Solares-Bravo, Melissa

ABSTRACT Objectives To explore the clinical impact of social determinants of health (SDoH) stress in bipolar disorder (BD) course of illness and to explore potential gender differences in SDoH stress endorsement. Methods 2280 individuals living with BD type I or II (62% women; 67.5% BD‐I) participating in the Mayo Clinic Bipolar Biobank who completed a questionnaire assessing positive and negative‐valent stressors in the 12 months prior to study enrollment were included for this analysis. Six negative stressors were used as proxies of SDoH. Generalized linear models were used to test the association between SDoH stressors and markers of illness severity as well as potential differences by sex by including an interaction term. Results After adjusting for age, sex, and recruitment site, SDoH‐related stressors were significantly associated with a more severe course of illness in both men and women. Except for unemployment rates, women reported more frequently SDoH‐related stress (i.e., lack of family support, financial problems, health coverage and access problems). Conclusions The study found significant gender differences in both the endorsement of specific SDoH stressors and in the prevalence of specific illness severity markers; however, it did not directly test the predictive relationship between gender differences in SDoH and illness severity markers. Future studies aiming to achieve health equity in mental health care should continue assessing social determinants of health stressors, incorporating a gender perspective to better understand their influence on bipolar disorder.

Abstract Background: Pharmacogenomic studies on antidepressant treatment outcomes could be conducted using previously collected data from electronic health record (EHR)-linked biobanks. However, absence of EHR based outcome measures is an unmet need in designing such studies We aimed to define EHR-derived antidepressant outcome measures and explore their utility in showing associations between treatment outcomes and Cytochrome P450 (CYP) metabolizer phenotypes in a proof-of-concept study. Methods: Using data from the EHR-linked cohort, Right Drug, Right Dose, Right Time: Using Genomic Data to Individualize Treatment (RIGHT 10K) Study, we collected prescription data and patient health questionnaire 9 (PHQ-9) scores to compute 3 proxy measures for antidepressant response, efficacy, and acceptability: change in PHQ-9 scores, longest treatment interval with a single antidepressant, and antidepressant non-refill. Subsequently, we tested the association of both prescription-based outcomes with DNA-predicted CYP metabolizer phenotypes in European-ancestry participants. Results: We identified 3920 RIGHT 10K participants with at least 1 antidepressant prescription and European-ancestry. Participants had a mean age of 61 years and 72% were women. Implementation of the PHQ-9 outcome was not feasible because of missingness. Of both prescription-based outcomes, antidepressant non-refill reproduced several known antidepressant-CYP interactions. However, the pilot was limited by small subgroups of participants with non-normal metabolizer phenotypes. Conclusions: Derived from structured data, antidepressant non-refill is a promising outcome measure for EHR-linked biobanks that partially reproduced antidepressant-CYP interactions. However, testing on larger datasets is necessary to understand whether it would be a useful for pharmacogenomic research.