DOP06 Histologic activity in Ulcerative Colitis patients achieving endoscopic healing is associated with higher rate of relapse and a distinct mucosal microbial and transcriptional profile
Bibliographic record
Abstract
Abstract Background Histologic remission (HR) in ulcerative colitis (UC) has been associated with better clinical outcomes. However, this therapeutic target is hard to achieve with the available medications. We aim to assess the relapse-free survival (RFS) of patients with persistent histologic activity (HA) upon achieving endoscopic healing (EH) and the microbial and molecular signatures associated with this condition. Methods Left-sided and extensive UC patients and healthy controls (HC) were recruited at colonoscopy and sigmoid colon (SC) biopsy samples were obtained for HA evaluation, 16S rRNA sequencing and host RNA-seq. EH was defined as Mayo endoscopic subscore (MES) 0–1, and HA and histologic remission (HR) as the presence and absence of neutrophil infiltration, respectively. UC relapse was retrospectively evaluated and defined as symptomatic and endoscopic worsening. Cumulative RFS was assessed by log-rank test. Principal component analysis (PCA) was used for capturing cluster structure. For differential expressed genes (DEG) and enrichment pathway analyses, glmQLFTest in EdgeR and enrichKEGG in clusterProfiler were applied, respectively. Genes with log fold change > 2 and a log count per million > 1 were considered DEG. Dada2 algorithm in QIIME2 for amplicon sequence variant generation and ANCOM-BC package for differential abundance (DA) taxa analysis were applied. P-values of omics data were corrected by false discovery rate (FDR) method. Results Sixty-five UC patients had EH (37 MES 0 and 28 MES 1). Nine (20.9%) MES 0 and 17 (40.5%) MES 1 showed HA. Over a median follow-up of 4.2 years (IQR 2.6–6.1), UC patients with HA showed a significant lower RFS compared to HR (p < 0.05; Figure 1). This difference was not observed between MES 0 and MES 1 (p = 0.2). Transcriptomics data was available for 27 samples with HR and 9 samples with HA, as well as 43 HC and 13 MES 2–3 samples. PCA showed that HR and HA samples cluster with HC and MES 2–3 samples, respectively (Figure 2). Compared to HR, 163 DEG (118 up and 45 downregulated) were found in HA. Enrichment analysis showed significant upregulation of cytokine-cytokine interaction, Th17 and chemokine signaling pathways (Figure 3). Microbiome data was available for 43 and 19 samples with HR and HA, respectively. Three genera had significant DA in HA compared to HR (Figure 4). Conclusion HA in UC is better than MES at distinguishing a higher risk of relapse and has a transcriptomics profile closer to active endoscopic disease despite EH with significant upregulation of chemokine, cytokine and Th17 pathways. A different microbial signature is also observed in HA. These particular signatures could help to define strategies to reduce the risk of UC relapse in patients achieving EH.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".