Distinct mechanisms involving diacylglycerol, ceramides, and inflammation underlie insulin resistance in oxidative and glycolytic muscles from high fat-fed rats
Bibliographic record
Abstract
This study investigated whether oxidative and glycolytic rat skeletal muscles respond differently to a high-fat (HF) sucrose-enriched diet with respect to diacylglycerol (DAG) and ceramides accumulation, protein kinase C (PKC) activation, glucose metabolism, and the expression of inflammatory genes. HF diet (8 weeks) suppressed insulin-stimulated glycogen synthesis and glucose oxidation in soleus (Sol), extensor digitorum longus (EDL) and epitrochlearis (Epit) muscles. However, DAG and ceramides levels increased in Sol and EDL, but not in Epit muscles of HF-fed rats. Additionally, membrane-bound PKC-delta and PKC-theta increased in Sol and EDL, whereas in Epit muscles both PKC isoforms were reduced by HF diet. In Epit muscles, HF diet also increased the expression of tumor necrosis factor-α (TNF-α) receptors (CD40 and FAS), toll-like receptor 4 (TLR4), and nuclear factor kappa light polypeptide gene enhancer in B cells (NF-kB), whereas in Sol and EDL muscles the expression of these inflammatory genes remained unchanged upon HF feeding. In conclusion, HF diet caused DAG and ceramides accumulation, PKC activation, and the induction of inflammatory pathways in a fiber type-specific manner. These findings help explain why oxidative and glycolytic muscles similarly develop insulin resistance, despite major differences in their metabolic characteristics and responsiveness to dietary lipid abundance.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".