Metabolomics

Metabolites, ions, and the mechanisms behind seasonal cold hardening of Pyrochroa coccinea (Pyrochroidae) larvae

The larvae of the black headed cardinal beetle Pyrochroa coccinea, overwinters under the bark of dead logs in northern European dioecious forests, and are thus exposed to temperatures below the melting point of their bodily fluids. Here we explore the mechanisms behind their seasonal cold hardening by characterising field samples collected monthly throughout the year. Both the lower lethal temperature and supercooling point dropped as much as 10℃ in the second half of November, reaching values around -15℃ by the beginning of December. This change was accompanied by a 320 mosmol/kg increase in hemolymph osmolality, which is a doubling compared to the summer levels. We used NMR metabolomics to identify and measure the absolute concentrations of the responsible cryoprotective C-H containing metabolites in the hemolymph. The largest increase was found to be in either glucose or trehalose, with an average total increase of 120 mM. Proline, alanine, and choline concentrations were found to increase by around 10 mM each. Contrarily, phosphocholine and phosphoethanolamine were halved, resulting in a total decrease of around 50 mM. These measurements were complemented with ion exchange chromatography measurements. This allowed us to account for all the osmotic pressure in the summer hemolymph, and the measured concentration changes explained as much as 40 % of the observed osmolality increase upon cold hardening. Preliminary results indicate that the remainder may be explained by non-colligative protein contributions.

Metabolomics: A New Tool in Our Understanding of Congenital Heart Disease

Although the genetic origins underpinning congenital heart disease (CHD) have been extensively studied, genes, by themselves, do not entirely predict phenotypes, which result from the complex interplay between genes and the environment. Consequently, genes merely suggest the potential occurrence of a specific phenotype, but they cannot predict what will happen in reality. This task can be revealed by metabolomics, the most promising of the "omics sciences". Though metabolomics applied to CHD is still in its infant phase, it has already been applied to CHD prenatal diagnosis, as well as to predict outcomes after cardiac surgery. Particular metabolomic fingerprints have been identified for some of the specific CHD subtypes. The hallmarks of CHD-related pulmonary arterial hypertension have also been discovered. This review, which is presented in a narrative format, due to the heterogeneity of the selected papers, aims to provide the readers with a synopsis of the literature on metabolomics in the CHD setting.

Prediction of semen analysis parameters improvement after varicocelectomy using 1 H NMR-based metabonomics assays

BACKGROUND

Varicocele is the most common correctable cause of male infertility, however, predicting varicocelectomy outcomes is difficult. "Omics" techniques have been increasingly used to develop new diagnostic and prognostics tools for several male infertility causes, and could be applied to study varicocele.

OBJECTIVES

To create metabolomics models capable of segregating men who improved semen analysis (SA) parameters or achieved natural pregnancy after microsurgical varicocelectomy (MV) from those who did not, using Hydrogen-1 nuclear magnetic resonance (¹ H NMR) spectra of seminal plasma of pre-operative samples.

MATERIAL AND METHODS

We recruited 29 infertile men with palpable varicocele. ¹ H NMR spectra of seminal plasma were obtained from pre-operative samples and used to create metabonomics models. Improvement was defined as an increase in the total motile progressive sperm count (TMC) of the post-operative SA when compared to the baseline, and pregnancy was assessed during 24 months after MV.

RESULTS

Using Linear Discriminant Analysis (LDA), we created a model that discriminated the men who improved SA from those who did not with accuracy of 93.1%. Another model segregated men who achieved natural pregnancy from men who did not. We identified 7 metabolites that were important for group segregation: caprylate, isoleucine, N-acetyltyrosine, carnitine, N-acetylcarnitine, creatine and threonine.

DISCUSSION

We described the use of metabonomics model to predict with high accuracy the outcomes of MV in infertile men with varicocele. The most important metabolites for groups segregation are involved in energy metabolism and oxidative stress response, highlighting the pivotal role of these mechanisms in the pathophysiology of varicocele.

CONCLUSIONS

¹ H NMR spectroscopy of seminal plasma can be used in conjunction with multivariate statistical tools to create metabonomics models useful segregate men with varicocele based on the reproductive outcomes of MV. These models may help counseling infertile men with varicocele regarding their prognosis after surgery. This article is protected by copyright. All rights reserved.

Understanding the Relationship between Intrinsic Cardiorespiratory Fitness and Serum and Skeletal Muscle Metabolomics Profile

Intrinsic cardiorespiratory fitness (iCRF) indicates the CRF level in the sedentary state. However, even among sedentary individuals, a wide interindividual variability is observed in the iCRF levels, whose associated molecular characteristics are little understood. This study aimed to investigate whether serum and skeletal muscle metabolomics profiles are associated with iCRF, measured by maximal power output (MPO). Seventy sedentary young adults were submitted to venous blood sampling, a biopsy of the vastus lateralis muscle and iCRF assessment. Blood serum and muscle tissue samples were analyzed by proton nuclear magnetic resonance (¹H NMR) spectroscopy. Metabolites related to iCRF were those supported by three levels of evidence: (1) correlation with iCRF, (2) significant difference between individuals with low and high iCRF, and (3) metabolite contribution to significant pathways associated with iCRF. From 43 serum and 70 skeletal muscle analyzed metabolites, iCRF was positively associated with levels of betaine, threonine, proline, ornithine, and glutamine in serum and lactate, fumarate, NADP+, and formate in skeletal muscle. Serum betaine and ornithine and skeletal muscle lactate metabolites explained 31.2 and 16.8%, respectively, of the iCRF variability in addition to body mass. The results suggest that iCRF in young adults is positively associated with serum and skeletal muscle metabolic levels, indicative of the amino acid and carbohydrate metabolism.

1H NMR-based metabonomics for infertility diagnosis in men with varicocele

PURPOSE

"Omics" techniques have been used to understand and to identify biomarkers of male infertility. We report on the first metabonomics models created to diagnose varicocele and infertility among men with varicocele.

METHODS

We recruited 35 infertile men with varicocele (VI group), 21 fertile men with varicocele (VF group) and 24 fertile men without varicocele (C group). All men underwent standard semen analysis, scrotal duplex ultrasonography, and sexual hormone level measurement. Hydrogen-1 nuclear magnetic resonance (1H NMR) spectra of seminal plasma were used to create metabonomics models to discriminate between men with and without varicocele, and between fertile and infertile men with varicocele.

