Inflammatory Bowel Disease Associated with Primary Sclerosing Cholangitis is Associated with an Altered Gut Microbiome and Bile Acid Profile
Bibliographic record
Abstract
BACKGROUND: Primary sclerosing cholangitis associated with inflammatory bowel disease (IBD-PSC) carries significant morbidity compared to IBD without PSC. Alterations in microbial composition and bile acid (BA) profiles have been shown to modulate chronic inflammation in IBD, but data in IBD-PSC is scarce. We aimed to assess the differences in gut microbiome composition as well as in the BA profile and BA-related microbial functions between IBD-PSC and IBD-only. METHODS: 54 IBD-PSC and 62 IBD-only subjects were enrolled from 2012 to 2021. Baseline samples were collected for fecal DNA shotgun metagenomic sequencing, fecal and serum BA quantitation using mass spectrometry and fecal calprotectin. Liver fibrosis measured by transient elastography (TE) was assessed in the IBD-PSC group. Data was analyzed using general linear regression models and Spearman rank correlation tests. RESULTS: Patients with IBD-PSC had reduced microbial gene richness (p=0.004) and significant compositional shifts (PERMANOVA: R2=0.01, p=0.03) compared to IBD-only. IBD-PSC was associated with altered microbial composition and function, including decreased abundance of Blautia obeum, increased abundance of Veillonella atypica, Veillonella dispar and Clostridium scindens (q<0.05 for all), and increased abundance of microbial genes involved in secondary BA metabolism. Decreased serum sulfated and increased serum conjugated secondary BA were associated with IBD-PSC and increased liver fibrosis. CONCLUSION: We identified differences in microbial species, functional capacity and serum BA profiles in IBD-PSC compared with IBD-only. Our findings provide insight into the pathophysiology of IBD associated with PSC and suggest possible targets for modulating the risk and course of IBD in subjects with PSC.
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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) | 0.002 | 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".