Genetic determinants of heart failure: facts and numbers

Cardiomyopathy, an uncommon phenotype of congenital disorders of glycosylation: Recommendations for baseline screening and follow-up evaluation

INTRODUCTION

Congenital disorders of glycosylation (CDG) are a continuously expanding group of monogenic disorders that disrupt glycoprotein and glycolipid biosynthesis, leading to multi-systemic manifestations. These disorders are categorized into various groups depending on which part of the glycosylation process is impaired. The cardiac manifestations in CDG can significantly differ, not only across different types but also among individuals with the same genetic cause of CDG. Cardiomyopathy is an important phenotype in CDG. The clinical manifestations and progression of cardiomyopathy in CDG patients have not been well characterized. This study aims to delineate common patterns of cardiomyopathy across a range of genetic causes of CDG and to propose baseline screening and follow-up evaluation for this patient population.

METHODS

Patients with molecular confirmation of CDG who were enrolled in the prospective or memorial arms of the Frontiers in Congenital Disorders of Glycosylation Consortium (FCDGC) natural history study were ascertained for the presence of cardiomyopathy based on a retrospective review of their medical records. All patients were evaluated by clinical geneticists who are members of FCDGC at their respective academic centers. Patients were screened for cardiomyopathy, and detailed data were retrospectively collected. We analyzed their clinical and molecular history, imaging characteristics of cardiac involvement, type of cardiomyopathy, age at initial presentation of cardiomyopathy, additional cardiac features, the treatments administered, and their clinical outcomes.

RESULTS

Of the 305 patients with molecularly confirmed CDG participating in the FCDGC natural history study as of June 2023, 17 individuals, nine females and eight males, were identified with concurrent diagnoses of cardiomyopathy. Most of these patients were diagnosed with PMM2-CDG (n = 10). However, cardiomyopathy was also observed in other diagnoses, including PGM1-CDG (n = 3), ALG3-CDG (n = 1), DPM1-CDG (n = 1), DPAGT1-CDG (n = 1), and SSR4-CDG (n = 1). All PMM2-CDG patients were reported to have hypertrophic cardiomyopathy. Dilated cardiomyopathy was observed in three patients, two with PGM1-CDG and one with ALG3-CDG; left ventricular non-compaction cardiomyopathy was diagnosed in two patients, one with PGM1-CDG and one with DPAGT1-CDG; two patients, one with DPM1-CDG and one with SSR4-CDG, were diagnosed with non-ischemic cardiomyopathy. The estimated median age of diagnosis for cardiomyopathy was 5 months (range: prenatal-27 years). Cardiac improvement was observed in three patients with PMM2-CDG. Five patients showed a progressive course of cardiomyopathy, while the condition remained unchanged in eight individuals. Six patients demonstrated pericardial effusion, with three patients exhibiting cardiac tamponade. One patient with SSR4-CDG has been recently diagnosed with cardiomyopathy; thus, the progression of the disease is yet to be determined. One patient with PGM1-CDG underwent cardiac transplantation. Seven patients were deceased, including five with PMM2-CDG, one with DPAGT1-CDG, and one with ALG3-CDG. Two patients died of cardiac tamponade from pericardial effusion; for the remaining patients, cardiomyopathy was not necessarily the primary cause of death.

CONCLUSIONS

In this retrospective study, cardiomyopathy was identified in ∼6% of patients with CDG. Notably, the majority, including all those with PMM2-CDG, exhibited hypertrophic cardiomyopathy. Some cases did not show progression, yet pericardial effusions were commonly observed, especially in PMM2-CDG patients, occasionally escalating to life-threatening cardiac tamponade. It is recommended that clinicians managing CDG patients, particularly those with PMM2-CDG and PGM1-CDG, be vigilant of the cardiomyopathy risk and risk for potentially life-threatening pericardial effusions. Cardiac surveillance, including an echocardiogram and EKG, should be conducted at the time of diagnosis, annually throughout the first 5 years, followed by check-ups every 2-3 years if no concerns arise until adulthood. Subsequently, routine cardiac examinations every five years are advisable. Additionally, patients with diagnosed cardiomyopathy should receive ongoing cardiac care to ensure the effective management and monitoring of their condition. A prospective study will be required to determine the true prevalence of cardiomyopathy in CDG.

Global epidemiology of heart failure

Heart failure (HF) is a heterogeneous clinical syndrome marked by substantial morbidity and mortality. The natural history of HF is well established; however, epidemiological data are continually evolving owing to demographic shifts, advances in treatment and variations in access to health care. Although the incidence of HF has stabilized or declined in high-income countries over the past decade, its prevalence continues to increase, driven by an ageing population, an increase in risk factors, the effectiveness of novel therapies and improved survival. This rise in prevalence is increasingly noted among younger adults and is accompanied by a shift towards HF with preserved ejection fraction. However, disparities exist in our epidemiological understanding of HF burden and progression in low-income and middle-income countries owing to the lack of comprehensive data in these regions. Therefore, the current epidemiological landscape of HF highlights the need for periodic surveillance and resource allocation tailored to geographically vulnerable areas. In this Review, we highlight global trends in the burden of HF, focusing on the variations across the spectrum of left ventricular ejection fraction. We also discuss evolving population-based estimates of HF incidence and prevalence, the risk factors for and aetiologies of this disease, and outcomes in different geographical regions and populations.

The Genetic Factors Influencing Cardiomyopathies and Heart Failure across the Allele Frequency Spectrum

Heart failure (HF) remains a major cause of mortality and morbidity worldwide. Understanding the genetic basis of HF allows for the development of disease-modifying therapies, more appropriate risk stratification, and personalised management of patients. The advent of next-generation sequencing has enabled genome-wide association studies; moving beyond rare variants identified in a Mendelian fashion and detecting common DNA variants associated with disease. We summarise the latest GWAS and rare variant data on mixed and refined HF aetiologies, and cardiomyopathies. We describe the recent understanding of the functional impact of titin variants and highlight FHOD3 as a novel cardiomyopathy-associated gene. We describe future directions of research in this field and how genetic data can be leveraged to improve the care of patients with HF.

