Comparative Effects of 11 Antipsychotics on Weight Gain and Metabolic Function in Patients With Acute Schizophrenia
Bibliographic record
Abstract
To investigate the association of metabolic side effects with antipsychotic dose, we conducted a dose-response meta-analysis of randomized controlled trials (RCTs) in which antipsychotics were administered to people with schizophrenia. The primary outcome was mean change in weight. The secondary outcomes were the mean changes in metabolic parameters. MEDLINE, Embase, PubMed, PsyARTICLES, PsycINFO, Cochrane Database of Systematic Reviews, and different trial registries were searched for articles published in English until February 2021. We identified fixed-dose RCTs with first- or second-generation antipsychotics. The quality of RCTs was measured with Cochrane's Risk of Bias tool. We performed a dose-response meta-analysis. We retained 52 RCTs including 22,588 participants. With the exception of aripiprazole long-acting injectable (LAI), all investigated antipsychotics presented significant dose-response associations with weight, from lurasidone with a quasi-parabolic shaped curve (9 studies, estimation of 95% effective dose [ED95; 59.93 mg/d] = 0.53 kg/6 wk) to olanzapine LAI with a curve that continued to increase with the dose (1 study, ED95 [15.05 mg/d] = 4.29 kg/8 wk). All curves could be ordered in 3 different classes of shapes-quasi-parabolic, plateau, and ascending. We found significant dose-response associations for weight and metabolic variables, with a unique signature for each antipsychotic. Weight gain can occur at a relatively low median effective dose, and increasing doses can be associated with greater weight gain for some drugs. Despite several limitations, including the limited number of available studies, our results may provide useful information for preventing weight gain and metabolic disturbance by adapting antipsychotic doses. PROSPERO ID number CRD42021176569.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.031 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".