Endothelial Function Increases after a 16-Week Diet and Exercise Intervention in Overweight and Obese Young Women
Bibliographic record
Abstract
Weight loss improves endothelial function in overweight individuals. The effects of weight loss through combined aerobic and resistance training and caloric restriction on in vivo vascular measures and blood markers associated with the regulation of endothelial function have not been comprehensively examined. Therefore, we investigated brachial artery endothelial function and potential regulatory blood markers in twenty overweight women (30.3 ± 2.0 years) who participated in 16 weeks of aerobic (5 d/wk) and resistance training (2 d/wk) (combined: ≥ 250 kcal/d) and caloric restriction (-500 kcal/d versus requirement). Resting brachial artery flow mediated dilation (FMD) and circulating endothelin-1 (ET-1) and interleukin-6 (IL-6) were assessed at baseline and following the intervention. Relative and absolute FMD increased (before: 4.0 ± 0.5% versus after: 6.9 ± 0.6%, P < 0.05, and before: 0.14 ± 0.02 mm versus after: 0.23 ± 0.02 mm, P < 0.05, resp.), while body mass decreased (before: 86.9 ± 2.4 kg versus after: 81.1 ± 2.4 kg, P < 0.05) following the intervention. There were no changes in either blood marker (IL-6: before: 1.5 ± 0.2 pg/mL versus after: 1.5 ± 0.1 pg/mL, P > 0.05, and ET-1: before: 0.55 ± 0.05 pg/mL versus after: 0.59 ± 0.09 pg/mL, P > 0.05). 16 weeks of combined aerobic/resistance training and diet-induced weight loss improved endothelial function in overweight and obese young women, but this increase was not associated with changes in blood markers of vasoconstriction or inflammation.
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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".