Statistical Methods for Large Scale Microbiome Studies of Cardiovascular Disease Risk

项目总结 心血管疾病(CVD)是全球主要的死亡原因，影响着全球近一半的成年人 在美国，每年死亡人数占所有死亡人数的四分之一，而且死亡率还在继续上升。这个 微生物组为研究和最终减轻全球心血管疾病负担提供了一个独特的范例。 在高通量测序技术的帮助下，微生物组图谱研究发现了细菌 社区与心血管疾病危险因素有关，包括高血压和收缩压。这 无论是从更好地理解病因学的角度还是从 治疗发展，因为微生物组是固有的可修改的。然而，尽管许多研究已经 证实了可能的关系，与心血管疾病危险因素相关的特定细菌分类，以及 人们对它们之间的联系方式知之甚少。最近，大规模的微生物组图谱研究 在现有的、正在进行的队列研究中进行了成百上千的个人研究。这些 研究为更彻底地研究微生物群对心血管疾病的影响提供了一个独特的机会。 风险因素。不幸的是，分析这些研究的统计和计算方法是 缺乏。这项提议旨在通过处理四个主要领域来填补方法学文献中的关键空白。 具体地说，我们的目标是开发一套全面的统计工具，用于(1)在以下方面处理批量效应 微生物组研究--随着研究规模的扩大，问题越来越多；(2)改进了对个体的识别 与心血管疾病危险因素相关的分类群；(3)进行中介分析并了解 微生物区系和对风险因素的暴露；以及(4)评估微生物区系作为效应调节剂的作用。 这些方法都是基于严格的先前数据，强调问题的重要性以及 现有战略的局限性或缺失。我们的工作是由并将直接支持分析 三项最大、最丰富的微生物组图谱研究：很少有研究结合 CARDIA、MEC和SOL微生物组研究的样本大小和协变量丰富度。因此，我们的 方法有可能促进对微生物在心血管疾病中的作用的理解，并促进 开发治疗方法和战略，以遏制不断上升的心血管疾病流行。

PROJECT SUMMARY Cardiovascular diseases (CVD) are the leading cause of death globally, affecting nearly half of all adults in the United States and responsible for a quarter of all deaths each year, with rates continuing to rise. The microbiome offers a unique paradigm for investigating and eventually mitigating the global burden of CVD. Aided by high-throughput sequencing technology, microbiome profiling studies have found bacterial communities to be related to CVD risk factors, including hypertension and systolic blood pressure. This knowledge is invaluable both from the perspective of better understanding etiology and from the perspective of therapeutic development, as the microbiome is inherently modifiable. However, although many studies have demonstrated possible relationships, the specific bacterial taxa associated with CVD risk factors, as well as the manner in which they are related, are poorly understood. Recently, large scale microbiome profiling studies of hundreds to thousands of individuals have been conducted within existing, on-going cohort studies. These studies offer a unique opportunity to achieve more thorough investigation of the impact of microbiomes on CVD risk factors. Unfortunately, the statistical and computational approaches for analyzing these studies are lacking. This proposal aims to fill critical gaps in the methodological literature by addressing four major areas. Specifically, we aim to develop comprehensive suite of statistical tools for (1) addressing batch effects in microbiome studies – increasingly problematic as studies get bigger; (2) improved identification of individual taxa associated with CVD risk factors; (3) conducting mediation analysis and understanding the relative role of microbiota and exposures on risk factors; and (4) assessing the role of the microbiota as an effect modifier. These approaches are all based on rigorous prior data emphasizing the importance of the problems as well as the limitations or absence of existing strategies. Our work is motivated by and will directly enable analyses in three of the largest, and richest microbiome profiling studies around: few studies have the combination of sample size and richness of covariates as the CARDIA, MEC, and SOL microbiome studies. Consequently, our methods have the potential for accelerating understanding of the role of microbes in CVD and facilitate development of therapies and strategies for stemming the rising CVD epidemic.