Characterization of subgroups of heart failure patients with preserved ejection fraction with possible implications for prognosis and treatment response

Mineralocorticoid receptor antagonists and heart failure with preserved ejection fraction: current understanding and future prospects

The mineralocorticoid receptor (MR), part of the steroid hormone receptor subfamily within nuclear hormone receptors, is found in the kidney and various non-epithelial tissues, including the heart and blood vessels. When improperly activated, it can contribute to heart failure processes such as cardiac hypertrophy, fibrosis, stiffening of arteries, inflammation, and oxidative stress. MR antagonists (MRAs) have shown clear clinical benefits in patients with heart failure with reduced ejection fraction (HFrEF). However, in cases of heart failure with preserved ejection fraction (HFpEF), there is considerable diversity due to its complex underlying mechanisms, resulting in conflicting findings regarding the effectiveness of MRAs in relevant studies. The concept of phenomapping presents an encouraging avenue for investigating different intervention targets and novel therapies for HFpEF. Post hoc analysis of the TOPCAT trial identified certain HFpEF phenotypes that responded favorably to spironolactone. Growing clinical and preclinical evidence suggests that non-steroidal MRAs, which exhibit greater receptor selectivity, stronger anti-fibrotic and anti-inflammatory properties, and fewer hormone-related side effects, may emerge as another promising treatment option for HFpEF alongside sodium-glucose co-transporter 2 (SGLT2) inhibitors. This review aims to outline the structural and functional characteristics of MR, discuss the physiological effects of its activation and inhibition, and delve into the potential for personalized MRA therapy based on the concept of HFpEF phenotype.

Distinct Comorbidity Clusters in Patients With Acute Heart Failure: Data From RELAX-AHF-2

BACKGROUND

Multimorbidity frequently occurs in patients with acute heart failure (AHF). The co-occurrence of comorbidities often follows specific patterns.

OBJECTIVES

This study investigated multimorbidity subtypes and their associations with clinical outcomes.

METHODS

From the prospective RELAX-AHF-2 (Relaxin for the Treatment of Acute Heart Failure-2) trial, 6,545 patients (26% with HF with preserved ejection fraction, defined as LVEF ≥50%) were classified into multimorbidity groups using latent class analysis. The association between subgroups and clinical outcomes was examined. Validation of these findings was conducted in the RELAX-AHF trial, which comprised 1,161 patients.

RESULTS

Five distinct multimorbidity groups emerged: 1) diabetes and chronic kidney disease (CKD) (often male, high prevalence of CKD and diabetes mellitus); 2) ischemic (ischemic HF); 3) elderly/atrial fibrillation (AF) (oldest, high prevalence of AF); 4) metabolic (obese, hypertensive, more often HF with preserved ejection fraction); and 5) young (fewest comorbidities). After adjusting for confounders, patients in the diabetes and CKD (HR: 1.80; 95% CI: 1.50-2.20), elderly/AF (HR: 1.42; 95% CI: 1.20-1.70), and metabolic (HR: 1.40; 95% CI: 1.20-1.80) groups had higher rates of the composite outcome than patients in the young group, primarily driven by differences in rehospitalization. Treatment allocation (placebo or serelaxin) modified these associations (Pinteraction <0.001). Serelaxin-treated patients in the young group were associated with a lower risk for all-cause mortality (HR: 0.59; 95% CI: 0.40-0.90). Similarly, patients from the RELAX-AHF trial clustered in 5 multimorbidity groups. The clinical characteristics and associations with outcomes could also be validated.

CONCLUSIONS

Comorbidities naturally clustered into 5 mutually exclusive groups in RELAX-AHF-2, showing variations in clinical outcomes. These data emphasize that the specific combination of comorbidities can influence adverse outcomes and treatment responses in patients with AHF.

Heart Failure With Preserved Ejection Fraction in the Elderly Population: Basic Mechanisms and Clinical Considerations

Heart failure with preserved ejection fraction (HFpEF) refers to a clinical condition in which the signs of heart failure, such as pulmonary congestion, peripheral edema, and increased natriuretic peptide levels, are present despite normal ejection fractions and the absence of other causes (eg, pericardial disease). The ejection fraction cutoff for the definition of HFpEF has varied in the past, but recent society guidelines have settled on a consensus of 50%. HFpEF is particularly common in the elderly population. The aim of this narrative review is to summarize the available literature regarding HFpEF in elderly patients in terms of evidence for the age dependence, specific clinical features, and underlying mechanisms. In the clinical arena, we review the epidemiology, discuss distinct clinical phenotypes typically seen in elderly patients, the importance of frailty, the role of biomarkers, and the role of medical therapies (including sodium-glucose cotransport protein 2 inhibitors, renin-angiotensin-aldosterone system blockers, angiotensin receptor/neprilysin inhibitors, diuretics, and β-adrenergic receptor blockers). We then go on to discuss the basic mechanisms implicated in HFpEF, including cellular senescence, fibrosis, inflammation, mitochondrial dysfunction, enhanced production of reactive oxygen species, abnormal cellular calcium handling, changes in microRNA signalling, insulin resistance, and sex hormone changes. Finally, we review knowledge gaps and promising areas of future investigation. Improved understanding of the specific clinical manifestations of HFpEF in elderly individuals and of the fundamental mechanisms that contribute to the age-related risk of HFpEF promises to lead to novel diagnostic and treatment approaches that will improve outcomes for this common cardiac disorder in a vulnerable population.

HFpEF and sex: understanding the role of sex differences

Heart failure is a complex clinical syndrome with many etiological factors and complex pathophysiology affecting millions worldwide. Males and females can have distinct clinical presentation and prognosis, and there is an emerging understanding of the factors that highlight the similarities and differences to synthesize and present available data for sex-specific differences in heart failure with preserved ejection fraction (HFpEF). While the majority of data demonstrate more similarities than differences between females and males in terms of heart failure, there are key differences. Data showed that females have a higher risk of developing HFpEF, but a lower risk of mortality and hospitalization. This can be conditioned by different profiles of comorbidities, postmenopausal changes in sex hormone levels, higher levels of inflammation and chronic microvascular dysfunction in females. These factors, combined with different left ventricular dimensions and function, which are more pronounced with age, lead to a higher prevalence of LV diastolic dysfunction at rest and exercise. As a result, females have lower exercise capacity and quality of life when compared to males. Females also have different activities of systems responsible for drug transformation, leading to different efficacy of drugs as well as higher risk of adverse drug reactions. These data prove the necessity for creating sex-specific risk stratification scales and treatment plans.

Phenogrouping heart failure with preserved or mildly reduced ejection fraction using electronic health record data

BACKGROUND

Heart failure (HF) with preserved or mildly reduced ejection fraction includes a heterogenous group of patients. Reclassification into distinct phenogroups to enable targeted interventions is a priority. This study aimed to identify distinct phenogroups, and compare phenogroup characteristics and outcomes, from electronic health record data.

METHODS

2,187 patients admitted to five UK hospitals with a diagnosis of HF and a left ventricular ejection fraction ≥ 40% were identified from the NIHR Health Informatics Collaborative database. Partition-based, model-based, and density-based machine learning clustering techniques were applied. Cox Proportional Hazards and Fine-Gray competing risks models were used to compare outcomes (all-cause mortality and hospitalisation for HF) across phenogroups.

RESULTS

Three phenogroups were identified: (1) Younger, predominantly female patients with high prevalence of cardiometabolic and coronary disease; (2) More frail patients, with higher rates of lung disease and atrial fibrillation; (3) Patients characterised by systemic inflammation and high rates of diabetes and renal dysfunction. Survival profiles were distinct, with an increasing risk of all-cause mortality from phenogroups 1 to 3 (p < 0.001). Phenogroup membership significantly improved survival prediction compared to conventional factors. Phenogroups were not predictive of hospitalisation for HF.

