Links between gut microbiome, metabolome, clinical variables and non‐alcoholic fatty liver disease severity in bariatric patients
Bibliographic record
Abstract
BACKGROUND AND AIMS: Bacterial species and microbial pathways along with metabolites and clinical parameters may interact to contribute to non-alcoholic fatty liver disease (NAFLD) and disease severity. We used integrated machine learning models and a cross-validation approach to assess this interaction in bariatric patients. METHODS: 113 patients undergoing bariatric surgery had clinical and biochemical parameters, blood and stool metabolite measurements as well as faecal shotgun metagenome sequencing to profile the intestinal microbiome. Liver histology was classified as normal liver obese (NLO; n = 30), simple steatosis (SS; n = 41) or non-alcoholic steatohepatitis (NASH; n = 42); fibrosis was graded F0 to F4. RESULTS: We found that those with NASH versus NLO had an increase in potentially harmful E. coli, a reduction of potentially beneficial Alistipes putredinis and an increase in ALT and AST. There was higher serum glucose, faecal 3-(3-hydroxyphenyl)-3-hydroxypropionic acid and faecal cholic acid and lower serum glycerophospholipids. In NAFLD, those with severe fibrosis (F3-F4) versus F0 had lower abundance of anti-inflammatory species (Eubacterium ventriosum, Alistipes finegoldii and Bacteroides dorei) and higher AST, serum glucose, faecal acylcarnitines, serum isoleucine and homocysteine as well as lower serum glycerophospholipids. Pathways involved with amino acid biosynthesis and degradation were significantly more represented in those with NASH compared to NLO, with severe fibrosis having an overall stronger significant association with Superpathway of menaquinol-10 biosynthesis and Peptidoglycan biosynthesis IV. CONCLUSIONS: In bariatric patients, NASH and severe fibrosis were associated with specific bacterial species, metabolic pathways and metabolites that may contribute to NAFLD pathogenesis and disease severity.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| 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".