Improving metabolite knowledge in stable atherosclerosis patients by association and correlation of GC-MS and 1H NMR fingerprints

Early vascular ageing phenotypes and urinary targeted metabolomics in children and young adults: the ExAMIN Youth SA and African-PREDICT studies

Some individuals are susceptible to accelerated biological ageing, resulting in premature alterations in arterial structure and function. Identifying early-onset vascular ageing characterised by arterial stiffening is vital for intervention and preventive strategies. We stratified and phenotyped healthy children (5-9 yrs) and young adults (20-30 yrs) into their vascular ageing extremes established by carotid-femoral pulse wave velocity (cfPWV) percentiles (i.e., healthy vascular ageing (HVA) and early vascular ageing (EVA)). We compared anthropometric, cardiovascular, and metabolomic profiles and explored associations between cfPWV and urinary metabolites. Children and adults in the EVA groups displayed higher levels of adiposity, cardiovascular, and lifestyle risk factors (adults only) (all p ≤ 0.018). In adults, several urinary metabolites were lower in the EVA group (all q ≤ 0.039) when compared to the HVA group, with no differences observed in children. In multiple regression analysis (adults only), we found inverse associations between cfPWV with histidine (adj. R2 = 0.038; β = -0.192; p = 0.013) and beta-alanine (adj. R2 = 0.034; β = -0.181; p = 0.019) in the EVA group, but with arginine (adj. R2 = 0.021; β = -0.160; p = 0.024) in the HVA group. The inverse associations of beta-alanine and histidine with cfPWV in the EVA group is suggestive that asymptomatic young adults who present with an altered metabolomic and less desired cardiovascular profile in combination with unfavourable lifestyle behaviours may be predisposed to early-onset vascular ageing. Taken together, screening on both a phenotypic and metabolic level may prove important in the early detection, prevention, and intervention of advanced biological ageing.

1H-NMR-Based Metabonomics Study to Reveal the Progressive Metabolism Regulation of SAP Deficiency on ApoE-/- Mice

Atherosclerosis is the most common disease of the vascular system and the metabolic disorder is one of its important molecular mechanisms. SAP protein is found to be highly expressed in atherosclerotic blood vessels. Our previous study found that SAP deficiency can significantly inhibit the development of atherosclerosis. However, the regulatory effect of SAP deficiency on AS metabolism is unknown. Based on ¹H-NMR metabonomics, this study investigated the serum metabolic changes in ApoE^-/-;SAP^-/- mice compared with ApoE^-/- mice during the whole progression of atherosclerosis. The results showed that acetate, pyruvate, choline and VLDL + LDL were statistically regulated to the normal levels as in C57 mice by SAP deficiency in ApoE^-/-;SAP^-/- mice at 8 w (without obvious plaques). With the appearance and aggravation of atherosclerotic plaques (8 + 4 w and 8 + 8 w), the four metabolites of acetate, pyruvate, choline and VLDL + LDL were continuously regulated, which were denoted as the metabolic regulatory markers of SAP deficiency. We also found that the changes in these four metabolites had nothing to do with high-fat diet. Therefore, it was revealed that SAP deficiency regulated the metabolic disorders in ApoE^-/- prior to the appearance of obvious atherosclerotic plaques, which is one of the important mechanisms leading to the inhibition of atherosclerosis, providing a new basis for the application of SAP in atherosclerosis.

Nuclear Magnetic Resonance Spectroscopy in Clinical Metabolomics and Personalized Medicine: Current Challenges and Perspectives

Personalized medicine is probably the most promising area being developed in modern medicine. This approach attempts to optimize the therapies and the patient care based on the individual patient characteristics. Its success highly depends on the way the characterization of the disease and its evolution, the patient’s classification, its follow-up and the treatment could be optimized. Thus, personalized medicine must combine innovative tools to measure, integrate and model data. Towards this goal, clinical metabolomics appears as ideally suited to obtain relevant information. Indeed, the metabolomics signature brings crucial insight to stratify patients according to their responses to a pathology and/or a treatment, to provide prognostic and diagnostic biomarkers, and to improve therapeutic outcomes. However, the translation of metabolomics from laboratory studies to clinical practice remains a subsequent challenge. Nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) are the two key platforms for the measurement of the metabolome. NMR has several advantages and features that are essential in clinical metabolomics. Indeed, NMR spectroscopy is inherently very robust, reproducible, unbiased, quantitative, informative at the structural molecular level, requires little sample preparation and reduced data processing. NMR is also well adapted to the measurement of large cohorts, to multi-sites and to longitudinal studies. This review focus on the potential of NMR in the context of clinical metabolomics and personalized medicine. Starting with the current status of NMR-based metabolomics at the clinical level and highlighting its strengths, weaknesses and challenges, this article also explores how, far from the initial “opposition” or “competition”, NMR and MS have been integrated and have demonstrated a great complementarity, in terms of sample classification and biomarker identification. Finally, a perspective discussion provides insight into the current methodological developments that could significantly raise NMR as a more resolutive, sensitive and accessible tool for clinical applications and point-of-care diagnosis. Thanks to these advances, NMR has a strong potential to join the other analytical tools currently used in clinical settings.

Biomarker Discovery in Atherosclerotic Diseases Using Quantitative Nuclear Magnetic Resonance Metabolomics

Despite great progress in the management of atherosclerosis (AS), its subsequent cardiovascular disease (CVD) remains the leading cause of morbidity and mortality. This is probably due to insufficient risk detection using routine lipid testing; thus, there is a need for more effective approaches relying on new biomarkers. Quantitative nuclear magnetic resonance (qNMR) metabolomics is able to phenotype holistic metabolic changes, with a unique advantage in regard to quantifying lipid-protein complexes. The rapidly increasing literature has indicated that qNMR-based lipoprotein particle number, particle size, lipid components, and some molecular metabolites can provide deeper insight into atherogenic diseases and could serve as novel promising determinants. Therefore, this article aims to offer an updated review of the qNMR biomarkers of AS and CVD found in epidemiological studies, with a special emphasis on lipoprotein-related parameters. As more researches are performed, we can envision more qNMR metabolite biomarkers being successfully translated into daily clinical practice to enhance the prevention, detection and intervention of atherosclerotic diseases.

