A Novel Lipid Biomarker Panel for the Detection of Heart Failure with Reduced Ejection Fraction

Reduced Immunity Regulator MAVS Contributes to Non-Hypertrophic Cardiac Dysfunction by Disturbing Energy Metabolism and Mitochondrial Homeostasis

Cardiac dysfunction is manifested as decline of cardiac systolic function, and multiple cardiovascular diseases (CVDs) can develop cardiac insufficiency. Mitochondrial antiviral signaling (MAVS) is known as an innate immune regulator involved in viral infectious diseases and autoimmune diseases, whereas its role in the heart remains obscure. The alteration of MAVS was analyzed in animal models with non-hypertrophic and hypertrophic cardiac dysfunction. Then, MAVS-deficient mice were generated to examine the heart function, mitochondrial status and energy metabolism. In vitro, CRISPR/Cas9-based gene editing was used to delete MAVS in H9C2 cell lines and the phenotypes of mitochondria and energy metabolism were evaluated. Here we observed reduced MAVS expression in cardiac tissue from several non-hypertrophic cardiac dysfunction models, contrasting to the enhanced MAVS in hypertrophic heart. Furthermore, we examined the heart function in mice with partial or total MAVS deficiency and found spontaneously developed cardiac pump dysfunction and cardiac dilation as assessed by echocardiography parameters. Metabonomic results suggested MAVS deletion probably promoted cardiac dysfunction by disturbing energy metabolism, especially lipid metabolism. Disordered and mitochondrial homeostasis induced by mitochondrial oxidative stress and mitophagy impairment also advanced the progression of cardiac dysfunction of mice without MAVS. Knockout of MAVS using CRISPR/Cas9 in cardiomyocytes damaged mitochondrial structure and function, as well as increased mitochondrial ROS production. Therefore, reduced MAVS contributed to the pathogenesis of non-hypertrophic cardiac dysfunction, which reveals a link between a key regulator of immunity (MAVS) and heart function.

Molecular Probes, Chemosensors, and Nanosensors for Optical Detection of Biorelevant Molecules and Ions in Aqueous Media and Biofluids

Synthetic molecular probes, chemosensors, and nanosensors used in combination with innovative assay protocols hold great potential for the development of robust, low-cost, and fast-responding sensors that are applicable in biofluids (urine, blood, and saliva). Particularly, the development of sensors for metabolites, neurotransmitters, drugs, and inorganic ions is highly desirable due to a lack of suitable biosensors. In addition, the monitoring and analysis of metabolic and signaling networks in cells and organisms by optical probes and chemosensors is becoming increasingly important in molecular biology and medicine. Thus, new perspectives for personalized diagnostics, theranostics, and biochemical/medical research will be unlocked when standing limitations of artificial binders and receptors are overcome. In this review, we survey synthetic sensing systems that have promising (future) application potential for the detection of small molecules, cations, and anions in aqueous media and biofluids. Special attention was given to sensing systems that provide a readily measurable optical signal through dynamic covalent chemistry, supramolecular host-guest interactions, or nanoparticles featuring plasmonic effects. This review shall also enable the reader to evaluate the current performance of molecular probes, chemosensors, and nanosensors in terms of sensitivity and selectivity with respect to practical requirement, and thereby inspiring new ideas for the development of further advanced systems.

Lipid Metabolite Biomarkers in Cardiovascular Disease: Discovery and Biomechanism Translation from Human Studies

Lipids represent a valuable target for metabolomic studies since altered lipid metabolism is known to drive the pathological changes in cardiovascular disease (CVD). Metabolomic technologies give us the ability to measure thousands of metabolites providing us with a metabolic fingerprint of individual patients. Metabolomic studies in humans have supported previous findings into the pathomechanisms of CVD, namely atherosclerosis, apoptosis, inflammation, oxidative stress, and insulin resistance. The most widely studied classes of lipid metabolite biomarkers in CVD are phospholipids, sphingolipids/ceramides, glycolipids, cholesterol esters, fatty acids, and acylcarnitines. Technological advancements have enabled novel strategies to discover individual biomarkers or panels that may aid in the diagnosis and prognosis of CVD, with sphingolipids/ceramides as the most promising class of biomarkers thus far. In this review, application of metabolomic profiling for biomarker discovery to aid in the diagnosis and prognosis of CVD as well as metabolic abnormalities in CVD will be discussed with particular emphasis on lipid metabolites.

