Pharmacological Strategies to Counteract Antipsychotic-Induced Weight Gain and Metabolic Adverse Effects in Schizophrenia: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Antipsychotic-induced metabolic adversities are often difficult to manage. Using concomitant medications to counteract these adversities may be a rational option. OBJECTIVE: To systematically determine the effectiveness of medications to counteract antipsychotic-induced metabolic adversities in patients with schizophrenia. DATA SOURCES: Published articles until November 2013 were searched using 5 electronic databases. Clinical trial registries were searched for unpublished trials. STUDY SELECTION: Double-blind randomized placebo-controlled trials focusing on patients with schizophrenia were included if they evaluated the effects of concomitant medications on antipsychotic-induced metabolic adversities as a primary outcome. DATA EXTRACTION: Variables relating to participants, interventions, comparisons, outcomes, and study design were extracted. The primary outcome was change in body weight. Secondary outcomes included clinically relevant weight change, fasting glucose, hemoglobin A1c, fasting insulin, insulin resistance, cholesterol, and triglycerides. DATA SYNTHESIS: Forty trials representing 19 unique interventions were included in this meta-analysis. Metformin was the most extensively studied drug in regard to body weight, the mean difference amounting to -3.17 kg (95% CI: -4.44 to -1.90 kg) compared to placebo. Pooled effects for topiramate, sibutramine, aripiprazole, and reboxetine were also different from placebo. Furthermore, metformin and rosiglitazone improved insulin resistance, while aripiprazole, metformin, and sibutramine decreased blood lipids. CONCLUSION: When nonpharmacological strategies alone are insufficient, and switching antipsychotics to relatively weight-neutral agents is not feasible, the literature supports the use of concomitant metformin as first choice among pharmacological interventions to counteract antipsychotic-induced weight gain and other metabolic adversities in schizophrenia.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.034 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".