1H NMR-metabolomics: Can they be a useful tool in our understanding of cardiac arrest?

Resuscitation 85 (2014) 595–601

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Resuscitation journal homepage: www.elsevier.com/locate/resuscitation

Review article 1

H NMR-metabolomics: Can they be a useful tool in our understanding of cardiac arrest?夽 Athanasios Chalkias a,b,∗ , Vassilios Fanos c , Antonio Noto c , Maaret Castrén d , Anil Gulati e , Hildigunnur Svavarsdóttir f , Nicoletta Iacovidou b,g , Theodoros Xanthos a,b a

MSc “Cardiopulmonary Resuscitation”, Medical School, National and Kapodistrian University of Athens, Athens, Greece Hellenic Society of Cardiopulmonary Resuscitation, Athens, Greece c Neonatal Intensive Care Unit, Puericulture Institute and Neonatal Section, AOU and University of Cagliari, Cagliari, Italy d Karolinska Institutet, Department of Clinical Science and Education, Södersjukhuset and Section of Emergency Medicine, Södersjukhuset, Stockholm, Sweden e Midwestern University, Downers Grove, IL, USA f School of Health Sciences, University of Akureyri, Akureyri, Iceland g 2nd Department of Obstetrics and Gynecology, Neonatal Division, Medical School, National and Kapodistrian University of Athens, Athens, Greece b

a r t i c l e

i n f o

Article history: Received 14 October 2013 Received in revised form 12 December 2013 Accepted 26 January 2014 Keywords: Cardiac arrest Cardiopulmonary resuscitation Metabolomics

a b s t r a c t Objective: This review focuses on the presentation of the emerging technology of metabolomics, a promising tool for the detection of identifying the unrevealed biological pathways that lead to cardiac arrest. Data sources: The electronic bases of PubMed, Scopus, and EMBASE were searched. Research terms were identified using the MESH database and were combined thereafter. Initial search terms were “cardiac arrest”, “cardiopulmonary resuscitation”, “post-cardiac arrest syndrome” combined with “metabolomics”. Results: Metabolomics allow the monitoring of hundreds of metabolites from tissues or body fluids and already influence research in the field of cardiac metabolism. This approach has elucidated several pathophysiological mechanisms and identified profiles of metabolic changes that can be used to follow the disease processes occurring in the peri-arrest period. This can be achieved through leveraging the strengths of unbiased metabolome-wide scans, which include thousands of final downstream products of gene transcription, enzyme activity and metabolic products of extraneously administered substances, in order to identify a metabolomic fingerprint associated with an increased risk of cardiac arrest. Conclusion: Although this technology is still under development, metabolomics is a promising tool for elucidating biological pathways and discovering clinical biomarkers, strengthening the efforts for optimizing both the prevention and treatment of cardiac arrest. © 2014 Elsevier Ireland Ltd. All rights reserved.

Contents 1. 2.

3.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Pathophysiology of cardiac arrest and resuscitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Cardiac arrest – the onset of the ischemic cascade . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Cardiopulmonary resuscitation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3. Post-resuscitation period . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4. The need for a better understanding of the pathophysiology of cardiac arrest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The metabolomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. The metabolomics workflow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

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夽 A Spanish translated version of the abstract of this article appears as Appendix in the final online version at http://dx.doi.org/10.1016/j.resuscitation.2014.01.025. ∗ Corresponding author at: National and Kapodistrian University of Athens, Medical School, MSc “Cardiopulmonary Resuscitation”, Hospital “Henry Dunant”, 107 Mesogion Av., 115 26 Athens, Greece. E-mail address: [email protected] (A. Chalkias). 0300-9572/$ – see front matter © 2014 Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.resuscitation.2014.01.025

