Semaglutide for the treatment of antipsychotic-associated weight gain in patients not responding to metformin – a case series
Bibliographic record
Abstract
Metformin is the currently accepted first-line treatment for antipsychotic-associated weight gain (AAWG). However, not all patients benefit from metformin. Glucagon-like peptide-1 receptor agonists (GLP1-RA) have shown promise in the management of obesity in the general population, with preliminary evidence supporting efficacy in AAWG. Semaglutide is a weekly injectable GLP-1RA which received recent approval for obesity management and noted superiority over other GLP-1RAs. This study explored the efficacy and tolerability of semaglutide in AAWG among individuals with severe mental illness. A retrospective chart review of patients treated with semaglutide in the Metabolic Clinic at the Center for Addiction and Mental Health (CAMH) between 2019 and 2021 was conducted. Patients failing a trial of metformin (<5% weight loss or continuing to meet criteria for metabolic syndrome) after 3 months at the maximum tolerated dose (1500–2000 mg/day) were initiated on semaglutide up to 2 mg/week. The primary outcome measure was a change in weight at 3, 6, and 12 months. Twelve patients on weekly semaglutide injections of 0.71 ± 0.47 mg/week were included in the analysis. About 50% were female; the average age was 36.09 ± 13.32 years. At baseline, mean weight was 111.4 ± 31.7 kg, BMI was 36.7 ± 8.2 kg/m 2 , with a mean waist circumference of 118.1 ± 19.3 cm. A weight loss of 4.56 ± 3.15 kg ( p < 0.001), 5.16 ± 6.27 kg ( p = 0.04) and 8.67 ± 9 kg ( p = 0.04) was seen at 3, 6, and 12 months, respectively, after initiation of semaglutide with relatively well-tolerated side-effects. Initial evidence from our real-world clinical setting suggests that semaglutide may be effective in reducing AAWG in patients not responding to metformin. Randomized control trials investigating semaglutide for AAWG are needed to corroborate these findings.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".