Background: Dilated cardiomyopathy (DCM) is the most common form of heart muscle disease characterized by progressive dilatation and ventricular dysfunction. Metabolomics is an emerging and powerful discipline that provides a global information on the phenotype of mammalian systems via the study of endogenous and exogenous metabolites in cells, tissues, and biofluids. These studies aid in the identification of biomarkers to prevent diseases in later life or help to early detect onset of diseases as well as aiding in the elucidation of disease mechanisms. Summary: Metabolomics provides a unique opportunity to discover novel biomarkers for DCM. This review demonstrates evidence of metabolite-based biomarkers useful for predicting, diagnosing, and monitoring therapeutic interventions of DCM. Key metabolites identified as potential biomarkers for diagnosing DCM include acylcarnitines, succinic acid, malate, methylhistidine, aspartate, methionine, and phenylalanine. In terms of differentiating DCM from ischemic cardiomyopathy, potential biomarkers including 1-pyrroline-2-carboxylate, norvaline, lysophosphatidylinositol (16:0/0:0), phosphatidylglycerol, fatty acid esters of hydroxy fatty acid, and phosphatidylcholine were identified. Acylcarnitines, isoleucine and linoleic acid, and tryptophan were the main biomarkers to monitor treatment response to DCM. Mapping metabolites to metabolic pathways revealed dysregulation of branch-chain amino acid, glycolysis, tricarboxylic acid cycle, and triacylglycerol and pentose phosphate metabolism, which have the therapeutic potential for DCM. This review shows several limitations including the use of small sample sizes, lack of interpretation of age and sex differences in most studies, and the fact that studies have so far been limited to case-control study designs. Key Messages: Metabolites have close proximity to disease phenotype. With recent advances in metabolomics field, potential biomarkers for DCM have been identified based on studies using different biological and metabolomics technologies. However, multicenter studies with larger populations that will lead to validation of these identified biomarkers to enable their clinical translation and utilization are still needed.

Dilated cardiomyopathy (DCM) is the prototypic form of systolic heart failure (HF) characterized by cardiac dilatation and reduced left ventricular ejection fraction (LVEF) [1, 2]. The European Society of Cardiology (ESC) defines DCM as a condition characterized by the presence of left ventricular (LV) or biventricular dilatation and systolic dysfunction in the absence of abnormal loading conditions (hypertension, valve disease) or coronary artery disease significant enough to cause global systolic impairment [3] The etiology of DCM may be through genetic or nongenetic as well as through the combined interaction between genetic predisposition and environmental factors [4]. DCM normally begins in the heart’s LV where the heart muscle begins to dilate causing the lower and upper chambers to enlarge, respectively. Consequently, the problem often spreads to the right ventricle and then to the atria. As a result of dilation of the heart chambers, the heart muscle fails to contract normal, leading to abnormal pumping of blood. DCM affects all age groups, both adult and pediatric population, thus defining early diagnostic biomarkers that can help to detect and possibly prevent the occurrence of DCM and associated accidents, which is of utmost importance [5, 6].

In the past decades, advances in technologies including electrocardiogram, advanced magnetic resonance imaging, or computed tomography imaging have made great breakthrough in investigating its pathogenesis [7-9]. However, recently, application of various “-omics” technologies including metabolomics provides a new opportunity to discover novel prognostic and diagnostic biomarkers of DCM as well as to increase mechanistic understanding underlying this disease [10]. The metabolome is defined as the complete set of low-molecular-weight metabolites, usually ranging from 50 to 1,500 Da in a biological system under a set of environmental conditions [11, 12]. These metabolites are mostly measured in a wide range of diverse body fluids and tissues including but not limited to serum, plasma, urine, cerebrospinal fluid, synovial fluid, heart, liver, brain, etc. [13-15]. Application of metabolomics on semi-invasive samples such as plasma and serum has been of increasing interest in DCM research, given that heart tissue is not easily available for investigation during malignancy, for instance. Serum/plasma study of metabolites can provide a comprehensive profile of the metabolic changes that can help detect the adaptive “metabolic shift” that cardiomyocytes undergo during DCM pathogenesis. Studying the metabolome, which is the downstream product of the genome [16], in DCM is therefore of increasing interest, given that metabolites provide a snapshot of disease phenotypes.

