Leveraging multi-omics for endotyping to identify subtypes and mechanisms of cardiometabolic diseases

利用多组学进行内分型来识别心脏代谢疾病的亚型和机制

• 批准号: 10664282
• 负责人: Minoo Sadat Bagheri Naraghi
• 金额: $ 16.44万
• 依托单位: VANDERBILT UNIVERSITY MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-05 至 2028-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10664282
• 关键词: Address; Adult; Big Data; Bioinformatics; Biological; Biological Markers; Biology; Blood; Cardiometabolic Disease; Clinical; Consensus; Coronary Arteriosclerosis; DNA; Data; Data Set; Development; Diabetes Mellitus; Disease; Disease Clusterings; Disease Progression; Disease susceptibility; Electronic Health Record; Framingham Heart Study; Gene Expression; Genotype; Goals; Hypertension; Individual; Jackson Heart Study; Knowledge; Link; Measurement; Measures; Mediating; Mediator; Metabolic syndrome; Metabolism; Methods; Morbidity - disease rate; Multi-Ethnic Study of Atherosclerosis; Multiomic Data; National Heart, Lung, and Blood Institute; Obesity; Onset of illness; Participant; Pathway Analysis; Patients; Phenotype; Plasma; Prospective cohort; Proteome; Proteomics; Public Health; Research; Resources; Risk; Risk Factors; Scientist; Subgroup; Testing; Therapeutic; Time; Training; Trans-Omics for Precision Medicine; bariatric surgery; biobank; cardiometabolic risk; cardiometabolism; clinical diagnosis; clinically relevant; disorder risk; disorder subtype; experience; genome wide association study; high dimensionality; high risk; improved; innovation; metabolome; metabolomics; mortality; multiple omics; network models; novel; obese patients; personalized diagnostics; phenome; phenotypic data; polygenic risk score; predict responsiveness; prevent; programs; risk stratification; skills; small molecule; study population; targeted treatment; tool; training opportunity; transcriptome; transcriptomics; treatment response; treatment strategy

Project Summary Prior studies have identified several risk factors related to cardiometabolic diseases. However, the rates of risk factor elevation are highly variable among patients. For example, despite obesity being identified as a primary risk factor for cardiometabolic diseases, a subgroup of obese patients does not develop downstream cardiometabolic complications. This suggests that there are other mediating mechanisms, independent of known risk factors, underlying cardiometabolic diseases. This project will leverage multi-omics data in conjunction with clustering approaches capitalizing on genome-wide association study (GWAS) data in BioVU, GWAS and other omics data generated by the NHLBI Trans-Omics for Precision Medicine (TOPMed) program and metabolomics data collected at 3 time points in a prospective cohort of bariatric surgery to identify: 1) clusters of individuals that represent cardiometabolic diseases (defined as novel/endotypic determinants of diseases), 2) endotypes of each cardiometabolic disease by clustering individuals within each disease (determined as pathobiological mechanisms related to disease) and 3) endotypes of responsiveness to bariatric surgery. To explore these overall study goals, the similarity network fusion method will be used along with the consensus clustering approach. Specifically, this project aims to 1) create genetically predicted levels of transcriptome, proteome and metabolome in 54,000 individuals in BioVU, use these predicted levels to construct clusters of individuals and examine whether these clusters represent diseases using the BioVU phenotype data, 2) construct clusters of individuals using direct measurements of multi-omics data (transcriptome, proteome and metabolome) in over 5,000 individuals in TOPMed, construct genetically predicted levels of the obtained clusters and impute them in BioVU using the BioVU GWAS data and explore whether these predicted levels represent cardiometabolic diseases using phenotype data in BioVU and 3) create metabolomic driven clusters using the plasma metabolomics data at baseline, 3 months and 12 months post bariatric surgery in 104 patients, explore the association of the identified clusters with cardiometabolic responsiveness and identify novel baseline metabolomic predictors of responsiveness to weight loss surgery . The proposed study will leverage multilayered –omics data using a novel and innovative network modeling analysis, providing Dr. Bagheri with critical skills, tools and experience to complete the research aims. This will be achieved by the accomplishment of the following two training goals which will help her to become an independent ‘big data’ cardiometabolic scientist: 1) gaining more in-depth knowledge of small molecule metabolism in biologically-informed cardiometabolic disease subtypes and 2) gaining expertise and expand her existing experience in bioinformatics and statistical multi-omics integration methods.

项目摘要 先前的研究已经确定了与心脏代谢疾病相关的几个风险因素。然而，风险率 因子升高在患者中高度可变。例如，尽管肥胖被确定为主要原因， 心脏代谢疾病的危险因素，肥胖患者的亚组不会发展为下游 心脏代谢并发症这表明还有其他独立于 已知的危险因素，潜在的心脏代谢疾病。该项目将利用多组学数据， 结合利用BioVU中全基因组关联研究（GWAS）数据的聚类方法， GWAS和其他组学数据由NHLBI Trans-Omics for Precision Medicine（TOPMed）计划生成 以及在减肥手术的前瞻性队列中在3个时间点收集的代谢组学数据，以确定：1） 代表心脏代谢疾病的个体集群（定义为新的/内源性决定因素， 疾病），2）通过将每种疾病内的个体聚类来确定每种心脏代谢疾病的内型 （确定为与疾病相关的病理生物学机制）和3）对 减肥手术为了探索这些总体研究目标，将沿着使用相似网络融合方法 共识聚类方法。具体来说，该项目旨在1）创造基因预测水平 转录组，蛋白质组和代谢组在54,000人在BioVU，使用这些预测水平， 使用BioVU构建个体集群，并检查这些集群是否代表疾病 表型数据，2）使用多组学数据的直接测量构建个体的聚类 （转录组，蛋白质组和代谢组）在超过5,000个人在TOPMed，构建遗传 获得的聚类的预测水平，并使用BioVU GWAS数据将其插补到BioVU中， 使用BioVU中的表型数据，这些预测水平是否代表心脏代谢疾病，以及3） 使用基线、3个月和12个月时的血浆代谢组学数据创建代谢组学驱动的聚类 在104例患者的减肥手术后，探索所确定的群集与心脏代谢的相关性， 反应性，并确定新的基线代谢组学预测的反应性减肥手术。 拟议的研究将利用多层组学数据使用一种新颖的和创新的网络建模 分析，为Bagheri博士提供关键技能，工具和经验，以完成研究目标。这将 通过以下两个培训目标的实现，这将有助于她成为一个 独立“大数据”心脏代谢科学家：1）获得更深入的小分子知识 在生物学上知情的心脏代谢疾病亚型的代谢和2）获得专业知识，并扩大她 生物信息学和统计多组学整合方法方面的现有经验。