<scp>Glucagon‐like peptide</scp>‐1/glucagon receptor agonism associates with reduced metabolic adaptation and higher fat oxidation: A randomized trial
Bibliographic record
Abstract
OBJECTIVE: This study tested the hypothesis that treatment with the glucagon-like peptide-1/glucagon receptor agonist SAR425899 would lead to a smaller decrease in sleeping metabolic rate (SMR; kilocalories/day) than expected from the loss of lean and fat mass (metabolic adaptation). METHODS: This Phase 1b, double-blind, randomized, placebo-controlled study was conducted at two centers in inpatient metabolic wards. Thirty-five healthy males and females with overweight and obesity (age = 36.5 ± 7.1 years) were randomized to a calorie-reduced diet (-1000 kcal/d) and escalating doses (0.06-0.2 mg/d) of SAR425899 (n = 17) or placebo (n = 18) for 19 days. SMR was measured by whole-room calorimetry. RESULTS: Both groups lost weight (-3.68 ± 1.37 kg placebo; -4.83 ± 1.44 kg SAR425899). Those treated with SAR425899 lost more weight, fat mass, and fat free mass (p < 0.05) owing to a greater achieved energy deficit than planned. The SAR425899 group had a smaller reduction in body composition-adjusted SMR (p = 0.002) as compared with placebo, but not 24-hour energy expenditure. Fat oxidation and ketogenesis increased in both groups, with significantly greater increases with SAR425899 (p < 0.05). CONCLUSIONS: SAR425899 led to reduced selective metabolic adaptation and increased lipid oxidation, which are believed to be beneficial for weight loss and weight-loss maintenance.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".