NMR-based plasma metabolic profiling in patients with unstable angina.

Autor: PouralijanAmiri M; Department of Genetics & Molecular Medicine, Faculty of Medicine, Zanjan University of Medical Sciences, Zanjan, Iran., Khoshkam M; Chemistry Group, Faculty of Basic Sciences, University of Mohaghegh Ardabili, Ardabil, Iran., Madadi R; Department of Cardiology, Mousavi Hospital, Zanjan University of Medical Sciences, Zanjan, Iran., Kamali K; Zanjan Metabolic Diseases Research Center, Zanjan University of Medical Sciences, Zanjan, Iran., Faghanzadeh Ganji G; Cardiac Surgery Department, Rohani Hospital, Babol University of Medical Sciences, Babol, Iran., Salek R; International Agency for Research on Cancer, 150cours Albert Thomas, 69372 Lyon CEDEX 08, Lyon, France., Ramazani A; Zanjan Metabolic Diseases Research Center, Zanjan University of Medical Sciences, Zanjan, Iran.; Cancer Gene Therapy Research Center, Zanjan University of Medical Sciences, Zanjan, Iran.
Jazyk: angličtina
Zdroj: Iranian journal of basic medical sciences [Iran J Basic Med Sci] 2020 Mar; Vol. 23 (3), pp. 311-320.
DOI: 10.22038/IJBMS.2020.39979.9475
Abstrakt: Objectives: Unstable angina (UA) is a form of the acute coronary syndrome (ACS) that affects more than a third of the population before age 70. Due to the limitations of diagnostic tests, appropriate identification of UA is difficult. In this study, we proceeded to investigate metabolite profiling in UA patients compared with controls to determine potential candidate biomarkers.
Materials and Methods: Ninety-four plasma samples from UA and 32 samples from controls were analyzed based on 1H NMR spectroscopy. The raw data were processed, analyzed, and subjected to partial least squares-discrimination analysis (PLS-DA), a supervised classification method with a good separation of control and UA patients was observed. The most important variables (VIP) ≥1 were selected and submitted to MetaboAnalyst pathway enrichment to identify the most important ones.
Results: We identified 17 disturbed metabolites in UA patients in comparison with the controls. These metabolites are involved in various biochemical pathways such as steroid hormone biosynthesis, aminoacyl-tRNA biosynthesis, and lysine degradation. Some of the metabolites were deoxycorticosterone, 17-hydroxyprogesterone, androstenedione, androstanedione, etiocholanolone, estradiol, 2-hydroxyestradiol, 2-hydroxyestrone, 2-methoxyestradiol, and 2-methoxyestrone. In order to determine test applicability in diagnosing UA, a diagnostic model was further created using the receiver operator characteristic (ROC) curve. The areas under the curve (AUC), sensitivity, specificity, and precision were 0.87, 90%, 65%, and 91%, respectively, for diagnosing of UA.
Conclusion: These metabolites could not only be useful for the diagnosis of UA patients but also provide more information for further deciphering of the biological processes of UA.
Databáze: MEDLINE
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