Body composition and energy intake — skeletal muscle mass is the strongest predictor of food intake in obese adolescents: The HEARTY trial
Bibliographic record
Abstract
There has been renewed interest in examining the relationship between specific components of energy expenditure and the overall influence on energy intake (EI). The purpose of this cross-sectional analysis was to determine the strongest metabolic and anthropometric predictors of EI. It was hypothesized that resting metabolic rate (RMR) and skeletal muscle mass would be the strongest predictors of EI in a sample of overweight and obese adolescents. 304 post-pubertal adolescents (91 boys, 213 girls) aged 16.1 (±1.4) years with body mass index at or above the 95th percentile for age and sex OR at or above the 85th percentile plus an additional diabetes risk factor were measured for body weight, RMR (kcal/day) by indirect calorimetry, body composition by magnetic resonance imaging (fat free mass (FFM), skeletal muscle mass, fat mass (FM), and percentage body fat), and EI (kcal/day) using 3 day food records. Body weight, RMR, FFM, skeletal muscle mass, and FM were all significantly correlated with EI (p < 0.005). After adjusting the model for age, sex, height, and physical activity, only FFM (β = 21.9, p = 0.007) and skeletal muscle mass (β = 25.8, p = 0.02) remained as significant predictors of EI. FFM and skeletal muscle mass also predicted dietary protein and fat intake (p < 0.05), but not carbohydrate intake. In conclusion, with skeletal muscle mass being the best predictor of EI, our results support the hypothesis that the magnitude of the body's lean tissue is related to absolute levels of EI in a sample of inactive adolescents with obesity.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".