Precision Cardio-Metabolic Phenotyping for Genetic Discovery and Risk Prediction

用于基因发现和风险预测的精准心脏代谢表型分析

• 批准号: 10409699
• 负责人: Scott Michael Damrauer
• 金额: --
• 依托单位: PHILADELPHIA VA MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2023-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10409699
• 关键词: Address; Affect; Aging; Algorithms; Architecture; Arteries; Artificial Intelligence; Blood Pressure; Blood flow; Brain; Cardiometabolic Disease; Cardiovascular Diseases; Cerebrovascular Disorders; Cessation of life; Characteristics; Cholesterol; Classification; Clinical; Code; Cohort Studies; Coronary heart disease; DNA; Data; Data Store; Development; Diabetes Mellitus; Diagnosis; Disease Outcome; Electronic Health Record; Environmental Risk Factor; Functional disorder; Genetic; Genetic Risk; Genomics; Genotype; Heart; Heart Diseases; Hereditary Disease; Heritability; Heterogeneity; Individual; Ischemic Stroke; Knowledge; Lead; Leg; Lipids; Literature; Modeling; Morbidity - disease rate; Myocardial Infarction; Non-Insulin-Dependent Diabetes Mellitus; Onset of illness; Outcome; Pattern; Peripheral arterial disease; Persons; Pharmaceutical Preparations; Phenotype; Population; Principal Component Analysis; Procedures; Publishing; Quality of life; Risk; Risk Factors; Smoking; Statistical Models; Stroke; Structure; Subgroup; Susceptibility Gene; Testing; Variant; Vascular Diseases; Veterans; Work; base; biobank; cardiometabolism; cardiovascular disorder prevention; cardiovascular disorder risk; cardiovascular risk factor; clinical data warehouse; clinical heterogeneity; clinical risk; data warehouse; design; diabetic; disease phenotype; genetic information; genetic variant; genome wide association study; glycemic control; health care service utilization; heart disease risk; high dimensionality; high risk; high risk population; improved; innovation; lifestyle factors; limb loss; mortality; multidimensional data; novel; novel therapeutics; personalized risk prediction; phenome; phenomics; phenotypic data; polygenic risk score; precision medicine; premature; prevent; programs; risk prediction; stem; trait

Type 2 diabetes (T2D) and cardiovascular disease (CVD) are among the leading causes of morbidity and mortality in US Veterans, as well as the US population at large. T2D is a widely-recognized risk factor for CVD, and T2D leads to worse CVD outcomes. However, there remains considerable clinical heterogeneity among individuals with T2D. Even among individuals with apparently similar glycemic control, there is significant variability with respect to who will develop CVD. To develop more effective strategies to prevent CVD in this high-risk population, better approaches for quantifying CVD risk are needed. Using novel computational approaches, we will consider dense phenotype and genotype data to identify the subpopulations of individuals with T2D who are at the highest risk of heart and vascular disease. In Aim 1, the relationship between traditional CVD risk factors, such as cholesterol, blood pressure, and smoking, and three heart and vascular disease phenotypes: peripheral artery disease (PAD), coronary heart disease (CHD), and cerebrovascular disease, will be tested. To account for the fact that these outcomes frequently occur in the same individuals, statistical models that treat the traits as correlated-within person outcomes will be used. To determine if the addition of genetic information improves the prediction of CVD outcomes, the impact of genetic risk scores, based on preliminary studies from the VA Million Veteran Program and other published work, on the models will be assed. In Aim 2 dense phenotype data will be extracted from the electronic health record and novel artificial intelligence based biclustering algorithms will be used to identify hidden subtypes of T2D. The association of these subtypes with CVD outcomes will then be assessed. In Aim 3, a similar approach will be taken to elaborate T2D subtypes based on DNA variants known to associate with T2D, CVD, and their risk factors. Finally, the genetic and phenotypic data will be jointly considered. These approaches will be applied across data from both US Veterans, using the Veterans Aging Cohort Study and the VA population at large (via the Corporate Data Warehouse), and non-Veterans, using data from the PennMedicine BioBank, Penn Data Store, and UK Biobank. Successful completion of this project will help to elucidate the phenotype structure of T2D and identify individuals at the highest risk of T2D. These results will lay the ground work for developing tailored strategizes for CVD prevention in T2D and help realize the promise of precision medicine for heart and vascular disease.

2 型糖尿病 (T2D) 和心血管疾病 (CVD) 是发病率和死亡率的主要原因之一 美国退伍军人以及整个美国人口的死亡率。 T2D 是公认的 CVD 危险因素， T2D 会导致更糟糕的 CVD 结果。然而，不同疾病之间仍存在相当大的临床异质性。 患有 T2D 的个体。即使在血糖控制明显相似的个体中，也存在显着的差异 哪些人会患上 CVD，存在差异。制定更有效的策略来预防CVD 对于高危人群，需要更好的方法来量化 CVD 风险。使用新颖的计算 方法，我们将考虑密集的表型和基因型数据来识别个体的亚群 T2D 患者患心脏病和血管疾病的风险最高。在目标 1 中，传统与传统之间的关系 CVD危险因素，如胆固醇、血压和吸烟，以及三种心脏和血管疾病 表型：外周动脉疾病（PAD）、冠心病（CHD）和脑血管疾病 被测试。为了解释这些结果经常发生在同一个人身上的事实，统计模型 将使用将这些特征视为与人内相关的结果。确定是否添加基因 信息可改善 CVD 结果的预测、遗传风险评分的影响（基于初步结果） 退伍军人管理局百万退伍军人计划和其他已发表的关于模型的研究将得到评估。目标 2 将从电子健康记录和基于新型人工智能的数据中提取密集的表型数据 双聚类算法将用于识别 T2D 的隐藏亚型。这些亚型与的关联 然后将评估 CVD 结果。在目标 3 中，将采用类似的方法来阐述 T2D 亚型 基于已知与 T2D、CVD 及其危险因素相关的 DNA 变异。最后，遗传和 表型数据将被共同考虑。这些方法将应用于美国退伍军人的数据， 使用退伍军人老龄化队列研究和广大退伍军人管理局人口（通过公司数据仓库），以及 非退伍军人，使用来自 PennMedicine BioBank、Penn Data Store 和 UK Biobank 的数据。成功的 该项目的完成将有助于阐明 T2D 的表型结构并识别个体 T2D 风险最高。这些结果将为制定定制的 CVD 预防策略奠定基础 T2D 领域的研究，并帮助实现针对心脏和血管疾病的精准医疗的承诺。