Omics phenotyping in heart failure: the next frontier

Machine learning in the prevention of heart failure

Heart failure (HF) is a global pandemic with a growing prevalence and is a growing burden on the healthcare system. Machine learning (ML) has the potential to revolutionize medicine and can be applied in many different forms to aid in the prevention of symptomatic HF (stage C). HF prevention currently has several challenges, specifically in the detection of pre-HF (stage B). HF events are missed in contemporary models, limited therapeutic options are proven to prevent HF, and the prevention of HF with preserved ejection is particularly lacking. ML has the potential to overcome these challenges through existing and future models. ML has limitations, but the many benefits of ML outweigh these limitations and risks in most scenarios. ML can be applied in HF prevention through various strategies such as refinement of incident HF risk prediction models, capturing diagnostic signs from available tests such as electrocardiograms, chest x-rays, or echocardiograms to identify structural/functional cardiac abnormalities suggestive of pre-HF (stage B HF), and interpretation of biomarkers and epigenetic data. Altogether, ML is able to expand the screening of individuals at risk for HF (stage A HF), identify populations with pre-HF (stage B HF), predict the risk of incident stage C HF events, and offer the ability to intervene early to prevent progression to or decline in stage C HF. In this narrative review, we discuss the methods by which ML is utilized in HF prevention, the benefits and pitfalls of ML in HF risk prediction, and the future directions.

Potential of plasma biomarkers for heart failure prediction, management, and prognosis: A multiomics perspective

Heart failure (HF) remains a major global health challenge, and more effective and comprehensive plasma biomarkers are needed to effectively treat HF patients. Multiomics studies have shown that DNA fragments, noncoding RNAs, proteins, and metabolites may be potential plasma biomarkers for HF. However, comprehensive reviews that focus on research on plasma biomarkers for HF from an omics perspective are lacking. This review summarizes the applications of various omics approaches in the exploration of biomarkers related to the risk assessment, diagnosis, subtype classification, medical management, and prognosis prediction of HF. Moreover, as heart transplantation and left ventricular assistant device (LVAD) implantation are terminal therapies for end-stage HF patients, this review also discusses the role of cell-free DNA as a biomarker for cardiac transplant rejection and omics studies of plasma biomarkers in patients who respond to LVAD therapy. Our findings suggest that future omics research on HF biomarkers should employ integrated multiomics methods and expand the sample size to increase the robustness of the results and that the identified biomarkers should be further validated in large cohorts.

New and future heart failure drugs

In the past decade, our understanding of heart failure pathophysiology has advanced significantly, resulting in the development of new medications such as angiotensin-neprilysin inhibitors, sodium-glucose cotransporter-2 inhibitors and oral soluble guanylate cyclase stimulators. Backed by positive findings from large randomized controlled trials, recommendations for their use were recently included in the 2022 AHA/ACC/HFSA guidelines and 2023 ESC guidelines for management of heart failure. Promising drugs for future heart failure treatment include agents that modulate the neurohormonal system, vasodilators, anti-inflammatory drugs, mitotropes, which improve deranged energy metabolism of the failing heart, and myotropes, which increase cardiac contractility by affecting cardiac sarcomere function. Here, we discuss these new and future heart failure drugs. We explain their mechanisms of action, critically evaluate their performance in clinical trials and summarize the clinical scenarios in which the latest guidelines recommend their use. This Review aims to offer clinicians and researchers a comprehensive overview of novel therapeutic classes in heart failure treatment.

Integrative proteomic and metabolomic elucidation of cardiomyopathy with in vivo and in vitro models and clinical samples

Cardiomyopathy is a prevalent cardiovascular disease that affects individuals of all ages and can lead to life-threatening heart failure. Despite its variety in types, each with distinct characteristics and causes, our understanding of cardiomyopathy at a systematic biology level remains incomplete. Mass spectrometry-based techniques have emerged as powerful tools, providing a comprehensive view of the molecular landscape and aiding in the discovery of biomarkers and elucidation of mechanisms. This review highlights the significant potential of integrating proteomic and metabolomic approaches with specialized databases to identify biomarkers and therapeutic targets across different types of cardiomyopathies. In vivo and in vitro models, such as genetically modified mice, patient-derived or induced pluripotent stem cells, and organ chips, are invaluable in exploring the pathophysiological complexities of this disease. By integrating omics approaches with these sophisticated modeling systems, our comprehension of the molecular underpinnings of cardiomyopathy can be greatly enhanced, facilitating the development of diagnostic markers and therapeutic strategies. Among the promising therapeutic targets are those involved in extracellular matrix remodeling, sarcomere damage, and metabolic remodeling. These targets hold the potential to advance precision therapy in cardiomyopathy, offering hope for more effective treatments tailored to the specific molecular profiles of patients.

The Use of Metabolomes in Risk Stratification of Heart Failure Patients: Protocol for a Scoping Review

BACKGROUND

Heart failure (HF) is a significant health problem that is often associated with major morbidity and mortality. Metabolic abnormalities occur in HF and may be used to identify individuals at risk of developing the condition. Furthermore, these metabolic changes may play a role in the pathogenesis and progression of HF. Despite this knowledge, the utility of metabolic changes in diagnosis, management, prognosis, and therapy for patients with chronic HF has not been systematically reviewed.

OBJECTIVE

This scoping review aims to systematically appraise the literature on metabolic changes in patients with HF, describe the role of these changes in pathogenesis, progression, and care, and identify knowledge gaps to inform future research.

METHODS

This review will be conducted using a strategy based on previous reports, the JBI Manual for Evidence Synthesis, and the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Reviews (PRISMA-ScR) guidelines. A comprehensive search of electronic databases (Medline, EBSCOhost, Scopus, and Web of Science) will be conducted using keywords related to HF, myocardial failure, metabolomes, metabonomics, and analytical chemistry techniques. The search will include original peer-reviewed research papers (clinical studies conducted on humans and systematic reviews with or without a meta-analysis) published between January 2010 and September 2023. Studies that include patients with HF younger than 18 years or those not published in English will be excluded. Two authors (UGA and MB) will screen the titles and abstracts independently and perform a full-text screen of the relevant and eligible papers. Relevant data will be extracted and synthesized, and a third author or group will be consulted to resolve discrepancies.

RESULTS

This scoping review will span from January 2010 to September 2023, and the results will be published in a peer-reviewed, open-access journal as a scoping review in 2024. The presentation of the findings will use the PRISMA-ScR flow diagram and descriptive and narrative formats, including tables and graphical displays, to provide a comprehensive overview of the extracted data.

CONCLUSIONS

This review aims to collect and analyze the available evidence on metabolic changes in patients with HF, aiming to enhance our current understanding of this topic. Additionally, this review will identify the most commonly used and suitable sample, analytical method, and specific metabolomes to facilitate standardization, reproducibility of results, and application in the diagnosis, treatment, and risk stratification of patients with HF. Finally, it is hoped that this review's outcomes will inspire further research into the metabolomes of patients with HF in low- and middle-income countries.

TRIAL REGISTRATION

Open Science Framework; https://osf.io/sp6xj.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/53905.

