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Discovery and validation of biomarkers of cardiovascular complications in type 1 diabetes

1 型糖尿病心血管并发症生物标志物的发现和验证

基本信息

批准号：
9923693
负责人：
Janet Kathleen Snell-Bergeon
金额：
$ 37.18万
依托单位：
UNIVERSITY OF COLORADO DENVER
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-05-01 至 2022-04-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9923693
关键词：
Address Adult Advanced Glycosylation End Products Area Biological Markers Blood Vessels Cardiovascular Diseases Cardiovascular system Cause of Death Clinical Cohort Studies Complications of Diabetes Mellitus Data Development Diabetes Mellitus Epidemiology Event Follow-Up Studies Genetic Genetic Markers Genetic Risk Genome Genotype Glycosylated hemoglobin A Health Heterogeneity Hyperglycemia Insulin-Dependent Diabetes Mellitus Intervention Measures Mediating Metabolic Microvascular Dysfunction Obesity Oxidative Stress Participant Patients Population Attributable Risks Proteins Proteome Proteomics Research Personnel Risk Risk Assessment Risk Factors Risk stratification Update biomarker validation cardiovascular disorder epidemiology cardiovascular disorder risk cardiovascular risk factor cohort coronary artery calcification diabetes control glycation hypercholesterolemia improved macrovascular disease metabolome metabolomics novel precision medicine protein biomarkers protein metabolite skills

项目摘要

Diabetes is a significant risk factor for cardiovascular disease (CVD), and accounts for more of the population attributable risk for CVD than hypercholesterolemia and obesity. Having type 1 diabetes (T1D) increases the risk for CVD at least 2-4 fold, with only half of this excess risk explained by known risk factors. Clearly, new collaborative partnerships are needed to identify novel risk factors and pathophysiological mechanisms for CVD in T1D. Hyperglycemia increases the risk of both microvascular and macrovascular complications, mediated through glycation of proteins, advanced glycation end products and oxidative stress. Heterogeneity in the risk for complications makes it difficult to determine which patients could benefit most from a specific treatment. Additional biomarkers are needed to refine the risk stratification through assessment of the patient's unique metabolic state in the context of her/his genotype. Large-scale studies of the genome, proteome and metabolome offer the promise of a personalized risk assessment by integrating the `omics' information. We propose to combine the skills of Co-Investigators with expertise in the areas of Cardiovascular Epidemiology and Omics along with biostatisticians with expertise in integration of Omics data to apply a precision medicine approach to assess novel proteomic, genetic, and metabolomic biomarkers of cardiovascular complications of T1D in the DCCT/EDIC study, and develop composite risk scores utilizing a combination of these biomarkers. We will then validate the developed risk score in an independent cohort study, the Coronary Artery Calcification in Type 1 Diabetes (CACTI) study. Specific Aims: SA#1: Identify novel glycated proteins and metabolites predicting cardiovascular events in the DCCT/EDIC cohort of adults with T1D by addressing the following hypotheses: SA#2: Develop and validate a composite risk score for CVD in the DCCT/EDIC study using a precision medicine approach, by selecting and integrating the glycated proteins and metabolites measured in this study with the underlying genetic risk and known CVD risk factors and other clinical parameters of study participants

糖尿病是心血管疾病（CVD）的一个重要危险因素，占人口的比例更大高胆固醇血症和肥胖的心血管疾病的归因风险。1型糖尿病（T1 D）增加了心血管疾病的风险至少是2-4倍，只有一半的这种过度风险是由已知的风险因素解释的。显然，新需要建立合作伙伴关系，以确定新的风险因素和病理生理 T1 D中的CVD机制。高血压增加了微血管和大血管并发症的风险，蛋白质的糖化、晚期糖化终产物和氧化应激。在风险中的异质性并发症使得很难确定哪些患者可以从特定治疗中获益最多。需要额外的生物标志物来通过评估患者的独特性来细化风险分层。代谢状态在她/他的基因型的背景下。对基因组、蛋白质组和代谢组学通过整合“组学”信息，为个性化风险评估提供了希望。我们建议联合收割机将合作研究者的技能与心血管领域的专业知识相结合流行病学和组学沿着生物统计学家，他们具有整合组学数据的专业知识，精确医学方法，以评估新的蛋白质组学，遗传和代谢生物标志物， DCCT/EDIC研究中T1 D的心血管并发症，并使用这些生物标志物的组合。然后，我们将在一个独立的队列中验证开发的风险评分 1型糖尿病患者冠状动脉钙化（CACTI）研究。具体目标： SA #1：在DCCT/EDIC中识别预测心血管事件的新型糖化蛋白和代谢物 T1 D成人队列通过解决以下假设： SA#2：使用精密度开发并验证DCCT/EDIC研究中CVD的复合风险评分医学方法，通过选择和整合本研究中测量的糖基化蛋白和代谢物与研究参与者的潜在遗传风险和已知CVD风险因素以及其他临床参数相关