Faecal microbiota from patients with cirrhosis has a low capacity to ferment non‐digestible carbohydrates into short‐chain fatty acids
Bibliographic record
Abstract
BACKGROUND AND AIMS: Cirrhosis is associated with dysbiosis, but its functional consequences are still largely unknown. Short-chain fatty acids (SCFAs) account for physiological interactions between the gut microbiota and host. Our aim was to assess the impact of cirrhotic dysbiosis on the production of SCFAs. METHODS: Seventeen patients with cirrhosis and 17 controls were selected. Microbiota composition in faecal samples was assessed by next-generation 16S rRNA gene sequencing. SCFAs were measured with GC-MS in faecal samples and after in vitro batch fermentations using arabinoxylan, resistant starch, pectin, and lactulose as substrates. RESULTS: Among the 17 cirrhotic patients (mean age 58, eight males), six, nine and two were, respectively, Child-Pugh class A, B and C. Eleven patients were on oral antibiotics, 11 on lactulose and 13 on proton pump inhibitors. Cirrhotic patients showed marked differences in the composition and diversity of gut microbiome when compared to controls, that were more pronounced with increased severity. Stool samples from cirrhotic patients showed lower SCFAs content and reduced capacity to produce SCFAs in batch fermentations, with butyrate production being the most abnormal. These functional aberrancies were more pronounced with greater liver disease severity. Abundance of Ruminococcus faecis (in family Ruminococcaceae), Faecalicatena fissicatena and Fusicatenibacter saccharivorans (in family Lachnospiraceae) was positively correlated with the SCFAs production. CONCLUSION: Cirrhotic dysbiosis is associated with a decreased capacity to ferment non-digestible carbohydrates into SCFAs, especially into butyrate. These functional abnormalities are more pronounced as disease progresses. These results might inform the design of gut-targeted therapies for cirrhosis.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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".