Consumption of a High-Protein Meal Replacement Leads to Higher Fat Oxidation, Suppression of Hunger, and Improved Metabolic Profile After an Exercise Session
Bibliographic record
Abstract
The aim of this study was to compare the impact of a high-protein meal replacement (HP-MR) versus a control (CON) breakfast on exercise metabolism. In this acute, randomized controlled, cross-over study, participants were allocated into two isocaloric arms: (a) HP-MR: 30% carbohydrate, 43% protein, and 27% fat; (b) CON: 55% carbohydrate, 15% protein, and 30% fat. Following breakfast, participants performed a moderate-intensity aerobic exercise while inside a whole-body calorimetry unit. Energy expenditure, macronutrient oxidation, appetite sensations, and metabolic blood markers were assessed. Forty-three healthy, normal-weight adults (24 males) participated. Compared to the CON breakfast, the HP-MR produced higher fat oxidation (1.07 ± 0.33 g/session; p = 0.003) and lower carbohydrate oxidation (−2.32 ± 0.98 g/session; p = 0.023) and respiratory exchange ratio (−0.01 ± 0.00; p = 0.003) during exercise. After exercise, increases in hunger were lower during the HP-MR condition. Changes in blood markers from the fasting state to post-exercise during the HP-MR condition were greater for insulin, low-density lipoprotein cholesterol, peptide tyrosine-tyrosine, and gluca-gon-like peptide 1, and lower for triglyceride and glycerol. Our primary findings were that a HP-MR produced higher fat oxidation during the exercise session, suppression of hunger, and improved metabolic profile after it.
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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 | 0.000 |
| 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 | 0.001 |
| 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".