Overexpression of Rad in muscle worsens diet-induced insulin resistance and glucose intolerance and lowers plasma triglyceride level
Bibliographic record
Abstract
Rad is a low molecular weight GTPase that is overexpressed in skeletal muscle of some patients with type 2 diabetes mellitus and/or obesity. Overexpression of Rad in adipocytes and muscle cells in culture results in diminished insulin-stimulated glucose uptake. To further elucidate the potential role of Rad in vivo, we have generated transgenic (tg) mice that overexpress Rad in muscle using the muscle creatine kinase (MCK) promoter-enhancer. Rad tg mice have a 6- to 12-fold increase in Rad expression in muscle as compared to wild-type littermates. Rad tg mice grow normally and have normal glucose tolerance and insulin sensitivity, but have reduced plasma triglyceride levels. On a high-fat diet, Rad tg mice develop more severe glucose intolerance than the wild-type mice; this is due to increased insulin resistance in muscle, as exemplified by a rightward shift in the dose-response curve for insulin stimulated 2-deoxyglucose uptake. There is also a unexpected further reduction of the plasma triglyceride levels that is associated with increased levels of lipoprotein lipase in the Rad tg mice. These results demonstrate a potential synergistic interaction between increased expression of Rad and high-fat diet in creation of insulin resistance and altered lipid metabolism present in type 2 diabetes.
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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) | 0.000 | 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 | 0.001 |
| 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".