Risk Factors and Management Strategies for Antipsychotic-Induced Weight Gain: A Prescriptive Review for Clinicians
Bibliographic record
Abstract
Antipsychotic-induced weight gain (AIWG) is a pervasive and concerning side effect of treatment with antipsychotic medications (APs) that has grave implications for morbidity and mortality rates in individuals with serious mental illness. As such, acknowledging, identifying, and addressing the metabolic side effects of these medications are critical to improve the overall health and quality of life of individuals treated with APs. This review summarizes the key risk factors and predictors of AIWG and provides a comprehensive and prescriptive overview of the best-researched and evidenced nonpharmacological and pharmacological therapies available to address this problem. Age, prior exposure to APs, and AP type are strong determinants of AIWG. Metabolic monitoring and lifestyle changes remain the methods of choice for addressing metabolic risk in this population. Clinical guidelines have recommended the off-label use of metformin when such interventions are not effective, and there is emerging evidence for the effectiveness of other novel weight loss agents in this population that may represent an opportunity for greater metabolic improvements. Most of the research and guidelines to date have focused on treating AIWG; however, prevention efforts may confer greater benefits given the challenges of reversing weight gain. To effectively mitigate the metabolic risks associated with these medications, substantial system-level reforms in both education and clinical service delivery are essential, with a focus on proactive monitoring and early intervention based on up-to-date evidence and best-practice recommendations.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".