Nuclear magnetic resonance‐based metabolomics predicts exercise‐induced ischemia in patients with suspected coronary artery disease

Abstract

The purpose of this study was to develop a 1 H-nuclear magnetic resonance metabolomic approach capable of predicting the occurrence of exercise-induced ischemia in patients with suspected coronary artery disease and to identify the metabolite patterns that contribute most importantly to the prediction. In 31 patients with suspected effort angina and without previous myocardial infarction, serum was obtained just prior to a stress single-photon emission computed tomography. Serum NMR spectra were acquired with pulse-and-acquire and T 2 -edited sequences. The region between 0.50 and 4.25 ppm was used for analysis. Twenty-two patients had reversible myocardial perfusion defects and nine did not. Both groups had similar age and clinical profile, except for more smokers and diabetics in the ischemia group, and attained a similar peak heart rate. The best separation was achieved with long T 2 -edited spectra, 84% of patients being correctly classified based on the partial least square discriminant analysis. The main contributors to discrimination were lactate, glucose, as well as methyl and methylene moieties of lipids and long-chain amino acids. Metabolomic analysis of serum can predict exercise-inducible ischemia in patients with suspected coronary artery disease. This capability could be useful in screening and risk stratification of patients with coronary risk factors. Magn Reson Med 60:27-32, 2008.

Metabolomics in Cardiovascular Disease: Towards Clinical Application

“…This work was done by analyzing serum samples obtained before, during and after stress testing by high performance liquid chromatography coupled to mass spectroscopy; ischaemic patients had higher circulating levels of metabolites belonging to the citric acid pathway. Later, we were able to show that it was possible to predict which patients would suffer from a positive stress test by analyzing serum samples obtained before the stress test using 1 H NMR spectroscopy [Barba et al, 2008]. Ischemic patients had relatively higher lactate levels than non ischemic suggesting an underlying ischemic process although it could not be directly related to myocardial ischemia.…”

“…There has been interest to apply metabolomic analysis to the clinical setting and it has been shown in the literature that various cardiovascular-related pathologies could be differentiated using metabolic analysis [Lee et al, 2005, Lewis et al, 2008, Barba et al, 2008, Kang et al, 2011 but there is still some work to be done for metabolomics in order becoming a clinical reality.…”

The omics era: what can nuclear magnetic resonance tell us on metabolomics?

“…Another unsupervised method for this purpose is the Hierarchical Cluster Analysis (HCA) (8,44). The most used supervised classification methods are k-nearest-Neighbors (kNN) (1,38), Soft Indipendent Modeling of Class Analogy (SIMCA) (12) and Partial Last Squares Discriminant Analysis (PLS-DA) (3,14). The supervised method use the sample class as a variable for establish discrimination rules.…”

The emergent role of metabolic phenotyping in dynamic patient stratification

“…Examples of static stratification from the cardiovascular field include the prediction of exercise-induced ischemia in patients with suspected coronary artery disease using NMR spectroscopy of serum [10] and prediction of adverse effects after coronary artery bypass surgery using a targeted MS approach based on 69 metabolites in 478 patients [11]. In the oncology field, there has been success in using the approach based on NMR spectra of serum for prediction of toxicity in 54 patients with inoperable colorectal cancer who were treated with capecitabine [12] and using MAS-NMR of tumor tissue samples, it was possible to identify profiles that enabled colorectal cancer staging and prognosis [13].…”