Effects of Eggs and Egg Components on Cognitive Performance, Glycemic Response, and Subjective Appetite in Children Aged 9–14 Years (P14-017-19)
Bibliographic record
Abstract
Childhood is a critical time for regular intake of high-quality nutrients to aid developing brains. Dietary protein and fat independently and additively enhance cognitive performance in adults, however there are limited studies in children. The objective of this study was to determine the effects of whole eggs and its components (egg whites [protein-rich, fat-free] vs. egg yolks [containing protein and essential fatty acids]) on cognitive performance, glycemic response, and subjective appetite in children. Using a randomized within-subject repeated-measures design, 19 children (age: 12.2 ± 0.4 years) completed five test conditions, at least seven days apart. Following the consumption of a standardized breakfast 3 h prior to arriving at the laboratory, children consumed one of the following treatments: whole eggs (147 kcal), egg yolks (112 kcal), egg whites (35 kcal), full fat yogurt (147 kcal), or continued to fast. Cognitive performance, blood glucose, and subjective appetite were assessed at baseline and 15, 30, 60, and 90-min post-treatment. A battery of cognitive performance tests assessed attention, learning and memory, executive functioning, and spatial working memory. Word recall (learning and memory) was higher after egg yolks (Δ = 1.7 ± 0.6 words; P = 0.03) compared with egg whites. Change from baseline cognitive processing test scores (attention) were significantly higher (P < 0.05) after egg yolks by 1.11, 1.25, and 1.21 trials compared with whole eggs, egg whites, and yogurt, respectively. Change from baseline subjective average appetite was lower after yogurt compared with snack skipping (Δ = 9.7 ± 2.9 mm; P = 0.01). Change from baseline blood glucose was lower after yogurt compared with egg yolks (P = 0.001) and egg whites (P = 0.01); and blood glucose was lower after whole eggs compared with egg yolks (P = 0.02). Egg yolks resulted in higher short-term learning and memory scores compared with egg whites, and attention was higher after egg yolks compared with egg whites, whole eggs and yogurt. In conclusion, consumption of egg yolks may be beneficial for supporting cognitive performance in children. Egg Nutrition Center.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".