Exercise‐induced appetite suppression: An update on potential mechanisms
Bibliographic record
Abstract
The first systematic reviews of the effects of exercise on appetite-regulation and energy intake demonstrated changes in appetite-regulating hormones consistent with appetite suppression and decreases in subsequent relative energy intake over a decade ago. More recently, an intensity-dependent effect and several potential mechanisms were proposed, and this review aims to highlight advances in this field. While exercise-induced appetite suppression clearly involves acylated ghrelin, glucagon-like peptide-1 may also be involved, though recent evidence suggests peptide tyrosine tyrosine may not be relevant. Changes in subjective appetite perceptions and energy intake continue to be equivocal, though these results are likely due to small sample sizes and methodological inconsistencies. Of the proposed mechanisms responsible for exercise-induced appetite suppression, lactate has garnered the most support through in vitro and in vivo rodent studies as well as a growing amount of work in humans. Other potential modulators of exercise-induced appetite suppression may include sex hormones, growth-differentiation factor 15, Lac-Phe, brain-derived neurotrophic factor, and asprosin. Research should focus on the mechanisms responsible for the changes and consider these other modulators (i.e., myokines/exerkines) of appetite to improve our understanding of the role of exercise on appetite regulation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".