Analysis of Metabolic Markers in Patients with Chronic Heart Failure before and after LVAD Implantation

Chronic heart failure (HF) is a clinical syndrome characterized by functional impairments of the myocardium. Metabolic and clinical changes develop with disease progression. In an advanced state, left ventricular assist devices (LVADs) are implanted for mechanical unloading. Our study aimed to assess the effects of LVAD implantation on the metabolic phenotypes and their potential to reverse the latter in patients with advanced HF. Plasma metabolites were analyzed by LC–MS/MS in 20 patients with ischemic cardiomyopathy (ICM), 20 patients with dilative cardiomyopathy (DCM), and 20 healthy controls. Samples were collected in HF patients before, 30 days after, and >100 days after LVAD implantation. Out of 188 measured metabolites, 63 were altered in HF. Only three metabolites returned to pre-LVAD concentrations 100 days after LVAD implantation. Pre-LVAD differences between DCM and ICM were mainly observed for amino acids and biogenic amines. This study shows a reversal of metabolite abnormalities in HF as a result of LVAD implantation. The etiology of the underlying disease plays an essential role in defining which specific metabolic parameter is altered in HF and reversed by LVAD implantation. Our findings provide a detailed insight into the disease pattern of ICM and DCM and the potential for reversibility of metabolic abnormalities in HF.

Performance of a cardiac lipid panel compared to four prognostic scores in chronic heart failure

The cardiac lipid panel (CLP) is a novel panel of metabolomic biomarkers that has previously shown to improve the diagnostic and prognostic value for CHF patients. Several prognostic scores have been developed for cardiovascular disease risk, but their use is limited to specific populations and precision is still inadequate. We compared a risk score using the CLP plus NT-proBNP to four commonly used risk scores: The Seattle Heart Failure Model (SHFM), Framingham risk score (FRS), Barcelona bio-HF (BCN Bio-HF) and Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC) score. We included 280 elderly CHF patients from the Cardiac Insufficiency Bisoprolol Study in Elderly trial. Cox Regression and hierarchical cluster analysis was performed. Integrated area under the curves (IAUC) was used as criterium for comparison. The mean (SD) follow-up period was 81 (33) months, and 95 (34%) subjects met the primary endpoint. The IAUC for FRS was 0.53, SHFM 0.61, BCN Bio-HF 0.72, MAGGIC 0.68, and CLP 0.78. Subjects were partitioned into three risk clusters: low, moderate, high with the CLP score showing the best ability to group patients into their respective risk cluster. A risk score composed of a novel panel of metabolite biomarkers plus NT-proBNP outperformed other common prognostic scores in predicting 10-year cardiovascular death in elderly ambulatory CHF patients. This approach could improve the clinical risk assessment of CHF patients.

Comprehensive plasma metabolites profiling reveals phosphatidylcholine species as potential predictors for cardiac resynchronization therapy response

Aims

This study aimed to identify the plasma metabolite fingerprint in patients with heart failure and to develop a prediction tool based on differential metabolites for predicting the response to cardiac resynchronization therapy (CRT).

