Metabolomic signatures of cardiac remodelling and heart failure risk in the community

Metabolomic profiling identifies novel metabolites associated with cardiac dysfunction

Metabolic comorbidities, such as obesity and diabetes, are associated with subclinical alterations in both cardiac structure/function and natriuretic peptides prior to the onset of heart failure (HF). Despite this, the exact metabolic pathways of cardiac dysfunction which precede HF are not well-defined. Among older individuals without HF in the Multi-Ethnic Study of Atherosclerosis (MESA), we evaluated the associations of 47 circulating metabolites measured by 1H-NMR with echocardiographic measures of cardiac structure and function. We then evaluated associations of significant metabolites with circulating N-terminal pro-B-type natriuretic peptide (NT-proBNP). In a separate cohort, we evaluated differences between top metabolites in patients with HF with preserved ejection fraction (HFpEF) and comorbidity-matched controls. Genetic variants associated with top metabolites (mQTLs) were then related to echocardiographic measures and NT-proBNP. Among 3440 individuals with metabolic and echocardiographic data in MESA (62 ± 10 years, 52% female, 38% White), 10 metabolites broadly reflective of glucose and amino acid metabolism were associated with at least 1 measure of cardiac structure or function. Of these 10 metabolites, 4 (myo-inositol, glucose, dimethylsulfone, carnitine) were associated with higher NT-proBNP and 2 (d-mannose, acetone) were associated with lower NT-proBNP. In a separate cohort, patients with HFpEF had higher circulating myo-inositol levels compared with comorbidity-matched controls. Genetic analyses revealed that 1 of 6 known myo-inositol mQTLs conferred risk of higher NT-proBNP. In conclusion, metabolomic profiling identifies several novel metabolites associated with cardiac dysfunction in a cohort at high risk for HF, revealing pathways potentially relevant to future HF risk.

Plasma metabolomics reveals the shared and distinct metabolic disturbances associated with cardiovascular events in coronary artery disease

Risk prediction for subsequent cardiovascular events remains an unmet clinical issue in patients with coronary artery disease. We aimed to investigate prognostic metabolic biomarkers by considering both shared and distinct metabolic disturbance associated with the composite and individual cardiovascular events. Here, we conducted an untargeted metabolomics analysis for 333 incident cardiovascular events and 333 matched controls. The cardiovascular events were designated as cardiovascular death, myocardial infarction/stroke and heart failure. A total of 23 shared differential metabolites were associated with the composite of cardiovascular events. The majority were middle and long chain acylcarnitines. Distinct metabolic patterns for individual events were revealed, and glycerophospholipids alteration was specific to heart failure. Notably, the addition of metabolites to clinical markers significantly improved heart failure risk prediction. This study highlights the potential significance of plasma metabolites on tailed risk assessment of cardiovascular events, and strengthens the understanding of the heterogenic mechanisms across different events.

Broadcasters, receivers, functional groups of metabolites, and the link to heart failure by revealing metabolomic network connectivity

BACKGROUND AND OBJECTIVE

Blood-based small molecule metabolites offer easy accessibility and hold significant potential for insights into health processes, the impact of lifestyle, and genetic variation on disease, enabling precise risk prevention. In a prospective study with records of heart failure (HF) incidence, we present metabolite profiling data from individuals without HF at baseline.

METHODS

We uncovered the interconnectivity of metabolites using data-driven and causal networks augmented with polygenic factors. Exploring the networks, we identified metabolite broadcasters, receivers, mediators, and subnetworks corresponding to functional classes of metabolites, and provided insights into the link between metabolomic architecture and regulation in health. We incorporated the network structure into the identification of metabolites associated with HF to control the effect of confounding metabolites.

RESULTS

We identified metabolites associated with higher and lower risk of HF incidence, such as glycine, ureidopropionic and glycocholic acids, and LPC 18:2. These associations were not confounded by the other metabolites due to uncovering the connectivity among metabolites and adjusting each association for the confounding metabolites. Examples of our findings include the direct influence of asparagine on glycine, both of which were inversely associated with HF. These two metabolites were influenced by polygenic factors and only essential amino acids, which are not synthesized in the human body and are obtained directly from the diet.

