Metabolomic approach to profile functional and metabolic changes in heart failure

Journal of Translational Medicine

Research

Emanuela Locci¹  Luigi Atzori¹  Cristina Piras¹  Federica Ascedu²  Christian Cadeddu Dessalvi²  Giuseppe Mercuro²  Martino Deidda²  Federica Torri²  Luigi Barberini³ 

[1] Department of Biomedical Sciences, University of Cagliari, Monserrato, Italy;Department of Medical Sciences “M. Aresu”, University of Cagliari, Asse didattico Medicina, SS Sestu KM 0.700, 09042, Monserrato, Italy;Department of Public Health, Clinical and Molecular Medicine, University of Cagliari, Monserrato, Italy;

关键词: Heart failure;    Metabolomics;    Energy metabolism;    Echocardiography;    Brain natriuretic peptide;   

DOI  :  10.1186/s12967-015-0661-3

 received in 2015-06-30, accepted in 2015-09-03,  发布年份 2015

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© Deidda et al. 2015