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

摘要 1型糖尿病（T1 D）患者发生冠状动脉疾病的风险显著增加 (CAD)，但与背景人群相比，这种过度风险的原因尚不完全清楚。 迫切需要在T1 D自然史早期识别有重大CAD事件风险的人群， 开发新的治疗干预措施，以降低CAD风险和负担。虽然先前的研究已经检查了 尽管遗传变异与T1 D中的CAD之间存在关联，但仍然缺乏强有力的候选基因。这种缺失是 至少部分原因是使用低精度表型的病例对照设计， 大小和忽视表型内异质性的方法一直是最常见的研究方法 迄今为止T1 D血管并发症的遗传基础。同样，虽然公认， 炎症和免疫应答生物标志物通常与CAD风险相关， 这些生物标志物在T1 D中升高，这些标志物与CAD之间的关联尚未被证实。 在T1 D中进行了全面研究。炎症和免疫反应生物标志物是中间表型 这可能有助于发现T1 D中CAD的新途径，并可能成为有希望的治疗靶点。 因此，我们的假设是，与CAD易感性或耐药性相关的未鉴定的遗传变异 存在，炎症和免疫反应生物标志物网络与CAD相关， 介导炎症/免疫反应基因-CAD关联。我们的方法是首先完善CAD 表型定义更好地反映了T1 D中CAD易感性和耐药性的遗传病因。 具体来说，研究高度特异性的“不一致”风险因素-CAD表型亚组可能有助于揭示 T1 D中CAD开发的新途径。我们的方法将提高基因测序的精确度 (by使用全基因组测序）和CAD表型定义。我们还将衡量一个全面的 92个靶向生物标志物的蛋白质组学面板，并导出相关标志物的网络以评估它们的关联 以及这些网络介导CAD和参与CAD的基因之间的关联的程度。 炎症/免疫反应。我们将利用糖尿病流行病学的数据和样本 并发症（EDC）研究，一项特征良好的T1 D队列，随访时间>30年， 表型，使我们能够全面检查许多中间表型（即，传统风险 因素和新的生物标志物）在基因到CAD途径。此外，我们将复制这一发现， 外部队列中的发现分析。通过这种方法，我们希望发现新途径的证据 这占T1 D中无法解释的CAD风险的比例，并指出新的干预目标。

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.