Skeletal muscle ceramides do not contribute to physical-inactivity-induced insulin resistance
Bibliographic record
Abstract
Physical inactivity increases the risk to develop type 2 diabetes, a disease characterized by a state of insulin resistance. By promoting inflammatory state, ceramides are especially recognized to alter insulin sensitivity in skeletal muscle. The present study was designed to analyze, in mice, whether muscle ceramides contribute to physical-inactivity-induced insulin resistance. For this purpose, we used the wheel lock model to induce a sudden reduction of physical activity, in combination with myriocin treatment, an inhibitor of de novo ceramide synthesis. Mice were assigned to 3 experimental groups: voluntary wheel access group (Active), a wheel lock group (Inactive), and wheel lock group treated with myriocin (Inactive-Myr). We observed that 10 days of physical inactivity induces hyperinsulinemia and increases basal insulin resistance (HOMA-IR). The muscle ceramide content was not modified by physical inactivity and myriocin. Thus, muscle ceramides do not play a role in physical-inactivity-induced insulin resistance. In skeletal muscle, insulin-stimulated protein kinase B phosphorylation and inflammatory pathway were not affected by physical inactivity, whereas a reduction of glucose transporter type 4 content was observed. Based on these results, physical-inactivity-induced insulin resistance seems related to a reduction in glucose transporter type 4 content rather than defects in insulin signaling. We observed in inactive mice that myriocin treatment improves glucose tolerance, insulin-stimulated protein kinase B, adenosine-monophosphate-activated protein kinase activation, and glucose transporter type 4 content in skeletal muscle. Such effects occur regardless of changes in muscle ceramide content. These findings open promising research perspectives to identify new mechanisms of action for myriocin on insulin sensitivity and glucose metabolism.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 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".