Serum Biomarker Identification by Mass Spectrometry in Acute Aortic Dissection

Abstract

Background/Aims: Aortic dissection (AD) is also known as intramural hematoma. This study aimed to screen peripheral blood biomarkers of small molecule metabolites for AD using high-performance liquid chromatography-mass spectrometry (HPLC-MS). Methods: Sera from 25 healthy subjects, 25 patients with well-established AD, and 25 patients with well-established hypertension were investigated by HPLC-MS to detect metabolites, screen differentially expressed metabolites, and analyze metabolic pathways. Results: Twenty-six and four metabolites were significantly up- and down-regulated in the hypertensive patients compared with the healthy subjects; 165 metabolites were significantly up-regulated and 109 significantly down-regulated in the AD patients compared with the hypertensive patients. Of these metabolites, 35 were up-regulated and 105 down-regulated only in AD patients. The metabolites that were differentially expressed in AD are mainly involved in tryptophan, histidine, glycerophospholipid, ether lipid, and choline metabolic pathways. As AD alters the peripheral blood metabolome, analysis of peripheral blood metabolites can be used in auxiliary diagnosis of AD. Conclusion: Eight metabolites are potential biomarkers for AD, 3 of which were differentially expressed and can be used for auxiliary diagnosis of AD and evaluation of treatment effectiveness.

Identification of Lysophosphatidylcholines and Sphingolipids as Potential Biomarkers for Acute Aortic Dissection via Serum Metabolomics

“…Recently, Ren et al conducted a metabolomics analysis on AAD and highlighted eight metabolites as potential biomarkers for AAD, including N1-acetyl-N2-formyl-5methoxykynuramine (AFMK), glycerophosphocholine, and ergothioneine. 28 However, they simply enrolled AD patients without using the Stanford standard for further classification and divided participants into control, hypertension, and AD groups, different to the groups included in the present study and perhaps contributing to different results. Moreover, the present study design could potentially reveal some different mechanisms between Stanford type A AAD and type B AAD.…”

Metabolomic Analysis of Influenza A Virus A/WSN/1933 (H1N1) Infected A549 Cells during First Cycle of Viral Replication

“…SIMCA-P 14.1 (Umetrics, Umea, Sweden) was used to establish a statistical model [18]. The data were preprocessed by Pareto-scaling for multidimensional statistical analysis, including unsupervised principal component analysis (PCA) [19,20], supervised partial least squares discriminant analysis (PLS-DA) [21], and orthogonal partial least squares discriminant analysis (OPLS-DA) [22]. Single-dimensional statistical analysis included Student’s t -test and variation multiple analyses, and the PCA maps, volcano maps, and cluster maps were generated with the R program.…”

LncRNA Expression Profile of Human Thoracic Aortic Dissection by High-Throughput Sequencing

“…Consequently, effective therapeutic targets are needed to prevent the pathological progression of TAD, including a loss of smooth muscle cells (SMC) and dynamic imbalance of the extracellular matrix (ECM). Despite great progress, the mechanisms underlying TAD are still not fully understood, thereby limiting the identification of potential targets [1, 2]. …”