Identification of potential diagnostic biomarkers of cerebral infarction using gas chromatography-mass spectrometry and chemometrics

Abstract

Cerebral infarction (CI) is one of the most common cerebrovascular diseases and remains a major health problem worldwide. In this study, we evaluated the potential diagnostic biomarkers and important relevant metabolic pathways associated with CI. Metabolomics based on gas chromatography-mass spectrometry coupled with the multivariate pattern recognition technique were used to characterize the potential serum metabolic profiles of CI. Forty healthy controls and thirty-three cerebral infarction patients were recruited for the nontargeted global metabolites' study and subsequent targeted fatty acid analysis. Overall, thirty-four endogenous metabolites were found in serum from the untargeted global study, four of which were detected to be significantly different between the CI group and healthy controls, including l-lysine, octadecanoic acid (fatty acid), l-tyrosine and lactic acid. Additionally, fourteen free fatty acids were identified by the subsequent targeted fatty acid analysis, and seven of them were detected to be significantly different between the CI group and healthy controls, which were mainly associated with arachidonic acid metabolism and fatty acid metabolism. Our results suggest several potential diagnostic biomarkers, and serum metabolism research is demonstrated as a powerful tool to explore the pathogenesis of CI.

Fig. 1. Typical total ion chromatograms (TICs) of the serum samples from the fatty acid analysis of the healthy participants (red), cerebral infarction patients (blue) and quality control (black). (1) Myristic acid; (2) pentadecanoic acid; (3) palmitic acid; (4) palmitoleic acid; (5) heptadecanoic acid (internal standard); (6) stearic acid; (7) 9-octadecenoic acid; (8) linoleic acid; (9) 6,9,12-octadecatrienoic acid; (10) 9,12,15-octadecatrienoic acid; (11) 8,11,14-eicosatrienoic acid; (12) arachidonic acid; (13) eicosapentaenoic acid; (14) docosapentaenoic acid; and (15) docosahexaenoic acid.

Fig. 2. Random forest model recognition to discriminate among the healthy participants (HP), cerebral infarction patients (CIP) and quality control (QC) groups: (A) nontargeted global metabolite study and (B) fatty acid analysis.

Fig. 3. Variable importance measure between CIP and HP in the fatty acid analysis obtained by RF (all 14 metabolites).

Fig. 4. Summary of the key metabolic pathway analysis in the fatty acid analysis with MetaboAnalyst 3.0. (a) Arachidonic acid metabolism and (b) fatty acid metabolism.