404-error "Disease not found": Unleashing the translational potential of -omics approaches beyond traditional disease classification in heart failure research

The emergence of personalized medicine, facilitated by the progress in -omics technologies, has initiated a new era in medical diagnostics and treatment. This review examines the potential of -omics approaches in heart failure, a condition that has not yet fully capitalized on personalized strategies compared to other medical fields like cancer therapy. Here, we argue that integrating multi-omics technology with systems medicine approaches could fundamentally transform heart failure management, moving away from the traditional paradigm of 'one size fits all'. Our review examines how omics can enhance understanding of heart failure's molecular foundations and contribute to a more comprehensive disease classification. We draw attention to the current state of medical practice that only relies on clinical evidence and a number of standard laboratory tests. At the same time, we propose a shift towards a universal approach that uses quantitative data from multi-omics to unravel complex molecular interactions. The discussion centres around the potential of the transition as a means to enhance individual risk assessment and emphasizes management within clinical settings. While the use of omics in cardiovascular research is not recent, many past studies have focused only on a single omics approach. In order to achieve a better understanding of disease mechanisms, we explore more holistic approaches using genomics, transcriptomics, epigenomics, and proteomics. This review concludes with a call to action to adopt multi-omics in clinical trials and practice to pave the way for more personalized disease management and more effective heart failure interventions.

Is atrial fibrillation in HFpEF a distinct phenotype? Insights from multiparametric MRI and circulating biomarkers

BACKGROUND

Heart failure with preserved ejection fraction (HFpEF) and atrial fibrillation (AF) frequently co-exist. There is a limited understanding on whether this coexistence is associated with distinct alterations in myocardial remodelling and mechanics. We aimed to determine if patients with atrial fibrillation (AF) and heart failure with preserved ejection fraction (HFpEF) represent a distinct phenotype.

METHODS

In this secondary analysis of adults with HFpEF (NCT03050593), participants were comprehensively phenotyped with stress cardiac MRI, echocardiography and plasma fibroinflammatory biomarkers, and were followed for the composite endpoint (HF hospitalisation or death) at a median of 8.5 years. Those with AF were compared to sinus rhythm (SR) and unsupervised cluster analysis was performed to explore possible phenotypes.

RESULTS

136 subjects were included (SR = 75, AF = 61). The AF group was older (76 ± 8 vs. 70 ± 10 years) with less diabetes (36% vs. 61%) compared to the SR group and had higher left atrial (LA) volumes (61 ± 30 vs. 39 ± 15 mL/m2, p < 0.001), lower LA ejection fraction (EF) (31 ± 15 vs. 51 ± 12%, p < 0.001), worse left ventricular (LV) systolic function (LVEF 63 ± 8 vs. 68 ± 8%, p = 0.002; global longitudinal strain 13.6 ± 2.9 vs. 14.7 ± 2.4%, p = 0.003) but higher LV peak early diastolic strain rates (0.73 ± 0.28 vs. 0.53 ± 0.17 1/s, p < 0.001). The AF group had higher levels of syndecan-1, matrix metalloproteinase-2, proBNP, angiopoietin-2 and pentraxin-3, but lower level of interleukin-8. No difference in clinical outcomes was observed between the groups. Three distinct clusters were identified with the poorest outcomes (Log-rank p = 0.029) in cluster 2 (hypertensive and fibroinflammatory) which had equal representation of SR and AF.

CONCLUSIONS

Presence of AF in HFpEF is associated with cardiac structural and functional changes together with altered expression of several fibro-inflammatory biomarkers. Distinct phenotypes exist in HFpEF which may have differing clinical outcomes.

Acute Coronary Syndrome Subphenotypes Based on Repeated Biomarker Measurements in Relation to Long-Term Mortality Risk

BACKGROUND

We aimed to identify patients with subphenotypes of postacute coronary syndrome (ACS) using repeated measurements of high-sensitivity cardiac troponin T, N-terminal pro-B-type natriuretic peptide, high-sensitivity C-reactive protein, and growth differentiation factor 15 in the year after the index admission, and to investigate their association with long-term mortality risk.

METHODS AND RESULTS

BIOMArCS (BIOMarker Study to Identify the Acute Risk of a Coronary Syndrome) was an observational study of patients with ACS, who underwent high-frequency blood sampling for 1 year. Biomarkers were measured in a median of 16 repeated samples per individual. Cluster analysis was performed to identify biomarker-based subphenotypes in 723 patients without a repeat ACS in the first year. Patients with a repeat ACS (N=36) were considered a separate cluster. Differences in all-cause death were evaluated using accelerated failure time models (median follow-up, 9.1 years; 141 deaths). Three biomarker-based clusters were identified: cluster 1 showed low and stable biomarker concentrations, cluster 2 had elevated concentrations that subsequently decreased, and cluster 3 showed persistently elevated concentrations. The temporal biomarker patterns of patients in cluster 3 were similar to those with a repeat ACS during the first year. Clusters 1 and 2 had a similar and favorable long-term mortality risk. Cluster 3 had the highest mortality risk. The adjusted survival time ratio was 0.64 (95% CI, 0.44-0.93; P=0.018) compared with cluster 1, and 0.71 (95% CI, 0.39-1.32; P=0.281) compared with patients with a repeat ACS.

CONCLUSIONS

Patients with subphenotypes of post-ACS with different all-cause mortality risks during long-term follow-up can be identified on the basis of repeatedly measured cardiovascular biomarkers. Patients with persistently elevated biomarkers have the worst outcomes, regardless of whether they experienced a repeat ACS in the first year.

A machine learning based approach to identify carotid subclinical atherosclerosis endotypes

AIMS

To define endotypes of carotid subclinical atherosclerosis.

