Metformin co-commencement at time of antipsychotic initiation for attenuation of weight gain: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Antipsychotic medications are associated with weight gain and metabolic derangement. However, comprehensive evidence for the efficacy of co-commenced pharmacological treatments to mitigate initial weight gain is limited. Metformin has been shown to be effective in reducing weight among people on antipsychotic medications who are already overweight, but the potential benefits of metformin co-commencement in mitigating antipsychotic-induced weight gain has not been systematically reviewed. Method: placebo for attenuation of weight gain and metabolic syndrome for people with schizophrenia commencing a new antipsychotic. Results: Fourteen studies from Australia, United States, Venezuela, and China with 1126 participants were included. We found that metformin was superior to placebo in terms of attenuating weight gain (-3.12 kg, 95% CI -4.22 to -2.01 kg). Metformin also significantly attenuated derangement of fasting glucose levels, total cholesterol, and total triglyceride levels. Sensitivity analysis on study quality, duration, and antipsychotic agent did not impact the results. Meta-analysis was also conducted on adverse drug reactions (ADR) reported in each study which showed no significant difference in ADR incidence between metformin and placebo groups. Subgroup analysis on antipsychotic-naïve participants and participants switching to new antipsychotic did not impact the results. Conclusion: Metformin led to statistically significant and clinically meaningful attenuation of weight gain as well as attenuation of several other metabolic parameters when commenced concomitantly with antipsychotic medications. Co-commencement of metformin with antipsychotic medications, where tolerated, should be considered in the clinical setting with aim to improve long-term cardiometabolic outcomes for patients with long-term need of antipsychotic treatments.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.034 |
| Bibliometrics | 0.004 | 0.005 |
| 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".