Leveraging multi-omics approaches to examine metabolic challenges of obesity in relation to cardiovascular diseases

摘要 心血管疾病（CVD）仍然是发病率、死亡率和早期残疾的主要原因， 因肥胖而加剧。众所周知，肥胖会使代谢途径紧张，从而加速心血管疾病 风险然而，具体的生物学机制仍然知之甚少。代谢物是生物活性小- 分子中间体和代谢的副产物，它们位于沿着将遗传易感性与 心血管疾病和肥胖，相关的健康行为，心血管疾病的危险因素。因此，代谢物可以是 强大的疾病生物标志物和治疗靶点，并可能提供可靶向的"机制桥梁"， 全基因组关联研究（GWAS）结果与CVD危险因素和临床疾病。我们假设 遗传易感性沿着特定的代谢途径影响CVD风险; 这些途径（i）影响CVD风险因素和（ii）受CVD风险因素影响，以（3）增加临床疾病风险;并且 (4)肥胖改变了一部分代谢物的作用。然而，迄今为止，大多数代谢组学研究都是 主要是在老年人，欧洲血统人群中进行的横断面或临床研究， 混杂因素，包括饮食，他们忽略了合理的修饰因素，包括肥胖。为了解决这些主要问题， 研究差距，我们将产生纵向非靶向和有针对性的代谢组学概况，在birthday（47%） 非裔美国人）CARDIA研究（n = 5，115; 1985 - 86年为18 - 30岁; 2020 - 21年n = 3，270）。CARDIA研究是 唯一适合测试拟议的研究假设，35年的纵向数据收集的关键 在你的成年生命周期中，CVD风险随着肥胖的增加而加速。创新和 采用先进的代谢组学和统计方法来表征已知和未知的代谢物 信号.纵向数据、孟德尔随机化和基于路径的建模能够评估（i） 影响CVD的代谢扰动和（ii）影响代谢扰动的CVD风险因素，（iii） 总体而言，在肥胖负担日益加重的背景下。我们致力于以下具体目标：1）识别 代谢物和主要代谢途径影响代谢CVD危险因素（胆固醇，血液 血压和血糖表型）; 2）确定影响代谢物和主要代谢物的代谢性CVD风险因素 代谢途径; 3）利用统计创新和现有的"组学"、表型和协变量数据， 因果推理，以评估机制框架，并表征新的代谢物;和4）测试 CARDIA研究中确定的代谢物与CVD风险因素和临床 终点（冠心病、心力衰竭和中风）在社区中的动脉粥样硬化风险 （ARIC）研究。我们预计，拟议的项目，由一个多学科的团队与专业知识， CVD和代谢流行病学，营养生物化学，代谢组学，生物信息学，生物统计学， 遗传学，将告知疾病机制，具有识别CVD风险生物标志物的强大潜力。在一起， 我们的创新将有助于确定新的治疗和营养目标，以减少CVD的全球负担。

ABSTRACT Cardiovascular diseases (CVD) remain leading causes of morbidity, mortality, and early disability, and are exacerbated by obesity. It is well known that obesity stresses metabolic pathways, thereby accelerating CVD risk. Yet, the specific biologic mechanisms remain poorly understood. Metabolites are biologically active small- molecule intermediates and byproducts of metabolism that lie along pathways linking genetic susceptibility with CVD and are responsive to obesity, related health behaviors, and CVD risk factors. Thus, metabolites can be powerful disease biomarkers and therapeutic targets and may provide targetable “mechanistic bridges” linking genome-wide association study (GWAS) findings with CVD risk factors and clinical disease. We hypothesize that: (1) genetic susceptibility influences CVD risk along specific metabolic pathways; (2) that metabolites on these pathways (i) affect and (ii) are affected by CVD risk factors to (3) increase clinical disease risk; and that (4) obesity modifies a subset of metabolite effects. Yet, the majority of metabolomics studies to-date have been largely cross-sectional or clinical efforts in older, European-ancestry populations, with inconsistent control of confounders, including diet, and they have ignored plausible modifiers, including obesity. To address these major research gaps, we will generate longitudinal untargeted and targeted metabolomics profiles in the biracial (47% African American) CARDIA study (n=5,115; 18-30 years in 1985-86; n~3,270 in 2020-21). The CARDIA study is uniquely suited to test the proposed study hypotheses, with 35 years of longitudinal data collected over the key your adult lifecycle period when CVD risk accelerates in concert with increasing obesity. We will develop and employ cutting-edge metabolomics and statistical methods to characterize known and unknown metabolite signals. Longitudinal data, Mendelian randomization, and pathway-based modeling enable assessment of (i) metabolic perturbations that influence CVD and (ii) CVD risk factors that influence metabolic perturbations, (iii) overall and in the context of a growing obesity burden. We address the following specific aims: 1) identify metabolites and major metabolic pathways that influence metabolic CVD risk factors (cholesterol, blood pressure, and glycemic phenotypes); 2) identify metabolic CVD risk factors that influence metabolites and major metabolic pathways; 3) leverage statistical innovations and existing `omics, phenotype, and covariate data for causal inference, to evaluate mechanistic frameworks, and characterize novel metabolites; and 4) test metabolites identified in the CARDIA study for evidence of association with CVD risk factors and clinical endpoints (coronary heart disease, heart failure, and stroke) in the biracial Atherosclerosis Risk in Communities (ARIC) study. We anticipate that the proposed project, prepared by a multi-disciplinary team with expertise in CVD and metabolic epidemiology, nutritional biochemistry, metabolomics, bioinformatics, biostatistics, and genetics, will inform disease mechanisms, with strong potential for identifying biomarkers of CVD risk. Together, our innovations will help identify novel therapeutic and nutritional targets to reduce the global burden of CVD.