METHODS AND RESULTS

We integrated demographic, clinical, and molecular data (n = 124) with ultrasonographic carotid measurements from study participants in the IMPROVE cohort (n = 3340). We applied a neural network algorithm and hierarchical clustering to identify carotid atherosclerosis endotypes. A measure of carotid subclinical atherosclerosis, the c-IMTmean-max, was used to extract atherosclerosis-related features and SHapley Additive exPlanations (SHAP) to reveal endotypes. The association of endotypes with carotid ultrasonographic measurements at baseline, after 30 months, and with the 3-year atherosclerotic cardiovascular disease (ASCVD) risk was estimated by linear (β, SE) and Cox [hazard ratio (HR), 95% confidence interval (CI)] regression models. Crude estimates were adjusted by common cardiovascular risk factors, and baseline ultrasonographic measures. Improvement in ASCVD risk prediction was evaluated by C-statistic and by net reclassification improvement with reference to SCORE2, c-IMTmean-max, and presence of carotid plaques. An ensemble stacking model was used to predict endotypes in an independent validation cohort, the PIVUS (n = 1061). We identified four endotypes able to differentiate carotid atherosclerosis risk profiles from mild (endotype 1) to severe (endotype 4). SHAP identified endotype-shared variables (age, biological sex, and systolic blood pressure) and endotype-specific biomarkers. In the IMPROVE, as compared to endotype 1, endotype 4 associated with the thickest c-IMT at baseline (β, SE) 0.36 (0.014), the highest number of plaques 1.65 (0.075), the fastest c-IMT progression 0.06 (0.013), and the highest ASCVD risk (HR, 95% CI) (1.95, 1.18-3.23). Baseline and progression measures of carotid subclinical atherosclerosis and ASCVD risk were associated with the predicted endotypes in the PIVUS. Endotypes consistently improved measures of ASCVD risk discrimination and reclassification in both study populations.

CONCLUSIONS

We report four replicable subclinical carotid atherosclerosis-endotypes associated with progression of atherosclerosis and ASCVD risk in two independent populations. Our approach based on endotypes can be applied for precision medicine in ASCVD prevention.

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.

Discovering Distinct Phenotypical Clusters in Heart Failure Across the Ejection Fraction Spectrum: a Systematic Review

REVIEW PURPOSE

This systematic review aims to summarise clustering studies in heart failure (HF) and guide future clinical trial design and implementation in routine clinical practice.

FINDINGS

34 studies were identified (n = 19 in HF with preserved ejection fraction (HFpEF)). There was significant heterogeneity invariables and techniques used. However, 149/165 described clusters could be assigned to one of nine phenotypes: 1) young, low comorbidity burden; 2) metabolic; 3) cardio-renal; 4) atrial fibrillation (AF); 5) elderly female AF; 6) hypertensive-comorbidity; 7) ischaemic-male; 8) valvular disease; and 9) devices. There was room for improvement on important methodological topics for all clustering studies such as external validation and transparency of the modelling process. The large overlap between the phenotypes of the clustering studies shows that clustering is a robust approach for discovering clinically distinct phenotypes. However, future studies should invest in a phenotype model that can be implemented in routine clinical practice and future clinical trial design. HF = heart failure, EF = ejection fraction, HFpEF = heart failure with preserved ejection fraction, HFrEF = heart failure with reduced ejection fraction, CKD = chronic kidney disease, AF = atrial fibrillation, IHD = ischaemic heart disease, CAD = coronary artery disease, ICD = implantable cardioverter-defibrillator, CRT = cardiac resynchronization therapy, NT-proBNP = N-terminal pro b-type natriuretic peptide, BMI = Body Mass Index, COPD = Chronic obstructive pulmonary disease.

Multimorbidity in Heart Failure: Leveraging Cluster Analysis to Guide Tailored Treatment Strategies

REVIEW PURPOSE

This review summarises key findings on treatment effects within phenotypical clusters of patients with heart failure (HF), making a distinction between patients with preserved ejection fraction (HFpEF) and reduced ejection fraction (HFrEF).

FINDINGS

Treatment response differed among clusters; ACE inhibitors were beneficial in all HFrEF phenotypes, while only some studies show similar beneficial prognostic effects in HFpEF patients. Beta-blockers had favourable effects in all HFrEF patients but not in HFpEF phenotypes and tended to worsen prognosis in older, cardiorenal patients. Mineralocorticoid receptor antagonists had more favourable prognostic effects in young, obese males and metabolic HFpEF patients. While a phenotype-guided approach is a promising solution for individualised treatment strategies, there are several aspects that still require improvements before such an approach could be implemented in clinical practice. Stronger evidence from clinical trials and real-world data may assist in establishing a phenotype-guided treatment approach for patient with HF in the future.

Biomarker Phenotypes in Heart Failure with Preserved Ejection Fraction Using Hierarchical Clustering-A Pilot Study

OBJECTIVES

We hypothesized the existence of distinct phenotype-based groups within the very heterogeneous population of patients of heart failure with preserved ejection fraction (HFpEF) and using an unsupervised hierarchical clustering applied to plasma concentration of various biomarkers. We sought to characterize them as "biomarker phenotypes" and to conclude differences in their overall characteristics.

SUBJECTS AND METHODS

A cross-sectional study was conducted on 75 patients with HFpEF. An agglomerative hierarchical clustering was performed using the concentrations of cardiac remodeling biomarkers, BNP and cystatin C.

RESULTS

According to the obtained heat map of this analysis, we concluded two distinctive biomarker phenotypes within the HFpEF. The "remodeled phenotype" presented with significantly higher concentrations of cardiac remodeling biomarkers and cystatin C (p < 0.001), higher prevalence of myocardial infarction (p = 0.047), STEMI (p = 0.045), atrial fibrillation (p = 0.047) and anemia: lower erythrocytes count (p=0.037), hemoglobin concentration (p = 0.034) and hematocrit (p = 0.046), compared to "non-remodeled phenotype". Echocardiography showed that patients within "remodeled phenotype" had significantly increased parameters of left ventricular remodeling: left ventricular mass index (p < 0.001), left ventricular mass (p = 0.001), diameters of the interventricular septum (p = 0.027) and posterior wall (p = 0.003) and function alterations, intermediate pauses duration >2.0 seconds (p < 0.006).

CONCLUSION

Unsupervised hierarchical clustering applied to plasma concentration of various biomarkers in patients with HFpEF enables the identification of two biomarker phenotypes, significantly different in clinical characteristics and cardiac structure and function, whereas one phenotype particularly relates to patients with reduced ejection fraction. These findings imply distinct underlying pathophysiology within a unique cohort of HFpEF.

HFrEF subphenotypes based on 4210 repeatedly measured circulating proteins are driven by different biological mechanisms

BACKGROUND

HFrEF is a heterogenous condition with high mortality. We used serial assessments of 4210 circulating proteins to identify distinct novel protein-based HFrEF subphenotypes and to investigate underlying dynamic biological mechanisms. Herewith we aimed to gain pathophysiological insights and fuel opportunities for personalised treatment.

METHODS

In 382 patients, we performed trimonthly blood sampling during a median follow-up of 2.1 [IQR:1.1-2.6] years. We selected all baseline samples and two samples closest to the primary endpoint (PEP; composite of cardiovascular mortality, HF hospitalization, LVAD implantation, and heart transplantation) or censoring, and applied an aptamer-based multiplex proteomic approach. Using unsupervised machine learning methods, we derived clusters from 4210 repeatedly measured proteomic biomarkers. Sets of proteins that drove cluster allocation were analysed via an enrichment analysis. Differences in clinical characteristics and PEP occurrence were evaluated.

