Journal of the American College of Cardiology
The Emerging Role of Metabolomics in the Diagnosis and Prognosis of Cardiovascular Disease
Author + information
- Received August 24, 2016
- Accepted September 9, 2016
- Published online December 19, 2016.
Author Information
- John R. Ussher, PhDa,b,
- Sammy Elmariah, MD, MPHc,
- Robert E. Gerszten, MDd and
- Jason R.B. Dyck, PhDa,∗ (jason.dyck{at}ualberta.ca)
- aCardiovascular Research Centre, Department of Pediatrics, Mazankowski Alberta Heart Institute, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, Canada
- bFaculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, Alberta, Canada
- cDivision of Cardiology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
- dDivision of Cardiovascular Medicine, Department of Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts
- ↵∗Reprint requests and correspondence:
Dr. Jason R.B. Dyck, Cardiovascular Research Centre, Department of Pediatrics, Mazankowski Alberta Heart Institute, Faculty of Medicine and Dentistry, University of Alberta, 458 Heritage Medical Research Centre, Edmonton, Alberta T6G 2S2, Canada.
Central Illustration
Abstract
Perturbations in cardiac energy metabolism are major contributors to a number of cardiovascular pathologies. In addition, comorbidities associated with cardiovascular disease (CVD) can alter systemic and myocardial metabolism, often contributing to the worsening of cardiac function and health outcomes. State-of-the-art metabolomic technologies give us the ability to measure thousands of metabolites in biological fluids or biopsies, providing us with a metabolic fingerprint of individual patients. These metabolic profiles may serve as diagnostic and/or prognostic tools that have the potential to significantly alter the management of CVD. Herein, the authors review how metabolomics can assist in the interpretation of perturbed metabolic processes, and how this has improved our ability to understand the pathology of ischemic heart disease, atherosclerosis, and heart failure. Taken together, the integration of metabolomics with other “omics” platforms will allow us to gain insight into pathophysiological interactions of metabolites, proteins, genes, and disease states, while advancing personalized medicine.
Over the last few decades, there has been a growing appreciation for the important contribution that myocardial energy metabolism plays in the regulation of cardiac function. As the heart is the most metabolically demanding organ in the body, it is not surprising that perturbations in cardiac energy metabolism are major contributors to a number of cardiovascular pathologies. In addition, comorbidities associated with cardiovascular disease (CVD) pathogenesis can alter systemic and myocardial metabolism, which often aids in the worsening of cardiac function and health outcomes. In no situation is this more relevant than in obesity and diabetes, where these conditions can cause major systemic metabolic disturbances that have a negative impact on organs such as the liver, skeletal muscle, adipose tissue, the vasculature, as well as the myocardium. However, even in the absence of obesity and diabetes, alterations in substrate metabolism of numerous organs resulting from the onset of CVD can contribute to changes in the metabolic profile of a patient. With numerous advancements in “omics” technology platforms, including genomics, transcriptomics, and proteomics, we now have a much broader understanding of the molecular/cellular/functional changes that take place in CVD, as well as predictions of how these changes may influence intermediary metabolism (Central Illustration). Furthermore, state-of-the-art metabolomic technologies that are now available give us the ability to measure thousands of metabolites in biological fluids or biopsies, providing us with a “snapshot” of the metabolic fingerprint of individual patients (Table 1). These snapshots can potentially serve as diagnostic and/or prognostic tools that can be used to identify impairments in systemic or myocardial metabolism occurring during the development and worsening of CVD, as well as help guide the types and timing of specific interventions/therapies. Thus, metabolomics is emerging as an important tool that can aid clinicians in better understanding the pathogenesis of CVD, and has the potential to significantly alter the management of CVD.
Perturbations in Cellular Metabolism Inferred via Metabolomic Profiling
Overview of Cellular Metabolism
Substrate metabolism is an essential component of cellular heath and survival. Both anabolic and catabolic processes are necessary to support the numerous cellular events that contribute to cell, organ, and organism survival. From a simplistic perspective, when cells in the body generate energy in the form of adenosine triphosphate (ATP), they catabolize the various energy sources available, either from endogenous energy stores (primarily glycogen or triacylglycerols), or from exogenous substrates circulating in the blood (e.g., carbohydrates, fatty acids, amino acids, and ketone bodies, among others). By contrast, when cells need to build materials (e.g., phospholipids for membranes, proteins for growth, fatty acids for de novo lipogenesis, and so on) or perform many cellular processes (e.g., growth, ionic homeostasis, signal transduction, contraction, among others), they consume ATP, releasing the energy needed to support these functions. For the vast majority of cells in our body, carbohydrates (primarily in the form of glucose and lactate) and fatty acids represent the most common energy substrates metabolized by our cells to produce ATP.
Of the many substrates that can be used to generate energy, glucose and fatty acids are the major contributors to overall ATP production. For the former, following transport from the circulation into the cell, glucose can undergo anaerobic glycolysis to produce pyruvate and small amounts of ATP (2 ATP per glucose molecule) (Figure 1). In some cell types, if the energy need is low, the majority of this pyruvate is converted into lactate, which can exit the cell into the circulation (Figure 1). However, if the cellular energy demands are high and oxygen is present, pyruvate derived from glucose and/or lactate can enter the mitochondria, where it is converted to acetyl coenzyme A (acetyl-CoA) by pyruvate dehydrogenase (PDH). Acetyl-CoA is the common intermediate that links oxidative metabolism of all nutrient energy sources, and this acetyl-CoA is utilized by the tricarboxylic acid (TCA) cycle (also known as the Krebs cycle) to produce reducing equivalents (e.g., nicotinamide adenine dinucleotide and flavin adenine dinucleotide), which act as electron donors to drive the proton motive force that fuels ATP synthesis (Figure 1). This latter process of glucose breakdown from pyruvate to eventual ATP production is termed glucose oxidation, and ultimately generates a significantly greater amount of ATP than glycolysis (31 ATP from glucose oxidation vs. 2 ATP from glycolysis). During cellular fatty acid catabolism, fatty acids are either transported into and/or passively enter the cell, converted into fatty acyl-CoA esters, and then converted into a fatty acylcarnitine, which permits the fatty acid to traverse the mitochondrial membrane. There, it is reconverted back into a fatty acyl-CoA ester for subsequent mitochondrial β-oxidation (Figures 1 and 2) (1). The mitochondrial β-oxidation enzymatic machinery proceeds to repeatedly remove acetyl-CoA from the fatty acyl-CoA ester until it has been completely oxidized. Hence, complete oxidation of a fatty acid molecule, such as oleate or palmitate, produces more acetyl-CoA and reducing equivalents, and thereby a much larger amount of ATP than the complete oxidation of a glucose molecule (104 ATP from palmitate vs. 31 ATP from glucose oxidation).
Cellular Metabolism and Implications of Metabolomic Profiling
The primary substrates used by our cells to produce energy (ATP) include carbohydrates, fatty acids, ketone bodies, and amino acids. In the absence of oxygen, ATP can be generated from glycolysis (step 1) as glucose is converted into pyruvate and then lactate. In the presence of oxygen, pyruvate can enter the mitochondria and be oxidized via PDH to produce acetyl-CoA for the TCA cycle. This process is referred to as glucose oxidation (step 2). Similarly, fatty acids are transported into the cell, converted to fatty acyl-CoA, and then enter the mitochondria via the CPT1/CPT2 system. Following this, the fatty acyl-CoA ester enters the β-oxidation pathway for further breakdown to produce acetyl-CoA (step 3). BCAAs can also be oxidized to produce acetyl-CoA, but must first be converted into their respective BCKA derivatives, which are subsequently oxidized by BCKD as they enter the mitochondria, undergoing a series of reactions that lead to the formation of acetyl-CoA (step 4). Ketone bodies such as β-hydroxybutyrate can also be oxidized (via BDH) in the mitochondria and result in the formation of acetyl-CoA (step 5). Thus, acetyl-CoA represents the common metabolite linking oxidative energy metabolism of the main substrates identified herein, and this acetyl-CoA feeds into the TCA cycle and results in the formation of reducing equivalents NADH and FADH2, which fuel ATP production. During cardiometabolic disease progression, metabolic pathways are often perturbed and lead to the accumulation or loss of various metabolites that can be detected in the circulation via metabolomic profiling. These key metabolites capable of spilling over into the circulation, and which can reflect a “snapshot” of cellular metabolism, are indicated in blue text. ATP = adenosine triphosphate; BCAA = branched-chain amino acid; BCKA = branched-chain α-keto-acid; BDH = β-hydroxybutyrate dehydrogenase; CoA = coenzyme A; CPT = carnitine palmitoyltransferase; FADH2 = flavin adenine dinucleotide; L-C = long-chain; NADH = nicotinamide adenine dinucleotide; PDH = pyruvate dehydrogenase; TCA = tricarboxylic acid.
