A randomization-based causal inference framework for uncovering environmental exposure effects on human gut microbiota

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 characteris...

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Vydáno v:	PLoS computational biology Ročník 18; číslo 5; s. e1010044
Hlavní autoři:	Sommer, Alice J., Peters, Annette, Rommel, Martina, Cyrys, Josef, Grallert, Harald, Haller, Dirk, Müller, Christian L., Bind, Marie-Abèle C.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States Public Library of Science 01.05.2022 Public Library of Science (PLoS)
Témata:	Air pollution Air pollution control Autoimmune diseases Biology and Life Sciences Cigarettes Cohort Studies Colorectal cancer Computer and Information Sciences Diabetes Ecology and Environmental Sciences Environmental aspects Environmental effects Environmental Exposure - adverse effects Environmental health Environmental statistics Epidemiology Exposure Gastrointestinal diseases Gastrointestinal Microbiome - genetics Genetic aspects Genomic analysis Gut microbiota Humans Inflammatory bowel disease Intestinal microflora Metabolic disorders Metabolites Microbiomes Microbiota Microbiota (Symbiotic organisms) Microorganisms Nitrogen dioxide Physical Sciences Physiological aspects Prevention Prospective Studies Random Allocation Randomization Research and Analysis Methods Smoking Smoking cessation Social Sciences Statistical analysis Statistical inference Statistical tests Statistics Wiring Germany
ISSN:	1553-7358, 1553-734X, 1553-7358
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Shrnutí:	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.
Bibliografie:	new_version ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 The authors have declared that no competing interests exist.
ISSN:	1553-7358 1553-734X 1553-7358
DOI:	10.1371/journal.pcbi.1010044