RESULTS

Using the statistical formalisms partial least square discriminants analysis and genetic algorithm-based linear discriminant analysis (GA-LDA), we created two models that discriminated the three groups from each other with accuracy of 92.17%. We also created metabonomics models using orthogonal partial least square discriminants analysis and GA-LDA that discriminated VF group from VI group, with an accuracy of 94.64% and 100% respectively. We identified 19 metabolites that were important in group segregation: caprate, 2-hydroxy-3-methylvalerate, leucine, valine, 3-hydroxybutyrate, lactate, alanine, 4-aminobutyrate, isoleucine, citrate, methanol, glucose, glycosides, glycerol-3-phosphocoline, n-acetyltyrosine, glutamine, tyrosine, arginine, and uridine.

CONCLUSIONS

1HNMR-based metabonomics of seminal plasma can be used to create metabonomics models to discriminate between men with varicocele from those without varicocele, and between fertile men with varicocele from those infertile with varicocele. Furthermore, the most important metabolites for group segregation are involved in the oxidative stress caused by varicocele.

Serum metabolites associate with lipid phenotypes among Bogalusa Heart Study participants

BACKGROUND AND AIMS

Dyslipidemia has been identified as a major risk factor for cardiovascular disease. We aimed to identify metabolites and metabolite modules showing novel association with lipids among Bogalusa Heart Study (BHS) participants using untargeted metabolomics.

METHODS AND RESULTS

Untargeted ultrahigh performance liquid chromatography-tandem mass spectroscopy was used to quantify serum metabolites of 1 243 BHS participants (816 whites and 427 African-Americans). The association of single metabolites with lipids was assessed using multiple linear regression models to adjust for covariables. Weighted correlation network analysis was utilized to identify modules of co-abundant metabolites and examine their covariable adjusted correlations with lipids. All analyses were conducted according to race and using Bonferroni-corrected α-thresholds to determine statistical significance. Thirteen metabolites with known biochemical identities showing novel association achieved Bonferroni-significance, p < 1.04 × 10^-5, and showed consistent effect directions in both whites and African-Americans. Twelve were from lipid sub-pathways including fatty acid metabolism (arachidonoylcholine, dihomo-linolenoyl-choline, docosahexaenoylcholine, linoleoylcholine, oleoylcholine, palmitoylcholine, and stearoylcholine), monohydroxy fatty acids (2-hydroxybehenate, 2-hydroxypalmitate, and 2-hydroxystearate), and lysoplasmalogens [1-(1-enyl-oleoyl)-GPE (P-18:1) and 1-(1-enyl-stearoyl)-GPE (P-18:0)]. The gamma-glutamylglutamine, peptide from the gamma-glutamyl amino acid sub-pathway, were also identified. In addition, four metabolite modules achieved Bonferroni-significance, p < 1.39 × 10^-3, in both whites and African-Americans. These four modules were largely comprised of metabolites from lipid sub-pathways, with one module comprised of metabolites which were not identified in the single metabolite analyses.

CONCLUSION

The current study identified 13 metabolites and 4 metabolite modules showing novel association with lipids, providing new insights into the physiological mechanisms regulating lipid levels.

Lipidomics Profile Changes of Type 2 Diabetes Mellitus with Acute Myocardial Infarction

The morbidity and mortality of cardiovascular disease (CVD)/acute myocardial infarction (AMI) of type 2 diabetes mellitus (T2DM) patients are extremely higher than those without T2DM. Biomarkers can be used to predict the occurrence of acute myocardial infarction, thus effectively reducing the incidence of CVD events, particularly in T2DM patients. Lipids have been shown to be biomarkers and potential therapeutic targets for human diseases. The aim of our study was to investigate the prognostic value of lipid biomarkers for predicting AMI in T2DM patients. A total of 420 subjects were recruited in this research. Liquid Chromatography-Electrospray Ionization-Quadrupole Time of Flight-Mass Spectrometer- (LC-ESI-QTOF-MS-) and Liquid Chromatography/Mass Spectrometer- (LC/MS-) based metabolomic methods were applied to characterize metabolic profiles in each plasma sample. In the first untargeted set, 40 T2DM patients with AMI, 40 T2DM patients without AMI, and 40 control subjects were gender- and age-matched. Eight lipid metabolites showed a significant difference among three groups. Then, in the second set, targeted metabolic profiling assays for these 8 lipid biomarker concentrations in plasma were performed; another 100 T2DM patients with AMI, 100 T2DM patients without AMI, and 100 control subjects were selected independently. Receiver operating characteristic (ROC) curves were constructed, and the area under the ROC curves (AUC) was calculated to determine the potential biomarkers. ROC curve analysis showed that the AUC value of lysophosphatidylcholine (LysoPC) 18:0 is more than 0.7, indicating that LysoPC 18:0 may be a potential sensitive and specific biomarker for T2DM with AMI. The changed plasma concentrations of lipids were significantly associated with T2DM with AMI, which showed great value to be biomarkers, though it requires a prospective cohort study for further validation.

Metabolomic Characterization of Human Model of Liver Rejection Identifies Aberrancies Linked to Cyclooxygenase (COX) and Nitric Oxide Synthase (NOS)

BACKGROUND Acute liver rejection (ALR), a significant complication of liver transplantation, burdens patients, healthcare payers, and the healthcare providers due to an increase in morbidity, cost, and resources. Despite clinical resolution, ALR is associated with an increased risk of graft loss. A unique protocol of delayed immunosuppression used in our institute provided a model to characterize metabolomic profiles in human ALR. MATERIAL AND METHODS Twenty liver allograft biopsies obtained 48 hours after liver transplantation in the absence of immunosuppression were studied. Hepatic metabolites were quantitated in these biopsies by liquid chromatography and mass spectroscopy (LC/MS). Metabolite profiles were compared among: 1) biopsies with reperfusion injury but no histological evidence of rejection (n=7), 2) biopsies with histological evidence of moderate or severe rejection (n=5), and 3) biopsies with histological evidence of mild rejection (n=8). RESULTS There were 133 metabolites consistently detected by LC/MS and these were prioritized using variable importance to projection (VIP) analysis, comparing moderate or severe rejection vs. no rejection or mild rejection using partial least squares discriminant statistical analysis (PLS-DA). Twenty metabolites were identified as progressively different. Further PLS-DA using these metabolites identified 3 metabolites (linoleic acid, γ-linolenic acid, and citrulline) which are associated with either cyclooxygenase or nitric oxide synthase functionality. CONCLUSIONS Hepatic metabolic aberrancies associated with cyclooxygenase and nitric oxide synthase function occur contemporaneous with ALR. Additional studies are required to better characterize the role of these metabolic pathways to enhance utility of the metabolomics approach in diagnosis and outcomes of ALR.

