Measuring Depression: Using biomarkers to investigate the biology of depression

测量抑郁症：使用生物标志物研究抑郁症的生物学

• 批准号: 10313696
• 负责人: Julia Sealock
• 金额: $ 3.08万
• 依托单位: VANDERBILT UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2022-05-13
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10313696
• 关键词: Academic Medical Centers; Affect; Anti-Inflammatory Agents; Antidepressive Agents; Antiinflammatory Effect; Attenuated; Automobile Driving; Biochemical; Biologic Development; Biological; Biological Markers; Biological Process; Biology; Blood Circulation; Brain region; Cells; Clinic; Clinical; Complete Blood Count; Complex; Coronary Arteriosclerosis; Country; Data; Diabetes Mellitus; Diagnosis; Disease; Electronic Health Record; Environmental Risk Factor; Future; Genes; Genetic; Genetic Markers; Genetic Research; Genetic Risk; Genetic Variation; Genetic study; Goals; Immune; Immune system; Immunologic Markers; Individual; Inflammatory; Interview; Investigation; Laboratories; Lead; Link; Measurement; Measures; Mediation; Mediator of activation protein; Medical; Mental Depression; Mental disorders; Methods; Modeling; Patients; Pharmaceutical Preparations; Phenotype; Physicians; Play; Population; Positioning Attribute; Resources; Role; Sample Size; Scanning; Selective Serotonin Reuptake Inhibitor; Smoking; Surveys; Test Result; Testing; Training; Variant; White Blood Cell Count procedure; antidepressant effect; base; biobank; career; circulating biomarkers; clinical care; comorbidity; cost; diagnostic panel; disability; druggable target; epidemiology study; genetic analysis; genetic information; genetic risk factor; genome wide association study; immune activation; immune depression; inflammatory marker; novel; pleiotropism; psychogenetics; response; training opportunity; treatment response

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.

抽象的 抑郁症是全球残疾的主要原因，影响六分之一的人。尽管面临全球负担， 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 生化测量、生物标志物，帮助诊断和治疗患者的各种疾病。 存储在电子健康记录 (EHR) 中的生物标志物结果在很大程度上是尚未开发的研究资源。 先前的流行病学研究确定了抑郁症状态与各种生物标志物之间的关联， 最值得注意的是免疫标记。然而，两者之间的关联方向和基础生物学 抑郁症和免疫系统之间的关系尚未被描述。我们假设整合遗传学 使用基于 EHR 的生物标志物和中介数据来治疗抑郁症将有助于了解发生在抑郁症中的生物过程 沮丧。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）框架作为 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 存储在电子病历中。我们的方法允许以前所未有的规模对生物标志物进行研究 分析中包含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并识别新的生物标志物关联。我们发现了一个关联 抑郁症 PGS 与免疫标记物白细胞计数 (WBC) 增加之间的关系 跨多个生物库。我们计划在整个提案中进一步研究这种关系。我们计划 使用两个目标来研究我们的假设：目标 1 将评估表型因素还是遗传因素 解释了抑郁症遗传学与白细胞之间的关联。抑郁症的诊断通常是合并症 患有已知对白细胞结果有影响的其他医疗状况。我们首先会进行敏感性分析 通过在抑郁症 PGS 和 WBC 之间的分析中对潜在的混淆诊断进行共变。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驱动 协会。目标 2 将描述抑郁症遗传学在调节人际关系中的作用 抑郁症诊断和抗抑郁药使用之间的白细胞计数。抗抑郁治疗 抗抑郁药此前曾被认为与循环生物标志物的变化有关。通过利用 电子病历中的药物信息，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抑郁症相关 治疗反应。这个项目的成功完成将首先分析抑郁症的影响 大规模生物标记物的遗传学，解析关联方向并识别遗传 抑郁症和免疫系统之间的调节因素，并研究遗传学对抑郁症和免疫系统变化的影响 免疫系统免受抑郁症治疗的影响。这些结果的未来影响可能包括发展 生物学信息的治疗方案、诊断组合和表型亚型。