Use of Acylcarnitine Profiling to Infer Changes in Fatty Acid Metabolism
During fatty acid oxidation, fatty acyl-CoA esters are converted into fatty acylcarnitine derivatives (via CPT-1), which allows fatty acids to enter the mitochondria where they are reconverted back into fatty acyl-CoA (via CPT-2) for subsequent mitochondrial β-oxidation. General defects in the enzymes of the β-oxidative machinery that inhibit the oxidation of L-C fatty acids will increase the accumulation of mitochondrial L-C acyl-CoAs and in turn lead to increases in L-C acylcarnitines via CPT-2. However, impaired L-C fatty acid β-oxidation will produce decreases in S-C and M-C acyl-CoAs, as L-C fatty acids are not broken down via repeated cycles of β-oxidation, leading to corresponding reductions in the respective S-C and M-C acylcarnitine derivatives. Therefore, reductions in S-C/L-C or M-C/L-C acylcarnitine ratios in the circulation often reflect impairments in mitochondrial L-C fatty acid oxidation rates. In contrast, general increases in numerous S-C, M-C, and L-C acylcarnitines often infer elevations in fatty acid oxidation. If fatty acid oxidation is impaired due to inhibition of mitochondrial fatty acid uptake, decreases in S-C, M-C, and L-C acylcarnitines are observed. CrAT = carnitine acetyltransferase; M-C = medium-chain; S-C = short-chain; other abbreviations as in Figure 1.
Although glucose and fatty acids represent the most common fuels used by our cells to produce energy, the majority of cells in our bodies are also capable of metabolizing amino acids (e.g., hepatocytes, skeletal muscle myocytes) and ketone bodies (e.g., neurons) to produce acetyl-CoA for oxidative energy metabolism (Figure 1). In particular, cellular metabolism of the branched-chain amino acids (BCAAs) leucine, isoleucine, and valine, has been extensively studied, as BCAAs are potent regulators of systemic metabolism, energy expenditure, and muscle protein synthesis (2). In order for BCAAs to enter the mitochondria for oxidative metabolism, they must first be converted into their respective branched-chain α-keto-acids (BCKAs), which are subsequently oxidized via BCKA dehydrogenase (Figure 3). BCAA/BCKA metabolism is of particular relevance during fasting/starvation, where skeletal muscle catabolism of BCAAs produces alanine to support hepatic gluconeogenesis, and hepatic catabolism of BCAAs leads to the formation of precursors for hepatic ketogenesis (Figure 3). Of interest, the increase in cellular metabolism of BCAAs in muscle has been strongly correlated with reduced insulin sensitivity and impaired glucose homeostasis (see the section Additional Considerations for the Use of Metabolomics in the Diagnosis and Prognosis of CVD) (2).
Cellular Fates of BCAA Catabolism
Although Figure 1 depicts that BCAA metabolism can be used to generate acetyl-CoA for oxidative energy metabolism, BCAA catabolism serves a number of other purposes. In particular, during fasting/starvation, the first step in BCAA catabolism involves transfer of amino groups to α-ketoglutarate, followed by subsequent amino group transfer to pyruvate, producing alanine. This is of particular importance in skeletal muscle, where this alanine is exported to the liver to support hepatic gluconeogenesis (step 1). Conversely, enhanced BCAA (leucine and isoleucine) catabolism in the liver during fasting/starvation can lead to the formation of precursors for the biosynthesis of ketone bodies (step 2). Finally, BCAA catabolism can be used to refuel TCA cycle intermediates through anaplerosis, either at the level of α-ketoglutarate, or through formation of succinyl-CoA produced via subsequent breakdown of valine or isoleucine (step 3). Abbreviations as in Figure 1.
Myocardial Metabolism
In the healthy adult heart, virtually all ATP production is derived from mitochondrial oxidative metabolism, with the remainder primarily arising from glycolysis (3). Because myocardial ATP stores are relatively low (∼300 mg of total ATP for an average heart) compared with the high rate of ATP utilization, there is a nearly complete turnover of the myocardial ATP pool every 10 s (3). In order to meet this enormous energy demand, the healthy heart metabolizes more than 30 g of fat and 20 g of carbohydrate per day, and uses the equivalent of nearly 35 liters of oxygen while doing so (3). Furthermore, the metabolic flexibility of the heart is extremely dynamic, as demonstrated by its ability to rapidly alter its pattern of fuel utilization in order to adapt to its substrate and hormonal environment, whereby virtually all energy substrates, including fatty acids, glucose, lactate, ketone bodies, and amino acids, can be consumed by the heart to generate ATP.
Interestingly, the normal patterns of cardiac substrate metabolism are often perturbed in a variety of CVDs, and it is widely accepted that these metabolic changes directly contribute to disease pathophysiology. Indeed, alterations in cardiac metabolism assessed via coronary sinus catheterization combined with infusion of radiolabeled tracers, positron emission tomography (PET) imaging, or cardiac magnetic resonance imaging have been observed in patients with obesity/diabetes, ischemic heart disease (IHD), and/or heart failure (HF) (3). However, these methods can be invasive and costly, present potential health hazards due to use of radioisotopes with long half-lives (e.g., carbon-14 [14C]), and are often deployed in patients who already have established or advanced CVD, thus limiting the diagnostic potential of these assessments. Perturbations in myocardial metabolism frequently lead to the accumulation or loss of specific metabolites from the various metabolic pathways described previously, and changes in these metabolites are often reflected within the circulation (Figure 1). These metabolites represent the terminal product of the environment’s interaction with the genome-transcriptome-proteome (Central Illustration) in response to a disease process (4,5). Hence, the ability to accurately identify and quantify changes in these metabolites, and to use this information to detect perturbations in a specific metabolic process, adds to the arsenal of diagnostic information available to clinicians. Fortunately, significant advancements in metabolomics-based technology platforms now enable us to detect these various metabolites with high sensitivity and selectivity. Furthermore, a number of these metabolites may have clinical utility with regard to predicting (screening biomarkers) and detecting (diagnostic biomarkers) disease, evaluating disease progression/remission (prognostic biomarkers), and assessing therapeutic effectiveness.
Metabolomics
The evolution of metabolomic profiling
In 1972, the concept of using quantitative and qualitative patterns of metabolites within biological fluids to understand the physiological status of a biological system was introduced by Pauling et al. (6). In the same year, Horning and Horning (7) coined the term metabolic profiling to describe data obtained from gas chromatography of a patient sample. Since these initial reports, the concept that metabolites can comprehensively characterize a phenotype, disease state, or physiological response to an applied stimulus or perturbation, has exponentially evolved over the past 4 decades into the field of metabolomics, the systematic assessment of small molecules in biological fluid or tissue (4,8).
