3.1-fold higher risk (p=0.02) of developing UC, while no significant association was observed between baseline LMR and UC onset. The relative abundance of genus Bilophila (HR=0.33 per SD, q=0.010) and Bifidobacteria (HR=0.16, q=0.02) were associated with UC risk, but lost significance after adjusting for FCP. Interestingly, the relative abundances of genera Alistipes (HR=0.15, q=0.0007), Phascolarctobacterium (HR=0.08, q=0.027), Christensenellaceae R.7 group (HR=8.37, q=0.02) and Angelakisella (HR=5.28, q=0.004) remained significantly associated with risk of UC even after adjusting for FCP. Thirty-seven pathways, predominantly involved in specific metabolic processes such as carbohydrates, lipid, or folate metabolism, were also associated with UC risk. Fur example, pathways involving hydroxymethylglutaryl-CoA reductase (HR=0.44, q=0.03), choline monooxygenase (HR=0.22, q=0.005), and 3-phenylpropionate/trans-cinnamate dioxygenase (HR=4.70, q=0.001) remained significantly associated with UC risk even after adjusting for FCP. Conclusion We report that elevated FCP, but not LMR, was significantly associated with future risk of UC. Furthermore, specific microbial taxa and functional pathways exhibited strong associations with future risk of UC, independent of FCP. These microbial signatures were distinct from the previously reported pre-CD microbial changes, suggesting a unique gut microbial contribution to the development of UC. References Raygoza Garay JA, Turpin W, Lee S-H, et al. Gastroenterology 2023;165:670–681.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".