CONCLUSIONS

Applying unsupervised machine learning to routinely collected electronic health record data identified phenogroups with distinct clinical characteristics and unique survival profiles.

One size does not fit all: Perioperative management of patients with heart failure with preserved ejection fraction

Heart failure with preserved ejection fraction (HFpEF) is recognized as an important risk factor for perioperative complications. However, anesthesia management of HFpEF patients remains a considerable challenge without clear guidance. HFpEF is heterogeneous in its pathophysiological processes, diverse clinical presentations, adverse remodeling of cardiovascular and other organs, and clinical outcomes. It is difficult to manage the disease with one fixed approach because of this. This review phenotypes HFpEF patients by combining their clinical features and anesthesia care issues. Five phenotypes of HFpEF patients are identified: A, O, P, C, and Y. The clinical features, anesthesia implications, and anesthesia management for each phenotype are highlighted and discussed. Such an approach to HFpEF patients in the operating room could deliver safe, high-quality perioperative care.

Clustering of Cardiovascular Risk Factors and Heart Failure in Older Adults from the Brazilian Far North

Given the aging global population, identifying heart failure (HF) phenotypes has become crucial, as distinct disease characteristics can influence treatment and prognosis in older adults. This study aimed to analyze the association between clustering of cardiovascular risk factors and HF in older adults. A cross-sectional epidemiological study was conducted with 1322 older adults (55% women, mean age 70.4) seen in primary health care. Diagnosis of HF was performed by a cardiologist based on diagnostic tests and medical history. Cardiovascular risk factors included hypertension, diabetes, hypercholesterolemia, and smoking. Using logistic regression, potential associations were tested. Individual risk factor analysis showed that older adults with hypertension, diabetes, or hypercholesterolemia had up to 7.6 times higher odds to have HF. The cluster where older adults had only one risk factor instead of none increased the odds of HF by 53.0%. Additionally, the odds of older patients having HF ranged from 3.59 times for the two-risk factor cluster to 20.61 times for the simultaneous presence of all four factors. The analysis of clusters substantially increasing HF risk in older adults revealed the importance of individualizing subgroups with distinct HF pathophysiologies. The clinical significance of these clusters can be beneficial in guiding a more personalized therapeutic approach.

The Therapy and Management of Heart Failure with Preserved Ejection Fraction: New Insights on Treatment

Heart failure with preserved ejection fraction (HFpEF) is a clinical syndrome characterised by the presence of diastolic dysfunction and elevated left ventricular filling pressure, in the setting of a left ventricular ejection fraction of at least 50%. Despite the epidemiological prevalence of HFpEF, a prompt diagnosis is challenging and many uncertainties exist. HFpEF is characterised by different phenotypes driven by various cardiac and non-cardiac comorbidities. This is probably the reason why several HFpEF clinical trials in the past did not reach strong outcomes to recommend a single therapy for this syndrome; however, this paradigm has recently changed, and the unmet clinical need for HFpEF treatment found a proper response as a result of a new class of drug, the sodium-glucose cotransporter 2 inhibitors, which beneficially act through the whole spectrum of left ventricular ejection fraction. The aim of this review was to focus on the therapeutic target of HFpEF, the role of new drugs and the potential role of new devices to manage the syndrome.

Association of Beta-Blocker Use With Exercise Capacity in Participants With Heart Failure With Preserved Ejection Fraction: A Post Hoc Analysis of the RELAX Trial

Patients with heart failure with preserved ejection fraction (HFpEF) often receive β-blocker (BB) therapy for management of co-morbidities. However, the association of BB therapy with exercise capacity and health-related quality of life (HRQL) in HFpEF is not well-studied. In this post hoc analysis of the Phosphodiesterase-5 Inhibition to Improve Clinical Status and Exercise Capacity in HFpEF (RELAX) trial, which included patients with chronic stable HFpEF with peak exercise capacity assessment at baseline and at 12 and 24 weeks of follow-up, we evaluated the association of BB use with the measures of exercise capacity (peak exercise oxygen uptake), anaerobic threshold, and HRQL (Minnesota living with heart failure questionnaire). Separate linear mixed-effect models were constructed for each outcome with adjustment for treatment arm, demographics, medical history, left ventricular ejection fraction, and duration of heart failure. Of the 216 study participants (median age 69 years, 48.2% women), 76% reported BB use at baseline. Participants with (vs without) BB therapy were older (70 vs 63.5 years, p = 0.001) and had a higher prevalence of ischemic heart disease (44% vs 23%, p = 0.01). In the adjusted linear mixed model, BB use over time was not associated with peak exercise oxygen uptake (β 95% confidence interval [CI] 0.2 (-0.31 to 0.7), p = 0.5) and 6-minute walk distance (β 95% CI 14.69 [-14.25 to 43.63], p = 0.3). However, BB use was associated with a higher anaerobic threshold (β 95% CI 0.32 (0.02 to 0.62), p = 0.036) and better HRQL (lower quality of life as assessed by Minnesota living with heart failure questionnaire score) (β 95% CI -6.68 [-10.96 to -2.4], p = 0.002). Future trials are needed to better evaluate the effects of BB on exercise capacity in patients with chronic stable HFpEF.

Does Heterogeneity Exist in Treatment Associations With Renin-Angiotensin-System Inhibitors or Beta-blockers According to Phenotype Clusters in Heart Failure with Preserved Ejection Fraction?

BACKGROUND

We explored the association between use of renin-angiotensin system inhibitors and beta-blockers, with mortality/morbidity in 5 previously identified clusters of patients with heart failure with preserved ejection fraction (HFpEF).

METHODS AND RESULTS

We analyzed 20,980 patients with HFpEF from the Swedish HF registry, phenotyped into young-low comorbidity burden (12%), atrial fibrillation-hypertensive (32%), older-atrial fibrillation (24%), obese-diabetic (15%), and a cardiorenal cluster (17%). In Cox proportional hazard models with inverse probability weighting, there was no heterogeneity in the association between renin-angiotensin system inhibitor use and cluster membership for any of the outcomes: cardiovascular (CV) mortality, all-cause mortality, HF hospitalisation, CV hospitalisation, or non-CV hospitalisation. In contrast, we found a statistical interaction between beta-blocker use and cluster membership for all-cause mortality (P = .03) and non-CV hospitalisation (P = .001). In the young-low comorbidity burden and atrial fibrillation-hypertensive cluster, beta-blocker use was associated with statistically significant lower all-cause mortality and non-CV hospitalisation and in the obese-diabetic cluster beta-blocker use was only associated with a statistically significant lower non-CV hospitalisation. The interaction between beta-blocker use and cluster membership for all-cause mortality could potentially be driven by patients with improved EF. However, patient numbers were diminished when excluding those with improved EF and the direction of the associations remained similar.

CONCLUSIONS

In patients with HFpEF, the association with all-cause mortality and non-CV hospitalisation was heterogeneous across clusters for beta-blockers. It remains to be elucidated how heterogeneity in HFpEF could influence personalized medicine and future clinical trial design.

Exercise intolerance in heart failure with preserved ejection fraction: Causes, consequences and the journey towards a cure

Heart failure with preserved ejection fraction (HFpEF) accounts for over 50% of all heart failure cases nationwide and continues to rise in its prevalence. The complex, multi-organ involvement of the HFpEF clinical syndrome requires clinicians and investigators to adopt an integrative approach that considers the contribution of both cardiac and non-cardiac function to HFpEF pathophysiology. Thus, this symposium review outlines the key points from presentations covering the contributions of disease-related changes in cardiac function, arterial stiffness, peripheral vascular function, and oxygen delivery and utilization to exercise tolerance in patients with HFpEF. While many aspects of HFpEF pathophysiology remain poorly understood, there is accumulating evidence for a decline in vascular health in this patient group that may be remediable through pharmacological and lifestyle interventions and could improve outcomes and clinical status in this ever-growing patient population.

