Adiponectin resistance precedes the accumulation of skeletal muscle lipids and insulin resistance in high-fat-fed rats
Bibliographic record
Abstract
High-fat (HF) diets can induce insulin resistance (IR) by altering skeletal muscle lipid metabolism. An imbalance between fatty acid (FA) uptake and oxidation results in intramuscular lipid accumulation, which can impair the insulin-signaling cascade. Adiponectin (Ad) is an insulin-sensitizing adipokine known to stimulate skeletal muscle FA oxidation and reduce lipid accumulation. Evidence of Ad resistance has been shown in obesity and following chronic HF feeding and may contribute to lipid accumulation observed in these conditions. Whether Ad resistance precedes and is associated with the development of IR is unknown. We conducted a time course HF feeding trial for 3 days, 2 wk, or 4 wk to determine the onset of Ad resistance and identify the ensuing changes in lipid metabolism and insulin signaling leading to IR in skeletal muscle. Ad stimulated FA oxidation (+28%, P < or = 0.05) and acetyl-CoA carboxylase phosphorylation (+34%, P < or = 0.05) in control animals but failed to do so in any HF-fed group (i.e., as early as 3 days). By 2 wk, plasma membrane FA transporters and intramuscular diacylglycerol (DAG) and ceramide were increased, and insulin-stimulated phosphorylation of both protein kinase B and protein kinase B substrate 160 was blunted compared with control animals. After 4 wk of HF feeding, maximal insulin-stimulated glucose transport was impaired compared with control. Taken together, our results demonstrate that an early loss of Ad's stimulatory effect on FA oxidation precedes an increase in plasmalemmal FA transporters and the accumulation of intramuscular DAG and ceramide, blunted insulin signaling, and ultimately impaired maximal insulin-stimulated glucose transport in skeletal muscle induced by HF diets.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".