EHR Phenotyping and Genomics of Opioid Addiction (Project 1)

阿片类药物成瘾的 EHR 表型分析和基因组学（项目 1）

• 批准号: 10493705
• 负责人: Vanessa Troiani
• 金额: $ 47.36万
• 依托单位: RESEARCH TRIANGLE INSTITUTE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-15 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10493705
• 关键词: Acute Pain; Algorithms; Anatomy; Autopsy; Bioinformatics; Brain; Brain imaging; Brain region; Classification; Clinical; Clinical Data; Code; Computerized Medical Record; Data; Data Collection; Data Sources; Diagnostic; Disease; Documentation; Dose; Drug usage; Electronic Health Record; Electronic Medical Records and Genomics Network; Emergency department visit; Equation; Face; Future; Gene Expression; Gene Expression Regulation; Genes; Genetic; Genetic study; Genomic medicine; Genomics; Genotype; Goals; Health system; Healthcare Systems; Heritability; Heterogeneity; Hospitalization; Human; Image; Link; Magnetic Resonance Imaging; Measurement; Measures; Mediator of activation protein; Medical; Mental disorders; Methods; Modeling; National Human Genome Research Institute; Neurobiology; Opiate Addiction; Opioid; Overdose; Pathway Analysis; Patient imaging; Patients; Pattern; Persons; Pharmaceutical Preparations; Phenotype; Publishing; Receptor Gene; Recording of previous events; Records; Research; Resources; Risk; Risk Factors; Rodent Model; Sampling; Series; Site; Structure; Substance Use Disorder; Substance abuse problem; United States; Validation; Variant; alcohol misuse; base; biobank; brain behavior; brain magnetic resonance imaging; brain volume; case control; chronic pain; cigarette smoking; clinical care; clinical diagnosis; cohort; experimental study; follow-up; gene network; genetic architecture; genetic risk factor; genome wide association study; high dimensionality; improved; in silico; injury-related death; insight; learning algorithm; morphometry; mu opioid receptors; multiple data types; multiple omics; novel; novel strategies; opioid abuse; opioid epidemic; opioid use; psychiatric comorbidity; psychogenetics; radiological imaging; rare variant; risk prediction; risk variant; sex; statistics; synergism; tool; unsupervised learning

PROJECT SUMMARY/ABSTRACT Drug overdose is the leading cause of injury-related death in the United States, and more than 2 million people in the United States are struggling with some form of opioid addiction (OA). Notably, many patients with OA are first introduced to opioids with a prescription for treatment of acute and chronic pain. Health care systems are also significantly impacted by the opioid epidemic, with opioid-related hospitalizations increasing by 150% and emergency department visits for opioid-related treatment doubling over the past 20 to 30 years. Thus, the use of prescription and clinical data from existing health system records offers a powerful opportunity to improve our understanding of opioid use and abuse. Several health systems with longitudinal data on millions of patients have also created biobanks to facilitate electronic health record (EHR)-based genomic research and implementation of genomic medicine. In 2007, the National Human Genome Research Institute organized the Electronic Medical Records and Genomics (eMERGE) network to develop EHR algorithms for medical disorders, and this was expanded in 2018 to include psychiatric disorders (PsycheMERGE). To date, however, EHR-based risk prediction and genomics have not been widely leveraged for substance abuse research. Evidence suggests that substance use disorders are highly heritable, although the underlying genetic risk factors remain unknown. In Project 1, we will leverage two powerful health system biobanks to develop EHR opioid phenotypes using prescription records and clinical diagnoses on more than 5 million people. We aim to (1) validate and harmonize case and control phenotypes across multiple disorders, (2) complete genome-wide association studies (GWAS) of opioid use phenotypes and the largest GWAS of OA to date, and (3) examine the interaction between genomics and brain structure in opioid-using patients. Successful completion of these aims will represent a major advance in demonstrating the utility of EHR resources for furthering our understanding of OA and will build a multi-site opioid research network for continued scientific discovery. Integrating Project 1 in the broader context of the Integrative Omics Center for Accelerating Neurobiological Understanding of Opioid Addiction (ICAN) creates multi-omic synergy that extends the impact of achieving these aims, linking them directly to differential gene regulation (Project 2) and experimental follow-up of key findings in rodent models (Project 3), as well as gene networks identification (Project 4). In this way, other ICAN Projects will enhance interpretation of Project 1 findings, and Project 1 GWAS and imaging results will provide opportunities to extend the other ICAN Projects, collectively achieving our goal to identify biologically meaningful drivers of OA.

项目概要/摘要 在美国，药物过量是导致伤害相关死亡的主要原因，超过 200 万人 美国人民正在与某种形式的阿片类药物成瘾 (OA) 作斗争。值得注意的是，很多患者 OA 首次引入阿片类药物作为治疗急性和慢性疼痛的处方。卫生保健 系统也受到阿片类药物流行的严重影响，与阿片类药物相关的住院人数不断增加 过去 20 至 30 年间，阿片类药物相关治疗的急诊就诊次数增加了一倍，增加了 150% 年。因此，使用来自现有卫生系统记录的处方和临床数据提供了强大的 提高我们对阿片类药物使用和滥用的了解的机会。多个纵向卫生系统 数百万患者的数据还创建了生物库，以促进基于电子健康记录 (EHR) 的 基因组研究和基因组医学的实施。 2007年，国家人类基因组 研究所组织电子病历和基因组学（eMERGE）网络开发 针对医疗疾病的 EHR 算法，并于 2018 年扩展至包括精神疾病 （心理合并）。然而，迄今为止，基于 EHR 的风险预测和基因组学尚未得到广泛应用。 用于药物滥用研究。有证据表明，物质使用障碍的发生率很高 虽然潜在的遗传危险因素仍不清楚，但具有遗传性。在项目 1 中，我们将利用两个 强大的卫生系统生物库，利用处方记录和临床数据开发 EHR 阿片类药物表型 确诊人数超过500万人。 我们的目标是 (1) 验证和协调多种疾病的病例和对照表型，(2) 阿片类药物使用表型的完整全基因组关联研究 (GWAS) 和最大的 OA GWAS 迄今为止，（3）检查阿片类药物使用患者的基因组学和大脑结构之间的相互作用。 成功完成这些目标将代表在展示电子病历的实用性方面取得了重大进步 进一步了解 OA 的资源，并将建立一个多站点阿片类药物研究网络 持续的科学发现。将项目 1 整合到综合组学中心的更广泛背景中 加速对阿片类药物成瘾的神经生物学理解 (ICAN) 创造了多组学协同作用， 扩大实现这些目标的影响，将它们直接与差异基因调控联系起来（项目2） 啮齿动物模型（项目 3）以及基因网络的主要发现的实验跟踪 识别（项目 4）。通过这种方式，其他 ICAN 项目将加强对项目 1 结果的解释， 项目 1 GWAS 和成像结果将为扩展其他 ICAN 项目提供机会， 共同实现我们的目标，即确定 OA 的具有生物学意义的驱动因素。