Disparate Metabolic Responses in Mice Fed a High-Fat Diet Supplemented with Maize-Derived Non-Digestible Feruloylated Oligo- and Polysaccharides Are Linked to Changes in the Gut Microbiota
Bibliographic record
Abstract
Studies have suggested links between colonic fermentation of dietary fibers and improved metabolic health. The objectives of this study were to determine if non-digestible feruloylated oligo- and polysaccharides (FOPS), a maize-derived dietary fiber, could counteract the deleterious effects of high-fat (HF) feeding in mice and explore if metabolic benefits were linked to the gut microbiota. C57BL/6J mice (n = 8/group) were fed a low-fat (LF; 10 kcal% fat), HF (62 kcal% fat), or HF diet supplemented with FOPS (5%, w/w). Pronounced differences in FOPS responsiveness were observed: four mice experienced cecal enlargement and enhanced short chain fatty acid production, indicating increased cecal fermentation (F-FOPS). Only these mice displayed improvements in glucose metabolism compared with HF-fed mice. Blooms in the gut microbial genera Blautia and Akkermansia were observed in three of the F-FOPS mice; these shifts were associated with reductions in body and adipose tissue weights compared with the HF-fed control mice. No improvements in metabolic markers or weights were detected in the four mice whose gut microbiota did not respond to FOPS. These findings demonstrate that FOPS-induced improvements in weight gain and metabolic health in mice depended on the ability of an individual's microbiota to ferment FOPS.
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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".