The effect of muraglitazar on adiponectin signalling, mitochondrial function and fat oxidation genes in human skeletal muscle <i>in vivo</i>
Bibliographic record
Abstract
AIMS: The molecular mechanisms by which muraglitazar (peroxisome proliferator-activated receptor γ/α agonist) improves insulin sensitivity in Type 2 diabetes mellitus are not fully understood. We hypothesized that muraglitazar would increase expression of 5'-monophosphate-activated protein kinase and genes involved in adiponectin signalling, free fatty acid oxidation and mitochondrial function in skeletal muscle. METHODS: Sixteen participants with Type 2 diabetes received muraglitazar, 5 mg/day (n = 12) or placebo (n = 4). Before and after 16 weeks, participants had vastus lateralis muscle biopsy followed by 180 min euglycaemic hyperinsulinaemic clamp. RESULTS: Muraglitazar increased plasma adiponectin (9.0 ± 1.1 to 17.8 ± 1.5 μg/ml, P < 0.05), while no significant change was observed with placebo. After 16 weeks with muraglitazar, fasting plasma glucose declined by 31%, fasting plasma insulin decreased by 44%, insulin-stimulated glucose disposal increased by 81%, HbA1c decreased by 21% and plasma triglyceride decreased by 39% (all P < 0.05). Muraglitazar increased mRNA levels of 5'-monophosphate-activated protein kinase, adiponectin receptor 1, adiponectin receptor 2, peroxisome proliferator-activated receptor gamma coactivator-1 alpha and multiple genes involved in mitochondrial function and fat oxidation. In the placebo group, there were no significant changes in expression of these genes. CONCLUSIONS: Muraglitazar increases plasma adiponectin, stimulates muscle 5'-monophosphate-activated protein kinase expression and increases expression of genes involved in adiponectin signalling, mitochondrial function and fat oxidation. These changes represent important cellular mechanisms by which dual peroxisome proliferator-activated receptor agonists improve skeletal muscle insulin sensitivity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".