Device therapy for patients with atrial fibrillation and heart failure with preserved ejection fraction

Device therapy is a nonpharmacological approach that presents a crucial advancement for managing patients with atrial fibrillation (AF) and heart failure with preserved ejection fraction (HFpEF). This review investigated the impact of device-based interventions and emphasized their potential for optimizing treatment for this complex patient demographic. Cardiac resynchronization therapy, augmented by atrioventricular node ablation with His-bundle pacing or left bundle-branch pacing, is effective for enhancing cardiac function and establishing atrioventricular synchrony. Cardiac contractility modulation and vagus nerve stimulation represent novel strategies for increasing myocardial contractility and adjusting the autonomic balance. Left ventricular expanders have demonstrated short-term benefits in HFpEF patients but require more investigation for long-term effectiveness and safety, especially in patients with AF. Research gaps regarding complications arising from left ventricular expander implantation need to be addressed. Device-based therapies for heart valve diseases, such as transcatheter aortic valve replacement and transcatheter edge-to-edge repair, show promise for patients with AF and HFpEF, particularly those with mitral or tricuspid regurgitation. Clinical evaluations show that these device therapies lessen AF occurrence, improve exercise tolerance, and boost left ventricular diastolic function. However, additional studies are required to perfect patient selection criteria and ascertain the long-term effectiveness and safety of these interventions. Our review underscores the significant potential of device therapy for improving the outcomes and quality of life for patients with AF and HFpEF.

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.

Unsupervised cluster analysis reveals different phenotypes in patients after transcatheter aortic valve replacement

Aims

The aim of this study was to identify phenotypes with potential prognostic significance in aortic stenosis (AS) patients after transcatheter aortic valve replacement (TAVR) through a clustering approach.

Methods and results

This multi-centre retrospective study included 1365 patients with severe AS who underwent TAVR between January 2015 and March 2019. Among demographics, laboratory, and echocardiography parameters, 20 variables were selected through dimension reduction and used for unsupervised clustering. Phenotypes and outcomes were compared between clusters. Patients were randomly divided into a derivation cohort (n = 1092: 80%) and a validation cohort (n = 273: 20%). Three clusters with markedly different features were identified. Cluster 1 was associated predominantly with elderly age, a high aortic valve gradient, and left ventricular (LV) hypertrophy; Cluster 2 consisted of preserved LV ejection fraction, larger aortic valve area, and high blood pressure; and Cluster 3 demonstrated tachycardia and low flow/low gradient AS. Adverse outcomes differed significantly among clusters during a median of 2.2 years of follow-up (P < 0.001). After adjustment for clinical and echocardiographic data in a Cox proportional hazards model, Cluster 3 (hazard ratio, 4.18; 95% confidence interval, 1.76-9.94; P = 0.001) was associated with increased risk of adverse outcomes. In sequential Cox models, a model based on clinical data and echocardiographic variables (χ2 = 18.4) was improved by Cluster 3 (χ2 = 31.5; P = 0.001) in the validation cohort.

Conclusion

Unsupervised cluster analysis of patients after TAVR revealed three different groups for assessment of prognosis. This provides a new perspective in the categorization of patients after TAVR that considers comorbidities and extravalvular cardiac dysfunction.

Evaluation of Nitrosative/Oxidative Stress and Inflammation in Heart Failure with Preserved and Reduced Ejection Fraction

Heart failure (HF) is a complex syndrome characterized by impaired cardiac function. Two common subtypes of HF include heart failure with preserved ejection fraction (HFpEF) and heart failure with reduced ejection fraction (HFrEF). In this study, we aimed to evaluate and compare the plasma levels of 3-nitrotyrosine (3-NT)-as a marker of nitrosative/oxidative stress and myeloperoxidase (MPO)-as an indicator of inflammation between HFpEF and HFrEF. Twenty-seven patients diagnosed with HFpEF and twenty-two with HFrEF were enrolled in this study. Additionally, forty-one patients were recruited for the control group. An echocardiographic assessment was conducted, followed by the collection of blood samples from all participants. Subsequently, the levels of 3-NT and MPO were quantified using the ELISA method. Comprehensive clinical characteristics and medical histories were obtained. Circulating levels of 3-NT were significantly higher in the HFpEF patients than in the control and the HFrEF groups. Nitrosative/oxidative stress is significantly intensified in HFpEF but not in HFrEF.

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.

Retrospective comparison of traditional and artificial intelligence-based heart failure phenotyping in a US health system to enable real-world evidence

OBJECTIVE

Quantitatively evaluate the quality of data underlying real-world evidence (RWE) in heart failure (HF).

DESIGN

Retrospective comparison of accuracy in identifying patients with HF and phenotypic information was made using traditional (ie, structured query language applied to structured electronic health record (EHR) data) and advanced (ie, artificial intelligence (AI) applied to unstructured EHR data) RWE approaches. The performance of each approach was measured by the harmonic mean of precision and recall (F1 score) using manual annotation of medical records as a reference standard.

SETTING

EHR data from a large academic healthcare system in North America between 2015 and 2019, with an expected catchment of approximately 5 00 000 patients.

POPULATION

4288 encounters for 1155 patients aged 18-85 years, with 472 patients identified as having HF.

OUTCOME MEASURES

HF and associated concepts, such as comorbidities, left ventricular ejection fraction, and selected medications.

RESULTS

The average F1 scores across 19 HF-specific concepts were 49.0% and 94.1% for the traditional and advanced approaches, respectively (p<0.001 for all concepts with available data). The absolute difference in F1 score between approaches was 45.1% (98.1% relative increase in F1 score using the advanced approach). The advanced approach achieved superior F1 scores for HF presence, phenotype and associated comorbidities. Some phenotypes, such as HF with preserved ejection fraction, revealed dramatic differences in extraction accuracy based on technology applied, with a 4.9% F1 score when using natural language processing (NLP) alone and a 91.0% F1 score when using NLP plus AI-based inference.

CONCLUSIONS

A traditional RWE generation approach resulted in low data quality in patients with HF. While an advanced approach demonstrated high accuracy, the results varied dramatically based on extraction techniques. For future studies, advanced approaches and accuracy measurement may be required to ensure data are fit-for-purpose.

Phenotype-Specific Outcome and Treatment Response in Heart Failure with Preserved Ejection Fraction with Comorbid Hypertension and Diabetes: A 12-Month Multicentered Prospective Cohort Study

Despite evidence of SGLT2 inhibitors in improving cardiovascular outcomes of heart failure with preserved ejection fraction (HFpEF), the heterogenous mechanism and characteristic multimorbidity of HFpEF require a phenotypic approach. Metabolic phenotype, one common HFpEF phenotype, has various presentations and prognoses worldwide. We aimed to identify different phenotypes of hypertensive-diabetic HFpEF, their phenotype-related outcomes, and treatment responses. The primary endpoint was time to the first event of all-cause mortality or hospitalization for heart failure (HHF). Among 233 recruited patients, 24.9% experienced primary outcomes within 12 months. A total of 3.9% was lost to follow-up. Three phenotypes were identified. Phenotype 1 (n = 126) consisted of lean, elderly females with chronic kidney disease, anemia, and concentric hypertrophy. Phenotype 2 (n = 62) included younger males with coronary artery disease. Phenotype 3 (n = 45) comprised of obese elderly with atrial fibrillation. Phenotype 1 and 2 reported higher primary outcomes than phenotype 3 (p = 0.002). Regarding treatment responses, SGLT2 inhibitor was associated with fewer primary endpoints in phenotype 1 (p = 0.003) and 2 (p = 0.001). RAAS inhibitor was associated with fewer all-cause mortality in phenotype 1 (p = 0.003). Beta blocker was associated with fewer all-cause mortality in phenotype 1 (p = 0.024) and fewer HHF in phenotype 2 (p = 0.011). Our pioneering study supports the personalized approach to optimize HFpEF management in hypertensive-diabetic patients.

