Adipokines as regulators of muscle metabolism and insulin sensitivityThis paper is one of a selection of papers published in this Special Issue, entitled 14th International Biochemistry of Exercise Conference – Muscles as Molecular and Metabolic Machines, and has undergone the Journal’s usual peer review process.
Bibliographic record
Abstract
Skeletal muscle is the largest tissue responsible for the insulin-stimulated disposal of glucose. However, identifying the link between excess body fat and impaired insulin sensitivity in skeletal muscle has been difficult. Several adipose-derived cytokines (adipokines) have been implicated in the impairment of insulin sensitivity, while adipokines such as leptin and adiponectin exert an insulin-sensitizing effect. Leptin and adiponectin have each been shown to increase fatty acid (FA) oxidation and decrease triglyceride storage in muscle, which may explain, in part, the insulin-sensitizing effect of these cytokines. Recent evidence strongly implicates an increased localization of the FA transporters to the plasma membrane (PM) as an important factor in the accumulation of intramuscular lipids with high-fat diets and obesity. Perhaps surprisingly, relatively little attention has been paid to the ability of insulin-sensitizing compounds, such as leptin and adiponectin, to decrease the abundance of FA transporters in the PM, thereby decreasing lipid accumulation. In the case of both adipokines, there is also evidence that a resistance to their ability to stimulate FA oxidation in skeletal muscle develops during obesity. One of our recent studies indicates that this development can be very rapid (i.e., within days), and precedes the increase in lipid uptake and accumulation that leads to insulin resistance. It is noteworthy that leptin resistance can be modulated by both diet and training in rodents. Further studies examining the underlying mechanisms of the development of leptin and adiponectin resistance are warranted.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 teacher head, 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".