Use of the Microbiome in the Practice of Epidemiology: A Primer on -Omic Technologies

Betsy Foxman; Emily T. Martin

Disclosures

Am J Epidemiol. 2015;182(1):1-8. 

In This Article

Abstract and Introduction

Abstract

The term microbiome refers to the collective genome of the microbes living in and on our bodies, but it has colloquially come to mean the bacteria, viruses, archaea, and fungi that make up the microbiota (previously known as microflora). We can identify the microbes present in the human body (membership) and their relative abundance using genomics, characterize their genetic potential (or gene pool) using metagenomics, and describe their ongoing functions using transcriptomics, proteomics, and metabolomics. Epidemiologists can make a major contribution to this emerging field by performing well-designed, well-conducted, and appropriately powered studies and by including measures of microbiota in current and future cohort studies to characterize natural variation in microbiota composition and function, identify important confounders and effect modifiers, and generate and test hypotheses about the role of microbiota in health and disease. In this review, we provide an overview of the rapidly growing literature on the microbiome, describe which aspects of the microbiome can be measured and how, and discuss the challenges of including the microbiome as either an exposure or an outcome in epidemiologic studies.

Introduction

The term microbiome refers to the collective genome of the microbes living in and on our bodies, but it has colloquially come to mean the bacteria, viruses, archaea, and fungi that make up the microbiota (previously known as microflora). "-Omic" technologies have transformed our perception of the microbiota by characterizing the microbes present and their relevant abundance, as well as their ongoing functions. We can identify the microbes present in the body (membership) and their relative abundance using genomics, characterize their genetic potential (or gene pool) using metagenomics, and describe their ongoing functions using transcriptomics, proteomics, and metabolomics.

The field of microbiomics is very new, and its application in epidemiology has barely begun, but excitement about its potential is high. Every day, new articles appear in the scientific literature—and often in the newspaper—touting the role of the microbiome in human health. Microbiota have been associated with obesity, the metabolic syndrome, and even autism.[1] Disruptions in the microbiota, termed dysbioses, are hypothesized to cause periodontal disease,[2] cause inflammatory bowel disease,[3] and potentially increase the risk of cancer.[4]

Unlike other reviews in this series on -omic technologies, the microbiome is not a technique but a reconceptualization of humans as superorganisms consisting of human cells and microorganisms. We argue that microbiota can be a marker of exposure and a prognostic factor as well as a factor in disease etiology. However, this will require the incorporation of laboratory analyses that generate data characterizing the presence and function of microbes in epidemiologic studies, assessments of the reliability and validity of these analyses and the putative biomarkers, and knowledge about how to best use these data to address questions of clinical and public health importance.

Microbiota are dynamic, and the variation within an individual can be high. As yet, we do not know what magnitude of difference in microbial membership and relative abundance (jointly known as community structure) or function corresponds to a clinically meaningful difference. This lack of knowledge creates challenges for good study design and sample size estimation. Further, because our understanding of the factors that affect the microbiome is limited, so too is our understanding of what factors might confound or modify observed associations between the microbiome and health and disease. This makes it difficult to differentiate between risk markers and causal factors and between microbiomic changes that result from human disease and those that cause human disease. Well-conducted, population-based longitudinal studies are essential to filling these knowledge gaps. In this review, we provide an overview of the rapidly growing literature on the microbiome, describe which aspects of the microbiome can be measured and how, and discuss the challenges of including the microbiome as either an exposure or an outcome in epidemiologic studies.

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