Heart failure (HF) is characterized by a series of maladaptive metabolic changes, which have recently been proposed as a new therapeutic target for HF. Metabolomics (MBS) enables the parallel assessment of a broad range of metabolites. A proton nuclear magnetic resonance (1H-NMR)-based MBS analysis was performed on blood samples of 3 groups of individuals: HF patients with BNP >200 pg/ml BNP (n=6; A); HF patients with BNP <100 pg/ml (n=7; B) and age- and sex-matched healthy controls (n=6; C). Specimens were analyzed with a 1H-NMR 400MHz spectrometer. A supervised PLS-DA Projection on Latent Structures Discriminant Analysis was applied to realize a descriptive model of HF, also on a still limited database of 1H-NMR spectra. BNP plasma concentration was measured using a non-competitive immunofluorimetric test. The application of Pattern-recognition methods to 1H-NMR spectra was able to identify 3 metabolic clusters related to A, B and C groups, respectively. The discrimination of the latent structures were related to a metabolic fingerprint depending on a limited set of metabolites: Acetone, Glucose, Glycerol, 3-Hydroxybutyric acid, Ornithine, Proline, Asparagine, Creatine, Creatinine, Valine, Aspartic acid and Phenylalanin. Our preliminary data show that MBS is a sensitive method, which can be effectively used in investigations on HF pathophysiology and therapy. This new tool may improve our knowledge of 1. perturbed metabolic pathways in HF, 2. their correlation with impaired myocardial function, 3. clinical monitoring in HF patients, 4. identification and management of innovative therapeutic approaches.
Metabolomics and heart failure: a new and promising scientific approach
DEIDDA, MARTINO;CADEDDU DESSALVI, CHRISTIAN;BARBERINI, LUIGI;FATTUONI, CLAUDIA;CADONI, ENZO;ATZORI, LUIGI;MERCURO, GIUSEPPE
2010-01-01
Abstract
Heart failure (HF) is characterized by a series of maladaptive metabolic changes, which have recently been proposed as a new therapeutic target for HF. Metabolomics (MBS) enables the parallel assessment of a broad range of metabolites. A proton nuclear magnetic resonance (1H-NMR)-based MBS analysis was performed on blood samples of 3 groups of individuals: HF patients with BNP >200 pg/ml BNP (n=6; A); HF patients with BNP <100 pg/ml (n=7; B) and age- and sex-matched healthy controls (n=6; C). Specimens were analyzed with a 1H-NMR 400MHz spectrometer. A supervised PLS-DA Projection on Latent Structures Discriminant Analysis was applied to realize a descriptive model of HF, also on a still limited database of 1H-NMR spectra. BNP plasma concentration was measured using a non-competitive immunofluorimetric test. The application of Pattern-recognition methods to 1H-NMR spectra was able to identify 3 metabolic clusters related to A, B and C groups, respectively. The discrimination of the latent structures were related to a metabolic fingerprint depending on a limited set of metabolites: Acetone, Glucose, Glycerol, 3-Hydroxybutyric acid, Ornithine, Proline, Asparagine, Creatine, Creatinine, Valine, Aspartic acid and Phenylalanin. Our preliminary data show that MBS is a sensitive method, which can be effectively used in investigations on HF pathophysiology and therapy. This new tool may improve our knowledge of 1. perturbed metabolic pathways in HF, 2. their correlation with impaired myocardial function, 3. clinical monitoring in HF patients, 4. identification and management of innovative therapeutic approaches.
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