Purine metabolite-based machine learning models for risk prediction, prognosis, and diagnosis of coronary artery disease

Alterations in xanthine oxidase activity are known to be pathologically influential on coronary artery disease (CAD), but the association between purine-related blood metabolites and CAD has only been partially elucidated. We performed global metabolomics profiling and network analysis on blood samples from the Wonju and Pyeongchang (WP) cohort study (n = 2055) to elucidate the importance of purine related metabolites associated with potential CAD risk. Then, 5 selected serum metabolites were quantified from the WP cohort, Shinchon cohort (n = 259), and Shinchon case control (n = 424) groups to develop machine learning models for 10-year risk prediction, relapse within 10 years and diagnosis of the disease via 100 repeated 5-fold cross-validations of logistic models. The combination of purine metabolite levels or only xanthine levels in blood could be applied for machine learning model development for major adverse cardiac and cerebrovascular event (MACCE, cerebrovascular death, nonfatal myocardial infarction, percutaneous transluminal coronary angioplasty, coronary artery bypass graft, and stroke) risk prediction, relapse of MACCEs among patients with myocardial infarction history and diagnosis of stable CAD. In particular, our research provided initial evidence that blood xanthine and uric acid levels play different roles in the development of machine learning models for primary/secondary prevention or diagnosis of CAD. In this research, we determined that purine-related metabolites in blood are applicable to machine learning model development for CAD risk prediction and diagnosis. Also, our work advances current CAD biomarker discovery strategies mainly relying on clinical features; emphasizes the differential biomarkers in first/secondary prevention or diagnosis studies.

Identifying unknown metabolites using NMR-based metabolic profiling techniques

Metabolic profiling of biological samples provides important insights into multiple physiological and pathological processes but is hindered by a lack of automated annotation and standardized methods for structure elucidation of candidate disease biomarkers. Here we describe a system for identifying molecular species derived from nuclear magnetic resonance (NMR) spectroscopy-based metabolic phenotyping studies, with detailed information on sample preparation, data acquisition and data modeling. We provide eight different modular workflows to be followed in a recommended sequential order according to their level of difficulty. This multi-platform system involves the use of statistical spectroscopic tools such as Statistical Total Correlation Spectroscopy (STOCSY), Subset Optimization by Reference Matching (STORM) and Resolution-Enhanced (RED)-STORM to identify other signals in the NMR spectra relating to the same molecule. It also uses two-dimensional NMR spectroscopic analysis, separation and pre-concentration techniques, multiple hyphenated analytical platforms and data extraction from existing databases. The complete system, using all eight workflows, would take up to a month, as it includes multi-dimensional NMR experiments that require prolonged experiment times. However, easier identification cases using fewer steps would take 2 or 3 days. This approach to biomarker discovery is efficient and cost-effective and offers increased chemical space coverage of the metabolome, resulting in faster and more accurate assignment of NMR-generated biomarkers arising from metabolic phenotyping studies. It requires a basic understanding of MATLAB to use the statistical spectroscopic tools and analytical skills to perform solid phase extraction (SPE), liquid chromatography (LC) fraction collection, LC-NMR-mass spectroscopy and one-dimensional and two-dimensional NMR experiments.

Central systolic pressure and a nonessential amino acid metabolomics profile: the African Prospective study on the Early Detection and Identification of Cardiovascular disease and Hypertension

OBJECTIVES

Early-life exposures to cardiovascular risk factors may manifest as early vascular ageing, a phenomenon to which black populations are more prone. The metabolome provides insight into the current state and regulation of physiological processes and was used to investigate the early molecular determinants of arterial stiffness.

METHODS

Black (N = 80) and white (N = 80) men and women (aged 20-30 years, clinic blood pressure <140 and 90 mmHg) across the arterial stiffness spectrum were included. Carotid-femoral pulse wave velocity, central SBP (cSBP) and central pulse pressure (cPP) were measured. NMR spectroscopy, liquid chromatography tandem mass spectrometry and gas chromatography-time of flight-mass spectrometry methods produced metabolomic data.

RESULTS

Differences (d ≥ 0.3) in 34 metabolites between black and white groups were found (adjusted for multiple comparisons). Only cSBP were higher in the black group (P = 0.003). Lower dietary protein intake (P < 0.001), but higher urinary nonessential amino acid levels were found in the black group (q ≤ 0.05). In multivariable-adjusted regression models cSBP and cPP inversely correlated with various nonessential amino acids, but only in black adults. These include associations of cSBP with 4-hydroxyproline (β = -0.24; P = 0.042), alanine: (β = -0.29; P = 0.015), glutamine (β = -0.25; P = 0.028), glycine (β = -0.26; P = 0.027), histidine (β = -0.30; P = 0.009), serine (β = -0.29; P = 0.012), and associations of cPP with alanine (β = -0.31; P = 0.005) and serine (β = -0.26; P = 0.019).

CONCLUSION

These amino acids play pivotal roles in collagen metabolism, glucose metabolism and oxidative stress and this ethnic-specific finding suggests that biosynthesis of nonessential amino acids may be upregulated to protect the vasculature against the onset of early vascular deterioration.

Metabolomics analysis of the therapeutic mechanism of Semen Descurainiae Oil on hyperlipidemia rats using 1 H-NMR and LC-MS

Semen descurainiae oil (SDO) is an important traditional Chinese medicine that was recently discovered to have the function of reducing blood lipids. Metabolomics analyses of plasma, liver and kidney in rats were performed using ¹ H-NMR and LC-MS to illuminate the lower blood lipid concentration effect of SDO, and niacin was considered as the active control. The measure of total cholesterol (TC) and low-density-lipoprotein cholesterol (LDL-C) in plasma showed that SDO treatment decreased significantly the content of TC and LDL-C. An orthogonal partial least squares-discriminant analysis approach was applied to identify the different metabolic profiles of plasma, liver and kidney in rats and to detect related potential biomarkers. The results suggested that the metabolic profiles of the control group and hyperlipidemia group showed significant difference and the SDO and niacin group had effective anti-hyperlipidemia function. The biomarkers primarily concern lipid metabolism, amino acid metabolism and glycometabolism, and the change in biomarkers indicated that hyperlipidemia could cause the unbalance of these metabolic pathways in vivo. SDO reduced blood lipids by repairing amino acid and lipid metabolism.

