Statistics analysis
Continuous variables were expressed as the mean ± standard deviation or
median (quartiles [Q]1, [Q]3) depending on their distribution.
Categorical variables are summarized as numbers and percentages. For
continuous variables, the differences among different inflammatory
asthma phenotypes and healthy were analyzed using A student’s t-test, or
ANOVA if they were normally distributed, otherwise Wilcoxon rank-sum
test were used to assess. Categorical data were compared using the
chi-squared test or Fisher’s test. Correlations between targeted
metabolites and clinical indexes in asthma patients in the validation
set were expressed using Spearman’s correlation coefficients and
adjusted by false discovery rate (FDR) correction (Benjamini–Hochberg
method). Between two groups, targeted metabolites were examined by
receiver operating characteristic (ROC) curves and the area under the
curve (AUC) of each ROC was calculated to show their capability of
discriminating different groups in validation set. Logistic and negative
binomial regression models were established to assess the association
between metabolites and severe asthma exacerbation during follow-up in
the validation set. These analyses were conducted using SPSS version
25.0 (IBM Corp, Armonk, NY, USA). In all statistical analyses, a double
tailed P -value of < 0.05 was considered statistically
significant.