Metabolomics and Cardiovascular Risk in Patients with Heart Failure: A Systematic Review and Meta-Analysis

The associations of plasma metabolites with adverse cardiovascular (CV) outcomes are still underexplored and may be useful in CV risk stratification. We performed a systematic review and meta-analysis to establish correlations between blood metabolites and adverse CV outcomes in patients with heart failure (HF). Four cohorts were included, involving 83 metabolites and 37 metabolite ratios, measured in 1158 HF patients. Hazard ratios (HR) of 42 metabolites and 3 metabolite ratios, present in at least two studies, were combined through meta-analysis. Higher levels of histidine (HR 0.74, 95% CI [0.64; 0.86]) and tryptophan (HR 0.82 [0.71; 0.96]) seemed protective, whereas higher levels of symmetric dimethylarginine (SDMA) (HR 1.58 [1.30; 1.93]), N-methyl-1-histidine (HR 1.56 [1.27; 1.90]), SDMA/arginine (HR 1.38 [1.14; 1.68]), putrescine (HR 1.31 [1.06; 1.61]), methionine sulfoxide (HR 1.26 [1.03; 1.52]), and 5-hydroxylysine (HR 1.25 [1.05; 1.48]) were associated with a higher risk of CV events. Our findings corroborate important associations between metabolic imbalances and a higher risk of CV events in HF patients. However, the lack of standardization and data reporting hampered the comparison of a higher number of studies. In a future clinical scenario, metabolomics will greatly benefit from harmonizing sample handling, data analysis, reporting, and sharing.

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.

Network-based identification of diagnosis-specific trans-omic biomarkers via integration of multiple omics data

The integration of multiple omics data promises to reveal new insights into the pathogenic mechanisms of complex human diseases, with the potential to identify avenues for the development of targeted therapies for disease subtypes. However, the extraction of diagnostic/disease-specific biomarkers from multiple omics data with biological pathway knowledge is a challenging issue in precision medicine. In this paper, we present a novel computational method to identify diagnosis-specific trans-omic biomarkers from multiple omics data. In the algorithm, we integrated multi-class sparse canonical correlation analysis (MSCCA) and molecular pathway analysis in order to derive discriminative molecular features that are correlated across different omics layers. We applied our proposed method to analyzing proteome and metabolome data of heart failure (HF), and extracted trans-omic biomarkers for HF subtypes; specifically, ischemic cardiomyopathy (ICM) and dilated cardiomyopathy (DCM). We were able to detect not only individual proteins that were previously reported from single-omics studies but also correlated protein-metabolite pairs characteristic of HF disease subtypes. For example, we identified hexokinase1(HK1)-d-fructose-6-phosphate as a paired trans-omic biomarker for DCM, which could significantly perturb amino-sugar metabolism. Our proposed method is expected to be useful for various applications in precision medicine.

Multiomics Analysis Provides Novel Pathways Related to Progression of Heart Failure

BACKGROUND

Despite major advances in pharmacological treatment for patients with heart failure, residual mortality remains high. This suggests that important pathways are not yet targeted by current heart failure therapies.

OBJECTIVES

We sought integration of genetic, transcriptomic, and proteomic data in a large cohort of patients with heart failure to detect major pathways related to progression of heart failure leading to death.

METHODS

We used machine learning methodology based on stacked generalization framework and gradient boosting algorithms, using 54 clinical phenotypes, 403 circulating plasma proteins, 36,046 transcript expression levels in whole blood, and 6 million genomic markers to model all-cause mortality in 2,516 patients with heart failure from the BIOSTAT-CHF (Systems BIOlogy Study to TAilored Treatment in Chronic Heart Failure) study. Results were validated in an independent cohort of 1,738 patients.

RESULTS

The mean age of the patients was 70 years (Q1-Q3: 61-78 years), 27% were female, median N-terminal pro-B-type natriuretic peptide was 4,275 ng/L (Q1-Q3: 2,360-8,486 ng/L), and 7% had heart failure with preserved ejection fraction. During a median follow-up of 21 months, 657 (26%) of patients died. The 4 major pathways with a significant association to all-cause mortality were: 1) the PI3K/Akt pathway; 2) the MAPK pathway; 3) the Ras signaling pathway; and 4) epidermal growth factor receptor tyrosine kinase inhibitor resistance. Results were validated in an independent cohort of 1,738 patients.

CONCLUSIONS

A systems biology approach integrating genomic, transcriptomic, and proteomic data identified 4 major pathways related to mortality. These pathways are related to decreased activation of the cardioprotective ERBB2 receptor, which can be modified by neuregulin.

Circulating Cardiovascular Biomarkers in Cancer Therapeutics-Related Cardiotoxicity: Review of Critical Challenges, Solutions, and Future Directions

Cardiotoxicity is a growing concern in the oncology population. Transthoracic echocardiography and multigated acquisition scans have been used for surveillance but are relatively insensitive and resource intensive. Innovative imaging techniques are constrained by cost and availability. More sensitive, cost-effective cardiotoxicity surveillance strategies are needed. Circulating cardiovascular biomarkers could provide a sensitive, low-cost solution. Biomarkers such as troponins, natriuretic peptides (NPs), novel upstream signals of oxidative stress, inflammation, and fibrosis as well as panomic technologies have shown substantial promise, and guidelines recommend baseline measurement of troponins and NPs in all patients receiving potential cardiotoxins. Nonetheless, supporting evidence has been hampered by several limitations. Previous reviews have provided valuable perspectives on biomarkers in cancer populations, but important analytic aspects remain to be examined in depth. This review provides comprehensive assessment of critical challenges and solutions in this field, with focus on analytical issues relating to biomarker measurement and interpretation. Examination of evidence pertaining to common and serious forms of cardiotoxicity reveals that improved study designs incorporating larger, more diverse populations, registry-based approaches, and refinement of current definitions are key. Further efforts to harmonize biomarker methodologies including centralized biobanking and analyses, novel decision limits, and head-to-head comparisons are needed. Multimarker algorithms incorporating machine learning may allow rapid, personalized risk assessment. These improvements will not only augment the predictive value of circulating biomarkers in cardiotoxicity but may elucidate both direct and indirect relationships between cardiovascular disease and cancer, allowing biomarkers a greater role in the development and success of novel anticancer therapies.

Unsupervised clustering to differentiate rheumatoid arthritis patients based on proteomic signatures

OBJECTIVE

Patients with rheumatoid arthritis (RA) have different presentations and prognoses. Cluster analysis based on proteomic signatures creates independent phenogroups of patients with different pathophysiological backgrounds. We aimed to identify distinct pathophysiological clusters of RA patients based on circulating proteomic biomarkers.

METHOD

This was a cohort study including 399 RA patients. Clustering was performed on 94 circulating proteins (92 CVDII Olink®, high-sensitivity troponin T, and C-reactive protein). Unsupervised clustering was performed using a partitioning cluster algorithm.

RESULTS

The clustering algorithm identified two distinct clusters: cluster 1 (n = 223) and cluster 2 (n = 176). Compared with cluster 1, cluster 2 included older patients with a higher burden of comorbidities (cardiovascular and RA related), more erosive and longer RA duration, more dyspnoea and fatigue, walking a shorter distance in the Six-Minute Walk Test, with more severe diastolic dysfunction, and a 4.5-fold higher risk of death or hospitalization for cardiovascular reasons. Tumour necrosis factor (TNF) receptor superfamily-related pathways were mainly responsible for the model's discriminative ability.

CONCLUSION

Using unsupervised cluster analysis based on proteomic phenotypes, we identified two clusters of RA patients with distinct biomarkers profiles, clinical characteristics, and different outcomes that could reflect different pathophysiological backgrounds. TNF receptor superfamily-related proteins may be used to distinguish subgroups.

