Biomarker Screening by LCMS and Liquid Chip Technology in Acute Aortic Dissection

Background Aortic dissection (AD) is a serious disease. Previous study, the use of peripheral blood biomarkers to diagnose AD showed strong clinical feasibility, but the possible molecular mechanism is unclear. Methods Sera from 79 healthy subjects, 73 patients with well-established AD, and 74 patients with well-established acute myocardial infarction (AMI) were investigated by Liquid Chromatograph-Mass Spectrometer to detect metabolites (AFMK, Glycerophosphocholine, Inosine, SPH). The cell factor expression in the 3 group were detected by Liquid Chip Technology. Results The serum content trends of 4 metabolic indexes in patients with AMI and AD group were used as the diagnostic models, and the effective diagnosis rate was 97.8%. The diagnosis rate is 89.8% in distinguishing patients with AMI from patients with AD. The expression in serum of the 3 groups showed that there were significant differences in the expression of 23 cytokines. By correlation analysis, it was found that miP-1, IL-7, MIP-1β, EGF and other cytokines were significantly correlated with the 4 metabolic molecules. Conclusions AFMK, Glycerophosphocholine, Inosine, Sphingfungin B (SPH) metabolites are potential biomarkers for AD, and the influence of related metabolic process may be related to the expression of miP-1, IL-7, MIP-1β, EGF, and other cytokines. Aortic dissection (AD) is a serious disease. Previous study, the use of peripheral blood biomarkers to diagnose AD showed strong clinical feasibility, but the possible molecular mechanism is unclear. Sera from 79 healthy subjects, 73 patients with well-established AD, and 74 patients with well-established acute myocardial infarction (AMI) were investigated by Liquid Chromatograph-Mass Spectrometer to detect metabolites (AFMK, Glycerophosphocholine, Inosine, SPH). The cell factor expression in the 3 group were detected by Liquid Chip Technology. The serum content trends of 4 metabolic indexes in patients with AMI and AD group were used as the diagnostic models, and the effective diagnosis rate was 97.8%. The diagnosis rate is 89.8% in distinguishing patients with AMI from patients with AD. The expression in serum of the 3 groups showed that there were significant differences in the expression of 23 cytokines. By correlation analysis, it was found that miP-1, IL-7, MIP-1β, EGF and other cytokines were significantly correlated with the 4 metabolic molecules. AFMK, Glycerophosphocholine, Inosine, Sphingfungin B (SPH) metabolites are potential biomarkers for AD, and the influence of related metabolic process may be related to the expression of miP-1, IL-7, MIP-1β, EGF, and other cytokines.