Alterations of gut microbiome in autoimmune hepatitis
Bibliographic record
Abstract
Objective The significance of the liver-microbiome axis has been increasingly recognised as a major modulator of autoimmunity. The aim of this study was to take advantage of a large well-defined corticosteroids treatment-naïve group of patients with autoimmune hepatitis (AIH) to rigorously characterise gut dysbiosis compared with healthy controls. Design We performed a cross-sectional study of individuals with AIH (n=91) and matched healthy controls (n=98) by 16S rRNA gene sequencing. An independent cohort of 28 patients and 34 controls was analysed to validate the results. All the patients were collected before corticosteroids therapy. Results The gut microbiome of steroid treatment-naïve AIH was characterised with lower alpha-diversity (Shannon and observed operational taxonomic units, both p<0.01) and distinct overall microbial composition compared with healthy controls (p=0.002). Depletion of obligate anaerobes and expansion of potential pathobionts including Veillonella were associated with disease status. Of note, Veillonella dispar , the most strongly disease-associated taxa (p=8.85E–8), positively correlated with serum level of aspartate aminotransferase and liver inflammation. Furthermore, the combination of four patients with AIH-associated genera distinguished AIH from controls with an area under curves of approximately 0.8 in both exploration and validation cohorts. In addition, multiple predicted functional modules were altered in the AIH gut microbiome, including lipopolysaccharide biosynthesis as well as metabolism of amino acids that can be processed by bacteria to produce immunomodulatory metabolites. Conclusion Our study establishes compositional and functional alterations of gut microbiome in AIH and suggests the potential for using gut microbiota as non-invasive biomarkers to assess disease activity.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".