Age-Dependent Sex Differences in the Prevalence of Selective Serotonin Reuptake Inhibitor Treatment: A Retrospective Cohort Analysis.

Background: Antidepressants are among the most prescribed medications in the United States. The aim of this study was to explore the prevalence of antidepressant prescriptions and investigate sex differences and age-sex interactions in adults enrolled in the Right Drug, Right Dose, Right Time: Using Genomic Data to Individualize Treatment (RIGHT) study. Materials and Methods: We conducted a retrospective analysis of the RIGHT study. Using electronic prescriptions, we assessed 12-month prevalence of antidepressant treatment. Sex differences and age-sex interactions were evaluated using multivariable logistic regression and flexible recursive smoothing splines. Results: The sample consisted of 11,087 participants (60% women). Antidepressant prescription prevalence was 22.24% (27.96% women, 13.58% men). After adjusting for age and enrollment year, women had significantly greater odds of antidepressant prescription (odds ratio = 2.29; 95% confidence interval = 2.07, 2.54). Furthermore, selective serotonin reuptake inhibitors (SSRIs) had a significant age-sex interaction. While SSRI prescriptions in men showed a sustained decrease with age, there was no such decline for women until after reaching ∼50 years of age. There are important limitations to consider in this study. Electronic prescription data were cross-sectional; information on treatment duration or adherence was not collected; this cohort is not nationally representative; and enrollment occurred over a broad period, introducing confounding by changes in temporal prescribing practices. Conclusions: Underscored by the significant interaction between age and sex on odds of SSRI prescription, our results warrant age to be incorporated as a mediator when investigating sex differences in mental illness, especially mood disorders and their treatment.

Risk assessment for antenatal depression among women who have undergone female genital mutilation or cutting: Are we missing the mark?

BACKGROUND: Although prohibited by specific legislation in Australia, patterns of global migration underscore the importance for local clinicians to recognise and manage potential complications associated with female genital mutilation/cutting (FGM/C). The incidence of antenatal depression in Australia is 10% and may be higher among those with a history of FGM/C (RANZCOG 2 statement: Perinatal Anxiety and Depression, 2012). The phenomenon of cultural embedding could represent a protective factor against an increase in mental health problems among these women. AIM: To determine whether women who have undergone FGM/C are at greater risk of depression in the antenatal period as defined by the Edinburgh Postnatal Depression Scale (EPDS). MATERIALS AND METHODS: A multicentre retrospective case-control study was performed. Participants who had delivered at either of two hospitals, had migrated from FGM/C-prevalent countries and who had undergone FGM/C were assessed and compared with the control group, case-matched by language and religion. RESULTS: Eighty-nine cases were included with an equal number of matched controls. No significant difference in the EPDS score was demonstrated when analysed as a continuous variable (P = 0.41) or as a categorical variable with a cut-off score of 12 (P = 0.12). There was no difference in the number of women who identified as having thoughts of self-harm between the two groups. CONCLUSION: There was no identified increase in the risk of antenatal depression among women who have undergone FGM/C from high-prevalence countries. Consideration must be given to the utility of the EPDS in this population, as well as factors such as cultural embedding.

Assessment of NICE and USPSTF guidelines for identifying women at high risk of pre-eclampsia for tailoring aspirin prophylaxis in pregnancy: An individual participant data meta-analysis.

