Deletion of Skeletal Muscle SOCS3 Prevents Insulin Resistance in Obesity
Bibliographic record
Abstract
Obesity is associated with chronic low-grade inflammation that contributes to defects in energy metabolism and insulin resistance. Suppressor of cytokine signaling (SOCS)-3 expression is increased in skeletal muscle of obese humans. SOCS3 inhibits leptin signaling in the hypothalamus and insulin signal transduction in adipose tissue and the liver. Skeletal muscle is an important tissue for controlling energy expenditure and whole-body insulin sensitivity; however, the physiological importance of SOCS3 in this tissue has not been examined. Therefore, we generated mice that had SOCS3 specifically deleted in skeletal muscle (SOCS MKO). The SOCS3 MKO mice had normal muscle development, body mass, adiposity, appetite, and energy expenditure compared with wild-type (WT) littermates. Despite similar degrees of obesity when fed a high-fat diet, SOCS3 MKO mice were protected against the development of hyperinsulinemia and insulin resistance because of enhanced skeletal muscle insulin receptor substrate 1 (IRS1) and Akt phosphorylation that resulted in increased skeletal muscle glucose uptake. These data indicate that skeletal muscle SOCS3 does not play a critical role in regulating muscle development or energy expenditure, but it is an important contributing factor for inhibiting insulin sensitivity in obesity. Therapies aimed at inhibiting SOCS3 in skeletal muscle may be effective in reversing obesity-related glucose intolerance and 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 | 0.000 | 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 | 0.001 |
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".