Global Public Health Burden of Heart Failure: An Updated Review

Heart failure (HF) is a rapidly growing public health issue with an estimated prevalence of 64 million people globally. Although the incidence of HF has stabilised worldwide and seems to be declining in developed countries, the prevalence is increasing due to the ageing of the population, improved survival after MI and improved treatment and survival of patients with HF. Yet, HF remains associated with high mortality and morbidity, poor quality of life and functional capacity, and confers a substantial burden to the healthcare system. The prevalence, incidence, mortality and morbidity rates reported show geographical variations, depending on the different aetiologies and clinical characteristics observed among patients with HF. In this review, we provide an overview of the global epidemiology of HF with updated data on prevalence, incidence, mortality and morbidity worldwide.

The Concept of "Heart Failure with Preserved Ejection Fraction": Time for a Critical Reappraisal

Heart failure with preserved ejection fraction (HFpEF) is frequently observed in elderly physically deconditioned subjects, mainly women with hypertension, obesity, glucose intolerance/diabetes, atrial fibrillation, anaemia, coronary artery disease, chronic pulmonary disease, and chronic renal insufficiency. In practice, these conditions represent the majority of cardiac diseases we deal with in our daily clinical practice. For this reason, the HFpEF disease does not exist as a single entity and, as such, no specific unifying therapy could be found. New classification attempts still do not consider the multifaceted aspect of the HF syndrome and appear rather as an artefactual attempt to categorize a condition which is indeed not categorizable. The aim of the present article is to critically review the construction of the concept of the HFpEF syndrome and propose the return of a pathophysiological approach in the evaluation and treatment of patients. Considering the huge economic efforts employed up to date to run awfully expensive trials and research in this field, it is time to call action and redirect such resources towards more specific pathophysiological classifications and potential specific therapeutic targets.

Phenomics and Robust Multiomics Data for Cardiovascular Disease Subtyping

The complex landscape of cardiovascular diseases encompasses a wide range of related pathologies arising from diverse molecular mechanisms and exhibiting heterogeneous phenotypes. This variety of manifestations poses significant challenges in the development of treatment strategies. The increasing availability of precise phenotypic and multiomics data of cardiovascular disease patient populations has spurred the development of a variety of computational disease subtyping techniques to identify distinct subgroups with unique underlying pathogeneses. In this review, we outline the essential components of computational approaches to select, integrate, and cluster omics and clinical data in the context of cardiovascular disease research. We delve into the challenges faced during different stages of the analysis, including feature selection and extraction, data integration, and clustering algorithms. Next, we highlight representative applications of subtyping pipelines in heart failure and coronary artery disease. Finally, we discuss the current challenges and future directions in the development of robust subtyping approaches that can be implemented in clinical workflows, ultimately contributing to the ongoing evolution of precision medicine in health care.

Trajectories of renal biomarkers and new-onset heart failure in the general population: Findings from the PREVEND study

AIMS

Renal dysfunction is one of the most critical risk factors for developing heart failure (HF). However, the association between repeated measures of renal function and incident HF remains unclear. Therefore, this study investigated the longitudinal trajectories of urinary albumin excretion (UAE) and serum creatinine and their association with new-onset HF and all-cause mortality.

METHODS AND RESULTS

Using group-based trajectory analysis, we estimated trajectories of UAE and serum creatinine in 6881 participants from the Prevention of Renal and Vascular End-stage Disease (PREVEND) study and their association with new-onset HF and all-cause death during the 11-years of follow-up. Most participants had stable low UAE or serum creatinine. Participants with persistently higher UAE or serum creatinine were older, more often men, and more often had comorbidities, such as diabetes, a previous myocardial infarction or dyslipidaemia. Participants with persistently high UAE had a higher risk of new-onset HF or all-cause mortality, whereas stable serum creatinine trajectories showed a linear association for new-onset HF and no association with all-cause mortality.

CONCLUSION

Our population-based study identified different but often stable longitudinal patterns of UAE and serum creatinine. Patients with persistently worse renal function, such as higher UAE or serum creatinine, were at a higher risk of HF or mortality.

Prevalence, characteristics and prognostic impact of aortic valve disease in patients with heart failure and reduced, mildly reduced, and preserved ejection fraction: An analysis of the ESC Heart Failure Long-Term Registry

AIMS

To assess the prevalence, clinical characteristics, and outcomes of patients with heart failure (HF) with or without moderate to severe aortic valve disease (AVD) (aortic stenosis [AS], aortic regurgitation [AR], mixed AVD [MAVD]).

METHODS AND RESULTS

Data from the prospective ESC HFA EORP HF Long-Term Registry including both chronic and acute HF were analysed. Of 15 216 patients with HF (62.5% with reduced ejection fraction, HFrEF; 14.0% with mildly reduced ejection fraction, HFmrEF; 23.5% with preserved ejection fraction, HFpEF), 706 patients (4.6%) had AR, 648 (4.3%) AS and 234 (1.5%) MAVD. The prevalence of AS, AR and MAVD was 6%, 8%, and 3% in HFpEF, 6%, 3%, and 2% in HFmrEF and 4%, 3%, and 1% in HFrEF. The strongest associations were observed for age and HFpEF with AS, and for left ventricular end-diastolic diameter with AR. AS (adjusted hazard ratio [HR] 1.43, 95% confidence interval [CI] 1.23-1.67), and MAVD (adjusted HR 1.37, 95% CI 1.07-1.74) but not AR (adjusted HR 1.13, 95% CI 0.96-1.33) were independently associated with the 12-month composite outcome of cardiovascular death and HF hospitalization. The associations between AS and the composite outcome were observed regardless of ejection fraction category.

CONCLUSIONS

In the ESC HFA EORP HF Long-Term Registry, one in 10 patients with HF had AVD, with AS and MAVD being especially common in HFpEF and AR being similarly distributed across all ejection fraction categories. AS and MAVD, but not AR, were independently associated with increased risk of in-hospital mortality and 12-month composite outcome, regardless of ejection fraction category.

The non-steroidal mineralocorticoid receptor antagonist finerenone and heart failure with preserved ejection fraction

Finerenone is a novel non-steroidal mineralocorticoid receptor (MR) antagonist (MRA) with high binding affinity, high MR selectivity and a short plasma half-life. In two major endpoint-driven clinical trials in patients with chronic kidney disease and type 2 diabetes mellitus (FIDELIO-DKD and FIGARO-DKD), finerenone induced significant cardiorenal protective actions, and has been recently approved for treatment of these patients. Heart failure with preserved ejection fraction (HFpEF) is a devastating clinical syndrome with increasing prevalence and poor prognosis. Pharmacological therapy of HFpEF is very limited and new therapeutic options are urgently needed. Finerenone has been shown to improve multiple pathophysiological parameters of HFpEF in preclinical models. In consonance, pre-specified subgroup analyses of FIDELIO-DKD and FIGARO-DKD suggested a potential beneficial effect of finerenone in HFpEF. This review will discuss the pharmacodynamic and -kinetic profile of finerenone. We will provide a general overview over the complex pathophysiology of HFpEF and data from pre-clinical studies, focusing on how finerenone improves multiple components of this pathophysiology. Finally, we will discuss current and future clinical trials with finerenone in heart failure patients focusing on 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.