Current Progress of Lipid Analysis in Metabolic Diseases by Mass Spectrometry Methods

BACKGROUND

Obesity, insulin resistance, diabetes, and metabolic syndrome are associated with lipid alterations, and they affect the risk of long-term cardiovascular disease. A reliable analytical instrument to detect changes in the composition or structures of lipids and the tools allowing to connect changes in a specific group of lipids with a specific disease and its progress, is constantly lacking. Lipidomics is a new field of medicine based on the research and identification of lipids and lipid metabolites present in human organism. The primary aim of lipidomics is to search for new biomarkers of different diseases, mainly civilization diseases.

OBJECTIVE

We aimed to review studies reporting the application of mass spectrometry for lipid analysis in metabolic diseases.

METHOD

Following an extensive search of peer-reviewed articles on the mass spectrometry analysis of lipids the literature has been discussed in this review article.

RESULTS

The lipid group contains around 1.7 million species; they are totally different, in terms of the length of aliphatic chain, amount of rings, additional functional groups. Some of them are so complex that their complex analyses are a challenge for analysts. Their qualitative and quantitative analysis of is based mainly on mass spectrometry.

CONCLUSION

Mass spectrometry techniques are excellent tools for lipid profiling in complex biological samples and the combination with multivariate statistical analysis enables the identification of potential diagnostic biomarkers.

Analysis of serum changes in response to a high fat high cholesterol diet challenge reveals metabolic biomarkers of atherosclerosis

Atherosclerotic plaques are characterized by an accumulation of macrophages, lipids, smooth muscle cells, and fibroblasts, and, in advanced stages, necrotic debris within the arterial walls. Dietary habits such as high fat and high cholesterol (HFHC) consumption are known risk factors for atherosclerosis. However, the key metabolic contributors to diet-induced atherosclerosis are far from established. Herein, we investigate the role of a 2-year HFHC diet challenge in the metabolic changes of development and progression of atherosclerosis. We used a non-human primate (NHP) model (baboons, n = 60) fed a HFHC diet for two years and compared metabolomic profiles in serum from animals on baseline chow with serum collected after the challenge diet using two-dimensional gas chromatography time-of-flight mass-spectrometry (2D GC-ToF-MS) for untargeted metabolomic analysis, to quantify metabolites that contribute to atherosclerotic lesion formation. Further, clinical biomarkers associated with atherosclerosis, lipoprotein measures, fat indices, and arterial plaque formation (lesions) were quantified. Using two chemical derivatization (i.e., silylation) approaches, we quantified 321 metabolites belonging to 66 different metabolic pathways, which revealed significantly different metabolic profiles of HFHC diet and chow diet fed baboon sera. We found heritability of two important metabolites, lactic acid and asparagine, in the context of diet-induced metabolic changes. In addition, abundance of cholesterol, lactic acid, and asparagine were sex-dependent. Finally, 35 metabolites correlated (R2, 0.068-0.271, P < 0.05) with total lesion burden assessed in three arteries (aortic arch, common iliac artery, and descending aorta) which could serve as potential biomarkers pending further validation. This study demonstrates the feasibility of detecting sex-specific and heritable metabolites in NHPs with diet-induced atherosclerosis using untargeted metabolomics allowing understanding of atherosclerotic disease progression in humans.

Metabolome- and genome-scale model analyses for engineering of Aureobasidium pullulans to enhance polymalic acid and malic acid production from sugarcane molasses

BACKGROUND

Polymalic acid (PMA) is a water-soluble biopolymer with many attractive properties for food and pharmaceutical applications mainly produced by the yeast-like fungus Aureobasidium pullulans. Acid hydrolysis of PMA, resulting in release of the monomer l-malic acid (MA), which is widely used in the food and chemical industry, is a competitive process for producing bio-based platform chemicals.

RESULTS

In this study, the production of PMA and MA from sucrose and sugarcane molasses by A. pullulans was studied in shake flasks and bioreactors. Comparative metabolome analysis of sucrose- and glucose-based fermentation identified 81 intracellular metabolites and demonstrated that pyruvate from the glycolysis pathway may be a key metabolite affecting PMA synthesis. In silico simulation of a genome-scale metabolic model (iZX637) further verified that pyruvate carboxylase (pyc) via the reductive tricarboxylic acid cycle strengthened carbon flux for PMA synthesis. Therefore, an engineered strain, FJ-PYC, was constructed by overexpressing the pyc gene, which increased the PMA titer by 15.1% compared with that from the wild-type strain in a 5-L stirred-tank fermentor. Sugarcane molasses can be used as an economical substrate without any pretreatment or nutrient supplementation. Using fed-batch fermentation of FJ-PYC, we obtained the highest PMA titers (81.5, 94.2 g/L of MA after hydrolysis) in 140 h with a corresponding MA yield of 0.62 g/g and productivity of 0.67 g/L h.

CONCLUSIONS

We showed that integrated metabolome- and genome-scale model analyses were an effective approach for engineering the metabolic node for PMA synthesis, and also developed an economical and green process for PMA and MA production from renewable biomass feedstocks.

Large-scale Metabolomic Analysis Reveals Potential Biomarkers for Early Stage Coronary Atherosclerosis

Coronary atherosclerosis (CAS) is the pathogenesis of coronary heart disease, which is a prevalent and chronic life-threatening disease. Initially, this disease is not always detected until a patient presents with seriously vascular occlusion. Therefore, new biomarkers for appropriate and timely diagnosis of early CAS is needed for screening to initiate therapy on time. In this study, we used an untargeted metabolomics approach to identify potential biomarkers that could enable highly sensitive and specific CAS detection. Score plots from partial least-squares discriminant analysis clearly separated early-stage CAS patients from controls. Meanwhile, the levels of 24 metabolites increased greatly and those of 18 metabolites decreased markedly in early CAS patients compared with the controls, which suggested significant metabolic dysfunction in phospholipid, sphingolipid, and fatty acid metabolism in the patients. Furthermore, binary logistic regression showed that nine metabolites could be used as a combinatorial biomarker to distinguish early-stage CAS patients from controls. The panel of nine metabolites was then tested with an independent cohort of samples, which also yielded satisfactory diagnostic accuracy (AUC = 0.890). In conclusion, our findings provide insight into the pathological mechanism of early-stage CAS and also supply a combinatorial biomarker to aid clinical diagnosis of early-stage CAS.