Typically, two main technologies [17] have been applied in metabolic profiling of DCM: nuclear magnetic resonance (NMR) spectrometry and mass spectrometry (MS) [18, 19]. While NMR generally has a better resolution compared to MS since this instrument does not interact directly with samples; however, it suffers from low sensitivity issue and limited to only high-abundance metabolites [20]. MS is a technology of choice and it is normally coupled with gas chromatography (GCMS) or liquid chromatography (LCMS) [21]. Contrary to NMR, MS methods have higher sensitivity, but lower resolution particularly LCMS compared to NMR. Again, MS requires a small amount of samples as low as 10 µL [20, 22].

Over the last decade, many metabolomics reviews have focused on the broader cardiovascular disease (CVD) [19, 23, 24] rather than specific CVDs such as DCM. This review sought to examine the literature to present evidence of metabolic changes in DCM and possible mechanisms behind these alterations. More importantly, it focused on specific metabolomics applications targeting biomarkers for diagnosis, prognosis, and treatment monitoring in DCM. The studies included in this review were identified from “PubMed,” “Scopus,” and “Google Scholar” for articles based on the search terms (DCM metabolomics and/or in combination with biomarkers/LCMS/GCMS/NMR/treatment/prediction/prognosis/mechanisms/ischemic cardiomyopathy [ICM]/progression/HF). In addition, electronic searches were supplemented with manual searching of bibliographic references. All articles were screened and added if metabolomics platforms were used to measure metabolites and DCM-related studies.

The various metabolite-based biomarkers that have been studied for their potential diagnosis, prognostic prediction, and treatment monitoring value are listed in Table 1.

Table 1.

Potential DCM biomarkers for differential diagnosis, progression, and prognosis prediction and treatment monitoring

Potential DCM biomarkers for differential diagnosis, progression, and prognosis prediction and treatment monitoring
Potential DCM biomarkers for differential diagnosis, progression, and prognosis prediction and treatment monitoring

Differential Diagnosis

Early diagnosis is a key factor for successful outcome of treatment in DCM research; however, despite established criteria currently based on clinical presentations, imaging techniques, echocardiography, and natriuretic peptides application to all CVDs [25, 26], there is the need for more robust, efficient, and cheap biomarkers that can be used to diagnose, predict, and monitor treatment of DCM. Serum biomarkers, such as troponin and natriuretic peptide, have been extensively studied and used biomarkers in HF [27, 28]; however, they are not generally specific to HF neither are they able to differentiate HF arising from different cardiac causes such as DCM, acute coronary syndrome, myocarditis, and cardioversion [28, 29]. Interestingly, application of metabolomics technologies offers a new opportunity to discover potential biomarkers for DCM. Application of biomarkers in blood or urine is more appreciated because of its semi- or noninvasive priority [30]. Alexander et al. [1] demonstrated that plasma metabolic profiles were different in primary DCM patients compared to normal control group. In the Alexander et al. [1] study, comprising 39 cases with primary DCM and 31 normal individuals (controls), they reported that DCM was associated with increased levels of citric acid cycle intermediates and lipid β-oxidation products including methylhistidine and prolylhydroxyproline and decreased levels of steroid metabolites, glutamine, threonine, and histidine. Another study by Haas et al. [31] analyzed serum metabolites from 82 patients with DCM and 51 healthy controls in a screening stage followed by a validation study with 57 healthy control and identified several metabolites involved in glycolysis and citric acid cycle including key metabolites such as lactate, succinic acid, and malate that were elevated in DCM patients compared to healthy controls. In addition to investigating differential metabolites between DCM and healthy controls, other studies have tried to identify metabolite biomarkers differentiating DCM from ICM (Table 2). In the Haas et al. [31] study, when comparing DCM with ICM, the potential DCM-identified metabolite biomarkers were not significantly different between the two groups based on the analysis of 52 DCM and 39 ICM patients’ serum samples. Further, researchers studied metabolite differences between HF patients with non-ICM and left ventricular systolic dysfunction and healthy controls via liquid chromatography quadrupole-time-of-flight mass spectrometry (LC-qTOF-MS). Here, 22 male HF patients and 19 healthy controls were studied. The metabolic signatures of HF patients with non-ICM and left ventricular systolic dysfunction were characterized by lower plasma levels of complex lipids and fatty acids, notably phosphatidylcholines (PC), cholesterol, and sphingolipids. In addition, reduced glutamine and increased glutamate plasma levels resulted in significantly increased purine degradation products in nonischemic HF patients [32]. Moreover, a potential differential diagnosis of DCM was proposed by Liu et al. [33] when they performed targeted LCMS-based metabolomics analysis to distinguish HF caused by coronary heart disease and DCM. Here, the researchers reported that plasma levels of arginine, glutamine, and hydroxytetradecanoylcarnitine could effectively distinguish coronary heart disease and DCM patients [33]. Further, in a study utilizing a high-resolution liquid chromatography coupled to the Q-Exactive quadrupole-Orbitrap tandem mass spectrometry (QE LC-MS/MS) approach, Zhao et al. [18] analyzed plasma samples of HF patients with different etiology (38 patients with DCM and 18 patients with ICM) and 20 healthy controls and revealed a combination panel of 6 metabolites including 1-pyrroline-2-carboxylate, norvaline, lysophosphatidylinositol (16:0/0:0), phosphatidylglycerol (6:0/8:0), fatty acid esters of hydroxy fatty acid (24:1), and PC (18:0/18:3) to differentiate patients with DCM and ICM with median areas under the curve (AUCs) of the ROC of 0.98 and 0.93 with accuracy of 95% and 87% in training and testing dataset, respectively, indicating an excellent discrimination of the combination model for the metabolites panel.