Knockdown of SCN5A alters metabolic-associated genes and aggravates hypertrophy in the cardiomyoblast

SCN5A mutations have been reported to cause various cardiomyopathies in humans. Most of the SCN5A mutations causes loss of function and thereby, alters the overall cellular function. Therefore, to understand the loss of SCN5A function in cardiomyocytes, we have knocked down the SCN5A gene (SCN5A-KD) in H9c2 cells and explored the cell phenotype and molecular behaviors in the presence and absence of isoproterenol (ISO), an adrenergic receptor agonist that induces cardiac hypertrophy. Expression of several genes related to hypertrophy, inflammation, fibrosis, and energy metabolism pathways were evaluated. It was found that the mRNA expression of hypertrophy-related gene, brain (B-type) natriuretic peptide (BNP) was significantly increased in SCN5A-KD cells as compared to 'control' H9c2 cells. There was a further increase in the mRNA expressions of BNP and βMHC in SCN5A-KD cells after ISO treatment compared to their respective controls. Pro-inflammatory cytokine, tumor necrosis factor-alpha expression was significantly increased in 'SCN5A-KD' H9c2 cells. Further, metabolism-related genes like glucose transporter type 4, cluster of differentiation 36, peroxisome proliferator-activated receptor alpha, and peroxisome proliferator-activated receptor-gamma were significantly elevated in the SCN5A-KD cells as compared to the control cells. Upregulation of these metabolic genes is associated with increased ATP production. The study revealed that SCN5A knock-down causes alteration of gene expression related to cardiac hypertrophy, inflammation, and energy metabolism pathways, which may promote cardiac remodelling and cardiomyopathy.

Uncovering genetic associations in the human diseasome using an endophenotype-augmented disease network

MOTIVATION

Many diseases, particularly cardiometabolic disorders, exhibit complex multimorbidities with one another. An intuitive way to model the connections between phenotypes is with a disease-disease network (DDN), where nodes represent diseases and edges represent associations, such as shared single-nucleotide polymorphisms (SNPs), between pairs of diseases. To gain further genetic understanding of molecular contributors to disease associations, we propose a novel version of the shared-SNP DDN (ssDDN), denoted as ssDDN+, which includes connections between diseases derived from genetic correlations with intermediate endophenotypes. We hypothesize that a ssDDN+ can provide complementary information to the disease connections in a ssDDN, yielding insight into the role of clinical laboratory measurements in disease interactions.

RESULTS

Using PheWAS summary statistics from the UK Biobank, we constructed a ssDDN+ revealing hundreds of genetic correlations between diseases and quantitative traits. Our augmented network uncovers genetic associations across different disease categories, connects relevant cardiometabolic diseases, and highlights specific biomarkers that are associated with cross-phenotype associations. Out of the 31 clinical measurements under consideration, HDL-C connects the greatest number of diseases and is strongly associated with both type 2 diabetes and heart failure. Triglycerides, another blood lipid with known genetic causes in non-mendelian diseases, also adds a substantial number of edges to the ssDDN. This work demonstrates how association with clinical biomarkers can better explain the shared genetics between cardiometabolic disorders. Our study can facilitate future network-based investigations of cross-phenotype associations involving pleiotropy and genetic heterogeneity, potentially uncovering sources of missing heritability in multimorbidities.

AVAILABILITY AND IMPLEMENTATION

The generated ssDDN+ can be explored at https://hdpm.biomedinfolab.com/ddn/biomarkerDDN.

Exploring TTN variants as genetic insights into cardiomyopathy pathogenesis and potential emerging clues to molecular mechanisms in cardiomyopathies

The giant protein titin (TTN) is a sarcomeric protein that forms the myofibrillar backbone for the components of the contractile machinery which plays a crucial role in muscle disorders and cardiomyopathies. Diagnosing TTN pathogenic variants has important implications for patient management and genetic counseling. Genetic testing for TTN variants can help identify individuals at risk for developing cardiomyopathies, allowing for early intervention and personalized treatment strategies. Furthermore, identifying TTN variants can inform prognosis and guide therapeutic decisions. Deciphering the intricate genotype-phenotype correlations between TTN variants and their pathologic traits in cardiomyopathies is imperative for gene-based diagnosis, risk assessment, and personalized clinical management. With the increasing use of next-generation sequencing (NGS), a high number of variants in the TTN gene have been detected in patients with cardiomyopathies. However, not all TTN variants detected in cardiomyopathy cohorts can be assumed to be disease-causing. The interpretation of TTN variants remains challenging due to high background population variation. This narrative review aimed to comprehensively summarize current evidence on TTN variants identified in published cardiomyopathy studies and determine which specific variants are likely pathogenic contributors to cardiomyopathy development.

Identification Drug Targets for Oxaliplatin-Induced Cardiotoxicity without Affecting Cancer Treatment through Inter Variability Cross-Correlation Analysis (IVCCA)

The successful treatment of side effects of chemotherapy faces two major limitations: the need to avoid interfering with pathways essential for the cancer-destroying effects of the chemotherapy drug, and the need to avoid helping tumor progression through cancer promoting cellular pathways. To address these questions and identify new pathways and targets that satisfy these limitations, we have developed the bioinformatics tool Inter Variability Cross-Correlation Analysis (IVCCA). This tool calculates the cross-correlation of differentially expressed genes, analyzes their clusters, and compares them across a vast number of known pathways to identify the most relevant target(s). To demonstrate the utility of IVCCA, we applied this platform to RNA-seq data obtained from the hearts of the animal models with oxaliplatin-induced CTX. RNA-seq of the heart tissue from oxaliplatin treated mice identified 1744 differentially expressed genes with False Discovery Rate (FDR) less than 0.05 and fold change above 1.5 across nine samples. We compared the results against traditional gene enrichment analysis methods, revealing that IVCCA identified additional pathways potentially involved in CTX beyond those detected by conventional approaches. The newly identified pathways such as energy metabolism and several others represent promising target for therapeutic intervention against CTX, while preserving the efficacy of the chemotherapy treatment and avoiding tumor proliferation. Targeting these pathways is expected to mitigate the damaging effects of chemotherapy on cardiac tissues and improve patient outcomes by reducing the incidence of heart failure and other cardiovascular complications, ultimately enabling patients to complete their full course of chemotherapy with improved quality of life and survival rates.

