Muscle insulin resistance: assault by lipids, cytokines and local macrophages
Bibliographic record
Abstract
PURPOSE OF REVIEW: The present review outlines possible mechanisms by which high fatty acids, associated with high-fat diet and obesity, impose insulin resistance on glucose uptake into skeletal muscle. RECENT FINDINGS: It is well established that muscle insulin resistance arises in conditions of high-fatty acid availability, and correlates with accumulation of triglycerides within skeletal muscle fibres. However, it is debated whether triglycerides or other lipid metabolites such as diacylglycerols and ceramides are directly responsible. These lipid metabolites can activate serine kinases that impair insulin signalling. Accumulation of acylcarnitines and reactive oxygen species could be additional causative agents of insulin resistance. Further, the precise defects in insulin signalling in muscle caused by high intramuscular lipid (i.e. lipotoxicity) remain unclear. In parallel, proinflammatory activation within the adipose tissue of obese and high-fat fed animals or humans causes muscle insulin resistance, and is ascribed to circulating inflammatory cytokines. Recent evidence also shows proinflammatory macrophages infiltrating muscle tissue and/or intermuscular adipose tissue, and there is growing evidence that fatty acids trigger macrophages to secrete factors that directly impair insulin actions. These factors are postulated to activate stress-signalling pathways in muscle that act on the same insulin-signalling components affected by lipotoxicity. SUMMARY: Altered intramuscular lipid metabolism, circulating cytokines, and inflammatory macrophage infiltration of muscle tissue have been recently linked to muscle insulin resistance provoked by fatty acids. Each is analysed separately in this review, but they may act simultaneously and synergistically to render skeletal muscle insulin-resistant.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.002 |
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".