Table 2.

Comparing differential metabolic changes between DCM and ICM

Comparing differential metabolic changes between DCM and ICM
Comparing differential metabolic changes between DCM and ICM

Progression and Prognosis Prediction

In plasma and urine metabolomics study, researchers identified distinctive metabolites associated with DCM severity (patients with DCM and normal left ventricular reverse remodeling (LVRR), asymptomatic DCM, and symptomatic DCM) [34]. In this study, they measured 149 metabolites in 273 plasma and urine samples from patients with DCM and with various stages of disease (i.e., patients with DCM and normal LVRR, n = 70; asymptomatic DCM, n = 72; and symptomatic DCM, n = 131). Although an absolute difference was marginal, metabolites such as acylcarnitines, sialic acid, and glutamic acid were found to be the most distinctive metabolites associated with disease severity in both plasma and urine. Decision tree analysis to differentiate patients with DCM and LVRR from symptomatic DCM was improved when the researchers combined N-terminal prohormone of brain natriuretic peptide (NT-proBNP) (AUC 0.813 versus. 0.739 for the combined panel versus. NT-proBNP, respectively) and the top metabolites, suggesting that combining NT-proBNP with acylcarnitines, sialic acid and glutamic acid provide a better biomarker panel to distinguish different stages of DCM [34]. Previously, a tandem targeted mass spectrometry (MS/MS) was used to quantify 60 metabolites (consisting of 45 acylcarnitines and 15 amino acids) in fasting plasma obtained from HF and preserved ejection fraction (HFpEF) patients (n = 282) defined by LVEF ≥45%, diastolic dysfunction grade ≥1, and history of HF; HF with reduced ejection fraction (HFrEF) controls (n = 279) defined similarly as HFpEF, except for having LVEF <45%; and no-HF controls (n = 191) who had LVEF ≥45%, normal diastolic function, and no HF diagnosis. Here, the authors reported that long-chain acylcarnitine (LCAC) factor levels were significantly higher in HFrEF controls than in HFpEF cases with both HF groups having greater factor levels than no-HF controls. Specifically, the plasma concentration of 5 LCAC metabolites including palmitoyl-carnitine, linoleyl-carnitine, oleyl-carnitine, 3-hydroxy-palmitoleoyl-carnitine, and arachidonoyl-carnitine differed between HFpEF and HFrEF. All 5 LCAC metabolites and stearoyl-carnitine differentiated HFrEF and no-HF controls with 4 of these 6 LCAC metabolites except for 3-hydroxy-palmitoleoyl-carnitine and arachidonoyl-carnitine significantly differed when comparing HFpEF with no-HF controls [35]. Another targeted metabolomics approach was used to quantify 181 metabolites by LC-MS/MS and proton NMR spectroscopy in serum from HFpEF (n = 24), HFrEF (n = 20), and age-matched non-HF controls (n = 38). Here, medium- and long-chain acylcarnitines and ketone bodies were observed to be higher in HFpEF compared to HFrEF patients. A panel of 4 metabolites including 2-hydroxybutyrate, octadecenoylcarnitine, hydroxyprionylcarnitine, and sphingomyelins was identified to discriminate between HFpEF and HFrEF with AUC of the ROC of 0.908. Moreover, compared to non-HF control, HFpEF and HFrEF patients showed higher serum concentrations of acylcarnitines, carnitine, creatinine, betaine, and amino acids; and lower levels of PCs, lysophosphatidylcholines, and sphingomyelins [36]. Furthermore, a targeted MS/MS analysis of 41 patients, aged 18 years and older, considered to have an end-stage HF and required a mechanical circulatory support with a continuous-flow left ventricular assist device (LVAD) as bridge to transplantation or destination therapy revealed C16, C18:1, and C18:2 acylcarnitine metabolite levels to be significantly higher in patients with end-stage HF as compared to those with chronic systolic HF [37]. Moreover, the Ahmad et al. [37] study revealed that long-chain acylcarnitines were associated with increased risk of all-cause mortality/all-cause hospitalization, all cause-hospitalization, and cardiovascular death or cardiovascular hospitalization. More longitudinal studies are suggested to confirm these progression and prognosis prediction biomarkers of DCM.

