The relation of serotonin-related gene and COMT gene polymorphisms with criminal behavior in schizophrenic disorder.

OBJECTIVE: It has been suggested that patients with schizophrenia might be involved in criminal behavior, such as homicidal and violent behavior. However, the relationship between criminal behavior and genes in patients with schizophrenia has not been clearly elucidated. The objective of this study was to examine the relation between criminal behavior and serotonin-related gene or catechol-O-methyltransferase (COMT) gene polymorphisms in patients with schizophrenia. METHOD: Serotonin-related and COMT polymorphic markers were assessed by using single nucleotide polymorphism (SNP) genotyping. Ninety-nine crime-related inpatients with schizophrenia (57 homicidal and 42 nonhomicidal violent) and 133 healthy subjects were enrolled between October 2005 and May 2008. Diagnoses were made according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria. The genotype frequencies of tryptophan hydroxylase-1 (TPH1) A218C and COMT V158M were compared between groups. RESULTS: The TPH1 CC genotype had 2.7-fold higher odds of crime-related schizophrenia compared with A-carrier genotype after the analysis was controlled for sex and age (OR, 2.69; 95% CI, 1.22 - 5.91; P = .01). In addition, the TPH1 CC genotype had 3.4-fold higher odds of homicidal schizophrenia compared with A-carrier genotype after the analysis was controlled for sex and age (OR, 3.38; 95% CI, 1.40 - 8.18; P = .007). However, no significant differences were found in the frequencies of genotype of COMT polymorphism between criminal schizophrenics and healthy subjects, nor were any significant differences found between nonhomicidal schizophrenics and healthy subjects. CONCLUSIONS: These results indicate that the TPH1 CC recessive genotype is likely to be a genetic risk factor for criminal behavior, especially homicidal behavior in patients with schizophrenia. However, COMT gene polymorphisms were not associated with criminal behavior in schizophrenic patients.

Application of support vector machine for prediction of medication adherence in heart failure patients.

OBJECTIVES: Heart failure (HF) is a progressive syndrome that marks the end-stage of heart diseases, and it has a high mortality rate and significant cost burden. In particular, non-adherence of medication in HF patients may result in serious consequences such as hospital readmission and death. This study aims to identify predictors of medication adherence in HF patients. In this work, we applied a Support Vector Machine (SVM), a machine-learning method useful for data classification. METHODS: Data about medication adherence were collected from patients at a university hospital through self-reported questionnaire. The data included 11 variables of 76 patients with HF. Mathematical simulations were conducted in order to develop a SVM model for the identification of variables that would best predict medication adherence. To evaluate the robustness of the estimates made with the SVM models, leave-one-out cross-validation (LOOCV) was conducted on the data set. RESULTS: THE TWO MODELS THAT BEST CLASSIFIED MEDICATION ADHERENCE IN THE HF PATIENTS WERE: one with five predictors (gender, daily frequency of medication, medication knowledge, New York Heart Association [NYHA] functional class, spouse) and the other with seven predictors (age, education, monthly income, ejection fraction, Mini-Mental Status Examination-Korean [MMSE-K], medication knowledge, NYHA functional class). The highest detection accuracy was 77.63%. CONCLUSIONS: SVM modeling is a promising classification approach for predicting medication adherence in HF patients. This predictive model helps stratify the patients so that evidence-based decisions can be made and patients managed appropriately. Further, this approach should be further explored in other complex diseases using other common variables.