Promising translatable pharmacological interventions for body weight management in individuals with severe mental illness – a narrative review
Bibliographic record
Abstract
INTRODUCTION: Psychotropic medications, especially antipsychotics, have been consistently shown to cause weight gain in individuals with severe mental illness (SMI), a population inherently challenged by poor physical health. Consequently, compared to the general population, this contributes to an increased cardiometabolic burden, including the risk of type 2 diabetes, dyslipidemia, and hypertension. Furthermore, comorbid obesity leads to treatment nonadherence, decreased quality of life, and increased risk of relapse, posing a challenge in the management of mental health. To address this, emerging agents investigated in the general population with potential to mitigate weight gain were explored to assess translatability to the SMI population. AREAS COVERED: A literature search was conducted including agents approved for the management of obesity in the general population, along with upcoming agents under investigation in phase III trials with weight loss properties. EXPERT OPINION: Metformin and topiramate along with lifestyle interventions are commonly prescribed for weight gain in individuals with SMI; however, their weight loss potential is modest at best. This review identified tirzepatide and cagrilintide-semaglutide among others as promising agents for adjunctive pharmacological management of weight gain.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".