Leveraging electronic medical records to perform large-scale diabetes pharmacogenomics among ancestrally diverse patient populations

利用电子病历在祖先不同的患者群体中进行大规模糖尿病药物基因组学研究

• 批准号: 9283738
• 负责人: Keoki Williams
• 金额: $ 64.2万
• 依托单位: HENRY FORD HEALTH SYSTEM
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-04-01 至 2022-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9283738
• 关键词: Address; Admixture; Adult; Affect; African American; American; Behavior Therapy; Blood; Blood Glucose; Blood Pressure; Blood Vessels; Candidate Disease Gene; Cause of Death; Cessation of life; Clinical; Clinical Trials; Complications of Diabetes Mellitus; Computerized Medical Record; Consensus; Custom; Data; Diabetes Mellitus; Diabetic Angiopathies; Diabetic Retinopathy; Diet therapy; Disadvantaged; Disease; Drug Exposure; Ethnic Origin; European; Event; Expenditure; Exposure to; Future Generations; Genes; Genetic; Genomic DNA; Genotype; Glucose; Glycosylated hemoglobin A; Goals; Health; Healthcare; Hepatic; Hypoglycemia; Incidence; Individual; Influentials; Insulin; Intestines; Kidney Diseases; Knowledge; Latino; Life; Lipids; Measures; Mediating; Medical; Metformin; Methodology; Minority Groups; Modernization; Molecular; Myocardial Infarction; Neuropathy; Non-Insulin-Dependent Diabetes Mellitus; Not Hispanic or Latino; Obesity; Oral; Outcome; Overweight; Patient Self-Report; Patients; Peripheral Vascular Diseases; Pharmaceutical Preparations; Pharmacogenomics; Population Group; Prevalence; Preventive therapy; Preventive treatment; Publishing; Race; Randomized; Reaction; Retinal Diseases; Risk Factors; Role; Saliva; Sampling; Scourge; Skeletal Muscle; Stroke; Sudden Death; Surveys; Testing; Therapeutic; Time; Time trend; Variant; Vision; Weight; Work; age group; base; burden of illness; cohort; cost; diabetes risk; diabetic; disability; effective therapy; fasting blood glucose level; follow-up; gene discovery; genetic predictors; genetic variant; genome wide association study; genome-wide; glucose production; glucose tolerance; glucose uptake; glycemic control; health equity; hepatic gluconeogenesis; high risk; improved; insight; insulin sensitivity; metropolitan; novel; novel strategies; patient population; predictive of treatment response; prevent; prophylactic; prospective; response; treatment response

ABSTRACT Diabetes mellitus is a modern day scourge, affecting an ever increasing proportion of individuals worldwide, including 26 million Americans currently. Moreover, type-2 diabetes (T2D) disproportionately affects historically disadvantaged U.S. minority groups, as evidenced by the much higher rates of disease and more severe complications among African American individuals. Although there are multiple therapeutic classes of oral medication available for treating T2D, metformin is currently recommended as the first-line therapy. Metformin lowers blood glucose levels by reducing hepatic gluconeogenesis, improving skeletal muscle insulin sensitivity, and limiting intestinal glucose uptake. It has also been shown to be an effective therapy for preventing incident diabetes. Despite being one of the most frequently prescribed drugs worldwide, very little is known about the biologic mechanism(s) through which metformin mediates its effect. This knowledge would be of value therapeutically to better understand and predict treatment response. By extension, even less is known about the activity of metformin among African American individuals, as few studies have included substantial numbers of non-European population groups. This application will help rectify existing knowledge gaps by studying a large and diverse patient population with T2D. Specifically, we will utilize electronic medical record (EMR) data for large-scale diabetes pharmacogenomics. These data have the advantage of being able to account for medication use and drug exposure over time; to provide substantial numbers of individuals for combined and population group specific analyses; and to assess clinical end-points both retrospectively and prospectively. In this application, we propose the following study aims: 1) To assess whether there are differences in metformin treatment response by self-reported race-ethnicity and genetic ancestry; 2) To use novel, gene-based association approaches to identify both shared and population group specific genetic variants influencing metformin's effect on blood glycemia (i.e., HbA1c levels); and 3) To replicate our findings in a separate group of patients and to include additional exploratory analyses to assess whether the identified genetic variants influence diabetes-related microvascular events, macrovascular events, and adverse drug reactions. The knowledge gained through this study will directly address the goals of Health People 2020 – “achieve health equity, eliminate disparities, and improve the health of all groups.”

抽象的 糖尿病是现代的祸害，影响了全球不断增加的个人， 包括2600万美国人。此外，2型糖尿病（T2D）不成比例地影响 历史上扰乱了美国少数群体，疾病率更高，更多 非裔美国人的严重并发症。尽管有多种治疗类别 目前建议可用于治疗T2D的口服药物作为一线疗法。 二甲双胍通过减少肝葡萄糖发作来降低血糖水平，从而改善骨骼肌胰岛素 灵敏度和限制肠葡萄糖的吸收。它也已被证明是一种有效的疗法 预防事件糖尿病。尽管是全球最常开处方的药物之一，但很少 了解二甲双胍介导其作用的生物学机制已知。这些知识会 具有价值理论，可以更好地理解和预测治疗反应。通过扩展，更少的是 关于二甲双胍在非洲裔美国人中的活性的了解，因为很少有研究包括 大量的非欧洲人口群体。该应用程序将有助于纠正现有知识 通过研究具有T2D的大型患者人群的差距。具体而言，我们将利用电子 大规模糖尿病药物基因组学的病历（EMR）数据。这些数据具有 能够随着时间的推移考虑药物使用和药物暴露；提供大量的 组合和人口组的特定分析；并评估临床终点 追溯和前瞻性。在此应用中，我们提出以下研究的目的：1）评估 自我报告的种族种族和遗传的二甲双胍治疗反应有差异 祖先; 2）使用基于基因的新型关联方法来识别共享和人口组 特定的遗传变异会影响二甲双胍对血糖的影响（即HbA1c水平）；和3）到 在单独的一组患者中复制我们的发现，并包括其他探索性分析以评估 确定的遗传变异是否影响与糖尿病相关的微血管事件，大血管事件， 和不良药物反应。通过这项研究获得的知识将直接解决健康的目标 2020年人民 - “实现健康平等，消除差异并改善所有群体的健康。”