S90. A SYSTEMATIC REVIEW AND META-ANALYSIS OF PHARMACOLOGICAL INTERVENTIONS FOR REDUCTION OF WEIGHT GAIN IN PEOPLE WITH SCHIZOPHRENIA: 2019 UPDATE
Bibliographic record
Abstract
Abstract Background Weight gain and obesity are common problems encountered by patients with schizophrenia. This is partially attributable to use of second-generation antipsychotics that are associated with weight gain and other metabolic disturbances. The significance of this prevalence and its impact on premature mortality and morbidity requires better consensus on its management. The objective of this review is to determine the effects of adjunctive pharmacological interventions aimed at reducing weight gain in schizophrenia. Methods We searched the Cochrane Schizophrenia Group’s Trials Register which is based on regular searches of CINAHL, BIOSIS, AMED, EMBASE, PubMed, MEDLINE, PsycINFO, and registries of clinical trials. Inclusion criteria consisted of all randomized controlled trials examining any adjunctive pharmacological intervention for weight loss in patients with schizophrenia or schizophrenia-like illnesses. The primary outcome of each study had to be body weight or a weight related measure. We reliably selected, quality assessed, and extracted data from studies. As endpoint and change data was combined in the analysis, mean differences (MD) of the change from baseline were calculated using Review Manager 5.3. Results Sixty-one randomized controlled trials met inclusion criteria for this review (pooled n = 3328). Metformin is effective in bringing about modest weight loss (Weight: MD -3.40 kg, 95% CI -4.63 to -2.16; participants = 731; studies = 12; BMI: MD -1.39, 95% CI -1.94 to -0.85; participants = 879; studies = 13). Heterogeneity was reduced by dividing populations into first episode psychosis (FEP) and chronic populations, where FEP patients appeared to benefit most from early metformin intervention (Weight: MD -5.18 kg, 95% CI -6.22 to -4.14; BMI: MD -1.87 kg/m2, 95% CI -2.19 to -1.56; participants = 214; studies = 3) as compared to chronic patients (Weight: MD -2.22 kg, 95% CI -3.07 to -1.37; participants = 517; studies = 9; BMI: MD -1.18 kg/m2, 95% CI -1.89 to -0.48; participants = 665; studies = 10). However, ethnicity could be a confounder for the apparent effect of illness stage, as all first episode metformin intervention studies were conducted in patients with Chinese ethnicity. Metformin as a treatment for weight gain may be associated with additional adaptive changes in fasting insulin levels and insulin resistance. The frequency of adverse effects did not differ between metformin and placebo groups. Moreover, glucagon-like peptide agonists (GLP-1RAs), such as liraglutide and exenatide, were also effective in reducing weight (Weight: MD -3.95 kg, 95% CI -7.08 to -0.83; participants = 165; studies = 3; BMI -1.26 kg/m2, 95% CI -2.21 to -0.30; participants = 165; studies = 3; waist circumference: MD -3.25, 95% CI -5.93 to -0.57; participants = 165, studies = 3). The frequency of adverse effects did not differ between GLP-1RA and placebo groups. Topiramate 200 mg was also effective for weight reduction (Weight: MD=-6.61 kg, 95% CI -9.62 to -3.61; BMI: MD=-2.72, 95% CI -3.25 to -2.20; participants = 181, studies = 3). Discussion This review highlights the promise of pharmacological interventions for decreasing weight gain associated with antipsychotic use. Of the drugs studied, metformin has the most evidence and was most effective in bringing about modest weight loss. Topiramate and GLP-1RA also have accumulating evidence supporting efficacy in reducing weight. Interpretation for other agents is limited by the small number of studies, sample size, and short study duration. Future studies that are adequately powered, with longer treatment duration, will be needed in evaluating the efficacy and safety of interventions for managing weight gain further.
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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 | 0.018 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.031 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".