Coombes, Brandon J.

Background: This study aimed to explore clinicians’ perspectives on the current practice of perinatal mood and anxiety disorder (PMAD) management and strategies to improve future implementation. Methods: This study had a cross-sectional, descriptive design. A 35-item electronic survey was sent to clinicians (N = 118) who treated perinatal women and practiced at several community clinics at an academic medical center in the United States. Results: Among clinicians who provided care for perinatal women, 34.7% reported never receiving PMAD management training and 66.3% had less than 10 years of experience. Out of 10 patients who reported psychiatric symptoms, 47.8% of clinicians on average reported providing PMAD management to 1 to 3 patients and 40.7% noted that they conducted screening only when patient expresses PMAD symptoms. Suggested future improvements were providing training, developing a referral list, and establishing integrated behavioral health services. Conclusions: Results from this study indicated that while PMAD screening and management was implemented, improvements are warranted to meet established guidelines. Additionally, clinicians endorsed providing PMAD management to a small percentage of perinatal patients. Suggested strategies to increase adoption and implementation of PMAD management should be explored to improve access to behavioral health services for perinatal women.

Abstract Polygenic risk scores (PRSs) have become an increasingly popular approach for demonstrating polygenic influences on complex traits and for establishing common polygenic signals between different traits. PRSs are typically constructed using pruning and thresholding (P+T), but the best choice of parameters is uncertain; thus multiple settings are used and the best is chosen. Optimization can lead to inflated Type I error. Permutation procedures can correct this, but they can be computationally intensive. Alternatively, a single parameter setting can be chosen a priori for the PRS, but choosing suboptimal settings results in loss of power. We propose computing PRSs under a range of parameter settings, performing principal component analysis (PCA) on the resulting set of PRSs, and using the first PRS–PC in association tests. The first PC reweights the variants included in the PRS to achieve maximum variation over all PRS settings used. Using simulations and a real data application to study PRS association with bipolar disorder and psychosis in bipolar disorder, we compare the performance of the proposed PRS–PCA approach with a permutation test and an a priori selected p ‐value threshold. The PRS–PCA approach is simple to implement, outperforms the other strategies in most scenarios, and provides an unbiased estimate of prediction performance.

Several antiepileptic drugs (AEDs) have US Food and Drug Administration (FDA) approval for use as mood stabilizers in bipolar disorder (BD), but not all BD patients respond to these AED mood stabilizers (AED‐MSs). To identify genetic polymorphisms that contribute to the variability in AED‐MS response, we performed a discovery genome‐wide association study (GWAS) of 199 BD patients from the Mayo Clinic Bipolar Disorder Biobank. Most of these patients had been treated with the AED‐MS valproate/divalproex and/or lamotrigine. AED‐MS response was assessed using the Alda scale, which quantifies clinical improvement while accounting for potential confounding factors. We identified two genome‐wide significant single‐nucleotide polymorphism (SNP) signals that mapped to the THSD7A (rs78835388, P = 7.1E‐09) and SLC35F3 (rs114872993, P = 3.2E‐08) genes. We also identified two genes with statistically significant gene‐level associations: ABCC1 (P = 6.7E‐07; top SNP rs875740, P = 2.0E‐6), and DISP1 (P = 8.9E‐07; top SNP rs34701716, P = 8.9E‐07). THSD7A SNPs were previously found to be associated with risk for several psychiatric disorders, including BD. Both THSD7A and SLC35F3 are expressed in excitatory/glutamatergic and inhibitory/γ‐aminobutyric acidergic (GABAergic) neurons, which are targets of AED‐MSs. ABCC1 is involved in the transport of valproate and lamotrigine metabolites, and the SNPs in ABCC1 and DISP1 with the strongest evidence of association in our GWAS are strong splicing quantitative trait loci in the human gut, suggesting a possible influence on drug absorption. In conclusion, our pharmacogenomic study identified novel genetic loci that appear to contribute to AED‐MS treatment response, and may facilitate precision medicine in BD.

Abstract Introduction “Spring forward,” the start of daylight savings time (DST) reduces sleep opportunity by an hour. The resulting sleep deprivation in healthcare workers can increase the potential for medical errors. We examined the change in patient safety-related adverse events (AEs) following the time change in both spring and fall. Methods Self-reported AEs that occurred 7 days prior to and following the spring and fall time changes for years 2010–2017 in a large healthcare organization were ascertained. AEs likely resulting from human errors were identified. The change in the number of AEs (all AEs or restricted to those resulting from human error) following the spring and fall time change were modeled using negative binomial mixed models using a random effect to correct for non-independent observations in consecutive. Results Over the 8 year period, there were more AEs (all and human) in the 7 days following the change in time both in spring (All: 2812 V. 2699; Human: 1902 V. 1625) and fall (All: 3207 V. 3007; Human: 2189 V. 2087). However, the only statistically significant increase was for the estimated 18% increase in human errors following time change in spring (95% CI: 6% to 34%; p = 0.004). The 18% AE increase in spring was also significantly greater than the 5% increase in AE in fall (p = 0.018). Conclusion There is a significant increase in human error related AEs following the “spring forward” clock change which can jeopardize patient safety. Based on safety considerations, DST might best be eliminated; alternatively, policy makers and healthcare organizations should evaluate measures to mitigate the increased risk during this period. Support NA

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.