CONCLUSION

Metabolites may play a critical role in linking genetic background and lifestyle factors to HF incidence. Revealing the underlying connectivity of metabolites associated with HF strengthens the findings and facilitates studying complex conditions like HF.

Using Omics to Identify Novel Therapeutic Targets in Heart Failure

Omics refers to the measurement and analysis of the totality of molecules or biological processes involved within an organism. Examples of omics data include genomics, transcriptomics, epigenomics, proteomics, metabolomics, and more. In this review, we present the available literature reporting omics data on heart failure that can inform the development of novel treatments or innovative treatment strategies for this disease. This includes polygenic risk scores to improve prediction of genomic data and the potential of multiomics to more efficiently identify potential treatment targets for further study. We also discuss the limitations of omic analyses and the barriers that must be overcome to maximize the utility of these types of studies. Finally, we address the current state of the field and future opportunities for using multiomics to better personalize heart failure treatment strategies.

Incremental Value of a Metabolic Risk Score for Heart Failure Mortality: A Population-Based Study

BACKGROUND

Heart failure is heterogeneous syndrome with persistently high mortality. Nuclear magnetic resonance spectroscopy enables high-throughput metabolomics, suitable for precision phenotyping. We aimed to use targeted metabolomics to derive a metabolic risk score (MRS) that improved mortality risk stratification in heart failure.

METHODS

Nuclear magnetic resonance was used to measure 21 metabolites (lipoprotein subspecies, branched-chain amino acids, alanine, GlycA (glycoprotein acetylation), ketone bodies, glucose, and citrate) in plasma collected from a heart failure community cohort. The MRS was derived using least absolute shrinkage and selection operator penalized Cox regression and temporal validation. The association between the MRS and mortality and whether risk stratification was improved over the Meta-Analysis Global Group in Chronic Heart Failure clinical risk score and NT-proBNP (N-terminal pro-B-type natriuretic peptide) levels were assessed.

RESULTS

The study included 1382 patients (median age, 78 years, 52% men, 43% reduced ejection fraction) with a 5-year survival rate of 48% (95% CI, 46%-51%). The MRS included 9 metabolites measured. In the validation data set, a 1 standard deviation increase in the MRS was associated with a large increased rate of death (hazard ratio, 2.2 [95% CI, 1.9-2.5]) that remained after adjustment for Meta-Analysis Global Group in Chronic Heart Failure score and NT-proBNP (hazard ratio, 1.6 [95% CI, 1.3-1.9]). These associations did not differ by ejection fraction. The integrated discrimination and net reclassification indices, and Uno's C statistic, indicated that the addition of the MRS improved discrimination over Meta-Analysis Global Group in Chronic Heart Failure and NT-proBNP.

CONCLUSIONS

This MRS developed in a heart failure community cohort was associated with a large excess risk of death and improved risk stratification beyond an established risk score and clinical markers.

WITHDRAWN: Broadcasters, receivers, functional groups of metabolites and the link to heart failure using polygenic factors

The full text of this preprint has been withdrawn, as it was submitted in error. Therefore, the authors do not wish this work to be cited as a reference. Questions should be directed to the corresponding author.

WITHDRAWN: Broadcasters, receivers, functional groups of metabolites and the link to heart failure using polygenic factors

The full text of this preprint has been withdrawn, as it was submitted in error. Therefore, the authors do not wish this work to be cited as a reference. Questions should be directed to the corresponding author.

Serum metabolite signatures of cardiac function and morphology in individuals from a population-based cohort

BACKGROUND

Changes in serum metabolites in individuals with altered cardiac function and morphology may exhibit information about cardiovascular disease (CVD) pathway dysregulations and potential CVD risk factors. We aimed to explore associations of cardiac function and morphology, evaluated using magnetic resonance imaging (MRI) with a large panel of serum metabolites.

METHODS

Cross-sectional data from CVD-free individuals from the population-based KORA cohort were analyzed. Associations between 3T-MRI-derived left ventricular (LV) function and morphology parameters (e.g., volumes, filling rates, wall thickness) and markers of carotid plaque with metabolite profile clusters and single metabolites as outcomes were assessed by adjusted multinomial logistic regression and linear regression models.