Metabolomics in early detection and prognosis of acute coronary syndrome

Acute coronary syndrome (ACS) is one of the most dangerous types of coronary heart disease (CHD) and contributes to significant mortality and morbidity worldwide. Outcomes in these patients remain a challenge despite improvements in diagnosis and treatment. Risk stratification continues to be problematic and the identification of novel predictors is crucial for improved outcomes. As such, there is a strong need for the development of novel analytical methods as well as the characterization of better predictive and prognostic biomarkers to enable more personalized treatment. Metabolite profile analysis may greatly assist in interpreting altered pathway dynamics, especially when combined with other 'omics' technologies such as transcriptomics and proteomics. In this review, we describe ACS pathophysiology and recent advances in the role of metabolomics in the diagnosis and the molecular pathogenesis of ACS. We briefly describe key technologies used in metabolomics research and statistical approaches for data reduction and pathway analysis and discuss their application to CHD.

Lipopolysaccharide (LPS)-induced septic shock causes profound changes in myocardial energy metabolites in pigs

INTRODUCTION

Energy deficiency is a cause for myocardial dysfunction during septic shock. In rodents, septic shock decreases the oxidation of long-chain fatty acids and glucose in the myocardium causing energy deficiency. However, the effect of septic shock on myocardial energy metabolites in large animals and human is unknown.

OBJECTIVES

Investigate the effects of septic shock on myocardial energy metabolites in domestic pigs.

METHODS

Seventeen female pigs divided into control and lipopolysaccharide (LPS)-induced septic shock groups. Myocardial metabolites were analyzed ex vivo by ¹H nuclear magnetic resonance spectroscopy and liquid chromatography-tandem mass spectrometry. Gene and protein expression analysis were analyzed by real-time PCR and western blot.

RESULTS

Septic shock was associated with an increase in myocardial levels of short- and medium-chain acylcarnitines, lactate, alanine, and pyruvate dehydrogenase kinase 4 gene expression. COX-2 and prostaglandin E4 receptor gene expression also increased in the septic myocardium, although the only elevated eicosanoid in the septic animals was thromboxane B2. Myocardial levels of niacin, taurine, glutamate, glutamine, and glutathione were higher, and hypoxanthine levels lower in septic pigs than controls.

CONCLUSIONS

In pigs, septic shock induced by LPS caused myocardial changes directed to decrease the oxidation of medium- and short-chain fatty acid without an effect on long-chain fatty acid oxidation. The increase in myocardial levels of lactate, alanine, and pyruvate dehydrogenase kinase 4 gene expression suggest that septic shock decreases pyruvate dehydrogenase complex activity and glucose oxidation. Homeostasis of niacin, taurine, glutamate, glutamine, glutathione, hypoxanthine and thromboxane B2 is also affected in the septic myocardium.

Increased branched-chain amino acid levels are associated with long-term adverse cardiovascular events in patients with STEMI and acute heart failure

AIMS

The long-term prognosis of ST-segment elevation myocardial infarction (STEMI) with acute heart failure (AHF) is poor. Identification of metabolic changes could provide understanding of the underlying pathological progress associated with adverse events in patients with STEMI and AHF. Therefore, the study aimed to identify new plasm metabolites associated with long-term adverse cardiovascular events in patients with STEMI and AHF.

MATERIALS AND METHODS

Mass spectrometry measurements of 26 amino acids were performed in 138 patients with STEMI and AHF. Endpoints were adverse cardiac events (composite of death and heart failure hospitalization). Survival analysis was performed to determine independent predictors of amino acids.

KEY FINDINGS

During a 3-year follow-up, there were 32 deaths and 21 hospitalizations for heart failure (HF). Multivariable Cox regression analysis showed that branched-chain amino acid (BCAA) levels were independent predictors for adverse cardiovascular events in patients with STEMI and AHF (adjusted HR: 2.67, p < 0.001). The prognostic value of BCAA was better than that of N-terminal pro-B-type natriuretic peptide (area under the curve: 0.77 vs. 0.72) and Kaplan-Meier curves for adverse cardiac events (log-rank: 14.91 vs. 10.05). The combination of BCAAs and NT-proBNP yielded a stronger predictive value (area under the curve: 0.81, log-rank: 27.14). Importantly, addition of BCAAs and NT-pro BNP to the Global Registry of Acute Coronary Events score increased the C-statistic from 0.707 to 0.813, with a net reclassification improvement of 0.714.

SIGNIFICANCE

Our study shows that increased plasma BCAA levels are associated with long-term adverse cardiovascular events in patients with STEMI and AHF. These findings suggest that dysregulated BCAA metabolism pathways affect clinical outcome after STEMI with AHF.

Elevated circulating levels of succinate in human obesity are linked to specific gut microbiota

Gut microbiota-related metabolites are potential clinical biomarkers for cardiovascular disease (CVD). Circulating succinate, a metabolite produced by both microbiota and the host, is increased in hypertension, ischemic heart disease, and type 2 diabetes. We aimed to analyze systemic levels of succinate in obesity, a major risk factor for CVD, and its relationship with gut microbiome. We explored the association of circulating succinate with specific metagenomic signatures in cross-sectional and prospective cohorts of Caucasian Spanish subjects. Obesity was associated with elevated levels of circulating succinate concomitant with impaired glucose metabolism. This increase was associated with specific changes in gut microbiota related to succinate metabolism: a higher relative abundance of succinate-producing Prevotellaceae (P) and Veillonellaceae (V), and a lower relative abundance of succinate-consuming Odoribacteraceae (O) and Clostridaceae (C) in obese individuals, with the (P + V/O + C) ratio being a main determinant of plasma succinate. Weight loss intervention decreased (P + V/O + C) ratio coincident with the reduction in circulating succinate. In the spontaneous evolution after good dietary advice, alterations in circulating succinate levels were linked to specific metagenomic signatures associated with carbohydrate metabolism and energy production with independence of body weight change. Our data support the importance of microbe-microbe interactions for the metabolite signature of gut microbiome and uncover succinate as a potential microbiota-derived metabolite related to CVD risk.

