Protein Intake at Breakfast Promotes a Positive Whole-Body Protein Balance in a Dose-Response Manner in Healthy Children: A Randomized Trial
Bibliographic record
Abstract
Background: Protein ingestion promotes whole-body net protein balance (NB) in children, which is a prerequisite for growth. Determining how much protein is required at breakfast to promote a positive NB, which may be negative after the traditional overnight fast in children, has yet to be determined. Objective: We determined the impact of incremental doses of milk protein at breakfast as well as the impact of daily dietary protein distribution on NB in children. Methods: A total of 28 children [14 boys, 14 girls; age range: 7-11 y; body mass index (mean ± SD, in kg/m2): 16.0 ± 1.9] completed 2 intervention trials. During the breakfast meal, participants consumed an isoenergetic beverage with different amounts of protein (0, 7, 14, or 21 g for Groups A-D, respectively) and [15N]-glycine to measure whole body protein metabolism. Whole-body nitrogen turnover, protein synthesis (PS), protein breakdown, and NB were measured over 9 and 24 h. Results: Following an overnight fast, children were in negative NB (-64.5 mg · kg-1 · h-1). Protein ingestion at breakfast induced a stepwise increase in NB over 9 h [Groups A (6.2 mg · kg-1 · h-1) < B (27.9 mg · kg-1 · h-1) < C (46.9 mg · kg-1 · h-1) < D (66.0 mg · kg-1 · h-1)] with all conditions different from each other (all P < 0.01). PS was 42% greater in Group D than in Group A over 9 h (P < 0.05). Conclusions: Consuming ≥7 g of the total daily protein intake at breakfast attenuates the observed overnight protein losses in children during the subsequent 9 h following breakfast consumption. The dose-dependent increase in NB over a daytime fed period, inclusive of breakfast and lunch, highlights the importance of breakfast protein intake on acute anabolism in healthy active children. This trial was registered at clinicaltrials.gov as NCT02465151.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".