Fig. 1

a Principal components analysis (PCA) is performed on the dataset containing the chemical shift of detected metabolites in plasma. On a PCA score scatter plots (a), each dot represents a sample (carotid artery stenosis [CAS] patient in red and control in green). The dot distribution shows homogeneous in each group, indicating no significant variation within group. The PCA score plot corresponding to the first two principal components (PC) shows that PC1 and 2 can explain 88.4 and 3.8%, respectively, of the dataset total variance. b To confirm the patterns observed in PCA and to identify metabolites responsible for these patterns, orthogonal-partial-least squares-discriminant-analysis (OPLS-DA) models are constructed to relate metabolic profiles to lipids, lactate, and choline. The 2-D score plot of metabolites in plasma samples between controls and CAS patients is shown in panel B which enables differentiation of controls and CAS patients