Comparison of self-reported energy availability and metabolic hormones to assess adequacy of dietary energy intake in young elite athletes
Bibliographic record
Abstract
Previous intervention studies suggest that leptin, insulin, insulin-like growth factor 1 (IGF-1), and triiodthyronine (T3) are sensitive markers of inadequate energy intake in relation to exercise expenditures. Because of limitations in metabolic hormone measurements, self-reported energy availability (EA) based on food and activity records may present an alternative for characterizing energy status in young athletes. The purpose of the current study was to assess whether self-reported EA is related to leptin, insulin, IGF-1, and T3 in 352 young athletes. Sex, body composition, sport participation, and acute weight changes were considered as confounding variables. Multiple linear regression revealed that EA was negatively associated with leptin (p < 0.05) but not with insulin, IGF-1, or T3. Female athletes with low EA (<30 kcal·kg(-1) fat-free mass (FFM)) had higher leptin concentrations (5.0 ± 4.7 ng·mL(-1)) and more body fat (18.3% ± 5.1%) than did females with normal EA (leptin, 3.1 ± 2.4 ng·mL(-1); body fat, 15.8% ± 4.2%; both, p < 0.001). Athletes reporting acute weight loss (>1 kg·week(-1)) had a lower EA (18.9 ± 7.4 kcal·kg(-1) FFM) than did weight-stable athletes (30.0 ± 11.2 kcal·kg(-1) FFM) or athletes reporting weight gain (>1 kg; 49.7 ± 13.1 kcal·kg(-1) FFM). IGF-1 and T3 were also reduced in athletes who lost weight (p < 0.01). This cross-sectional study reveals a lack of association between self-reported EA and metabolic hormones indicative of energy status in young athletes. Further studies are needed to investigate whether self-reported EA and metabolic hormones are in better agreement when measured repeatedly.
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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 | 0.000 | 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".