GLP-1 Receptor Agonists as a Novel Solution for Antipsychotic-Induced Weight Gain in Severe and Persistent Mental Illness
Bibliographic record
Abstract
Patients with severe and persistent mental illness (SPMI) experience significant metabolic side effects from antipsychotic medications, including antipsychotic-induced weight gain (AIWG). This contributes to a high prevalence of obesity, insulin resistance, and type 2 diabetes in this population, ultimately reducing life expectancy. Traditional weight management strategies, such as behavioural interventions, are often less feasible in this group. Glucagon-like peptide-1 receptor agonists (GLP-1RAs), initially developed for type 2 diabetes, have shown promise in addressing AIWG by reducing weight, improving metabolic parameters, and offering potential neuroprotective and psychiatric benefits. Evidence supports the efficacy of GLP-1RAs in managing AIWG, with studies demonstrating substantial reductions in weight and body mass index without exacerbating psychiatric symptoms. However, access to these medications remains limited due to high costs and restrictive healthcare policies. Expanding access to GLP-1RAs could bridge a critical gap in care for patients with SPMI, improving both physical and mental health outcomes. Future research should focus on evaluating long-term efficacy and cost-effectiveness, particularly in the Canadian healthcare context, to inform policy changes and optimize treatment strategies.Plain Language Summary TitleCan diabetes medications help treat weight gain caused by antipsychotics?
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".