Measuring Depression: Using biomarkers to investigate the biology of depression

摘要 抑郁症是全球残疾的主要原因，影响六分之一的人。尽管全球负担沉重， 抑郁症的生物学仍然知之甚少。实验室检测为医生提供有针对性的 生物化学测量、生物标志物，以帮助诊断和治疗各种疾病的患者。 存储在电子健康记录（EHR）中的生物标志物结果是一种尚未开发的研究资源。 先前的流行病学研究确定了抑郁状态与各种生物标志物之间的关联， 尤其是免疫标记。然而，协会和潜在的生物学之间的方向 抑郁症和免疫系统的关系还没有被描述过。我们假设， 抑郁症与EHR为基础的生物标志物和调解数据将有助于告知生物过程发生在 萧条在之前的分析中，我们创建了一个实验室范围的关联研究（LabWAS）框架， 用于扫描多基因评分（PGS）和生物标志物之间关联的假设生成方法 存储在EHR中。我们的方法允许以前所未有的规模调查生物标志物， 分析中包括的个体数量和生物标志物数量，使我们有机会 复制以前的生物标志物关联以及识别新的生物标志物。我们发现了 在抑郁症PGS和增加的免疫标记物之间，白色血细胞计数（WBC）复制了 在多个生物库中我们计划在整个提案中进一步研究这种关系。我们计划 研究我们的假设使用两个目标：目标1将评估是否表型或遗传因素 解释了抑郁症遗传学和白细胞之间的联系。抑郁症的诊断往往是共病 其他已知对WBC结果有影响的疾病。我们会先进行敏感性分析 在抑郁症PGS和WBC之间的分析中，通过协变潜在的混淆诊断。我们将 然后进行条件分析，以解析关联方向和潜在的遗传区域， 协会。目的2将描述抑郁症遗传学在调节关系中的作用 抑郁症诊断和抗抑郁药使用与WBC之间的关系。抗肿瘤治疗， 抗抑郁药以前与循环生物标志物的变化有关。通过利用 药物信息，我们计划检查抗抑郁药对免疫生物标志物水平的影响， 确定抑郁症PGS的调节作用，并确定免疫生物标志物水平是否与 治疗反应。成功完成这个项目将是第一个分析抑郁症的影响 在生物标志物的景观规模遗传学，解析关联的方向，并确定遗传 抑郁症和免疫系统之间的介质，并研究遗传学对 免疫系统从抑郁症治疗。这些结果的未来影响可能包括发展 生物学上知情的治疗方案，诊断面板，和表型亚型。

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.