Blood metabolomic fingerprint is distinct in healthy coronary and in stenosing or microvascular ischemic heart disease

BACKGROUND

The endothelium is a key variable in the pathogenesis of atherosclerosis and its complications, particularly coronary artery disease (CAD). Current evidence suggests that the endothelial status can be regarded as an integrated index of individual atherogenic and anti-atherogenic properties, and that the interaction between circulating factors and the arterial wall might be critical for atherogenesis. In organism-level investigations, a functional view is provided by metabolomics, the study of the metabolic profile of small molecules. We sought to verify whether metabolomic analysis can reveal the presence of coronary microenvironment peculiarities associated with distinct manifestations of CAD.

METHODS

Thirty-two coronary blood samples were analyzed using ¹H-NMR-based metabolomics. Samples collected from patients with evidence of myocardial ischemia formed the case group, and were further divided into the stenotic-disease (SD) group (N = 13) and absence of stenosis (microvascular disease; "Micro") group (N = 8); specimens of patients presenting no evidence of ischemic heart disease (dilated cardiomyopathy, valvular diseases) constituted the control group (N = 11).

RESULTS

Application of an orthogonal partial least squares discriminant analysis (OPLS-DA) model to the entire dataset clearly separated the samples into 3 groups, indicating 3 distinct metabolic fingerprints. Relative to control-group members, Micro patients showed a higher content of 2-hydroxybutirate, alanine, leucine, isoleucine, and N-acetyl groups and lower levels of creatine/phosphocreatine, creatinine, and glucose, whereas SD patients showed higher levels of 3-hydroxybutirate and acetate and a lower content of 2-hydroxybutirate. Moreover, relative to SD patients, Micro patients showed higher levels of 2-hydroxybutirate, alanine, leucine, and N-acetyl groups and lower levels of 3-hydroxybutirate and acetate.

CONCLUSIONS

Specific coronary microenvironments are likely associated with distinct development and pathological expression of CAD.

Metabolomic-guided discovery of Alzheimer's disease biomarkers from body fluid

The rapid increase in the older population has made age-related diseases like Alzheimer's disease (AD) a global concern. Given that there is still no cure for this neurodegenerative disease, the drastic growth in the number of susceptible individuals represents a major emerging threat to public health. The poor understanding of the mechanisms underlying AD is deemed the greatest stumbling block against progress in definitive diagnosis and management of this disease. There is a dire need for biomarkers that can facilitate early diagnosis, classification, prognosis, and treatment response. Efforts have been directed toward discovery of reliable and distinctive AD biomarkers but with very little success. With the recent emergence of high-throughput technology that is able to collect and catalogue vast datasets of small metabolites, metabolomics offers hope for a better understanding of AD and subsequent identification of biomarkers. This review article highlights the potential of using multiple metabolomics platforms as useful means in uncovering AD biomarkers from body fluids. © 2017 Wiley Periodicals, Inc.

Urine Metabolomics in Hypertension Research

Functional genomics requires an understanding of the complete network of changes within an organism by extensive measurements of moieties from mRNA, proteins, and metabolites. Metabolomics utilizes analytic chemistry tools to profile the complete spectrum of metabolites found in a tissue, cells, or biofluids using a wide range of tools from infrared spectroscopy, fluorescence spectroscopy, NMR spectroscopy, and mass spectrometry. In this protocol, we outline a procedure for performing metabolomic analysis of urine samples using liquid chromatography-mass spectrometry (LC-MS). We outline the advantages of using this approach and summarize some of the early promising studies in cardiovascular diseases using this approach.

Proteomics and metabolomics in biomarker discovery for cardiovascular diseases: progress and potential

INTRODUCTION

The process of discovering novel biomarkers and potential therapeutic targets may be shortened using proteomic and metabolomic approaches.

AREAS COVERED

Several complementary strategies, each one presenting different advantages and limitations, may be used with these novel approaches. In vitro studies show how cells involved in cardiovascular disease react, although the phenotype of cultured cells differs to that occurring in vivo. Tissue analysis either in human specimens or animal models may show the proteins that are expressed in the pathological process, although the presence of structural proteins may be confounding. To identify circulating biomarkers, analyzing the secretome of cultured atherosclerotic tissue, analysis of blood cells and/or plasma may be more straightforward. However, in the latter approach, high-abundant proteins may mask small molecules that could be potential biomarkers. The study of sub-proteomes such as high-density lipoproteins may be useful to circumvent this limitation. Regarding metabolomics, most studies have been performed in small populations, and we need to perform studies in large populations in order to discover robust biomarkers. Expert commentary: It is necessary to involve the clinicians in these areas to improve the design of clinical studies, including larger populations, in order to obtain consistent novel biomarkers.

Omics-based approaches to understand mechanosensitive endothelial biology and atherosclerosis

Atherosclerosis is a multifactorial disease that preferentially occurs in arterial regions exposed to d-flow can be used to indicate disturbed flow or disturbed blood flow. The mechanisms by which d-flow induces atherosclerosis involve changes in the transcriptome, methylome, proteome, and metabolome of multiple vascular cells, especially endothelial cells. Initially, we begin with the pathogenesis of atherosclerosis and the changes that occur at multiple levels owing to d-flow, especially in the endothelium. Also, there are a variety of strategies used for the global profiling of the genome, transcriptome, miRNA-ome, DNA methylome, and metabolome that are important to define the biological and pathophysiological mechanisms of endothelial dysfunction and atherosclerosis. Finally, systems biology can be used to integrate these 'omics' datasets, especially those that derive data based on a single animal model, in order to better understand the pathophysiology of atherosclerosis development in a holistic manner and how this integrative approach could be used to identify novel molecular diagnostics and therapeutic targets to prevent or treat atherosclerosis. WIREs Syst Biol Med 2016, 8:378-401. doi: 10.1002/wsbm.1344 For further resources related to this article, please visit the WIREs website.

