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: 10710159
• 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/10710159
• 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; 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; Recommendation; Risk; Risk Factors; Smoking; Statistical Models; Stroke; Structure; Subgroup; Susceptibility Gene; Testing; Variant; Vascular Diseases; Veterans; Work; 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 美国退伍军人的死亡率以及美国的整个人口。 T2D是CVD的广泛认可的危险因素， T2D导致CVD结果较差。但是，之间仍然存在相当大的临床异质性 患有T2D的人。即使在显然具有相似血糖控制的个体中，也有明显的 关于谁将开发CVD的可变性。制定更有效的策略以防止CVD 需要高风险的人群，量化CVD风险的更好方法。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 与心脏和血管疾病风险最高的T2D。在AIM 1中，传统之间的关系 CVD危险因素，例如胆固醇，血压和吸烟，三个心脏和血管疾病 表型：外周动脉疾病（PAD），冠心病（CHD）和脑血管疾病，将 进行测试。为了说明这些结果经常出现在同一个人中的事实，统计模型 将使用将这些特征视为相关的人的结果。确定是否添加遗传 信息改善了基于初步的CVD结果的预测，遗传风险评分的影响 VA百万退伍军人计划和其他已发表的作品的研究将得到解决。在目标2中 密集的表型数据将从电子健康记录和基于人工智能的新型数据中提取 双簇算法将用于识别T2D的隐藏子类型。这些亚型与 然后将评估CVD结果。在AIM 3中，将采用类似的方法来详细介绍T2D子类型 基于已知与T2D，CVD及其风险因素相关的DNA变体。最后，遗传和 表型数据将被共同考虑。这些方法将在美国退伍军人的数据中应用 使用退伍军人老化队列研究和大型VA人口（通过公司数据仓库），以及 非退伍军人，使用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，并在 T2D的最高风险。这些结果将为开发量身定制的CVD预防策略奠定基础工作 在T2D中，有助于实现心脏和血管疾病的精确医学的希望。

项目成果

Association of Kidney Comorbidities and Acute Kidney Failure With Unfavorable Outcomes After COVID-19 in Individuals With the Sickle Cell Trait

• DOI: 10.1001/jamainternmed.2022.2141
• 发表时间: 2022-06-27
• 期刊: JAMA INTERNAL MEDICINE
• 影响因子: 39
• 作者: Verma, Anurag;Huffman, Jennifer E.;Luoh, Shiuh-Wen
• 通讯作者: Luoh, Shiuh-Wen

Elevated plasma complement factor H related 5 protein is associated with venous thromboembolism.

• DOI: 10.1038/s41467-023-38383-y
• 发表时间: 2023-06-07
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Iglesias MJ;Sanchez-Rivera L;Ibrahim-Kosta M;Naudin C;Munsch G;Goumidi L;Farm M;Smith PM;Thibord F;Kral-Pointner JB;Hong MG;Suchon P;Germain M;Schrottmaier W;Dusart P;Boland A;Kotol D;Edfors F;Koprulu M;Pietzner M;Langenberg C;Damrauer SM;Johnson AD;Klarin DM;Smith NL;Smadja DM;Holmström M;Magnusson M;Silveira A;Uhlén M;Renné T;Martinez-Perez A;Emmerich J;Deleuze JF;Antovic J;Soria Fernandez JM;Assinger A;Schwenk JM;Souto Andres JC;Morange PE;Butler LM;Trégouët DA;Odeberg J
• 通讯作者: Odeberg J