The metabolome encompasses an immense variety of endogenous small molecules, including amino acids, sugars, lipids, nucleic acids, amines, organic acids, fatty acids, and urea-cycle and methyl-transfer metabolites, as well as a myriad of exogenous chemicals, such as pharmacological agents, toxins, and xenobiotics. Chemical characteristics, including hydrophilicity, lipophilicity, polarity, mass, and charge, vary tremendously across these compound classes, as does the dynamic range of metabolite concentrations, varying from picomolar to millimolar amounts (5,8). The broad range of concentrations and the biochemical diversity of metabolites preclude the measurement of all metabolites using a single analytical technique or platform. Instead, combined analytical approaches using several metabolomic-profiling platforms are utilized to span the diversity of the metabolome (5,8). However, metabolic pathways are highly conserved across species, readily allowing for the mechanistic and functional evaluation of human metabolomic findings in model organisms (4).
Metabolomic analytic techniques
Nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), often coupled to a chromatographic technique, are the primary methods by which the metabolome is assessed. Both techniques enable high-throughput profiling of large numbers of metabolites simultaneously within biological fluids, but each is associated with different analytical strengths and weaknesses.
Metabolite profiling of biological samples using NMR spectroscopy was pioneered in 1983. This technique identifies metabolites by chemical shifts in resonance frequency when they are subjected to a magnetic field. Within a strong magnetic field, nuclei absorb electromagnetic radiation in a characteristic frequency that can be used to assign local molecular structure. Measuring all frequencies can identify metabolites within a sample. Advantages of NMR include that it is robust, reproducible, requires minimal sample preparation, is low in cost, and nondestructive. Although NMR spectroscopy is also quantitative, without the need for standards, its sensitivity, which relates to the strength of the magnet, is limited, especially for low abundance metabolites (4). Using standard equipment, NMR is able to resolve metabolite concentrations down to the low micromolar range (4,8). In addition, within a biological sample, current technology enables the accurate quantification of approximately 100 of the most abundant metabolites (8).
MS identifies metabolite species on the basis of their mass/charge ratio (m/z). Although samples can be infused directly into the mass spectrometer, the separation of metabolite components using either liquid chromatography (LC) or gas chromatography is often first performed to facilitate analyte identification and quantification. Chromatographic separation involves dissolving the sample into a solvent and passing this mobile phase through a stationary phase, such as a column containing surfaces with specific interaction chemistries that allow the dissolved metabolites to remain in the column or to pass through at varying speeds. Analytes are subsequently ionized for introduction into the mass separation unit. Mass separation can be performed by several techniques, including time-of-flight, quadrupole, and ion trap mass analyzers, each of which has varying dynamic ranges, resolution, and accuracy. Tandem MS (MS/MS) allows for the use of 2 or more stages of mass analysis to focus on the fragmentation of an ion within a mixture in order to enhance resolution and accuracy (5). Integration of these serial analyte manipulations provides the highly sensitive capability of identifying and analyzing thousands of metabolites with concentrations down to the low femtomolar range (5,8).
Metabolite profiling using NMR or MS can be performed in a targeted or untargeted manner (5). Targeted metabolomics measures a distinct, well-characterized set of metabolites within a biological sample. Absolute quantification of analytes is possible by spiking in internal standards, usually isotope-labeled versions of endogenous metabolites, across a range of concentrations. Using the targeted approach, key metabolites within metabolic pathways act as sentinels to reflect changes within a metabolic pathway (Table 1). Although instruments functioning within a targeted mode are more sensitive, targeted metabolomics only permits the assessment of several hundred known metabolites at a time. By contrast, untargeted metabolomics attempts to analyze all small molecules within a sample in an unbiased manner and is often not quantitative. In untargeted metabolite profiling, closer to 8,000 metabolites are analyzed, the identities of most of which are unknown (9). This approach relies on patterns or signatures reflective of a metabolic state or, alternatively, the investment of tremendous time and resources in order to identify unknown metabolites.
Limitations with the use of metabolomics to assess intermediary metabolism
Metabolomic profiling performed on blood or urine samples can provide significant insight into metabolic pathways that may be altered during the progression of numerous pathologies (Figures 4, 5, and 6, Table 1). Many of these diseases, such as obesity and/or type 2 diabetes (T2D) are complex syndromes that involve pathological changes in multiple organ systems, each of which may significantly contribute to the blood- and urine-based metabolomic profile. As such, using blood- and urine-based metabolomics as a diagnostic or prognostic tool is valuable, but cannot be considered definitive in informing us about new molecular pathophysiological processes that are occurring within the heart or any specific organ. Indeed, assessment of metabolism at the organ level necessitates the measurement of changes in metabolites across the organ. For example, the delta in metabolite levels between arterial blood and the coronary sinus for the heart or the renal artery and vein for the kidney provides more precise information as to the metabolic changes that are occurring in those organs. In addition, because metabolomics only provides a snapshot of metabolic flux through a particular pathway, the reason for increased or decreased metabolite levels may not be readily apparent. For instance, an aberrant metabolite concentration may be due to a defective metabolic pathway, changes in substrate supply, or abnormal metabolic rate of the primary product. Thus, predictions of defective metabolic processes on the basis of blood- or urine-based samples alone must be interpreted with caution. Regardless of these limitations, identification of numerous altered metabolites related to a common metabolic pathway in the blood or urine can yield significant insight into potential perturbations in metabolism that take place during disease progression. As such, metabolomic signatures in blood or urine can often yield hypothesis-generating findings. These findings ultimately can lead to intricate molecular explorations in a diseased organ, cell, or animal model, which in turn will enhance clinical utility and our understanding of disease pathobiology.
Metabolomics in IHD and Atherosclerosis
During myocardial ischemia, a reduction in blood flow and oxidative metabolism decreases substrate extraction by the heart, which contributes to the elevation in circulating FFAs and BCAAs, whereas anaerobic glycolysis increases to produce ATP in the absence of oxygen. Hence, increases in circulating lactate levels are frequently observed during ischemia. CAD resulting from atherosclerosis is often a major contributor to ischemic heart disease or myocardial infarction. It has been demonstrated that gut microbiota are capable of converting phosphatidylcholine/choline and L-carnitine into TMA, which the liver converts into TMAO. TMAO increases the risk for atherosclerosis potentially via interfering with reverse cholesterol transport and promoting plaque formation, which promotes IHD. Together, all of these changes in the metabolomic profile can provide significant diagnostic insight into the risk for atherosclerosis or presence of CAD. CAD = coronary artery disease; FFA = free fatty acid; IHD = ischemic heart disease; TMA = trimethylamine; TMAO = trimethylamine-N-oxide; other abbreviations as in Figure 1.
Metabolomics in HF
The failing heart undergoes a well-characterized metabolic remodeling that consists of general reductions in mitochondrial function and oxidative energy metabolism, which is compensated via an increase in anaerobic glucose metabolism (glycolysis). Hence, circulating metabolomic profiles often yield increases in L-C acylcarnitines and lactate in HF patients. Circulating BCAAs are also often elevated in HF patients, although reasons for this increase are unknown. As recent studies in HF patients demonstrate impairments in the heart’s ability to break down BCKAs (Figure 3), this may lead to a buildup of myocardial BCAAs that spill over into the circulation. Furthermore, recent studies have identified that the failing heart may increase its reliance on ketone bodies as an oxidative energy source to compensate for the reduction in mitochondrial fatty acid oxidation, and circulating ketone bodies (e.g., β-hydroxybutyrate and acetoacetate) are often decreased in HF patients. Of interest, decreased circulating ketone bodies and increased circulating L-C acylcarnitines may also have diagnostic capability in differentiating HFrEF from HFpEF patients, in that serum concentrations of ketone bodies and L-C acylcarnitines are decreased and increased, respectively, in HFrEF patients compared with HFpEF patients. HF = heart failure; HFpEF = heart failure with preserved ejection fraction; HFrEF = heart failure with reduced ejection fraction; other abbreviations as in Figures 1 and 2.
