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(T1D)患者患冠状动脉疾病的风险显著增加 (CAD)，但与背景人群相比，这种额外风险的原因尚不完全清楚。 迫切需要在T1D自然病史的早期识别有重大CAD事件风险的人，并 开发新的治疗干预措施，以降低冠心病风险和负担。虽然之前的研究已经检查了 遗传变异与冠心病的相关性在T1D中，仍然缺乏强有力的候选基因。这种缺失就是 至少部分是因为病例对照设计使用低精度表型来最大化样本 大小和忽略表型内的异质性一直是最常见的研究方法 迄今T1D血管并发症的遗传学基础。同样，尽管众所周知， 炎症和免疫反应生物标记物通常与冠心病风险相关，且 这些生物标志物在T1D中升高，这些标志物与CAD之间的相关性尚未得到证实 在T1D全面学习。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T1D中CAD的新途径，并可能成为有前景的治疗靶点。 因此，我们的假设是，未知的遗传变异与冠心病易感性或耐药性有关 且炎症和免疫反应生物标记物网络与冠心病相关，并可能 介导炎症/免疫反应基因-CAD关联。我们的方法将是首先完善CAD 表型定义为更好地反映T1D中冠心病易感性和耐药性的遗传病因学。 具体地说，研究高度特异的“不协调”风险因素--CAD表型亚群可能有助于揭示 T1D中CAD开发的新途径。我们的方法将提高基因测序的精确度 (通过使用全基因组测序)和CAD表型定义。我们还将衡量一个全面的 92个靶向生物标记物的蛋白质组学小组和衍生的相关标记网络以评估它们的相关性 以及这些网络在多大程度上调节CAD和涉及的基因之间的关联 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，具有良好特征的T1D队列，具有30年的随访和深入研究 表型，使我们能够全面检查许多中间表型(即，传统风险 因素和新的生物标记物)在基因到CAD的途径。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 这在无法解释的T1D冠心病风险中占了一定比例，并指向了新的干预目标。

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.