Does Switching Antipsychotics Ameliorate Weight Gain in Patients With Severe Mental Illness? A Systematic Review and Meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Obesity and adverse metabolic outcomes in patients with severe mental illness are clinically significant but potentially preventable. Importantly, the evidence for switching to antipsychotics to reduce cardiometabolic burden is unclear. METHOD: PubMED, Embase, PsycINFO, and Cochrane were searched from inception to March 8, 2020. Articles reporting weight and metabolic changes after antipsychotic switching vs staying on the previous antipsychotic were meta-analyzed both across and within group. RESULTS: Of 61 identified studies, 59 were meta-analyzed (40% rated high quality). In the switch-vs-stay pairwise meta-analyses, only aripiprazole significantly reduced weight (-5.52 kg, 95% CI -10.63, -0.42, P = .03), while olanzapine significantly increased weight (2.46 kg, 95% CI 0.34, 4.57, P = .02). Switching to aripiprazole also significantly improved fasting glucose (-3.99 mg/dl, 95% CI -7.34, -0.64, P = .02) and triglycerides (-31.03 mg/dl, 95% CI -48.73, -13.34, P = .0001). Dropout and psychosis ratings did not differ between switch and stay groups for aripiprazole and olanzapine. In before-to-after switch meta-analyses, aripiprazole (-1.96 kg, 95% CI -3.07, -0.85, P < .001) and ziprasidone (-2.22 kg, 95% CI -3.84, -0.60, P = .007) were associated with weight loss, whereas olanzapine (2.71 kg, 95% CI 1.87, 3.55, P < .001), and clozapine (2.80 kg, 95% CI 0.26, 5.34, P = .03) were associated with weight gain. No significant weight or other cardiometabolic changes were observed when switching to amisulpride, paliperidone/risperidone, quetiapine, or lurasidone. CONCLUSIONS: Switching antipsychotics to agents with lower weight gain potential, notably to aripiprazole and ziprasidone, can improve weight profile and other cardiometabolic outcomes. When choosing switch agents, both the weight gain potential of the pre- and post-switch antipsychotic must be considered. Antipsychotic switching in psychiatrically stable patients must be weighed against the risk of psychiatric worsening.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.039 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".