RESULTS

In 360 individuals (mean age 56.3 years; 41.9% female), 146 serum metabolites clustered into three distinct profiles that reflected high-, intermediate- and low-CVD risk. Higher stroke volume (relative risk ratio (RRR): 0.53, 95%-CI [0.37; 0.76], p-value < 0.001) and early diastolic filling rate (RRR: 0.51, 95%-CI [0.37; 0.71], p-value < 0.001) were most strongly protectively associated against the high-risk profile compared to the low-risk profile after adjusting for traditional CVD risk factors. Moreover, imaging markers were associated with 10 metabolites in linear regression. Notably, negative associations of stroke volume and early diastolic filling rate with acylcarnitine C5, and positive association of function parameters with lysophosphatidylcholines, diacylphosphatidylcholines, and acylalkylphosphatidylcholines were observed. Furthermore, there was a negative association of LV wall thickness with alanine, creatinine, and symmetric dimethylarginine. We found no significant associations with carotid plaque.

CONCLUSIONS

Serum metabolite signatures are associated with cardiac function and morphology even in individuals without a clinical indication of CVD.

Predictive biomarkers for the early detection and management of heart failure

Cardiovascular disease (CVD) is a serious public health concern whose incidence has been on a rise and is projected by the World Health Organization to be the leading global cause of mortality by 2030. Heart failure (HF) is a complicated syndrome resulting from various CVDs of heterogeneous etiologies and exhibits varying pathophysiology, including activation of inflammatory signaling cascade, apoptosis, fibrotic pathway, and neuro-humoral system, thereby leading to compromised cardiac function. During this process, several biomolecules involved in the onset and progression of HF are released into circulation. These circulating biomolecules could serve as unique biomarkers for the detection of subclinical changes and can be utilized for monitoring disease severity. Hence, it is imperative to identify these biomarkers to devise an early predictive strategy to stop the deterioration of cardiac function caused by these complex cellular events. Furthermore, measurement of multiple biomarkers allows clinicians to divide HF patients into sub-groups for treatment and management based on early health outcomes. The present article provides a comprehensive overview of current omics platform available for discovering biomarkers for HF management. Some of the existing and novel biomarkers for the early detection of HF with special reference to endothelial biology are also discussed.

Metabolomic Association and Risk Prediction With Heart Failure in Older Adults

BACKGROUND

Older adults have markedly increased risks of heart failure (HF), specifically HF with preserved ejection fraction (HFpEF). Identifying novel biomarkers can help in understanding HF pathogenesis and improve at-risk population identification. This study aimed to identify metabolites associated with incident HF, HFpEF, and HF with reduced ejection fraction and examine risk prediction in older adults.

METHODS

Untargeted metabolomic profiling was performed in Black and White adults from the ARIC study (Atherosclerosis Risk in Communities) visit 5 (n=3719; mean age, 75 years). We applied Cox regressions to identify metabolites associated with incident HF and its subtypes. The metabolite risk score (MRS) was constructed and examined for associations with HF, echocardiographic measures, and HF risk prediction. Independent samples from visit 3 (n=1929; mean age, 58 years) were used for replication.

RESULTS

Sixty metabolites (hazard ratios range, 0.79-1.49; false discovery rate, <0.05) were associated with incident HF after adjusting for clinical risk factors, eGFR, and NT-proBNP (N-terminal pro-B-type natriuretic peptide). Mannonate, a hydroxy acid, was replicated (hazard ratio, 1.36 [95% CI, 1.19-1.56]) with full adjustments. MRS was associated with an 80% increased risk of HF per SD increment, and the highest MRS quartile had 8.7× the risk of developing HFpEF than the lowest quartile. High MRS was also associated with unfavorable values of cardiac structure and function. Adding MRS over clinical risk factors and NT-proBNP improved 5-year HF risk prediction C statistics from 0.817 to 0.850 (∆C, 0.033 [95% CI, 0.017-0.047]). The association between MRS and incident HF was replicated after accounting for clinical risk factors (P<0.05).

CONCLUSIONS

Novel metabolites associated with HF risk were identified, elucidating disease pathways, specifically HFpEF. An MRS was associated with HF risk and improved 5-year risk prediction in older adults, which may assist at at-risk population identification.