Methods and results

We prospectively recruited 32 healthy individuals and 42 consecutive patients with HF who underwent CRT between January 2018 and January 2019. Peripheral venous blood samples, clinical data, and echocardiographic signatures were collected before CRT implantation. Liquid chromatography‐mass spectrometry was used to perform untargeted metabolites profiling for peripheral plasma under ESI+ and ESI− modes. After 6 month follow‐up, patients were categorized as CRT responders or non‐responders based on the alterations of echocardiographic characteristics. Compared with healthy individuals, patients with HF had distinct metabolomic profiles under both ESI+ and ESI− modes, featuring increased free fatty acids, carnitine, β‐hydroxybutyrate, and dysregulated lipids with heterogeneous alterations such as phosphatidylcholines (PCs) and sphingomyelins. Disparities of baseline metabolomics profile were observed between CRT responders and non‐responders under ESI+ mode but not under ESI− mode. Further metabolites analysis revealed that a group of 20 PCs metabolites under ESI+ mode were major contributors to the distinct profiles between the two groups. We utilized LASSO regression model and identified a panel of four PCs metabolites [including PC (20:0/18:4), PC (20:4/20:0), PC 40:4, and PC (20:4/18:0)] as major predictors for CRT response prediction. Among our whole population (n = 42), receive operating characteristics analysis revealed that the four PCs‐based model could nicely discriminate the CRT responders from non‐responders (area under the curve = 0.906) with a sensitivity of 83.3% and a specificity of 90.0%. Cross‐validation analysis also showed a satisfactory and robust performance of the model with the area under the curve of 0.910 in the training dataset and 0.880 in the testing dataset.

Conclusions

Patients with HF held significantly altered plasma metabolomics profile compared with the healthy individuals. Within the HF group, the non‐responders had a distinct plasma metabolomics profile in contrast to the responders to CRT, which was characterized by increased PCs species. A novel predictive model incorporating four PCs metabolites performed well in identifying CRT non‐responders. These four PCs might severe as potential biomarkers for predicting CRT response. Further validations are needed in multi‐centre studies with larger external cohorts.

Incremental prognostic value of a novel metabolite-based biomarker score in congestive heart failure patients

Aims

The Cardiac Lipid Panel (CLP) is a newly discovered panel of metabolite‐based biomarkers that has shown to improve the diagnostic value of N terminal pro B type natriuretic peptide (NT‐proBNP). However, little is known about its usefulness in predicting outcomes. In this study, we developed a risk score for 4‐year cardiovascular death in elderly chronic heart failure (CHF) patients using the CLP.

Methods and results

From the Cardiac Insufficiency Bisoprolol Study in Elderly trial, we included 280 patients with CHF aged >65 years. A targeted metabolomic analysis of the CLP biomarkers was performed on baseline serum samples. Cox regression was used to determine the association of the biomarkers with the outcome after accounting for established risk factors. A risk score ranging from 0 to 4 was calculated by counting the number of biomarkers above the cut‐offs, using Youden index. During the mean (standard deviation) follow‐up period of 50 (8) months, 35 (18%) subjects met the primary endpoint of cardiovascular death. The area under the receiver operating curve for the model based on clinical variables was 0.84, the second model with NT‐proBNP was 0.86, and the final model with the CLP was 0.90. The categorical net reclassification index was 0.25 using three risk categories: 0–60% (low), 60–85% (intermediate), and >85% (high). The continuous net reclassification index was 0.772, and the integrated discrimination index was 0.104.

Conclusions

In patients with CHF, incorporating a panel of three metabolite‐based biomarkers into a risk score improved the prognostic utility of NT‐proBNP by predicting long‐term cardiovascular death more precisely. This novel approach holds promise to improve clinical risk assessment in CHF patients.

Comprehensive plasma and tissue profiling reveals systemic metabolic alterations in cardiac hypertrophy and failure

AIMS

Heart failure is characterized by structural and metabolic cardiac remodelling. The aim of the present study is to expand our understanding of the complex metabolic alterations in the transition from pathological hypertrophy to heart failure and exploit the results from a translational perspective.

METHODS AND RESULTS

Mice were subjected to transverse aortic constriction (TAC) or sham surgery and sacrificed 2 weeks, 4 weeks, or 6 weeks after the procedure. Samples from plasma, liver, skeletal muscle, and heart were collected and analysed using metabolomics. Cardiac samples were also analysed by transcriptional profiling. Progressive alterations of key cardiac metabolic pathways and gene expression patterns indicated impaired mitochondrial function and a metabolic switch during transition to heart failure. Similar to the heart, liver, and skeletal muscle revealed significant metabolic alterations such as depletion of essential fatty acids and glycerolipids in late stages of heart failure. Circulating metabolites, particularly fatty acids, reflected cardiac metabolic defects, and deteriorating heart function. For example, inverse correlation was found between plasma and the heart levels of triacylglycerol (C18:1, C18:2, C18:3), and sphingomyelin (d18:1, C23:0) already at an early stage of heart failure. Interestingly, combining metabolic and transcriptional data from cardiac tissue revealed that decreased carnitine shuttling and transportation preceded mitochondrial dysfunction. We, thus, studied the therapeutic potential of OCTN2 (Organic Cation/Carnitine Transporter 2), an important factor for carnitine transportation. Cardiac overexpression of OCTN2 using an adeno-associated viral vector significantly improved ejection fraction and reduced interstitial fibrosis in mice subjected to TAC.

