The role of adipokines as regulators of skeletal muscle fatty acid metabolism and insulin sensitivity
Bibliographic record
Abstract
Several adipose-derived cytokines (adipokines) have been suggested to act as a link between accumulated fat mass and altered insulin sensitivity. Resistin and tumour necrosis factor-alpha (TNF-alpha) have been implicated in impairing insulin sensitivity in rodents; conversely, two other adipokines, leptin and adiponectin, increase insulin sensitivity in lean and obese rodents. Currently, there is considerable focus on the concept that lipid accumulation in skeletal muscle leads to the development of insulin resistance. Adiponectin and leptin have each been demonstrated to increase rates of fatty acid (FA) oxidation and decrease muscle lipid content, which may in part be the underlying mechanism to their insulin sensitizing effect. These effects on FA metabolism appear to be mediated in part through the activation of AMP-activated protein kinase. Evidence derived from animal and human studies suggests that the ability of leptin and adiponectin to stimulate FA oxidation in muscle is impaired in the obese condition. Thus, leptin and adiponectin resistance may be an initiating factor in the accumulation of intramuscular lipids, such as diacylglycerol and ceramide, and the ensuing development of insulin resistance. Lifestyle factors such as diet and exercise are able to restore the sensitivity of muscle to leptin. The actual physiological roles of resistin and TNF-alpha in altering muscle lipid metabolism are more controversial, but each has been shown to directly impair insulin signalling and consequently, insulin stimulated glucose uptake in muscle. However, the possibility that resistin and TNF-alpha reduces insulin sensitivity in muscle by directly impairing FA metabolism in this tissue leading to lipid accumulation, has been virtually unexamined. Thus, the contribution of various adipokines to the development of insulin resistance is complex and not fully understood. Finally, the effects of these adipokines on metabolism and insulin sensitivity are generally studied in isolation, making it difficult to predict the interactive effects and the net impact on insulin sensitivity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".