The interaction between adipokines, diet and exercise on muscle insulin sensitivity
Bibliographic record
Abstract
PURPOSE OF REVIEW: High-fat diets lead to obesity and increase the risk of developing insulin resistance and type 2 diabetes. Adipose tissue and skeletal muscle act as endocrine organs, and produce various cytokines that can potentially alter peripheral insulin sensitivity. The purpose of the present review is to briefly summarize the effects of major cytokines (leptin, adiponectin, tumor necrosis factor-alpha, and interleukin-6) on muscle metabolism and insulin response, with a focus on the effects of diet and exercise. RECENT FINDINGS: Leptin and adiponectin improve insulin sensitivity. However, in obesity there is a diminished response to these adipokines. This resistance can be induced very rapidly and may lead to subsequent impairments in insulin response. Tumor necrosis factor-alpha is a proinflammatory cytokine that has been implicated as a mediator of insulin resistance, particularly in obesity. Interleukin-6 was the first identified myokine. There is evidence to implicate interleukin-6 both as a mediator of impaired insulin action in obesity, and also as a facilitator of increased fuel metabolism during exercise. The effect of each of these cytokines on muscle insulin sensitivity can be modulated by diet and exercise. SUMMARY: Much of the information summarized in the present review focuses on the effects of various cytokines in isolation, although in vivo there can be considerable interaction with each other. Future research should consider these potential interactions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".