A comprehensive review of heart failure: Unraveling the etiology, decoding pathophysiological mechanisms, navigating diagnostic modalities, exploring pharmacological interventions, advocating lifestyle modifications, and charting the horizon of emerging therapies in the complex landscape of chronic cardiac dysfunction

Heart failure (HF) poses a significant global health burden, necessitating a profound understanding of its multifaceted dimensions. This comprehensive review aims to unravel the etiology, decode pathophysiological mechanisms, navigate diagnostic modalities, explore pharmacological interventions, advocate lifestyle modifications, and chart the horizon of emerging therapies in the complex landscape of chronic cardiac dysfunction. The exploration of HF begins with an insightful journey into its diverse etiological factors, encompassing genetic predispositions, hypertension, and coronary artery disease. Delving into pathophysiological mechanisms, this review elucidates the intricate processes of cardiac remodeling, neurohormonal activation, and cellular dysfunction that underlie the progression of HF. Diagnostic modalities play a pivotal role in unraveling the mysteries of HF by examining advanced imaging techniques, biomarkers, and comprehensive clinical assessments. The pharmacological interventions section provides an in-depth analysis of traditional medications, such as diuretics and angiotensin-converting enzyme inhibitors, while highlighting the emergence of novel drug classes transforming HF management. Advocating lifestyle modifications emphasizes the crucial role of diet, exercise, smoking cessation, and alcohol moderation in enhancing patient outcomes. Lastly, the review delves into the promising horizon of emerging therapies, offering a glimpse into current research, innovative treatment approaches, and potential breakthroughs. As HF management faces challenges in patient compliance, healthcare access, and education, this comprehensive review aims to equip healthcare professionals and researchers with a holistic understanding of chronic cardiac dysfunction's intricacies. In conclusion, synthesizing key findings emphasizes the need for an integrated and multidimensional approach to effectively address the complex landscape of heart failure.

The Role of Personalized Medicine in Companion Animal Cardiology

Cardiomyopathies remain one of the most common inherited cardiac diseases in both human and veterinary patients. To date, well over 100 mutated genes are known to cause cardiomyopathies in humans with only a handful known in cats and dogs. This review highlights the need and use of personalized one-health approaches to cardiovascular case management and advancement in pharmacogenetic-based therapy in veterinary medicine. Personalized medicine holds promise in understanding the molecular basis of disease and ultimately will unlock the next generation of targeted novel pharmaceuticals and aid in the reversal of detrimental effects at a molecular level.

The Role of Genetics in the Management of Heart Failure Patients

Over the last decades, the relevance of genetics in cardiovascular diseases has expanded, especially in the context of cardiomyopathies. Its relevance extends to the management of patients diagnosed with heart failure (HF), given its capacity to provide invaluable insights into the etiology of cardiomyopathies and identify individuals at a heightened risk of poor outcomes. Notably, the identification of an etiological genetic variant necessitates a comprehensive evaluation of the family lineage of the affected patients. In the future, these genetic variants hold potential as therapeutic targets with the capability to modify gene expression. In this complex setting, collaboration among cardiologists, specifically those specializing in cardiomyopathies and HF, and geneticists becomes paramount to improving individual and family health outcomes, as well as therapeutic clinical results. This review is intended to offer geneticists and cardiologists an updated perspective on the value of genetic research in HF and its implications in clinical practice.

A network medicine approach to study comorbidities in heart failure with preserved ejection fraction

BACKGROUND

Comorbidities are expected to impact the pathophysiology of heart failure (HF) with preserved ejection fraction (HFpEF). However, comorbidity profiles are usually reduced to a few comorbid disorders. Systems medicine approaches can model phenome-wide comorbidity profiles to improve our understanding of HFpEF and infer associated genetic profiles.

METHODS

We retrospectively explored 569 comorbidities in 29,047 HF patients, including 8062 HFpEF and 6585 HF with reduced ejection fraction (HFrEF) patients from a German university hospital. We assessed differences in comorbidity profiles between HF subtypes via multiple correspondence analysis. Then, we used machine learning classifiers to identify distinctive comorbidity profiles of HFpEF and HFrEF patients. Moreover, we built a comorbidity network (HFnet) to identify the main disease clusters that summarized the phenome-wide comorbidity. Lastly, we predicted novel gene candidates for HFpEF by linking the HFnet to a multilayer gene network, integrating multiple databases. To corroborate HFpEF candidate genes, we collected transcriptomic data in a murine HFpEF model. We compared predicted genes with the murine disease signature as well as with the literature.

RESULTS

We found a high degree of variance between the comorbidity profiles of HFpEF and HFrEF, while each was more similar to HFmrEF. The comorbidities present in HFpEF patients were more diverse than those in HFrEF and included neoplastic, osteologic and rheumatoid disorders. Disease communities in the HFnet captured important comorbidity concepts of HF patients which could be assigned to HF subtypes, age groups, and sex. Based on the HFpEF comorbidity profile, we predicted and recovered gene candidates, including genes involved in fibrosis (COL3A1, LOX, SMAD9, PTHL), hypertrophy (GATA5, MYH7), oxidative stress (NOS1, GSST1, XDH), and endoplasmic reticulum stress (ATF6). Finally, predicted genes were significantly overrepresented in the murine transcriptomic disease signature providing additional plausibility for their relevance.