Indoleamine 2,3-Dioxygenase 1 Deletion-Mediated Kynurenine Insufficiency Inhibits Pathological Cardiac Hypertrophy

BACKGROUND

Aberrant amino acid metabolism is implicated in cardiac hypertrophy, while the involvement of tryptophan metabolism in pathological cardiac hypertrophy remains elusive. Herein, we aimed to investigate the effect and potential mechanism of IDO1 (indoleamine 2,3-dioxygenase) and its metabolite kynurenine (Kyn) on pathological cardiac hypertrophy.

METHODS

Transverse aortic constriction was performed to induce cardiac hypertrophy in IDO1-knockout (KO) mice and AAV9-cTNT-shIDO1 mice. Liquid chromatography-mass spectrometry was used to detect the metabolites of tryptophan-Kyn pathway. Chromatin immunoprecipitation assay and dual luciferase assay were used to validate the binding of protein and DNA.

RESULTS

IDO1 expression was upregulated in both human and murine hypertrophic myocardium, alongside with increased IDO1 activity and Kyn content in transverse aortic constriction-induced mice's hearts using liquid chromatography-mass spectrometry analysis. Myocardial remodeling and heart function were significantly improved in transverse aortic constriction-induced IDO1-KO mice, but were greatly exacerbated with subcutaneous Kyn administration. IDO1 inhibition or Kyn addition confirmed the alleviation or aggravation of hypertrophy in cardiomyocyte treated with isoprenaline, respectively. Mechanistically, IDO1 and metabolite Kyn contributed to pathological hypertrophy via the AhR (aryl hydrocarbon receptor)-GATA4 (GATA binding protein 4) axis.

CONCLUSIONS

This study demonstrated that IDO1 deficiency and consequent Kyn insufficiency can protect against pathological cardiac hypertrophy by decreasing GATA4 expression in an AhR-dependent manner.

Broadcasters, receivers, functional groups of metabolites and the link to heart failure progression using polygenic factors

In a prospective study with records of heart failure (HF) incidence, we present metabolite profiling data from individuals without HF at baseline. We uncovered the interconnectivity of metabolites using data-driven and causal networks augmented with polygenic factors. Exploring the networks, we identified metabolite broadcasters, receivers, mediators, and subnetworks corresponding to functional classes of metabolites, and provided insights into the link between metabolomic architecture and regulation in health. We incorporated the network structure into the identification of metabolites associated with HF to control the effect of confounding metabolites. We identified metabolites associated with higher or lower risk of HF incidence, the associations that were not confounded by the other metabolites, such as glycine, ureidopropionic and glycocholic acids, and LPC 18:2. We revealed the underlying relationships of the findings. For example, asparagine directly influenced glycine, and both were inversely associated with HF. These two metabolites were influenced by polygenic factors and only essential amino acids which are not synthesized in the human body and come directly from the diet. Metabolites may play a critical role in linking genetic background and lifestyle factors to HF progression. Revealing the underlying connectivity of metabolites associated with HF strengthens the findings and facilitates a mechanistic understanding of HF progression.

Targeting gut microbiota-derived kynurenine to predict and protect the remodeling of the pressure-overloaded young heart

Pressure-overloaded left ventricular remodeling in young population is progressive and readily degenerate into heart failure. The aims of this study were to identify a plasma metabolite that predicts and is mechanistically linked to the disease. Untargeted metabolomics determined elevated plasma kynurenine (Kyn) in both the patient cohorts and the mice model, which was correlated with remodeling parameters. In vitro and in vivo evidence, combined with single-nucleus RNA sequencing (snRNA-seq), demonstrated that Kyn affected both cardiomyocytes and cardiac fibroblasts by activating aryl hydrocarbon receptors (AHR) to up-regulate hypertrophy- and fibrosis-related genes. Shotgun metagenomics and fecal microbiota transplantation revealed the existence of the altered gut microbiota-Kyn relationship. Supplementation of selected microbes reconstructed the gut microbiota, reduced plasma Kyn, and alleviated ventricular remodeling. Our data collectively discovered a gut microbiota-derived metabolite to activate AHR and its gene targets in remodeling young heart, a process that could be prevented by specific gut microbiota modulation.