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3.2. The technology of 1 H NMR/metabolomics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3. Metabolomics data analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4. Metabolomics and cardiac arrest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5. Metabolomics in the prevention of cardiac arrest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conflict of interest statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix A. Supplementary data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1. Introduction Cardiac arrest is a leading cause of death, affecting more than one million individuals worldwide every year. Patients who restore spontaneous circulation (ROSC) have a long way to go until recovery, as they have to pass through the “Clashing Rocks” of postcardiac arrest syndrome. Indeed, following successful resuscitation from cardiac arrest, neurological impairment as well as other types of organ dysfunction still cause significant morbidity and mortality. Research so far has shown that the best chance of survival with good neurological outcome is gained by strengthening the links of the Chain of Survival, i.e. early recognition of cardiac arrest, highquality cardiopulmonary resuscitation (CPR), early defibrillation, and subsequent care in a specialist center.1 In fact, prognostication for cardiac arrest victims remains dismal, as only about 17% survive to hospital.1,2 In this regard the scientific world has improved the knowledge on cardiopulmonary diseases, especially regarding risk factors and early diagnosis of the diseases; however, the management of the risk stratification and complications need more extensive study. Apart from the reduced financial resources, the lack of research may also be due to the complexities on the study of cardiopulmonary patients. The advent of metabolomics may be useful because the study of the metabolome is comparable to observe a snapshot of a particular biological sample in a particular moment, which may reveal biochemical pattern that could be correlated with the diagnosis and classification of diseases. From the clinical point of view, the metabolomics approach, offers unique insight into small molecule regulation and signaling, by providing access to a portion of biomolecular space not covered by other approaches such as genomics and proteomics.

2. Pathophysiology of cardiac arrest and resuscitation 2.1. Cardiac arrest – the onset of the ischemic cascade With the onset of cardiac arrest the effective blood flow stops and noradrenaline (norepinephrine) and neuropeptide Y are released from the cardiac sympathetic nerve terminals, while the release of acetylcholine diminishes.3 The acute onset of ischemia activates the adrenal glands which release noradrenaline, but despite the 1- to 100-fold elevation in endogenous plasma catecholamines, tissue perfusion remains poor. The impaired coronary flow diminishes the removal of noradrenaline from the interstitial spaces, resulting in prolonged vasoconstriction and myocardial hypoperfusion. Interestingly, the adrenal blood flow further worsens due to microvessel contraction, a phenomenon which is partly mediated by adrenomedullin.4 At the same time, various cytokines, complement components, and other molecules are synthesized and released in response to global hypoxia, such as tumor necrosis factor-a (TNF-a), interleukin 1b (IL-1b), C3, C4, C5, C5b-9, P-selectin, and intercellular adhesion molecule-1, and reactive oxygen species (ROS) are released from activated polymorphonuclear leukocytes (PMN).3 The cellular

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response to hypoxia is coordinated by the hypoxia-inducible factor (HIF) and its regulator, the Von Hippel–Lindau tumor suppressor protein.5 HIF 1-a is accumulated under hypoxic conditions and activates the transcription of endothelial nitric oxide (NO) synthase. The formation of NO involves arginine and its metabolites, ornithine and citrulline, along with enzymes that induce or inhibit NO synthase such as asymmetric dimethylarginine or l-NGmonomethylarginine.6 In addition, the activated platelets release 5-hydroxytryptamine and the expression of cyclooxygenase-2 is induced, enhancing the contractile response and the development of intracellular acidosis which increases the concentration of inorganic phosphate, pyruvate, hydrogen ions, and lactate.4 Furthermore, the ROS-mediated damage of the fatty acids of membrane phospholipids increases cell membrane permeability, diminishing the concentration of intracellular K+ and Mg2+ and increases that of Na+ and Ca2+ which together with the depletion of adenosine triphosphate (ATP) and the increased oxidative stress form the mitochondrial permeability transition pore (MTP).4,7 Of note, the mitochondrial metabolism is impaired and free fatty acids, long-chain acyl CoA, and acylcarnitine accumulate and are incorporated into membranes impairing their function. Shortly after the development of cardiac arrest, the blood–brain barrier is disrupted, allowing serum proteins, Na+ , and water to enter the brain microfluid environment.8–10 Brain edema and intracranial pressure increase and neuronal damage occurs, although mitochondrial swelling is not observed. Nevertheless, mitochondria can kill neurons by releasing apoptotic factors into cytosol, by releasing Ca2+ , and/or by generating ROS.11 Meanwhile, disturbances in Ca2+ homeostasis, inhibition of glycolysis, and oxidative stress triggers the release of signaling proteins and the activation of apoptotic mechanisms, such as the apoptotic pathway of stress-activated protein kinases (SAPKs) 1 and 2.12,13 Also, MTP opening can lead to apoptosis via cytochrome c release and/or may promote autophagy,14 while apoptosis may be also initiated by hypoxia-induced p53 accumulation which, in turn, it directly interacts with HIF 1-a and limits its expression.15 2.2. Cardiopulmonary resuscitation With the onset of chest compressions blood flow increases, although during optimal CPR, the cardiac output is between 25 and 40% of pre-arrest values.3 During the CPR interval, the coronary blood flow is low and cannot maintain aerobic myocardial metabolism, but it is sufficient enough to promote the deleterious effects of reperfusion. At this stage, generation of ROS, further activation of PMN, exacerbation of intracellular Ca2+ concentration, and the production of small amounts of ATP in myocardium lead to the formation of ischemic contracture,8 although, the badrenergic action of exogenous adrenaline [epinephrine] decrease the myocardial ATP content and increases the lactate content.16 The reperfusion-induced activation of blood coagulation which leads to the formation of microthrombi, together with the microvessel accumulation of activated PMN and platelets contribute to microvascular obstruction and to the onset of “no-reflow” phenomenon.3 Of note, microvascular obstruction in adrenal