CONCLUSIONS

We applied systems medicine concepts to analyze comorbidity profiles in a HF patient cohort. We were able to identify disease clusters that helped to characterize HF patients. We derived a distinct comorbidity profile for HFpEF, which was leveraged to suggest novel candidate genes via network propagation. The identification of distinctive comorbidity profiles and candidate genes from routine clinical data provides insights that may be leveraged to improve diagnosis and identify treatment targets for HFpEF patients.

Uncovering genetic associations in the human diseasome using an endophenotype-augmented disease network

Many diseases exhibit complex multimorbidities with one another. An intuitive way to model the connections between phenotypes is with a disease-disease network (DDN), where nodes represent diseases and edges represent associations, such as shared single-nucleotide polymorphisms (SNPs), between pairs of diseases. To gain further genetic understanding of molecular contributors to disease associations, we propose a novel version of the shared-SNP DDN (ssDDN), denoted as ssDDN+, which includes connections between diseases derived from genetic correlations with endophenotypes. We hypothesize that a ssDDN+ can provide complementary information to the disease connections in a ssDDN, yielding insight into the role of clinical laboratory measurements in disease interactions. Using PheWAS summary statistics from the UK Biobank, we constructed a ssDDN+ revealing hundreds of genetic correlations between disease phenotypes and quantitative traits. Our augmented network uncovers genetic associations across different disease categories, connects relevant cardiometabolic diseases, and highlights specific biomarkers that are associated with cross-phenotype associations. Out of the 31 clinical measurements under consideration, HDL-C connects the greatest number of diseases and is strongly associated with both type 2 diabetes and diabetic retinopathy. Triglycerides, another blood lipid with known genetics causes in non-mendelian diseases, also adds a substantial number of edges to the ssDDN. Our study can facilitate future network-based investigations of cross-phenotype associations involving pleiotropy and genetic heterogeneity, potentially uncovering sources of missing heritability in multimorbidities.

Metabolic Cardiomyopathies and Cardiac Defects in Inherited Disorders of Carbohydrate Metabolism: A Systematic Review

Heart failure (HF) is a progressive chronic disease that remains a primary cause of death worldwide, affecting over 64 million patients. HF can be caused by cardiomyopathies and congenital cardiac defects with monogenic etiology. The number of genes and monogenic disorders linked to development of cardiac defects is constantly growing and includes inherited metabolic disorders (IMDs). Several IMDs affecting various metabolic pathways have been reported presenting cardiomyopathies and cardiac defects. Considering the pivotal role of sugar metabolism in cardiac tissue, including energy production, nucleic acid synthesis and glycosylation, it is not surprising that an increasing number of IMDs linked to carbohydrate metabolism are described with cardiac manifestations. In this systematic review, we offer a comprehensive overview of IMDs linked to carbohydrate metabolism presenting that present with cardiomyopathies, arrhythmogenic disorders and/or structural cardiac defects. We identified 58 IMDs presenting with cardiac complications: 3 defects of sugar/sugar-linked transporters (GLUT3, GLUT10, THTR1); 2 disorders of the pentose phosphate pathway (G6PDH, TALDO); 9 diseases of glycogen metabolism (GAA, GBE1, GDE, GYG1, GYS1, LAMP2, RBCK1, PRKAG2, G6PT1); 29 congenital disorders of glycosylation (ALG3, ALG6, ALG9, ALG12, ATP6V1A, ATP6V1E1, B3GALTL, B3GAT3, COG1, COG7, DOLK, DPM3, FKRP, FKTN, GMPPB, MPDU1, NPL, PGM1, PIGA, PIGL, PIGN, PIGO, PIGT, PIGV, PMM2, POMT1, POMT2, SRD5A3, XYLT2); 15 carbohydrate-linked lysosomal storage diseases (CTSA, GBA1, GLA, GLB1, HEXB, IDUA, IDS, SGSH, NAGLU, HGSNAT, GNS, GALNS, ARSB, GUSB, ARSK). With this systematic review we aim to raise awareness about the cardiac presentations in carbohydrate-linked IMDs and draw attention to carbohydrate-linked pathogenic mechanisms that may underlie cardiac complications.

Investigating genes associated with heart failure, atrial fibrillation, and other cardiovascular diseases, and predicting disease using machine learning techniques for translational research and precision medicine

Cardiovascular disease (CVD) is the leading cause of mortality and loss of disability adjusted life years (DALYs) globally. CVDs like Heart Failure (HF) and Atrial Fibrillation (AF) are associated with physical effects on the heart muscles. As a result of the complex nature, progression, inherent genetic makeup, and heterogeneity of CVDs, personalized treatments are believed to be critical. Rightful application of artificial intelligence (AI) and machine learning (ML) approaches can lead to new insights into CVDs for providing better personalized treatments with predictive analysis and deep phenotyping. In this study we focused on implementing AI/ML techniques on RNA-seq driven gene-expression data to investigate genes associated with HF, AF, and other CVDs, and predict disease with high accuracy. The study involved generating RNA-seq data derived from the serum of consented CVD patients. Next, we processed the sequenced data using our RNA-seq pipeline and applied GVViZ for gene-disease data annotation and expression analysis. To achieve our research objectives, we developed a new Findable, Accessible, Intelligent, and Reproducible (FAIR) approach that includes a five-level biostatistical evaluation, primarily based on the Random Forest (RF) algorithm. During our AI/ML analysis, we have fitted, trained, and implemented our model to classify and distinguish high-risk CVD patients based on their age, gender, and race. With the successful execution of our model, we predicted the association of highly significant HF, AF, and other CVDs genes with demographic variables.