Oxidative stress and inflammation distinctly drive molecular mechanisms of diastolic dysfunction and remodeling in female and male heart failure with preserved ejection fraction rats

Heart failure with preserved ejection fraction (HFpEF) is a complex cardiovascular insufficiency syndrome presenting with an ejection fraction (EF) of greater than 50% along with different proinflammatory and metabolic co-morbidities. Despite previous work provided key insights into our understanding of HFpEF, effective treatments are still limited. In the current study we attempted to unravel the molecular basis of sex-dependent differences in HFpEF pathology. We analyzed left ventricular samples from 1-year-old female and male transgenic (TG) rats homozygous for the rat Ren-2 renin gene (mRen2) characterized with hypertension and diastolic dysfunction and compared it to age-matched female and male wild type rats (WT) served as control. Cardiomyocytes from female and male TG rats exhibited an elevated titin-based stiffness (F_passive), which was corrected to control level upon treatment with reduced glutathione indicating titin oxidation. This was accompanied with high levels of oxidative stress in TG rats with more prominent effects in female group. In vitro supplementation with heat shock proteins (HSPs) reversed the elevated F_passive indicating restoration of their cytoprotective function. Furthermore, the TG group exhibited high levels of proinflammatory cytokines with significant alterations in apoptotic and autophagy pathways in both sexes. Distinct alterations in the expression of several proteins between both sexes suggest their differential impact on disease development and necessitate distinct treatment options. Hence, our data suggested that oxidative stress and inflammation distinctly drive diastolic dysfunction and remodeling in female and male rats with HFpEF and that the sex-dependent mechanisms contribute to HF pathology.

Heart failure with preserved ejection fraction phenogroup classification using machine learning

AIMS

Heart failure (HF) with preserved ejection fraction (HFpEF) is a complex syndrome with a poor prognosis. Phenotyping is required to identify subtype-dependent treatment strategies. Phenotypes of Japanese HFpEF patients are not fully elucidated, whose obesity is much less than Western patients. This study aimed to reveal model-based phenomapping using unsupervised machine learning (ML) for HFpEF in Japanese patients.

METHODS AND RESULTS

We studied 365 patients with HFpEF (left ventricular ejection fraction >50%) as a derivation cohort from the Nara Registry and Analyses for Heart Failure (NARA-HF), which registered patients with hospitalization by acute decompensated HF. We used unsupervised ML with a variational Bayesian-Gaussian mixture model (VBGMM) with common clinical variables. We also performed hierarchical clustering on the derivation cohort. We adopted 230 patients in the Japanese Heart Failure Syndrome with Preserved Ejection Fraction Registry as the validation cohort for VBGMM. The primary endpoint was defined as all-cause death and HF readmission within 5 years. Supervised ML was performed on the composite cohort of derivation and validation. The optimal number of clusters was three because of the probable distribution of VBGMM and the minimum Bayesian information criterion, and we stratified HFpEF into three phenogroups. Phenogroup 1 (n = 125) was older (mean age 78.9 ± 9.1 years) and predominantly male (57.6%), with the worst kidney function (mean estimated glomerular filtration rate 28.5 ± 9.7 mL/min/1.73 m2 ) and a high incidence of atherosclerotic factor. Phenogroup 2 (n = 200) had older individuals (mean age 78.8 ± 9.7 years), the lowest body mass index (BMI; 22.78 ± 3.94), and the highest incidence of women (57.5%) and atrial fibrillation (56.5%). Phenogroup 3 (n = 40) was the youngest (mean age 63.5 ± 11.2) and predominantly male (63.5 ± 11.2), with the highest BMI (27.46 ± 5.85) and a high incidence of left ventricular hypertrophy. We characterized these three phenogroups as atherosclerosis and chronic kidney disease, atrial fibrillation, and younger and left ventricular hypertrophy groups, respectively. At the primary endpoint, Phenogroup 1 demonstrated the worst prognosis (Phenogroups 1-3: 72.0% vs. 58.5% vs. 45%, P = 0.0036). We also successfully classified a derivation cohort into three similar phenogroups using VBGMM. Hierarchical and supervised clustering successfully showed the reproducibility of the three phenogroups.

CONCLUSIONS

ML could successfully stratify Japanese HFpEF patients into three phenogroups (atherosclerosis and chronic kidney disease, atrial fibrillation, and younger and left ventricular hypertrophy groups).

Personalized Management for Heart Failure with Preserved Ejection Fraction

Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous clinical syndrome with multiple underlying mechanisms and comorbidities that leads to a variety of clinical phenotypes. The identification and characterization of these phenotypes are essential for better understanding the precise pathophysiology of HFpEF, identifying appropriate treatment strategies, and improving patient outcomes. Despite accumulating data showing the potentiality of artificial intelligence (AI)-based phenotyping using clinical, biomarker, and imaging information from multiple dimensions in HFpEF management, contemporary guidelines and consensus do not incorporate these in daily practice. In the future, further studies are required to authenticate and substantiate these findings in order to establish a more standardized approach for clinical implementation.

Differences in Cardiac Mechanics and Exercise Physiology Among Heart Failure With Preserved Ejection Fraction Phenomapping Subgroups

Unsupervised machine learning (phenomapping) has been used successfully to identify novel subgroups (phenogroups) of heart failure with preserved ejection fraction (HFpEF). However, further investigation of pathophysiological differences between HFpEF phenogroups is necessary to help determine potential treatment options. We performed speckle-tracking echocardiography and cardiopulmonary exercise testing (CPET) in 301 and 150 patients with HFpEF, respectively, as part of a prospective phenomapping study (median age 65 [25th to 75th percentile 56 to 73] years, 39% Black individuals, 65% female). Linear regression was used to compare strain and CPET parameters by phenogroup. All indicies of cardiac mechanics except for left ventricular global circumferential strain worsened in a stepwise fashion from phenogroups 1 to 3 after adjustment for demographic and clinical factors. After further adjustment for conventional echocardiographic parameters, phenogroup 3 had the worst left ventricular global longitudinal, right ventricular free wall, and left atrial booster and reservoir strain. On CPET, phenogroup 2 had the lowest exercise time and absolute peak oxygen consumption (VO2), driven primarily by obesity, whereas phenogroup 3 achieved the lowest workload, relative peak oxygen consumption (VO2), and heart rate reserve on multivariable-adjusted analyses. In conclusion, HFpEF phenogroups identified by unsupervised machine learning analysis differ in the indicies of cardiac mechanics and exercise physiology.

Phenotyping of Elderly Patients With Heart Failure Focused on Noncardiac Conditions: A Latent Class Analysis From a Multicenter Registry of Patients Hospitalized With Heart Failure

Background The burden of noncardiovascular conditions is becoming increasingly prevalent in patients with heart failure (HF). We aimed to identify novel phenogroups incorporating noncardiovascular conditions to facilitate understanding and risk stratification in elderly patients with HF. Methods and Results Data from a total of 1881 (61.2%) patients aged ≥65 years were extracted from a prospective multicenter registry of patients hospitalized for acute HF (N=3072). We constructed subgroups of patients with HF with preserved ejection fraction (HFpEF; N=826, 43.9%) and those with non-HFpEF (N=1055, 56.1%). Latent class analysis was performed in each subgroup using 17 variables focused on noncardiovascular conditions (including comorbidities, Clinical Frailty Scale, and Geriatric Nutritional Risk Index). The latent class analysis revealed 3 distinct clinical phenogroups in both HFpEF and non-HFpEF subgroups: (1) robust physical and nutritional status (Group 1: HFpEF, 41.2%; non-HFpEF, 46.0%); (2) multimorbid patients with renal impairment (Group 2: HFpEF, 40.8%; non-HFpEF, 41.9%); and (3) malnourished patients (Group 3: HFpEF, 18.0%; non-HFpEF, 12.1%). After multivariable adjustment, compared with Group 1, patients in Groups 2 and 3 had a higher risk for all-cause death over the 1-year postdischarge period (hazard ratio [HR], 2.79 [95% CI, 1.64-4.81] and HR, 2.73 [95% CI, 1.39-5.35] in HFpEF; HR, 1.96 [95% CI, 1.22-3.14] and HR, 2.97 [95% CI, 1.64-5.38] in non-HFpEF; respectively). Conclusions In elderly patients with HF, the phenomapping focused on incorporating noncardiovascular conditions identified 3 phenogroups, each representing distinct clinical outcomes, and the discrimination pattern was similar for both patients with HFpEF and non-HFpEF. This classification provides novel risk stratification and may aid in clinical decision making.

