Dietary fiber combinations to mitigate the metabolic, microbial, and cognitive imbalances resulting from diet‐induced obesity in rats
Bibliographic record
Abstract
Dietary fiber promotes a healthy gut microbiome and shows promise in attenuating the unfavorable microbial changes resulting from a high-fat/sucrose (HFS) diet. High-fiber diets consisting of oligofructose alone (HFS/O) or in combination with β-glucan (HFS/OB), resistant starch (HFS/OR), or β-glucan and resistant starch (HFS/OBR) were fed to diet-induced obese rats for 8 weeks to determine if these fibers could attenuate the obese phenotype. Only the HFS/O group displayed a decrease in body weight and body fat, but all fiber interventions improved insulin sensitivity and cognitive function. The HFS/O diet was the least effective at improving cognitive function and only the HFS/OB group showed improvements in glucose tolerance, thus highlighting the differential effects of fiber types. Hippocampal cytokines (IL-6, IL-10) were more pronounced in the HFS/OB group which coincided with the most time spend in the open arms of the elevated plus maze. All fiber groups showed an increase in beneficial Bifidobacterium and Lactobacillus abundance while the HFS group showed higher abundance of Clostridium. Fecal microbiota transplant from fiber-treated rats into germ-free mice did not alter body composition in the mice but did result in a higher abundance of Bacteroides in the HFS/O and HFS/OB groups compared to HFS. The HFS/OB recipient mice also had higher insulin sensitivity compared to the other groups. This study highlights the influence of dietary fiber type on metabolic and cognitive outcomes suggesting that the type of supplementation (single or combined fibers) could be tailored to specific targeted outcomes.
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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 | 0.001 |
| 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".