Exercise Suppresses Appetite in Obesity: A Biochemical, Metabolic, and Molecular Approach
Bibliographic record
Abstract
Exercise suppresses appetite in individuals with obesity irrespective of the type, duration, or intensity of the exercise. This effect is mediated through various physiological and biochemical mechanisms. Exercise influences appetite-regulatory hormones such as ghrelin and leptin, reducing hunger signals. Additionally, exercise generates metabolites and myokines, along with hepatokines, which modulate appetite suppression. Brain-derived neurotrophic factor (BDNF) is also implicated in modulating appetite. Changes in eating behaviors, gastric motility, and gastric emptying further contribute to a reduced appetite. Mental stress and body temperature alterations during exercise can also impact hunger levels. This review synthesizes current evidence and provides specific biochemical, metabolic and molecular mechanisms of how exercise and obesity affect appetite regulation. More specifically, it is extensively discussed the effect of exercise and obesity on: (1) endocrine mediators (hepatokines, metabolites, myokines, and neurotrophins); (2) physiological modulators (gastric emptying and body temperature); and (3) behavioral influences (eating patterns and visual food cues) in association with appetite regulation. Collectively, these factors highlight the complex interplay between physical activity and appetite regulation, offering insights into potential therapeutic strategies for managing obesity through exercise.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".