Energy balance and obesity: the emerging role of glucagon like peptide-1 receptor agonists
Bibliographic record
Abstract
PURPOSE OF REVIEW: With obesity affecting over one billion people globally, understanding and managing this complex condition is more crucial than ever. This review explores the emerging role of GLP-1 receptor agonists (GLP-1RA) in weight management, focusing on their impact on energy balance. It highlights the necessity of this investigation due to the limited knowledge on both the short-term and long-term implications of GLP-1RA on energy expenditure (EE) and energy intake (EI). RECENT FINDINGS: GLP-1RA, such as liraglutide and semaglutide, have shown significant efficacy in promoting weight loss by reducing appetite, cravings and consequently, EI. Newer medications such as tirzepatide have demonstrated even greater weight loss success. Emerging evidence also suggests potential effects on EE, which could explain the greater weight loss success achieved with GLP-1 RA rather than typical lifestyle changes. However, comprehensive data on the total impact of these drugs on energy balance remain limited. SUMMARY: The findings underscore the promising role of GLP-1RA in obesity management, particularly through mechanisms influencing both EI and EE. Future research should focus on systematically measuring all components of energy balance to fully elucidate the mechanisms of GLP-1RA and optimize their therapeutic use for personalized medicine.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.002 |
| 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".