Phenomapping Heart Failure with Preserved Ejection Fraction Using Machine Learning Cluster Analysis: Prognostic and Therapeutic Implications

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.

Animal models of heart failure with preserved ejection fraction (HFpEF): from metabolic pathobiology to drug discovery

Heart failure (HF) with preserved ejection fraction (HFpEF) is currently a preeminent challenge for cardiovascular medicine. It has a poor prognosis, increasing mortality, and is escalating in prevalence worldwide. Despite accounting for over 50% of all HF patients, the mechanistic underpinnings driving HFpEF are poorly understood, thus impeding the discovery and development of mechanism-based therapies. HFpEF is a disease syndrome driven by diverse comorbidities, including hypertension, diabetes and obesity, pulmonary hypertension, aging, and atrial fibrillation. There is a lack of high-fidelity animal models that faithfully recapitulate the HFpEF phenotype, owing primarily to the disease heterogeneity, which has hampered our understanding of the complex pathophysiology of HFpEF. This review provides an updated overview of the currently available animal models of HFpEF and discusses their characteristics from the perspective of energy metabolism. Interventional strategies for efficiently utilizing energy substrates in preclinical HFpEF models are also discussed.

Phenotyping of heart failure with preserved ejection faction using electronic health records and echocardiography

Aims

Patients presenting symptoms of heart failure with preserved ejection fraction (HFpEF) are not a homogenous population. Different phenotypes can differ in prognosis and optimal management strategies. We sought to identify phenotypes of HFpEF by using the medical information database from a large university hospital centre using machine learning.

Methods and results

We explored the use of clinical variables from electronic health records in addition to echocardiography to identify different phenotypes of patients with HFpEF. The proposed methodology identifies four phenotypic clusters based on both clinical and echocardiographic characteristics, which have differing prognoses (death and cardiovascular hospitalization).

Conclusion

This work demonstrated that artificial intelligence-derived phenotypes could be used as a tool for physicians to assess risk and to target therapies that may improve outcomes.

Insights into the Interaction of Heart Failure with Preserved Ejection Fraction and Sleep-Disordered Breathing

Heart failure with preserved ejection fraction (HFpEF) is emerging as a widespread disease with global socioeconomic impact. Patients with HFpEF show a dramatically increased morbidity and mortality, and, unfortunately, specific treatment options are limited. This is due to the various etiologies that promote HFpEF development. Indeed, cluster analyses with common HFpEF comorbidities revealed the existence of several HFpEF phenotypes. One especially frequent, yet underappreciated, comorbidity is sleep-disordered breathing (SDB), which is closely intertwined with the development and progression of the "obese HFpEF phenotype". The following review article aims to provide an overview of the common HFpEF etiologies and phenotypes, especially in the context of SDB. As general HFpEF therapies are often not successful, patient- and phenotype-individualized therapeutic strategies are warranted. Therefore, for the "obese HFpEF phenotype", a better understanding of the mechanistic parallels between both HFpEF and SDB is required, which may help to identify potential phenotype-individualized therapeutic strategies. Novel technologies like single-cell transcriptomics or CRISPR-Cas9 gene editing further broaden the groundwork for deeper insights into pathomechanisms and precision medicine.

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.

Cardiac Structure and Function in Young Adults With Various Cardiometabolic Profiles

Background Cardiovascular diseases are a leading cause of mortality worldwide. Cardiometabolic abnormalities result in alterations in the myocardial structure and function. Limited data are available on these changes in young adults with various cardiometabolic risk profiles. The goal was to study the relationship between cardiometabolic risk and echocardiographic changes in young patients of both sexes in a Russian population, using a risk-based cardiometabolic disease staging (CMDS) system. Methods A total of 191 patients were included. The patients were classified into five groups based on the CMDS system. We gathered patient history and performed a physical exam, biochemical blood analysis, and echocardiography. Statistical analyses were performed using IBM SPSS Statistics for Windows, Version 23 (Released 2015; IBM Corp., Armonk, New York, United States). Results The median age of the participants was 35 (30.0-39.0) years. Elevated systolic and diastolic blood pressure and hypertriglyceridemia were more frequent (p < 0.05) in males than in females. An increase in the end-diastolic volume (EDV) and end-systolic volume (ESV) and a decrease in the ejection fraction were noted from CMDS 0 to 3. The EDV and ESV were associated with most cardiometabolic risk factors and strongly correlated with the visceral fat level, waist circumference, and body mass index. We identified a new subgroup as CMDS 3-overly high in patients with CMDS 3 and an excess level of visceral fat. Conclusion When designing strategies for cardiovascular disease prevention in young adults apart from CMDS parameters, bioimpedance analysis should be considered to assess the level of visceral fat, especially in individuals with CMDS 3 because they are at a higher risk of cardiac chamber enlargements. These results can be used to identify new dominants or phenotypes of heart failure with preserved ejection fraction.

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.