Phenomapping in heart failure with preserved ejection fraction: insights, limitations, and future directions

Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous entity with complex pathophysiology and manifestations. Phenomapping is the process of applying statistical learning techniques to patient data to identify distinct subgroups based on patterns in the data. Phenomapping has emerged as a technique with potential to improve the understanding of different HFpEF phenotypes. Phenomapping efforts have been increasing in HFpEF over the past several years using a variety of data sources, clinical variables, and statistical techniques. This review summarizes methodologies and key takeaways from these studies, including consistent discriminating factors and conserved HFpEF phenotypes. We argue that phenomapping results to date have had limited implications for clinical care and clinical trials, given that the phenotypes, as currently described, are not reliably identified in each study population and may have significant overlap. We review the inherent limitations of aggregating and utilizing phenomapping results. Lastly, we discuss potential future directions, including using phenomapping to optimize the likelihood of clinical trial success or to drive discovery in mechanisms of the disease process of HFpEF.

Association of Plasma Myeloperoxidase with Inflammation and Diabetic status in HFpEF

Background

Inflammation and oxidative stress are thought to play an important role in the pathophysiology of heart failure with preserved ejection fraction (HFpEF) through the development of endothelial dysfunction. Myeloperoxidase (MPO) functions as a link between oxidative stress and inflammation and is an interesting therapeutic target. The objective of this observational cohort study was to compare MPO levels between HFpEF and old controls, to define clinical characteristics associated with high levels of MPO and to assess the relation between MPO levels and vascular function.

Methods

Patients with HFpEF (N = 55) and controls > 60 years (N = 18) were prospectively included. All subjects underwent complete echocardiography and blood sampling. MPO levels were dosed by ELISA assay. Effective arterial elastance (Ea) and peripheral arterial tonometry (EndoPAT reactive hyperemia index RHI and augmentation index AIx) were used to assess vascular function. Characteristics between groups defined by the median of MPO were compared using independent samples t-test or chi square test.

Results

Patients with HFpEF (80 ± 8.7 years, 65% female) had higher levels of MPO compared to controls (75 ± 5.0 years, 72% female) (34.7 ng/mL [22.7; 44.0] vs 22.6 [18.2; 32.0], p = 0.026). MPO levels were correlated with markers of inflammation; C-reactive protein (Pearson's R = 0.46, p = 0.001) and neutrophile to lymphocyte ratio (R = 0.36, p = 0.031) and with signs of left ventricular (LV) remodelling and elevated filling pressures, namely NT-proBNP levels (R = 0.32, p = 0.019), decreased LV ejection fraction (LVEF, R = -0.36, p = 0.008) and E/e' ratio (R = 0.35, p = 0.011). HFpEF patients with levels of MPO above the median were more often men (48% vs 21%, p = 0.037) and suffered more often from diabetes (48% vs 18%, p = 0.017). Intriguingly, they had lower indices of vascular stiffness (augmentation index 11.1 [0.1; 30.7] vs 19.9 [10.5; 33.4], p = 0.018 and arterial elastance Ea 2.06 ± 0.676 vs 2.43 ± 0.721, p = 0.065) and there was no difference in endothelial function (1.82 [1.34; 2.30] vs 1.66 [1.32; 1.95], p = 0.55).

Conclusions

HFpEF patients have higher levels of MPO than controls, reflecting leukocyte activation and oxidative stress. Among patients, high levels of MPO are associated with male sex, diabetic status, subtle left ventricular dysfunction and pronounced diastolic dysfunction. The association between oxidative stress and vascular stiffness, on the other hand could not be demonstrated.

Clinical Trial Registration

Clinical trial NCT03197350.

The NO-cGMP-PKG Axis in HFpEF: From Pathological Mechanisms to Potential Therapies

Heart failure with preserved ejection fraction (HFpEF) accounts for almost half of all heart failure (HF) cases worldwide. Unfortunately, its incidence is expected to continue to rise, and effective therapy to improve clinical outcomes is lacking. Numerous efforts currently directed towards the pathophysiology of human HFpEF are uncovering signal transduction pathways and novel therapeutic targets. The nitric oxide-cyclic guanosine phosphate-protein kinase G (NO-cGMP-PKG) axis has been described as an important regulator of cardiac function. Suppression of the NO-cGMP-PKG signalling pathway is involved in the progression of HFpEF. Therefore, the NO-cGMP-PKG signalling pathway is a potential therapeutic target for HFpEF. In this review, we aim to explore the mechanism of NO-cGMP-PKG in the progression of HFpEF and to summarize potential therapeutic drugs that target this signalling pathway.

Cardiac Structure and Function Phenogroups and Risk of Incident Heart Failure (from the Multi-ethnic Study of Atherosclerosis)

Indices of cardiac structure and function, such as left ventricular (LV) mass and ejection fraction, have been associated with risk of incident heart failure (HF), but the clinical relevance of data-driven grouping of a comprehensive set of cardiac parameters is unclear. In Multi-Ethnic Study of Atherosclerosis participants, latent class analysis was applied in the sample stratified by gender to define phenogroups on the basis of cardiovascular magnetic resonance imaging parameters of right ventricular and LV structure and function at baseline. Cox proportional hazard models in gender-stratified analyses were used to assess the association between phenogroup membership and risk of HF subtypes adjusting for potential confounders. In the 4,204 participants (mean age 61 ± 10 years, 53% women), the mean follow-up time was 14 ± 4 years for men and 15 ± 4 years for women. For both genders, 4 distinct phenogroups were identified: (1) ideal cardiac mechanics; (2) higher output/hypertrophied LV; (3) impaired ejection fraction/dilated LV; and (4) higher output/hyperdynamic (LV). Men in phenogroups 4 (hazard ratio [HR] 2.91, 95% confidence interval [CI] 1.60 to 5.31, p = 0.0005), 3 (HR 3.52, 95% CI 1.90 to 6.53, p <0.0001), and 2 (HR 3.49, 95% CI 1.94 to 6.28, p <0.0001) had higher rates of incident HF than did men in phenogroup 1, in fully adjusted models. No significant associations were found between phenogroup membership and incident HF in women. In conclusion, phenogroup membership based on cardiac structure and function in men was significantly associated with incident HF. Integration of cardiac magnetic resonance imaging variables may help identify differential risk for HF in men.

Updates in the Pharmacotherapy of Pulmonary Hypertension in Patients with Heart Failure with Preserved Ejection Fraction

Pulmonary hypertension (PH) associated with left heart disease (LHD) is a complex cardiopulmonary condition where a variable degree of pulmonary congestion, arterial vasoconstriction and vascular remodeling can lead to PH and right heart strain. Right heart dysfunction has a significant prognostic impact on these patients. Therefore, preserving right ventricular (RV) function is an important treatment goal. However, the treatment of PH in patients with left heart disease has produced conflicting evidence. The transition from pure LHD to LHD with PH is a continuum and clinically challenging. The heart failure with preserved ejection fraction (HFpEF) patient population is heterogeneous when it comes to PH and RV function. Appropriate clinical and hemodynamic phenotyping of patients with HFpEF and concomitant PH is paramount to making the appropriate treatment decision. This manuscript will summarize the current evidence for the use of pulmonary arterial vasodilators in patients with HFpEF.

