Improved glucose tolerance in insulin-resistant rats after pea hull feeding is associated with changes in lipid metabolism-targeted transcriptome
Bibliographic record
Abstract
Understanding of the mechanisms by which pulse grain fractions elicit beneficial effects on glucose tolerance is incomplete. An untargeted metabolomic analysis of serum from insulin-resistant rats was carried out to identify potential metabolic pathways affected by feeding rats the hull fraction of dried peas for 4 weeks. From this, we hypothesized that transcription of hepatic genes involved in lipid metabolism would be altered. cDNA was prepared from total RNA extracted from livers of rats fed a high-fat diet (HFD) or HFD + pea hulls (PH) diet. The liver lipid transcriptome of each cDNA sample was characterized using a PCR-based array of 84 genes. The activity of peroxisome-proliferator-activated receptor alpha (PPAR-α) was measured in hepatocyte nuclei. The predominant findings of the metabolomic analysis revealed a significant increase in the intermediaries of β-oxidation: C16-OH and C16:1 acylcarnitines (>50%, p < 0.05) and 3-hydroxybutyrate (100%, p < 0.05) in the PH group compared with the HFD group. mRNA of hadha, a gene involved in β-oxidation, was significantly reduced by 53% (p < 0.005) in the PH group compared with the HFD group, but no differences in PPAR-α activity were detected. 3-Hydroxybutyrate concentrations were associated with insulin sensitivity and reduced demand for insulin. The results indicate that feeding PH alters lipid metabolism in liver, which may contribute to improved glucose tolerance in insulin-resistant rats.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".