Pharmacologic and Nonpharmacologic Strategies for Weight Gain and Metabolic Disturbance in Patients Treated with Antipsychotic Medications
Bibliographic record
Abstract
OBJECTIVES: To provide an overview of pharmacologic and nonpharmacologic strategies for antipsychotic-associated weight gain and metabolic disturbance, to identify important areas for future research, and to make practice recommendations based on current knowledge. METHODS: We undertook a selective review of interventions for weight gain and metabolic disturbance in the general population and in individuals treated with antipsychotic medications, focusing on randomized controlled trials in schizophrenia. RESULTS: Pharmacologic strategies include medication choice, medication dosage and formulation, choice of concomitant psychotropic medications, medication switching, medication addition to effect weight loss or prevent weight gain, and medications to increase insulin sensitivity. Medication choice and medication switching may have the most potent influence on weight and metabolic parameters. Modest short-term weight loss can occur with the addition of selective medications and (or) lifestyle interventions. However, more rigorous and longer-term studies are needed. CONCLUSIONS: Although difficult, the prevention of weight gain and the promotion of weight loss are possible for individuals treated with antipsychotic medications. Further research, including diabetes prevention studies, is required. We suggest a pathway for the management of weight gain and emerging metabolic disturbance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".