Combined effects of oligofructose and <i>Bifidobacterium animalis</i> on gut microbiota and glycemia in obese rats
Bibliographic record
Abstract
OBJECTIVE: Prebiotics and probiotics may be able to modify an obesity-associated gut microbiota. The aim of this study was to examine the individual and combined effects of the prebiotic oligofructose (OFS) and the probiotic Bifidobacterium animalis subsp. lactis BB-12 (BB-12) on gut microbiota and host metabolism in obese rats. METHODS: Adult male, diet-induced obese Sprague Dawley rats were randomized to: (1) Control (C); (2) 10% OFS; (3) BB-12; (4) OFS + BB-12 for 8 weeks (n = 9-10 rats/group). Body composition, glycemia, gut permeability, satiety hormones, cytokines, and gut microbiota were examined. RESULTS: Prebiotic, but not probiotic reduced energy intake, weight gain, and fat mass (P < 0.01). OFS, BB-12, and the combined OFS + BB-12 improved glycemia (P < 0.05). Individually, OFS and BB-12 reduced insulin levels (P < 0.05). Portal GLP-1 was increased with OFS, whereas probiotic increased GLP-2 (P < 0.05). There was a marked increase in bifidobacteria and lactobacilli (P < 0.01) with OFS that was not observed with probiotic alone. CONCLUSIONS: The impact of prebiotic intake on body composition and gut microbiota was of greater magnitude than the probiotic BB-12. Despite this, an improvement in glucose AUC with both prebiotic or probiotic demonstrates the beneficial role of each of these "biotic" agents in glycemic control.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".