Plasma fingerprinting with GC-MS in acute coronary syndrome.
ABSTRACT New biomarkers of cardiovascular disease are needed to augment the information obtained from traditional indicators and to illuminate disease mechanisms. One of the approaches used in metabolomics/metabonomics for that purpose is metabolic fingerprinting aiming to profile large numbers of chemically diverse metabolites in an essentially nonselective way. In this study, gas chromatography-mass spectrometry was employed to evaluate the major metabolic changes in low molecular weight plasma metabolites of patients with acute coronary syndrome (n = 9) and with stable atherosclerosis (n = 10) vs healthy subjects without significant differences in age and sex (n = 10). Reproducible differences between cases and controls were obtained with pattern recognition techniques, and metabolites accounting for higher weight in the classification have been identified through their mass spectra. On this basis, it seems inherently plausible that even a simple metabolite profile might be able to offer improved clinical diagnosis and prognosis, but in addition, specific markers are being identified.
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ABSTRACT: How to design a state-of-the art proteomic/metabolomic analysis.•Latest advances in atherothrombosis through proteomics/metabolomics.•Potential of systems biology for integrating omics projects.•Biomarker and therapeutical target implementation in the clinic.Translational Proteomics. 10/2014;
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ABSTRACT: Abnormal lipid composition and metabolism of plasma lipoproteins play a crucial role in the pathogenesis of coronary heart disease (CHD). A 1H NMR-based lipidomic approach was used to investigate the correlation of coronary arteries stenosis with the atherogenic (non-HDL) and atheroprotective (HDL) lipid profiles in 99 patients with CHD of various stages of disease and compared with 60 patients with normal coronary arteries (NCA), all documented in coronary angiography. The Pattern Recognition models created from lipid profiles predicted the presence of CHD with a sensitivity of 87% and a specificity of 88% in the HDL model, and with 90% and 89% in the non-HDL model, respectively. Patients with mild, moderate, and severe coronary arteries stenosis were progressively differentiated from those with NCA in the non-HDL model with a statistically significant separation of severe stage from both mild and moderate. In the HDL model, the progressive differentiation of the disease stages was statistically significant only between patients with mild and severe coronary arteries stenosis. The lipid constituents of lipoproteins that mainly characterized the initial stages and then the progression of the disease were the high levels of saturated fatty acids in lipids in both HDL and non-HDL particles, the low levels of HDL-phosphatidylcholine, HDL-sphingomyelin, and omega-3 fatty acids and linoleic acid in lipids in non-HDL particles. The conventional lipid marker, total cholesterol, found in low levels in HDL and in high levels in non-HDL, also contributed to the onset of the disease but with a much lower coefficient of significance. 1H NMR-based lipidomic analysis of atherogenic and atheroprotective lipoproteins could contribute to the early evaluation of the onset of coronary artery disease, and possibly to the establishment of an appropriate therapeutic option.Journal of Proteome Research 04/2014; · 5.06 Impact Factor
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ABSTRACT: Gestational Diabetes (GDM) is causing severe short- and long-term complications for mother, fetus or neonate. As yet, the metabolic alterations that are specific for the development of GDM have not been fully determined, which also precludes the early diagnosis and prognosis of this pathology. In this pilot study, we determine the metabolic fingerprint, using a multiplatform LC-QTOF/MS, GC-Q/MS and CE-TOF/MS system, of plasma and urine samples of 20 women with GDM and 20 with normal glucose tolerance in the second trimester of pregnancy. Plasma fingerprints allowed for the discrimination of GDM pregnant women from controls. In particular, lysoglycerophospholipids showed a close association with the glycemic state of the women. In addition, we identified some metabolites with a strong discriminative power, such as LPE(20:1), (20:2), (22:4); LPC(18:2), (20:4), (20:5); LPI(18:2), (20:4); LPS(20:0) and LPA(18:2), as well as taurine-bile acids and long-chain polyunsaturated fatty acids derivatives. Finally, we provide evidence for the implication of these compounds in metabolic routes, indicative of low-grade inflammation and altered redox-balance, that may be related with the specific pathophysiological context of the genesis of GDM. This highlights their potential use as prognostic markers for the identification of women at risk to develop severe glucose intolerance during pregnancy. Gestational Diabetes Mellitus (GDM) is increasing worldwide and, although diabetes usually remits after pregnancy, women with GDM have a high risk of developing postpartum type 2-diabetes, particularly when accompanied by obesity. Therefore, understanding the pathophysiology of GDM, as well as the identification of potentially modifiable risk factors and early diagnostic markers for GDM are relevant issues. In the present study, we devised a multiplatform metabolic fingerprinting approach to obtain a comprehensive picture of the early metabolic alternations that occur in GDM, and may reflect on the specific pathophysiological context of the disease. Future studies at later stages of gestation will allow us to validate the discriminant power of the identified metabolites.Journal of proteomics 03/2014; · 5.07 Impact Factor