Measuring Depression: Using biomarkers to investigate the biology of depression

摘要

Abstract Depression is a leading cause of disability worldwide, affecting 1 in 6 individuals. Despite the global burden, biology of depression remains poorly understood. Laboratory testing provides physicians with targeted biochemical measurements, biomarkers, to aid in diagnosing and treating patients for a variety of diseases. Biomarker results stored in electronic health records (EHRs) are a largely untapped resource for research. Previous epidemiologic studies identified associations between depression status and various biomarkers, most notably immune markers. However, the direction of association and underlying biology between depression and the immune system has not been described. We hypothesize that integrating genetics of depression with EHR-based biomarker and mediation data will help inform biological processes occurring in depression. In previous analyses, we created a lab-wide association study (LabWAS) framework as a hypothesis-generating approach to scan for associations between polygenic scores (PGS) and biomarkers stored in EHRs. Our method allows for an investigation of biomarkers at an unprecedented scale with both the number of individuals and the number of biomarkers included in the analyses, giving us the opportunity to replicate previous biomarker associations as well as identify novel ones. We discovered an association between depression PGS and an increased immune marker, white blood cell count (WBC) which replicated across multiple biobanks. We plan to further investigate this relationship throughout the proposal. We plan to investigate our hypothesis using two aims: Aim 1 will evaluate whether a phenotype or genetic factors explains the association between depression genetics and WBC. Depression diagnosis is often comorbid with other medical conditions that have known effects on WBC results. We will first conduct sensitivity analyses by covarying for potentially confounding diagnoses in the analysis between depression PGS and WBC. We will then perform conditional analyses to parse the direction of association and underlying genetic regions driving the association. Aim 2 will characterize the role of depression genetics in moderating the relationships between depression diagnosis and antidepressant usage with WBC. Antidepressant treatment with antidepressants has previously been associated with changes in circulating biomarkers. By leveraging medication information in EHRs, we plan to examine the effect of antidepressants on immune biomarker levels, determine the moderating role of depression PGS, and determine if immune biomarker levels associate with treatment response. Successful completion of this project would be the first to analyze the effect of depression genetics on the landscape of biomarkers at scale, parse the direction of association and identify genetic mediators between depression and the immune system, and investigate the effects of genetics on changes in immune system from depression treatment. The future impact of these results could include the development of biologically-informed treatment options, diagnostic panels, and phenotypic subtyping.