Increased Secretion and Expression of Myostatin in Skeletal Muscle From Extremely Obese Women
Bibliographic record
Abstract
OBJECTIVE: Obesity is associated with endocrine abnormalities that predict the progression of insulin resistance to type 2 diabetes. Because skeletal muscle has been shown to secrete proteins that could be used as biomarkers, we characterized the secreted protein profile of muscle cells derived from extremely obese (BMI 48.8 +/- 14.8 kg/m(2); homeostasis model assessment [HOMA] 3.6 +/- 1.0) relative to lean healthy subjects (BMI 25.7 +/- 3.2 kg/m(2); HOMA 0.8 +/- 0.2). RESEARCH DESIGN AND METHODS: We hypothesized that skeletal muscle would secrete proteins that predict the severity of obesity. To test this hypothesis, we used a "bottom-up" experimental design using stable isotope labeling by amino acids in culture (SILAC) and liquid chromatography/mass spectometry/mass spectometry (LC-MS/MS) to both identify and quantify proteins secreted from cultured myotubes derived from extremely obese compared with healthy nonobese women. RESULTS: Using SILAC, we discovered a 2.9-fold increase in the secretion of myostatin from extremely obese human myotubes. The increased secretion and biological activity of myostatin were validated by immunoblot (3.16 +/- 0.18, P < 0.01) and a myoblast proliferation assay using conditioned growth medium. Myostatin was subsequently shown to increase in skeletal muscle (23%, P < 0.05) and plasma (35%, P < 0.05) and to correlate (r(2) = 0.6, P < 0.05) with the severity of insulin resistance. CONCLUSIONS: Myostatin is a potent antianabolic regulator of muscle mass that may also play a role in energy metabolism. These findings show that increased expression of myostatin in skeletal muscle with obesity and insulin resistance results in elevated circulating myostatin. This may contribute to systemic metabolic deterioration of skeletal muscle with the progression of insulin resistance to 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".