Billions of microbes sculpt the gut ecosystem, affecting physiology. Since endurance athletes’ performance is often physiology-limited, understanding the composition and interactions within athletes’ gut microbiota could improve performance. Individual studies describe differences in the relative abundance of bacterial taxa in endurance athletes, suggesting the existence of an “endurance microbiota”, yet the taxa identified are mostly non-overlapping. To narrow down the source of this variation, we created a bioinformatics workflow and reanalyzed fecal microbiota from four 16S rRNA gene sequence datasets associated with endurance athletes and controls, examining diversity, relative abundance, correlations, and association networks. There were no significant differences in alpha diversity among all datasets and only one out of four datasets showed a significant overall difference in bacterial community abundance. When bacteria were examined individually, there were no genera with significantly different relative abundance in all four datasets. Two genera were significantly different in two datasets (Veillonella and Romboutsia). No changes in correlated abundances were consistent across datasets. A power analysis using the variance in relative abundance detected in each dataset indicated that much larger sample sizes will be necessary to detect a modest difference in relative abundance especially given the multitude of covariates. Our analysis confirms several challenges when comparing microbiota in general, and indicates that microbes consistently or universally associated with human endurance remain elusive.

Billions of microbial cells sculpt the gut ecosystem, playing essential roles in human physiology. Since endurance athletes' performance is often physiology-limited, understanding the composition and interactions within these athletes' gut microbiomes could lead to improved performance. Previous studies describe differences in the relative abundance of bacterial taxa when comparing athletes versus controls or athletes before and after an endurance event, suggesting the existence of an "endurance microbiome". However, there are inconsistencies among studies in which taxa correlate with extended physical exertion. Although these studies employed similar barcoding methods, variation in downstream bioinformatic analyses makes it difficult to determine whether inconsistencies are due to methodological differences or biological factors. Herein, we created a metagenomic bioinformatics workflow reanalyzing four 16S rDNA sequence datasets reflecting endurance athletes' gut microbiomes, looking at alpha diversity, changes in relative abundance of gut microbiome genera, changes in pairwise correlations between bacterial genera and compared bacterial association networks. There were no significant differences in alpha diversity between any of the four treatment group comparisons. For relative abundance, there were no consistent differences in all four datasets, and only two genera were significantly different in 50% of the datasets. Although many genera showed changes in pairwise correlations in endurance microbiome samples from individual datasets, none were consistent across datasets. Collectively, these results suggest that either there is no universal endurance microbiome, or that it remains elusive even after controlling for the bioinformatic workflow and statistical analyses. Using this data, a power analysis indicates that sample sizes 150- to 800-fold larger than these published studies would be necessary to detect a 10% difference in relative abundance. Furthermore, 10- to 20-fold more samples will be needed to control for the multitude of covariates (genetic, metabolic, dietary, environmental, and pharmacological factors) that mold the gut microbiome of athletes and non-athletes alike.