FINDINGS

We identified four subphenotypes with different protein profiles, prognosis and clinical characteristics, including age (median [IQR] for subphenotypes 1-4, respectively:70 [64, 76], 68 [60, 79], 57 [47, 65], 59 [56, 66]years), EF (30 [26, 36], 26 [20, 38], 26 [22, 32], 33 [28, 37]%), and chronic renal failure (45%, 65%, 36%, 37%). Subphenotype allocation was driven by subsets of proteins associated with various biological functions, such as oxidative stress, inflammation and extracellular matrix organisation. Clinical characteristics of the subphenotypes were aligned with these associations. Subphenotypes 2 and 3 had the worst prognosis compared to subphenotype 1 (adjHR (95%CI):3.43 (1.76-6.69), and 2.88 (1.37-6.03), respectively).

INTERPRETATION

Four circulating-protein based subphenotypes are present in HFrEF, which are driven by varying combinations of protein subsets, and have different clinical characteristics and prognosis.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov Identifier: NCT01851538https://clinicaltrials.gov/ct2/show/NCT01851538.

FUNDING

EU/EFPIA IMI2JU BigData@Heart grant n°116074, Jaap Schouten Foundation and Noordwest Academie.

Distinct genetic liability profiles define clinically relevant patient strata across common diseases

Genome-wide association studies have unearthed a wealth of genetic associations across many complex diseases. However, translating these associations into biological mechanisms contributing to disease etiology and heterogeneity has been challenging. Here, we hypothesize that the effects of disease-associated genetic variants converge onto distinct cell type specific molecular pathways within distinct subgroups of patients. In order to test this hypothesis, we develop the CASTom-iGEx pipeline to operationalize individual level genotype data to interpret personal polygenic risk and identify the genetic basis of clinical heterogeneity. The paradigmatic application of this approach to coronary artery disease and schizophrenia reveals a convergence of disease associated variant effects onto known and novel genes, pathways, and biological processes. The biological process specific genetic liabilities are not equally distributed across patients. Instead, they defined genetically distinct groups of patients, characterized by different profiles across pathways, endophenotypes, and disease severity. These results provide further evidence for a genetic contribution to clinical heterogeneity and point to the existence of partially distinct pathomechanisms across patient subgroups. Thus, the universally applicable approach presented here has the potential to constitute an important component of future personalized medicine concepts.

Proteomics profiling reveals a distinct high-risk molecular subtype of hypertrophic cardiomyopathy

OBJECTIVE

Hypertrophic cardiomyopathy (HCM) is a heterogeneous disease, likely encompassing several subtypes of disease with distinct biological mechanisms (ie, molecular subtypes). Current models based solely on clinical data have yielded limited accuracy in predicting the risk of major adverse cardiovascular events (MACE). Our aim in this study was to derive molecular subtypes in our multicentre prospective cohort of patients with HCM using proteomics profiling and to examine their longitudinal associations with MACE.

METHODS

We applied unsupervised machine learning methods to plasma proteomics profiling data of 1681 proteins from 258 patients with HCM who were prospectively followed for a median of 2.8 years. The primary outcome was MACE, defined as a composite of arrhythmia, heart failure, stroke and sudden cardiac death.

RESULTS

We identified four molecular subtypes of HCM. Time-to-event analysis revealed significant differences in MACE-free survival among the four molecular subtypes (plogrank=0.007). Compared with the reference group with the lowest risk of MACE (molecular subtype A), patients in molecular subtype D had a higher risk of subsequently developing MACE, with an HR of 3.41 (95% CI 1.54 to 7.55, p=0.003). Pathway analysis of proteins differentially regulated in molecular subtype D demonstrated an upregulation of the Ras/mitogen-activated protein kinase and associated pathways, as well as pathways related to inflammation and fibrosis (eg, transforming growth factor-β pathway).

CONCLUSIONS

Our prospective plasma proteomics study not only exhibited the presence of HCM molecular subtypes but also identified pathobiological mechanisms associated with a distinct high-risk subtype of HCM.

Identifying novel subgroups in heart failure patients with unsupervised machine learning: A scoping review

Background

Heart failure is currently divided into three main forms, HFrEF, HFpEF, and HFmrEF, but its etiology is diverse and highly heterogeneous. Many studies reported a variety of novel subgroups in heart failure patients, with unsupervised machine learning methods. The aim of this scoping review is to provide insights into how these techniques can diagnose and manage HF faster and better, thus providing direction for future research and facilitating its routine use in clinical practice.

Methods

The review was performed following PRISMA-SCR guideline. We searched the PubMed database for eligible publications. Studies were included if they defined new subgroups in HF patients using clustering analysis methods, and excluded if they are (1) Reviews, commentary, or editorials, (2) Studies not about defining new sub-types, or (3) Studies not using unsupervised algorithms. All study screening and data extraction were conducted independently by two investigators and narrative integration of data extracted from included studies was performed.

Results

Of the 498 studies identified, 47 were included in the analysis. Most studies (61.7%) were published in 2020 and later. The largest number of studies (46.8%) coming from the United States, and most of the studies were authored and included in the same country. The most commonly used machine learning method was hierarchical cluster analysis (46.8%), the most commonly used cluster variable type was comorbidity (61.7%), and the least used cluster variable type was genomics (12.8%). Most of the studies used data sets of less than 500 patients (48.9%), and the sample size had negative correlation with the number of clustering variables. The majority of studies (85.1%) assessed the association between cluster grouping and at least one outcomes, with death and hospitalization being the most commonly used outcome measures.

Conclusion

This scoping review provides an overview of recent studies proposing novel HF subgroups based on clustering analysis. Differences were found in study design, study population, clustering methods and variables, and outcomes of interests, and we provided insights into how these studies were conducted and identify the knowledge gaps to guide future research.

Heart failure subphenotypes based on repeated biomarker measurements are associated with clinical characteristics and adverse events (Bio-SHiFT study)

BACKGROUND

This study aimed to identify heart failure (HF) subphenotypes using 92 repeatedly measured circulating proteins in 250 patients with heart failure with reduced ejection fraction, and to investigate their clinical characteristics and prognosis.