CONCLUSION

Comprehensive plasma and tissue profiling reveals systemic metabolic alterations in heart failure, which can be used for identification of novel biomarkers and potential therapeutic targets.

Machine-learning facilitates selection of a novel diagnostic panel of metabolites for the detection of heart failure

The metabolic derangement is common in heart failure with reduced ejection fraction (HFrEF). The aim of the study was to check feasibility of the combined approach of untargeted metabolomics and machine learning to create a simple and potentially clinically useful diagnostic panel for HFrEF. The study included 67 chronic HFrEF patients (left ventricular ejection fraction-LVEF 24.3 ± 5.9%) and 39 controls without the disease. Fasting serum samples were fingerprinted by liquid chromatography-mass spectrometry. Feature selection based on random-forest models fitted to resampled data and followed by linear modelling, resulted in selection of eight metabolites (uric acid, two isomers of LPC 18:2, LPC 20:1, deoxycholic acid, docosahexaenoic acid and one unknown metabolite), demonstrating their predictive value in HFrEF. The accuracy of a model based on metabolites panel was comparable to BNP (0.85 vs 0.82), as verified on the test set. Selected metabolites correlated with clinical, echocardiographic and functional parameters. The combination of two innovative tools (metabolomics and machine-learning methods), both unrestrained by the gaps in the current knowledge, enables identification of a novel diagnostic panel. Its diagnostic value seems to be comparable to BNP. Large scale, multi-center studies using validated targeted methods are crucial to confirm clinical utility of proposed markers.

The metabolites urobilin and sphingomyelin (30:1) are associated with incident heart failure in the general population

Aims

We aimed to investigate whether metabolomic profiling of blood can lead to novel insights into heart failure pathogenesis or improved risk prediction.

Methods and results

Mass spectrometry‐based metabolomic profiling was performed in plasma or serum samples from three community‐based cohorts without heart failure at baseline (total n = 3924; 341 incident heart failure events; median follow‐up ranging from 4.6 to 13.9 years). Cox proportional hazard models were applied to assess the association of each of the 206 identified metabolites with incident heart failure in the discovery cohorts Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) (n = 920) and Uppsala Longitudinal Study of Adult Men (ULSAM) (n = 1121). Replication was undertaken in the independent cohort TwinGene (n = 1797). We also assessed whether metabolites could improve the prediction of heart failure beyond established risk factors (age, sex, body mass index, low‐density and high‐density lipoprotein cholesterol, triglycerides, lipid medication, diabetes, systolic and diastolic blood pressure, blood pressure medication, glomerular filtration rate, smoking status, and myocardial infarction prior to or during follow‐up). Higher circulating urobilin and lower sphingomyelin (30:1) were associated with incident heart failure in age‐adjusted and sex‐adjusted models in the discovery and replication sample. The hazard ratio for urobilin in the replication cohort was estimated to 1.29 per standard deviation unit, 95% confidence interval (CI 1.03–1.63), and for sphingomyelin (30:1) to 0.72 (95% CI 0.58–0.89). Results remained similar after further adjustment for established heart failure risk factors in meta‐analyses of all three cohorts. Urobilin concentrations were inversely associated with left ventricular ejection fraction at baseline in the PIVUS cohort (β = −0.70, 95% CI −1.03 to −0.38). No major improvement in risk prediction was observed when adding the top 2 metabolites (C‐index 0.787, 95% CI 0.752–0.823) or nine Lasso‐selected metabolites (0.790, 95% CI 0.754–0.826) to a modified Atherosclerosis Risk in Communities heart failure risk score model (0.780, 95% CI 0.745–0.816).