Genetic architecture of heart failure with preserved versus reduced ejection fraction

Pharmacologic clinical trials for heart failure with preserved ejection fraction have been largely unsuccessful as compared to those for heart failure with reduced ejection fraction. Whether differences in the genetic underpinnings of these major heart failure subtypes may provide insights into the disparate outcomes of clinical trials remains unknown. We utilize a large, uniformly phenotyped, single cohort of heart failure sub-classified into heart failure with reduced and with preserved ejection fractions based on current clinical definitions, to conduct detailed genetic analyses of the two heart failure sub-types. We find different genetic architectures and distinct genetic association profiles between heart failure with reduced and with preserved ejection fraction suggesting differences in underlying pathobiology. The modest genetic discovery for heart failure with preserved ejection fraction (one locus) compared to heart failure with reduced ejection fraction (13 loci) despite comparable sample sizes indicates that clinically defined heart failure with preserved ejection fraction likely represents the amalgamation of several, distinct pathobiological entities. Development of consensus sub-phenotyping of heart failure with preserved ejection fraction is paramount to better dissect the underlying genetic signals and contributors to this highly prevalent condition.

Empagliflozin, irrespective of blood pressure, improves outcomes in heart failure with preserved ejection fraction: the EMPEROR-Preserved trial

AIMS

Empagliflozin reduces the risk of cardiovascular death or heart failure (HF) hospitalization in patients with HF and preserved ejection fraction. This study aims to evaluate if systolic blood pressure (SBP) moderates these effects.

METHODS AND RESULTS

The association of SBP and the treatment effects of empagliflozin in EMPEROR-Preserved (empagliflozin outcome trial in patients with chronic heart failure with preserved ejection fraction) was evaluated. Randomized patients (n 5988) were grouped according to SBP at baseline (110 mmHg, n 455; 110130 mmHg, n 2415; 130 mmHg, n 3118). The effect of empagliflozin on blood pressure, cardiovascular death or HF hospitalization (primary outcome), total HF hospitalizations, and rate of decline in estimated glomerular filtration rate was studied. Over a median of 26.2 months, the placebo-corrected decline was small and not significantly different across baseline SBP. On placebo, the risk of cardiovascular death or hospitalization for HF was 8.58 at 130 mmHg, 8.26 at 110130 mmHg, and 11.59 events per 100 patient-years at 110 mmHg (P 0.12 vs. 130 mmHg, P 0.08 vs. 110130 mmHg). There was no evidence for baseline SBP moderating the effect of empagliflozin on risk of HF events (primary endpoint interaction P 0.69, recurrent HF hospitalizations interaction P 0.55). When comparing empagliflozin with placebo, SBP did not meaningfully associate with adverse events such as hypotension, volume depletion, and acute renal failure.

CONCLUSION

In EMPEROR-Preserved, empagliflozin was effective and safe without SBP meaningfully moderating empagliflozins treatment effects. This analysis of EMPEROR-Preserved shows that empagliflozin can be used safely and effectively without blood pressure being a meaningful moderator of the drug benefit.

CLINICAL TRIAL REGISTRATION

URL: https://www.clinicaltrials.gov Unique identifier: NCT03057951.

Impact of multiple substance use on circulating ST2, a biomarker of adverse cardiac remodelling, in women

CONTEXT

Cardiovascular disease (CVD) and heart failure (HF) are major causes of mortality in low-income populations and differ by sex. Risk assessment that incorporates cardiac biomarkers is common. However, research evaluating the utility of biomarkers rarely includes controlled substances, which may influence biomarker levels and thus influence CVD risk assessment.

MATERIALS AND METHODS

We identified the effects of multiple substances on soluble "suppression of tumorigenicity 2" (sST2), a biomarker of adverse cardiac remodelling, in 245 low-income women. Adjusting for CVD risk factors, we examined associations between substance use and sST2 over six monthly visits.

RESULTS

Median age was 53 years and 74% of participants were ethnic minority women. An sST2 level > 35 ng/mL (suggesting cardiac remodelling) during ≥1 study visit was observed in 44% of participants. In adjusted analysis, higher sST2 levels were significantly and positively associated with the presence of cocaine (Adjusted Linear Effect [ALE]:1.10; 95% CI:1.03-1.19), alcohol (ALE:1.10; 95% CI:1.04-1.17), heroin (ALE:1.25; 95% CI:1.10-1.43), and the interaction between heroin and fentanyl use.

CONCLUSION

Results suggest that the use of multiple substances influences the level of sST2, a biomarker often used to evaluate cardiovascular risk. Incorporating substance use alongside cardiac biomarkers may improve CVD risk assessment in vulnerable women.

Machine learning to define phenotypes and outcomes of patients hospitalized for heart failure with preserved ejection fraction: Findings from ASCEND-HF

BACKGROUND

Heart Failure with Preserved Ejection Fraction (HFpEF) is a heterogenous disease with few therapies proven to provide clinical benefit. Machine learning can characterize distinct phenotypes and compare outcomes among patients with HFpEF who are hospitalized for acute HF.

METHODS

We applied hierarchical clustering using demographics, comorbidities, and clinical data on admission to identify distinct clusters in hospitalized HFpEF (ejection fraction >40%) in the ASCEND-HF trial. We separately applied a previously developed latent class analysis (LCA) clustering method and compared in-hospital and long-term outcomes across cluster groups.

RESULTS

Of 7141 patients enrolled in the ASCEND-HF trial, 812 (11.4%) were hospitalized for HFpEF and met the criteria for complete case analysis. Hierarchical Cluster 1 included older women with atrial fibrillation (AF). Cluster 2 had elevated resting blood pressure. Cluster 3 had young men with obesity and diabetes. Cluster 4 had low resting blood pressure. Mortality at 180 days was lowest among Cluster 3 (KM event-rate 6.2 [95% CI: 3.5, 10.9]) and highest among Cluster 4 (18.8 [14.6, 24.0], P < .001). Twenty four-hour urine output was higher in Cluster 3 (2700 mL [1800, 3975]) than Cluster 4 (2100 mL [1400, 3055], P < .001). LCA also identified four clusters: A) older White or Asian women, B) younger men with few comorbidities, C) older individuals with AF and renal impairment, and D) patients with obesity and diabetes. Mortality at 180 days was lowest among LCA Cluster B (KM event-rate 5.5 [2.0, 10.3]) and highest among LCA Cluster C (26.3 [19.2, 35.4], P < .001).

CONCLUSIONS

In patients hospitalized for HFpEF, cluster analysis demonstrated distinct phenotypes with differing clinical profiles and outcomes.

Defining the Phenotypes for Heart Failure With Preserved Ejection Fraction

PURPOSE OF REVIEW

Heart failure with preserved ejection fraction (HFpEF) imposes a significant burden on society and healthcare. The lack in efficacious therapies is likely due to the significant heterogeneity of HFpEF. In this review, we define various phenotypes based on underlying comorbidities or etiologies, discuss phenotypes arrived at by novel methods, and explore therapeutic targets.

RECENT FINDINGS

A few studies have used machine learning methods to uncover sub-phenotypes within HFpEF in an unbiased manner based on clinical features, echocardiographic findings, and biomarker levels. We synthesized the literature and propose three broad phenotypes: (1) young, with few comorbidities, usually obese and with low natriuretic peptide levels, (2) obese with substantive cardiometabolic burden and comorbidities and impaired ventricular relaxation, (3) old, multimorbid, with high rates of atrial fibrillation, renal and coronary artery disease, chronic obstructive pulmonary disease, and left ventricular hypertrophy. We also propose potential therapeutic strategies for these phenotypes.

