Interactive effects of oligofructose and obesity predisposition on gut hormones and microbiota in diet‐induced obese rats
Bibliographic record
Abstract
OBJECTIVE: Oligofructose (OFS) is a prebiotic that reduces energy intake and fat mass via changes in gut satiety hormones and microbiota. The effects of OFS may vary depending on predisposition to obesity. The aim of this study was to examine the effect of OFS in diet-induced obese (DIO) and diet-resistant (DR) rats. METHODS: Adult, male DIO, and DR rats were randomized to: high-fat/high-sucrose (HFS) diet or HFS diet + 10% OFS for 6 weeks. Body composition, food intake, gut microbiota, plasma gut hormones, and cannabinoid CB(1) receptor expression in the nodose ganglia were measured. RESULTS: OFS reduced body weight, energy intake, and fat mass in both phenotypes (P < 0.05). Select gut microbiota differed in DIO versus DR rats (P < 0.05), the differences being eliminated by OFS. OFS did not modify plasma ghrelin or CB(1) expression in nodose ganglia, but plasma levels of GIP were reduced and PYY were elevated (P < 0.05) by OFS. CONCLUSIONS: OFS was able to reduce body weight and adiposity in both prone and resistant obese phenotypes. OFS-induced changes in gut microbiota profiles in DIO and DR rats, along with changes in gut hormone levels, likely contribute to the sustained lower body weights.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".