Predicting Cardiovascular Outcomes Using Diabetes-Induced Transcriptomic Networks

使用糖尿病诱导的转录组网络预测心血管结果

• 批准号: 10679593
• 负责人: Keaton Karlinsey
• 金额: $ 4.55万
• 依托单位: UNIVERSITY OF CONNECTICUT SCH OF MED/DNT
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10679593
• 关键词: Address; Affect; Algorithms; Atherosclerosis; Biological; Biology; Blood Glucose; Cardiovascular Diseases; Cardiovascular system; Cholesterol; Chronic; Chronic Disease; Clinical; Clinical Data; Computer Models; Connecticut; Data; Data Set; Diabetes Mellitus; Disease; Disease Outcome; Environment; Equation; Event; Foam Cells; Future; Genes; Genetic; Genetic Transcription; Goals; Immunologics; Individual; Inflammation; Inflammatory; Intervention; Knowledge; Leukocytes; Lipids; Metabolic Diseases; Modeling; Modernization; Molecular Profiling; NCOR2 gene; Noise; Non-Insulin-Dependent Diabetes Mellitus; Outcome; Output; Pathogenesis; Pathogenicity; Pathology; Pathway Analysis; Pathway interactions; Patients; Performance; Persons; Physicians; Play; Proteins; Publications; Risk; Risk Assessment; Risk Factors; Role; SNW1 Gene; Sampling; Scientist; Ski-interacting protein; Testing; Training; Universities; Virulence Factors; blood glucose regulation; cardiovascular disorder risk; cardiovascular risk factor; career; clinical predictive model; cohort; design; differential expression; efficacy evaluation; feature selection; follow-up; gene network; genetic signature; glycemic control; high risk; improved; innovation; machine learning algorithm; medical schools; model development; monocyte; multi-ethnic; next generation; non-diabetic; novel; predict clinical outcome; predictive modeling; prevent; skills; standard of care; supervised learning; tool; transcriptomics

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.

摘要 2型糖尿病（T2 DM）是一种日益流行的慢性疾病，影响超过4亿人 世界各地的人们。2型糖尿病的主要并发症之一是动脉粥样硬化性心血管疾病 疾病（CVD）。即使应用现代脂质和葡萄糖控制策略，T2 DM也与 CVD风险增加2至4倍，表明其他病理学的影响，如炎症。 然而，目前预测T2 DM患者CVD结局的工具仅结合了临床和人口统计学 变量到他们的模型中，因此他们只能获得中等的能力来区分最高风险的患者 需要有针对性的临床干预。我们的实验室最近发现，单核细胞衍生的泡沫细胞， 众所周知，在动脉粥样硬化性CVD中发挥核心作用，可以经历稳态（非炎症） 和致病性（炎性）发泡。利用致病泡沫细胞的转录组特征，我们的实验室 开发了一个名为CR 30的CVD预测模型，该模型优于现有工具。为了解决关键问题， 识别T2 DM患者CVD风险的知识差距，我分析了单核细胞转录组学数据 多种族动脉粥样硬化研究（梅萨）通过初步分析，我发现了一个 T2 DM伴CVD患者特有的转录组学特征，包含一个超网络下游的共- 调节蛋白SNW 1、NCOR 2和CITED 2。我们假设这个转录组超级网络 代表了一种独特的分子标记，可用于改善动脉粥样硬化的预测 T2 DM患者的心血管事件。在这个建议中，我将通过应用两个 不同的策略来开发预测模型。在目标1中，我将应用监督机器学习 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结合。在目标2中，我将使用另一种方法， T2 DM-CVD分子特征通过聚焦于转录组学超网络建模。我将生成 超级网络的丰富分数，然后将分数作为变量纳入模型开发中。 本项目的长期目标是确定T2 DM患者中CVD的生物学风险因素。的 预期的影响是确定机制研究的新目标和推进 生物学知情的方法来预测临床结果。本提案的培训目标将提供 我的生物学定量技能。这个跨学科的，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 医学院的。这个项目的预期成果将促进我的职业目标，成为一个 下一代的物理学家，科学家能够整合生物学知识和定量技能，以解决 慢性病患者的临床问题。