Metabolomics in Obesity and Diabetes
Obesity and type 2 diabetes contribute to a number of deleterious changes in multiple organs in the body. These changes include adipose tissue inflammation, skeletal muscle insulin resistance, nonalcoholic fatty liver disease, β-cell dysfunction, vascular dysfunction, and cardiomyopathy. Associated with these changes are metabolic derangements in these organs that can contribute to an increased risk for CVD in these individuals. Blood-based metabolomic profiles of individuals with obesity and type 2 diabetes often yield increased acylcarnitines, lactate, and BCAAs. CVD = cardiovascular disease; other abbreviations as in Figure 1.
Integration of Various Omics Platforms for Patient Phenotyping
The metabolome represents one of the downstream end products of the environment’s interaction with the genome-transcriptome-proteome.
Metabolomics and IHD
IHD is caused by inadequate perfusion of the myocardium and encompasses both stable and unstable angina, as well as myocardial infarction (MI). IHD represents the most common form of CVD, and although survival rates following an acute MI have greatly improved in recent years, mortality due to IHD still remains the leading cause of cardiovascular death globally (10). Because oxygen and nutrient supply/delivery to the myocardium are markedly reduced during ischemic periods, there are a number of key changes that manifest with regard to myocardial intermediary energy metabolism (Figure 4), often marked by a reduction in overall oxidative metabolism. In response to this decline in oxidative metabolism, glycogen breakdown and glycolysis rates are increased, as glycolysis can produce ATP anaerobically at a rapid rate (3). The extent of the increase in myocardial glycolysis is dependent on the severity of ischemia and its duration. Whether these myocardial metabolic alterations during myocardial ischemia can be detected via use of blood-based metabolomics is a question of great interest due to its diagnostic potential.
TCA cycle activity
Application of LC/MS blood-based metabolomic profiling in 36 individuals subjected to exercise stress testing demonstrated that individuals exhibiting myocardial ischemia (the majority confirmed via coronary angiography) had reductions in a number of TCA cycle metabolites (e.g., oxaloacetate), consistent with myocardial ischemia-induced reductions in oxidative metabolism (11). In patients undergoing alcohol septal ablation for hypertrophic obstructive cardiomyopathy, a model of planned MI, serial blood samples collected as early as 10 min and up to 24 h post-ablation from the coronary sinus and periphery revealed alterations in numerous metabolites (12). This included changes in a number of organic acids, including increases in succinic and malic acid, reflecting dysregulated TCA cycle activity. Hence, blood-based metabolomics frequently identifies defects in TCA cycle activity and oxidative metabolism in IHD patients.
Fatty acid utilization
Fatty acid oxidation rates are decreased in the ischemic myocardium as a direct result of reduced blood flow and oxygen delivery. As such, paired collection of arterial and coronary sinus blood before cardiac surgery demonstrated significant reductions in the myocardial extraction of free fatty acids in patients with coronary artery disease (CAD) versus those without CAD (13). However, one must be cautious when interpreting circulating metabolomic profiles in patients with IHD, because metabolism within the ischemic myocardium differs from that of the viable nonischemic myocardium. Consistent with this, fatty acid oxidation rates in the viable nonischemic myocardium appear normal, and thus the circulating metabolomic profile from this region may mask the metabolic changes present within ischemic tissue. Likewise, direct coronary sinus catheterization combined with infusion of [1-14C]-oleate or [1-14C]-palmitate demonstrated similar fatty acid oxidation rates in patients who had just underwent coronary angiography for symptoms of IHD versus healthy volunteers (14). Paired collection of arterial and coronary sinus blood before cardiac surgery has also demonstrated increases in baseline short-chain (S-C) dicarboxylacylcarnitines, which were shown to predict risk for cardiovascular events in patients with CAD (15). In addition, LC/MS/MS-based serum metabolomic profiling in elderly patients with established CAD, but without HF, demonstrates that medium-chain (M-C) and long-chain (L-C) acylcarnitines are increased, and predict incident risk for subsequent cardiovascular events independent of standard predictors (16). The source of circulating S-C dicarboxylacylcarnitines and how they may contribute to the pathology of CAD are unknown. Because peroxisomes are another cellular organelle capable of oxidizing fatty acids like mitochondria, such changes may reflect alterations in peroxisomal fatty acid metabolism because L-C dicarboxylic acids are often shortened via peroxisomal oxidation (17).
Glucose utilization
Glucose oxidation rates are also markedly depressed in the ischemic myocardium, whereas glycolytic rates are robustly increased due to the stimulation of glycogenolysis (14,18). The more serious the ischemic episode, the faster myocardial lactate accumulates, because there is limited flow to wash out lactate and other glycolytic intermediates (e.g., protons), which also contributes to the eventual inhibition of glycolysis during prolonged ischemia (18). Blood-based metabolomic profiling in patients undergoing global ischemia/reperfusion as a result of planned cardiac surgery with cardioplegic arrest or in patients undergoing alcohol septal ablation for hypertrophic obstructive cardiomyopathy leads to marked increases in circulating lactate levels, reflective of enhanced myocardial anaerobic glycolytic metabolism (12,13). These findings have been validated in a cohort of patients who underwent spontaneous MI (12), and have also been reported in stable angina patients undergoing coronary angioplasty, where ischemia for at least 1 min following balloon inflation produced marked increases in circulating lactate levels collected 10 min post-ischemia (19).
Amino acid utilization
Current information suggests that the normal heart’s reliance on amino acids as a source of ATP is minimal, as infusion of l-[ring-2,6-3H]phenylalanine demonstrates that amino acids are used primarily for anabolic purposes in the myocardium (20). This is supported by measurements of leucine oxidation in the isolated rat heart, which contributes between 3% to 5% of overall cardiac oxygen consumption, depending on experimental conditions (21). Regardless, it has been suggested that amino acid metabolism in the heart may be particularly important during ischemia. Amino acids such as glutamate and glutamine, as well as the BCAAs, can serve as anaplerotic substrates that refuel the TCA cycle via intraconversion into α-ketoglutarate or succinyl-CoA, respectively (Figure 3) (22). Moreover, guanosine triphosphate (which can be converted to ATP) can be generated in the absence of oxygen during the metabolism of glutamate and glutamine via substrate-level phosphorylation (22). Direct comparisons of arteriovenous differences in 8 healthy and 11 CAD patients demonstrates that IHD is associated with a net myocardial release of alanine and net myocardial uptake of glutamate (23). Interestingly, circulating BCAA signatures have also been shown to predict both prevalent and subsequent cardiovascular risk in patients with CAD recruited through the CATHGEN (CATHeterization GENetics) biorepository at the Duke University Medical Center (15,24). However, a limitation of these studies is that all patients underwent cardiac catheterization for suspected CAD and were not healthy subjects. However, recent studies directly comparing blood-based metabolite profiles also observed that serum BCAA levels assessed via LC/MS/MS-based metabolomics were elevated in age- and sex-matched CAD patients versus healthy individuals, and this was found to be independent of other traditional risk factors, including diabetes, hypertension, and dyslipidemia (25). Although studies to date are limited in scope, BCAA uptake appears negligible in the ischemic heart (22) and, in general, our mechanistic understanding of the relevance of amino acid and protein metabolism in the progression of IHD remains poorly understood.
