Performance of a neuro-fuzzy model in predicting weight changes of chronic schizophrenic patients exposed to antipsychotics

Abstract

Artificial intelligence has become a possible solution to resolve the problem of loss of information when complexity of a disease increases. Obesity phenotypes are observable clinical features of drug-naive schizophrenic patients. In addition, atypical antipsychotic medications may cause these unwanted effects. Here we examined the performance of neurofuzzy modeling (NFM) in predicting weight changes in chronic schizophrenic patients exposed to antipsychotics. Two hundred and twenty inpatients meeting DSMIV diagnosis of schizophrenia, treated with antipsychotics, either typical or atypical, for more than 2 years, were recruited. All subjects were assessed in the same study period between mid-November 2003 and mid-April 2004. The baseline and first visit's physical data including weight, height and circumference were used in this study. Clinical information (Clinical Global Impression and Life Style Survey) and genotype data of five single nucleotide polymorphisms were also included as predictors. The subjects were randomly assigned into the first group (105 subjects) and second group (115 subjects), and NFM was performed by using the FuzzyTECH 5.54 software package, with a network-type structure constructed in the rule block. A complete learned model trained from merged data of the first and second groups demonstrates that, at a prediction error of 5, 93% subjects with weight gain were identified. Our study suggests that NFM is a feasible prediction tool for obesity in schizophrenic patients exposed to antipsychotics, with further improvements required.

C825T Polymorphism of the GNB3 Gene on Valproate-Related Metabolic Abnormalities in Bipolar Disorder Patients

“…The discrepancies between ethnicities might arise from environmental factors; further analyses considering the gene-environment interacting effect on VPA-induced metabolic abnormalities are warranted. 47,48 It should be considered whether the possible confounding effect, age, could influence the results of the current study because accumulating evidence indicated that aging was an important factor in metabolic disturbances. 49,50 In addition, although it is not certainly known whether a patient treated with VPA had different degrees of metabolic abnormalities in the populations of different ages, reports showed that VPA induced metabolic abnormalities both in children and in adults.…”

Metabolic and Cardiovascular Adverse Effects Associated with Treatment with Antipsychotic Drugs

“…Possible risk factors include younger age (children and adolescents being at great risk), female gender, first-time use of antipsychotic medication, longer duration of antipsychotic treatment, good clinical response, high parental BMI, and high BMI before the first antipsychotic treatment. 35 Lan et al 36 applied neuro-fuzzy modeling to physical factors (weight, height as well as waist and hip circumferences), psychiatric factors (severity of psychopathology), lifestyle factors (tobacco use, dietary patterns, and exercise levels), and genetic factors to predict the weight changes of chronic schizophrenic patients receiving antipsychotic drugs. These findings may help to identify patients who are prone to developing metabolic adverse effects.…”

Genetics of Common Antipsychotic-Induced Adverse Effects

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