METHODS

Clinical data and blood samples were collected tri-monthly until the primary endpoint (PEP) or censoring occurred, with a maximum of 11 visits. The Olink Cardiovascular III panel was measured in baseline samples and the last two samples before the PEP (in 66 PEP cases), or the last sample before censoring (in 184 PEP-free patients). The PEP comprised cardiovascular death, heart transplantation, Left Ventricular Assist Device implantation, and hospitalization for HF. Cluster analysis was performed on individual biomarker trajectories to identify subphenotypes. Then biomarker profiles and clinical characteristics were investigated, and survival analysis was conducted.

RESULTS

Clustering revealed three clinically diverse subphenotypes. Cluster 3 was older, with a longer duration of, and more advanced HF, and most comorbidities. Cluster 2 showed increasing levels over time of most biomarkers. In cluster 3, there were elevated baseline levels and increasing levels over time of 16 remaining biomarkers. Median follow-up was 2.2 (1.4-2.5) years. Cluster 3 had a significantly poorer prognosis compared to cluster 1 (adjusted event-free survival time ratio 0.25 (95%CI:0.12-0.50), p < 0.001). Repeated measurements clusters showed incremental prognostic value compared to clusters using single measurements, or clinical characteristics only.

CONCLUSIONS

Clustering based on repeated biomarker measurements revealed three clinically diverse subphenotypes, of which one has a significantly worse prognosis, therefore contributing to improved (individualized) prognostication.

Applications of artificial intelligence and machine learning in heart failure

Machine learning (ML) is a sub-field of artificial intelligence that uses computer algorithms to extract patterns from raw data, acquire knowledge without human input, and apply this knowledge for various tasks. Traditional statistical methods that classify or regress data have limited capacity to handle large datasets that have a low signal-to-noise ratio. In contrast to traditional models, ML relies on fewer assumptions, can handle larger and more complex datasets, and does not require predictors or interactions to be pre-specified, allowing for novel relationships to be detected. In this review, we discuss the rationale for the use and applications of ML in heart failure, including disease classification, early diagnosis, early detection of decompensation, risk stratification, optimal titration of medical therapy, effective patient selection for devices, and clinical trial recruitment. We discuss how ML can be used to expedite implementation and close healthcare gaps in learning healthcare systems. We review the limitations of ML, including opaque logic and unreliable model performance in the setting of data errors or data shift. Whilst ML has great potential to improve clinical care and research in HF, the applications must be externally validated in prospective studies for broad uptake to occur.

Endotypes and the Path to Precision in Moderate and Severe Traumatic Brain Injury

Heterogeneity is recognized as a major barrier in efforts to improve the care and outcomes of patients with traumatic brain injury (TBI). Even within the narrower stratum of moderate and severe TBI, current management approaches do not capture the complexity of this condition characterized by manifold clinical, anatomical, and pathophysiologic features. One approach to heterogeneity may be to resolve undifferentiated TBI populations into endotypes, subclasses that are distinguished by shared biological characteristics. The endotype paradigm has been explored in a range of medical domains, including psychiatry, oncology, immunology, and pulmonology. In intensive care, endotypes are being investigated for syndromes such as sepsis and acute respiratory distress syndrome. This review provides an overview of the endotype paradigm as well as some of its methods and use cases. A conceptual framework is proposed for endotype research in moderate and severe TBI, together with a scientific road map for endotype discovery and validation in this population.

Heart Failure Spending Function: An Investment Framework for Sequencing and Intensification of Guideline-Directed Medical Therapies

Heart failure with reduced ejection fraction is managed with increasing numbers of guideline-directed medical therapies (GDMT). Benefits tend to be additive. Burdens can also be additive. We propose a heart failure spending function as a conceptual framework for tailored intensification of GDMT that maximizes therapeutic opportunity while limiting adverse events and patient burden. Each patient is conceptualized to have reserve in physiological and psychosocial domains, which can be spent for a future return on investment. Key domains are blood pressure, heart rate, serum creatinine, potassium, and out-of-pocket costs. For each patient, GDMT should be initiated and intensified in a sequence that prioritizes medications with the greatest expected cardiac benefit while drawing on areas where the patient has ample reserves. When reserve is underspent, patients fail to gain the full benefit of GDMT. Conversely, when a reserve is fully spent, addition of new drugs or higher doses that draw upon a domain will lead to patient harm. The benefit of multiple agents drawing upon varied physiological domains should be balanced against cost and complexity. Thresholds for overspending are explored, as are mechanisms for implementing these concepts into routine care, but further health care delivery research is needed to validate and refine clinical use of the spending function. The heart failure spending function also suggests how newer therapies may be considered in terms of relative value, prioritizing agents that draw on different spending domains from existing GDMT.

Rationale and design of the preserved versus reduced ejection fraction biomarker registry and precision medicine database for ambulatory patients with heart failure (PREFER-HF) study

INTRODUCTION

Patients with heart failure (HF) are classically categorised by left ventricular ejection fraction (LVEF). Efforts to predict outcomes and response to specific therapy among LVEF-based groups may be suboptimal, in part due to the underlying heterogeneity within clinical HF phenotypes. A multidimensional characterisation of ambulatory patients with and without HF across LVEF groups is needed to better understand and manage patients with HF in a more precise manner.

METHODS AND ANALYSIS

To date, the first cohort of 1313 out of total planned 3000 patients with and without HF has been enroled in this single-centre, longitudinal observational cohort study. Baseline and 1-year follow-up blood samples and clinical characteristics, the presence and duration of comorbidities, serial laboratory, echocardiographic data and images and therapy information will be obtained. HF diagnosis, aetiology of disease, symptom onset and clinical outcomes at 1 and 5 years will be adjudicated by a team of clinicians. Clinical outcomes of interest include all-cause mortality, cardiovascular mortality, all-cause hospitalisation, cardiovascular hospitalisation, HF hospitalisation, right-sided HF and acute kidney injury. Results from the Preserved versus Reduced Ejection Fraction Biomarker Registry and Precision Medicine Database for Ambulatory Patients with Heart Failure (PREFER-HF) trial will examine longitudinal clinical characteristics, proteomic, metabolomic, genomic and imaging data to better understand HF phenotypes, with the ultimate goal of improving precision medicine and clinical outcomes for patients with HF.

ETHICS AND DISSEMINATION

Information gathered in this research will be published in peer-reviewed journals. Written informed consent for PREFER-HF was obtained from all participants. All study procedures were approved by the Mass General Brigham Institutional Review Board in Boston, Massachusetts and performed in accordance with the Declaration of Helsinki (Protocol Number: 2016P000339).