Conclusions

Our metabolomic profiling of three community‐based cohorts study identified associations of circulating levels of the haem breakdown product urobilin, and sphingomyelin (30:1), a cell membrane component involved in signal transduction and apoptosis, with incident heart failure.

Disturbed energy and amino acid metabolism with their diagnostic potential in mitral valve disease revealed by untargeted plasma metabolic profiling

INTRODUCTION

Mitral valve disease (MVD), including mitral valve regurgitation (MR) and mitral valve stenosis (MS), is a chronic and progressive cardiac malady. However, the metabolic alterations in MVD is not well-understood till now. The current gold standard diagnostic test, transthoracic echocardiography, has limitations on high-throughput measurement and lacks molecular information for early diagnosis of the disease.

OBJECTIVE

The present study aimed to investigate the biochemical alterations and to explore their diagnostic potential for MVD.

METHODS

Plasma metabolic profile derangements and their diagnostic potential were non-invasively explored in 34 MR and 20 MS patients against their corresponding controls, using high-throughput NMR-based untargeted metabolomics.

RESULTS

Eighteen differential metabolites were identified for MR and MS patients respectively, on the basis of multivariate and univariate data analysis, which were mainly involved in energy metabolism, amino acid metabolism, calcium metabolism and inflammation. These differential metabolites, notably the significantly down-regulated formate and lactate, showed high diagnostic potential for MVD by using Spearman's rank-order correlation analysis and ROC analysis.

CONCLUSIONS

To the best of our knowledge, the present study is the first one that explores the metabolic derangements and their diagnostic values in MVD patients using metabolomics. The findings indicated that metabolic disturbance occurred in MVD patients, with plasma formate and lactate emerged as important candidate biomarkers for MVD.

Metabolomic analysis of serum and myocardium in compensated heart failure after myocardial infarction

AIMS

To determine the metabolic adaptations to compensated heart failure using a reproducible model of myocardial infarction and an unbiased metabolic screen. To address the limitations in sample availability and model variability observed in preclinical and clinical metabolic investigations of heart failure.

MAIN METHODS

Metabolomic analysis was performed on serum and myocardial tissue from rabbits after myocardial infarction (MI) was induced by cryo-injury of the left ventricular free wall. Rabbits followed for 12 weeks after MI exhibited left ventricular dilation and depressed systolic function as determined by echocardiography. Serum and tissue from the viable left ventricular free wall, interventricular septum and right ventricle were analyzed using a gas chromatography time of flight mass spectrometry-based untargeted metabolomics assay for primary metabolites.

KEY FINDINGS

Unique results included: a two- three-fold increase in taurine levels in all three ventricular regions of MI rabbits and similarly, the three regions had increased inosine levels compared to sham controls. Reduced myocardial levels of myo-inositol in the myocardium of MI animals point to altered phospholipid metabolism and membrane receptor function in heart failure. Metabolite profiles also provide evidence for responses to oxidative stress and an impairment in TCA cycle energy production in the failing heart.

SIGNIFICANCE

Our results revealed metabolic changes during compensated cardiac dysfunction and suggest potential targets for altering the progression of heart failure.

A review of validated biomarkers obtained through metabolomics

INTRODUCTION

Studying changes in the whole set of small molecules, final products of biochemical reactions in living systems or metabolites, is extremely appealing because they represent the best approach to identifying what occurs in an organism when samples are collected. However, their usefulness as potential biomarkers is limited by discoveries obtained in small groups without proper validation or even confirmation of the chemical structure. Areas covered: During the past 5 years, more than 900 papers have been published on metabolomics for biomarker discovery, but the numbers are much lower when some criteria of validation are applied. In total, 102 papers have been included in this review. The most frequent disease areas in which these markers have been discovered include the following: cancer, diabetes, and related diseases and neurodegenerative, cardiovascular, autoimmune, liver, and kidney diseases. Expert commentary: Metabolomics has been demonstrated as rapidly growing due to the improvements in instrumentation, mainly mass spectrometry, and data mining software. For application in the clinic, the results should be validated in different stages, from analytical validation to validation in independent sets of samples, using thousands of samples from different sources.

