Semaglutide impacts skeletal muscle to a similar extent as caloric restriction in mice with diet‐induced obesity
Bibliographic record
Abstract
Semaglutide is a GLP-1 receptor agonist that is highly efficacious in reducing food intake and body weight. While semaglutide reduces adipose tissue, there is also a loss of lean mass including skeletal muscle, though it is unclear whether this translates to a loss of muscle function. The effect of discontinuation of semaglutide on rebound weight gain and shifts in body composition is also not well understood. We investigated the impact of semaglutide and matched caloric restriction on body composition in mice with diet-induced obesity. Mice were treated with semaglutide or fed a calorie-matched diet for 4 weeks. Semaglutide and pair-feeding induced significant weight loss with a concomitant reduction in energy expenditure. Weight loss was greater with semaglutide than caloric restriction, despite matched energy intake. Muscle transcriptomic analyses revealed distinct molecular responses between semaglutide and pair-feeding. In a follow-up experiment, semaglutide and pair-feeding was discontinued after 4 weeks, and body weight and food intake were tracked for 6 weeks. At the end of the withdrawal period there was a loss of treatment effects. Lean and fat mass rebounded to baseline levels at the end of the withdrawal period. Muscle size and strength were also comparable between groups. These findings demonstrate that semaglutide reduces muscle size and strength to the same extent as caloric restriction but may be more effective at promoting fat loss. Interestingly, the loss of lean mass and skeletal muscle recovered following treatment discontinuation. KEY POINTS: Semaglutide results in greater weight loss than caloric restriction. Semaglutide treatment increases fat loss compared with caloric restriction. Muscle mass and strength is reduced to a similar extent by semaglutide and restricted feeding.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".