Long-term effects of high-intensity resistance and endurance exercise on plasma leptin and ghrelin in overweight individuals: the RESOLVE Study
Bibliographic record
Abstract
The objective of this study was to evaluate the effects of high-intensity resistance and endurance exercise on body composition and plasma leptin and ghrelin concentrations in overweight individuals. One hundred participants were randomly assigned to 3 exercise interventions: high-resistance–low-aerobic exercise (Re), low-resistance–high-aerobic exercise (rE), low-resistance–low-aerobic exercise (re). Interventions began with 3 weeks of residential supervision (phase 1) after which participants had to manage the physical activity programs individually (phase 2). Body composition and plasma variables were measured at baseline and after phase 1 as well as after 3, 6, and 12 months. Significant decreases in body weight and fat were observed after phase 1 (p < 0.001) and continued at a lower rate for up to 3 months and then remained stable for the rest of the protocol. Once a body weight plateau was reached, body fat loss after the Re and rE conditions exceeded the fat loss observed in the re condition by 1.5–2 kg (p < 0.05). Leptin was significantly decreased after day 21 and month 3 (p < 0.001) and remained stable for the rest of the study. Ghrelin was significantly increased after day 21 and month 3 (p < 0.001) and returned to a level comparable to baseline between month 6 and 12 when body weight and fat had reached a plateau. In conclusion, this study reinforces the idea that an increase in exercise intensity may accentuate body fat loss before the occurrence of a body weight plateau. Resistance to further fat loss was accompanied by a decrease in plasma leptin and an increase in plasma ghrelin.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".