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1.
ESC Heart Fail ; 4(2): 178-189, 2017 May.
Article in English | MEDLINE | ID: mdl-28451455

ABSTRACT

AIMS: Identification of metabolic signatures in heart failure (HF) patients and evaluation of their diagnostic potential to discriminate HF patients from healthy controls during baseline and exercise conditions. METHODS: Plasma samples were collected from 22 male HF patients with non-ischemic idiopathic cardiomyopathy and left ventricular systolic dysfunction and 19 healthy controls before (t0), at peak (t1) and 1 h after (t2) symptom-limited cardiopulmonary exercise testing. Two hundred fifty-two metabolites were quantified by gas chromatography-mass spectrometry (GC-MS) and liquid chromatography (LC)-MS/MS-based metabolite profiling. RESULTS: Plasma metabolite profiles clearly differed between HF patients and controls at t0 (P < 0.05). The metabolic signature of HF was characterized by decreased levels of complex lipids and fatty acids, notably phosphatidylcholines, cholesterol, and sphingolipids. Moreover, reduced glutamine and increased glutamate plasma levels, significantly increased purine degradation products, as well as signs of impaired glucose metabolism were observed. The metabolic differences increased strongly according to New York Heart Association functional class and the addition of three metabolites further improved prediction of exercise capacity (Q2 = 0.24 to 0.35). Despite a high number of metabolites changing significantly with exercise (30.2% at t1/t0), the number of significant alterations between HF and controls was almost unchanged at t1 and t2 (30.7 and 29.0% vs. 31.3% at t0) with a similar predictive group separation (Q2 = 0.50 for t0, 0.52 for t1, and 0.56 for t2, respectively). CONCLUSIONS: Our study identified a metabolic signature of non-ischemic HF with prominent changes in complex lipids including phosphatidylcholines, cholesterol, and sphingolipids. The metabolic changes were already evident at rest and largely preserved under exercise.

2.
PLoS One ; 7(2): e31501, 2012.
Article in English | MEDLINE | ID: mdl-22359596

ABSTRACT

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive loss of cognitive functions. Today the diagnosis of AD relies on clinical evaluations and is only late in the disease. Biomarkers for early detection of the underlying neuropathological changes are still lacking and the biochemical pathways leading to the disease are still not completely understood. The aim of this study was to identify the metabolic changes resulting from the disease phenotype by a thorough and systematic metabolite profiling approach. For this purpose CSF samples from 79 AD patients and 51 healthy controls were analyzed by gas and liquid chromatography-tandem mass spectrometry (GC-MS and LC-MS/MS) in conjunction with univariate and multivariate statistical analyses. In total 343 different analytes have been identified. Significant changes in the metabolite profile of AD patients compared to healthy controls have been identified. Increased cortisol levels seemed to be related to the progression of AD and have been detected in more severe forms of AD. Increased cysteine associated with decreased uridine was the best paired combination to identify light AD (MMSE>22) with specificity and sensitivity above 75%. In this group of patients, sensitivity and specificity above 80% were obtained for several combinations of three to five metabolites, including cortisol and various amino acids, in addition to cysteine and uridine.


Subject(s)
Alzheimer Disease/cerebrospinal fluid , Metabolome , Alzheimer Disease/diagnosis , Alzheimer Disease/metabolism , Case-Control Studies , Cysteine/cerebrospinal fluid , Disease Progression , Gas Chromatography-Mass Spectrometry , Humans , Hydrocortisone/cerebrospinal fluid , Sensitivity and Specificity , Uridine/cerebrospinal fluid
3.
J Proteome Res ; 8(12): 5568-79, 2009 Dec.
Article in English | MEDLINE | ID: mdl-19810704

ABSTRACT

Dietary preferences influence basal human metabolism and gut microbiome activity that in turn may have long-term health consequences. The present study reports the metabolic responses of free living subjects to a daily consumption of 40 g of dark chocolate for up to 14 days. A clinical trial was performed on a population of 30 human subjects, who were classified in low and high anxiety traits using validated psychological questionnaires. Biological fluids (urine and blood plasma) were collected during 3 test days at the beginning, midtime and at the end of a 2 week study. NMR and MS-based metabonomics were employed to study global changes in metabolism due to the chocolate consumption. Human subjects with higher anxiety trait showed a distinct metabolic profile indicative of a different energy homeostasis (lactate, citrate, succinate, trans-aconitate, urea, proline), hormonal metabolism (adrenaline, DOPA, 3-methoxy-tyrosine) and gut microbial activity (methylamines, p-cresol sulfate, hippurate). Dark chocolate reduced the urinary excretion of the stress hormone cortisol and catecholamines and partially normalized stress-related differences in energy metabolism (glycine, citrate, trans-aconitate, proline, beta-alanine) and gut microbial activities (hippurate and p-cresol sulfate). The study provides strong evidence that a daily consumption of 40 g of dark chocolate during a period of 2 weeks is sufficient to modify the metabolism of free living and healthy human subjects, as per variation of both host and gut microbial metabolism.


Subject(s)
Anxiety/metabolism , Cacao/metabolism , Energy Metabolism/drug effects , Intestines/microbiology , Metagenome/drug effects , Adolescent , Adult , Anxiety/drug therapy , Blood , Female , Hormones/metabolism , Humans , Male , Metabolome/drug effects , Metabolomics , Stress, Physiological/drug effects , Urine/chemistry , Young Adult
4.
Angew Chem Int Ed Engl ; 38(11): 1634-1637, 1999 Jun 01.
Article in English | MEDLINE | ID: mdl-29710992

ABSTRACT

The occurrence in ambient air of organic nitrates of isoprene, which can form according to Equation (1), was established for the first time. The analytical method was a combination of NP-HPLC and capillary gas chromatography with mass-selective detection by means of (methane)-NCI after high-volume sample collection (NCI=negative chemical ionization).

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