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glands seems to be partly responsible for the low concentration of serum cortisol which is often observed after cardiac arrest.17 On the other hand, the electrical shock during CPR cause myocardial injury that is proportional to the amount of energy delivered. It increases intramyocardial temperature, potentiates excitation–contraction uncoupling, and is linked with dose-dependent release of free radicals and mitochondrial dysfunction.18 After countershocks, the oxidative metabolism is depressed and lactate is produced, while cell membrane permeability further increases, converting myocardium into a depressed and unexcitable tissue. The stunned myocardium is characterized by significant amounts of NO which were generated shortly after the onset of cardiac arrest and may exert protective as well as deleterious effects. During CPR, NO participates in mitochondrial oxygen sensing and inhibits cytochrome c oxidase, while it can interact with superoxide to form peroxynitrate, exacerbating oxidative stress. 2.3. Post-resuscitation period With the onset of ROSC, the automaticity of the heart is restored, although its mechanical function is impaired.3 The transient increase of catecholamines results in normal or elevated blood pressure, decreased microcirculatory blood flow, and increased Ca2+ overload.19 Despite the fact that ROSC is characterized by the upregulation and release of several cytokines and especially TNF-a and IL-8,20 targeted temperature management suppresses the production of proinflammatory cytokines and may block other manifestations associated with post-cardiac arrest syndrome, such as the increase of intracellular Ca2+ and glutamate which is observed after exposure to excitotoxin.11 The chemical changes that occur during cardiac arrest predispose to a massive burst of ROS production during the first minutes after ROSC, although free-radical production and vascular permeability may be attenuated by induced hypothermia.21 The endothelium becomes more dysfunctional and NO formation decreases, resulting in impaired vasodilation, further activation of PMN and platelets and extend tissue injury.22 However, the already increased levels of NO depress cardiac contractility and exacerbate post-resuscitation myocardial stunning.6 Although anaerobic metabolism is impaired, metabolism of lipids, glutamate, ␥-aminobutyric acid, and glutamate accelerates.23 The oxidation of fatty acids inhibits glucose oxidation rates and glycolysis continuous uncoupled from the oxidative process resulting in a net increase in cytosolic H+ concentration.24 The low levels of ATP and the increased amounts of ROS preserve the intracellular influx of Ca2+ which is further exacerbated by the increased activation of renin–angiotensin system and the production of angiotensin II.3 Approximately 2.5 h after ROSC, the levels of the soluble receptors for tumor necrosis factor type II (sTNFII) and other interleukins reach their peak,25 while cortisol concentration increases.17 Ischemia/reperfusion triggers apoptotic cell death through the protein kinase C family pathway, the Fas/Fas ligand pathway, and the caspase pathway which is activated by TNF-a and Fas receptors.26 In addition, the caspase cascade is activated by the increased release of cytochrome c from mitochondria. Nevertheless, cooling reduces delayed cell death including apoptosis and inhibits the neuroexcitatory cascade. During the recovery phase, the pathophysiological changes subside, although delayed neuronal degeneration may occur, as morphological changes in brain tissue reach maximum levels only after three weeks.27 Regarding the outcome, the post-resuscitation myocardial stunning is important in the early phases of ROSC, but thereafter, multi-organ dysfunction, irreversible myocardial necrosis, and irreversible cerebral injury play major roles.