Metabolites as biomarkers of adverse reactions following vaccination: A pilot study using nuclear magnetic resonance metabolomics

An Adverse Event Following Immunization (AEFI) is an adverse reaction to a vaccination that goes above and beyond the usual side effects associated with vaccinations. One serious AEFI related to the smallpox vaccine is myopericarditis. Metabolomics involves the study of the low molecular weight metabolite profile of cells, tissues, and biological fluids, and provides a functional readout of the phenotype. Metabolomics may help identify a particular metabolic signature in serum of subjects who are predisposed to developing AEFIs. The goal of this study was to identify metabolic markers that may predict the development of adverse events following smallpox vaccination. Serum samples were collected from military personnel prior to and following receipt of smallpox vaccine. The study population included five subjects who were clinically diagnosed with myopericarditis, 30 subjects with asymptomatic elevation of troponins, and 31 subjects with systemic symptoms following immunization, and 34 subjects with no AEFI, serving as controls. Two-hundred pre- and post-smallpox vaccination sera were analyzed by untargeted metabolomics using ¹H nuclear magnetic resonance (NMR) spectroscopy. Baseline (pre-) and post-vaccination samples from individuals who experienced clinically verified myocarditis or asymptomatic elevation of troponins were more metabolically distinguishable pre- and post-vaccination compared to individuals who only experienced systemic symptoms, or controls. Metabolomics profiles pre- and post-receipt of vaccine differed substantially when an AEFI resulted. This study is the first to describe pre- and post-vaccination metabolic profiles of subjects who developed an adverse event following immunization. The study demonstrates the promise of metabolites for determining mechanisms associated with subjects who develop AEFI and the potential to develop predictive biomarkers.

Methionine and Kynurenine Activate Oncogenic Kinases in Glioblastoma, and Methionine Deprivation Compromises Proliferation

PURPOSE

We employed a metabolomics-based approach with the goal to better understand the molecular signatures of glioblastoma cells and tissues, with an aim toward identifying potential targetable biomarkers for developing more effective and novel therapies.

EXPERIMENTAL DESIGN

We used liquid chromatography coupled with mass spectrometry (LC-MS/Q-TOF and LC-MS/QQQ) for the discovery and validation of metabolites from primary and established glioblastoma cells, glioblastoma tissues, and normal human astrocytes.

RESULTS

We identified tryptophan, methionine, kynurenine, and 5-methylthioadenosine as differentially regulated metabolites (DRM) in glioblastoma cells compared with normal human astrocytes (NHAs). Unlike NHAs, glioblastoma cells depend on dietary methionine for proliferation, colony formation, survival, and to maintain a deregulated methylome (SAM:SAH ratio). In methylthioadenosine phosphorylase (MTAP)-deficient glioblastoma cells, expression of MTAP transgene did not alter methionine dependency, but compromised tumor growth in vivo We discovered that a lack of the kynurenine-metabolizing enzymes kynurenine monooxygenase and/or kynureninase promotes the accumulation of kynurenine, which triggers immune evasion in glioblastoma cells. In silico analysis of the identified DRMs mapped the activation of key oncogenic kinases that promotes tumorigenesis in glioblastoma. We validated this result by demonstrating that the exogenous addition of DRMs to glioblastoma cells in vitro results in oncogene activation as well as the simultaneous downregulation of Ser/Thr phosphatase PP2A.

CONCLUSIONS

We have connected a four-metabolite signature, implicated in the methionine and kynurenine pathways, to the promotion and maintenance of glioblastoma. Together, our data suggest that these metabolites and their respective metabolic pathways serve as potential therapeutic targets for glioblastoma. Clin Cancer Res; 22(14); 3513-23. ©2016 AACR.

A targeted metabolomics assay for cardiac metabolism and demonstration using a mouse model of dilated cardiomyopathy

Metabolomics can be performed either as an 'open profiling' tool where the aim is to measure, usually in a semi-quantitative manner, as many metabolites as possible or perform 'closed' or 'targeted' analyses where instead a pre-defined set of metabolites are measured. Targeted methods can be designed to be more sensitive and quantitative and so are particularly appropriate to systems biology for quantitative models of systems or when metabolomics is performed in a hypothesis driven manner to test whether a particular pathway is perturbed. We describe a targeted metabolomics assay that quantifies a broad range of over 130 metabolites relevant to cardiac metabolism including the pathways of the citric acid cycle, fatty acid oxidation, glycolysis, the pentose phosphate pathway, amino acid metabolism, the urea cycle, nucleotides and reactive oxygen species using tandem mass spectrometry to produce quantitative, sensitive and robust data. This assay is illustrated by profiling cardiac metabolism in a lamin A/C (Lmna) mouse model of dilated cardiomyopathy (DCM). The model of DCM was characterised by increases in concentrations of proline and methyl-histidine suggestive of increased myofibrillar and collagen degradation, as well as decreases in a number of citric acid cycle intermediates and carnitine derivatives indicating reduced energy metabolism in the dilated heart. These assays could be used for any other cardiac or cardiovascular disease in that they cover central core metabolism and key pathways involved in cardiac metabolism, and may provide a general start for many mammalian systems.

Blood-borne biomarkers and bioindicators for linking exposure to health effects in environmental health science

Environmental health science aims to link environmental pollution sources to adverse health outcomes to develop effective exposure intervention strategies that reduce long-term disease risks. Over the past few decades, the public health community recognized that health risk is driven by interaction between the human genome and external environment. Now that the human genetic code has been sequenced, establishing this "G × E" (gene-environment) interaction requires a similar effort to decode the human exposome, which is the accumulation of an individual's environmental exposures and metabolic responses throughout the person's lifetime. The exposome is composed of endogenous and exogenous chemicals, many of which are measurable as biomarkers in blood, breath, and urine. Exposure to pollutants is assessed by analyzing biofluids for the pollutant itself or its metabolic products. New methods are being developed to use a subset of biomarkers, termed bioindicators, to demonstrate biological changes indicative of future adverse health effects. Typically, environmental biomarkers are assessed using noninvasive (excreted) media, such as breath and urine. Blood is often avoided for biomonitoring due to practical reasons such as medical personnel, infectious waste, or clinical setting, despite the fact that blood represents the central compartment that interacts with every living cell and is the most relevant biofluid for certain applications and analyses. The aims of this study were to (1) review the current use of blood samples in environmental health research, (2) briefly contrast blood with other biological media, and (3) propose additional applications for blood analysis in human exposure research.

Methods for integration of transcriptomic data in genome-scale metabolic models

Several computational methods have been developed that integrate transcriptomic data with genome-scale metabolic reconstructions to infer condition-specific system-wide intracellular metabolic flux distributions. In this mini-review, we describe each of these methods published to date with categorizing them based on four different grouping criteria (requirement for multiple gene expression datasets as input, requirement for a threshold to define a gene's high and low expression, requirement for a priori assumption of an appropriate objective function, and validation of predicted fluxes directly against measured intracellular fluxes). Then, we recommend which group of methods would be more suitable from a practical perspective.

