Examining Susceptibility and Resistance Phenotypes to Enhance Understanding of the Genetic Basis of Major Coronary Artery Disease in Type 1 Diabetes

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ABSTRACT People with type 1 diabetes (T1D) are at dramatically increased risk of developing coronary artery disease (CAD), but the reasons for this excess risk compared to the background population are not fully understood. There is a critical need to identify people at risk of major CAD events early in their T1D natural history and to develop new therapeutic interventions to reduce CAD risk and burden. While prior studies have examined associations between genetic variants and CAD in T1D, a lack of strong candidate genes remains. This lack is at least partially due to the fact that case-control designs using low-precision phenotypes to maximize sample size and which disregard within-phenotype heterogeneity have been the most common approach to studying the genetic basis of vascular complications in T1D to date. Likewise, while it is well established that inflammatory and immune response biomarkers are associated with CAD risk in general and that levels of these biomarkers are elevated in T1D, the association between such markers and CAD has not been comprehensively studied in T1D. Inflammatory and immune response biomarkers are intermediate phenotypes that hold potential to help uncover novel pathways to CAD in T1D and may be promising treatment targets. Thus, our hypotheses are that unidentified genetic variants associated with CAD susceptibility or resistance exist and that networks of inflammatory and immune response biomarkers are associated with CAD and may mediate inflammatory/immune response gene-CAD associations. Our approach will be to first refine the CAD phenotype definition to one that better reflects the genetic etiology of CAD susceptibility and resistance in T1D. Specifically, studying highly specific “discordant” risk factor-CAD phenotype subgroups may help uncover novel pathways to CAD development in T1D. Our approach will increase precision of both genetic sequencing (by using whole genome sequencing) and CAD phenotype definitions. We will also measure a comprehensive proteomic panel of 92 targeted biomarkers and derive networks of related markers to assess their associations with CAD and the degree to which those networks mediate associations between CAD and genes involved in inflammation/immune response. We will utilize data and specimens from the Epidemiology of Diabetes Complications (EDC) study, a well-characterized T1D cohort with >30 years of follow-up and deep phenotyping, allowing us to comprehensively examine many intermediate phenotypes (i.e., traditional risk factors and novel biomarkers) in gene-to-CAD pathways. Furthermore, we will replicate the findings from this discovery analyses in external cohorts. With this approach we expect to uncover evidence of novel pathways that account for a proportion of unexplained CAD risk in T1D and point to new intervention targets.