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2.4. The need for a better understanding of the pathophysiology of cardiac arrest The increased quantity and quality of research efforts during the last decades have led the scientific community to the conclusion that only a better understanding of the pathophysiology of cardiac arrest and resuscitation will lead to the optimization of survival rates. Despite the recent progress in CPR, it was only during the recent years that we began to deepen into the pathophysiological mechanisms governing cardiac arrest and post-resuscitation syndrome. Throughout the evolution of resuscitation knowledge, however, research has shown an abundance of metabolites in several biological fluids or tissues whose presence has not been adequately explained. Today, the emerging technology of proton nuclear magnetic resonance (1 H NMR)/metabolomics might help us to identify and study the biomarker profile of metabolic systems and metabolic perturbations that occur in response to cardiac arrest and resuscitation and to establish the unrevealed pathways and mechanisms of post-resuscitation syndrome. 3. The metabolomics The metabolomics approach is based on the quantitative analysis of a large number of small metabolites (<1000 Da) within cell, tissue, and bio-fluids which is called the metabolome. The term metabolome refers to all small molecules that exclude nucleic acids and proteins (Fig. 1). Indeed, these metabolites represent the end products of gene and offer an instantaneous snapshot of both physiological and pathophysiological status. However, as metabolites in tissue or body fluids are present across a broad range of concentrations, no single analytical method is capable of analyzing all of

Fig. 1. Integration of metabolomics with other “omics” approaches and relationship to phenotype. The term metabolome refers to all small molecules that exclude nucleic acids and proteins.

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Table 1 Metabolomics approaches: advantages vs. disadvantages. Technique

Advantages

Disadvantages

GC–MS

Small sample quantity needed Accurate measurement High sensibility Analytical sensitivity pico/nano molar range Suitable for measuring lipids, dipeptides and other macromolecules Identification of polar, high molecular weight organic compounds and enables semiquantitative determination of concentration Enabling to analyze a large number of metabolites simultaneously Can be used for the targeted and the non-targeted approach Analytical time (20–60 min for each sample)

Sample manipulation

LC–MS

1

H NMR

Table 2 Currently profiled endogenous compounds with metabolomics technologies. Amino acids Cholesterols Hydroxyacids Steroids Organic acids

Carbohydrates Coenzyme A derivatives Lipids Peptides Glycerol/glycerophospholipids

Catecholamine Fatty acids Phosphates Ketones Acetylcarnitines

Information is from Ref. [30]. Time consuming Expensive instrument

Analytical sensitivity ranging 1–10 ␮mol L−1 Expensive instrument

GC–MS, gas chromatography coupled by mass spectrometry; LC–MS, liquid chromatography coupled by mass spectrometry; 1 H NMR, nuclear magnetic resonance.

