Influence of obesity and insulin sensitivity on insulin signaling genes in human omental and subcutaneous adipose tissue
Bibliographic record
Abstract
Obesity and insulin resistance are independent risk factors for metabolic syndrome, diabetes, and cardiovascular disease. Adipose tissue samples from nonobese (NO), insulin-sensitive obese (ISO), and insulin-resistant obese (IRO) subjects from subcutaneous (SC) and omental (OM) adipose tissue (n = 28) were analyzed by microarray and confirmed by real-time PCR. Insulin signaling gene expression changes were greater in OM than in SC tissue and were related to insulin resistance rather than to obesity; few genes correlated with body mass index. Insulin receptor and insulin receptor substrate 1 (IRS-1) increased in the IRO versus pooled insulin-sensitive (NO+ISO) subjects. In glucose transport, PI3Kalpha and PDK2 decreased in IRO subjects, whereas PI3Kgamma, Akt2, GLUT4, and GLUT1 increased. IRS-1 regulators Jnk and IKK increased in IRO (P < 0.01 and P < 0.001 respectively). In protein synthesis, most genes examined were downregulated in IRO subjects, including mTor, Rheb, and 4EBP and eIF members (all P < 0.05). In proliferation, SHC, SOS, and Raf1 (P < 0.05) were increased, whereas Ras and MEK1/2 kinase 1 (P < 0.05) were decreased, in IRO subjects. Finally, in differentiation, PPARgamma, CEBPalpha, and CEBPbeta decreased, whereas PPARdelta, CEBPgamma, and CEBPepsilon increased, in IRO subjects (P < 0.05). Together, microarray and real-time PCR data demonstrate that insulin resistance rather than obesity is associated with altered gene expression of insulin signaling genes, especially in OM adipose tissue.
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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".