Interaction of HSP70-2 gene polymorphism with body mass index and alcohol consumption on the prognosis of Uyghur patients with ischemic heart failure

OBJECTIVES

To investigate the interaction of HSP70-2 gene polymorphism with body mass index (BMI) and alcohol consumption on the prognosis of Uyghur patients with ischemic heart failure (IHF).

METHODS

A total of 205 Uyghur patients with IHF admitted in Urumqi Friendship Hospital from June 2014 to June 2017 were enrolled; 200 age and sex-matched healthy Uyghur physical examiners in the hospital were enrolled as healthy controls. The HSP70-2 gene +1267 polymorphism was detected by PCR. Multivariate unconditional logistic regression was used to analyze the risk factors associated with prognosis in patients with IHF, and the relative excess risk of interaction (RERI) was calculated by crossover analysis to determine the interaction of HSP70-2 gene polymorphism with BMI and alcohol consumption.

RESULTS

Patients were followed up for 3 years, there were 56 cases with poor prognosis (27.32%) and 149 cases with good prognosis (72.68%). Compared with the healthy control group and the good prognosis group, the poor prognosis group had a significantly higher proportion of subjects with alcohol consumption, abnormal alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels as well as lower BMI and left ventricular ejection fraction (all P<0.05). There were significant differences in distributions of HSP70-2 genotype AA/AG/GG and A/G allele between the good prognosis group and the poor prognosis group (both P<0.05). There were significant differences in the distribution of HSP70-2 genotype (χ2=45.42, P<0.01) and A/G allele among IHF patients with different NYHA cardiac function class; the frequency of A allele of HSP70-2 gene increased, and G allele decreased with the increase of cardiac function class (χ2=19.14, P<0.01). Multivariate logistic regression analysis showed that alcohol consumption as well as abnormal ALT and AST were risk factors for poor prognosis in patients with IHF, while BMI and GG type of HSP70-2 gene (compared with AA type) were protective factors (all P<0.05). Crossover analysis showed a significant additive interaction between BMI and HSP70-2 gene polymorphism (RERI=1.15, 95%CI: 0.54-1.76, P<0.01), and for patients carrying HSP70-2 gene type AA/AG, BMI<26.5 kg/m2 increased the risk of poor prognosis (OR=7.47, 95%CI: 2.51-22.22, P<0.01); there was no significant additive interaction between alcohol consumption and HSP70-2 gene polymorphism (RERI=0.56, 95%CI: －6.07-7.20, P>0.05).

CONCLUSIONS

The HSP70-2 gene polymorphism interacts with BMI in Uyghur IHF patients, and BMI<26.5 kg/m2 increases the risk of poor prognosis in IHF patients carrying the HSP70-2 AA/AG genotype.

RhoA rescues cardiac senescence by regulating Parkin-mediated mitophagy

Heart failure is one of the leading causes of death worldwide. RhoA, a small GTPase, governs actin dynamics in various tissue and cell types, including cardiomyocytes; however, its involvement in cardiac function has not been fully elucidated. Here, we generated cardiomyocyte-specific RhoA conditional knockout (cKO) mice, which demonstrated a significantly shorter lifespan with left ventricular dilation and severely impaired ejection fraction. We found that the cardiac tissues of the cKO mice exhibited structural disorganization with fibrosis and also exhibited enhanced senescence compared with control mice. In addition, we show that cardiomyocyte mitochondria were structurally abnormal in the aged cKO hearts. Clearance of damaged mitochondria by mitophagy was remarkably inhibited in both cKO cardiomyocytes and RhoA-knockdown HL-1 cultured cardiomyocytes. In RhoA-depleted cardiomyocytes, we reveal that the expression of Parkin, an E3 ubiquitin ligase that plays a crucial role in mitophagy, was reduced, and expression of N-Myc, a negative regulator of Parkin, was increased. We further reveal that the RhoA-Rho kinase axis induced N-Myc phosphorylation, which led to N-Myc degradation and Parkin upregulation. Re-expression of Parkin in RhoA-depleted cardiomyocytes restored mitophagy, reduced mitochondrial damage, attenuated cardiomyocyte senescence, and rescued cardiac function both in vitro and in vivo. Finally, we found that patients with idiopathic dilated cardiomyopathy without causal mutations for dilated cardiomyopathy showed reduced cardiac expression of RhoA and Parkin. These results suggest that RhoA promotes Parkin-mediated mitophagy as an indispensable mechanism contributing to cardioprotection in the aging heart.

Deciphering the Basis of Molecular Biology of Selected Cardiovascular Diseases: A View on Network Medicine

Cardiovascular diseases are the leading cause of death across the world. For decades, researchers have been studying the causes of cardiovascular disease, yet many of them remain undiscovered or poorly understood. Network medicine is a recently expanding, integrative field that attempts to elucidate this issue by conceiving of disease as the result of disruptive links between multiple interconnected biological components. Still in its nascent stages, this revolutionary application of network science facilitated a number of important discoveries in complex disease mechanisms. As methodologies become more advanced, network medicine harbors the potential to expound on the molecular and genetic complexities of disease to differentiate how these intricacies govern disease manifestations, prognosis, and therapy. This is of paramount importance for confronting the incredible challenges of current and future cardiovascular disease research. In this review, we summarize the principal molecular and genetic mechanisms of common cardiac pathophysiologies as well as discuss the existing knowledge on therapeutic strategies to prevent, halt, or reverse these pathologies.

Mendelian randomization of circulating proteome identifies actionable targets in heart failure

Background

Heart failure (HF) is a prevalent cause of mortality and morbidity. The molecular drivers of HF are still largely unknown.