Hypertrophic cardiomyopathy: a heart in need of an energy bar?

Hypertrophic cardiomyopathy (HCM) has been recently recognized as the most common inherited cardiovascular disorder, affecting 1 in 500 adults worldwide. HCM is characterized by myocyte hypertrophy resulting in thickening of the ventricular wall, myocyte disarray, interstitial and/or replacement fibrosis, decreased ventricular cavity volume and diastolic dysfunction. HCM is also the most common cause of sudden death in the young. A large proportion of patients diagnosed with HCM have mutations in sarcomeric proteins. However, it is unclear how these mutations lead to the cardiac phenotype, which is variable even in patients carrying the same causal mutation. Abnormalities in calcium cycling, oxidative stress, mitochondrial dysfunction and energetic deficiency have been described constituting the basis of therapies in experimental models of HCM and HCM patients. This review focuses on evidence supporting the role of cellular metabolism and mitochondria in HCM.

Text-mining-assisted biocuration workflows in Argo

Biocuration activities have been broadly categorized into the selection of relevant documents, the annotation of biological concepts of interest and identification of interactions between the concepts. Text mining has been shown to have a potential to significantly reduce the effort of biocurators in all the three activities, and various semi-automatic methodologies have been integrated into curation pipelines to support them. We investigate the suitability of Argo, a workbench for building text-mining solutions with the use of a rich graphical user interface, for the process of biocuration. Central to Argo are customizable workflows that users compose by arranging available elementary analytics to form task-specific processing units. A built-in manual annotation editor is the single most used biocuration tool of the workbench, as it allows users to create annotations directly in text, as well as modify or delete annotations created by automatic processing components. Apart from syntactic and semantic analytics, the ever-growing library of components includes several data readers and consumers that support well-established as well as emerging data interchange formats such as XMI, RDF and BioC, which facilitate the interoperability of Argo with other platforms or resources. To validate the suitability of Argo for curation activities, we participated in the BioCreative IV challenge whose purpose was to evaluate Web-based systems addressing user-defined biocuration tasks. Argo proved to have the edge over other systems in terms of flexibility of defining biocuration tasks. As expected, the versatility of the workbench inevitably lengthened the time the curators spent on learning the system before taking on the task, which may have affected the usability of Argo. The participation in the challenge gave us an opportunity to gather valuable feedback and identify areas of improvement, some of which have already been introduced. Database URL: http://argo.nactem.ac.uk.

Lipidomics Profiling and Risk of Cardiovascular Disease in the Prospective Population-Based Bruneck Study

Background—

The bulk of cardiovascular disease risk is not explained by traditional risk factors. Recent advances in mass spectrometry allow the identification and quantification of hundreds of lipid species. Molecular lipid profiling by mass spectrometry may improve cardiovascular risk prediction.

Methods and Results—

Lipids were extracted from 685 plasma samples of the prospective population-based Bruneck Study (baseline evaluation in 2000). One hundred thirty-five lipid species from 8 different lipid classes were profiled by shotgun lipidomics with the use of a triple-quadrupole mass spectrometer. Levels of individual species of cholesterol esters (CEs), lysophosphatidylcholines, phosphatidylcholines, phosphatidylethanolamines (PEs), sphingomyelins, and triacylglycerols (TAGs) were associated with cardiovascular disease over a 10-year observation period (2000–2010, 90 incident events). Among the lipid species with the strongest predictive value were TAGs and CEs with a low carbon number and double-bond content, including TAG(54:2) and CE(16:1), as well as PE(36:5) ( P =5.1×10 −7 , 2.2×10 −4 , and 2.5×10 −3 , respectively). Consideration of these 3 lipid species on top of traditional risk factors resulted in improved risk discrimination and classification for cardiovascular disease (cross-validated ΔC index, 0.0210 [95% confidence interval, 0.0010-0.0422]; integrated discrimination improvement, 0.0212 [95% confidence interval, 0.0031-0.0406]; and continuous net reclassification index, 0.398 [95% confidence interval, 0.175-0.619]). A similar shift in the plasma fatty acid composition was associated with cardiovascular disease in the UK Twin Registry (n=1453, 45 cases).

Conclusions—

This study applied mass spectrometry-based lipidomics profiling to population-based cohorts and identified molecular lipid signatures for cardiovascular disease. Molecular lipid species constitute promising new biomarkers that outperform the conventional biochemical measurements of lipid classes currently used in clinics.

Targeted metabolic profiling of hepatocellular carcinoma and hepatitis C using LC-MS/MS

Hepatitis C virus (HCV) infection of the liver is a global health problem and a major risk factor for the development of hepatocellular carcinoma (HCC). Sensitive methods are needed for the improved and earlier detection of HCC, which would provide better therapy options. Metabolic profiling of the high-risk population (HCV patients) and those with HCC provides insights into the process of liver carcinogenesis and possible biomarkers for earlier cancer detection. Seventy-three blood metabolites were quantitatively profiled in HCC (n = 30) and cirrhotic HCV (n = 22) patients using a targeted approach based on LC-MS/MS. Sixteen of 73 targeted metabolites differed significantly (p < 0.05) and their levels varied up to a factor of 3.3 between HCC and HCV. Four of these 16 metabolites (methionine, 5-hydroxymethyl-2'-deoxyuridine, N2,N2-dimethylguanosine, and uric acid) that showed the lowest p values were used to develop and internally validate a classification model using partial least squares discriminant analysis. The model exhibited high classification accuracy for distinguishing the two groups with sensitivity, specificity, and area under the receiver operating characteristic curve of 97%, 95%, and 0.98, respectively. A number of perturbed metabolic pathways, including amino acid, purine, and nucleotide metabolism, were identified based on the 16 biomarker candidates. These results provide a promising methodology to distinguish cirrhotic HCV patients, who are at high risk to develop HCC, from those who have already progressed to HCC. The results also provide insights into the altered metabolism between HCC and HCV.

Metabolomics in the diagnosis of acute myocardial ischemia

Despite recent advances in the diagnosis of myocardial ischemia, its biochemical identification in patients with acute chest pain is still a challenge, and alternative approaches for further improvement are needed. Metabolic alterations are the first consequences of acute myocardial ischemia. Metabolomics coupled with potent multivariate analyses allows for a simultaneous and relative quantification of thousands of different metabolites within a given sample. Thus, this discipline might exert a great impact on medical practice in cardiovascular medicine by providing a wealth of relevant biochemical data. Metabolomics is a promising tool to improve current, single biomarker-based approaches by identifying metabolic biosignatures that embody global biochemical changes in disease. This is especially relevant for conditions requiring early treatment like myocardial ischemia. This review discusses the potential application of metabolomics in the diagnosis of myocardial ischemia.