Cardiac proteomic profiling suggests that hypertrophic and dilated cardiomyopathy share a common pathogenetic pathway of the calcium signalling pathway

OBJECTIVE

Hypertrophic cardiomyopathy (HCM) and dilated cardiomyopathy (DCM) are classified as different diseases but have many similar pathogenic genes and clinical symptoms. Previous research has focused on mutated genes. This study was conducted to identify key molecular mechanisms and explore effective therapeutic targets.

METHODS

Myocardial tissue was harvested from patients with HCM (n = 3) or DCM (n = 4) during surgery. Hearts donated by healthy traffic accident victims were treated as controls (n = 4). Total proteins were extracted for liquid chromatography-tandem mass spectrometry. Differentially expressed proteins (DEPs) were annotated via GO and KEGG analyses. Selected distinguishing protein abundance was confirmed by western blotting.

RESULTS

Compared with the control group, there were 121 and 76 DEPs in the HCM and DCM groups, respectively. GO terms for these two comparisons are associated with contraction-related components and actin binding. Additionally, the most significantly upregulated and downregulated proteins were periostin and tropomyosin alpha-3 chain in both comparisons. Moreover, when comparing the HCM and DCM groups, we found 60 significant DEPs, and the GO and KEGG terms are related to the calcium signalling pathway. Expression of the calcium regulation-related protein peptidyl-prolyl cis-trans isomerase (FKBP1A) was significantly upregulated in multiple samples.

CONCLUSION

HCM and DCM have many mutual pathogenetic pathways. Calcium ion-related processes are among the most significant factors affecting disease development. For HCM and DCM, research on regulating linchpin protein expression or interfering with key calcium-related pathways may be more beneficial than genetic research.

Microbiome-based risk prediction in incident heart failure: a community challenge

Heart failure (HF) is a major public health problem. Early identification of at-risk individuals could allow for interventions that reduce morbidity or mortality. The community-based FINRISK Microbiome DREAM challenge (synapse.org/finrisk) evaluated the use of machine learning approaches on shotgun metagenomics data obtained from fecal samples to predict incident HF risk over 15 years in a population cohort of 7231 Finnish adults (FINRISK 2002, n=559 incident HF cases). Challenge participants used synthetic data for model training and testing. Final models submitted by seven teams were evaluated in the real data. The two highest-scoring models were both based on Cox regression but used different feature selection approaches. We aggregated their predictions to create an ensemble model. Additionally, we refined the models after the DREAM challenge by eliminating phylum information. Models were also evaluated at intermediate timepoints and they predicted 10-year incident HF more accurately than models for 5- or 15-year incidence. We found that bacterial species, especially those linked to inflammation, are predictive of incident HF. This highlights the role of the gut microbiome as a potential driver of inflammation in HF pathophysiology. Our results provide insights into potential modeling strategies of microbiome data in prospective cohort studies. Overall, this study provides evidence that incorporating microbiome information into incident risk models can provide important biological insights into the pathogenesis of HF.

An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture

This review discusses the transformative potential of integrating multi-omics data and artificial intelligence (AI) in advancing horticultural research, specifically plant phenotyping. The traditional methods of plant phenotyping, while valuable, are limited in their ability to capture the complexity of plant biology. The advent of (meta-)genomics, (meta-)transcriptomics, proteomics, and metabolomics has provided an opportunity for a more comprehensive analysis. AI and machine learning (ML) techniques can effectively handle the complexity and volume of multi-omics data, providing meaningful interpretations and predictions. Reflecting the multidisciplinary nature of this area of research, in this review, readers will find a collection of state-of-the-art solutions that are key to the integration of multi-omics data and AI for phenotyping experiments in horticulture, including experimental design considerations with several technical and non-technical challenges, which are discussed along with potential solutions. The future prospects of this integration include precision horticulture, predictive breeding, improved disease and stress response management, sustainable crop management, and exploration of plant biodiversity. The integration of multi-omics and AI holds immense promise for revolutionizing horticultural research and applications, heralding a new era in plant phenotyping.

Phenotyping heart failure by cardiac magnetic resonance imaging of cardiac macro- and microscopic structure: state of the art review

Heart failure demographics have evolved in past decades with the development of improved diagnostics, therapies, and prevention. Cardiac magnetic resonance (CMR) has developed in a similar timeframe to become the gold-standard non-invasive imaging modality for characterizing diseases causing heart failure. CMR techniques to assess cardiac morphology and function have progressed since their first use in the 1980s. Increasingly efficient acquisition protocols generate high spatial and temporal resolution images in less time. This has enabled new methods of characterizing cardiac systolic and diastolic function such as strain analysis, exercise real-time cine imaging and four-dimensional flow. A key strength of CMR is its ability to non-invasively interrogate the myocardial tissue composition. Gadolinium contrast agents revolutionized non-invasive cardiac imaging with the late gadolinium enhancement technique. Further advances enabled quantitative parametric mapping to increase sensitivity at detecting diffuse pathology. Novel methods such as diffusion tensor imaging and artificial intelligence-enhanced image generation are on the horizon. Magnetic resonance spectroscopy (MRS) provides a window into the molecular environment of the myocardium. Phosphorus (31P) spectroscopy can inform the status of cardiac energetics in health and disease. Proton (1H) spectroscopy complements this by measuring creatine and intramyocardial lipids. Hyperpolarized carbon (13C) spectroscopy is a novel method that could further our understanding of dynamic cardiac metabolism. CMR of other organs such as the lungs may add further depth into phenotypes of heart failure. The vast capabilities of CMR should be deployed and interpreted in context of current heart failure challenges.

Multimodality Imaging in Sarcomeric Hypertrophic Cardiomyopathy: Get It Right…on Time

Hypertrophic cardiomyopathy (HCM) follows highly variable paradigms and disease-specific patterns of progression towards heart failure, arrhythmias and sudden cardiac death. Therefore, a generalized standard approach, shared with other cardiomyopathies, can be misleading in this setting. A multimodality imaging approach facilitates differential diagnosis of phenocopies and improves clinical and therapeutic management of the disease. However, only a profound knowledge of the progression patterns, including clinical features and imaging data, enables an appropriate use of all these resources in clinical practice. Combinations of various imaging tools and novel techniques of artificial intelligence have a potentially relevant role in diagnosis, clinical management and definition of prognosis. Nonetheless, several barriers persist such as unclear appropriate timing of imaging or universal standardization of measures and normal reference limits. This review provides an overview of the current knowledge on multimodality imaging and potentialities of novel tools, including artificial intelligence, in the management of patients with sarcomeric HCM, highlighting the importance of specific "red alerts" to understand the phenotype-genotype linkage.

Mass Spectrometry Imaging-Based Single-Cell Lipidomics Profiles Metabolic Signatures of Heart Failure

Heart failure (HF), leading as one of the main causes of mortality, has become a serious public health issue with high prevalence around the world. Single cardiomyocyte (CM) metabolomics promises to revolutionize the understanding of HF pathogenesis since the metabolic remodeling in the human hearts plays a vital role in the disease progression. Unfortunately, current metabolic analysis is often limited by the dynamic features of metabolites and the critical needs for high-quality isolated CMs. Here, high-quality CMs were directly isolated from transgenic HF mice biopsies and further employed in the cellular metabolic analysis. The lipids landscape in individual CMs was profiled with a delayed extraction mode in time-of-flight secondary ion mass spectrometry. Specific metabolic signatures were identified to distinguish HF CMs from the control subjects, presenting as possible single-cell biomarkers. The spatial distributions of these signatures were imaged in single cells, and those were further found to be strongly associated with lipoprotein metabolism, transmembrane transport, and signal transduction. Taken together, we systematically studied the lipid metabolism of single CMs with a mass spectrometry imaging method, which directly benefited the identification of HF-associated signatures and a deeper understanding of HF-related metabolic pathways.

