Influence of Brown or Germinated Brown Rice Supplementation on Fecal Short-Chain Fatty Acids and Microbiome in Diet-Induced Insulin-Resistant Mice
Bibliographic record
Abstract
Intake of whole grain foods is associated with improving metabolic profile compared to refined grain products, but the underlying mechanism remains unclear. The present study examined the effects of brown rice (BRR) or germinated brown rice (GBR) supplementation on fecal short-chain fatty acids (SCFAs), and relationship with gut microbiota, metabolism and inflammation in high fat (HF)-diet-fed mice. The results demonstrated that an HF diet supplemented with BRR or GBR comparably increased the abundance of fecal isobutyric acid compared to that in mice receiving HF+white rice (WHR) diet (p < 0.01). The abundance of valeric acid in HF+GBR-diet-fed mice was higher than those receiving HF+WHR diet (p < 0.05). The abundances of fecal isobutyric acid negatively correlated with fasting plasma glucose, insulin, cholesterol, triglycerides, tumor necrosis factor-α, plasminogen activator inhibit-1, monocyte chemotactic protein-1 and homeostatic model assessment of insulin resistance (p < 0.01). The abundance of valeric acids negatively correlated with insulin resistance (p < 0.05). The abundances of isobutyric acid positively correlated with Lactobacillus, but negatively correlated with Dubosiella genus bacteria (p < 0.05). The findings demonstrated that the increases in SCFAs in the feces of BRR and GBR-treated mice were associated with improvements in gut microbiome, metabolic and inflammatory profile, which may contribute to the antidiabetic and anti-inflammatory effects of the whole grains in HF-diet-fed mice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".