Department of Statistics, Harvard University, Cambridge, Massachusetts, United States of America.
Institute for Medical Information Processing, Biometry, and Epidemiology, Faculty of Medicine, Ludwig-Maximilians-University München, Munich, Germany.
PLoS Comput Biol. 2022 May 9;18(5):e1010044. doi: 10.1371/journal.pcbi.1010044. eCollection 2022 May.
Statistical analysis of microbial genomic data within epidemiological cohort studies holds the promise to assess the influence of environmental exposures on both the host and the host-associated microbiome. However, the observational character of prospective cohort data and the intricate characteristics of microbiome data make it challenging to discover causal associations between environment and microbiome. Here, we introduce a causal inference framework based on the Rubin Causal Model that can help scientists to investigate such environment-host microbiome relationships, to capitalize on existing, possibly powerful, test statistics, and test plausible sharp null hypotheses. Using data from the German KORA cohort study, we illustrate our framework by designing two hypothetical randomized experiments with interventions of (i) air pollution reduction and (ii) smoking prevention. We study the effects of these interventions on the human gut microbiome by testing shifts in microbial diversity, changes in individual microbial abundances, and microbial network wiring between groups of matched subjects via randomization-based inference. In the smoking prevention scenario, we identify a small interconnected group of taxa worth further scrutiny, including Christensenellaceae and Ruminococcaceae genera, that have been previously associated with blood metabolite changes. These findings demonstrate that our framework may uncover potentially causal links between environmental exposure and the gut microbiome from observational data. We anticipate the present statistical framework to be a good starting point for further discoveries on the role of the gut microbiome in environmental health.
在流行病学队列研究中对微生物基因组数据进行统计分析，有望评估环境暴露对宿主和宿主相关微生物组的影响。然而，前瞻性队列数据的观察性特征和微生物组数据的复杂特征使得发现环境与微生物组之间的因果关系具有挑战性。在这里，我们引入了一个基于 Rubin 因果模型的因果推理框架，该框架可以帮助科学家研究环境-宿主-微生物组之间的关系，利用现有的、可能强大的检验统计量，并检验合理的尖锐零假设。我们使用来自德国 KORA 队列研究的数据，通过设计两个具有干预措施的假设随机实验，说明了我们的框架：（i）减少空气污染和（ii）预防吸烟。我们通过基于随机化的推理，在匹配的受试者组之间测试微生物多样性的变化、个体微生物丰度的变化以及微生物网络布线，来研究这些干预措施对人类肠道微生物组的影响。在预防吸烟的情况下，我们确定了一小群值得进一步研究的相互关联的分类群，包括 Christensenellaceae 和 Ruminococcaceae 属，这些分类群以前与血液代谢物变化有关。这些发现表明，我们的框架可以从观察性数据中揭示环境暴露与肠道微生物组之间潜在的因果联系。我们预计，目前的统计框架将成为进一步发现肠道微生物组在环境健康中的作用的良好起点。
