The human microbiome and bile acid metabolism: dysbiosis, dysmetabolism, disease and intervention
Bibliographic record
Abstract
INTRODUCTION: Recent evidence indicates that the human gut microbiome plays a significant role in health and disease. Dysbiosis, defined as a pathological imbalance in a microbial community, is becoming increasingly appreciated as a 'central environmental factor' that is both associated with complex phenotypes and affected by host genetics, diet and antibiotic use. More recently, a link has been established between the dysmetabolism of bile acids (BAs) in the gut to dysbiosis. AREAS COVERED: BAs, which are transformed by the gut microbiota, have been shown to regulate intestinal homeostasis and are recognized as signaling molecules in a wide range of metabolic processes. This review will examine the connection between BA metabolism as it relates to the gut microbiome and its implication in health and disease. EXPERT OPINION: A disrupted gut microbiome, including a reduction of bile salt hydrolase (BSH)-active bacteria, can significantly impair the metabolism of BAs and may result in an inability to maintain glucose homeostasis as well as normal cholesterol breakdown and excretion. To better understand the link between dysbiosis, BA dysmetabolism and chronic degenerative disease, large-scale metagenomic sequencing studies, metatranscriptomics, metaproteomics and metabolomics should continue to catalog functional diversity in the gastrointestinal tract of both healthy and diseased populations. Further, BSH-active probiotics should continue to be explored as treatment options to help restore metabolic levels.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".