Metabolomics and Atherosclerosis
Because the vast majority of IHD is due to the presence of atherosclerotic plaques in the coronary vessels, there has been great interest in determining whether metabolomic profiling can identify individuals at increased risk for IHD due to underlying atherosclerosis. This is especially important considering a large number of patients with clinically diagnosed CAD exhibit derangements in myocardial metabolism, and the use of techniques such as PET imaging to confirm these derangements is costly. Hence, the use of blood-based metabolomic profiling to identify subclinical atherosclerosis and early CAD may be of clinical value. Indeed, a number of metabolomic studies in patient cohorts have identified a variety of potential biomarkers that can predict risk for CAD and subsequent cardiovascular events, including MI and death. Most notably, sophisticated studies have demonstrated that circulating trimethylamine-N-oxide (TMAO) is a significant predictor for atherosclerosis and incident risk for MI and stroke (26,27), with the gut microbiome being implicated as a critical factor regulating this process (26). Specifically, dietary phosphatidylcholine, choline, and carnitine are substrates for the generation of trimethylamine (TMA) via the host gut microbiome. This TMA is subsequently released into the circulation and is then oxidized by the liver to TMAO. TMAO, in turn, promotes the progression of atherosclerosis, potentially via interfering with reverse cholesterol transport, which subsequently increases the risk of cardiovascular events (Figure 4) (26). Recent studies indicate that TMAO may also increase platelet hyper-reactivity, as application of exogenous TMAO at physiological levels to human platelet-rich plasma enhanced platelet aggregation, while increasing the adherence of fluorescently labeled platelets within human whole blood to collagen (27). Furthermore, studies have shown that interfering with the gut microbiome’s ability to convert dietary choline or carnitine into TMA, via inhibiting bacterial TMA lyase with 3,3-dimethyl-1-butanol in a nonlethal manner, reduces circulating TMAO levels and the subsequent progression of atherosclerotic lesions in a mouse model of atherosclerosis (28). Interestingly, these studies support that increased circulating levels of phosphotidylcholine/choline and/or carnitine are associated with increased cardiovascular risk, yet only appear to provide true prognostic value in those individuals in which TMAO is also elevated (26). Together, these findings support TMAO as a novel biomarker for the diagnosis of atherosclerosis/CAD, and exemplify the power of metabolomics to produce hypothesis-generating findings that can lead to intricate molecular explorations, which ultimately improve our understanding of disease pathology.
Other identified biomarkers from blood-based metabolomic profiling include observations that increased levels of 18:2 monoglyceride or decreased levels of 18:2 lysophosphatidylcholine and 28:1 sphingomyelin are associated with increased risk for incident CAD events in pooled data from 3 case-cohort studies (29). In patients with angina or MI versus healthy subjects, an increased prevalence for CAD was associated with increased circulating levels of lysophosphatidylcholines containing unsaturated fatty acids, and decreased circulating levels of lysophosphatidylcholines containing saturated fatty acids (30). In addition, this study also identified that circulating phosphatidylcholines containing ceramide, sphingomyelin, diacylglycerol, or palmitic acid were associated with increased prevalence for MI. The diagnostic information these circulating metabolites harbor is currently unknown, but once again illustrates the ability of blood-based metabolomics to potentially identify novel metabolic processes that may contribute to disease pathology. In obese and T2D patients, numerous tissues often display marked accumulation of lipid intermediates, including sphingomyelins, ceramides, and DAGs (31). These lipids often contribute to a phenomenon referred to as lipotoxicity to reflect lipid-induced cellular toxicity and dysfunction. If increases in these circulating metabolites also reflect accumulation of these metabolites in organs such as the liver and skeletal muscle, perhaps these metabolites may have clinical utility in the diagnosis and prognosis of insulin resistance and T2D (see the section Additional Considerations for the Use of Metabolomics in the Diagnosis and Prognosis of CVD). As alluded to previously, circulating BCAAs predict risk for CAD, often even after correcting for T2D (15,24,25), and LC/MS-based metabolic profiling in a cross-sectional study of 472 Chinese subjects demonstrated that serum BCAAs are increased and positively correlated with carotid intima-media thickness, a well-established index of subclinical atherosclerosis (32). Importantly, if changes in these circulating metabolites are truly indicative of atherosclerotic risk, the detection of these biomarkers may allow for early intervention with the appropriate therapies.
Metabolomics and HF
HF affects more than 5 million people in North America, with more than one-half-million new cases in the United States every year (33,34). Despite the advent of several new medications and devices used to manage HF, there remains a high mortality rate of approximately 30% to 50% within 5 years (35). Although a vast array of pathophysiological and molecular events contributes to the development and progression of HF (36–39), a long-standing concept in HF is that the failing heart has defects in metabolic processes that normally permit proper ATP production necessary to maintain contractile function (39) (Figure 5). Within the failing heart, oxidative phosphorylation is impaired, oxygen consumption is depressed, and ATP production is compromised (39), with failing human hearts demonstrating approximately 30% lower ATP levels than nonfailing hearts (40).
Metabolomic profiling, when used alone or in combination with standard HF biomarkers (e.g., B-type natriuretic peptide [BNP]), has significant diagnostic and prognostic value (40). Although the majority of reports utilizing metabolomic analyses in HF are comparing non-HF controls with HF patients (41–43), other studies have shown that blood-based metabolomics can also differentiate between the degree of HF severity (44), or between HF with preserved ejection fraction (HFpEF) and HF with reduced ejection fraction (HFrEF) (42,45). As a result of the profound changes that occur in energy metabolism during HF, the identification of blood- or urine-based metabolic signatures for these patients may uniquely assist clinicians with the clinical management of HF through detection, classification, and/or markers of effective treatment (46,47). Even though changes in the levels of numerous metabolites have been shown to occur in HF, there appear to be some key metabolite classes that consistently change in metabolomic profiles of HF patients (Table 2), which will be discussed herein.
Seminal HF Studies of Metabolomics in CVD and their Principal Findings
Fatty acid metabolism
It has been well established that impaired fatty acid flux in the mitochondria is associated with HF (48). Although multiple fatty acid metabolites are altered in the blood and/or urine of HF patients, attention has primarily focused on changes in acylcarnitine profiles, as acylcarnitines are derivatives of fatty acyl-CoAs that can reflect changes in fatty acid oxidation rates (Figure 2). Indeed, changes in numerous S-C, M-C, and L-C acylcarnitines, as well as the ratios of S-C/M-C, S-C/L-C, or M-C/L-C often indicate perturbations in fatty acid oxidation. Decreases in the ratios of S-C/M-C or M-C/L-C acylcarnitines often reflect specific defects in the mitochondrial β-oxidation machinery, whereas increases in these ratios often reflect increases in fatty-acid oxidation (49). In contrast, decreases in numerous S-C, M-C, and L-C acylcarnitines often imply a defect in mitochondrial fatty acid uptake and subsequent oxidation, whereas general increases over a broad range of S-C, M-C, and L-C acylcarnitines indicate increases in fatty acid oxidation (Figure 2) (49,50).
Metabolomic profiling in a subset of patients from the HF-ACTION (Exercise Training Program to Improve Clinical Outcomes in Individuals With Congestive Heart Failure) trial demonstrated elevations in circulating C16 and C18:1 acylcarnitines in patients with end-stage HF versus those with chronic systolic HF, which were associated with increased risk for mortality and hospitalization for HF (46). Intriguingly, metabolomic profiling following left ventricular (LV) assist device implantation in these end-stage HF patients resulted in a decrease in these circulating L-C acylcarnitines. Consistent with these results, in a cohort of patients taken from the CATHGEN biorepository, Hunter et al. (45) identified that circulating L-C acylcarnitines are increased in HFpEF patients compared with non-HF controls, and are further increased in HFrEF patients. Conversely, Bedi et al. (51) observed a reduction in a broad range of S-C, M-C, and L-C acylcarnitines in myocardial tissue from end-stage HF patients at the time of transplant compared with levels in heart tissue from subjects with no history of HF. Reasons for the discrepancy in circulating versus myocardial acylcarnitine profiles in these studies may be attributed to a large fraction of the patients from the HF-ACTION trial and CATHGEN biorepository being diabetic, whereas no end-stage HF group patients were diabetic in the study by Bedi et al. Circulating acylcarnitines are often elevated in obese and diabetic subjects (52), and may actually reflect general elevations in skeletal muscle fatty acid oxidation (see the section Additional Considerations for the Use of Metabolomics in the Diagnosis and Prognosis of CVD), although proper interpretation of this dataset also requires assessment of S-C and M-C acylcarnitine profiles (49). Conversely, the general reduction in myocardial S-C, M-C, and L-C acylcarnitine content observed by Bedi et al. (51) is reflective of impaired mitochondrial function and subsequent fatty acid oxidation, which is entirely consistent with the reduction in fatty acid oxidation observed in the more severe stages of HF (1,3,38). These findings observed from metabolomic analyses align with molecular/metabolic studies in mice, whereby both protein expression and activity of fatty acid oxidation enzymes only exhibit a mild decrease in compensated hypertrophy, but are markedly decreased in mice with decompensated HF (1). As there is controversy surrounding myocardial fatty acid oxidation rates in the failing heart, with some studies reporting an increase, no change, or a decrease in fatty acid oxidation (1), it is plausible that many of these discrepancies can be explained by the severity of HF (e.g., compensated hypertrophy vs. decompensated HF), the overall decline in LV function (HFpEF vs. HFrEF), and the presence of underlying obesity/diabetes. Hence, future metabolomic studies in HF populations will need to make distinct comparisons between these subgroups of HF patients, while taking into account their associated comorbidities (e.g., obesity and/or T2D) and the type of sample collected (e.g., serum/plasma vs. tissue biopsy).
