Appetite sensations and substrate metabolism at rest, during exercise, and recovery: impact of a high-calcium meal
Bibliographic record
Abstract
The aim of this study was to investigate the effects of the calcium content of a high-carbohydrate, pre-exercise meal on substrate metabolism and appetite sensations before, during, and after exercise. Nine active males participated in 2 trials in a double-blind, randomised, crossover design. After consuming a high carbohydrate (1.5 g · kg(-1) of body mass) breakfast with a calcium content of either 3 (control trial) or 9 mg · kg(-1) of body mass (high milk-calcium (CAL)), participants ran at 60% peak oxygen uptake for 60 min. Following exercise, a recovery drink was consumed and responses were investigated for a further 90 min. Blood and expired gas were sampled throughout to determine circulating substrate and hormone concentrations and rates of substrate oxidation. Visual analogue scales were also administered to determine subjective appetite sensations. Neither whole-body lipid oxidation nor non-esterified fatty acid availability differed between trials. The area under the curve for the first hour following breakfast consumption was 16% (95% confidence interval: 0%-35%) greater for fullness and 10% (95% confidence interval: 2%-19%) greater for insulin in the CAL trial but these differences were transient and not apparent later in the trial. This study demonstrates that increasing the calcium content of a high carbohydrate meal transiently increases insulinemia and fullness but substrate metabolism is unaffected.
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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 | 0.000 |
| 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) | 0.002 | 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".