TRIAL REGISTRATION NUMBER

PREFER-HF ClinicalTrials.gov identifier: NCT03480633.

Endotyping in Heart Failure - Identifying Mechanistically Meaningful Subtypes of Disease

Endotyping is an emerging concept in which diseases are classified into distinct subtypes based on underlying molecular mechanisms. Heart failure (HF) is a complex clinical syndrome that encompasses multiple endotypes with differential risks of adverse events, and varying responses to treatment. Identifying these distinct endotypes requires molecular-level investigation involving multi-"omics" approaches, including genomics, transcriptomics, proteomics, and metabolomics. The derivation of these HF endotypes has important implications in promoting individualized treatment and facilitating more targeted selection of patients for clinical trials, as well as in potentially revealing new pathways of disease that may serve as therapeutic targets. One challenge in the integrated analysis of high-throughput omics and detailed clinical data is that it requires the ability to handle "big data", a task for which machine learning is well suited. In particular, unsupervised machine learning has the ability to uncover novel endotypes of disease in an unbiased approach. In this review, we will discuss recent efforts to identify HF endotypes and cover approaches involving proteomics, transcriptomics, and genomics, with a focus on machine-learning methods.

Atrial Fibrillation and Heart Failure with Preserved Ejection Fraction: Two Chronic Troublemakers

In this article, the definition; mechanisms; diagnostic strategies, including scoring systems; treatments; prognosis; and future perspectives in heart failure with preserved ejection fraction with atrial fibrillation, which are common comorbid conditions, are reviewed thoroughly.

Variation in intraabdominal pressure in patients with acute heart failure according to left ventricular ejection fraction. Results of an intraabdominal pressure study

BACKGROUND

The increase in intraabdominal pressure (IAP) has been correlated with increased creatinine levels in patients with heart failure with severely reduced left ventricular ejection fraction (HFrEF). However, IAP has not been examined in more stable patients or those with heart failure with preserved ejection fraction (HFpEF).

PATIENTS AND METHOD

We conducted an observational, prospective descriptive study that measured the IAP of patients hospitalised for decompensated heart failure (HF). The sample was stratified according to left ventricular ejection fraction (LVEF), with a cut-off of 50%. The objective was to analyse the IAP, the baseline characteristics and degree of congestion using clinical ultrasonography and impedance audiometry.

RESULTS

The study included 56 patients, 22 with HFrEF and 34 with HFpEF. The patients with HFrEF presented a higher prevalence of ischaemic heart disease (11% vs. 6%; p = 0.010) and chronic obstructive pulmonary disease/asthma (6% vs. 2%; p = 0.025). The IAP was higher in the patients with HFrEF (17.2 vs. 13.3 mmHg; p = 0.004), with no differences in renal function at admission according to the LVEF (CKD-EPI creatinine) (HFrEF 55.0 mL/min/1.73 m² [32.6-83.6] vs. HFpEF 55.0 mL/min/1.73 m² [44.0-74.9]; p = 0.485). The patients with HFrEF presented a more congestive profile determined through ultrasonography (inferior vena cava collapse [26% vs. 50%; p = 0.001]), impedance audiometry (total body water at admission, 46 L vs. 41 L; p = 0.052; and at 72 h, 50.2 L vs. 39.1 L; p = 0.038) and CA125 concentration (68 U/mL vs. 39 U/mL; p = 0.037).

CONCLUSIONS

During the decompensation episodes, the patients with HFrEF had a greater increase in IAP and a higher degree of systemic congestion.

Classification of Heart Failure According to Ejection Fraction: JACC Review Topic of the Week

The recent U.S. Food and Drug Administration expanded indication for sacubitril/valsartan introduces a new potential taxonomy for heart failure, with no reference to "preserved" ejection fraction but referring to "below normal" ejection fraction as those most likely to benefit. This review summarizes the evolution of nomenclature in heart failure and examines evidence showing that patients with ejection fraction in the "mid range" may benefit from neurohormonal blockade similar to those with more severely reduced (<40%) ejection fraction. Furthermore, prominent sex differences have been observed wherein the benefit of neurohormonal blockade appears to extend to a higher ejection fraction range in women compared to men. Based on emerging evidence, revised nomenclature is proposed defining heart failure with "reduced" (<40%), "mildly reduced," and "normal" (≥55% in men, ≥60% in women) ejection fraction. Such nomenclature signals consideration of potentially beneficial therapies in the largest group of patients with reduced or mildly reduced ejection fraction.

The year in cardiology: heart failure

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Phenotypic clustering of dilated cardiomyopathy patients highlights important pathophysiological differences

AIMS

The dilated cardiomyopathy (DCM) phenotype is the result of combined genetic and acquired triggers. Until now, clinical decision-making in DCM has mainly been based on ejection fraction (EF) and NYHA classification, not considering the DCM heterogenicity. The present study aimed to identify patient subgroups by phenotypic clustering integrating aetiologies, comorbidities, and cardiac function along cardiac transcript levels, to unveil pathophysiological differences between DCM subgroups.

METHODS AND RESULTS

We included 795 consecutive DCM patients from the Maastricht Cardiomyopathy Registry who underwent in-depth phenotyping, comprising extensive clinical data on aetiology and comorbodities, imaging and endomyocardial biopsies. Four mutually exclusive and clinically distinct phenogroups (PG) were identified based upon unsupervised hierarchical clustering of principal components: [PG1] mild systolic dysfunction, [PG2] auto-immune, [PG3] genetic and arrhythmias, and [PG4] severe systolic dysfunction. RNA-sequencing of cardiac samples (n = 91) revealed a distinct underlying molecular profile per PG: pro-inflammatory (PG2, auto-immune), pro-fibrotic (PG3; arrhythmia), and metabolic (PG4, low EF) gene expression. Furthermore, event-free survival differed among the four phenogroups, also when corrected for well-known clinical predictors. Decision tree modelling identified four clinical parameters (auto-immune disease, EF, atrial fibrillation, and kidney function) by which every DCM patient from two independent DCM cohorts could be placed in one of the four phenogroups with corresponding outcome (n = 789; Spain, n = 352 and Italy, n = 437), showing a feasible applicability of the phenogrouping.

CONCLUSION

The present study identified four different DCM phenogroups associated with significant differences in clinical presentation, underlying molecular profiles and outcome, paving the way for a more personalized treatment approach.

Future perspective of heart failure care: benefits and bottlenecks of artificial intelligence and eHealth

Tweetable abstract #eHealth and #ArtificialIntelligence (AI) bring new possibilities for #HeartFailure (HF) care. We elaborate on potential benefits of #AI in #HF and highlight important bottlenecks for its implementation. #Editorial #Cardiology.

