The type of sport, but not sex, impacts body composition and metabolic response to a complete weight loss–weight regain episode in weight cycling athletes: results from the WAVE study
Bibliographic record
Abstract
Athletes may engage in weight cycling—successive episodes of weight loss and weight (re)gain—for performance reasons, but risk metabolic adaptations and regaining more fat that was lost (fat overshoot). This study aimed to assess the influence of a complete weight cycling episode on body composition, thermoregulation, and metabolism in athletes, considering sex and the type of sport practiced. Forty-eight athletes (28 males, 20 females) engaged in combat ( n = 23), strength (n = 12), or endurance ( n = 13) sports were examined under three experimental conditions (weight maintenance, weight loss, weight (re)gain) during a weight cycling episode using their habitual strategies. Body composition (dual-energy X-ray absorptiometry), core body temperature (telemetric temperature sensor), energy expenditure and substrate oxidation at rest and during moderate exercise (indirect calorimetry), and energy intake (48 h food record) were assessed. Overall, athletes lost 4.4 ± 2.3% body weight, 12.2 ± 10.6% fat mass, and 2.6 ± 2.3% fat-free mass ( p < 0.001). All variables returned to baseline values during the regain period, and a higher fat mass regain was observed in endurance than combat athletes ( p < 0.01). During weight loss, a transient increase in lipid and decrease in carbohydrate oxidation occurred at rest and during exercise ( p < 0.001). Energy expenditure and core body temperature remained unchanged across the three experimental conditions, and no specific sex effect was observed. Overall, no apparent body weight nor fat overshoot was observed in athletes after a complete weight cycling episode. Nonetheless, the greater fat mass gain in endurance, compared with combat athletes, highlights a need for further specific long-term studies in this population. Clinical Trial registration: NCT04107545.
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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) | 0.001 | 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".