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

基本信息

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
项目类别：
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

项目摘要

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.

抽象的 2 型糖尿病 (T2DM) 是一种日益流行的慢性疾病，影响超过 4 亿人 T2DM 的主要并发症之一是加剧动脉粥样硬化性心血管疾病。即使应用现代血脂和血糖控制策略，T2DM 仍与以下疾病相关： CVD 风险增加两到四倍，表明炎症等其他病理的影响。然而，目前预测 T2DM 患者 CVD 结果的工具仅包含临床和人口统计数据变量进入他们的模型，因此他们只能获得中等程度的能力来区分最高风险的患者我们的实验室最近发现单核细胞衍生的泡沫细胞，它需要有针对性的临床干预。众所周知，它们在动脉粥样硬化 CVD 中发挥着核心作用，可以实现稳态（非炎症）我们的实验室利用致病性泡沫细胞的转录组学特征。开发了一种名为 CR30 的 CVD 预测模型，其性能优于现有工具，可以解决关键问题。由于识别 T2DM 患者 CVD 风险的知识差距，我分析了单核细胞转录组数据从多种族动脉粥样硬化研究 (MESA) 中，我确定了一项初步分析。患有 CVD 的 T2DM 患者特有的转录组特征，包含共转录组下游的超级网络我们利用了这个转录组超级网络。代表了独特的分子特征，可用于改善动脉粥样硬化的预测 T2DM 患者的心血管事件在本提案中，我将通过应用两个来检验这一假设。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可预测 T2DM-CVD 的基因的方法然后，我将测试训练和构建 T2DM-CVD 预测模型的几种建模策略。在目标 2 中，我将使用另一种方法来合并该基因集和临床数据。通过关注我将生成的转录组超级网络，将 T2DM-CVD 分子特征纳入建模。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。该项目的长期目标是确定 T2DM 患者 CVD 的生物学危险因素。预期的影响是确定机械研究的新目标和推进该提案的培训目标将提供基于生物学的临床结果预测方法。这个跨学科、高度转化的项目将利用我的生物学定量技能。赞助商实验室和康涅狄格大学的创新环境和独特机会该项目的预期成果将促进我成为一名医学院的职业目标。能够整合生物学知识和定量技能来解决问题的下一代医师科学家慢性病患者的临床问题。