OBJECTIVE: To assess the accuracy of the National Institute of Health and Care Excellence (NICE) and United States Preventive Services Task Force (USPSTF) guidelines for predicting pre-eclampsia in pregnancy to guide aspirin prophylaxis. STUDY DESIGN: We conducted an individual participant data meta-analysis using the Perinatal Antiplatelet Review of International Studies (PARIS) dataset. This dataset includes randomised controlled trials (RCTs) of antiplatelet therapy for primary prevention of pre-eclampsia conducted in international antenatal care settings. RCTs were eligible if they enrolled pregnant women up to 28 weeks'gestation, reported risk factors, and assessed pre-eclampsia. Women assigned to the control arm (no antiplatelet agent) were included. Both guidelines recommend aspirin if ≥1 high-risk factors or ≥2 moderate-risk factors. Two moderate-risk factors (body mass index and pregnancy interval) were unavailable. Pre-eclampsia was the primary outcome. The secondary outcomes were pre-eclampsia defined by gestational age at delivery (<37 weeks versus ≥37 weeks; <34 weeks versus ≥34 weeks). We assessed the performance of the NICE and USPSTF approaches for parous and nulliparous women by estimating sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for predicting pre-eclampsia, the number-needed-to-screen (NNS) and the number-needed-to-treat (NNT) to prevent one pre-eclampsia event. RESULTS: Three RCTs were eligible (4524 women, 221 pre-eclampsia cases). Using the NICE guidelines, 9.4% of 1020 parous women were classified as screen-positive with a sensitivity of 26.4% (95% confidence interval 16.4-39.6%), specificity 91.5% (89.6-93.1%), PPV 14.6% (8.9-23.0%) and NPV 95.8% (94.3-96.9%). The NNS was 729 and NNT 69. For 3504 nulliparous women, 3% were classified as screen-positive with a sensitivity of 8.9% (5.5-14.4%), specificity 97.2% (96.6-97.8%), PPV 14.2% (8.7-21.9%), NPV 95.5% (94.8-96.1%). The NNS was 2336 and NNT 71. The USPSTF approach demonstrated similar performance. CONCLUSION: The NICE and USPSTF guidelines offer a simple and specific approach for recommending aspirin prophylaxis for women at high-risk of pre-eclampsia where more advanced screening methods are not available. However, the low detection rate limits its value in clinical practice, in particular for nulliparous women, and raises the need for development of an improved simple risk prediction tool.

Two-phase designs to follow-up genome-wide association signals with DNA resequencing studies.

Genome-wide association studies (GWAS) of complex traits have generated many association signals for single nucleotide polymorphisms (SNPs). To understand the underlying causal genetic variant(s), focused DNA resequencing of targeted genomic regions is commonly used, yet the current cost of resequencing limits sample sizes for resequencing studies. Information from the large GWAS can be used to guide choice of samples for resequencing, such as the SNP genotypes in the targeted genomic region. Viewing the GWAS tag-SNPs as imperfect surrogates for the underlying causal variants, yet expecting that the tag-SNPs are correlated with the causal variants, a reasonable approach is a two-phase case-control design, with the GWAS serving as the first-phase and the resequencing study serving as the second-phase. Using stratified sampling based on both tag-SNP genotypes and case-control status, we explore the gains in power of a two-phase design relative to randomly sampling cases and controls for resequencing (i.e., ignoring tag-SNP genotypes). Simulation results show that stratified sampling based on both tag-SNP genotypes and case-control status is not likely to have lower power than stratified sampling based only on case-control status, and can sometimes have substantially greater power. The gain in power depends on the amount of linkage disequilibrium between the tag-SNP and causal variant alleles, as well as the effect size of the causal variant. Hence, the two-phase design provides an efficient approach to follow-up GWAS signals with DNA resequencing.

A Bayesian integrative genomic model for pathway analysis of complex traits.

With new technologies, multiple types of genomic data are commonly collected on a single set of samples. However, standard analysis methods concentrate on a single data type at a time and ignore the relationships between genes, proteins, and biochemical reactions that give rise to complex phenotypes. In this paper, we propose a novel integrative model to incorporate multiple types of genomic data into an association analysis with a complex phenotype. The method combines path analysis and stochastic search variable selection into a Bayesian hierarchical model that simultaneously identifies both direct and indirect genomic effects on the phenotype. Results from a simulation study and application of the Bayesian model to a pharmacogenomic study of the drug gemcitabine demonstrate greater sensitivity to detect genomic effects in some simulation scenarios, when compared to the standard single data type analysis. Further research is required to extend and modify this integrative modeling framework to increase computational efficiency to best capitalize on the wealth of information available across multiple genomic data types.