Potential DNA markers have been identified for risk and protective factors involved in metabolic side effects of atypical antipsychotic drugs, according to an article published online January 2 by Molecular Psychiatry.

"From a clinical standpoint, these drugs are an important part of the physician's armamentarium, and the ability to select the appropriate one for each patient to gain therapeutic impact without metabolic side effects would be a major advance," noted Jose de Leon, MD, co-author of the paper and Professor of Psychiatry, College of Medicine, University of Kentucky, and Medical Director, University of Kentucky Mental Health Research Center at Eastern State Hospital. "This approach is precisely how we envision the process of personalized medicine affecting the practice of psychiatry."

The studies were undertaken using a proprietary biomedical platform that analyzes DNA variation within a patient population and compares these differences to physiological characteristics or reactions. PhysioGenomics Technology looks at the entire distribution of patients' responses and determines how the frequency of single nucleotide polymorphisms varies among individuals with similar responses to a drug. When configured into polymorphism ensembles with interpretative algorithms, the company's product, termed "PhyzioType" system, enables clinicians to perform DNA-guided drug selection considering an individual's innate likelihood of developing side effects.

The Molecular Psychiatry study followed patients who were part of ongoing genotyping studies at the Institute of Living, Hartford, Connecticut and at three Kentucky state hospitals: 67 patients taking olanzapine and 101 taking risperidone were sampled for genotyping. A total of 29 single-nucleotide polymorphisms were selected from 13 candidate genes related to peripheral lipid homeostasis or central appetite regulation, key indicators of pre-diabetic conditions.

The investigators assessed the physiological-genomic associations with the weight profiles of patients taking each drug. Age, gender, race, and site (Kentucky or Connecticut) were also analyzed as potential covariates. The data show that physiogenomic associations of patient weight profiles can be established for genes in the pathways encompassing appetite peptides and peripheral lipid homoeostasis, thereby differentiating olanzapine and risperidone side-effect profiles.

Specifically, the researchers found that a certain series of polymorphisms in cholesterol metabolism-related genes coding for apolipoproteins E and A4 were significantly associated with the weight profile of olanzapine-treated patients but not risperidone patients.

Conversely, they found that a different series of polymorphisms in appetite-related genes coding for leptin receptor and neuropeptide Y receptor Y5 were significantly associated with the weight profile of risperidone patients but not in their olanzapine counterparts. Gender was also found to be significantly associated with risperidone, with men being heavier on average.