Abstract

While clinical gut microbiota research is ever-expanding, extending reference knowledge of healthy between- and within-subject gut microbiota variation and its drivers remains essential; in particular, temporal variability is under-explored, and a comparison with cross-sectional variation is missing. Here, we perform daily quantitative microbiome profiling on 713 fecal samples from 20 Belgian women over six weeks, combined with extensive anthropometric measurements, blood panels, dietary data, and stool characteristics. We show substantial temporal variation for most major gut genera; we find that for 78% of microbial genera, day-to-day absolute abundance variation is substantially larger within than between individuals, with up to 100-fold shifts over the study period. Diversity, and especially evenness indicators also fluctuate substantially. Relative abundance profiles show similar but less pronounced temporal variation. Stool moisture, and to a lesser extent diet, are the only significant host covariates of temporal microbiota variation, while menstrual cycle parameters did not show significant effects. We find that the dysbiotic Bact2 enterotype shows increased between- and within-subject compositional variability. Our results suggest that to increase diagnostic as well as target discovery power, studies could adopt a repeated measurement design and/or focus analysis on community-wide microbiome descriptors and indices.

Highlights

  • Namely the minimum, median, and maximum values, for dietary data, such as the energy derived from fat, proteins, and carbohydrates, as well as the amount of fibers, were calculated based on the respective values for the time series data of each participant

  • We limited the investigated genera to those present in more than 60% of the samples

  • To bin samples into classes based on their microbiota composition several methods have been used in the past

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Summary

Objectives

If the sole purpose of the study is the diagnostics-oriented detection of differences between groups, RMPs might be the better option because of the reduced temporal variation

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