them. Nevertheless, capturing a subset of “sentinel” metabolites in critical pathways is easier than other “omics” techniques such as proteomics.30 The metabolomics’ most common technique includes gas or liquid chromatography coupled by mass spectrometry and nuclear magnetic resonance, which are able to identify a large number of metabolites simultaneously. Mass spectrometry is still considered the gold standard in metabolite detection and quantification; in fact, the sensitivity is in the picomolar and nanomolar range, while 1 H NMR spectroscopy is able to analyze a large number of metabolites (more than 100) simultaneously. However the analytical sensitivity ranges between 1 and 10 ␮mol L−1 . Considering that all the techniques have drawbacks (Table 1), as well as that there is no single technology available to analyze the entire metabolome, it is the opinion of the authors that complementary approaches should be used. 3.1. The metabolomics workflow A metabolomics experiment can be divided in at least three phases. The first phase is focused on the creation of the experiment which, in turn, can be summarized in two points, the aim of the study and the type of biofluids, number of samples, collection of samples, and the storage temperature of the samples. In the second phase, the choice of the experimental technique takes place, e.g. 1 H NMR, LC/GC–MS, while during the third phase, the multivariate statistical analysis is performed. Among the aforementioned techniques, however, high resolution 1 H NMR seems to be very attractive due to the fact that it offers an intriguing insight into the early and intermediate changes in the metabolomic profile, which may be invaluable during the very early period post-cardiac arrest. Indeed, resuscitation from cardiac arrest is rapid and “time sensitive”, and high resolution 1 H NMR may be of great benefit in dynamic experimental and clinical models of cardiac arrest while taking into account that profiling metabolic changes during the hypoperfusion state (global ischemia) may take several minutes to be detected while during reperfusion changes occur much more rapidly.

metabolites present in the samples. The 1 H NMR spectrum is characterized by a number of peaks, which are mapped as spectral intensities and frequencies, resulting from different functional groups of the molecule (Fig. 2). The NMR spectroscopy technique relies on the fact that certain nuclei possess the quantum mechanical property of magnetic spin, and when placed inside a magnetic field can adopt different energy levels, which can be observed using radiofrequency waves; therefore, this method is applicable to any nucleus possessing spin. The application of 1 H NMR spectroscopy to the study of biological fluids for medical purposes is extensively documented and from these studies it can be showed that the several metabolites present in the sample give rise to a unique 1 H NMR profile of the biofluid under examination.28–31 1 H NMR/metabolomics can be defined as the study of the smallmolecule (within 1 kDa) characterizing certain biological fluid or tissue and due to the ability of identifying and quantifying a broad range of metabolites simultaneously, it could provide a global metabolic state that reflects several biological events (Table 2).28,29 The blooming of metabolomics began when it was appreciated that a metabolite profile derived in an unbiased manner may be informative even if the constituents or their association to the disease are initially unknown.30 Presently, over 8500 have already been identified and recorded in the Human Metabolome Database.29–32 However, this number is constantly changing due to the improvement of the reliability of the equipment used. Indeed, it can be considered a real time representation of the final product of the organism going from the DNA to the proteins. For this reason this new tool seems to be promising. 3.3. Metabolomics data analysis The analysis of the data aims at capturing the complexity of the global metabolome within a sample. In this regard, there are two major approaches in multivariate statistics: unsupervised and supervised approaches. The unsupervised uses an exploratory approach such as Principal Component Analysis, which is based

3.2. The technology of 1 H NMR/metabolomics 1 H nuclear magnetic resonance (NMR) spectroscopy is an analytical tool that allows detection of the low molecular weight

Fig. 2. Typical 1 H nuclear magnetic resonance spectrum.

