Determining the gut microbiota‐independent effects of prebiotic fiber in diet‐induced obese rats
Bibliographic record
Abstract
Prebiotics, which are indigestible carbohydrates that are selectively fermented in the gut, have been shown to improve glycemia, inflammation, satiety, and body weight. The health benefits elicited by prebiotics are believed to be mediated through compositional changes in gut microbiota and associated metabolic activity. The extent to which prebiotics are dependent on gut microbiota for improved metabolic health is not clear. Our aim was to examine the gut‐microbiota independent effects of prebiotics on body weight, adiposity, intestinal permeability, and glycemia in a model of antibiotic‐induced intestinal decontamination. Diet induced obese male Sprague Dawley rats were randomized into 1 of 6 groups: 1) High energy (HE) ; 2) HE+Ampicillin (AMP); 3) HE+AMP+Neomycin (NEO); 4) HE+10% oligofructose (OFS); 5) HE+OFS+AMP; 6) HE+OFS+AMP+NEO (n=10 rats/gp). Decontamination of the gut with AMP and NEO did not limit classical prebiotic effects including reduced body fat and food intake and improved glucose tolerance (P<0.05). Decontamination with AMP, which is known to decrease the beneficial bacteria associated with OFS consumption, prevented prebiotic‐mediated improvements in adiposity and intestinal permeability (P<0.05). Taken together, this data suggests that prebiotics elicit both microbiota‐independent and microbiota‐dependent effects on metabolism in obesity. Funded by CIHR, NSERC, and AIHS. Grant Funding Source : Canadian Institutes of Health Research
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".