Treatment Monitoring

Metabolomics studies have been conducted to predict the outcome of medical intervention in several diseases [38]. However, only a few metabolomics studies have been conducted to identify potential metabolite biomarkers that can predict outcomes of treatment or surgical intervention in DCM. In 2014, Padeletti et al. [39] conducted a metabolomics study with the aim of identifying metabolite fingerprints that could predict response to cardiac resynchronization therapy (CRT) in patients with HF. Using proton NMR technology, they reported different metabolite fingerprints in HF patients from healthy controls with high accuracy level, but their study did not differentiate between patients with ischemic and nonischemic DCM based on metabolic profile analyses [39]. This may be due to that fact that NMR technology in general is less sensitive and as a result it failed to capture many metabolites particularly those with trace concentrations in biological samples [20]. A LC-MS/MS-based metabolomics study used a least absolute shrinkage and selection operator regression model to identify a panel of four plasma PC metabolites (PC [20:0/18:4], PC [20:4/20:0], PC 40:4, and PC [20:4/18:0]) as major predictors for CRT response prediction with AUCs of the ROC of 0.906 in addition to sensitivity of 83.3% and 90.0% specificity. Similarly, a cross-validation analysis also showed a satisfactory and robust performance of the model with AUC of 0.910 in training dataset and 0.880 in the testing dataset, demonstrating potential metabolite markers for predicting HF response to CRT treatment [40]. Another study was conducted by Gong et al. [41] using an ultra-performance liquid chromatography quadrupole-time-of-flight mass spectrometry (UPLC-qTOF-MS) for comprehensive serum metabolomics analyses to help predict response to CRT. In their study, a discovery set of 27 responders versus 24 nonresponders and a validation set of 36 responders and 18 nonresponders were investigated and they reported increased concentrations of isoleucine and linoleic acid and a decreased concentration of tryptophan in response to CRT relative to nonresponders. This suggests that utilizing a more sensitive metabolomics technology such as MS-based approaches can help to identify potential metabolic biomarkers that could predict which HF patient will favorably or not respond to CRT. Moreover, a study to identify biomarkers that can predict which patients with advanced HF will benefit from an LVAD, based on analyses of urine samples, showed an increase in acylcarnitines, lysophosphatidylcholine derivatives, and PC derivatives in HF patients [42]. Further, in the Ahmad et al. [37] study, following the mechanical circulatory support with continuous-flow LVAD, they observed a decrease in levels of C16, C18:1, and C18:2 acylcarnitine metabolites in patients with end-stage HF.