Missing value imputation in proximity extension assay-based targeted proteomics data

Targeted proteomics utilizing antibody-based proximity extension assays provides sensitive and highly specific quantifications of plasma protein levels. Multivariate analysis of this data is hampered by frequent missing values (random or left censored), calling for imputation approaches. While appropriate missing-value imputation methods exist, benchmarks of their performance in targeted proteomics data are lacking. Here, we assessed the performance of two methods for imputation of values missing completely at random, the previously top-benchmarked ‘missForest’ and the recently published ‘GSimp’ method. Evaluation was accomplished by comparing imputed with remeasured relative concentrations of 91 inflammation related circulating proteins in 86 samples from a cohort of 645 patients with venous thromboembolism. The median Pearson correlation between imputed and remeasured protein expression values was 69.0% for missForest and 71.6% for GSimp (p = 5.8e-4). Imputation with missForest resulted in stronger reduction of variance compared to GSimp (median relative variance of 25.3% vs. 68.6%, p = 2.4e-16) and undesired larger bias in downstream analyses. Irrespective of the imputation method used, the 91 imputed proteins revealed large variations in imputation accuracy, driven by differences in signal to noise ratio and information overlap between proteins. In summary, GSimp outperformed missForest, while both methods show good overall imputation accuracy with large variations between proteins.

Tumour biomarkers: association with heart failure outcomes

BACKGROUND

There is increasing recognition that heart failure (HF) and cancer are conditions with a number of shared characteristics.

OBJECTIVES

To explore the association between tumour biomarkers and HF outcomes.

METHODS

In 2,079 patients of BIOSTAT-CHF cohort, we measured six established tumour biomarkers: CA125, CA15-3, CA19-9, CEA, CYFRA 21-1 and AFP.

RESULTS

During a median follow-up of 21 months, 555 (27%) patients reached the primary end-point of all-cause mortality. CA125, CYFRA 21-1, CEA and CA19-9 levels were positively correlated with NT-proBNP quartiles (all P < 0.001, P for trend < 0.001) and were, respectively, associated with a hazard ratio of 1.17 (95% CI 1.12-1.23; P < 0.0001), 1.45 (95% CI 1.30-1.61; P < 0.0001), 1.19 (95% CI 1.09-1.30; P = 0.006) and 1.10 (95% CI 1.05-1.16; P < 0.001) for all-cause mortality after correction for BIOSTAT risk model (age, BUN, NT-proBNP, haemoglobin and beta blocker). All tumour biomarkers (except AFP) had significant associations with secondary end-points (composite of all-cause mortality and HF hospitalization, HF hospitalization, cardiovascular (CV) mortality and non-CV mortality). ROC curves showed the AUC of CYFRA 21-1 (0.64) had a noninferior AUC compared with NT-proBNP (0.68) for all-cause mortality (P = 0.08). A combination of CYFRA 21-1 and NT-proBNP (AUC = 0.71) improved the predictive value of the model for all-cause mortality (P = 0.0002 compared with NT-proBNP).

CONCLUSIONS

Several established tumour biomarkers showed independent associations with indices of severity of HF and independent prognostic value for HF outcomes. This demonstrates that pathophysiological pathways sensed by these tumour biomarkers are also dysregulated in HF.

Variation in intraabdominal pressure in patients with acute heart failure according to left ventricular ejection fraction. Results of an intraabdominal pressure study

BACKGROUND

The increase in intraabdominal pressure (IAP) has been correlated with increased creatinine levels in patients with heart failure with severely reduced left ventricular ejection fraction (HFrEF). However, IAP has not been examined in more stable patients or those with heart failure with preserved ejection fraction (HFpEF).

PATIENTS AND METHOD

We conducted an observational, prospective descriptive study that measured the IAP of patients hospitalised for decompensated heart failure (HF). The sample was stratified according to left ventricular ejection fraction (LVEF), with a cut-off of 50%. The objective was to analyse the IAP, the baseline characteristics and degree of congestion using clinical ultrasonography and impedance audiometry.

RESULTS

The study included 56 patients, 22 with HFrEF and 34 with HFpEF. The patients with HFrEF presented a higher prevalence of ischaemic heart disease (11% vs. 6%; p = 0.010) and chronic obstructive pulmonary disease/asthma (6% vs. 2%; p = 0.025). The IAP was higher in the patients with HFrEF (17.2 vs. 13.3 mmHg; p = 0.004), with no differences in renal function at admission according to the LVEF (CKD-EPI creatinine) (HFrEF 55.0 mL/min/1.73 m2 [32.6-83.6] vs. HFpEF 55.0 mL/min/1.73 m2 [44.0-74.9]; p = 0.485). The patients with HFrEF presented a more congestive profile determined through ultrasonography (inferior vena cava collapse [26% vs. 50%; p = 0.001]), impedance audiometry (total body water at admission, 46 L vs. 41 L; p = 0.052; and at 72 h, 50.2 L vs. 39.1 L; p = 0.038) and CA125 concentration (68 U/mL vs. 39 U/mL; p = 0.037).

CONCLUSIONS

During the decompensation episodes, the patients with HFrEF had a greater increase in IAP and a higher degree of systemic congestion.

Rationale, design and baseline characteristics of the MyoVasc study: A prospective cohort study investigating development and progression of heart failure

Background

Heart failure (HF) is a poly-aetiological syndrome with large heterogeneity regarding clinical presentation, pathophysiology, clinical outcome and response to therapy. The MyoVasc study (NCT04064450) is an epidemiological cohort study investigating the development and progression of HF.

Methods

The primary objective of the study is (a) to improve the understanding of the pathomechanisms of HF across the full spectrum of clinical presentation, (b) to investigate the current clinical classifications of HF, and (c) to identify and characterize homogeneous subgroups regarding disease development using a systems-oriented approach. Worsening of HF, that is, the composite of transition from asymptomatic to symptomatic HF, hospitalization due to HF, or cardiac death, was defined as the primary endpoint of the study. During a six-year follow-up period, all study participants receive a highly standardized, biannual five-hour examination in a dedicated study centre, including detailed cardiovascular phenotyping and biobanking of various biomaterials. Annual follow-up examinations are conducted by computer-assisted telephone interviews recording comprehensively the participants´ health status, including subsequent validation and adjudication of adverse events.

