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. PURPOSE: To determine whether Ad resistance precedes and is associated with the development of IR. METHODS: We conducted a time course HF feeding trial for 3 days, 2 weeks or 4 weeks to determine the onset of Ad resistance and identify the ensuing changes in lipid metabolism and insulin signaling leading to IR in skeletal muscle. RESULTS: Adiponectin stimulated FA oxidation (+28%, p≤0.05) and ACC phosphorylation (+34%, p≤0.05) in control animals, but failed to do so in any HF fed group (i.e. as early as 3 days). By 2 weeks, plasma membrane FA transporters and intramuscular diacylglycerol (DAG) and ceramide were increased and insulin-stimulated phosphorylation of both Akt and AS160 was blunted compared to control animals. After 4 weeks of HF feeding, maximal insulin stimulated glucose transport was impaired compared to control. CONCLUSIONS: 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 into skeletal muscle induced by HF diets.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".