Gut microbiota and obesity: Impact of antibiotics and prebiotics and potential for musculoskeletal health
Bibliographic record
Abstract
Obesity is a complex disease with multiple contributing factors. One of the most intensely studied factors during the past decade has been the gut microbiota, which is the community of all microbes in the intestinal tract. The gut microbiota, via energy extraction, inflammation, and other actions, is now recognized as an important player in the pathogenesis of obesity. Dysbiosis, or an imbalance in the microbial community, can initiate a cascade of metabolic disturbances in the host. Early life is a particularly important period for the development of the gut microbiota, and perturbations such as with antibiotic exposure can have long-lasting consequences for host health. In early life and throughout the life span, diet is one of the most important factors that shape the gut microbiota. Although diets high in fat and sugar have been shown to contribute to dysbiosis and disease, dietary fiber is recognized as an important fermentative fuel for the gut microbiota and results in the production of short-chain fatty acids that can act as signaling molecules in the host. One particular type of fiber, prebiotic fiber, contributes to changes in the gut microbiota, the most notable of which is an increase in the abundance of Bifidobacterium. This review highlights our current understanding of the role of gut microbiota in obesity development and the ways in which manipulating the microbiota through dietary means, specifically prebiotics, could contribute to improved health in the host, including musculoskeletal health.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".