Abstract
BackgroundHeart failure (HF) is characterized by a series of adaptive changes in energy metabolism. The use of metabolomics enables the parallel assessment of a wide range of metabolites. In this study, we appraised whether metabolic changes correlate with HF severity, assessed as an impairment of functional contractility, and attempted to interpret the role of metabolic changes in determining systolic dysfunction.MethodsA 500 MHz proton nuclear magnetic resonance (1H-NMR)-based analysis was performed on blood samples from three groups of individuals: 9 control subjects (Group A), 9 HF patients with mild to moderate impairment of left ventricle ejection fraction (LVEF: 41.9 ± 4.0 %; Group B), and 15 HF patients with severe LVEF impairment (25.3 ± 10.3 %; Group C). In order to create a descriptive model of HF, a supervised orthogonal projection on latent structures discriminant analysis (OPLS-DA) was applied using speckle tracking-derived longitudinal strain rate as the Y-variable in the multivariate analysis.ResultsOPLS-DA identified three metabolic clusters related to the studied groups achieving good values for R2 [R2(X) = 0.64; R2(Y) = 0.59] and Q2 (0.39). The most important metabolites implicated in the clustering were 2-hydroxybutyrate, glycine, methylmalonate, and myo-inositol.ConclusionsThe results demonstrate the suitability of metabolomics in combination with functional evaluation techniques in HF staging. This innovative tool should facilitate investigation of perturbed metabolic pathways in HF and their correlation with the impairment of myocardial function.Electronic supplementary materialThe online version of this article (doi:10.1186/s12967-015-0661-3) contains supplementary material, which is available to authorized users.
Highlights
Heart failure (HF) is characterized by a series of adaptive changes in energy metabolism
This study focused on the MBS investigation of two groups of HF patients with mild-to-moderate and severe impairment of systolic function
It is important to emphasize that, while the orthogonal partial least square discriminant analysis (OPLS-DA) model based on different metabolic profiles in our study showed a significant cluster among the three groups, brain natriuretic peptide (BNP) levels were unable to differentiate patients with mild-to-moderate HF from control subjects
Summary
Heart failure (HF) is characterized by a series of adaptive changes in energy metabolism. Metabolomics (MBS) is the study of the complete profile of small-molecule metabolites in an organism and may provide a metabolic overview, resulting from changes in the expression of genes and RNA, but Deidda et al J Transl Med (2015) 13:297 as a result of protein activity and environmental factors, including nutrition and drug therapies [7, 8]. Mass spectrometry-based profiling of plasma metabolites was performed in over 400 HF patients by Cheng et al in order to assess the diagnostic and prognostic value of MBS in HF. Their results showed that MBS is able to provide significant prognostic value, independent of brain natriuretic peptide (BNP) and other traditional risk factors [11]
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