The potential role of prebiotic fibre for treatment and management of non‐alcoholic fatty liver disease and associated obesity and insulin resistance
Bibliographic record
Abstract
Non-alcoholic fatty liver disease (NAFLD) and the more severe non-alcoholic steatohepatitis (NASH) represent a spectrum of diseases involving hepatic fat accumulation and histological features essentially identical to alcoholic liver disease; however, they occur in the absence of excessive alcohol intake. They typically arise in conjunction with one or more features of the metabolic syndrome. Lifestyle mediated weight loss remains the primary mode of therapy for NAFLD and NASH, but this is often ineffective and adjunctive medical and surgical treatments are presently lacking. Prebiotic fibres are a group of non-digestible carbohydrates that modulate the human microbiota in a manner that is advantageous to host health. Rodent studies suggest that dietary supplementation with prebiotic fibres positively impacts NAFLD by modifying the gut microbiota, reducing body fat, and improving glucoregulation. Future research should focus on placebo-controlled, human, clinical trials using histological endpoints to address the effects of prebiotics on NAFLD and NASH. The aim of this review is to summarize current knowledge about prebiotics as an emerging therapeutic target for NAFLD.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".