J Munro

Lack of association between a polymorphism in the promoter region of the dopamine-2 receptor gene and clozapine response

Dopamine receptors are strong candidates for involvement in schizophrenia and are targeted by a wide variety of antipsychotics. We hypothesized that genetic variation in these neurotransmitter receptors may influence clinical response to clozapine, an antipsychotic which displays high affinity for dopamine D2 receptors in the limbic system. To test this hypothesis, we studied a functional polymorphism in the promoter region of the D2 receptor gene (-141C Ins/Del) in a sample of 151 clozapine treated patients of British origin. In addition, the influence of this polymorphism on antipsychotic response in general was investigated on a sample of 146 Han Chinese schizophrenic patients treated with a variety of antipsychotics. No association was found between this polymorphism and clinical response in either of the two samples suggesting that genetic variation in D2 receptors does not play a major role in determining clinical response to antipsychotic treatment.

Relaxin polymorphisms associated with metabolic disturbance in patients treated with antipsychotics

People with schizophrenia have an increased risk of metabolic syndrome, with consequent elevated morbidity and mortality, largely due to cardiovascular disease. Metabolic disorders comprise obesity, dyslipidemia and elevated levels of triglycerides, hypertension, and disturbed insulin and glucose metabolism. The elevated risk of metabolic syndrome in individuals suffering from schizophrenia is believed to be multifactorial, related to a genetic predisposition, lifestyle characteristics and treatment with antipsychotic medications. Relaxin 3 (RLN3, also known as INSL7) is a recently identified member of the insulin/relaxin superfamily that plays a role in the regulation of appetite and body weight control. RLN3 stimulates relaxin-3 receptor 1 (relaxin/insulin-like family peptide receptor 3, RXFP3) and relaxin receptor 2 (relaxin/insulin-like family peptide receptor 4, RXFP4). We have investigated the role of ten polymorphisms in these genes (RLN3 rs12327666, rs1982632, and rs7249702, RLN3R1 rs42868, rs6861957, rs7702361, and rs35399, and RLN3R2 rs11264422, rs1018730 and rs12124383) in the occurrence of metabolic syndrome phenotypes (obesity, diabetes, hypercholesterolemia, hypertrigyceridemia, and hypertension) in a cross-sectional cohort of 419 US Caucasian patients treated with antipsychotic drugs. We found several associations between relaxin polymorphisms and hypecholesterolemia, obesity and diabetes, suggesting a role for the relaxin/insulin pathway in the development of metabolic disturbance observed in patients treated with antipsychotics.

Toward understanding genetic risk for differential antipsychotic response in individuals with schizophrenia.

Antipsychotic treatment response is highly heterogeneous and unpredictable in terms of both efficacy and side effects. A combination of factors influences treatment outcome, including clinical, demographic, environmental and genetic factors. No comprehensive studies have quantified the relative contributions of these factors to date. Two decades of pharmacogenetic and pharmacogenomic studies have attempted to identify key gene variants associated with antipsychotic response, with a dual goal of better understanding the mechanisms underlying drug response and guiding prescribing decisions in clinical care. Pharmacogenetic studies have succeeded in identifying several genes that are associated with antipsychotic treatment efficacy or side effects. However, the strength of these associations is modest and they are of limited clinical value. Genome-wide association studies, moving beyond hypothesis-based discovery, have greater potential to identify novel gene associations, although only a few genome-wide association studies have been conducted and they have not revealed clearly significant findings. The first pharmacogenetic tests to guide the selection or dosing of antipsychotic drugs are now commercially available. Their uptake has been limited by the modest level of prediction they offer, compounded by the lack of data supporting their clinical or economic benefits. Advances in genomic analysis techniques, the better characterization of larger subject cohorts and an improved understanding of the role of environmental factors and gene–environment interactions are renewing hope that future research will identify genetic variants with stronger associations with treatment outcome. Tests incorporating such genetic data, with environmental and clinical variables, may offer the potential to personalize medicine and significantly improve the efficacy and tolerability of antipsychotic treatment.