Metabolomics in diabetic complications

With a global prevalence of 9%, diabetes is the direct cause of millions of deaths each year and is quickly becoming a health crisis. Major long-term complications of diabetes arise from persistent oxidative stress and dysfunction in multiple metabolic pathways. The most serious complications involve vascular damage and include cardiovascular disease as well as microvascular disorders such as nephropathy, neuropathy, and retinopathy. Current clinical analyses like glycated hemoglobin and plasma glucose measurements hold some value as prognostic indicators of the severity of complications, but investigations into the underlying pathophysiology are still lacking. Advancements in biotechnology hold the key to uncovering new pathways and establishing therapeutic targets. Metabolomics, the study of small endogenous molecules, is a powerful toolset for studying pathophysiological processes and has been used to elucidate metabolic signatures of diabetes in various biological systems. Current challenges in the field involve correlating these biomarkers to specific complications to provide a better prediction of future risk and disease progression. This review will highlight the progress that has been made in the field of metabolomics including technological advancements, the identification of potential biomarkers, and metabolic pathways relevant to macro- and microvascular diabetic complications.

Metabolomic study on the faecal extracts of atherosclerosis mice and its application in a Traditional Chinese Medicine

The intestinal microbiota and their metabolites are closely related to the formation of atherosclerosis (AS). In this study, a metabolomic approach based on the reversed-phase liquid chromatography/quadrupole time-of-flight mass spectrometry (LC-Q-TOF-MS) platform was established to analyze the metabolic profiling of fecal extracts from AS mice model. The established metabolomic platform was also used for clearing the effective mechanism of a Traditional Chinese Medicine (TCM) named Sishen granule (SSKL). Totally, sixteen potential biomarkers in faeces of AS mice were identified and 5 of them could be reversed by SSKL. Through functional analysis of these biomarkers and the established network, lipid metabolism, cholesterol metabolism, energy cycle, and inflammation reaction were considered as the most relevant pathological changes in gastrointestinal tract of AS mice. The metabolomic study not only revealed the potential biomarkers in AS mice' faeces but also supplied a systematic view of the pathological changes in gastrointestinal metabolite in AS mice. This metabolomic study also demonstrated that SSKL had the therapeutic effectiveness on AS through partly reversing the lipid metabolism, inflammation and energy metabolism.

Fasting but not changes of plasma metabolome during oral glucose tolerance tests improves the diagnosis of severe coronary arterial stenosis

BACKGROUND

Noninvasive stress tests for the diagnosis of significant coronary arterial stenosis requiring intervention are not perfect. We investigated whether plasma metabolome during the oral glucose tolerance test (OGTT) can improve the diagnosis.

METHODS

A total of 117 subjects with positive stress test results who received coronary angiography were recruited. After excluding subjects with a history of myocardial infarction and subjects who did not receive OGTT, the 18 subjects without significant stenosis were selected as controls. Another 18 age- and sex-matched subjects with significant stenosis were selected as cases. Plasma metabolome from samples obtained in fasting, 30 and 120 min after OGTT was measured using liquid chromatography combined with time-of-flight mass spectrometry.

RESULTS

We found five metabolites which can identify patients with significant stenosis independent to clinical risk factors, including diabetes, hypertension, hypercholesterolaemia, smoking and history of percutaneous coronary intervention (all P < 0·05). The area under the receiver operating characteristic (AUROC) curve of these metabolites was 0·799-0·818 at fasting and 30 min after OGTT. The addition of metabolites to clinical factors increases the AUROC (0·616, 95%CI 0·429-0·803 for model with clinical factors only; 0·824, 95%CI 0·689-0·959 for model with four metabolites and clinical factors). The changes of plasma metabolite levels during OGTT did not significantly improve the diagnostic performance.

CONCLUSIONS

Fasting plasma metabolome, but not change of plasma metabolome during OGTT, can improve the diagnosis of significant stenosis in patients with positive noninvasive stress test results.

Unveiling differences between patients with acute coronary syndrome with and without ST elevation through fingerprinting with CE-MS and HILIC-MS targeted analysis

Differences in the degree and severity of Acute Coronary Syndrome, associated to differences in the electrocardiogram, together with blood tests of biomarkers classify patients for diagnosis and treatment. Cases where the electrocardiogram and/or biomarkers are not conclusive still appear, and there is a need for complementary biomarkers for routine determinations. Metabolomics approaches with blind fingerprinting could reveal differences in metabolites, which must be confirmed by means of targeted determinations. CE-MS and HILIC-MS are well suited for the determination of highly polar compounds, like those from to the intermediate metabolism, altered due to acute stress induced by myocardial infarction. Serum from patients with ST-elevated and non-ST elevated myocardial infarction was collected at intensive care and emergency units, and fingerprinted with CE-MS. Data pretreatment and analysis showed up carnitine-related compounds and amino acids differentially present in both groups. Acylcarnitines and amino acids were then quantitatively measured with HILIC-MS-QqQ. The significance of the differences and the sensitivity/specificity of each compound were individually evaluated. The ratio of free carnitine to acylcarnitines, together with the ratios of acetylcarnitine to betaine, to threonine, and to citrulline, showed high significance and area under the curve in the respective receiver operating characteristic curves. This study opens new possibilities for defining new sets of biomarkers for refining the diagnosis of the patients with difficult classification.

Metabolomics in cardiovascular diseases

Cardiovascular diseases (CVDs) are the main cause of death globally. There is a need for the development of specific diagnostic methods, more effective therapeutic procedures as well as drugs, which can decrease the risk of deaths in the course of CVDs. For this reason, better understanding and explanation of molecular pathomechanisms of CVDs are essential. Metabolomics is focused on analysis of metabolites, small molecules which reflect the state of an organism in a certain point of time. Application of metabolomics approach in the investigation of molecular processes responsible for CVDs development may provide valuable information. In this article we overviewed recent reports employing application of untargeted and targeted metabolomic analyses in particular CVDs. Moreover, we focused on applications of various analytical platforms and metabolomics approaches which may contribute to the explanation of the pathomechanisms of different cardiovascular diseases.