Results

We aimed to identify circulating proteins causally associated with HF by leveraging genome-wide genetic association data for HF including 47,309 cases and 930,014 controls. We performed two-sample Mendelian randomization (MR) with multiple cis instruments as well as network and enrichment analysis using data from blood protein quantitative trait loci (pQTL) (2,965 blood proteins) measured in 3,301 individuals. Nineteen blood proteins were causally associated with HF, were not subject to reverse causality and were enriched in ligand-receptor and glycosylation molecules. Network pathway analysis of the blood proteins showed enrichment in NF-kappa B, TGF beta, lipid in atherosclerosis and fluid shear stress. Cross-phenotype analysis of HF identified genetic overlap with cardiovascular drugs, myocardial infarction, parental longevity and low-density cholesterol. Multi-trait MR identified causal associations between HF-associated blood proteins and cardiovascular outcomes. Multivariable MR showed that association of BAG3, MIF and APOA5 with HF were mediated by the blood pressure and coronary artery disease. According to the directional effect and biological action, 7 blood proteins are targets of existing drugs or are tractable for the development of novel therapeutics. Among the pathways, sialyl Lewis x and the activin type II receptor are potential druggable candidates.

Conclusions

Integrative MR analyses of the blood proteins identified causally-associated proteins with HF and revealed pleiotropy of the blood proteome with cardiovascular risk factors. Some of the proteins or pathway related mechanisms could be targeted as novel treatment approach in HF.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08811-2.

Reduction of A-to-I RNA editing in the failing human heart regulates formation of circular RNAs

Alterations of RNA editing that affect the secondary structure of RNAs can cause human diseases. We therefore studied RNA editing in failing human hearts. Transcriptome sequencing showed that adenosine-to-inosine (A-to-I) RNA editing was responsible for 80% of the editing events in the myocardium. Failing human hearts were characterized by reduced RNA editing. This was primarily attributable to Alu elements in introns of protein-coding genes. In the failing left ventricle, 166 circRNAs were upregulated and 7 circRNAs were downregulated compared to non-failing controls. Most of the upregulated circRNAs were associated with reduced RNA editing in the host gene. ADAR2, which binds to RNA regions that are edited from A-to-I, was decreased in failing human hearts. In vitro, reduction of ADAR2 increased circRNA levels suggesting a causal effect of reduced ADAR2 levels on increased circRNAs in the failing human heart. To gain mechanistic insight, one of the identified upregulated circRNAs with a high reduction of editing in heart failure, AKAP13, was further characterized. ADAR2 reduced the formation of double-stranded structures in AKAP13 pre-mRNA, thereby reducing the stability of Alu elements and the circularization of the resulting circRNA. Overexpression of circAKAP13 impaired the sarcomere regularity of human induced pluripotent stem cell-derived cardiomyocytes. These data show that ADAR2 mediates A-to-I RNA editing in the human heart. A-to-I RNA editing represses the formation of dsRNA structures of Alu elements favoring canonical linear mRNA splicing and inhibiting the formation of circRNAs. The findings are relevant to diseases with reduced RNA editing and increased circRNA levels and provide insights into the human-specific regulation of circRNA formation.

Integrated Bioinformatics Identifies FREM1 as a Diagnostic Gene Signature for Heart Failure

Objective

This study is aimed at integrating bioinformatics and machine learning to determine novel diagnostic gene signals in the progression of heart failure disease.

Methods

The heart failure microarray datasets and RNA-seq datasets have been downloaded from the public database. Differentially expressed genes (DE genes) are screened out, and then, we analyze their biological functions and pathways. Integrating three machine learning methods, the least absolute shrinkage and selection operator (LASSO) algorithm, random forest (RF) algorithm, and support vector machine recursive feature elimination (SVM-RFE) are used to determine candidate diagnostic gene signals. Then, external independent RNA-seq datasets evaluate the diagnostic value of gene signals. Finally, the convolution tool CIBERSORT estimated the composition pattern of immune cell subtypes in heart failure and carried out a correlation analysis combined with gene signals.

Results

Under the set threshold, we obtained 47 DE genes with the most significant differences. Enrichment analysis shows that most of them are related to hypertrophy, matrix structural constituent, protein binding, inflammatory immune pathway, cardiovascular disease, and inflammatory disease. Three machine learning methods assisted in determining the potential characteristic signals Fras1-related extracellular matrix 1 (FREM1) and meiosis-specific nuclear structural 1 (MNS1). Validation of external datasets confirms that FREM1 is a diagnostic gene signal for heart failure. Immune cell subtypes of tissue specimens found T cell CD8, mast cell resting, T cell CD4 memory resting, T cell regulation (Tregs), monocytes, macrophages M2, T cell CD4 naive, macrophages M0, and neutrophils are associated with HF.

Conclusion

The gene signal FREM1 may be a potential molecular target in the development of HF and is related to the difference in immune infiltration of HF tissue.

A Mild Dyssynchronous Contraction Pattern Detected by SPECT Myocardial Perfusion Imaging Predicts Super-Response to Cardiac Resynchronization Therapy

Background

Using single photon emission computed tomography myocardial perfusion imaging (SPECT MPI) with phase analysis (PA), we aimed to identify the predictive value of a new contraction pattern in cardiac resynchronization therapy (CRT) response.

Methods

Left ventricular mechanical dyssynchrony (LVMD) was evaluated using SPECT MPI with PA in non-ischemic dilated cardiomyopathy (DCM) patients with left bundle branch block (LBBB) indicated for CRT. CRT super-response was defined as LV ejection fraction (EF) ≥50% or an absolute increase of LVEF >15%. The LV contraction was categorized as the mild dyssynchronous pattern when the phase standard deviation (PSD) ≤ 40.3° and phase histogram bandwidth (PBW) ≤ 111.9°, otherwise it was defined as severe dyssynchronous pattern which was further characterized as U-shaped, heterogeneous or homogenous pattern.