Most DCM metabolomics studies in humans have been performed on biofluids: serum, plasma, or urine, in part because of the noninvasive nature and easy accessibility of blood. However, due to the challenges in obtaining human heart tissue samples, tissue metabolomics studies are mostly limited to animal models or human postmortem samples [43], but even so these studies are crucial to understand the physiological processes during DCM development and HF. Tissue analysis, in particular, is perhaps the most powerful approach for studying localized and specific responses to stimuli and pathogenesis, yielding explicit biochemical information about the mechanisms of disease [44]. As it is well reported in the literature, a normal human heart obtains about 40% of its energy needs from the metabolism of metabolites such as ketones, glucose, lactate, and amino acids, with approximately 60% originating from the metabolism of fatty acids (including free fatty acid, acylcarnitine, and acyl-CoA). These metabolite energy substrates are used to produce energy in the form of ATP, which must be supplied continuously from the circulation due to the heart’s low ability to store energy substrates intracellularly. A large amount of ATP (about 95%) is derived from the mitochondrial oxidative metabolism, out of which 70–90% is obtained from fatty acid oxidation and the remaining from the metabolism of glucose, lactate, ketone bodies, and amino acids particularly BCAAs. The remaining 5% of ATP is derived from glycolysis [45]. Moreover, cellular and even subcellular metabolite profiles can provide further insights into structure-to-function relationships; this is particularly important in the case of heterogeneous tissues such as brain and cancers [46]. Therefore, using metabolomics approach to simultaneously monitor these metabolites’ regulation in DCM tissues provides a better mechanistic understanding. In a human metabolomics study where samples from DCM patients (male n = 11; female n = 3) and healthy control nontransplanted donor hearts from male (n = 5) and female (n = 2) individuals were analyzed using NMR spectroscopy revealed a dramatic elevation of cardiac BCAA in the left ventricular samples of patients with DCM compared to control samples. To determine if stimulating BCAA catabolism could lower the severity of HF, the researchers subjected C57BL/6J mice to a transverse aortic constriction and treated them between 1 and 4weeks postsurgery with either a vehicle or a stimulator of BCAA oxidation (BT2, 40 mg/kg/day). Here, similar to the human DCM hearts, the accumulation of BCAA was also evident in the mouse failing heart induced by transverse aortic constriction, and cardiac BCAA oxidation in isolated working hearts was significantly enhanced by BT2, compared to a vehicle, following either acute or chronic treatment [47]. Again, targeted tissue metabolomics analyses of 44 cryopreserved human ICM and DCM hearts using ultra-high performance liquid chromatography systems with multiple reaction monitoring mass spectrometry (UHPLC-MRM-MS) showed a core set of 13 metabolites regulated in both ICM and DCM, while 10 were unique to ICM and 5 unique to DCM. Importantly, riboflavin-5-mononucleotide, a metabolite related to oxidative stress, was the most significant metabolite in both ICM and DCM hearts, while mitochondrial substrate, 2-methylbutyrylcarnitine, decreased in both ICM and DCM, indicating mitochondrial stress and alteration in myocardial substrate utilization (acylcarnitines transport fatty acids into the mitochondria for oxidation) [48]. In a metabolomics profiling of cardiac metabolism in a lamin A/C (Lmna) mouse model of DCM using ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS), DCM mice were characterized by the increased concentration of proline, methyl-histidine, as well as a number of citric acid cycle intermediates and carnitine derivatives indicating reduced energy metabolism [49]. Moreover, a metabolomics study to understand DCM pathogenesis and to identify new therapeutic targets using capillary electrophoresis mass spectrometry and LC-MS/MS compared J2N-k cardiomyopathic hamsters (n = 7) with J2N-n-healthy controls (n = 7) reported a reduced level of metabolites in glycolysis, pentose phosphate pathway, tricarboxylic acid cycle, and triacylglycerol, suggesting that a decrease in energy production leads to cardiac contractile dysfunction in diseased tissues [50]. Furthermore, a previous study using both targeted and untargeted LCMS approaches to investigate myocardial lipid content from a nondiabetic, lean, predominantly nonischemic advanced HF patients at the time of heart transplantation or LVAD implantation revealed a significantly decreased concentration of lipid intermediates including long-chain acylcarnitines. These metabolic changes were associated with the increased myocardial concentration of acetyl-CoA, ketogenic β-hydroxybutyryl CoA, and expression of the gene encoding succinyl-CoA:3oxoacid-CoA transferase (SCOT) [51]. An NMR-based metabolomics study in a mouse model revealed hypertrophied and failing heart shifts to oxidizing ketone bodies (indicated by an increase in hydroxybutyrylcarnitine, acetylcarnitine, succinate concentrations in the heart) as an alternative to compensate energy deficit as a result of reduced capacity to oxidize fatty acids. These changes were associated with downregulation of proteins involved in fatty acid utilization and an increased expression of β-hydroxybutyrate dehydrogenase 1, a key enzyme in the ketone oxidation pathway in HF samples [52]. Similarly, a targeted LC-MS/MS metabolomics analysis of heart tissue from end-stage arrhythmogenic cardiomyopathy (a form of DCM) revealed a moderate decrease in long-chain acylcarnitine (C14-22) and a significant decrease in medium-chain acylcarnitine (C8-12) and short-chain acylcarnitine (≤C6) [53].

