Substituting Carbohydrate at Lunch for Added Protein Increases Fat Oxidation During Subsequent Exercise in Healthy Males
Bibliographic record
Abstract
CONTEXT: How pre-exercise meal composition influences metabolic and health responses to exercise later in the day is currently unclear. OBJECTIVE: Examine the effects of substituting carbohydrate for protein at lunch on subsequent exercise metabolism, appetite, and energy intake. METHODS: Twelve healthy males completed 3 trials in randomized, counterbalanced order. Following a standardized breakfast (779 ± 66 kcal; ∼08:15), participants consumed a lunch (1186 ± 140 kcal; ∼13:15) containing either 0.2 g·kg-1 carbohydrate and ∼2 g·kg-1 protein (LO-CARB), or 2 g·kg-1 carbohydrate and ∼0.4 g·kg-1 protein (HI-CARB), or they fasted (FAST). Participants later cycled at ∼60% V̇O2peak for 1 hour (∼16:15) and post-exercise ad libitum energy intake was measured (∼18:30). Substrate oxidation, subjective appetite, and plasma concentrations of glucose, insulin, nonesterified fatty acids (NEFA), peptide YY (PYY), glucagon-like peptide 1 (GLP-1), and acylated ghrelin were measured for 5 hours post-lunch. RESULTS: Fat oxidation was greater during FAST (+11.66 ± 6.63 g) and LO-CARB (+8.00 ± 3.83 g) than HI-CARB (P < .001), with FAST greater than LO-CARB (+3.67 ± 5.07 g; P < .05). NEFA were lowest in HI-CARB and highest in FAST, with insulin demonstrating the inverse response (all P < .01). PYY and GLP-1 demonstrated a stepwise pattern, with LO-CARB greatest and FAST lowest (all P < .01). Acylated ghrelin was lower during HI-CARB and LO-CARB vs FAST (P < .01). Energy intake in LO-CARB was lower than FAST (-383 ± 233 kcal; P < .001) and HI-CARB (-313 ± 284 kcal; P < .001). CONCLUSION: Substituting carbohydrate for protein in a pre-exercise lunch increased fat oxidation, suppressed subjective and hormonal appetite, and reduced post-exercise energy intake.
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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 | 0.000 |
| 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) | 0.002 | 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".