1/2 Leveraging electronic health records for pharmacogenomics of psychiatric disorders

1/2 利用电子健康记录进行精神疾病的药物基因组学研究

基本信息

批准号：
10064583
负责人：
ROY H. Perlis
金额：
$ 42.13万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-01-01 至 2022-11-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10064583
关键词：
Academic Medical Centers Address Affect Antidepressive Agents Antipsychotic Agents Biology Case-Control Studies Clinical Clinical Informatics Clozapine Code Consumption Coupled DNA Data Development Discrimination Disease Effectiveness Electroconvulsive Therapy Electronic Health Record Engineering Functional disorder General Hospitals Generations Genetic Genetic Variation Genetic study Genomics Genotype Goals Health system Healthcare Systems Heritability Hospitals Individual Intervention Investigation Label Lead Link Literature Machine Learning Major Depressive Disorder Massachusetts Measures Medical Genetics Mental disorders Methods Modeling Morbidity - disease rate Outcome Patient Triage Patients Pharmaceutical Preparations Pharmacogenomics Pharmacology Pharmacotherapy Phenotype Population Prevalence Probability Psychiatric therapeutic procedure Psychiatry Public Health Publishing Reporting Research Research Personnel Resistance Risk Rodent Role Sample Size Sampling Schizophrenia Sequential Treatment Site Structure Suicide attempt Supervision System Therapeutic Time Treatment Failure Treatment Step Treatment outcome Variant Work adverse outcome algorithmic methodologies base biobank biomedical resource clinical predictors clinical risk cohort cost design determinants of treatment resistance effective therapy efficacious treatment genetic association genome wide association study genomic data genomic predictors genomic variation high risk improved in silico mortality neuropsychiatric disorder personalized intervention portability predictive modeling prevent prospective recruit response risk stratification risk variant success symptomatic improvement therapy resistant trait treatment response treatment risk treatment strategy treatment trial

项目摘要

Schizophrenia (SCZ) and major depressive disorder (MDD) are highly heritable, debilitating diseases with lifetime prevalences of ~1% and 15%, respectively. Both disorders carry substantial morbidity and mortality and are associated with severe societal and personal costs. Despite the availability of efficacious treatments for both disorders, ~1/3 of individuals will not achieve symptomatic improvement even after multiple rounds of medication. Identifying individuals at greater risk for such treatment nonresponse, or treatment resistance, could facilitate more targeted interventions for these individuals. A burgeoning literature has identified genomic variation associated with treatment response. IN particular, antidepressant response has been suggested to be highly heritable; convergent data from rodent studies likewise suggest that antipsychotic and antidepressant response phenotypes are influenced by genetic variation. However, treatment studies to date have had minimal success in identifying variants associated with psychotropic response, likely as a result of limited sample sizes: prior efforts required sequential treatment trials and prospective assessment to characterize outcomes. Longitudinal electronic health records (EHR) data provide an opportunity to efficiently characterize treatment response in many individuals in real-world settings. Coupled with large and expanding biobanks, these cohorts allow for low- cost, large-scale genomic studies that finally achieve sufficient power to detect realistic effect sizes. The investigators now propose to apply these approaches to the EHRs of two large regional health systems, each linked to a large biobank, to investigate treatment resistance in SCZ and MDD. They will apply canonical indicators of treatment resistance - clozapine treatment for SCZ, and electroconvulsive therapy (ECT) for MDD - to identify coded and uncoded clinical features associated with high probability of treatment resistance in EHR data. These predictors will themselves provide a useful baseline for identifying high risk individuals. Then, they will apply these to study the entire affected population of each biobank, extending existing genomic data with additional genome-wide association, yielding more than 26,000 antidepressant-treated individuals and 2,500 antipsychotic-treated individuals. Rather than simply conducting a case-control study, they will examine treatment resistance as a quantitative trait, applying a method developed by the investigators and shown to substantially increase power for such traits. The project combines expertise in clinical informatics, machine learning, and analysis of large scale genomics, as well as domain-specific expertise in psychiatric treatment resistance. Spanning two distinct health systems, the algorithms and methods developed have maximal portability, facilitating next- step investigations. Successful identification of risk variants will facilitate efforts at clinical risk stratification as well as investigation of the biology underlying treatment resistance.

精神分裂症（SCZ）和主要抑郁症（MDD）高度遗传，令人衰弱终生患病率分别为约1％和15％的疾病。两种疾病都有大量发病率和死亡率，与严重的社会和个人成本有关。尽管有可用性两种疾病的有效治疗方法，〜1/3的个体将无法实现症状改善即使经过多轮药物。确定这种治疗风险更大的人无响应或抗治疗性可以促进这些人更有针对性的干预措施。新兴文献已经确定了与治疗反应相关的基因组变异。在特别是，抗抑郁反应被认为是高度遗传的。收敛数据啮齿动物研究同样表明抗精神病药和抗抑郁反应表型受到影响通过遗传变异。但是，迄今为止的治疗研究在识别变体方面取得了最小的成功与精神反应有关，可能是由于样本量有限的结果：所需的先前努力顺序治疗试验和前瞻性评估以表征结果。纵向电子健康记录（EHR）数据提供了有效表征许多治疗反应的机会在现实世界中的个人。加上大型和扩大的生物库，这些同类群体可使成本，大规模的基因组研究，最终获得足够的能力来检测逼真的效果大小。研究人员现在建议将这些方法应用于两个大区域健康的EHR 系统，每个系统都与大型生物库相连，以研究SCZ和MDD中的治疗耐药性。他们会的应用耐药性的规范指标 - 氯氮平治疗SCZ和电惊厥 MDD的治疗（ECT） - 确定与高可能性相关的编码和未编码的临床特征 EHR数据中的治疗耐药性。这些预测因素本身将为确定高风险个人。然后，他们将应用这些来研究每个人的整个受影响的人群生物库，扩展现有的基因组数据，并具有额外的全基因组关联，产生超过 26,000个抗抑郁药治疗的个体和2,500个抗精神病药物治疗的个体。而不是简单进行病例对照研究，他们将研究抗药性作为定量性状，应用A 研究人员开发的方法并证明可以大大增加此类特征的能力。该项目结合了临床信息学，机器学习和大型分析方面的专业知识比例基因组学以及精神病治疗抗性的领域特定专业知识。跨越两个独特的卫生系统，开发的算法和方法具有最大的可移植性，促进了接下来的步骤调查。成功识别风险变异将有助于临床风险分层的努力以及对生物学基础治疗耐药性的研究。