Glucose metabolism
A number of studies have demonstrated that mitochondrial glucose oxidation is defective within the failing myocardium (38,53). However, there is also reported controversy regarding glucose oxidation alterations in the failing heart, where it has also been suggested that myocardial glucose oxidation rates are increased in HF (38). This discrepancy may be explained by the magnitudes of difference between the higher absolute rates of glycolysis versus glucose oxidation in the heart (∼10-fold higher). Those studies reporting an increase in glucose oxidation in the failing myocardium may simply be observing the result of the mass increase in glycolysis-derived pyruvate for PDH, despite PDH activity being impaired. Moreover, increased circulating pyruvate and lactate levels are often observed in metabolomic panels from HF patients, consistent with the HF-mediated increase in myocardial glucose uptake and glycolysis (43,54). The elevation in circulating lactate levels is likely due to a combination of the increase in glycolysis and the inability of the failing heart to sufficiently oxidize the increased pyruvate generated from glycolysis (Figure 5) (38,53). Thus, blood-based metabolomic profiles from failing hearts are consistent with the increased glycolysis rates and the general defect in absolute glucose oxidation rates observed in animal and human studies directly assessing myocardial metabolism (1,38).
Ketone body metabolism
Early metabolomic profiling of blood from HF patients showed higher levels of ketone bodies in HF patients than in healthy controls (55,56). More recent work also showed that β-hydroxybutyrate and acetone (a breakdown product of acetoacetate and β-hydroxybutyrate) levels were significantly increased in HF patients (57). As such, it has been proposed that the failing heart cannot adequately extract ketone bodies from the blood to use for energy production, thus leading to the elevation in circulating ketone bodies (58). On the contrary, we have shown that the serum concentrations of the ketone bodies, acetoacetate, α-hydroxybutyrate, and β-hydroxybutyrate, were lower in HFrEF patients than in non-HF controls (42). Interestingly, we also showed that serum ketone bodies were lower in HFrEF patients compared with HFpEF patients (42), suggesting potential changes in ketone body metabolism on the basis of the “type” of HF, similar to what is observed with L-C acylcarnitines (Figure 5). In agreement with our studies, lower concentrations of α-hydroxybutyrate were reported in HF patients with severe LV dysfunction (LVEF <35%) compared with HF patients with less severe declines in LVEF and healthy controls (44). It has been proposed that the extent to which the heart extracts ketone bodies from the blood is dependent on their circulating concentration (44). Thus, it is apparent that the absolute levels of ketone bodies in the blood represent more than just ketone body utilization by the heart, and other factors, such as the severity and type of HF (i.e., compensated hypertrophy vs. decompensated HF and/or HFpEF vs. HFrEF), other comorbidities, and/or the influence that other organs have in regulating alterations in circulating ketone bodies are also involved. Likewise, recent studies also support that the failing heart exhibits elevations in ketone body oxidation, because metabolomic profiling in myocardial extracts from end-stage HF patients revealed elevations in β-hydroxybutyryl-CoA, which were coupled to decreases in β-hydroxybutyrate and increased expression of enzymes involved in ketone body oxidation (51). Metabolomic profiling from myocardial extracts of mice subjected to HF via combined transverse aortic constriction and apical MI have produced similar findings (59). Although we cannot yet determine the physiological importance of these metabolomic findings in HF, the potential HF-mediated increase in myocardial ketone body oxidation rates may represent an adaptive mechanism to compensate for the decline in myocardial fatty acid oxidation.
Amino acid metabolism
The contribution of cardiac amino acid metabolism to changes in the metabolome of HF patients is currently unknown. As mentioned in the section Metabolomics and IHD, amino acids appear to make a minimal contribution to overall cardiac energy metabolism (20,21). Nevertheless, renewed interest in studying amino acid metabolism in the myocardium stems from demonstrations that decreases in the circulating methylarginine/arginine ratio and essential amino acid levels from metabolomic panels in HF patients yield a similar diagnostic value to BNP, while having a prognostic value that exceeds BNP for distinguishing between control and HF patients (41). Consistent with this, circulating levels of essential and nonessential amino acids were found to be lower in patients with chronic HF compared with non-HF controls (43), and treatment that results in improvement of HF (LV function, quality of life, and survival) also partially normalizes circulating levels of these amino acids (47). Furthermore, amino acids, such as leucine and isoleucine, can also be catabolized and used to generate ketone bodies (Figure 3), suggesting there may be a link between the levels of ketogenic amino acids and ketone bodies in HF (60).
In addition to the amino acids mentioned in the preceding text, BCAAs may also play a role in HF pathogenesis. Interestingly, it has been shown that circulating leucine and isoleucine levels are elevated in chronic HF patients compared with healthy controls (43). These results appear independent of obesity (mean body mass index of 25) or dyslipidemia (14% of HF patients demonstrated dyslipidemia), although it was not considered whether diabetes was a potential confounder, because ∼70% of these HF patients had T2D. Furthermore, animal models suggest that BCAA metabolism may impair glucose utilization in the heart (61), and that BCAAs may accumulate in the heart and interfere with insulin receptor-mediated signal transduction (2,62). Studies of myocardial extracts revealed increased levels of the BCKAs α-ketoisovalerate (valine-derived BCKA), α-ketoisocaproate (leucine-derived BCKA), and α-keto-β-methylvalerate (isoleucine-derived BCKA) in mice and humans with HF, whereas pharmacological manipulations that enhanced systemic BCKA metabolism attenuated pressure overload-induced HF (63). Despite a number of studies now supporting aberrant BCAA metabolism in HF, whether abnormal circulating BCAA levels in HF patients is a component of the metabolomic signature of HF itself, or is due to associated comorbidities, such as insulin resistance and/or T2D, is currently unknown (see the section Additional Considerations for the Use of Metabolomics in the Diagnosis and Prognosis of CVD).
Metabolomics and CVD in Obesity and Diabetes
The prevalence of T2D is increasing at an alarming rate, due in large part to the obesity epidemic and our aging population (64–66). The disease usually develops later in life, and is often detected incidentally or after sequelae of the associated vascular disease or CVD has occurred (65,67). The application of high-throughput metabolomics to populations at risk of T2D has generated considerable excitement, given the potential to identify biomarkers predictive of incident T2D and its associated sequelae, and the possibility of gaining insight into novel modifiable disease pathways and therapeutic strategies for reversing the disease and its associated comorbidities (Figure 6) (68). Furthermore, identification of circulating metabolites that correlate with risk for insulin resistance (e.g., BCAAs) via metabolomics may aid in the treatment and overall prognosis of CVD. Hence, the use of metabolomics to screen for numerous metabolites linked to insulin resistance could theoretically aid in the diagnosis, prognosis, and management of CVD in obese and/or T2D patients. In the last decade, metabolomic profiling in obese and/or T2D subjects has identified a number of potential metabolites that may be mechanistically involved in the pathogenesis of these conditions and their associated comorbidities. We will herein discuss some of these perturbed metabolic pathways and their associated metabolites that may reflect perturbed myocardial metabolism, as well as their potential diagnostic implications.