Using Artificial Intelligence to Better Predict and Develop Biomarkers

Advancements in technology have improved biomarker discovery in the field of heart failure (HF). What was once a slow and laborious process has gained efficiency through use of high-throughput omics platforms to phenotype HF at the level of genes, transcripts, proteins, and metabolites. Furthermore, improvements in artificial intelligence (AI) have made the interpretation of large omics data sets easier and improved analysis. Use of omics and AI in biomarker discovery can aid clinicians by identifying markers of risk for developing HF, monitoring care, determining prognosis, and developing druggable targets. Combined, AI has the power to improve HF patient care.

Untargeted Metabolomics Identifies Potential Hypertrophic Cardiomyopathy Biomarkers in Carriers of MYBPC3 Founder Variants

Hypertrophic cardiomyopathy (HCM) is the most prevalent monogenic heart disease, commonly caused by pathogenic MYBPC3 variants, and a significant cause of sudden cardiac death. Severity is highly variable, with incomplete penetrance among genotype-positive family members. Previous studies demonstrated metabolic changes in HCM. We aimed to identify metabolite profiles associated with disease severity in carriers of MYBPC3 founder variants using direct-infusion high-resolution mass spectrometry in plasma of 30 carriers with a severe phenotype (maximum wall thickness ≥20 mm, septal reduction therapy, congestive heart failure, left ventricular ejection fraction <50%, or malignant ventricular arrhythmia) and 30 age- and sex-matched carriers with no or a mild phenotype. Of the top 25 mass spectrometry peaks selected by sparse partial least squares discriminant analysis, XGBoost gradient boosted trees, and Lasso logistic regression (42 total), 36 associated with severe HCM at a p < 0.05, 20 at p < 0.01, and 3 at p < 0.001. These peaks could be clustered to several metabolic pathways, including acylcarnitine, histidine, lysine, purine and steroid hormone metabolism, and proteolysis. In conclusion, this exploratory case-control study identified metabolites associated with severe phenotypes in MYBPC3 founder variant carriers. Future studies should assess whether these biomarkers contribute to HCM pathogenesis and evaluate their contribution to risk stratification.

Understanding How Heart Metabolic Derangement Shows Differential Stage Specificity for Heart Failure with Preserved and Reduced Ejection Fraction

Heart failure (HF) is a clinical condition defined by structural and functional abnormalities in the heart that gradually result in reduced cardiac output (HFrEF) and/or increased cardiac pressures at rest and under stress (HFpEF). The presence of asymptomatic individuals hampers HF identification, resulting in delays in recognizing patients until heart dysfunction is manifested, thus increasing the chance of poor prognosis. Given the recent advances in metabolomics, in this review we dissect the main alterations occurring in the metabolic pathways behind the decrease in cardiac function caused by HF. Indeed, relevant preclinical and clinical research has been conducted on the metabolite connections and differences between HFpEF and HFrEF. Despite these promising results, it is crucial to note that, in addition to identifying single markers and reliable threshold levels within the healthy population, the introduction of composite panels would strongly help in the identification of those individuals with an increased HF risk. That said, additional research in the field is required to overcome the current drawbacks and shed light on the pathophysiological changes that lead to HF. Finally, greater collaborative data sharing, as well as standardization of procedures and approaches, would enhance this research field to fulfil its potential.

Using Artificial Intelligence to Better Predict and Develop Biomarkers

Advancements in technology have improved biomarker discovery in the field of heart failure (HF). What was once a slow and laborious process has gained efficiency through use of high-throughput omics platforms to phenotype HF at the level of genes, transcripts, proteins, and metabolites. Furthermore, improvements in artificial intelligence (AI) have made the interpretation of large omics data sets easier and improved analysis. Use of omics and AI in biomarker discovery can aid clinicians by identifying markers of risk for developing HF, monitoring care, determining prognosis, and developing druggable targets. Combined, AI has the power to improve HF patient care.

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.

Progress and Research Priorities in Imaging Genomics for Heart and Lung Disease: Summary of an NHLBI Workshop

Imaging genomics is a rapidly evolving field that combines state-of-the-art bioimaging with genomic information to resolve phenotypic heterogeneity associated with genomic variation, improve risk prediction, discover prevention approaches, and enable precision diagnosis and treatment. Contemporary bioimaging methods provide exceptional resolution generating discrete and quantitative high-dimensional phenotypes for genomics investigation. Despite substantial progress in combining high-dimensional bioimaging and genomic data, methods for imaging genomics are evolving. Recognizing the potential impact of imaging genomics on the study of heart and lung disease, the National Heart, Lung, and Blood Institute convened a workshop to review cutting-edge approaches and methodologies in imaging genomics studies, and to establish research priorities for future investigation. This report summarizes the presentations and discussions at the workshop. In particular, we highlight the need for increased availability of imaging genomics data in diverse populations, dedicated focus on less common conditions, and centralization of efforts around specific disease areas.