Characteristics and clinical outcomes of patients with acute heart failure with a supranormal left ventricular ejection fraction

AIM

Recent data suggest that guideline-directed medical therapy of patients with heart failure (HF) with reduced ejection fraction (HFrEF) might improve clinical outcomes in patients with HF up to a left ventricular ejection fraction (LVEF) of 55-65%, whereas patients with higher LVEF do not seem to benefit. Recent data have shown that LVEF may have a U-shaped relation with outcome, with poorer outcome also in patients with supranormal values. This suggests that patients with supranormal LVEF may be a distinctive group of patients.

METHODS AND RESULTS

RELAX-AHF-2 was a multicentre, placebo-controlled trial on the effects of serelaxin on 180-day cardiovascular (CV) mortality and worsening HF at day 5 in patients with acute HF. Echocardiograms were performed at hospital admission in 6128 patients: 155 (2.5%) patients were classified as HF with supranormal ejection fraction (HFsnEF; LVEF >65%), 1440 (23.5%) as HF with preserved ejection fraction (HFpEF; LVEF 50-65%), 1353 (22.1%) as HF with mildly reduced ejection fraction (HFmrEF; LVEF 41-49%) and 3180 (51.9%) as HFrEF (LVEF <40%). Patients with HFsnEF compared to HFpEF were more often women, had higher prevalence of non-ischaemic HF, had lower levels of natriuretic peptides, were less likely to be treated with beta-blockers and had higher blood urea nitrogen plasma levels. All-cause mortality was not statistically different between groups, although patients with HFsnEF had the highest numerical rate. A declining trend was seen in the proportion of 180-day deaths due to CV causes from HFrEF (290/359, 80.8%) to HFsnEF (14/24, 58.3%). The reverse was observed with death from non-CV causes. No treatment effect of serelaxin was observed in any of the subgroups.

CONCLUSIONS

In this study, only 2.5% of patients were classified as HFsnEF. HFsnEF was primarily characterized by female sex, lower natriuretic peptides and a higher risk of non-CV death.

Elevated circulating level of β-aminoisobutyric acid (BAIBA) in heart failure patients with type 2 diabetes receiving sodium-glucose cotransporter 2 inhibitors

AIMS

The mechanism by which a sodium-glucose cotransporter inhibitor (SGLT2i) induces favorable effects on diabetes and cardiovascular diseases including heart failure (HF) remains poorly understood. Metabolomics including amino acid profiling enables detection of alterations in whole body metabolism. The aim of this study was to determine whether plasma amino acid profiles are modulated by SGLT2i use in HF patients with type 2 diabetes mellitus (T2DM).

METHODS

We retrospectively examined 81 HF patients with T2DM (68 ± 11 years old; 78% male). Plasma amino acid concentrations in a fasting state after stabilization of HF were determined using ultraperformance liquid chromatography. To minimize potential selection bias in the retrospective analyses, the differences in baseline characteristics between patients receiving an SGLT2i and patients not receiving an SGLT2i were controlled by using an inverse probability of treatment weighting (IPTW)-adjusted analysis.

RESULTS

Of amino acids measurable in the present assay, plasma β-aminoisobutyric acid (BAIBA), an exercise-induced myokine-like molecule also known as 3-aminoisobutyric acid or 3-amino-2-methyproponic acid, was detected in 77% of all patients and the proportion of patients in whom plasma BAIBA was detected was significantly higher in patients receiving an SGLT2i than in patients not receiving an SGLT2i (93% vs. 67%, p = 0.01). Analyses in patients in whom plasma BAIBA was detected showed that plasma BAIBA concentration was significantly higher in patients receiving an SGLT2i than in patients not receiving an SGLT2i (6.76 ± 4.72 vs. 4.56 ± 2.93 nmol/ml, p = 0.03). In multivariate logistic regression analyses that were adjusted for age and sex, SGLT2i use was independently associated with BAIBA detection. The independent association between BAIBA and SGLT2i use remained after inclusion of body mass index, HF with reduced ejection fraction, ischemic etiology, renal function, NT-proBNP, albumin, hemoglobin, and HbA1c into the Cox proportional hazards model. When the differences in baseline characteristics between patients receiving an SGLT2i and patients not receiving an SGLT2i were controlled by using an IPTW-adjusted analysis, least squares mean of plasma BAIBA concentration was significantly higher in patients receiving an SGLT2i than in patients not receiving an SGLT2i.

CONCLUSION

SGLT2i use is closely associated with increased circulating BAIBA concentration in HF patients with T2DM.

Insulin-like growth factor-binding protein-7 (IGFBP7) links senescence to heart failure

Heart failure (HF) is a rising global cardiovascular epidemic driven by aging and chronic inflammation. As elderly populations continue to increase, precision treatments for age-related cardiac decline are urgently needed. Here we report that cardiac and blood expression of IGFBP7 is robustly increased in patients with chronic HF and in an HF mouse model. In a pressure overload mouse HF model, Igfbp7 deficiency attenuated cardiac dysfunction by reducing cardiac inflammatory injury, tissue fibrosis and cellular senescence. IGFBP7 promoted cardiac senescence by stimulating IGF-1R/IRS/AKT-dependent suppression of FOXO3a, preventing DNA repair and reactive oxygen species (ROS) detoxification, thereby accelerating the progression of HF. In vivo, AAV9-shRNA-mediated cardiac myocyte Igfbp7 knockdown indicated that myocardial IGFBP7 directly regulates pathological cardiac remodeling. Moreover, antibody-mediated IGFBP7 neutralization in vivo reversed IGFBP7-induced suppression of FOXO3a, restored DNA repair and ROS detoxification signals and attenuated pressure-overload-induced HF in mice. Consequently, selectively targeting IGFBP7-regulated senescence pathways may have broad therapeutic potential for HF.

Biomarkers in heart failure clinical trials. A review from the Biomarkers Working Group of the Heart Failure Association of the European Society of Cardiology

The approval of new heart failure (HF) therapies has slowed over the past two decades in part due to the high costs of conducting large randomized clinical trials that are needed to adequately power major clinical endpoint studies. Several biomarkers have been identified reflecting different elements of HF pathophysiology, with possible applications in diagnosis, risk stratification, treatment monitoring, and even in the design of clinical trials. Biomarkers could potentially be used to refine study inclusion criteria to enable enrolment of patients who are more likely to respond to a therapeutic intervention, despite being at sufficient risk to meet pre-determined study endpoint rates. When there is a close relationship between biomarker levels and clinical endpoints, changes in biomarker levels after a given treatment can act as a surrogate endpoint, potentially reducing the duration and cost of a clinical trial. Natriuretic peptides have been widely used in clinical trials with a variable amount of added value, which such variation being probably due to the absence of a close pathophysiological connection to the study drug. Notable exceptions to this include sacubitril/valsartan and vericiguat. Future studies should seek to adopt unbiased approaches for discovery of true companion diagnostics; with -omics-based tools, biomarkers might be more precisely selected for use in clinical trials to identify responses that closely reflect the biological effects of the drug under investigation. Finally, biomarkers associated with cardiac damage and remodelling, such as cardiac troponin, could be employed as safety endpoints provided that standardization between different assays is achieved.

Alerting Clinicians to 1-Year Mortality Risk in Patients Hospitalized With Heart Failure: The REVEAL-HF Randomized Clinical Trial

Importance

Heart failure is a major cause of morbidity and mortality worldwide. The use of risk scores has the potential to improve targeted use of interventions by clinicians that improve patient outcomes, but this hypothesis has not been tested in a randomized trial.