PET imaging with 1-11C-palmitate demonstrates increases in both myocardial fatty acid uptake and oxidation in obese and insulin-resistant women, as well as in men with uncomplicated T2D (1,69). Furthermore, numerous studies directly assessing fatty acid oxidation rates in hearts from obese and/or T2D animals have observed similar results (1,69). Though no assessment of the acylcarnitine profile has been made in myocardial biopsies in obese and/or T2D subjects, myocardial extracts in obese versus lean mice demonstrate elevations in S-C, M-C, and L-C acylcarnitines (70), reflecting observations seen in circulating and skeletal muscle acylcarnitine profiles of obese and/or T2D humans and animals (see the section Additional Considerations for the Use of Metabolomics in the Diagnosis and Prognosis of CVD) (50,52). Although the heart’s contribution to the circulating acylcarnitine pool remains unknown, the expression of carnitine acetyltransferase, a key enzyme involved in mediating carnitine transfer to acetyl-CoA for mitochondrial export (Figure 2), is greater in the heart than the skeletal muscle (71). This observation supports the possibility that the heart may contribute to the circulating acylcarnitine profile, and that increases in S-C, M-C, and L-C acylcarnitines in the circulation of obese and/or T2D patients do reflect increases in myocardial fatty acid oxidation rates. With regard to myocardial glucose metabolism in obese and/or T2D patients, the most frequently observed defect appears to take place at the level of glucose oxidation (1,70). Nevertheless, the vast majority of studies supporting this concept have been performed in animals, due to the lack of available methods to accurately assess myocardial glucose oxidation rates in humans. Currently, studies of myocardial ketone body and amino acid metabolism in humans are limited, and even more so in obese and/or T2D patients. Nevertheless, with recent metabolomic findings illustrating that the failing heart relies on ketone bodies as an adaptive mechanism to meet its oxidative energy needs, whereas myocardial BCAA/BCKA metabolism is impaired (see the section Metabolomics and HF) (59,63), it is likely that investigations on myocardial ketone body and BCAA metabolism in obesity and/or T2D will increase in order to elucidate their link with HF (72,73).
Additional Considerations for the Use of Metabolomics in the Diagnosis and Prognosis of CVD
Although the majority of this review has focused on how blood-based metabolomics can aid in the diagnosis and prognosis of various CVDs, patients with CVD often also have associated comorbidities, such as kidney disease and hepatic steatosis, as well as obesity and/or T2D (8,17). In fact, obesity and/or T2D may add extreme complexity to the understanding of the metabolomic profile, because these conditions often result in significant pathology and perturbed metabolism in other peripheral tissues (e.g., skeletal muscle, liver, adipose tissue, among others), and these peripheral tissues also contribute to the circulating metabolomic profile measured in patients with CVD. Indeed, systemic insulin resistance is a hallmark feature of obesity and T2D, and metabolomic profiling in obese and/or diabetic subjects often reveals increases in circulating lactate levels (74,75), which on the surface suggest increases in anaerobic glycolysis (Figure 1). However, increases in anaerobic glycolysis are not consistent with systemic insulin resistance, which impairs glucose metabolism in numerous tissues, including skeletal muscle. Conversely, proteomic studies have observed increases in the expression of glycolytic enzymes in vastus lateralis biopsies from obese and T2D patients (76). Furthermore, the source of elevated lactate in the circulation is difficult to ascertain, and can arise from other sources in addition to skeletal muscle. Because adipose tissue dysfunction in obesity is often associated with tissue hypoxia (77), increased anaerobic glycolysis in the expanded adipose depots of obese individuals may also contribute to this elevation in circulating lactate.
As circulating free fatty acids and triacylglycerols are markedly increased during obesity/T2D, fatty acid supply, uptake, and metabolism are frequently increased in peripheral tissues, such as the skeletal muscle and heart. Metabolomic profiling to assess alterations in fatty acid metabolism in obese and/or diabetic subjects has revealed increases in numerous circulating acylcarnitine species (52), whereas other studies have reported no differences (78). It has been suggested that a very large portion of the circulating acylcarnitine pool arises from skeletal muscle-mediated export, and muscle biopsies in obese humans also demonstrate increases in M-C and L-C acylcarnitines compared with lean controls (79). Thus, assessment of acylcarnitine profiles via blood-based metabolomics supports increases in both skeletal muscle and myocardial fatty acid oxidation rates (see the section Metabolomics and CVD in Obesity and Diabetes), although additional investigation is required to determine the contributions individual organs make to the circulating acylcarnitine profile.
Circulating ketone bodies (e.g., β-hydroxybutyrate and acetoacetate) are frequently elevated in obese and/or T2D patients (80), and elevations in circulating ketone bodies predict worsening of hyperglycemia in men with or without newly diagnosed T2D (81). Changes in circulating β-hydroxybutyrylcarnitine may reflect changes in peripheral tissue ketone body oxidation because β-hydroxybutyrylcarnitine is a mitochondrial intermediate derived directly from β-hydroxybutyryl-CoA (30). Of interest, recent findings from the EMPA-REG OUTCOME trial have observed that treatment with the sodium-glucose cotransporter 2 inhibitor, empagliflozin, markedly lowers cardiovascular risk in T2D patients, including a 35% reduction in hospitalization for HF (82). As these exciting findings were associated with a significant increase in circulating β-hydroxybutyrate levels, it has been suggested that empagliflozin reduces cardiovascular risk by increasing ketone body uptake and oxidation in the heart (83). This is consistent with increased ketone body oxidation being a potential beneficial and adaptive mechanism in the failing heart (see the section Metabolomics and HF), and highlights the need to understand the significance of blood-based metabolomic profiles revealing perturbations in ketone body metabolism.
Numerous studies have illustrated a tight correlation between circulating BCAA levels and risk for insulin resistance/T2D (2,75). Since these initial observations, several investigators have utilized novel metabolomic profiling techniques to implicate BCAAs in obesity and insulin resistance (2,17), with BCAAs and aromatic amino acids emerging as predictors of future T2D. Intriguingly, generation of an amino acid score on the basis of 3 of these amino acids (isoleucine, phenylalanine, and tyrosine) was found to significantly predict incident T2D (84). In light of findings suggesting impaired myocardial BCAA/BCKA metabolism in HF (63), it will be important to understand how the circulating BCAA profile in obesity reflects deranged systemic and myocardial metabolism.
Taken together, although blood-based metabolomics is proving to be a powerful tool that can assist in the diagnosis and prognosis of CVD, in the context of obesity and/or T2D, caution must be applied in how we infer the associated peripheral metabolic perturbations. In such instances, assessment of changes in metabolites across the diseased organ would prove more valuable, such as sampling arterial blood and the coronary sinus for the heart (see limitations of metabolomics described in the section Metabolomics), or having a tissue biopsy to compare against the serum/plasma sample. Nonetheless, due to the ease of obtaining a serum/plasma sample versus collecting a tissue biopsy or invasive catheterization, we must rely on molecular explorations in animal models of disease to confirm and/or understand novel metabolic alterations identified from human blood-based metabolomic signatures.
