Statistical Methods for Large Scale Microbiome Studies of Cardiovascular Disease Risk

项目摘要 心血管疾病（CVD）是全球死亡的主要原因，影响到全球近一半的成年人。 在美国，每年有四分之一的人死于疟疾，而且死亡率还在继续上升。的 微生物组为研究并最终减轻CVD的全球负担提供了一个独特的范例。 在高通量测序技术的帮助下，微生物组分析研究发现了细菌 社区与CVD危险因素有关，包括高血压和收缩压。这 从更好地理解病因学的角度和从 治疗发展，因为微生物组本质上是可改变的。尽管许多研究 证明了可能的关系，与CVD危险因素相关的特定细菌分类群，以及 他们之间的关系，我们知之甚少。最近，大规模的微生物组分析研究 在现有的、正在进行的队列研究中，已经对数百至数千人进行了研究。这些 研究提供了一个独特的机会，以实现更彻底的调查微生物对CVD的影响 危险因素不幸的是，分析这些研究的统计和计算方法是 缺乏这项建议旨在通过处理四个主要领域，填补方法学文献中的关键空白。 具体来说，我们的目标是开发一套全面的统计工具，用于（1）解决批量效应， 微生物组研究-随着研究规模的扩大，问题越来越多;（2）改善个体识别 分类群与心血管疾病的危险因素;（3）进行调解分析和理解的相对作用， 微生物群和风险因素的暴露;以及（4）评估微生物群作为效应调节剂的作用。 这些方法都是基于严格的先验数据，强调问题的重要性， 现有战略的局限性或缺乏。我们的工作是由以下因素推动的，并将直接使分析成为可能： 三个最大，最丰富的微生物组分析研究：很少有研究结合了 样本量和协变量的丰富程度，如CARDIA、MEC和SOL微生物组研究。因此，我们 这些方法有可能加速理解微生物在CVD中的作用， 开发治疗方法和策略，以遏制心血管疾病的流行。

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.