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: 9895775
• 负责人: Keoki Williams
• 金额: $ 63.59万
• 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/9895775
• 关键词: Address; Admixture; Adult; Affect; African American; American; Biological; 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; Drug Prescriptions; Ethnic Origin; European; Event; Expenditure; Exposure to; Financial Hardship; 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; Life Style Modification; Lipids; Longterm Follow-up; 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; Prediction of Response to Therapy; Prevalence; Preventive therapy; Preventive treatment; Publishing; Race; Randomized; Retinal Diseases; Risk Factors; Role; Saliva; Sampling; Scourge; Skeletal Muscle; Stroke; Sudden Death; Surveys; Testing; Therapeutic; Time; Time trend; Variant; Vision; Weight; Work; adverse drug reaction; 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; health goals; hepatic gluconeogenesis; high risk; improved; insight; insulin sensitivity; large scale data; metropolitan; novel; novel strategies; patient population; 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 糖尿病是一种现代祸害，影响着全世界越来越多的人， 目前包括2600万美国人。此外，2 型糖尿病 (T2D) 不成比例地影响 美国少数族裔在历史上处于不利地位，其疾病发病率高得多以及更多 非裔美国人的严重并发症。尽管有多种治疗类别 口服药物可用于治疗 T2D，目前推荐二甲双胍作为一线治疗。 二甲双胍通过减少肝脏糖异生、改善骨骼肌胰岛素来降低血糖水平 敏感性，并限制肠道葡萄糖的摄取。它也被证明是一种有效的治疗方法 预防糖尿病的发生。尽管是全世界最常开处方的药物之一，但很少 已知二甲双胍介导其作用的生物学机制。这些知识将 对于更好地理解和预测治疗反应具有治疗价值。推而广之，甚至更少 人们了解二甲双胍在非裔美国人中的活性，但很少有研究涉及 大量非欧洲人口群体。该应用程序将有助于纠正现有知识 通过研究大量不同的 T2D 患者群体来弥补差距。具体来说，我们将利用电子 用于大规模糖尿病药物基因组学的病历 (EMR) 数据。这些数据的优点是 能够解释一段时间内的药物使用和药物暴露；提供大量 用于综合分析和特定人群分析的个体；并评估临床终点 回顾性和前瞻性。在本申请中，我们提出以下研究目标：1）评估 自我报告的种族和遗传对二甲双胍治疗反应是否存在差异 祖先; 2) 使用新颖的、基于基因的关联方法来识别共享群体和人口群体 影响二甲双胍对血糖影响的特定遗传变异（即 HbA1c 水平）； 3) 至 在另一组患者中复制我们的研究结果，并纳入额外的探索性分析来评估 确定的遗传变异是否影响糖尿病相关的微血管事件、大血管事件、 以及药物不良反应。通过这项研究获得的知识将直接解决健康目标 人民2020——“实现健康公平，消除差距，改善所有群体的健康。”