Predicting Cardiovascular Outcomes Using Diabetes-Induced Transcriptomic Networks

摘要 2型糖尿病(T2 DM)是一种日益流行的慢性病，影响着4亿多人 世界各地的人们。T2 DM的主要并发症之一是加剧了心血管动脉粥样硬化 疾病(CVD)。即使应用了现代的血脂和血糖控制策略，T2 DM也与 心血管疾病风险增加两到四倍，这表明其他病理因素的影响，如炎症。 然而，目前预测T2 DM患者心血管疾病预后的工具仅包括临床和人口统计学 变量引入他们的模型，因此他们只获得了中等的区分高危患者的能力 需要有针对性的临床干预。我们的实验室最近发现单核细胞来源的泡沫细胞，这是 众所周知，它们在动脉粥样硬化性心血管疾病中发挥核心作用，可以经历体内平衡(非炎症性) 和致病(炎性)泡沫。利用致病泡沫细胞的转录签名，我们的实验室 开发了一个名为CR30的心血管疾病预测模型，该模型的表现优于现有工具。要解决关键问题 在识别T2 DM患者心血管疾病风险方面的知识差距，我分析了单核细胞转录数据 来自关于动脉粥样硬化的多种族研究(MESA)。从这个初步分析中，我确定了一个 2型糖尿病合并心血管疾病患者独有的转录转录信号，包含一个超级网络下游的co- 调节蛋白SNW1、NCOR2和CITED2。我们假设这个转录的超级网络 代表了一种独特的分子特征，可用于改善动脉粥样硬化的预测 2型糖尿病患者的心血管事件。在这个提议中，我将通过应用两个 开发预测模型的不同策略。在目标1中，我将应用有监督的机器学习 从我的初步分析中选择一组预测T2 DM-CVD的基因的方法 结果。然后，我将在训练和构建T2 DM-CVD预测模型时测试几种建模策略 将该基因集与临床数据相结合。在目标2中，我将使用另一种方法来合并 T2 DM-CVD分子签名通过聚焦转录的超网络进行建模。我将生成 丰富超级网络的得分，然后将得分作为变量纳入模型开发。 该项目的长期目标是确定T2 DM患者心血管疾病的生物学危险因素。这个 预期的影响是为机械研究确定新的目标，并推动 临床结果预测的生物学信息方法。该提案的培训目标将提供 我有生物信息量的技能。这个跨学科、高度翻译的项目将利用 赞助商实验室和康涅狄格大学的创新环境和独特机会 医学院。这个项目的预期结果将促进我成为一名 能够结合生物学知识和定量技能解决问题的下一代医生-科学家 慢性病患者的临床问题。

ABSTRACT Type 2 diabetes mellitus (T2DM) is an increasingly prevalent chronic disease that affects more than 400 million people worldwide. One of the major complications of T2DM is exacerbated atherosclerotic cardiovascular disease (CVD). Even when modern lipid and glucose control strategies are applied, T2DM is associated with a two- to four-fold increase in CVD risk, suggesting the effect of additional pathologies, such as inflammation. However, current tools to predict CVD outcomes for T2DM patients incorporate only clinical and demographic variables into their models, and they thus attain only a moderate ability to discriminate the highest-risk patients in need of targeted clinical intervention. Our lab recently discovered that monocyte-derived foam cells, which are well-known to play a central role in atherosclerotic CVD, can undergo both homeostatic (non-inflammatory) and pathogenic (inflammatory) foaming. Using a transcriptomic signature from pathogenic foam cells, our lab developed a CVD prediction model called CR30 which outperformed existing tools. To address the critical knowledge gap of identifying CVD risk specifically in T2DM patients, I analyzed monocyte transcriptomic data from the Multi-Ethnic Study on Atherosclerosis (MESA). From this preliminary analysis, I identified a transcriptomic signature unique to T2DM patients with CVD, containing a super-network downstream of the co- regulator proteins SNW1, NCOR2, and CITED2. We hypothesize that this transcriptomic super-network represents a unique molecular signature which can be used to improve prediction of atherosclerotic cardiovascular events in individuals with T2DM. In this proposal, I will test this hypothesis by applying two different strategies to develop predictive models. In Aim 1, I will apply supervised machine learning approaches to select a set of genes from my preliminary analysis which are predictive of T2DM-CVD outcomes. I will then test several modeling strategies in training and building a T2DM-CVD prediction model incorporating this gene set combined with clinical data. In Aim 2, I will use another approach to incorporate T2DM-CVD molecular signature into modeling by focusing on the transcriptomic super-network. I will generate enrichment scores for the super-network, then incorporate the scores as variables into model development. The long-term goal of this project is to identify biological risk factors for CVD in patients with T2DM. The anticipated impacts are the identification of novel targets for mechanistic studies and the advancement of biology-informed approaches to clinical outcomes prediction. The training goals of this proposal will provide me with biologically-informed quantitative skills. This interdisciplinary, highly translational project will leverage the innovative environment and unique opportunities in the sponsor’s lab and the University of Connecticut School of Medicine. The expected outcomes from this project will promote my career goals of becoming a next-generation physician-scientist capable of integrating biological knowledge and quantitative skills to solve clinical problems for patients with chronic disease.