Skeletal muscle inflammation is not responsible for the rapid impairment in adiponectin response with high-fat feeding in rats
Bibliographic record
Abstract
Adiponectin (Ad) is an insulin-sensitizing adipokine known to stimulate fatty acid (FA) oxidation in skeletal muscle. Skeletal muscle can become resistant to Ad very rapidly, after only 3 days of high saturated fat feeding in rats. Whether the same occurs following a high polyunsaturated fat diet is unknown. Obesity, insulin resistance, and hyperlipidemia are recognized as low-grade inflammatory diseases; therefore, we hypothesized that high-fat feeding induces inflammation, which interferes with Ad action at skeletal muscle. To this end, rats were placed into one of three dietary groups, control (CON, 10% kcal from fat), high saturated (SAT), or high polyunsaturated (PUFA) fat (60% kcal from fat) for 3 days to determine whether Ad resistance develops. Half of the animals from each group were further supplemented with aspirin, a common anti-inflammatory drug. Ad stimulated FA metabolism, Ad signaling intermediates [AdipoR1, APPL1, LKB1, AMPK, and acetyl-CoA carboxylase (ACC)], and inflammatory proteins [Toll-like receptor (TLR4), IKK alpha/beta, IkappaB alpha, NF-kappaB, suppressor of cytokine signaling-3 (SOCS3), and JNK] were measured in soleus muscle. Three days of SAT feeding induced Ad resistance in soleus muscle, assessed as an inability of Ad to phosphorylate ACC and increase FA oxidation. In PUFA-fed animals, Ad-stimulated FA oxidation and ACC phosphorylation to the same degree as CON animals (FA oxidation: +35%, +41%; pACC +29%, +19%; CON, PUFA, P < 0.05). However, neither SAT nor PUFA feeding for 3 days induced skeletal muscle inflammation. Surprisingly, aspirin prevented Ad-stimulated increases in FA oxidation. In conclusion, FA type is critical in the development of Ad resistance, but this does not appear to be mediated by inflammation.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".