Intestinal microbiota contributes to individual susceptibility to alcoholic liver disease
Bibliographic record
Abstract
OBJECTIVE: There is substantial inter-individual diversity in the susceptibility of alcoholics to liver injury. Alterations of intestinal microbiota (IM) have been reported in alcoholic liver disease (ALD), but the extent to which they are merely a consequence or a cause is unknown. We aimed to demonstrate that a specific dysbiosis contributes to the development of alcoholic hepatitis (AH). DESIGN: We humanised germ-free and conventional mice using human IM transplant from alcoholic patients with or without AH. The consequences on alcohol-fed recipient mice were studied. RESULTS: A specific dysbiosis was associated with ALD severity in patients. Mice harbouring the IM from a patient with severe AH (sAH) developed more severe liver inflammation with an increased number of liver T lymphocyte subsets and Natural Killer T (NKT) lymphocytes, higher liver necrosis, greater intestinal permeability and higher translocation of bacteria than mice harbouring the IM from an alcoholic patient without AH (noAH). Similarly, CD45+ lymphocyte subsets were increased in visceral adipose tissue, and CD4(+)T and NKT lymphocytes in mesenteric lymph nodes. The IM associated with sAH and noAH could be distinguished by differences in bacterial abundance and composition. Key deleterious species were associated with sAH while the Faecalibacterium genus was associated with noAH. Ursodeoxycholic acid was more abundant in faeces from noAH mice. Additionally, in conventional mice humanised with the IM from an sAH patient, a second subsequent transfer of IM from an noAH patient improved alcohol-induced liver lesions. CONCLUSIONS: Individual susceptibility to ALD is substantially driven by IM. It may, therefore, be possible to prevent and manage ALD by IM manipulation.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".