One week of overeating upregulates angiogenic and lipolytic gene expression in human subcutaneous adipose tissue from exercise trained and untrained adults
Bibliographic record
Abstract
Effective storage of excess energy in abdominal subcutaneous adipose tissue during periods of overeating may help attenuate weight-gain-related insulin resistance. The objective of this study was to assess changes in the expression of factors regulating abdominal subcutaneous adipose tissue storage capacity in response to a brief exposure to overeating in nonobese adults. Because exercise can alter the expression of genes involved in regulating adipose tissue storage capacity, we compared the responses to overeating in regular exercisers (EX, n = 11) and nonexercisers (nonEX, n = 11). Abdominal subcutaneous adipose tissue samples and oral glucose tolerance tests were performed before and after participants ate 30% above their estimated daily energy requirements for 1 week. Both EX and nonEX gained ∼1 kg ( P < 0.01), and Matsuda insulin sensitivity index was reduced ∼15% ( P = 0.04) in both groups. Gene expression of factors involved in lipid metabolism ( HSL, ATGL, DGAT, and PPARγ) and angiogenesis ( HIF1α and KDR) were increased ( P < 0.05), with no differences observed between EX and nonEX. In contrast, protein abundance of these factors did not change. The modest overeating stimulus did not increase markers of inflammation in the systemic circulation or adipose tissue. Overall, our findings indicate that a brief and modest overeating stimulus can impair insulin sensitivity and upregulate genes involved in abdominal adipose tissue storage capacity similarly in exercisers and nonexercisers. ClinicalTrials.gov ID#: NCT02701738.
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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".