Abdominal adipocyte populations in women with visceral obesity
Bibliographic record
Abstract
BACKGROUND: Visceral obesity is independently related to numerous cardiometabolic alterations, with adipose tissue dysfunction as a central feature. OBJECTIVE: To examine whether omental (OM) and subcutaneous (SC) adipocyte size populations in women relate to visceral obesity, cardiometabolic risk factors and adipocyte lipolysis independent of total adiposity. DESIGN AND METHODS: OM and SC fat samples were obtained during gynecological surgery in 60 women (mean age, 46.1±5.9 years; mean BMI, 27.1±4.5 kg/m² (range, 20.3-41. kg/m²)). Fresh samples were treated with osmium tetroxide and were analyzed with a Multisizer Coulter. Cell size distributions were computed for each sample with exponential and Gaussian function fits. RESULTS: Computed tomography-measured visceral fat accumulation was the best predictor of larger cell populations as well as the percentage of small cells in both OM and SC fat (P<0.0001 for all). Accordingly, women with visceral obesity had larger cells in the main population and higher proportion of small adipocytes independent of total adiposity (P≤0.05). Using linear regression analysis, we found that women characterized by larger-than-predicted adipocytes in either OM or SC adipose tissue presented higher visceral adipose tissue area, increased percentage of small cells and homeostasis model assessment insulin resistance index as well as higher OM adipocyte isoproterenol-, forskolin- and dbcAMP-stimulated lipolysis compared to women with smaller-than-predicted adipocytes, independent of total adiposity (P≤0.05). CONCLUSION: Excess visceral adipose tissue accumulation is a strong marker of both adipocyte hypertrophy and increased number of small cells in either fat compartment, which relates to higher insulin resistance index and lipolytic response, independent of total adiposity.
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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 | 0.001 |
| 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) | 0.002 | 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".