Final Summary and Implications
Metabolomic profiling technologies have significantly evolved to a point where high-resolution and high-throughput assessment of thousands of metabolites within a biological sample can be performed with relative ease. Due to the expansive biochemical diversity of metabolites within the human metabolome, several complementary platforms and techniques are necessary for comprehensive metabolic characterization. These techniques, MS and NMR coupled with LC or gas chromatography, are exquisitely sensitive, generating the metabolic fingerprint of a biological sample that not only reflects the condition of interest, but also diet, drug effects, sex, and other comorbidities and exposures. Thus, human metabolomic studies are consequently prone to clinical confounding factors that may lead to spurious diagnoses/conclusions. Yet the application of metabolomic profiling to several large population-based epidemiological cohorts has allowed for robust statistical adjustment for potential confounders, and has resulted in externally reproducible findings. In addition, metabolomics has been applied in smaller studies of unique clinical scenarios that allow for serial sampling before and after a controlled biological perturbation (e.g., exercise testing, planned MI, drug administration, and so on). Such paired experiments allow subjects to serve as their own controls, mitigating the risk of confounding factors. In addition, emerging investigational techniques are integrating metabolomics with other “omics” platforms in order to gain insight into pathophysiological interactions of metabolites, proteins, genes, and disease states. Furthermore, the state-of-the-art metabolomic technologies now available provide us with a snapshot of the metabolic fingerprints of individual patients, which can serve as diagnostic and/or prognostic tools that can be used to identify impairments in systemic or myocardial metabolism occurring during the development and worsening of CVD, as well as identifying the types and timing of specific interventions/therapies. Thus, metabolomics is a powerful technology that is transforming our ability to predict, detect, and understand a myriad of cardiometabolic disease states, and to monitor the effectiveness of therapeutic interventions. In doing so, metabolomics continues to advance our societal objective of personalizing the practice of medicine.
Footnotes
Dr. Ussher is a New Investigator of the Heart and Stroke Foundation of Alberta, NWT & Nunavut, and is a Scholar of the Canadian Diabetes Association (CDA). Dr. Elmariah is supported by the American Heart Association (4FTF20440012) and by the Massachusetts General Hospital Heart Center Hassenfeld Scholar Award. Dr. Gerszten is supported by NIH grant R01-DK081572. Dr. Dyck is a Canada Research Chair in Molecular Medicine, and is supported by the Heart and Stroke Foundation of Canada, the CDA, and the Canadian Institutes for Health Research. The authors have reported that they have no relationships relevant to the contents of this paper to disclose.
Listen to this manuscript's audio summary by JACC Editor-in-Chief Dr. Valentin Fuster.
- Abbreviations and Acronyms
- ATP
- adenosine triphosphate
- BCAA
- branched-chain amino acid
- BCKA
- branched-chain α-keto-acid
- BNP
- B-type natriuretic peptide
- CAD
- coronary artery disease
- CoA
- coenzyme A
- CVD
- cardiovascular disease
- HF
- heart failure
- HFpEF
- heart failure with preserved ejection fraction
- HFrEF
- heart failure with reduced ejection fraction
- IHD
- ischemic heart disease
- L-C
- long-chain
- LC
- liquid chromatography
- LV
- left ventricular
- M-C
- medium-chain
- MI
- myocardial infarction
- MS
- mass spectrometry
- NMR
- nuclear magnetic resonance
- PDH
- pyruvate dehydrogenase
- PET
- positron emission tomography
- S-C
- short-chain
- T2D
- type 2 diabetes
- TCA
- tricarboxylic acid
- TMA
- trimethylamine
- TMAO
- trimethylamine-N-oxide
- Received August 24, 2016.
- Accepted September 9, 2016.
- American College of Cardiology Foundation
References
- ↵
- Lopaschuk G.D.,
- Ussher J.R.,
- Folmes C.D.,
- et al.
- ↵
- ↵
- Taegtmeyer H.,
- Young M.E.,
- Lopaschuk G.D.,
- et al.,
- American Heart Association Council on Basic Cardiovascular Sciences
- ↵
- Lewis G.D.,
- Asnani A.,
- Gerszten R.E.
- ↵
- ↵
- Pauling L.,
- Robinson A.B.,
- Teranishi R.,
- et al.
- ↵
- ↵
- Shah S.H.,
- Kraus W.E.,
- Newgard C.B.
- ↵
- Wishart D.S.,
- Tzur D.,
- Knox C.,
- et al.
- ↵
- ↵
- Sabatine M.S.,
- Liu E.,
- Morrow D.A.,
- et al.
- ↵
- ↵
- Turer A.T.,
- Stevens R.D.,
- Bain J.R.,
- et al.
- ↵
- ↵
- Shah S.H.,
- Bain J.R.,
- Muehlbauer M.J.,
- et al.
- ↵
- ↵
- Shah S.H.,
- Newgard C.B.
- ↵
- ↵
- Bodi V.,
- Sanchis J.,
- Morales J.M.,
- et al.
- ↵
- ↵
- Ichihara K.,
- Neely J.R.,
- Siehl D.L.,
- et al.
- ↵
- Drake K.J.,
- Sidorov V.Y.,
- McGuinness O.P.,
- et al.
- ↵
- ↵
- Yang R.Y.,
- Wang S.M.,
- Sun L.,
- et al.
- ↵
- ↵
- ↵
- ↵
- ↵
- ↵
- ↵
- ↵
- Kim D.H.,
- Hunt S.A.
- ↵
- ↵
- Sharma K.,
- Kass D.A.
- ↵
- Stanley W.C.,
- Recchia F.A.,
- Lopaschuk G.D.
- ↵
- ↵
- Nascimben L.,
- Ingwall J.S.,
- Pauletto P.,
- et al.
- ↵
- Cheng M.L.,
- Wang C.H.,
- Shiao M.S.,
- et al.
- ↵
- ↵
- ↵
- Deidda M.,
- Piras C.,
- Dessalvi C.C.,
- et al.
- ↵
- Hunter W.G.,
- Kelly J.P.,
- McGarrah R.W. III.,
- et al.
- ↵
- Ahmad T.,
- Kelly J.P.,
- McGarrah R.W.,
- et al.
- ↵
- Nemutlu E.,
- Zhang S.,
- Xu Y.Z.,
- et al.
- ↵
- Lai L.,
- Leone T.C.,
- Keller M.P.,
- et al.
- ↵
- Bain J.R.,
- Stevens R.D.,
- Wenner B.R.,
- et al.
- ↵
- ↵
- Bedi K.C. Jr..,
- Snyder N.W.,
- Brandimarto J.,
- et al.
- ↵
- Gupte A.A.,
- Hamilton D.J.,
- Cordero-Reyes A.M.,
- et al.
- ↵
- Lommi J.,
- Kupari M.,
- Koskinen P.,
- et al.
- ↵
- ↵
- ↵
- Aubert G.,
- Martin O.J.,
- Horton J.L.,
- et al.
- ↵
- ↵
- Sansbury B.E.,
- DeMartino A.M.,
- Xie Z.,
- et al.
- ↵
- Sun H.,
- Olson K.C.,
- Gao C.,
- et al.
- ↵
- ↵
- Paneni F.,
- Beckman J.A.,
- Creager M.A.,
- et al.
- Wild S.,
- Roglic G.,
- Green A.,
- et al.
- ↵
- Zlobine I.,
- Gopal K.,
- Ussher J.R.
- ↵
- Ussher J.R.,
- Koves T.R.,
- Jaswal J.S.,
- et al.
- ↵
- Noland R.C.,
- Koves T.R.,
- Seiler S.E.,
- et al.
- ↵
- Riehle C.,
- Abel E.D.
- ↵
- Hansen J.S.,
- Zhao X.,
- Irmler M.,
- et al.
- ↵
- ↵
- ↵
- Baker P.R. II.,
- Boyle K.E.,
- Koves T.R.,
- et al.
- ↵
- ↵
- Robinson A.M.,
- Williamson D.H.
- ↵
- Mahendran Y.,
- Vangipurapu J.,
- Cederberg H.,
- et al.
- ↵
- ↵
- Ferrannini E.,
- Mark M.,
- Mayoux E.
- ↵
Toolbox
Citation Manager Formats
Article Outline
- Top
- Central Illustration
- Abstract
- Overview of Cellular Metabolism
- Myocardial Metabolism
- Metabolomics
- Metabolomics and IHD
- Metabolomics and Atherosclerosis
- Metabolomics and HF
- Metabolomics and CVD in Obesity and Diabetes
- Additional Considerations for the Use of Metabolomics in the Diagnosis and Prognosis of CVD
- Final Summary and Implications
- Footnotes
- References