Objective

To evaluate whether prognostic information in heart failure translates into improved decisions about initiation and intensity of treatment, more appropriate end-of-life care, and a subsequent reduction in rates of hospitalization or death.

Design, Setting, and Participants

This was a pragmatic, multicenter, electronic health record-based, randomized clinical trial across the Yale New Haven Health System, comprising small community hospitals and large tertiary care centers. Patients hospitalized for heart failure who had N-terminal pro-brain natriuretic peptide (NT-proBNP) levels of greater than 500 pg/mL and received intravenous diuretics within 24 hours of admission were automatically randomly assigned to the alert (intervention) or usual-care groups.

Interventions

The alert group had their risk of 1-year mortality calculated using an algorithm that was derived and validated using similar historic patients in the electronic health record. This estimate, including a categorical risk assessment, was presented to clinicians while they were interacting with a patient's electronic health record.

Main Outcomes and Measures

The primary outcome was a composite of 30-day hospital readmissions and all-cause mortality at 1 year.

Results

Between November 27, 2019, through March 7, 2021, 3124 patients were randomly assigned to the alert (1590 [50.9%]) or usual-care (1534 [49.1%]) group. The alert group had a median (IQR) age of 76.5 (65-86) years, and 796 were female patients (50.1%). Patients from the following race and ethnicity groups were included: 13 Asian (0.8%), 324 Black (20.4%), 136 Hispanic (8.6%), 1448 non-Hispanic (91.1%), 1126 White (70.8%), 6 other ethnicity (0.4%), and 127 other race (8.0%). The usual-care group had a median (IQR) age of 77 (65-86) years, and 788 were female patients (51.4%). Patients from the following race and ethnicity groups were included: 11 Asian (1.4%), 298 Black (19.4%), 162 Hispanic (10.6%), 1359 non-Hispanic (88.6%), 1077 White (70.2%), 13 other ethnicity (0.9%), and 137 other race (8.9%). Median (IQR) NT-proBNP levels were 3826 (1692-8241) pg/mL in the alert group and 3867 (1663-8917) pg/mL in the usual-care group. A total of 284 patients (17.9%) and 270 patients (17.6%) were admitted to the intensive care unit in the alert and usual-care groups, respectively. A total of 367 patients (23.1%) and 359 patients (23.4%) had a left ventricular ejection fraction of 40% or less in the alert and usual-care groups, respectively. The model achieved an area under the curve of 0.74 in the trial population. The primary outcome occurred in 619 patients (38.9%) in the alert group and 603 patients (39.3%) in the usual-care group (P = .89). There were no significant differences between study groups in the prescription of heart failure medications at discharge, the placement of an implantable cardioverter-defibrillator, or referral to palliative care.

Conclusions and Relevance

Provision of 1-year mortality estimates during heart failure hospitalization did not affect hospitalization or mortality, nor did it affect clinical decision-making.

Trial Registration

ClinicalTrials.gov Identifier NCT03845660.

Biomarkers of HFpEF: Natriuretic Peptides, High-Sensitivity Troponins and Beyond

Heart failure (HF) is a significant cause of morbidity and mortality worldwide. HF with preserved ejection fraction (HFpEF) is a complex syndrome, often participated by several cardiac and extracardiac conditions, including chronic kidney disease, pulmonary disease, anaemia and advanced age. Circulating biomarkers reflecting pathophysiological pathways involved in HFpEF development and progression may assist clinicians in early diagnosis and management of this condition. Natriuretic peptides (NPs) are cardioprotective hormones released by cardiomyocytes in response to pressure or volume overload and in response to activation of neuro-endocrine-immune system. The relevance of B-type NP (BNP) and N-terminal pro-B-type NP (NT-proBNP) for diagnosis and risk stratification has been extensively demonstrated, and these biomarkers are emerging tools for population screening and as guides to the start of treatment in subclinical HF. On the contrary, conflicting evidence exists on the value of NPs to guide HF therapy. Among the other biomarkers, high-sensitivity troponins and soluble suppression of tumorigenesis-2 are the most promising biomarkers for risk stratification, predicting outcome independently from NPs. In this review, some novel biomarkers are being tested in such clinical scenario, more tightly linked to specific pathophysiological processes of cardiac damage.

Cardiac remodelling - Part 1: From cells and tissues to circulating biomarkers. A review from the Study Group on Biomarkers of the Heart Failure Association of the European Society of Cardiology

Cardiac remodelling refers to changes in left ventricular structure and function over time, with a progressive deterioration that may lead to heart failure (HF) development (adverse remodelling) or vice versa a recovery (reverse remodelling) in response to HF treatment. Adverse remodelling predicts a worse outcome, whilst reverse remodelling predicts a better prognosis. The geometry, systolic and diastolic function and electric activity of the left ventricle are affected, as well as the left atrium and on the long term even right heart chambers. At a cellular and molecular level, remodelling involves all components of cardiac tissue: cardiomyocytes, fibroblasts, endothelial cells and leucocytes. The molecular, cellular and histological signatures of remodelling may differ according to the cause and severity of cardiac damage, and clearly to the global trend toward worsening or recovery. These processes cannot be routinely evaluated through endomyocardial biopsies, but may be reflected by circulating levels of several biomarkers. Different classes of biomarkers (e.g. proteins, non-coding RNAs, metabolites and/or epigenetic modifications) and several biomarkers of each class might inform on some aspects on HF development, progression and long-term outcomes, but most have failed to enter clinical practice. This may be due to the biological complexity of remodelling, so that no single biomarker could provide great insight on remodelling when assessed alone. Another possible reason is a still incomplete understanding of the role of biomarkers in the pathophysiology of cardiac remodelling. Such role will be investigated in the first part of this review paper on biomarkers of cardiac remodelling.

The Cardiomyocyte in Heart Failure with Preserved Ejection Fraction-Victim of Its Environment?

Heart failure (HF) with preserved left ventricular ejection fraction (HFpEF) is becoming the predominant form of HF. However, medical therapy that improves cardiovascular outcome in HF patients with almost normal and normal systolic left ventricular function, but diastolic dysfunction is missing. The cause of this unmet need is incomplete understanding of HFpEF pathophysiology, the heterogeneity of the patient population, and poor matching of therapeutic mechanisms and primary pathophysiological processes. Recently, animal models improved understanding of the pathophysiological role of highly prevalent and often concomitantly presenting comorbidity in HFpEF patients. Evidence from these animal models provide first insight into cellular pathophysiology not considered so far in HFpEF disease, promising that improved understanding may provide new therapeutical targets. This review merges observation from animal models and human HFpEF disease with the intention to converge cardiomyocytes pathophysiological aspects and clinical knowledge.