Results

The final cohort comprised 74 patients, including 32 (43.2%) in mild dyssynchronous group, 17 (23%) in U-shaped group, 19 (25.7%) in heterogeneous group, and 6 (8.1%) in homogenous group. The mild dyssynchronous group had lower PSD and PBW than U-shaped, heterogeneous, and homogenous groups (P < 0.0001). Compared to patients with the heterogeneous pattern, the odds ratios (ORs) with 95% confidence intervals (CIs) for CRT super-response were 10.182(2.43–42.663), 12.8(2.545–64.372), and 2.667(0.327–21.773) for patients with mild dyssynchronous, U-shaped, and homogenous pattern, respectively. After multivariable adjustment, mild dyssynchronous group remained associated with increased CRT super-response (adjusted OR 5.709, 95% CI 1.152–28.293). Kaplan-Meier curves showed that mild dyssynchronous group demonstrated a better long-term prognosis.

Conclusions

The mild dyssynchronous pattern in patients with DCM is associated with an increased CRT super-response and better long-term prognosis.

Genetic predisposition study of heart failure and its association with cardiomyopathy

Heart failure (HF) is a clinical condition distinguished by structural and functional defects in the myocardium, which genetic and environmental factors can induce. HF is caused by various genetic factors that are both heterogeneous and complex. The incidence of HF varies depending on the definition and area, but it is calculated to be between 1 and 2% in developed countries. There are several factors associated with the progression of HF, ranging from coronary artery disease to hypertension, of which observed the most common genetic cause to be cardiomyopathy. The main objective of this study is to investigate heart failure and its association with cardiomyopathy with their genetic variants. The selected novel genes that have been linked to human inherited cardiomyopathy play a critical role in the pathogenesis and progression of HF. Research sources collected from the human gene mutation and several databases revealed that numerous genes are linked to cardiomyopathy and thus explained the hereditary influence of such a condition. Our findings support the understanding of the genetics aspect of HF and will provide more accurate evidence of the role of changing disease accuracy. Furthermore, a better knowledge of the molecular pathophysiology of genetically caused HF could contribute to the emergence of personalized therapeutics in future.

Sodium tanshinone IIA sulfonate improves adverse ventricular remodeling post MI by reducing myocardial necrosis, modulating inflammation and promoting angiogenesis

BACKGROUND AND OBJECTIVE

Myocardial infarction (MI) leads to pathological cardiac remodeling and heart failure. Sodium tanshinone IIA sulfonate (STS) shows therapeutic values. The present study aimed to explore the potential role of STS in ventricular remodeling post-MI.

METHODS

Mice were randomly divided into sham, MI + normal saline (NS) and MI + STS (20.8 mg/kg/day intraperitoneally) groups. MI was established following left anterior descending artery ligation. Cardiac function was evaluated using echocardiography. Scar size and myocardial fibrosis-associated markers were detected using Masson's trichrome staining and western blot analysis (WB). Necrosis and inflammation were assessed using H&E staining, lactate dehydrogenase (LDH) detection, ELISA, immunohistochemical staining, and WB. Furthermore, angiogenesis markers and associated proteins were detected using immunohistochemical staining and WB.

RESULTS

Mice treated with STS exhibited significant improvements in cardiac function, smaller scar size, and low expression levels of α-smooth muscle actin and collagen I and III at 28 days following surgery, compared with the NS-treated group. Moreover, treatment with STS reduced eosinophil necrosis, the infiltration of inflammatory cells, plasma levels of LDH, high mobility group protein B1, interleukin-1β and tumor necrosis factor-α, and protein expression of these cytokines at 3 days. Macrophage infiltration was also decreased in the STS group in the early phase. Additionally, CD31+ vascular density, protein levels of hypoxia-inducible factor-1α, and vascular endothelial growth factor were elevated in the STS-treated mice at 28 days.

CONCLUSION

STS improved pathological remodeling post-MI, and the associated therapeutic effects may result from a decrease in myocardial necrosis, modulation of inflammation, and an increase in angiogenesis.

RNA-seq driven expression and enrichment analysis to investigate CVD genes with associated phenotypes among high-risk heart failure patients

Background

Heart failure (HF) is one of the most common complications of cardiovascular diseases (CVDs) and among the leading causes of death in the US. Many other CVDs can lead to increased mortality as well. Investigating the genetic epidemiology and susceptibility to CVDs is a central focus of cardiology and biomedical life sciences. Several studies have explored expression of key CVD genes specially in HF, yet new targets and biomarkers for early diagnosis are still missing to support personalized treatment. Lack of gender-specific cardiac biomarker thresholds in men and women may be the reason for CVD underdiagnosis in women, and potentially increased morbidity and mortality as a result, or conversely, an overdiagnosis in men. In this context, it is important to analyze the expression and enrichment of genes with associated phenotypes and disease-causing variants among high-risk CVD populations.

Methods

We performed RNA sequencing focusing on key CVD genes with a great number of genetic associations to HF. Peripheral blood samples were collected from a broad age range of adult male and female CVD patients. These patients were clinically diagnosed with CVDs and CMS/HCC HF, as well as including cardiomyopathy, hypertension, obesity, diabetes, asthma, high cholesterol, hernia, chronic kidney, joint pain, dizziness and giddiness, osteopenia of multiple sites, chest pain, osteoarthritis, and other diseases.

Results

We report RNA-seq driven case–control study to analyze patterns of expression in genes and differentiating the pathways, which differ between healthy and diseased patients. Our in-depth gene expression and enrichment analysis of RNA-seq data from patients with mostly HF and other CVDs on differentially expressed genes and CVD annotated genes revealed 4,885 differentially expressed genes (DEGs) and regulation of 41 genes known for HF and 23 genes related to other CVDs, with 15 DEGs as significantly expressed including four genes already known (FLNA, CST3, LGALS3, and HBA1) for HF and CVDs with the enrichment of many pathways. Furthermore, gender and ethnic group specific analysis showed shared and unique genes between the genders, and among different races. Broadening the scope of the results in clinical settings, we have linked the CVD genes with ICD codes.