Analysis of risk factors and diseases associated with atherosclerosis in the progression of carotid artery stenosis

Objective

The objective of this study was to analyze the roles of demographic, clinical, and laboratory factors on the progression of atherosclerotic stenosis in carotid bifurcation. It was based on prospective information from records entered on a specific application form for follow-up outpatients at a tertiary university service.

Methods

Consecutive symptomatic and asymptomatic patients ( n = 210) who had undergone more than one carotid duplex scan but no surgical intervention were selected for the analysis. The patients were divided into two groups: patients whose duplex scans did not show bilateral progression of carotid stenosis and patients with carotid stenosis progression of <50%, 50%–69%, or >70%. Clinical and demographic parameters were compared between groups.

Results

Group II levels of plasma urea (51.6 ± 27.8 mg/dl) and fibrinogen (493.2 ± 113.3 mg/dl) were higher than the Group I levels (43.0 ± 14.9 mg/dl and 441.3 ± 106.7 mg/dl, respectively) with statistical significance (p urea = 0.013 and p fibrinogen = 0.018). Paradoxically, the mean body mass index was higher in Group I (26.4 ± 4.6 kg/m2) than in Group II (24.6 ± 3.9 kg/m2; p = 0.02).

Conclusions

Traditional risk factors for the development of atherosclerosis in a carotid bifurcation are important but not unique. Metabolic and inflammatory factors can contribute to disease progression.

KLK1 and ZG16B proteins and arginine-proline metabolism identified as novel targets to monitor atherosclerosis, acute coronary syndrome and recovery

We pursued here the identification of specific signatures of proteins and metabolites in urine which respond to atherosclerosis development, acute event and/or recovery. An animal model (rabbit) of atherosclerosis was developed and molecules responding to atherosclerosis silent development were identified. Those molecules were investigated in human urine from patients suffering an acute coronary syndrome (ACS), at onset and discharge. Kallikrein1 (KLK1) and zymogen granule protein16B (ZG16B) proteins, and l-alanine, l-arabitol, scyllo-inositol, 2-hydroxyphenilacetic acid, 3-hydroxybutyric acid and N-acetylneuraminic acid metabolites were found altered in response to atherosclerosis progression and the acute event, composing a molecular panel related to cardiovascular risk. KLK1 and ZG16B together with 3-hydroxybutyric acid, putrescine and 1-methylhydantoin responded at onset but also showed normalized levels at discharge, constituting a molecular panel to monitor recovery. The observed decreased of KLK1 is in alignment with the protective mechanism of the kallikrein-kinin system. The connection between KLK1 and ZG16B shown by pathway analysis explains reduced levels of toll-like receptor 2 described in atherosclerosis. Metabolomic analysis revealed arginine and proline metabolism, glutathione metabolism and degradation of ketone bodies as the three main pathways altered. In conclusion, two novel urinary panels of proteins and metabolites are here for the first time shown related to atherosclerosis, ACS and patient's recovery.

Implementation strategies of Systems Medicine in clinical research and home care for cardiovascular disease patients

Insights from the "-omics" science have recently emphasized the need to implement an overall strategy in medical research. Here, the development of Systems Medicine has been indicated as a potential tool for clinical translation of basic research discoveries. Systems Medicine also gives the opportunity of improving different steps in medical practice, from diagnosis to healthcare management, including clinical research. The development of Systems Medicine is still hampered however by several challenges, the main one being the development of computational tools adequate to record, analyze and share a large amount of disparate data. In addition, available informatics tools appear not yet fully suitable for the challenge because they are not standardized, not universally available, or with ethical/legal concerns. Cardiovascular diseases (CVD) are a very promising area for translating Systems Medicine into clinical practice. By developing clinically applied technologies, the collection and analysis of data may improve CV risk stratification and prediction. Standardized models for data recording and analysis can also greatly broaden data exchange, thus promoting a uniform management of CVD patients also useful for clinical research. This advance however requires a great organizational effort by both physicians and health institutions, as well as the overcoming of ethical problems. This narrative review aims at providing an update on the state-of-art knowledge in the area of Systems Medicine as applied to CVD, focusing on current critical issues, providing a road map for its practical implementation.

The emergence of proton nuclear magnetic resonance metabolomics in the cardiovascular arena as viewed from a clinical perspective

The ability to phenotype metabolic profiles in serum has increased substantially in recent years with the advent of metabolomics. Metabolomics is the study of the metabolome, defined as those molecules with an atomic mass less than 1.5 kDa. There are two main metabolomics methods: mass spectrometry (MS) and proton nuclear magnetic resonance (¹H NMR) spectroscopy, each with its respective benefits and limitations. MS has greater sensitivity and so can detect many more metabolites. However, its cost (especially when heavy labelled internal standards are required for absolute quantitation) and quality control is sub-optimal for large cohorts. ¹H NMR is less sensitive but sample preparation is generally faster and analysis times shorter, resulting in markedly lower analysis costs. ¹H NMR is robust, reproducible and can provide absolute quantitation of many metabolites. Of particular relevance to cardio-metabolic disease is the ability of ¹H NMR to provide detailed quantitative data on amino acids, fatty acids and other metabolites as well as lipoprotein subparticle concentrations and size. Early epidemiological studies suggest promise, however, this is an emerging field and more data is required before we can determine the clinical utility of these measures to improve disease prediction and treatment.

This review describes the theoretical basis of ¹H NMR; compares MS and ¹H NMR and provides a tabular overview of recent ¹H NMR-based research findings in the atherosclerosis field, describing the design and scope of studies conducted to date. ¹H NMR metabolomics-CVD related research is emerging, however further large, robustly conducted prospective, genetic and intervention studies are needed to advance research on CVD risk prediction and to identify causal pathways amenable to intervention.

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¹H NMR metabolomics is being increasingly applied to large cohort studies.

