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: Heart failure is a multifactorial disease associated with staggeringly high morbidity and motility. Recently, alterations of multiple metabolites have been implicated in heart failure; however, the lack of an effective technology platform to assess these metabolites has limited our understanding on how they contribute to this disease phenotype. We have successfully developed a new workflow combining specific sample preparation with tandem mass spectrometry that enables us to extract most of the targeted metabolites. 19 metabolites were chosen ascribing to their biological relevance to heart failure, including extracellular matrix remodeling, inflammation, insulin resistance, renal dysfunction, and cardioprotection against ischemic injury. In this report, we systematically engineered, optimized and refined a protocol applicable to human plasma samples; this study contributes to the methodology development with respect to deproteinization, incubation, reconstitution, and detection with mass spectrometry. The deproteinization step was optimized with 20% methanol/ethanol at a plasma:solvent ratio of 1:3. Subsequently, an incubation step was implemented which remarkably enhanced the metabolite signals and the number of metabolite peaks detected by mass spectrometry in both positive and negative modes. With respect to the step of reconstitution, 0.1% formic acid was designated as the reconstitution solvent vs. 6.5 mM ammonium bicarbonate, based on the comparable number of metabolite peaks detected in both solvents, and yet the signal detected in the former was higher. By adapting this finalized protocol, we were able to retrieve 13 out of 19 targeted metabolites from human plasma. We have successfully devised a simple albeit effective workflow for the targeted plasma metabolites relevant to human heart failure. This will be employed in tandem with high throughput liquid chromatography mass spectrometry platform to validate and characterize these potential metabolic biomarkers for diagnostic and therapeutic development of heart failure patients.Clinical Proteomics 07/2013; 10(1):7.
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ABSTRACT: The purpose of this research was to use metabolomics to investigate the cystic phenotype in the Lewis polycystic kidney rat. Spot urine samples were collected from four male Lewis control and five male Lewis polycystic kidney rats aged 5 weeks, before kidney function was significantly impaired. Metabolites were extracted from urine and analysed using gas chromatography-mass spectrometry. Principal component analysis was used to determine key metabolites contributing to the variance observed between sample groups. With the development of a metabolomics method to analyse Lewis and Lewis polycystic kidney rat urine, 2-ketoglutaric acid, allantoin, uric acid and hippuric acid were identified as potential biomarkers of cystic disease in the rat model. The findings of this study demonstrate the potential of metabolomics to further investigate kidney disease.Nephrology 02/2012; 17(2):104-10. · 1.69 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