Novel heart failure biomarkers: why do we fail to exploit their potential?

Plasma biomarkers are useful tools in the diagnosis and prognosis of heart failure (HF). In the last decade, numerous studies have aimed to identify novel HF biomarkers that would provide superior and/or additional diagnostic, prognostic, or stratification utility. Although numerous biomarkers have been identified, their implementation in clinical practice has so far remained largely unsuccessful. Whereas cardiac-specific biomarkers, including natriuretic peptides (ANP and BNP) and high sensitivity troponins (hsTn), are widely used in clinical practice, other biomarkers have not yet proven their utility. Galectin-3 (Gal-3) and soluble suppression of tumorigenicity 2 (sST2) are the only novel HF biomarkers that are included in the ACC/AHA HF guidelines, but their clinical utility still needs to be demonstrated. In this review, we will describe natriuretic peptides, hsTn, and novel HF biomarkers, including Gal-3, sST2, human epididymis protein 4 (HE4), insulin-like growth factor-binding protein 7 (IGFBP-7), heart fatty acid-binding protein (H-FABP), soluble CD146 (sCD146), interleukin-6 (IL-6), growth differentiation factor 15 (GDF-15), procalcitonin (PCT), adrenomedullin (ADM), microRNAs (miRNAs), and metabolites like 5-oxoproline. We will discuss the biology of these HF biomarkers and conclude that most of them are markers of general pathological processes like fibrosis, cell death, and inflammation, and are not cardiac- or HF-specific. These characteristics explain to a large degree why it has been difficult to relate these biomarkers to a single disease. We propose that, in addition to clinical investigations, it will be pivotal to perform comprehensive preclinical biomarker investigations in animal models of HF in order to fully reveal the potential of these novel HF biomarkers.

Cardiovascular Implications of Sphingomyelin Presence in Biological Membranes

Sphingomyelin (SM) is a type of sphingolipid found within plasma, cellular membranes and plasma lipoproteins. Here we highlight the basic biochemical features of SMs and their role in biological membranes. We further discuss evidence of the association between SM and cardiovascular diseases such as atherosclerosis, valvular disease, heart failure and diabetes mellitus.

Heart Failure with Preserved and Reduced Ejection Fraction in Hemodialysis Patients: Prevalence, Disease Prediction and Prognosis

BACKGROUND/AIMS

Heart failure (HF) is a main cause of mortality of hemodialysis (HD) patients. While HF with reduced ejection fraction (HFrEF) is known to only affect a minority of patients, little is known about the prevalence, associations with clinical characteristics and prognosis of HF with preserved ejection fraction (HFpEF).

METHODS

We included 105 maintenance HD patients from the Medical University of Vienna into this prospective single-center cohort study and determined the prevalence of HFpEF (per the 2013 criteria of the European Society of Cardiology) and HFrEF (EF <45%), using standardized post-HD transthoracic echocardiography. We also assessed clinical, laboratory and volume status parameters (by bioimpedance spectroscopy). These parameters served to calculate prediction models for both disease entities, while clinical outcomes (frequency of cardiovascular hospitalizations and/or cardiac death) were assessed prospectively over 27±4 months of follow-up.

RESULTS

All but 4 patients (96%) had evidence of diastolic dysfunction. 70% of the entire cohort fulfilled HF criteria (81% HFpEF, 19% HFrEF). Age, female sex, body mass index, blood pressure and dialysis vintage were predictive of HFpEF (sensitivity 86%, specificity 63%; AUC 0.87), while age, female sex, NT pro-BNP, history of coronary artery disease and atrial fibrillation were predictive of HFrEF (sensitivity 85%, specificity 90%; AUC 0.95). Compared to patients without HF, those with HFpEF and HFrEF had a higher risk of hospitalization for cardiovascular reason and/or cardiac death (adjusted HR 4.31, 95% CI 0.46-40.03; adjusted HR 3.24, 95% CI 1.08-9.75, respectively).

CONCLUSION

Diastolic dysfunction and HFpEF are highly prevalent in HD patients while HFrEF only affects a minority. Distinct patient-specific characteristics predict diagnosis of either entity with good accuracy.