Acylcarnitines: potential implications for skeletal muscle insulin resistance
Bibliographic record
Abstract
Insulin resistance may be linked to incomplete fatty acid β-oxidation and the subsequent increase in acylcarnitine species in different tissues including skeletal muscle. It is not known if acylcarnitines participate in muscle insulin resistance or simply reflect dysregulated metabolism. The aims of this study were to determine whether acylcarnitines can elicit muscle insulin resistance and to better understand the link between incomplete muscle fatty acid β-oxidation, oxidative stress, inflammation, and insulin-resistance development. Differentiated C2C12, primary mouse, and human myotubes were treated with acylcarnitines (C4:0, C14:0, C16:0) or with palmitate with or without carnitine acyltransferase inhibition by mildronate. Treatment with C4:0, C14:0, and C16:0 acylcarnitines resulted in 20-30% decrease in insulin response at the level of Akt phosphorylation and/or glucose uptake. Mildronate reversed palmitate-induced insulin resistance concomitant with an ∼25% decrease in short-chain acylcarnitine and acetylcarnitine secretion. Although proinflammatory cytokines were not affected under these conditions, oxidative stress was increased by 2-3 times by short- or long-chain acylcarnitines. Acylcarnitine-induced oxidative stress and insulin resistance were reversed by treatment with antioxidants. Results are consistent with the conclusion that incomplete muscle fatty acid β-oxidation causes acylcarnitine accumulation and associated oxidative stress, raising the possibility that these metabolites play a role in muscle insulin resistance.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".