The future of heart failure with preserved ejection fraction : Deep phenotyping for targeted therapeutics

Heart failure (HF) with preserved ejection fraction (HFpEF) is a multi-organ, systemic syndrome that involves multiple cardiac and extracardiac pathophysiologic abnormalities. Because HFpEF is a heterogeneous syndrome and resistant to a "one-size-fits-all" approach it has proven to be very difficult to treat. For this reason, several research groups have been working on methods for classifying HFpEF and testing targeted therapeutics for the HFpEF subtypes identified. Apart from conventional classification strategies based on comorbidity, etiology, left ventricular remodeling, and hemodynamic subtypes, researchers have been combining deep phenotyping with innovative analytical strategies (e.g., machine learning) to classify HFpEF into therapeutically homogeneous subtypes over the past few years. Despite the growing excitement for such approaches, there are several potential pitfalls to their use, and there is a pressing need to follow up on data-driven HFpEF subtypes in order to determine their underlying mechanisms and molecular basis. Here we provide a framework for understanding the phenotype-based approach to HFpEF by reviewing (1) the historical context of HFpEF; (2) the current HFpEF paradigm of comorbidity-induced inflammation and endothelial dysfunction; (3) various methods of sub-phenotyping HFpEF; (4) comorbidity-based classification and treatment of HFpEF; (5) machine learning approaches to classifying HFpEF; (6) examples from HFpEF clinical trials; and (7) the future of phenomapping (machine learning and other advanced analytics) for the classification of HFpEF.

Advances in Machine Learning Approaches to Heart Failure with Preserved Ejection Fraction

Heart failure with preserved ejection fraction (HFpEF) represents a prototypical cardiovascular condition in which machine learning may improve targeted therapies and mechanistic understanding of pathogenesis. Machine learning, which involves algorithms that learn from data, has the potential to guide precision medicine approaches for complex clinical syndromes such as HFpEF. It is therefore important to understand the potential utility and common pitfalls of machine learning so that it can be applied and interpreted appropriately. Although machine learning holds considerable promise for HFpEF, it is subject to several potential pitfalls, which are important factors to consider when interpreting machine learning studies.

Global status and trends in heart failure with preserved ejection fraction over the period 2009-2020: A bibliometric analysis

BACKGROUND

Heart failure with preserved ejection fraction (HFpEF) comprises about 50% of the cases of heart failure (HF), but so far there is no effective treatment strategy. This study used bibliometric methods to analyze the scientific literature on HFpEF in 2009 to 2020, and evaluate the global scientific output of HFpEF research, in order to explore the research status and trends in this field.

METHODS

Documents about the HFpEF research published in 2009 to 2020 were retrieved from Science Citation Index Expanded (SCIE) in Web of Science. This study used bibliometrix R-package, VOSviewer, and CiteSpace to conduct the bibliometric analysis.

RESULTS

A total of 1971 documents (1508 articles and 283 reviews) were retrieved to construct the local HFpEF literature collection for analysis. The number of annual documents had increased year by year in general, from 24 to 353. Relevant documents were mainly written in English, and mostly focused on the field of "Cardiovascular System Cardiology." USA ranked first in the relevant countries/regions with most documents, and the leading affiliation was Mayo Clin. Shah SJ was the most productive author, while Borlaug BA ranked highest among the local cited authors and G-index. Circulation was the most local cited source, while Eur J Heart Fail published the most documents and was rated as the top source in terms of G-index. "Paulus WJ, 2013, J Am Coll Cardiol" was the top local cited document within the local HFpEF literature collection, while "Owan TE, 2006, New Engl J Med" outside the local HFpEF literature collection was the most local cited reference. The keywords such as "mortality," "dysfunction," "diagnosis," "outcomes," and "diastolic dysfunction" were most frequent, while "hemodynamics," "comorbidity," "myocardial infarction," "inflammation," and "phenotype" indicated research frontiers or emerging trends. Furthermore, this study also found some deeper bibliometric relationships through bibliographic networks.

CONCLUSIONS

Due to the multi-dimensional bibliometric analysis, this study shows a wide view of scientific productivity related to HFpEF, and provides valuable guidance for researchers interested in HFpEF, assisting them in understanding the research status, identifying potential collaborators, discovering research hotspots and frontiers, and conducting more in-depth research.

Enhanced oxidative stress mediates pathological autophagy and necroptosis in cardiac myocytes in pressure overload induced heart failure in rats

In cardiac myocytes in vitro, hydrogen peroxide induces autophagic cell death and necroptosis. Oxidative stress, myocyte autophagy and necroptosis coexist in heart failure (HF). In this study, we tested the hypothesis that excessive oxidative stress mediates pathological autophagy and necroptosis in myocytes in pressure overload-induced HF. HF was produced by chronic pressure overload induced by abdominal aortic constriction (AAC) in rats. Rats with AAC or sham operation were randomised to orally receive an antioxidant N-acetylcysteine (NAC) or placebo for 4 weeks. Echocardiography was performed for the assessments of left ventricular (LV) structure and function. AAC rats exhibited decreased LV fractional shortening (FS) at 4 weeks after surgery. NAC treatment attenuated decreased LV FS in AAC rats. In AAC rats, myocardial level of 8-hydroxydeoxyguanosine assessed by immunohistochemical staining, indicative of oxidative stress, was increased, LC3 II protein, a marker of autophagy, Beclin1 protein and Atg4b, Atg5, Atg7 and Atg12 mRNA expression were markedly increased, RIP1, RIP3 and MLKL expression, indicative of necroptosis, was increased, and all of the alterations in AAC rats were prevented by the NAC treatment. NAC treatment also attenuated myocyte cross-sectional area and myocardial fibrosis in AAC rats. In conclusion, NAC treatment prevented the increases in oxidative stress, myocyte autophagy and necroptosis and the decrease in LV systolic function in pressure overload-induced HF. These findings suggest that enhanced oxidative stress mediates pathological autophagy and necroptosis in myocytes, leading to LV systolic dysfunction, and antioxidants may be of value to prevent HF through the inhibition of excessive autophagy and necroptosis.