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

点击翻译按钮获取中文摘要

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