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Studies have identified potentially novel lipoprotein and metabolite predictors for CVD.

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Potential exists for the use of metabolomics in cardiovascular clinical practice.

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Current findings are too preliminary to translate into clinical recommendations.

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Further large scale studies are now needed to advance the field in a robust manner.

UPLC-Q/TOF MS standardized Chinese formula Xin-Ke-Shu for the treatment of atherosclerosis in a rabbit model

Xin-Ke-Shu (XKS), a patent traditional Chinese medicine (TCM) preparation, has been commonly used for the treatment of coronary heart disease in China. In order to understand its mechanism of action, a metabonomic approach based on ultra performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q/TOF MS) was utilized to profile the plasma metabolic fingerprints of atherosclerosis (AS) rabbits with and without XKS treatment. The metabolic profile of model group clearly separated from normal, and that of XKS group was closer to the control group. Metabolites with significant changes during atherosclerosis were characterized as potential biomarkers related to the development of atherosclerosis by using orthogonal partial least-squares-discriminate analysis (OPLS-DA). Twenty potential biomarkers, including l-acetylcarnitine (1), propionylcarnitine (2), unknown (3), phytosphingosine (4), glycoursodeoxycholic acid (5), LPC(14:0) (6), sphinganine (7), LPC(20:5) (8), LPC(16:1) (9), LPC(18:2) (10), LPC(18:3) (11), LPC(22:5) (12), LPC(16:0) (13), LPC(18:1) (14), LPC(22:4) (15), LPC(17:0) (16), LPC(20:2) (17), elaidic carnitine (18), LPC(18:0) (19) and LPC(20:1) (20), were identified by their accurate mass and MS(E) spectra. The derivations of those biomarkers can be regulated by administration of XKS, which suggested that the intervention effect of XKS against AS may involve in regulating the lipid perturbation including fatty acid β-oxidation pathway, sphingolipid metabolism, glycerophospholipid metabolism and bile acid biosynthesis. This study indicated that the UPLC-Q/TOF MS-based metabonomics not only gave a systematic view of the pathomechanism of AS, but also provided a powerful tool to study the efficacy and mechanism of complex TCM prescriptions.

Technology platform development for targeted plasma metabolites in human heart failure

Background

Heart failure is a multifactorial disease associated with staggeringly high morbidity and motility. Recently, alterations of multiple metabolites have been implicated in heart failure; however, the lack of an effective technology platform to assess these metabolites has limited our understanding on how they contribute to this disease phenotype. We have successfully developed a new workflow combining specific sample preparation with tandem mass spectrometry that enables us to extract most of the targeted metabolites. 19 metabolites were chosen ascribing to their biological relevance to heart failure, including extracellular matrix remodeling, inflammation, insulin resistance, renal dysfunction, and cardioprotection against ischemic injury.

Results

In this report, we systematically engineered, optimized and refined a protocol applicable to human plasma samples; this study contributes to the methodology development with respect to deproteinization, incubation, reconstitution, and detection with mass spectrometry. The deproteinization step was optimized with 20% methanol/ethanol at a plasma:solvent ratio of 1:3. Subsequently, an incubation step was implemented which remarkably enhanced the metabolite signals and the number of metabolite peaks detected by mass spectrometry in both positive and negative modes. With respect to the step of reconstitution, 0.1% formic acid was designated as the reconstitution solvent vs. 6.5 mM ammonium bicarbonate, based on the comparable number of metabolite peaks detected in both solvents, and yet the signal detected in the former was higher. By adapting this finalized protocol, we were able to retrieve 13 out of 19 targeted metabolites from human plasma.

Conclusions

We have successfully devised a simple albeit effective workflow for the targeted plasma metabolites relevant to human heart failure. This will be employed in tandem with high throughput liquid chromatography mass spectrometry platform to validate and characterize these potential metabolic biomarkers for diagnostic and therapeutic development of heart failure patients.

Metabolomic analyses for atherosclerosis, diabetes, and obesity

Insulin resistance associated with type 2 diabetes mellitus (T2DM), obesity, and atherosclerosis is a global health problem. A portfolio of abnormalities of metabolic and vascular homeostasis accompanies T2DM and obesity, which are believed to conspire to lead to accelerated atherosclerosis and premature death. The complexity of metabolic changes in the diseases presents challenges for a full understanding of the molecular pathways contributing to the development of these diseases. The recent advent of new technologies in this area termed “Metabolomics” may aid in comprehensive metabolic analysis of these diseases. Therefore, metabolomics has been extensively applied to the metabolites of T2DM, obesity, and atherosclerosis not only for the assessment of disease development and prognosis, but also for the biomarker discovery of disease diagnosis. Herein, we summarize the recent applications of metabolomics technology and the generated datasets in the metabolic profiling of these diseases, in particular, the applications of these technologies to these diseases at the cellular, animal models, and human disease levels. In addition, we also extensively discuss the mechanisms linking the metabolic profiling in insulin resistance, T2DM, obesity, and atherosclerosis, with a particular emphasis on potential roles of increased production of reactive oxygen species (ROS) and mitochondria dysfunctions.

¹H NMR-based metabolomics approach to evaluate the effect of Xue-Fu-Zhu-Yu decoction on hyperlipidemia rats induced by high-fat diet

An NMR-based metabolomics approach was conducted to holisticly explore the effect of XFZYD (a traditional Chinese medicine formula) on high-fat diet induced hyperlipidemia rats with one of the commonly used antihyperlipidemic agents, simvastatin as the positive control. NMR spectra from blood plasma combined with statistical analysis revealed compounds distinguishing hyperlipidemia rats from normal control rats. XFZYD could ameliorate hyperlipidemia by intervening in some major metabolic pathways, such as decreasing the accumulation of ketone body (β-hydroxybutyrate) and acetyl-glycoproteins, enhancing glutathione (GSH) biosynthesis, partially reversing energy and lipid metabolism disturbance. Oral administration of XFZYD could also be helpful to hyperlipidemia rats in bettering the serum chemistry profile. The combined results demonstrated that XFZYD could ameliorate the hyperlipidemic symptoms in a global scale and restore the abnormal metabolic state to a near normal level in a time-dependent pattern.

