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，而且 肥胖症更是雪上加霜。众所周知，肥胖强调新陈代谢途径，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。代谢产物是具有生物活性的小- 分子中间体和代谢副产物，位于连接遗传易感性和 心血管疾病，并对肥胖、相关的健康行为和心血管疾病风险因素做出反应。因此，代谢物可以 强大的疾病生物标志物和治疗靶点，并可能提供有针对性的“机械桥梁”连接 全基因组研究结果与心血管疾病危险因素和临床疾病的关系。我们假设 即：(1)遗传易感性沿着特定的代谢途径影响心血管疾病风险；(2)代谢产物对 这些途径(I)影响和(Ii)受心血管疾病危险因素影响(3)增加临床疾病风险； (4)肥胖改变了代谢物效应的一个子集。然而，到目前为止，大多数代谢组学研究都是 在欧洲血统的老年人群中，主要是横断面或临床努力，控制不一致 包括饮食在内的混杂因素，他们忽视了包括肥胖在内的看似合理的修饰性因素。要解决这些主要问题 研究差距，我们将产生纵向的非靶向和靶向代谢组学概况(47% 非洲裔美国人)CARDIA研究(n=5,115；1985-86年为18-30年；2020-21年为n~3,270)。CARDIA的研究是 独一无二地适合测试提出的研究假设，通过关键的35年纵向数据收集 你的成年生命周期中，心血管疾病的风险随着肥胖的增加而加速。我们将发展和 使用尖端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) 影响心血管疾病的代谢扰动和(Ii)影响代谢扰动的心血管危险因素，(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)确定 影响代谢性心血管疾病危险因素(胆固醇、血液)的代谢物和主要代谢途径 压力和血糖表型)；2)确定影响代谢物和主要代谢产物的代谢性心血管疾病危险因素 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；以及4)测试 CARDIA研究中确定的代谢物与心血管疾病危险因素和临床相关证据 社区中双源性动脉粥样硬化风险的终点(冠心病、心力衰竭和中风) (ARIC)研究。我们预计，拟议的项目由一个具有以下专业知识的多学科团队准备 心血管疾病和代谢流行病学、营养生物化学、代谢组学、生物信息学、生物统计学和 遗传学将为疾病机制提供信息，具有识别心血管疾病风险的生物标记物的强大潜力。一起， 我们的创新将有助于确定新的治疗和营养目标，以减轻全球心血管疾病的负担。

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.