Clinical data were analysed using SPSS Statistics 25 software (IBM®, Armonk, NY, USA). Data are represented as the mean ± standard deviation or median and interquartile range. Continuous variables were compared using Student’s t test or the Mann−Whitney U test. Student’s t test is used when two samples are small and meet the conditions of normal distribution and homogeneity of variance. The Mann−Whitney U test was used when the samples did not meet the conditions of normal distribution and homogeneity of variance. Categorical variables between the two groups were compared by Fisher’s exact probability method. P < 0.05 was considered statistically significant.
The metabolic profiles were imported into R for principal component analysis (PCA) to observe the overall distribution among the samples and the stability of the entire analysis process. Partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to distinguish differential metabolites between groups. To prevent overfitting, 7-fold cross-validation and 200 response permutation tests were utilized to evaluate the quality of the model. Variable importance of projection (VIP) values obtained from the OPLS-DA model were used to rank the overall contribution of each variable to group discrimination. A two-tailed Student’s t test was further used to verify whether the differences in metabolites between groups were significant. Differential metabolites were selected with VIP >1.0, P < 0.05, and fold change (FC) >1.5 or <0.7. Binary logistic regression analysis was constructed to screen independent risk factors. Receiver operating characteristic (ROC) curves were constructed to evaluate the diagnostic ability of differential metabolites between the tested groups.
The metabolic profiles were imported into R for principal component analysis (PCA) to observe the overall distribution among the samples and the stability of the entire analysis process. Partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to distinguish differential metabolites between groups. To prevent overfitting, 7-fold cross-validation and 200 response permutation tests were utilized to evaluate the quality of the model. Variable importance of projection (VIP) values obtained from the OPLS-DA model were used to rank the overall contribution of each variable to group discrimination. A two-tailed Student’s t test was further used to verify whether the differences in metabolites between groups were significant. Differential metabolites were selected with VIP >1.0, P < 0.05, and fold change (FC) >1.5 or <0.7. Binary logistic regression analysis was constructed to screen independent risk factors. Receiver operating characteristic (ROC) curves were constructed to evaluate the diagnostic ability of differential metabolites between the tested groups.
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