Table 1.
Mean ± SD of study variables in control and ischemic stroke groups.
Fig 1.
Two-dimensional principal components analysis (PCA) score plots of stroke and control groups in the positive mode.
t[1] = first principal component; t[2] = second principal component. The QC samples were tightly clustered in the PCA score plot and showing minimal analytical variation.
Fig 2.
Serum metabolic profiling and score plot with PLS-DA.
A and B: metabolic profiling in control and stroke groups, respectively. Blue box marked the differences of high or area between the two chromatograms; C: the controls are indicated by triangles and stroke patients by blue triangles. Each data point represents one subject. Comp, component. t[1], component 1; t[2], component 2; One data point represents one subject; D: permutation test result of the PLS-DA model in the positive ESI mode. The R2Y value represents the goodness of fit of the model. The Q2 value represents the predictability of the models. R2Y (green triangle) = 0.927 and Q2 (blue box) = 0.853. All R2Y and Q2 values to the left were lower than the original points to the right, showing that the PLS-DA model was valid.
Table 2.
Identification and changing trends for the principal metabolites of ischemic stroke.
Fig 3.
Areas under ROC curve of the biomarkers and combination of the three biomarkers.
Marker 1, uric acid; marker 2, sphinganine; marker 3, adrenoyl ethanolamide. Red line combination of three biomarkers: AUC = 0.941.
Table 3.
Receiver operator characteristic curve analysis of 12 metabolites.
Fig 4.
Summary of pathway analysis for biomarkers in MetaboAnalyst3.0.
(1) Glycerophospholipid metabolism; (2) Sphingolipid metabolism; (3) Glycosylphosphatidylinositol (GPI)-anchor biosynthesis.