The Effect of Semaglutide on Antipsychotic-Induced Weight Gain and Other Metabolic Parameters, among a Cohort of Inpatients
Bibliographic record
Abstract
BACKGROUND AND HYPOTHESIS: Antipsychotic use in severe mental illnesses (SMI) is associated with metabolic dysregulation, including antipsychotic-induced weight gain (AIWG), type 2 diabetes (T2D), and dyslipidemia. In the case of non-response to metformin which is currently recommended for AIWG mitigation, no clear alternatives exist. Semaglutide, a weekly injectable glucagon like peptide-1 receptor agonist, represents a promising option. However, effectiveness and safety data in SMI are lacking. With initiation of semaglutide, we hypothesized weight loss, improvements in metabolic indices, and good tolerability. STUDY DESIGN: A retrospective chart review was conducted for inpatients receiving antipsychotics, initiated on once-weekly subcutaneous semaglutide between January 4, 2018 and March 31, 2024 at the Centre for Addiction and Mental Health in Toronto, Canada, that analyzed weight loss, metabolic parameters, and side effects. STUDY RESULTS: 47 individuals with SMI were included: 59.6% males, mean age of 43 ± 13.2 years, 83% had dysglycemia, and 66% had T2D at baseline. The maximum dose of semaglutide in this cohort was 2 mg/week. There was a mean weight loss of 3.15 ± 0.77 kg (n = 47), 7.27 ± 0.97 kg (n = 24), 9.33 ± 1.16 kg (n = 15), and 12.25 ± 1.32 kg (n = 11) at 3, 6, 9, and 12 months, respectively (P < .001). Individuals without T2D experienced greater weight loss versus those with T2D (P = .022). Additionally, significant decreases were noted in body mass index and HbA1c (P < .001), alongside improvements in lipid parameters; most common side effects were gastrointestinal in nature but did not result in semaglutide discontinuation. CONCLUSION: This retrospective chart review of inpatients with SMI supports efficacy and safety of semaglutide up to a year of follow-up, warranting future adequately powered randomized controlled trials.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".