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on the analysis of a set of samples that are solved into principal components describing the maximum variation within the data. Using this approach the complexity of the spectra is reduced into plots where the intrinsic sample clustering is described with no prior assumptions made about the samples. In contrast supervised approaches can be more useful when it is necessary to elucidate the difference which maximizes the separation between classes. An example of a supervised method is the partial least square discriminate analysis (PLS-DA) which uses a linear regression method, where the classes of samples are included in the calculation. The validity of the PLS-DA model is assessed by statistical parameters: the correlation coefficient R2 and the cross-validation correlation coefficient Q2 . R2 represents the goodness of the fit to the model and Q2 reveals the predictability of the model. Moreover, R2 and cross-Q2 are frequently used as measures of clinical utility, rather than for the performance of the test. From the clinical point of view they provide very little transparency and they are not a readily interpretable especially among the clinicians because they are not familiar with these parameters. In addition, despite the strong effort applied to the field of biomarker discovery (using the metabolomics approach), very little consistency and relatively little rigor were used in how the candidate biomarkers were selected. In particular, less than 2% of the published studies (15 out of the 823) provided a receiver operator characteristic (ROC) curves, which is considered the standard method for assessing the performance of a medical diagnostic test. In addition, most of the studies were based on relatively small numbers of case and control. Therefore, it is definitely recommended the use of a test such as the ROC curve together with parameters of multivariate statistical. These may be useful for moving a certain number of metabolite from the “bench top to the bedside”. 3.4. Metabolomics and cardiac arrest A major research issue among scientists involved in resuscitation, is the complete elucidation of the pathophysiological mechanisms underlying the deterioration of long-duration ventricular fibrillation to asystole. Consequently, the identification of the biomarker panel characterizing this undesirable change will enlighten many of the unknown aspects hidden in this foggy landscape. In a recent study investigating key metabolic alterations associated with the incidence of asystole during 20-min of global ischemia and long-duration ventricular fibrillation in blood-perfused dog hearts,33 lactate accumulation due to enhanced glycolysis was the key metabolic determinant of electrical depression and asystole, indicating that the rate of glycolysis may depend on the degree of energy imbalance occurring soon after the onset of ventricular fibrillation.3,32–34 Furthermore, endothelin-1 (ET-1) levels have been reported to be elevated in a swine model of cardiac arrest and the peak ET-1 level prior to onset of spontaneous ventricular fibrillation was predictive of unsuccessful resuscitation (Table 3).35 There are reports suggesting that ET-1 levels are not elevated following cardiac arrest and resuscitation.46 However, a decrease in ET-1 was observed in non-survivors indicating a positive association between ET-1 levels and outcome.47 Administration of ET-1 plus noradrenaline was found to improve coronary perfusion pressure in a swine model of prolonged ventricular fibrillation, but was related with worse post-resuscitation outcome; it appears that ET-1 mediated intense vasoconstriction had detrimental post-resuscitation effects.48 In a study conducted in swine where ET-1 was administered with and without adrenaline, it was found that the combination of ET-1 and adrenaline increased coronary perfusion pressure and improved cerebral blood flow post-resuscitation but did not improve myocardial blood flow during cardiac arrest.49 In another study, ET-1 was found to enhance cerebral blood flow

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Table 3 Currently known end-point metabolites of biological events after cardiac arrest and resuscitation. Metabolite

Biological fluid/tissue

3,4-Dihydroxy-mandelic acid 3-Hydroxyanthranilic acid 3-Methoxy-4-hydroxyphenylglycol 5-Hydroxyindole-3-acetic acid 5-Hydroxyindoleacetic acid 5-Hydroxytryptamine Acetylcholine Acylcarnitine Adenosine triphosphate Adrenaline Adrenomedullin Alanine Angiotensin II Arginine B-type natriuretic peptide Choline Citrate Citrulline CK-MB Complement components Creatine Cytochrome c Dopamine Endothelin Fatty acids Glial fibrillary acidic protein Glucose Glutamate Glycogen Homoprotocatechic acid Homovanillic acid Hydrogen ions Hypoxia-inducible factor Inorganic phosphate Interleukins Intercellular adhesion molecule-1 Ketones Kynurenic acid Lactate Lipids l-kynurenine Long chain acyl CoA Methoxy-hydroxy-phenyl-ethanol Methoxy-hydroxy-phenyl-glycol Myo-inositol N-acetylaspartate N-terminal prohormone of brain natriuretic peptide Neuron specific enolase Neuropeptide Y Nitric oxide Noradrenaline Ornithine Oxaloacetate Peroxynitrate Phosphocreatine Procalcitonin P-selectin Pyruvate Reactive oxygen species S-100B sTNF II Stress-activated protein kinases Succinyl-CoA Taurine TNF-a Troponin I Vanylmandelic acid Von Hippel–Lindau tumor suppressor protein ␣-Ketoglutarate ␤-Hydroxy-butryl-carnitine ␥-Aminobutyric acid