Heart Failure Incidence Following ST-Elevation Myocardial Infarction

ST-elevation myocardial infarction (STEMI) survivors have a heightened risk of developing heart failure (HF). The magnitude of this risk with the advent of primary percutaneous coronary intervention is less characterized. We aimed to examine the incidence and predictors of incident HF and all-cause mortality in a contemporary STEMI cohort. We performed a retrospective analysis of 700 consecutive patients with STEMI treated with primary percutaneous coronary intervention at a tertiary hospital. The primary outcome was the occurrence of HF during follow-up. HF was defined by HF hospitalization or the presence of congestion that led to de novo prescription or up-titration of diuretics in the outpatient clinic. The secondary outcome was defined by the occurrence of HF or all-cause mortality. During a median follow-up period of 43.6 months, HF events occurred in 110 patients (15.7%), 34 (4.8%) managed as outpatient and 76 (10.9%) requiring hospitalization. Left ventricular ejection fraction (LVEF) <50% was present in 76% of those who developed HF. Age (hazard ratio [HR] 1.03, 95% confidence interval [CI] 1.01 to 1.06), diabetes (HR 1.85, 95% CI 1.12 to 3.05), door-to-balloon time (HR 1.002, 95% CI 1.000 to 1.003), Killip-Kimball class ≥II (HR 2.24, 95% CI 1.32 to 3.80) and LVEF <50% (HR 1.71, 95% CI 1.01 to 2.92) were independent predictors. All-cause mortality incidence was 8.7%. HF was independently associated with a threefold increased risk of dying (HR 3.52, 95% CI 1.85 to 6.69, p <0.001). In conclusion, a substantial proportion of contemporary patients with STEMI develop HF, which triplicates the risk of dying. Older age, diabetes and LVEF <50% independently predicted the development of HF and all-cause death.

Integrative omics of schizophrenia: from genetic determinants to clinical classification and risk prediction

Schizophrenia (SCZ) is a debilitating neuropsychiatric disorder with high heritability and complex inheritance. In the past decade, successful identification of numerous susceptibility loci has provided useful insights into the molecular etiology of SCZ. However, applications of these findings to clinical classification and diagnosis, risk prediction, or intervention for SCZ have been limited, and elucidating the underlying genomic and molecular mechanisms of SCZ is still challenging. More recently, multiple Omics technologies - genomics, transcriptomics, epigenomics, proteomics, metabolomics, connectomics, and gut microbiomics - have all been applied to examine different aspects of SCZ pathogenesis. Integration of multi-Omics data has thus emerged as an approach to provide a more comprehensive view of biological complexity, which is vital to enable translation into assessments and interventions of clinical benefit to individuals with SCZ. In this review, we provide a broad survey of the single-omics studies of SCZ, summarize the advantages and challenges of different Omics technologies, and then focus on studies in which multiple omics data are integrated to unravel the complex pathophysiology of SCZ. We believe that integration of multi-Omics technologies would provide a roadmap to create a more comprehensive picture of interactions involved in the complex pathogenesis of SCZ, constitute a rich resource for elucidating the potential molecular mechanisms of the illness, and eventually improve clinical assessments and interventions of SCZ to address clinical translational questions from bench to bedside.

Interplay Between Gut Microbiota and Amino Acid Metabolism in Heart Failure

Heart failure (HF) is a complex clinical syndrome of which the incidence is on the rise worldwide. Cardiometabolic disorders are associated with the deterioration of cardiac function and progression of HF. Recently, there has been renewed interest in gut microbiota (GM) and its metabolites in the cardiovascular disease. HF-caused hypoperfusion could increase intestinal permeability, and a “leaky” bowel leads to bacterial translocation and make its metabolites more easily enter the circulation. Considerable evidence shows that the composition of microbiota and amino acids (AAs) has been altered in HF patients, and AAs could serve as a diagnostic and prognostic biomarker in HF. The findings indicate that the gut–amino acid–HF axis may play a key role in the progression of HF. In this paper, we focus on the interrelationship between the AA metabolism and GM alterations during the development of heart failure. We also discuss the potential prognostic and therapeutic value of the gut–amino acid–HF axis in the cortex of HF.

Blood Differential Gene Expression in Patients with Chronic Heart Failure and Systemic Iron Deficiency: Pathways Involved in Pathophysiology and Impact on Clinical Outcomes

Background: Iron deficiency is a common disorder in patients with heart failure and is related with adverse outcomes and poor quality of life. Previous experimental studies have shown biological connections between iron homeostasis, mitochondrial metabolism, and myocardial function. However, the mechanisms involved in this crosstalk are yet to be unfolded. Methods: The present research attempts to investigate the intrinsic biological mechanisms between heart failure and iron deficiency and to identify potential prognostic biomarkers by determining the gene expression pattern in the blood of heart failure patients, using whole transcriptome and targeted TaqMan^® low-density array analyses. Results: We performed a stepwise cross-sectional longitudinal study in a cohort of chronic heart failure patients with and without systemic iron deficiency. First, the full transcriptome was performed in a nested case-control exploratory cohort of 7 paired patients and underscored 1128 differentially expressed transcripts according to iron status (cohort1#). Later, we analyzed the messenger RNA levels of 22 genes selected by their statistical significance and pathophysiological relevance, in a validation cohort of 71 patients (cohort 2#). Patients with systemic iron deficiency presented lower mRNA levels of mitochondrial ferritin, sirtuin-7, small integral membrane protein 20, adrenomedullin and endothelin converting enzyme-1. An intermediate mitochondrial ferritin gene expression and an intermediate or low sirtuin7 and small integral membrane protein 20 mRNA levels were associated with an increased risk of all-cause mortality and heart failure admission ((HR 2.40, 95% CI 1.04–5.50, p-value = 0.039), (HR 5.49, 95% CI 1.78–16.92, p-value = 0.003), (HR 9.51, 95% CI 2.69–33.53, p-value < 0.001), respectively). Conclusions: Patients with chronic heart failure present different patterns of blood gene expression depending on systemic iron status that affect pivotal genes involved in iron regulation, mitochondrial metabolism, endothelial function and cardiovascular physiology, and correlate with adverse clinical outcomes.

Cardiovascular Aging and Physical Activity: Insights From Metabolomics

The purpose of this review is to explore how metabolomics can help uncover mechanisms through which physical activity may influence the progression of cardiovascular aging. Cardiovascular aging is a process of functional and structural changes in older adults which can progress to cardiovascular disease. Metabolomics profiling is an investigative tool that can track the diverse changes which occur in human biochemistry with physical activity and aging. This mini review will summarize published investigations in metabolomics and physical activity, with a specific focus on the metabolic pathways that connect physical activity with cardiovascular aging.

Effects of canagliflozin on NT-proBNP stratified by left ventricular diastolic function in patients with type 2 diabetes and chronic heart failure: a sub analysis of the CANDLE trial

BACKGROUND

Identification of the effective subtypes of treatment for heart failure (HF) is an essential topic for optimizing treatment of the disorder. We hypothesized that the beneficial effect of SGLT2 inhibitors (SGLT2i) on the levels of N-terminal pro-brain natriuretic peptide (NT-proBNP) might depend on baseline diastolic function. To elucidate the effects of SGLT2i in type 2 diabetes mellitus (T2DM) and chronic HF we investigated, as a post-hoc sub-study of the CANDLE trial, the effects of canagliflozin on NT-proBNP levels from baseline to 24 weeks, with the data stratified by left ventricular (LV) diastolic function at baseline.

METHODS

Patients (n = 233) in the CANDLE trial were assigned randomly to either an add-on canagliflozin (n = 113) or glimepiride treatment groups (n = 120). The primary endpoint was a comparison between the two groups of the changes from baseline to 24 weeks in NT-pro BNP levels, stratified according to baseline ventricular diastolic function.

RESULTS

The change in the geometric mean of NT-proBNP level from baseline to 24 weeks was 0.98 (95% CI 0.89-1.08) in the canagliflozin group and 1.07 (95% CI 0.97-1.18) in the glimepiride group. The ratio of change with canagliflozin/glimepiride was 0.93 (95% CI 0.82-1.05). Responder analyses were used to investigate the response of an improvement in NT-proBNP levels. Although the subgroup analyses for septal annular velocity (SEP-e') showed no marked heterogeneity in treatment effect, the subgroup with an SEP-e' < 4.7 cm/s indicated there was an association with lower NT-proBNP levels in the canagliflozin group compared with that in the glimepiride group (ratio of change with canagliflozin/glimepiride (0.83, 95% CI 0.66-1.04).

