Subcutaneous and Visceral Adipose Tissue Secretions from Extremely Obese Men and Women both Acutely Suppress Muscle Insulin Signaling
Bibliographic record
Abstract
Adipose tissue plays a key role in the development of type-2 diabetes via the secretion of adipokines. The current study investigated if secretion media derived from intact visceral (VAT) and subcutaneous (SAT) adipose tissues from extremely obese men and women differently suppressed insulin signaling in human skeletal myotubes derived from a healthy, non-diabetic male and female donor, respectively. Adipose tissue samples were collected from men and women during laparoscopic bariatric surgery. In general, secretion media collected from both SAT and VAT depots caused impaired insulin signaling in myotubes, independent of sex. In females, this was true regardless of the protein kinase B (Akt) phosphorylation site (Akt Thr308 and Akt Ser473) assessed (p < 0.01). In males, both SAT and VAT secretion media reduced Akt Thr308 activation in insulin-stimulated myotubes compared to controls (p < 0.001); however, only the VAT secretion media impaired Akt Ser473 phosphorylation. Independent of sex, 13 out of 18 detected cytokines, chemokines, and growth factors were more abundant in VAT versus SAT secretion media (p < 0.01). Both SAT and VAT secretion media from obese men and women acutely suppress insulin signaling in myotubes, despite different secretion profiles. We propose that this crosstalk model will help to extend our understanding of the interplay between adipose and muscle, as well as the pathogenesis of 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".