RMR and RMR ratio are not related to energy availability in elite and pre-elite athletes
Bibliographic record
Abstract
Low energy availability (LEA), defined as an imbalance between dietary intake and exercise energy expenditure, has been associated with physiological and psychological dysfunction in athletes. The resting metabolic rate ratio (RMR ratio ), calculated as measured RMR divided by predicted RMR, is increasingly used as a surrogate marker for chronic energy deficiency when values fall below 0.9. Therefore, this study aimed to (1) compare the RMR ratio across different predictive equations, (2) assess the association between energy availability (EA) and both RMR and RMR ratio , and (3) explore the relationship between the RMR ratio and phenotypical characteristics, including body composition, dietary intake, physical activity, sleep, and subjective well-being in Spanish competitive athletes. A total of 49 competitive athletes (33 males; 25.2 ± 5.6 years) from various sports were evaluated in this cross-sectional study. RMR was measured using indirect calorimetry and predicted using eleven validated equations. EA was estimated through dietary recalls and training logs. The prevalence of athletes classified as having metabolic suppression (RMR ratio < 0.9) ranged from 34.7% to 93.9%, depending on the predictive equation used. No significant associations were observed between EA and either RMR or RMR ratio . However, athletes with RMR ratio ≥ 0.9 had significantly higher visceral adipose tissue mass, bone mineral content, and density. They also showed more favorable behavioral and psychological profiles, including greater sleep regularity, fewer sustained inactivity bouts, higher self-perceived performance, and lower general stress scores. In conclusion, while no relationship was observed between RMR or RMR ratio and EA, the RMR ratio was associated with different physiological, behavioral, and psychological parameters.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".