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Background: There is evidence suggesting racial disparities in diagnosis and treatment in bipolar disorder (BD) and schizophrenia (SZ). The purpose of this study is to compare psychiatric diagnoses and psychotropic use preceding a first episode of mania (FEM) or psychosis (FEP) in racially diverse patients. Methods: Using a comprehensive medical records linkage system (Rochester Epidemiology Project, REP), we retrospectively identified individuals diagnosed with BD or SZ and a documented first episode of mania or psychosis. Illness trajectory before FEP/FEM were characterized as the time from first visit for a mental health complaint to incident case. Pathways to care and clinical events preceding FEP/FEM were compared based on subsequent incident case diagnosis (BD or SZ) and self-reported race (White vs. non-White). Results: A total of 205 (FEM = 74; FEP = 131) incident cases were identified in the REP. Duration of psychiatric antecedents was significantly shorter in non-White patients, compared to White patients (2.2 ± 4.3 vs. 7.4 ± 6.6 years; p < 0.001) with an older age at time of first visit for a mental health complaint (15.7 ± 6.3 vs. 11.1 ± 6.0 years; p = 0.005). There were no significant differences by race in FEM pathway to care or age of first seeking mental health. Overall non-White patients had lower rates of psychotropic use. Conclusion: These data are unable to ascertain reasons for shorter duration of psychiatric antecedents and later age of seeking care, and more broadly first age of initial symptom presentation. If symptoms are confirmed to be earlier than first time seeking care in both groups, it would be important to identify barriers that racial minorities face to access timely psychiatric care and optimize early intervention strategies.
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BACKGROUND: Treatment in bipolar disorder (BD) is commonly applied as a multimodal therapy based on decision algorithms that lack an integrative understanding of molecular mechanisms or a biomarker associated clinical outcome measure. Pharmacogenetics/genomics study the individual genetic variation associated with drug response. This selective review of pharmacogenomics and pharmacogenomic testing (PGT) in BD will focus on candidate genes and genome wide association studies of pharmacokinetic drug metabolism and pharmacodynamic drug response/adverse event, and the potential role of decision support tools that incorporate multiple genotype/phenotype drug recommendations. MAIN BODY: We searched PubMed from January 2013 to May 2019, to identify studies reporting on BD and pharmacogenetics, pharmacogenomics and PGT. Studies were selected considering their contribution to the field. We summarize our findings in: targeted candidate genes of pharmacokinetic and pharmacodynamic pathways, genome-wide association studies and, PGT platforms, related to BD treatment. This field has grown from studies of metabolizing enzymes (i.e., pharmacokinetics) and drug transporters (i.e., pharmacodynamics), to untargeted investigations across the entire genome with the potential to merge genomic data with additional biological information. CONCLUSIONS: The complexity of BD genetics and, the heterogeneity in BD drug-related phenotypes, are important considerations for the design and interpretation of BD PGT. The clinical applicability of PGT in psychiatry is in its infancy and is far from reaching the robust impact it has in other medical disciplines. Nonetheless, promising findings are discovered with increasing frequency with remarkable relevance in neuroscience, pharmacology and biology.
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Gene-environment (GE) interaction has important implications in the etiology of complex diseases that are caused by a combination of genetic factors and environment variables. Several authors have developed GE analysis in the context of independent subjects or longitudinal data using a gene-set. In this paper, we propose to analyze GE interaction for discrete and continuous phenotypes in family studies by incorporating the relatedness among the relatives for each family into a generalized linear mixed model (GLMM) and by using a gene-based variance component test. In addition, we deal with collinearity problems arising from linkage disequilibrium among single nucleotide polymorphisms (SNPs) by considering their coefficients as random effects under the null model estimation. We show that the best linear unbiased predictor (BLUP) of such random effects in the GLMM is equivalent to the ridge regression estimator. This equivalence provides a simple method to estimate the ridge penalty parameter in comparison to other computationally-demanding estimation approaches based on cross-validation schemes. We evaluated the proposed test using simulation studies and applied it to real data from the Baependi Heart Study consisting of 76 families. Using our approach, we identified an interaction between BMI and the Peroxisome Proliferator Activated Receptor Gamma (PPARG) gene associated with diabetes.