CONCLUSIONS

In the subgroup with a lower LV diastolic function, canagliflozin showed a trend of reduced NT-pro BNP levels compared to that observed with glimepiride. This study suggests that the beneficial effects of canagliflozin treatment may be different in subgroups classified by the severity of LV diastolic dysfunction.

Circulating heart failure biomarkers beyond natriuretic peptides: review from the Biomarker Study Group of the Heart Failure Association (HFA), European Society of Cardiology (ESC)

New biomarkers are being evaluated for their ability to advance the management of patients with heart failure. Despite a large pool of interesting candidate biomarkers, besides natriuretic peptides virtually none have succeeded in being applied into the clinical setting. In this review, we examine the most promising emerging candidates for clinical assessment and management of patients with heart failure. We discuss high-sensitivity cardiac troponins (Tn), procalcitonin, novel kidney markers, soluble suppression of tumorigenicity 2 (sST2), galectin-3, growth differentiation factor-15 (GDF-15), cluster of differentiation 146 (CD146), neprilysin, adrenomedullin (ADM), and also discuss proteomics and genetic-based risk scores. We focused on guidance and assistance with daily clinical care decision-making. For each biomarker, analytical considerations are discussed, as well as performance regarding diagnosis and prognosis. Furthermore, we discuss potential implementation in clinical algorithms and in ongoing clinical trials.

Plasma amino acid profiling improves predictive accuracy of adverse events in patients with heart failure

Aims

The clinical outcome of heart failure (HF) is complicated by the presence of multiple comorbidities including malnutrition and cachexia, and prediction of the outcome is still difficult in each patient. Metabolomics including amino acid profiling enables detection of alterations in whole body metabolism. The aim of this study was to determine whether plasma amino acid profiling improves prediction of clinical outcomes in patients with HF.

Methods and results

We retrospectively examined 301 HF patients (70 ± 15 years old; 59% male). Blood samples for measurements of amino acid concentrations were collected in a fasting state after stabilization of HF. Plasma amino acid concentrations were measured using ultraperformance liquid chromatography. Clinical endpoint of this study was adverse event defined as all‐cause death and unscheduled readmission due to worsening HF or lethal arrhythmia. During a mean follow‐up period of 380 ± 214 days, 40 patients (13%) had adverse events. Results of analyses of variable importance in projection score, a measure of a variable's importance in partial least squares–discriminant analysis (PLS‐DA) showed that the top five amino acids being associated with adverse events were 3‐methylhistidine (3‐Me‐His), β‐alanine, valine, hydroxyproline, and tryptophan. Multivariate Cox‐proportional hazard analyses indicated that a high 3‐Me‐His concentration and low β‐alanine and valine concentrations were independently associated with adverse events. When HF patients were divided according to the cut‐off values of amino acids calculated from receiver operating characteristic curves, Kaplan–Meier survival curves showed that event‐free survival rates were lower in HF patients with high 3‐Me‐His than in HF patients with low 3‐Me‐His (68% vs. 91%, P < 0.01). In a subgroup with high 3‐Me‐His, HF patients with low β‐alanine and those with low valine had significantly lower event‐free survival rates than did HF patients with high β‐alanine and those with high valine, respectively. On the other hand, Kaplan–Meier curves of event‐free survival rates did not differ between HF patients with and those without low β‐alanine and low valine in subgroups of patients with low 3‐Me‐His. Inclusion of both high 3‐Me‐His and low β‐alanine or low valine into the adjustment model including N‐terminal pro‐brain natriuretic peptide improved the accuracy of prediction of adverse events after discharge. 3‐Me‐His concentration was associated with muscle mass and nutritional status.

Conclusions

Simple measurement of 3‐Me‐His with either β‐alanine or valine improved the predictive ability for adverse events, indicating the utility of plasma amino acid profiling in risk stratification of hospitalized HF patients.

Management of cardiac fibrosis is the largest unmet medical need in heart failure Cardiac fibrosis in heart failure

Cardiac fibrosis is a major driver associated with the growing burden of heart failure, especially in older people. However, integrating cardiac fibrosis in heart failure management is still an unmet medical need, which may be explained by its high tissue heterogeneity and clinical diversity, and, as a consequence, the very real limitations of its diagnosis and treatment. In this viewpoint article we summarize the challenges and requirements in the clinical management of cardiac fibrosis in heart failure patients.

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.

Multiomics, virtual reality and artificial intelligence in heart failure

Aim: Multiomics delivers more biological insight than targeted investigations. We applied multiomics to patients with heart failure (HF) and reduced ejection fraction (HFrEF), with machine learning applied to advanced ECG (AECG) and echocardiography artificial intelligence (Echo AI). Patients & methods: In total, 46 patients with HFrEF and 20 controls underwent metabolomic profiling, including liquid/gas chromatography-mass spectrometry and solid-phase microextraction volatilomics in plasma and urine. HFrEF was defined using left ventricular (LV) global longitudinal strain, EF and N-terminal pro hormone BNP. AECG and Echo AI were performed over 5 min, with a subset of patients undergoing a virtual reality mental stress test. Results: A-ECG had similar diagnostic accuracy as N-terminal pro hormone BNP for HFrEF (area under the curve = 0.95, 95% CI: 0.85-0.99), and correlated with global longitudinal strain (r = -0.77, p < 0.0001), while Echo AI-generated measurements correlated well with manually measured LV end diastolic volume r = 0.77, LV end systolic volume r = 0.8, LVEF r = 0.71, indexed left atrium volume r = 0.71 and indexed LV mass r = 0.6, p < 0.005. AI-LVEF and other HFrEF biomarkers had a similar discrimination for HFrEF (area under the curve AI-LVEF = 0.88; 95% CI: -0.03 to 0.15; p = 0.19). Virtual reality mental stress test elicited arrhythmic biomarkers on AECG and indicated blunted autonomic responsiveness (alpha 2 of RR interval variability, p = 1 × 10^-4) in HFrEF. Conclusion: Multiomics-related machine learning shows promise for the assessment of HF.

Design and rationale of the EMPA-VISION trial: investigating the metabolic effects of empagliflozin in patients with heart failure

Aims

Despite substantial improvements over the last three decades, heart failure (HF) remains associated with a poor prognosis. The sodium‐glucose co‐transporter‐2 inhibitor empagliflozin demonstrated significant reductions of HF hospitalization in patients with HF independent of the presence or absence of type 2 diabetes mellitus in the EMPEROR‐Reduced trial and cardiovascular mortality in the EMPA‐REG OUTCOME trial. To further elucidate the mechanisms behind these positive outcomes, this study aims to determine the effects of empagliflozin treatment on cardiac energy metabolism and physiology using magnetic resonance spectroscopy (MRS) and cardiovascular magnetic resonance (CMR).

Methods and results

The EMPA‐VISION trial is a double‐blind, randomized, placebo‐controlled, mechanistic study. A maximum of 86 patients with HF with reduced ejection fraction (n = 43, Cohort A) or preserved ejection fraction (n = 43, Cohort B), with or without type 2 diabetes mellitus, will be enrolled. Participants will be randomized 1:1 to receive either 10 mg of empagliflozin or placebo for 12 weeks. Eligible patients will undergo cardiovascular magnetic resonance, resting and dobutamine stress MRS, echocardiograms, cardiopulmonary exercise tests, serum metabolomics, and quality of life questionnaires at baseline and after 12 weeks. The primary endpoint will be the change in resting phosphocreatine‐to‐adenosine triphosphate ratio, as measured by ³¹Phosphorus‐MRS.