Heart failure risk estimation based on novel biomarkers

Introduction: Despite advances in medical care, heart failure (HF)-associated morbidity and mortality remains high. Consequently, there is increased effort to find better ways for predicting, screening, and prognosticating HF in order to facilitate effective primary and secondary prevention.Areas covered: In this review, we describe the various biomarkers associated with different etiologic pathways implicated in HF, and discuss their roles in screening, diagnosing, prognosticating and predicting HF. We explore the emerging role of multi-omic approaches. We performed electronic searches in databases (PubMed and Google Scholar) through December 2020, using the following key terms: biomarker, novel, heart failure, risk, prediction, and estimation.Circulating BNP and troponin concentrations have been established in clinical care as key biomarkers for diagnosing and prognosticating HF. Emerging biomarkers (such as galectin-3 and ST-2) have gained further recognition for use in evaluating prognosis of HF patients. Promising biomarkers that are yet to be part of clinical recommendations include biomarkers of cardiorenal disease.Expert opinion: Increasing recognition of the complex and interdependent nature of pathophysiological pathways of HF has led to the application of multi-marker approaches including multi-omic high throughput assays. These newer approaches have the potential for new therapeutic discoveries and improving precision medicine in HF.

Framingham Heart Study: JACC Focus Seminar, 1/8

The Framingham Heart Study is the longest-running cardiovascular epidemiological study, starting in 1948. This paper gives an overview of the various cohorts, collected data, and most important research findings to date. In brief, the Framingham Heart Study, funded by the National Institutes of Health and managed by Boston University, spans 3 generations of well phenotyped White persons and 2 cohorts comprised of racial and ethnic minority groups. These cohorts are densely phenotyped, with extensive longitudinal follow-up, and they continue to provide us with important information on human cardiovascular and noncardiovascular physiology over the lifespan, as well as to identify major risk factors for cardiovascular disease. This paper also summarizes some of the more recent progress in molecular epidemiology and discusses the future of the study.

[Biomarkers in Heart Failure: Current and Future]

Heart failure (HF) is the ending of practically all cardiovascular diseases and the reason for hospitalization of 49% of patients in a cardiological hospital. Available instrumental diagnostic methods and biomarkers not always allow verification of HF, particularly in patients with preserved left ventricular ejection fraction. Prediction of chronic HF in patients with risk factors faces great difficulties. Currently, natriuretic peptides (NUP) are widely used for the diagnosis, prognosis and management of patients with HF and are included in clinical guidelines for diagnosis and treatment of HF. Following multiple studies, the understanding of NUP significance has changed. This resulted in a need for new biomarkers to improve the insight into the process of HF and to personalize the treatment by better individual phenotyping. In addition, current technologies, such as transcriptomic, proteomic and metabolomic analyses, provide identification of new biomarkers and better understanding of features of the HF pathogenesis. The aim of this study was to discuss recent reports on NUP and novel, most promising biomarkers in respect of their possible use in clinical practice.

Neurohumoral, cardiac and inflammatory markers in the evaluation of heart failure severity and progression

Heart failure is common in adult population, accounting for substantial morbidity and mortality worldwide. The main risk factors for heart failure are coronary artery disease, hypertension, obesity, diabetes mellitus, chronic pulmonary diseases, family history of cardiovascular diseases, cardiotoxic therapy. The main factor associated with poor outcome of these patients is constant progression of heart failure. In the current review we present evidence on the role of established and candidate neurohumoral biomarkers for heart failure progression management and diagnostics. A growing number of biomarkers have been proposed as potentially useful in heart failure patients, but not one of them still resembles the characteristics of the “ideal biomarker.” A single marker will hardly perform well for screening, diagnostic, prognostic, and therapeutic management purposes. Moreover, the pathophysiological and clinical significance of biomarkers may depend on the presentation, stage, and severity of the disease. The authors cover main classification of heart failure phenotypes, based on the measurement of left ventricular ejection fraction, including heart failure with preserved ejection fraction, heart failure with reduced ejection fraction, and the recently proposed category heart failure with mid-range ejection fraction. One could envisage specific sets of biomarker with different performances in heart failure progression with different left ventricular ejection fraction especially as concerns prediction of the future course of the disease and of left ventricular adverse/reverse remodeling. This article is intended to provide an overview of basic and additional mechanisms of heart failure progression will contribute to a more comprehensive knowledge of the disease pathogenesis.