A practical guide to metabolomic profiling as a discovery tool for human heart disease

Metabolomics has refreshed interest in metabolism across biology and medicine, particularly in the areas of functional genomics and biomarker discovery. In this review we will discuss the experimental techniques and challenges involved in metabolomic profiling and how these technologies have been applied to cardiovascular disease. Open profiling and targeted approaches to metabolomics are compared, focusing on high resolution NMR spectroscopy and mass spectrometry, as well as discussing how to analyse the large amounts of data generated using multivariate statistics. Finally, the current literature on metabolomic profiling in human cardiovascular disease is reviewed to illustrate the diversity of approaches, and discuss some of the key metabolites and pathways found to be perturbed in plasma, urine and tissue from patients with these diseases. This includes studies of coronary artery disease, myocardial infarction, and ischemic heart disease. These studies demonstrate the potential of the technology for biomarker discovery and elucidating metabolic mechanisms associated with given pathologies, but also in some cases provide a warning of the pitfalls of poor study design. This article is part of a Special Issue entitled "Focus on Cardiac Metabolism".

Current metabolomics: practical applications

The field of metabolomics continues to grow rapidly over the last decade and has been proven to be a powerful technology in predicting and explaining complex phenotypes in diverse biological systems. Metabolomics complements other omics, such as transcriptomics and proteomics and since it is a 'downstream' result of gene expression, changes in the metabolome is considered to best reflect the activities of the cell at a functional level. Thus far, metabolomics might be the sole technology capable of detecting complex, biologically essential changes. As one of the omics technology, metabolomics has exciting applications in varied fields, including medical science, synthetic biology, medicine, and predictive modeling of plant, animal and microbial systems. In addition, integrated applications with genomics, transcriptomics, and proteomics provide greater understanding of global system biology. In this review, we discuss recent applications of metabolomics in microbiology, plant, animal, food, and medical science.

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.

Vascular proteomics

Cardiovascular diseases constitute the largest of death in developed countries, being atherosclerosis the major contributor. Atherosclerosis is a process of chronic inflammation, characterized by the accumulation of lipids, cells, and fibrous elements in medium and large arteries. There is a continuum in atherosclerotic cardiovascular pathology that extends from the initial endothelial damage to diseases such as angina, myocardial infarction, and stroke. The extent of inflammation, proteolysis, calcification, and neovascularization influences the development of advanced lesions (atheroma plaques) on the arteries. Plaque rupture and the ensuing thrombosis cause the acute complications of atherosclerosis, i.e., myocardial infarction and cerebral ischemia. Thus, identification of early biomarkers of plaque unstability and susceptibility to rupture is of capital importance in preventing acute events. In recent years proteomics has been successfully applied to study proteins involved in these pathological processes. Thus, proteomic studies have been carried out focusing on different elements such as vascular tissues (arteries), artery layers, cells looking at proteomes and secretomes, plasma/serum, exosomes, lipoproteins, and metabolites. This chapter will provide an overview of latest advances in proteomic studies of atherosclerosis and related vascular diseases.

Applications of mass spectrometry to metabolomics and metabonomics: detection of biomarkers of aging and of age-related diseases

Every 5 years or so new technologies, or new combinations of old ones, seemingly burst onto the science scene and are then sought after until they reach the point of becoming commonplace. Advances in mass spectrometry instrumentation, coupled with the establishment of standardized chemical fragmentation libraries, increased computing power, novel data-analysis algorithms, new scientific applications, and commercial prospects have made mass spectrometry-based metabolomics the latest sought-after technology. This methodology affords the ability to dynamically catalogue and quantify, in parallel, femtomole quantities of cellular metabolites. The study of aging, and the diseases that accompany it, has accelerated significantly in the last decade. Mutant genes that alter the rate of aging have been found that increase lifespan by up to 10-fold in some model organisms, and substantial progress has been made in understanding fundamental alterations that occur at both the mRNA and protein level in tissues of aging organisms. The application of metabolomics to aging research is still relatively new, but has already added significant insight into the aging process. In this review we summarize these findings. We have targeted our manuscript to two audiences: mass spectrometrists interested in applying their technical knowledge to unanswered questions in the aging field, and gerontologists interested in expanding their knowledge of both mass spectrometry and the most recent advances in aging-related metabolomics.

Metabonomics-based omics study and atherosclerosis

Atherosclerosis results from dyslipidemia and systemic inflammation, associated with the strong metabolism and interaction between diet and disease. Strategies based on the global profiling of metabolism would be important to define the mechanisms involved in pathological alterations. Metabonomics is the quantitative measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification. Metabonomics has been used in combination with proteomics and transcriptomics as the part of a systems biology description to understand the genome interaction with the development of atherosclerosis. The present review describes the application of metabonomics to explore the potential role of metabolic disturbances and inflammation in the initiation and development of atherosclerosis. Metabonomics-based omics study offers a new potential for biomarker discovery by disentangling the impacts of diet, environment and lifestyle.

Metabolomics as a tool for cardiac research

Metabolomics represents a paradigm shift in metabolic research, away from approaches that focus on a limited number of enzymatic reactions or single pathways, to approaches that attempt to capture the complexity of metabolic networks. Additionally, the high-throughput nature of metabolomics makes it ideal to perform biomarker screens for diseases or follow drug efficacy. In this Review, we explore the role of metabolomics in gaining mechanistic insight into cardiac disease processes, and in the search for novel biomarkers. High-resolution NMR spectroscopy and mass spectrometry are both highly discriminatory for a range of pathological processes affecting the heart, including cardiac ischemia, myocardial infarction, and heart failure. We also discuss the position of metabolomics in the range of functional-genomic approaches, being complementary to proteomic and transcriptomic studies, and having subdivisions such as lipidomics (the study of intact lipid species). In addition to techniques that monitor changes in the total sizes of pools of metabolites in the heart and biofluids, the role of stable-isotope methods for monitoring fluxes through pathways is examined. The use of these novel functional-genomic tools to study metabolism provides a unique insight into cardiac disease progression.