Conclusions

EMPA‐VISION is the first clinical trial assessing the effects of empagliflozin treatment on cardiac energy metabolism in human subjects in vivo. The results will shed light on the mechanistic action of empagliflozin in patients with HF and help to explain the results of the safety and efficacy outcome trials (EMPEROR‐Reduced and EMPEROR‐Preserved).

Biomarkers for the diagnosis and management of heart failure

Heart failure (HF) is a significant cause of morbidity and mortality worldwide. Circulating biomarkers reflecting pathophysiological pathways involved in HF development and progression may assist clinicians in early diagnosis and management of HF patients. Natriuretic peptides (NPs) are cardioprotective hormones released by cardiomyocytes in response to pressure or volume overload. The roles of B-type NP (BNP) and N-terminal pro-B-type NP (NT-proBNP) for diagnosis and risk stratification in HF have been extensively demonstrated, and these biomarkers are emerging tools for population screening and as guides to the start of treatment in subclinical HF. On the contrary, conflicting evidence exists on the role of NPs as a guide to HF therapy. Among the other biomarkers, high-sensitivity troponins and soluble suppression of tumorigenesis-2 are the most promising biomarkers for risk stratification, with independent value to NPs. Other biomarkers evaluated as predictors of adverse outcome are galectin-3, growth differentiation factor 15, mid-regional pro-adrenomedullin, and makers of renal dysfunction. Multi-marker scores and genomic, transcriptomic, proteomic, and metabolomic analyses could further refine HF management.

Metabolomics and cardiovascular imaging: a combined approach for cardiovascular ageing

The purpose of this review is to explore how metabolomics can help uncover new biomarkers and mechanisms for cardiovascular ageing. Cardiovascular ageing refers to cardiovascular structural and functional alterations that occur with chronological ageing and that can lead to the development of cardiovascular disease. These alterations, which were previously only detectable on tissue histology or corroborated on blood samples, are now detectable with modern imaging techniques. Despite the emergence of powerful new imaging tools, clinical investigation into cardiovascular ageing is challenging because ageing is a life course phenomenon involving known and unknown risk factors that play out in a dynamic fashion. Metabolomic profiling measures large numbers of metabolites with diverse chemical properties. Metabolomics has the potential to capture changes in biochemistry brought about by pathophysiologic processes as well as by normal ageing. When combined with non-invasive cardiovascular imaging tools, metabolomics can be used to understand pathological consequences of cardiovascular ageing. This review will summarize previous metabolomics and imaging studies in cardiovascular ageing. These methods may be a clinically relevant and novel approach to identify mechanisms of cardiovascular ageing and formulate or personalize treatment strategies.

Association between the nurse-led program with mental health status, quality of life, and heart failure rehospitalization in chronic heart failure patients

ABSTRACT

The nurse-led program is associated with a short-term improvement of mental health status (MHS) and quality of life (QOL) in patients with chronic heart failure (CHF). Nonetheless, the long-term effect of this program is undetermined. The aims of the current study were to evaluate the 1-year effects of the nurse-led program on MHS, QOL, and heart failure (HF) rehospitalization among patients with CHF.CHF patients in the control group received standard care, and patients in the treatment group received standard care plus telehealth intervention including inquiring patients' medical condition, providing feedbacks, counseling and providing positive and emotional talk with the patients. At the third, sixth, and twelfth month's follow-up, patients were called by registered nurses to assess the Mental Health Inventory-5 (MHI-5) and Kansas City Cardiomyopathy Questionnaire (KCCQ) scores. HF rehospitalization was also assessed.A total of 300 patients were included and 46% (n = 138) of the patients were in the treatment group. There were no significant between-group differences in the MHI-5 and KCCQ scores at baseline. In the control group, the MHI-5 score was gradually decreased with follow-up and the score was significantly lower than that in the treatment group since the third month's follow-up (63.5 ± 10.6 vs 73.6 ± 10.3). Compared with the treatment group, KCCQ score was lower in the control group from the third month's follow-up (64.3 ± 10.6 vs 73.5 ± 12.3) until the end of the twelfth months' follow-up (45.3 ± 11.2 vs 60.8 ± 11.1). During 12 months' follow-up, the proportion of patients who experienced HF rehospitalization was lower in the treatment group (19.6% vs 24.1%). After adjusting for covariates, the utilization of the nurse-led program, and increase of MHI-5 and KCCQ scores were associated with reduced risk of HF rehospitalization.The nurse-led program is beneficial for the improvement of MHS and QOL for CHF patients, which might contribute to the reduction of HF rehospitalization.

Metabolomic profile of patients with left ventricular assist devices: a pilot study

Background

Metabolomic profiling has important diagnostic and prognostic value in heart failure (HF). We investigated whether left ventricular assist device (LVAD) support has an impact on the metabolomic profile of chronic HF patients and if specific metabolic patterns are associated with the development of adverse events.

Methods

We applied untargeted metabolomics to detect and analyze molecules such as amino acids, sugars, fatty acids and other metabolites in plasma samples collected from thirty-three patients implanted with a continuous-flow LVAD. Data were analyzed at baseline, i.e., before implantation of the LVAD, and at long-term follow-up.

Results

Our results reveal significant changes in the metabolomic profile after LVAD implant compared to baseline. In detail, we observed a pre-implant reduction in amino acid metabolism (aminoacyl-tRNA biosynthesis) and increased galactose metabolism, which reversed over the course of support [median follow-up 187 days (63-334 days)]. These changes were associated with improved patient functional capacity driven by LVAD therapy, according to NYHA functional classification of HF (NYHA class I-II: pre-implant =0% of the patients; post-implant =97% of the patients; P<0.001). Moreover, patients who developed adverse thromboembolic events (n=4, 13%) showed a pre-operative metabolomic fingerprint mainly associated with alterations of fatty acid biosynthesis and mitochondrial beta-oxidation of short-chain saturated fatty acids.

Conclusions

Our data provide preliminary evidence that LVAD therapy is associated with changes in the metabolomic profile of HF and suggest the potential use of metabolomics as a new tool to stratify LVAD patients in regard to the risk of adverse events.

Treatment of HF in an Era of Multiple Therapies: Statement From the HF Collaboratory

The treatment of heart failure with reduced ejection fraction (HFrEF) has changed considerably over time, particularly with the sequential development of therapies aimed at antagonism of maladaptive biologic pathways, including inhibition of the sympathetic nervous system and the renin-angiotensin aldosterone system. The sequential nature of earlier HFrEF trials allowed the integration of new therapies tested against the background therapy of the time. More recently, multiple heart failure therapies are being evaluated simultaneously, and the number of therapeutic choices for treating HFrEF has grown considerably. In addition, implementation science has lagged behind discovery science in heart failure. Furthermore, given there are currently >200 ongoing clinical trials in heart failure, further complexities are anticipated. In an effort to provide a decision-making framework in the current era of expanding therapeutic options in HFrEF, the Heart Failure Collaboratory convened a multi-stakeholder group, including patients, clinicians, clinical investigators, the U.S. Food and Drug Administration, industry, and payers who met at the U.S. Food and Drug Administration campus on March 6, 2020. This paper summarizes the discussions and expert consensus recommendations.