Temporal variability in quantitative human gut microbiome profiles and implications for clinical research

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.


Statistics
For all statistical analyses, confirm that the following items are present in the figure legend, table legend, main text, or Methods section.
n/a Confirmed The exact sample size (n) for each experimental group/condition, given as a discrete number and unit of measurement A statement on whether measurements were taken from distinct samples or whether the same sample was measured repeatedly The statistical test(s) used AND whether they are one-or two-sided Only common tests should be described solely by name; describe more complex techniques in the Methods section.
A description of all covariates tested A description of any assumptions or corrections, such as tests of normality and adjustment for multiple comparisons A full description of the statistical parameters including central tendency (e.g. means) or other basic estimates (e.g. regression coefficient) AND variation (e.g. standard deviation) or associated estimates of uncertainty (e.g. confidence intervals) For null hypothesis testing, the test statistic (e.g. F, t, r) with confidence intervals, effect sizes, degrees of freedom and P value noted Give P values as exact values whenever suitable.

For Bayesian analysis, information on the choice of priors and Markov chain Monte Carlo settings
For hierarchical and complex designs, identification of the appropriate level for tests and full reporting of outcomes Estimates of effect sizes (e.g. Cohen's d, Pearson's r), indicating how they were calculated Our web collection on statistics for biologists contains articles on many of the points above.

Software and code
Policy information about availability of computer code Data collection

Data analysis
For manuscripts utilizing custom algorithms or software that are central to the research but not yet described in published literature, software must be made available to editors/reviewers. We strongly encourage code deposition in a community repository (e.g. GitHub). See the Nature Research guidelines for submitting code & software for further information.

Data
Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A list of figures that have associated raw data -A description of any restrictions on data availability Field-specific reporting Please select the one below that is the best fit for your research. If you are not sure, read the appropriate sections before making your selection.

Life sciences Behavioural & social sciences Ecological, evolutionary & environmental sciences
For a reference copy of the document with all sections, see nature.com/documents/nr-reporting-summary-flat.pdf

Life sciences study design
All studies must disclose on these points even when the disclosure is negative.

MRI-based neuroimaging
At the moment of study set up, there were no available methods for sample size estimation and little information on effect size in microbiome studies. We here aimed for a sample size of 20 individuals. Sample size estimation was based on a case study showing a significant shift of some gut bacteria during the menstruation phase and a longitudinal diet intervention study showing that a significant effect of diet could be noted with daily sampling in 10 individuals. Previously, we were able to show a significant effect of stool consistency with 53 independent datapoints. The longitudinal set up of this study would lead to replicated data, increasing the power of the analysis by incorporating the temporal variation, hence allowing a smaller sample size. However, sample sizes of non-parametric tests for testing differences between two or more groups should generally be >10 to be valuable. We therefore opted for a sample size of 20. The study design, which included one and a half menstrual cycle, allowed to repeat measurements for several menstrual phase parameters, increasing the power of those analyses further.
Of twenty-two recruited volunteers, two did not complete the study protocol and were excluded from analyses. For statements regarding normal temporal variation, only non-perturbed time-series were included, leaving out one time-series in which an infection event took place.
This study includes a discovery cohort only. Replication was not performed.
This study did not allocate participants into groups, hence no randomization was applied. Factors known to influence microbiome variation (e.g. BMI, age, stool consistency, dietary information, ... ) were recorded, summary measures were determined and outliers were investigated to evaluate possible confounding. Statistical analyses were performed considering confounding factors and linear relationships between the collected data.
This study did not involve allocation of participants into groups.