Statistical analysis
Visualization techniques (Q-Q plots and histograms) and
Kolmogorov-Smirnov tests were applied to assess the distribution of
continuous variables. Mean \(\pm\) standard deviation (SD) and median
and 25th and 75th percentiles were
reported for continuous variables that followed normal and non-normal
distributions, respectively. Demographic and clinical characteristics of
DPI and MDI device users were compared using parametric and
nonparametric tests (Mann-Whitney U test, Pearson Chi-Square, or
Fisher’s Exact tests). According to the literature and consulting with
experts in the team, confounding factors were drawn in the directed
acyclic graph (DAG, Figure 2) and, if applicable, considered for
adjustment in further analyses. Bacterial microbiome composition and
global diversity (α and β diversity using phyloseq, 26and vegan 27 R-packages) were compared between DPI and
MDI groups. The richness and the Shannon index were used to assess the
differences in α diversity. The Wilcoxon rank-sum test evaluated the
significant difference in α diversity. The Bray-Curtis and weighted
UniFrac distance measures were used to compare the two groups’ β
diversity with p-values calculated by PERMANOVA models after adjusting
for multiple covariates (as defined in the DAG, Figure 2). A p-value
compared between the two groups using the Analysis of Compositions of
Microbiomes with the Bias Correction (ANCOM-BC) method by ANCOMBC
R-package, 28 including an internal normalization.
Taxons present in \(\geq\) 5% of samples, 20,29 and
covariates, as defined in the DAG (Figure 2), were included in the
ANCOM-BC differential abundance model. Multiple testing was corrected
using the Benjamini–Hochberg method, with 0.05 as a significant cut-off
if applicable. R (version 4.2.2, 2022-10-31) and R-studio (version
2023.03.1+446) were used for the analyses and data visualizations.