Regional differences in cellular mechanisms of adipose tissue gain with overfeeding
Bibliographic record
Abstract
Body fat distribution is an important predictor of the metabolic consequences of obesity, but the cellular mechanisms regulating regional fat accumulation are unknown. We assessed the changes in adipocyte size (photomicrographs) and number in response to overfeeding in upper- and lower-body s.c. fat depots of 28 healthy, normal weight adults (15 men) age 29 ± 2 y. We analyzed how these changes relate to regional fat gain (dual energy X-ray absorptiometry and computed tomography) and baseline preadipocyte proliferation, differentiation [peroxisome proliferator-activated receptor-γ2 (PPARγ2) and CCAAT/enhancer binding protein-α (C/EBPα) mRNA]), and apoptotic response to TNF-α. Fat mass increased by 1.9 ± 0.2 kg in the upper body and 1.6 ± 0.1 kg in the lower body. Average abdominal s.c. adipocyte size increased by 0.16 ± 0.06 μg lipid per cell and correlated with relative upper-body fat gain (r = 0.74, P < 0.0001). However, lower-body fat responded to overfeeding by fat-cell hyperplasia, with adipocyte number increasing by 2.6 ± 0.9 × 10(9) cells (P < 0.01). We found no depot-differences in preadipocyte replication or apoptosis that would explain lower-body adipocyte hyperplasia and abdominal s.c. adipocyte hypertrophy. However, baseline PPARγ2 and C/EBPα mRNA were higher in abdominal than femoral s.c. preadipocytes (P < 0.005 and P < 0.03, respectively), consistent with the ability of abdominal s.c. adipocytes to achieve a larger size. Inherent differences in preadipocyte cell dynamics may contribute to the distinct responses of different fat depots to overfeeding, and fat-cell number increases in certain depots in adults after only 8 wk of increased food intake.
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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".