Associations between metabolomic compounds and incident heart failure among African Americans: the ARIC Study

Heart failure is more prevalent among African Americans than in the general population. Metabolomic studies among African Americans may efficiently identify novel biomarkers of heart failure. We used untargeted methods to measure 204 stable serum metabolites and evaluated their associations with incident heart failure hospitalization (n = 276) after a median follow-up of 20 years (1987-2008) by using Cox regression in data from 1,744 African Americans aged 45-64 years without heart failure at baseline from the Jackson, Mississippi, field center of the Atherosclerosis Risk in Communities (ARIC) Study. After adjustment for established risk factors, we found that 16 metabolites (6 named with known structural identities and 10 unnamed with unknown structural identities, the latter denoted by using the format X-12345) were associated with incident heart failure (P < 0.0004 based on a modified Bonferroni procedure). Of the 6 named metabolites, 4 are involved in amino acid metabolism, 1 (prolylhydroxyproline) is a dipeptide, and 1 (erythritol) is a sugar alcohol. After additional adjustment for kidney function, 2 metabolites remained associated with incident heart failure (for metabolite X-11308, hazard ratio = 0.75, 95% confidence interval: 0.65, 0.86; for metabolite X-11787, hazard ratio = 1.23, 95% confidence interval: 1.10, 1.37). Further structural analysis revealed X-11308 to be a dihydroxy docosatrienoic acid and X-11787 to be an isoform of either hydroxyleucine or hydroxyisoleucine. Our metabolomic analysis revealed novel biomarkers associated with incident heart failure independent of traditional risk factors.

Proteomics and metabolomics for mechanistic insights and biomarker discovery in cardiovascular disease

In the last decade, proteomics and metabolomics have contributed substantially to our understanding of cardiovascular diseases. The unbiased assessment of pathophysiological processes without a priori assumptions complements other molecular biology techniques that are currently used in a reductionist approach. In this review, we highlight some of the "omics" methods used to assess protein and metabolite changes in cardiovascular disease. A discrete biological function is very rarely attributed to a single molecule; more often it is the combined input of many proteins. In contrast to the reductionist approach, in which molecules are studied individually, "omics" platforms allow the study of more complex interactions in biological systems. Combining proteomics and metabolomics to quantify changes in metabolites and their corresponding enzymes will advance our understanding of pathophysiological mechanisms and aid the identification of novel biomarkers for cardiovascular disease.

Metabolomics and incident hypertension among blacks: the atherosclerosis risk in communities study

Development of hypertension is influenced by genes, environmental effects, and their interactions, and the human metabolome is a measurable manifestation of gene-environment interaction. We explored the metabolomic antecedents of developing incident hypertension in a sample of blacks, a population with a high prevalence of hypertension and its comorbidities. We examined 896 black normotensives (565 women; aged, 45-64 years) from the Atherosclerosis Risk in Communities study, whose metabolome was measured in serum collected at the baseline examination and analyzed by high-throughput methods. The analyses presented here focus on 204 stably measured metabolites during a period of 4 to 6 weeks. Weibull parametric models considering interval censored data were used to assess the hazard ratio for incident hypertension. We used a modified Bonferroni correction accounting for the correlations among metabolites to define a threshold for statistical significance (P<3.9 × 10(-4)). During 10 years of follow-up, 38% of baseline normotensives developed hypertension (n=344). With adjustment for traditional risk factors and estimated glomerular filtration rate, each +1SD difference in baseline 4-hydroxyhippurate, a product of gut microbial fermentation, was associated with 17% higher risk of hypertension (P=2.5 × 10(-4)), which remained significant after adjusting for both baseline systolic and diastolic blood pressure (P=3.8 × 10(-4)). After principal component analyses, a sex steroids pattern was significantly associated with risk of incident hypertension (highest versus lowest quintile hazard ratio, 1.72; 95% confidence interval, 1.05-2.82; P for trend, 0.03), and stratified analyses suggested that this association was consistent in both sexes. Metabolomic analyses identify novel pathways in the pathogenesis of hypertension.

Plasma metabolomic profiles predict near-term death among individuals with lower extremity peripheral arterial disease

BACKGROUND

Individuals with peripheral arterial disease (PAD) have a nearly two-fold increased risk of all-cause and cardiovascular disease mortality compared to those without PAD. This pilot study determined whether metabolomic profiling can accurately identify patients with PAD who are at increased risk of near-term mortality.

METHODS

We completed a case-control study using (1)H NMR metabolomic profiling of plasma from 20 decedents with PAD, without critical limb ischemia, who had blood drawn within 8 months prior to death (index blood draw) and within 10 to 28 months prior to death (preindex blood draw). Twenty-one PAD participants who survived more than 30 months after their index blood draw served as a control population.

RESULTS

Results showed distinct metabolomic patterns between preindex decedent, index decedent, and survivor samples. The major chemical signals contributing to the differential pattern (between survivors and decedents) arose from the fatty acyl chain protons of lipoproteins and the choline head group protons of phospholipids. Using the top 40 chemical signals for which the intensity was most distinct between survivor and preindex decedent samples, classification models predicted near-term all-cause death with overall accuracy of 78% (32/41), a sensitivity of 85% (17/20), and a specificity of 71% (15/21). When comparing survivor with index decedent samples, the overall classification accuracy was optimal at 83% (34/41) with a sensitivity of 80% (16/20) and a specificity of 86% (18/21), using as few as the top 10 to 20 chemical signals.

CONCLUSIONS

Our results suggest that metabolomic profiling of plasma may be useful for identifying PAD patients at increased risk for near-term death. Larger studies using more sensitive metabolomic techniques are needed to identify specific metabolic pathways associated with increased risk of near-term all-cause mortality among PAD patients.