Results

In total, 3289 study participants (age range: 35 to 84 years; female sex: 36.8%) including the full range of HF stages were enrolled from 2013 to 2018. Approximately half of the subjects (n=1741) presented at baseline with symptomatic HF (i.e. HF stage C/D). Among these, HF with preserved ejection fraction was the most frequent phenotype.

Conclusions

By providing a large-scale, multi-dimensional biodatabase with sequential, comprehensive medical-technical (sub)clinical phenotyping and multi-omics characterization (i.e. genome, transcriptome, proteome, lipidome, metabolome and exposome), the MyoVasc study will help to advance our knowledge about the heterogeneous HF syndrome by a systems-oriented biomedicine approach.

Trial registration

ClinicalTrials.gov; NCT04064450.

Aptamer-Based Proteomic Platform Identifies Novel Protein Predictors of Incident Heart Failure and Echocardiographic Traits

BACKGROUND

We used a large-scale, high-throughput DNA aptamer-based discovery proteomic platform to identify circulating biomarkers of cardiac remodeling and incident heart failure (HF) in community-dwelling individuals.

METHODS

We evaluated 1895 FHS (Framingham Heart Study) participants (age 55±10 years, 54% women) who underwent proteomic profiling and echocardiography. Plasma levels of 1305 proteins were related to echocardiographic traits and to incident HF using multivariable regression. Statistically significant protein-HF associations were replicated in the HUNT (Nord-Trøndelag Health) study (n=2497, age 63±10 years, 43% women), and results were meta-analyzed. Genetic variants associated with circulating protein levels (pQTLs) were related to echocardiographic traits in the EchoGen (n=30 201) and to incident HF in the CHARGE (n=20 926) consortia.

RESULTS

Seventeen proteins associated with echocardiographic traits in cross-sectional analyses (false discovery rate <0.10), and 8 of these proteins had pQTLs associated with echocardiographic traits in EchoGen (P<0.0007). In Cox models adjusted for clinical risk factors, 29 proteins demonstrated associations with incident HF in FHS (174 HF events, mean follow-up 19 [limits, 0.2-23.7] years). In meta-analyses of FHS and HUNT, 6 of these proteins were associated with incident HF (P<3.8×10-5; 3 with higher risk: NT-proBNP [N-terminal proB-type natriuretic peptide], TSP2 [thrombospondin-2], MBL [mannose-binding lectin]; and 3 with lower risk: ErbB1 [epidermal growth factor receptor], GDF-11/8 [growth differentiation factor-11/8], and RGMC [hemojuvelin]). For 5 of the 6 proteins, pQTLs were associated with echocardiographic traits (P<0.0006) in EchoGen, and for RGMC, a protein quantitative trait loci was associated with incident HF (P=0.001).

CONCLUSIONS

A large-scale proteomics approach identified new predictors of cardiac remodeling and incident HF. Future studies are warranted to elucidate how biological pathways represented by these proteins may mediate cardiac remodeling and HF risk and to assess if these proteins can improve HF risk prediction.

A network analysis to identify pathophysiological pathways distinguishing ischaemic from non-ischaemic heart failure

Aims

Heart failure (HF) is frequently caused by an ischaemic event (e.g. myocardial infarction) but might also be caused by a primary disease of the myocardium (cardiomyopathy). In order to identify targeted therapies specific for either ischaemic or non‐ischaemic HF, it is important to better understand differences in underlying molecular mechanisms.

Methods and results

We performed a biological physical protein–protein interaction network analysis to identify pathophysiological pathways distinguishing ischaemic from non‐ischaemic HF. First, differentially expressed plasma protein biomarkers were identified in 1160 patients enrolled in the BIOSTAT‐CHF study, 715 of whom had ischaemic HF and 445 had non‐ischaemic HF. Second, we constructed an enriched physical protein–protein interaction network, followed by a pathway over‐representation analysis. Finally, we identified key network proteins. Data were validated in an independent HF cohort comprised of 765 ischaemic and 100 non‐ischaemic HF patients. We found 21/92 proteins to be up‐regulated and 2/92 down‐regulated in ischaemic relative to non‐ischaemic HF patients. An enriched network of 18 proteins that were specific for ischaemic heart disease yielded six pathways, which are related to inflammation, endothelial dysfunction superoxide production, coagulation, and atherosclerosis. We identified five key network proteins: acid phosphatase 5, epidermal growth factor receptor, insulin‐like growth factor binding protein‐1, plasminogen activator urokinase receptor, and secreted phosphoprotein 1. Similar results were observed in the independent validation cohort.

Conclusions

Pathophysiological pathways distinguishing patients with ischaemic HF from those with non‐ischaemic HF were related to inflammation, endothelial dysfunction superoxide production, coagulation, and atherosclerosis. The five key pathway proteins identified are potential treatment targets specifically for patients with ischaemic HF.

Distinct Pathological Pathways in Patients With Heart Failure and Diabetes

OBJECTIVES

The aims of this study were to compare the characteristics of patients with and without diabetes and to use network analyses to compare biomarker profiles and associated pathways in patients with diabetes compared with those without diabetes, which might offer new avenues for potential therapeutic targets.

BACKGROUND

Diabetes adversely affects clinical outcomes and complicates treatment in patients with heart failure (HF). A clear understanding of the pathophysiological processes associated with type 2 diabetes in HF is lacking.

METHODS

Network and pathway over-representation analyses were performed to identify unique pathological pathways in patients with and without diabetes using 92 biomarkers from different pathophysiological domains measured in plasma samples from 1,572 patients with HF (31% with diabetes) with reduced ejection fraction (left ventricular ejection fraction <40%). The results were validated in an independent cohort of 729 patients (30% with diabetes).

RESULTS

Biomarker profiles were first compared between patients with HF with and without diabetes. Patients with diabetes showed higher levels of galectin-4, growth differentiation factor 15, and fatty acid binding protein 4 and lower levels of paraoxonase 3. Network analyses were then performed, revealing that epidermal growth factor receptor and galectin-3 were the most prominent connecting proteins. Translation of these networks to biologic pathways revealed that diabetes was associated with inflammatory response and neutrophil degranulation. Diabetes conferred worse outcomes after correction for an established risk model (hazard ratio: 1.20; 95% confidence interval: 1.01 to 1.42).

CONCLUSIONS

Concomitant diabetes in patients with HF with reduced ejection fraction is associated with distinct pathophysiological pathways related to inflammation, protein phosphorylation, and neutrophil degranulation. These data support the evaluation of anti-inflammatory therapeutic approaches, epidermal growth factor receptor in particular, for patients with HF and diabetes.

