Standardization of gut microbiome analysis in sports

Abstract

The gut microbiome plays a significant role in physiological functions such as nutrient processing, vitamin production, inflammatory response, and immune modulation, which, in turn, are important contributors to athlete health and performance. To date, the interpretation, discussion, and visualization of microbiome results of athletes are challenging, due to a lack of standard parameters and reference data for collection and comparison. The purpose of this perspective piece is to provide researchers with an easy-to-understand framework for the collection, analysis, and data management related to the gut microbiome with a specific focus on athletic populations. In the absence of a consensus on microbiome research in the sports field, we hope that these considerations serve as foundational "best practice." Adherence to these standard operating procedures will accelerate the path toward improving the quality of data and ultimately our understanding of the influence of the gut microbiome in sport settings.

Keywords: exercise; gut microbiome; metabolism; methodology; sport.

Figure 1

Factors associated with influencing the gut microbiome The genetic makeup of human gut microbiome is seeded at birth; however, the gut microbiome at strain level is shaped by a combination of extrinsic and intrinsic factors. Main drivers include genetics, dietary habits (plant and animal based diets), medications (nonsteroidal anti-inflammatory drugs, antibiotics, etc.), lifestyle, early-life exposures, childhood environment, adulthood exposome (neighborhood urbanicity and income), circadian rhythms, and geographical origin. Intrinsic factors include genetics and individual characteristics. In addition, the gut microbiome is shaped across a lifetime with the unique makeup of bacterial taxa acquired from other individuals (e.g., mother to offspring and social interactions transmissions). Created with BioRender.com.

Figure 2

Schematic overview of “best practices for microbiome research” in the field of sport and exercise Step 1: into the field and into the lab. Most appropriate methods for study design. Step 2: statistical analyses. Computational analyses are performed to determine the taxonomic content (which organisms are present within the sample) and the functional capacity of the sample (which genes are present). Analyses can include both read-based and assembly-based approaches depending on the experimental design. Read-based metabolic profiling analysis can be also included. Step 3: advanced statistical analysis and data interpretation. Different advanced statistical methods can be used to interpret the data. In this case, it could be useful to discover potential correlation between markers of disease or metabolic health and specific bacterial taxa (at strain level). Association between microbiome variation and athlete’s phenotype variation can be also identified with longitudinal and epidemiological studies. Created with BioRender.com.