Appetite-related peptides in childhood and adolescence: role of ghrelin, PYY, and GLP-1
Bibliographic record
Abstract
During childhood and adolescence, a number of factors, including age, puberty, sex, race, and body composition, may contribute to differences in satiety, food intake, and appetite-related peptides. These peptides include the orexigenic peptide ghrelin and anorexigenic gut peptides peptide YY (PYY) and glucagon-like peptide-1 (GLP-1). For example, lower fasting ghrelin levels, lower postprandial ghrelin suppression, and blunted PYY and GLP-1 responses to food intake could contribute to a dysregulation of appetite in already obese children and adolescents. Whereas, changes in these peptides observed during puberty could facilitate growth. A greater understanding of the major moderating factors of appetite-related peptides in the pediatric population is essential to improve interpretation of study findings and for effective tailoring of strategies targeting appetite control to individuals. While more studies are needed, there is some evidence to suggest that exercise-based lifestyle interventions could be a potential therapeutic strategy to improve appetite-peptide profiles in overweight and obese children and adolescents. The aim of this review is (i) to discuss the potential moderating factors of ghrelin, PYY, and GLP-1, including age and puberty, sex, race and body composition; and (ii) to examine the effects of exercise interventions on these appetite-related gut peptides in children and adolescents.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".