S90. A SYSTEMATIC REVIEW AND META-ANALYSIS OF PHARMACOLOGICAL INTERVENTIONS FOR REDUCTION OF WEIGHT GAIN IN PEOPLE WITH SCHIZOPHRENIA: 2019 UPDATE
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,017 | 0,004 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».