Compound discover 2

The raw data were entered into Compound Discover 2.1 (Thermofisher). Intensities were rectified by fitting the loess regression model to the quality control (QC) samples. The α parameter was set to 2 to prevent overfitting. Then, the features with >80% missing values and relative standard deviations (RSDs) >30% (in QCs) were ruled out. Features were identified by performing retention time (RT) alignment (maximum time shift: 2 min), unknown compound detection (mass tolerance: 10.0 ppm), compound grouping (time tolerance: 0.5 min), and compound annotation (mass tolerance: 5.0 ppm). Features that fully matched the MS2 spectrum in the mzCloud database underwent further analyses. The pretreated data were transformed with log and scaled with Pareto, then analysed using partial least squares discrimination analysis (PLS-DA) in MetaboAnalyst version 5.0 (https://www.metaboanalyst.ca/). The cross-validation and 100-times permutation tests were used to confirm the accuracy. Differential metabolites were screened by variable importance in projection (VIP) >1 and p < 0.05 (Student’s t-test). Heatmaps and metabolic pathway analysis were processed by the MetaboAnalyst website.

For sample preparation for metabolomics study, serum (50 μL) was thoroughly mixed with methanol (200 μL, containing 30 μg/mL chlorophenylalanine, internal standards). Then, the sample was centrifuged at 12000 r/min at 4°C for 15 min. The obtained supernatant was used for metabolomics study.
For UPLC-MS condition and data analysis, the serum metabolites profiling was performed on Ultimate 3000 UPLC system (Thermo Fisher Scientific) coupled with an Orbitrap Elite Mass Spectrometer (Thermo Fisher Scientific).
The metabolites were chromatographically separated on a Hss T3 column (100 mm × 2.1 mm, 1.8 μm, ACQUITY UPLC) at a flow rate of 0.3 mL/min for 17 min. Buffer A consisted of 0.1% formic acid in water and buffer B consisted of 0.1% formic acid in acetonitrile. The gradient was set as follows: 0–2 min, 95% A; 2–12 min, 5% A; 12–15 min, 5% A; and 15–17 min, 95% A.
For metabolite identification and pathway analysis, the collected data were processed by Compound Discover 2.0 (Thermo Fisher Scientific) to identify potential biomarkers according to the online database (HMDB, KEGG, m/z cloud). The preprocessed data was imported into SIMCA-P software (Umetrics, Umea, Sweden) for principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA). MetaboAnalyst 4.0 was used for pathway analysis.