Metabolomic analysis of complex chinese remedies: examples of induced nephrotoxicity in the mouse from a series of remedies containing aristolochic Acid

Aristolochic acid nephropathy is caused by aristolochic acid (AA) and AA-containing herbs. In traditional Chinese medicine, a principle called “Jun-Chen-Zou-Shi” may be utilized to construct a remedial herbal formula that attempts to mitigate the toxicity of the main ingredient. This study used Bu-Fei-A-Jiao-Tang (BFAJT) to test if the compound remedy based on a principle of “Jun-Chen-Zou-Shi” can decrease the toxicity of AA-containing herbs. We compared the three toxicities of AA standard, Madouling (an Aristolochia herb), and a herbal formula BFAJT. AA standard was given for BALB/c mice at a dose of 5 mg/kg bw/day or 7.5 mg/kg bw/day for 10 days. Madouling and BFAJT were given at an equivalence of AA 0.5 mg/kg bw/day for 21 days. Nephrotoxicity was evaluated by metabolomics and histopathology. The urinary metabolomics profiles were characterized by ¹H NMR spectroscopy. The spectral data was analyzed with partial least squares discriminant analysis, and the significant differential metabolites between groups were identified. The result showed different degrees of acute renal tubular injuries, and metabolomics analysis found that the kidney injuries were focused in proximal renal tubules. Both metabolomics and pathological studies revealed that AA standard, Madouling, and BFAJT were all nephrotoxicants. The compositions of the compound remedy did not diminish the nephrotoxicity caused by AA.

MicroRNAs within the continuum of postgenomics biomarker discovery

The postgenomic shift in paradigm from reductionism to systems-wide network inference has increased recognition that cardiovascular diseases are not simply determined by the genome but arise from an interaction and dynamic dysregulation of gene regulatory networks, proteins, and metabolic alterations. The advent of postgenomic technologies promises to interrogate these complex pathophysiological perturbations by applying concepts of systemic relationships to biomarker discovery. A multibiomarker panel consisting of biomarkers capturing different levels of information (eg, microRNAs to assess endothelial and platelet activation, molecular lipid species to profile metabolic status, and proteolytic degradation products to assess vascular integrity) could outperform inflammatory biomarkers without vascular specificity in their ability of predicting cardiovascular risk. As atherosclerosis develops over decades, different biomarkers may be required for different stages of disease. Thus far, there is no simple blood test to directly assess the health of blood vessels or identify vulnerable patients. We discuss strategies for biomarker discovery using post genomics technologies, with a particular focus on circulating microRNAs. The aim is to reveal distinctive cardiovascular phenotypes and identify biomarker signatures that complement the Framingham risk scores in clinical decision-making and in a stratified medicine approach for early preventive treatment of disease.

Metabolomics and ischaemic heart disease

Ischaemic heart disease accounts for nearly half of the global cardiovascular disease burden. Aetiologies relating to heart disease are complex, but dyslipidaemia, oxidative stress and inflammation are cardinal features. Despite preventative measures and advancements in treatment regimens with lipid-lowering agents, the high prevalence of heart disease and the residual risk of recurrent events continue to be a significant burden to the health sector and to the affected individuals and their families. The development of improved risk models for the early detection and prevention of cardiovascular events in addition to new therapeutic strategies to address this residual risk are required if we are to continue to make inroads into this most prevalent of diseases. Metabolomics and lipidomics are modern disciplines that characterize the metabolite and lipid complement respectively, of a given system. Their application to ischaemic heart disease has demonstrated utilities in population profiling, identification of multivariate biomarkers and in monitoring of therapeutic response, as well as in basic mechanistic studies. Although advances in magnetic resonance and mass spectrometry technologies have given rise to the fields of metabolomics and lipidomics, the plethora of data generated presents challenges requiring specific statistical and bioinformatics applications, together with appropriate study designs. Nonetheless, the predictive and re-classification capacity of individuals with various degrees of risk by the plasma lipidome has recently been demonstrated. In the present review, we summarize evidence derived exclusively by metabolomic and lipidomic studies in the context of ischaemic heart disease. We consider the potential role of plasma lipid profiling in assessing heart disease risk and therapeutic responses, and explore the potential mechanisms. Finally, we highlight where metabolomic studies together with complementary -omic disciplines may make further inroads into the understanding, detection and treatment of ischaemic heart disease.

Proteomics: from single molecules to biological pathways

The conventional reductionist approach to cardiovascular research investigates individual candidate factors or linear signalling pathways but ignores more complex interactions in biological systems. The advent of molecular profiling technologies that focus on a global characterization of whole complements allows an exploration of the interconnectivity of pathways during pathophysiologically relevant processes, but has brought about the issue of statistical analysis and data integration. Proteins identified by differential expression as well as those in protein–protein interaction networks identified through experiments and through computational modelling techniques can be used as an initial starting point for functional analyses. In combination with other ‘-omics’ technologies, such as transcriptomics and metabolomics, proteomics explores different aspects of disease, and the different pillars of observations facilitate the data integration in disease-specific networks. Ultimately, a systems biology approach may advance our understanding of cardiovascular disease processes at a ‘biological pathway’ instead of a ‘single molecule’ level and accelerate progress towards disease-modifying interventions.

Circulating, imaging, and genetic biomarkers in cardiovascular risk prediction

In the primary prevention of cardiovascular disease, the study of biomarkers to identify at-risk individuals is an expanding field. Several developments have fueled this trend, including improved understanding of the pathophysiological processes underlying atherosclerosis, advances in imaging technology to enable the quantification of subclinical disease burden, and the identification of new genetic susceptibility variants for cardiovascular disease. Furthermore, the advent of high-throughput platforms for molecular profiling has increased the pace of biomarker discovery. The rising interest in biomarkers has been balanced by the recognition that standardized and rigorous statistical approaches are needed to evaluate the clinical utility of candidate risk markers. This article reviews the issues surrounding the evaluation of biomarkers, evidence from studies of existing biomarkers, and recent applications of biomarker discovery platforms.

MicroRNAs in vascular and metabolic disease

Recent findings demonstrated the importance of microRNAs (miRNAs) in the vasculature and the orchestration of lipid metabolism and glucose homeostasis. MiRNA networks represent an additional layer of regulation for gene expression that absorbs perturbations and ensures the robustness of biological systems. This function is very elegantly demonstrated in cholesterol metabolism where miRNAs reducing cellular cholesterol export are embedded in the very same genes that increase cholesterol synthesis. Often their alteration does not affect normal development but changes under stress conditions and in disease. A detailed understanding of the molecular and cellular mechanisms of miRNA-mediated effects on metabolism and vascular pathophysiology could pave the way for the development of novel diagnostic markers and therapeutic approaches. In the first part of this review, we summarize the role of miRNAs in vascular and metabolic diseases and explore potential confounding effects by platelet miRNAs in preclinical models of cardiovascular disease. In the second part, we discuss experimental strategies for miRNA target identification and the challenges in attributing miRNA effects to specific cell types and single targets.