Long-term survivors of early breast cancer treated with chemotherapy are characterized by a pro-inflammatory biomarker profile compared to matched controls

Background

Chemo‐ and radiotherapy for breast cancer (BC) can lead to cardiotoxicity even years after the initial treatment. The pathophysiology behind these late cardiac effects is poorly understood. Therefore, we studied a large panel of biomarkers from different pathophysiological domains in long‐term BC survivors, and compared these to matched controls.

Methods and results

In total 91 biomarkers were measured in 688 subjects: 342 BC survivors stratified either to treatment with chemotherapy ± radiotherapy (n = 170) or radiotherapy alone (n = 172) and matched controls. Mean age was 59 ± 9 years and 65 ± 8 years for women treated with chemotherapy ± radiotherapy and radiotherapy alone, respectively, with a mean time since treatment of 11 ± 5.5 years. No biomarkers were differentially expressed in survivors treated with radiotherapy alone vs. controls (P for all >0.1). In sharp contrast, a total of 19 biomarkers were elevated, relative to controls, in BC survivors treated with chemotherapy ± radiotherapy after correction for multiple comparisons (P <0.05 for all). Network analysis revealed upregulation of pathways relating to collagen degradation and activation of matrix metalloproteinases. Furthermore, several inflammatory biomarkers including growth differentiation factor 15, monocyte chemoattractant protein 1, chemokine (C‐X‐C motif) ligand 16, tumour necrosis factor super family member 13b and proprotein convertase subtilisin/kexin type 9, elevated in survivors treated with chemotherapy, showed an independent association with lower left ventricular ejection fraction.

Conclusion

Breast cancer survivors treated with chemotherapy ± radiotherapy show a distinct biomarker profile associated with mild cardiac dysfunction even 10 years after treatment. These results suggest that an ongoing pro‐inflammatory state and activation of matrix metalloproteinases following initial treatment with chemotherapy might play a role in the observed cardiac dysfunction in late BC survivors.

Conceptualization of Heterogeneity of Chronic Diseases and Atherosclerosis as a Pathway to Precision Medicine: Endophenotype, Endotype, and Residual Cardiovascular Risk

The article discusses modern approaches to the conceptualization of pathogenetic heterogeneity in various branches of medical science. The concepts of endophenotype, endotype, and residual cardiovascular risk and the scope of their application in internal medicine and cardiology are considered. Based on the latest results of studies of the genetic architecture of atherosclerosis, five endotypes of atherosclerosis have been proposed. Each of the presented endotypes represents one or another pathophysiological mechanism of atherogenesis, having an established genetic substrate, a characteristic panel of biomarkers, and a number of clinical features. Clinical implications and perspectives for the study of endotypes of atherosclerosis are briefly reviewed.

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

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

The role of cathepsin D in the pathophysiology of heart failure and its potentially beneficial properties: a translational approach

Aims

Cathepsin D is a ubiquitous lysosomal protease that is primarily secreted due to oxidative stress. The role of circulating cathepsin D in heart failure (HF) is unknown. The aim of this study is to determine the association between circulating cathepsin D levels and clinical outcomes in patients with HF and to investigate the biological settings that induce the release of cathepsin D in HF.

Methods and results

Cathepsin D levels were studied in 2174 patients with HF from the BIOSTAT‐CHF index study. Results were validated in 1700 HF patients from the BIOSTAT‐CHF validation cohort. The primary combined outcome was all‐cause mortality and/or HF hospitalizations. Human pluripotent stem cell‐derived cardiomyocytes were subjected to hypoxic, pro‐inflammatory signalling and stretch conditions. Additionally, cathepsin D expression was inhibited by targeted short hairpin RNAs (shRNA). Higher levels of cathepsin D were independently associated with diabetes mellitus, renal failure and higher levels of interleukin‐6 and N‐terminal pro‐B‐type natriuretic peptide (P < 0.001 for all). Cathepsin D levels were independently associated with the primary combined outcome [hazard ratio (HR) per standard deviation (SD): 1.12; 95% confidence interval (CI) 1.02–1.23], which was validated in an independent cohort (HR per SD: 1.23, 95% CI 1.09–1.40). In vitro experiments demonstrated that human stem cell‐derived cardiomyocytes released cathepsin D and troponin T in response to mechanical stretch. ShRNA‐mediated silencing of cathepsin D resulted in increased necrosis, abrogated autophagy, increased stress‐induced metabolism, and increased release of troponin T from human stem cell‐derived cardiomyocytes under stress.

Conclusions

Circulating cathepsin D levels are associated with HF severity and poorer outcome, and reduced levels of cathepsin D may have detrimental effects with therapeutic potential in HF.

Risk factor-based subphenotyping of heart failure in the community

Background

Heart failure (HF) is a heterogeneous clinical syndrome with varying prognosis. Subphenotyping of HF is a research priority to advance our understanding of the syndrome. We formulated a subphenotyping schema and compared long-term mortality risk among the HF subphenotypes in the community-based Framingham Study.

Methods and results

In hierarchical order, we grouped participants with new-onset HF (stratified by HF with reduced [HFrEF] vs. preserved ejection fraction [HFpEF]) according to the presence of: (1) coronary heart disease (CHD), (2) metabolic syndrome (MetS), (3) hypertension, and (4) ‘other’ causes. Age at HF onset was lowest in people with the MetS (mean 76 vs. 77 years for HFrEF and HFpEF, respectively) and highest in those with hypertension only (mean 82 and 85 years for HFrEF and HFpEF, respectively). For HFrEF, 10-year cumulative mortality and hazards ratios [HR] were 87% for CHD (n = 219; referent group), 88% for MetS (n = 105; HR 0.95 [95% CI 0.73–1.23]), 82% for hypertension (n = 104; HR 0.71 [0.55–0.91]), and 78% for other (n = 37; HR 0.81 [0.55–1.19]). Corresponding 10-year cumulative mortality and HR data for HFpEF were: 85% for CHD (n = 84; referent), 83% for MetS (n = 118; HR 0.98 [0.72–1.33]), 81% for hypertension (n = 127; HR 0.71 [0.52–0.95]), and 76% for other (n = 43; HR 0.76 [0.50–1.14]). In a sample without overt heart failure (n = 5536), several echocardiographic and vascular indices showed graded worsening of age- and sex adjusted-values among those having CHD, MetS, hypertension, or obesity, compared with individuals not having these risk factors.

Conclusions

HF subphenotypes characterized by the presence of CHD or metabolic syndrome present at a younger age and are marked by greater mortality risk. The clinical utility of the proposed subphenotyping schema warrants further research.