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Atypical Antipsychotic-induced Weight Gain: Insights into Mechanisms of Action

Overview
Journal CNS Drugs
Specialties Neurology
Pharmacology
Date 2011 Dec 3
PMID 22133326
Citations 54
Authors
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Abstract

Prescriptions for second-generation antipsychotics (SGAs) have surpassed those for first-generation agents in the treatment of schizophrenia and bipolar disorder. While SGAs have the benefit of a much reduced risk of causing movement disorders, they have been associated with weight gain and metabolic effects. These adverse reactions are not uncommon, and threaten to have a significant impact on the patient's health over the long-term treatment that the patient requires. Currently, the aetiology of these effects is not known. This article reviews the data exploring the weight gain phenomenon. The literature was reviewed from searches of PubMed and the references of major articles in the field. The SGAs present a heterogeneous risk for weight gain. In addition, different individuals receiving the same drug can exhibit substantially different weight changes. This pattern suggests that a group of factors are associated with the weight gain phenomenon rather than a single mechanism. Coupled with the genetic profile that the patient brings to the treatment, the risk for SGA-induced weight gain will be different for different drugs and different individuals. Targets for exploration of the weight gain phenomenon include receptor interactions involving serotonin, histamine, dopamine, adrenergic, cannabinoid and muscarinic receptors. The association of SGA-induced weight gain and the role of orexigenic and anorexigenic peptides are reviewed. Also, a brief discussion of genetic factors associated with SGA-induced weight gain is presented, including that of the serotonin 5-HT(2C) receptor gene (HTR2C) and the cannabinoid 1 receptor gene (CNR1). The most promising data associated with SGA-induced weight gain include investigations of the histamine H(1), 5-HT(2A), 5-HT(2C), muscarinic M(3) and adrenergic receptors. In addition, work in the genetic area promises to result in a better understanding of the variation in risk associated with different individuals.

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