A major limitation observed is the fact that DCM metabolomics-related biomarker search investigations so far have been mostly small studies with a relatively sample size of less than 100 samples as a result, making it difficult to draw a definitive conclusion about the translation of identified DCM biomarkers for clinical applications. A large sample size is known to decrease downstream statistical bias that may arise from the batch effect, missing data points which are very common in metabolomics studies [54, 55]. Again in majority of human metabolomics studies of DCM, only biofluids such as serum, serum, or urine have been investigated. However, further studies are needed to explore metabolite correlations between DCM heart and biofluid as changes in plasma metabolites alone may reflect the contribution of several organs. Exploring human heart will also help better understand the mechanisms underlining DCM as they have been partially explored in few preclinical studies so far. Another important limitation observed in several of these studies is that sex-specific metabolic differences in DCM were not interpreted warranting future studies to take this into consideration.

The strength of this review is that it provides a very comprehensive picture of the current state of the knowledge and research on biomarkers for DCM. The review demonstrates that different metabolomics studies have been performed and validated with various biological samples and metabolomics technologies and these suggested biomarkers represent very similar metabolic pathways that were altered in a different state of DCM progression. This reemphasizes the need for future studies to focus on using multicenter studies with larger populations to validate these biomarkers for potential clinical applications to complement clinical biomarkers currently in used for DCM diagnosis and treatment.

Better understanding of the DCM phenotype and recent advances in metabolomics technology to define DCM metabolome will eventually improve the diagnosis, prevention, prognosis, and treatment of this disease. Metabolomics offers a unique opportunity to influence DCM prevention and treatment since it provides a comprehensive understanding of the underlying mechanisms of DCM and the metabolome has close proximity to the disease phenotype [56, 57]. Metabolites with diagnostic potential that have repeatedly been identified in several studies included acylcarnitines, succinic acid, malate, methylhistidine, aspartate, methionine, and phenylalanine. DCM could be differentiated from ICM based on biomarkers such as 1-pyrroline-2-carboxylate, norvaline, lysophosphatidylinositol (16:0/0:0), phosphatidylglycerol, fatty acid esters of hydroxy fatty acid, and PC, whereas acylcarnitines, isoleucine and linoleic acid, and tryptophan were the main biomarkers to monitor treatment response to DCM. Mechanistically, BCAA, glycolysis, tricarboxylic acid cycle, and acylcarnitine induced proinflammatory pathways were the key biological pathways dysregulated in DCM.

The author declares no conflicts of interest.

The author received no funding support for this work.

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