Data analysis
Complex chromatograms were deconvoluted and peaks were aligned, and a
data matrix was constructed using MS‐DIAL
(v.2.56, http://prime.psc.riken.jp/compms/msdial/main.html).
In MS‐DIAL setting, the range for mass detection was 50 - 650 Da and the
minimum peak height for trustable feature detection was chosen to be1000
amplitude. The preferred tolerance limit for retention time was 0.05
min. The identification cut-off score was 70%. Identificaiton of the
metabolites was performed by using the Fiehn Retention Index database.
The data matrix normalization was based on the sum of the total peak
area for individaul samples. In the data matrix, metabolites that had
traits more than 50% of the values missing were excluded. Missing
values in the data matrix were filled with the half value of the lowest
concentration in the metabolite group. PLS‐DA analysis in Metaboanalyst
4.0 platform was used for class discrimination, simplifying
interpretation and finding candidate biomarkers. The variable importance
in projection (VIP) value is estimated to discriminate the most
significant metabolites for stratified groups. Student’s unpaired t‐test
was used to compare changes in mean expression per metabolites between
groups. p < 0·05 was accepted to be statistically significant.
Altered metabolites were evaluated in pathway analysis within the
Metaboanalyst 4.0 platform.