Abstract Background Our understanding of the interplay between genetic and environmental factors (Gene x Environment Interaction, or GxE) determining mental health disorders has improved through the proliferation of genome-wide interaction association studies (GWIAS) and targeted GxE analyses. Moreover, multivariate modelling approaches, such as structural equation modelling (SEM) and polygenic risk scores (PRS), offer opportunities for the integration of clinical and genome-wide genotype data in building improved biopsychosocial models of mental illness aetiology and their response to treatment. Aims & Objectives We propose to construct a SEM framework to uncover the inter-correlation and directed structure of mental health phenotypes by leveraging the joint predictive capacity of PRS for comorbid traits that share underlying biological and environmental risk pathways. The proposed model will be capable of linking latent constructs to their observed measurements; these will include disease severity, comorbidities and clinical histories, and behaviours and lifestyle factors such as physical and social activity. Method Our gene-by-environment SEM (GESEM) will be initially developed and tested using four well- characterized clinical cohorts for older adults diagnosed with late-life depression and treated with antidepressants (CAN-BIND, IRL-GREY, STOP-PD II and IMPACT; n =1,238). The primary outcome will be antidepressant remission. Multiple PRS will be calculated to capture underlying genetic risk across vulnerable pathways which contribute to comorbidities. This selection will be made based on new, largely unpublished work from our group on the impact of PRS and targeted GxE studies on psychiatric outcomes across the lifespan. Each PRS will be calculated using both clumping and thresholding (PRSice-2) and continuous shrinkage (PRS-CS-auto) methods across selected cohorts using well-powered publicly available GWAS summary statistics. The multilevel GESEM model will include interactions between symptoms and comorbidities (i.e., observed measurements), which are caused by unobserved factors (i.e.,latent constructs), and are subject to modification by background PRS. We will compare our GESEM model against existing SEM-based approaches to GxE, including local SEM (LOSEM). Discussion & Conclusion An open-source R package of the analytical code will be created and shared with the research community. This work has the potential to improve upon existing PRS-based predictive models in a clinical setting.
Although well described in the current literature, Neurocysticercosis [NCC] remains an enigma when confronted by practitioners. This is in part due to the haphazard nature of the parasitic infection on the central nervous system [CNS]. These include single or multiple anatomic sites of infection, stage of parasitosis, and the resultant inflammatory response. As a result, NCC can present with a complex constellation of symptomatic presentations, making therapeutic regiments highly individualized. Despite intervention, other impediments may arise post-therapy due to the nature of the infection. We present a case of rapidly progressive symptomatic NCC that initially was successfully treated, however would eventually succumb to complications of ventriculitis.
Schizophrenia spectrum disorders (SSDs) are associated with significant functional impairments, disability, and low rates of personal recovery, along with tremendous economic costs linked primarily to lost productivity and premature mortality. Efforts to delineate the contributors to disability in SSDs have highlighted prominent roles for a diverse range of symptoms, physical health conditions, substance use disorders, neurobiological changes, and social factors. These findings have provided valuable advances in knowledge and helped define broad patterns of illness and outcomes across SSDs. Unsurprisingly, there have also been conflicting findings for many of these determinants that reflect the heterogeneous population of individuals with SSDs and the challenges of conceptualizing and treating SSDs as a unitary categorical construct. Presently it is not possible to identify the functional course on an individual level that would enable a personalized approach to treatment to alter the individual’s functional trajectory and mitigate the ensuing disability they would otherwise experience. To address this ongoing challenge, this study aims to conduct a longitudinal multimodal investigation of a large cohort of individuals with SSDs in order to establish discrete trajectories of personal recovery, disability, and community functioning, as well as the antecedents and predictors of these trajectories. This investigation will also provide the foundation for the co-design and testing of personalized interventions that alter these functional trajectories and improve outcomes for people with SSDs.
Abstract Background Extended spectrum β-lactamase (ESBL) bacteria are resistant to many antibiotics, which increases the risk of inadequate early antibiotic therapy. A previous single-center study had created a prediction tool to assist clinicians in identifying patients at risk for ESBL bloodstream infections. The purpose of our research project was to assess validity of this tool while also identifying risk factors for ESBL bacteremia within our own institution, which would allow for assessment of alternative prediction tools. Methods We performed a retrospective chart review of adult patients admitted to an urban university hospital who were found to have bacteremia with Escherichia coli, Klebsiella pneumoniae, and/or Klebsiella oxytoca between October 2016 and April 2018. Demographics and comorbidities were assessed, along with other potential risk factors including exposure to antibiotics and hospitalizations within the past 6 months. Results A total of 214 instances of bacteremia were identified and 14% were due to ESBL organisms. Risk factors for ESBL bacteremia in our cohort included history of positive culture for ESBL (RR = 5.9) or MRSA (RR = 3.5) and antibiotic usage in the past 6 months (RR = 2.3). Patients with ESBL bacteremia were hospitalized longer (mean 16 days vs. 6 days for non-ESBL), received longer durations of antibiotic therapy (11.7 days vs. 5.3 days), and were exposed to greater numbers of different antibiotics (1.9 vs. 0.7) in the previous 6 months. Multivariate logistic regression showed that history of prior ESBL infection (OR 14.7, CI 1.8–120) and increasing number of different antibiotic classes administered in the prior 6 months (OR 4.3, CI 1.7–11.2) were significant risk factors for ESBL bacteremia. The previously created prediction tool did not sufficiently differentiate higher and lower risk for ESBL bacteremia in our cohort. Conclusion Although risk factors were similar, the previously derived stepwise prediction tool did not predict ESBL bacteremia in our external cohort. Point-based prediction modeling might better assess risk across institutions. Additionally, the number of different antibiotics received was associated with risk for ESBL bacteremia and should be investigated further. Disclosures All authors: No reported disclosures.
The 5-HT2C receptor has been hypothesized to represent an important modulator in feeding behaviour. Evidence was based on the observation that knock-out mice for the 5-HT2C receptor gene (HTR2C) develop obesity and that many atypical antipsychotics with potent 5-HT2C antagonism may induce weight gain in susceptible individuals. Pharmacogenetic studies focusing mainly on the -759C/T promoter polymorphism (rs3813929) of the X-linked HTR2C gene revealed controversial results. We investigated the association of the HTR2C gene and weight gain using meta-analytical techniques, combining all published data while restricting our analysis to studies investigating the 759C/T. We also investigated whether ancestry (Caucasian vs. Asian) and clinical factors moderated any association. We found evidence for a slight association of -759C/T with weight gain and significance between studies for heterogeneity. Our meta-analysis provides support for the association of HTR2C in weight gain but indicates that firmly establishing the role of pharmacogenetics in clinical psychiatry requires much larger sample sizes that have been hitherto reported.
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