Metabolomic Profiles and Heart Failure Risk in Black Adults: Insights From the Jackson Heart Study

BACKGROUND

Heart failure (HF) is a heterogeneous disease characterized by significant metabolic disturbances; however, the breadth of metabolic dysfunction before the onset of overt disease is not well understood. The purpose of this study was to determine the association of circulating metabolites with incident HF to uncover novel metabolic pathways to disease.

METHODS

We performed targeted plasma metabolomic profiling in a deeply phenotyped group of Black adults from the JHS (Jackson Heart Study; n=2199). We related metabolites associated with incident HF to established etiological mechanisms, including increased left ventricular mass index and incident coronary heart disease. Furthermore, we evaluated differential associations of metabolites with HF with preserved ejection fraction versus HF with reduced ejection fraction.

RESULTS

Metabolites associated with incident HF included products of posttranscriptional modifications of RNA, as well as polyamine and nitric oxide metabolism. A subset of metabolite-HF associations was independent of well-established HF pathways such as increased left ventricular mass index and incident coronary heart disease and included homoarginine (per 1 SD increase in metabolite level, hazard ratio, 0.77; P=1.2×10-3), diacetylspermine (hazard ratio, 1.34; P=3.4×10-3), and uridine (hazard ratio, 0.79; P, 3×10-4). Furthermore, metabolites involved in pyrimidine metabolism (orotic acid) and collagen turnover (N-methylproline) among others were part of a distinct metabolic signature that differentiated individuals with HF with preserved ejection fraction versus HF with reduced ejection fraction.

CONCLUSIONS

The integration of clinical phenotyping with plasma metabolomic profiling uncovered novel metabolic processes in nontraditional disease pathways underlying the heterogeneity of HF development in Black adults.

Metabolomic signatures of cardiac remodelling and heart failure risk in the community

Aims

Heart failure (HF) is associated with several metabolic changes, but it is unknown whether distinct components of the circulating metabolome may be related to cardiac structure and function, and with incident HF in the community.

Methods and results

We assayed 217 circulating metabolites in 2336 Framingham Study participants (mean age 55 ± 10 years, 53% women) without HF at baseline. We used linear and Cox regression to relate concentrations of metabolites to left ventricular (LV) diastolic dimension, LV wall thickness, LV ejection fraction, left atrial dimension, LV ventricular mass, and aortic root size cross‐sectionally and to incident HF prospectively. Bonferroni‐adjusted P‐values <0.05 denoted statistical significance. Circulating concentrations of kynurenine [β = −0.12 cm per standard deviation (SD) increment in normalized residual of metabolite, P = 7.3 × 10⁻⁸] and aminoadipate (−0.11 cm per SD increment, P = 2.61 × 10⁻⁵) were associated with left ventricular diastolic dimension, phosphatidylcholine (carbon:double bound = 38:6) with left atrial dimension (0.10 cm per SD increment, P = 9.7 × 10⁻⁶), and cholesterol ester (carbon:double bound = 20:5) with left atrial dimension (0.10 cm per SD increment, P = 1.4 × 10⁻⁵) in multivariable‐adjusted models. During an average follow‐up of 15.8 (range 0.02–23.2) years, 113 participants (5%) were diagnosed with HF with reduced ejection fraction and 106 individuals (5%) with HF with preserved ejection fraction. In multivariable analyses, concentrations of phosphatidylcholine (hazard ratio 0.63, P = 1.3 × 10⁻⁵) and ornithine (hazard ratio 1.44, P = 0.00014) were associated with HF with reduced ejection fraction.

Conclusions

Several metabolites, including the vasoactive metabolite kynurenine, were related to cardiac structure and function in our sample. Additional research is warranted to confirm our observations and investigate if these metabolites can risk stratify ambulatory individuals.