Biomarkers and cardiovascular risk assessment for primary prevention: an update

BACKGROUND

Interest in cardiovascular biomarkers in primary prevention has increased dramatically in the past decade. This increase has been fueled by an improved understanding of cardiovascular pathophysiology, as well as novel technologies for biomarker identification.

CONTENT

In this review we provide a brief overview of recent concepts in the evaluation of screening biomarkers, because biomarkers may behave differently when used for screening as opposed to diagnosis or disease staging. The following specific biomarker examples are then discussed, with a focus on data from primary prevention studies: high-sensitivity C-reactive protein, B-type natriuretic peptide, lipoprotein-associated phospholipase A2, and high-sensitivity troponin T. The article concludes by addressing novel platforms for biomarker discovery, reviewing recent examples from the field of metabolomics.

SUMMARY

An ongoing challenge is to develop screening strategies that can identify individuals at risk for cardiovascular events well before symptoms appear. For this purpose, the measurement of soluble biomarkers could be an important adjunct to traditional cardiovascular risk assessment. Recent studies highlight both the strengths and limitations of "novel" circulating biomarkers, and suggest that substantial work is still needed to identify biomarkers that are sufficiently accurate and cost-effective for routine use in primary prevention.

Metabolomics and cardiovascular biomarker discovery

BACKGROUND

Metabolomics, the systematic analysis of low molecular weight biochemical compounds in a biological specimen, has been increasingly applied to biomarker discovery.

CONTENT

Because no single analytical method can accommodate the chemical diversity of the entire metabolome, various methods such as nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) have been employed, with the latter coupled to an array of separation techniques including gas and liquid chromatography. Whereas NMR can provide structural information and absolute quantification for select metabolites without the use of exogenous standards, MS tends to have much higher analytical sensitivity, enabling broader surveys of the metabolome. Both NMR and MS can be used to characterize metabolite data either in a targeted manner or in a nontargeted, pattern-recognition manner. In addition to technical considerations, careful sample selection and study design are important to minimize potential confounding influences on the metabolome, including diet, medications, and comorbitidies. To this end, metabolite profiling has been applied to human biomarker discovery in small-scale interventions, in which individuals are extremely well phenotyped and able to serve as their own biological controls, as well as in larger epidemiological cohorts. Understanding how metabolites relate to each other and to established risk markers for diseases such as diabetes and renal failure will be important in evaluating the potential value of these metabolites as clinically useful biomarkers.

SUMMARY

Applied to both experimental and epidemiological study designs, metabolite profiling has begun to highlight the breadth metabolic disturbances that accompany human disease. Experimental work in model systems and integration with other functional genomic approaches will be required to establish a causal link between select biomarkers and disease pathogenesis.

The cardiovascular risk of healthy individuals studied by NMR metabonomics of plasma samples

The identification and the present wide acceptance of cardiovascular risk factors such as age, sex, hypertension, hyperlipidemia, smoking, obesity, diabetes, and physical inactivity have led to dramatic reductions in cardiovascular morbidity and mortality. However, novel risk predictors present opportunities to identify more patients at risk and to more accurately define the biochemical signature of that risk. In this paper, we present a comprehensive metabonomic analysis of 864 plasma samples from healthy volunteers, through Nuclear Magnetic Resonance (NMR) and multivariate statistical analysis (regression and classification). We have found that subjects that are classified as at high or at low risk using the common clinical markers can also be discriminated using NMR metabonomics. This discrimination is not only due to common markers (such as total cholesterol, triglycerides, LDL, HDL), but also to (p < 0.05 after Bonferroni correction) other metabolites (e.g., 3-hydroxybutyrate, α-ketoglutarate, threonine, dimethylglycine) previously not associated with cardiovascular diseases.

Comparative lipidomics profiling of human atherosclerotic plaques

BACKGROUND

We sought to perform a systematic lipid analysis of atherosclerotic plaques using emerging mass spectrometry techniques.

METHODS AND RESULTS

A chip-based robotic nanoelectrospray platform interfaced to a triple quadrupole mass spectrometer was adapted to analyze lipids in tissue sections and extracts from human endarterectomy specimens by shotgun lipidomics. Eighteen scans for different lipid classes plus additional scans for fatty acids resulted in the detection of 150 lipid species from 9 different classes of which 24 were detected in endarterectomies only. Further analyses focused on plaques from symptomatic and asymptomatic patients and stable versus unstable regions within the same lesion. Polyunsaturated cholesteryl esters with long-chain fatty acids and certain sphingomyelin species showed the greatest relative enrichment in plaques compared to plasma and formed part of a lipid signature for vulnerable and stable plaque areas in a systems-wide network analysis. In principal component analyses, the combination of lipid species across different classes provided a better separation of stable and unstable areas than individual lipid classes.

CONCLUSIONS

This comprehensive analysis of plaque lipids demonstrates the potential of lipidomics for unraveling the lipid heterogeneity within atherosclerotic lesions.

Taking pressure off the heart: the ins and outs of atrophic remodelling

Our work on atrophic remodelling of the heart has led us to appreciate the simple principles in biology: (i) the dynamic nature of intracellular protein turnover, (ii) the return to the foetal gene programme when the heart remodels, and (iii) the adaptive changes of cardiac metabolism. Although the molecular mechanisms of cardiac hypertrophy are many, much less is known regarding the molecular mechanisms of cardiac atrophy. We state the case that knowing more about mechanisms of atrophic remodelling may provide insights into cellular consequences of metabolic and haemodynamic unloading of the stressed heart. Overall we strive to find an answer to the question: 'What makes the failing heart shrink and become stronger?' We speculate that signals arising from intermediary metabolism of energy-providing substrates are likely candidates.

Early identification of cardiovascular risk using genomics and proteomics

Coronary heart disease (CHD) will soon become the leading cause of death and morbidity in the world. Early detection and treatment of CHD is thus imperative to improve global health. Atherosclerosis of the coronary arteries is a complex multifactorial disease process involving multiple pathways that can be influenced by both genetic and environmental factors. With the recent advances in genomics and proteomics, many new risk factors with small-to-moderate effects are likely to be identified. Additionally, individualized risk stratification and targeted therapy may become feasible; each individual could potentially be assessed with a panel of tests for genomic and proteomic markers and, on the basis of the individual's composite risk profile, preventive and therapeutic steps could then be undertaken. With a multimarker approach, it may also be possible to identify alterations in pathways involved in atherogenesis, rather than focus on individual risk factors. In this article, we use the specific example of atherosclerosis to discuss the role of genomics and proteomics in cardiovascular risk assessment.