Blood/various Blood CSFa /brain CSFa /brain Blood Blood/various Blood/heart, brain Blood/various Blood/various Blood/various Blood/various Blood/various Blood/various Blood, CSFa /various Blood/heart CSFa /brain Blood/various Blood/various Blood/heart Blood/various CSFa /brain Blood, urine/various Blood/various Blood Blood, CSFa /various Blood/brain Blood, urine/various Blood/various Blood/various Blood/various Blood, urine, CSFa /various Blood, urine/various Blood/various Blood, urine/various Blood, urine/various Blood/various Blood, urine/various Blood Blood/various Blood, urine/various Blood Blood/various Blood/various Blood/various CSFa /brain CSFa /brain Blood/heart

a

Blood, CSFa /brain Blood/heart Blood, CSFa /various Blood/various Blood/various Blood/various Blood/various CSFa /brain Blood/various Blood/various Blood/various Blood, urine/various Blood, urine, CSFa /various CSFa /various Blood/various Blood/various CSFa /brain Blood, urine/various Blood/heart Blood, urine/various Blood/various Blood/various Blood/various Blood/various

Cerebrospinal fluid. Information is from Refs. [35–45,51,52].

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during cardiopulmonary resuscitation better than adrenaline and produced better outcome.50 In a fully translational investigation, Ristagno et al.51 measured kynurenine and tryptophan metabolites in plasma from rats, pigs and humans after cardiac arrest and reported that the kynurenine pathway was activated early following resuscitation and might have contributed to post-resuscitation outcome. Specifically, after absolute metabolite quantification using liquid chromatography–multiple reaction monitoring-mass spectrometry, they found that tryptophan plasma concentration fell significantly very early in the post-resuscitation phase, while its metabolites, l-kynurenine, kynurenic acid, 3-hydroxyanthranilic acid and 5-hydroxyindoleacetic acid, rose significantly. Of note, changes in their concentration reflected changes in rat postresuscitation myocardial dysfunction, while elevated plasma level of kynurenic acid, and 3-hydroxyanthranilic acid were associated with significant decrease in ejection fraction and stroke volume. Collectively, the aforementioned studies clearly indicate that metabolomics may help to clarify unexplored biochemical pathways in CPR, increasing the therapeutic modalities and survival rates of cardiac arrest victims.52 As cellular metabolic pathways are highly conserved across species, the identification of metabolic changes in humans will rapidly provide insight into homeostatic and disease pathways governing cardiac arrest and resuscitation. 3.5. Metabolomics in the prevention of cardiac arrest Today, over 5 million individuals in the United States have advanced left ventricular (LV) dysfunction, usually secondary to myocardial infarction or non-ischemic cardiomyopathy and are considered to be at high-risk of sudden cardiac death (SCD). Although the current guidelines recommend a prophylactic placement of an implantable cardioverter defibrillator (ICD) in patients with a LV ejection fraction of <35%, the procedure itself is not curative, is expensive, and is not free from short- and long-term adverse effects. The fact that ICDs are implanted ten times more than needed to save a life reflects the limited understanding of the biologic pathways predisposing to SCD and, consequently, the need for the development of better risk-stratification techniques. The new research proposals should aim at developing a novel biomarker panel that will help identify individuals who are at an increased risk of cardiac arrest. This will be achieved through leveraging the strengths of unbiased metabolome-wide scans, which include thousands of final downstream products of gene transcription and/or enzyme activity and metabolic products of extraneously administered substances, in order to identify a metabolomic fingerprint associated with an increased risk of cardiac arrest. However, future studies have to be of large sample size in order to eliminate false positive associations and to provide novel insight into the biologic and mechanistic pathways predisposing to SCD, taking into account the heterogeneous physiological responses to cardiac arrest. As the metabolome is the most proximal ‘snapshot’, the identification of the proposed metabolite panel will be a significant addition to the currently used risk-prediction algorithms for SCD. Most importantly, the results of these studies not only will have direct implication over all individuals who are considered ICD eligible, but also, much of these findings may be applicable to the general population where majority of SCDs occur. 4. Conclusion Metabolomics allow the monitoring of hundreds of metabolites from tissues or body fluids and is already influencing research in the field of cardiac metabolism. This approach has elucidated several biological mechanisms of pathology and identified profiles

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