Cargando…

Metabolic profiles in heart failure due to non‐ischemic cardiomyopathy at rest and under exercise

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 idiopa...

Descripción completa

Detalles Bibliográficos
Autores principales: Mueller‐Hennessen, Matthias, Sigl, Johanna, Fuhrmann, Jens C., Witt, Henning, Reszka, Regina, Schmitz, Oliver, Kastler, Jürgen, Fischer, Jenny J., Müller, Oliver J., Giannitsis, Evangelos, Weis, Tanja, Frey, Norbert, Katus, Hugo A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5396036/
https://www.ncbi.nlm.nih.gov/pubmed/28451455
http://dx.doi.org/10.1002/ehf2.12133
Descripción
Sumario: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 (Q(2) = 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 t(1) and t(2) (30.7 and 29.0% vs. 31.3% at t(0)) with a similar predictive group separation (Q(2) = 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.