The Conundrum of HFpEF Definition: Non-Invasive Assessment Uncertainties and Alternative Diagnostic Strategies

Heart failure (HF) with preserved ejection fraction (HFpEF) is a heterogeneous syndrome including several morphological phenotypes and varying pathophysiological mechanisms. The conventional classification of HF based on left ventricular ejection fraction (LVEF) has created an oversimplification in diagnostic criteria. Although LVEF is a standardized parameter easy to calculate and broadly applied in the large clinical trials, but it is erroneously considered an index of left ventricular (LV) systolic function. Indeed,  it is affected by preload and afterload and it has limitations related to reproducibility, reduced sensitivity and scarce prognostic values especially when above 50%. Notably, additional diagnostic parameters have been recently proposed in order to improve diagnostic accuracy and to homogenise the different HFpEF populations. Unfortunately, these algorithms comprise sophisticated measurements that are difficult to apply in the daily clinical practice. Additionally, the scarce diffusion of these diagnostic criteria may have led to neutral or negative results in interventional phase 3 trials . We propose changes to the current HFpEF diagnostic approach mainly based on LVEF stratification measurement aiming towards a more inclusive model taking into consideration an integrative approach starting from the main diseases responsible for cardiac dysfunction  through to cardiac structural and functional alterations. Accordingly, with recent universal HF definitions, a stepwise model could be helpful in recognizing patients with early vs. overt HFpEF by the appraisal of specific Doppler echocardiographic variables. Thus, we would encourage the application of new criteria in order to better identify the different phenotypes and to move towards more personalized medicine.

Identifying novel phenotypes of elevated left ventricular end diastolic pressure using hierarchical clustering of features derived from electromechanical waveform data

Introduction

Elevated left ventricular end diastolic pressure (LVEDP) is a consequence of compromised left ventricular compliance and an important measure of myocardial dysfunction. An algorithm was developed to predict elevated LVEDP utilizing electro-mechanical (EM) waveform features. We examined the hierarchical clustering of selected features developed from these EM waveforms in order to identify important patient subgroups and assess their possible prognostic significance.

Materials and methods

Patients presenting with cardiovascular symptoms (N = 396) underwent EM data collection and direct LVEDP measurement by left heart catheterization. LVEDP was classified as non-elevated ( ≤ 12 mmHg) or elevated (≥25 mmHg). The 30 most contributive features to the algorithm output were extracted from EM data and input to an unsupervised hierarchical clustering algorithm. The resultant dendrogram was divided into five clusters, and patient metadata overlaid.

Results

The cluster with highest LVEDP (cluster 1) was most dissimilar from the lowest LVEDP cluster (cluster 5) in both clustering and with respect to clinical characteristics. In contrast to the cluster demonstrating the highest percentage of elevated LVEDP patients, the lowest was predominantly non-elevated LVEDP, younger, lower BMI, and males with a higher rate of significant coronary artery disease (CAD). The next adjacent cluster (cluster 2) to that of the highest LVEDP (cluster 1) had the second lowest LVEDP of all clusters. Cluster 2 differed from Cluster 1 primarily based on features extracted from the electrical data, and those that quantified predictability and variability of the signal. There was a low predictability and high variability in the highest LVEDP cluster 1, and the opposite in adjacent cluster 2.

Conclusion

This analysis identified subgroups of patients with varying degrees of LVEDP elevation based on waveform features. An approach to stratify movement between clusters and possible progression of myocardial dysfunction may include changes in features that differentiate clusters; specifically, reductions in electrical signal predictability and increases in variability. Identification of phenotypes of myocardial dysfunction evidenced by elevated LVEDP and knowledge of factors promoting transition to clusters with higher levels of left ventricular filling pressures could permit early risk stratification and improve patient selection for novel therapeutic interventions.

Worsening of Renal Function Among Hospitalized Patients With Acute Heart Failure: Phenotyping, Outcomes, and Predictors

OBJECTIVE

To define clinical phenotyping and its associated outcome of worsening of renal function (WRF) in hospitalized acute heart failure (AHF) patients.

PATIENTS AND METHODS

Latent class analysis was performed in 113 AHF patients who developed WRF within 72 hours in the DOSE (Diuretic Optimization Strategies Evaluation) trial (from March 2008 to November 2009) and ROSE-AHF (Renal Optimization Strategies Evaluation in Acute Heart Failure) trial (from September 2010 to March 2013) to identify potential WRF phenotypes. Clinical characteristics and outcome (in-hospital and post-discharge) were compared between different phenotypes.

RESULTS

Two WRF phenotypes were identified by latent class analysis, which we named WRF minimally responsive to diuretics (WRF-MRD) and WRF responsive to diuretics (WRF-RD). Among the population, 58 (9.5%) developed WRF-MRD and 55 (9.0%) developed WRF-RD. Patients with WRF-MRD had more comorbidities than WRF-RD. In WRF-MRD, there were an early increase in serum creatinine, a smaller amount of net fluid loss and weight loss, and a higher rate of worsening or persistent heart failure over 72 hours. In contrast, for those with WRF-RD, they had faster in-hospital net fluid loss and weight loss and a better 60-day survival after discharge even compared with patients without WRF (P=.004). Furthermore, baseline chronic obstructive pulmonary disease, diabetes, and cystatin C were independent predictors of WRF-MRD, whereas serum hemoglobin and sodium predicted WRF-RD.

CONCLUSIONS

Among hospitalized AHF patients, we identified two phenotypes of WRF with distinct response to heart failure treatment, predictors, and short-term prognosis after discharge. The results could help early differentiation of WRF phenotypes in clinical practice.

Epidemiology and Clinical Features of Heart Failure with Preserved Ejection Fraction

Heart failure (HF) with preserved ejection (HFpEF) constitutes a large and growing proportion of patients with HF around the world, and is now responsible for more than half of all HF cases in ageing societies. While classically described as a condition of elderly, hypertensive women, recent studies suggest heterogeneity in clinical phenotypes involving differential characteristics and pathophysiological mechanisms. Despite a paucity of disease-modifying therapy for HFpEF, an understanding of phenotypic similarities and differences among patients with HFpEF around the world provides the foundation to recognise the clinical condition for early treatment, as well as to identify modifiable risk factors for preventive intervention. This review summarises the epidemiology of HFpEF, its common clinical features and risk factors, as well as differences by age, comorbidities, race/ethnicity and geography.

Clinical Phenotypes of Heart Failure across the spectrum of Ejection Fraction: A Cluster Analysis

INTRODUCTION

Heart failure (HF), and especially HF with preserved ejection fraction (HFpEF), remains a challenging condition to define. The heterogenous nature of this population may be related to a variety of underlying etiologies interacting myocardial dysfunction.

METHOD

Alberta HEART study was a prospective, observational cohort that enrolled participants along the spectrum of heart failure including: healthy controls, people at risk of HF, and patients with HF and preserved (HFpEF) or reduced ejection fraction (HFrEF). We aimed to explore phenotypes of patients with HF and at-risk of developing HF. Utilising 27 detailed clinical, echocardiographic and biomarker variables, latent class analysis with and without multiple imputation was undertaken to identify distinct clinical phenotypes.

RESULTS

Of 621 participants, 191 (30.8%) and 169 (27.2%) were adjudicated by cardiologists to have HFpEF and HFrEF respectively. In the overall cohort, latent class analysis identified four distinct phenotypes. Phenotype A (n=152, 24.5%) was a healthy and low risk group. Phenotype B (n=129, 20.8%) demonstrated increased left ventricular mass and end-diastolic volumes, with elevated natriuretic peptides and clinical features of congestion. Phenotype C (n=128, 20.6%) was primarily characterised by obesity (80%) and normal indexed cardiac chamber sizes, low natriuretic peptide levels and minimal features of congestion. Phenotype D (n=212, 34.1%) consisted of elderly patients with clinical features of congestions. Phenotypes B and D demonstrated the highest risk of mortality and hospitalization over a median follow-up of 3.7 years.

CONCLUSION

Phenotypes with congestive features demonstrated increased risk profiles. Heart failure is a heterogenous classification which requires further work to appropriately categorise patients based on the underlying etiology or mechanism of impairment.

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.