Conclusions

Many pathways were found to be enriched, and gender-specific analysis showed shared and unique genes between the genders. Additional testing of these genes may lead to the development of new clinical tools to improve diagnosis and prognosis of CVD patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40246-021-00367-8.

Ring-like late gadolinium enhancement for predicting ventricular tachyarrhythmias in non-ischaemic dilated cardiomyopathy

AIMS

Myocardial fibrosis is associated with clinical ventricular tachyarrhythmia (VTA) events in patients with non-ischaemic dilated cardiomyopathy (DCM). Subepicardial or mid-wall ring-like late gadolinium enhancement (LGE) has received increasing attention in recent years. The aim of this study was to investigate the relationship between ring-like LGE and VTAs in DCM.

METHODS AND RESULTS

Patients diagnosed with non-ischaemic DCM who underwent cardiac magnetic resonance with LGE imaging at baseline were investigated. The composite outcome was the occurrence of VTAs defined as sustained ventricular tachycardia, ventricular fibrillation/flutter, aborted sudden cardiac death (SCD), SCD, and appropriate implantable cardioverter-defibrillator intervention. The final cohort comprised 157 patients, including 36 (22.9%) in no LGE group, 48 (30.6%) in focal LGE group, 40 (25.5%) in multi-focal LGE group, and 33 (21%) in ring-like LGE group. Ring-like LGE group patients were younger compared to focal and multi-focal LGE group (P < 0.001) with higher left ventricular ejection fraction (33.0% vs. 24.4% vs. 22.1%, P < 0.001). After a median of 13 ± 7 months follow-up, compared to patients with no LGE, the hazard ratios (HRs) with 95% confidence intervals (CIs) for VTAs were 2.90 (0.56-15.06), 5.55 (1.21-25.44), and 11.75 (2.66-51.92) for patients with focal LGE, multi-focal LGE, and ring-like LGE, respectively. After multivariable adjustment, ring-like LGE group remained associated with increased risk of VTAs (adjusted HR 10.00, 95% CI 1.54-64.98; P = 0.016) independent of the global LGE burden.

CONCLUSION

The ring-like pattern of LGE is independently associated with an increased risk of VTAs in patients with non-ischaemic DCM.

Clinical and Genetic Analysis of KATP Variants With Heart Failure Risk in Patients With Decreased Serum ApoA-I Levels

CONTEXT

Lower serum concentration of apolipoprotein A-I (ApoA-I) is causally associated with heart failure (HF) risk. Adenosine triphosphate-sensitive potassium channels (KATP), as gating channels coupling vascular reactivity and metabolism with ischemic protection, become a new potential target of management for HF. The KATP gene sequence is highly polymorphic and has a high degree of genetic heterogeneity.

OBJECTIVE

This work aimed to determine whether KATP variants predict the risks of decreased ApoA-I concentration and its related HF.

METHODS

A total of 634 individuals, including 317 patients with decreased ApoA-I concentration (< 120 mg/dL) and 317 counterpart participants (≥ 120 mg/dL), were retrospectively selected. Five KATP variants were genotyped through the MassARRAY platform. Exosome-derived microRNAs (exo-miRs) expression profiles were identified by next-generation sequencing, and the top 10 differentially expressed (DE) exo-miRs were verified using quantitative polymerase chain reaction in a validation cohort of 240 individuals with decreased ApoA-I concentration.

RESULTS

KATP rs141294036 was related to an increased risk of lower ApoA-I levels (adjusted odds ratio [OR] = 1.95, P = .002) and HF incidence (adjusted OR = 2.38, P = .009), especially heart failure with preserved ejection fraction (HFpEF; adjusted OR = 2.13, P = .015). After a median 48.6-month follow-up, participants carrying the CC genotype of rs141294036 were associated with an elevated HF rehospitalization risk (adjusted hazard ratio = 1.91, P = .005). Thirty-six exo-miRs were significantly DE between different genotypes of rs141294036 in participants with lower ApoA-I levels, but only 5 exo-miRs (miR-31-5p, miR-126-5p, miR-106a-5p, miR-378i, and miR-181c-5p) were further confirmed.

CONCLUSION

KATP rs141294036 was associated with increased risks of lower ApoA-I levels, HF incidence (especially HFpEF), and HF rehospitalization in those with the 5 confirmed exo-miRs and its related metabolic pathways.

Machine learning and network medicine: a novel approach for precision medicine and personalized therapy in cardiomyopathies

The early identification of pathogenic mechanisms is essential to predict the incidence and progression of cardiomyopathies and to plan appropriate preventive interventions. Noninvasive cardiac imaging such as cardiac computed tomography, cardiac magnetic resonance, and nuclear imaging plays an important role in diagnosis and management of cardiomyopathies and provides useful prognostic information. Most molecular factors exert their functions by interacting with other cellular components, thus many diseases reflect perturbations of intracellular networks. Indeed, complex diseases and traits such as cardiomyopathies are caused by perturbations of biological networks. The network medicine approach, by integrating systems biology, aims to identify pathological interacting genes and proteins, revolutionizing the way to know cardiomyopathies and shifting the understanding of their pathogenic phenomena from a reductionist to a holistic approach. In addition, artificial intelligence tools, applied to morphological and functional imaging, could allow imaging scans to be automatically analyzed to extract new parameters and features for cardiomyopathy evaluation. The aim of this review is to discuss the tools of network medicine in cardiomyopathies that could reveal new candidate genes and artificial intelligence imaging-based features with the aim to translate into clinical practice as diagnostic, prognostic, and predictive biomarkers and shed new light on the clinical setting of cardiomyopathies. The integration and elaboration of clinical habits, molecular big data, and imaging into machine learning models could provide better disease phenotyping, outcome prediction, and novel drug targets, thus opening a new scenario for the implementation of precision medicine for cardiomyopathies.

Racial and socioeconomic disparity associates with differences in cardiac DNA methylation among men with end-stage heart failure

Heart failure (HF) is a multifactorial syndrome that remains a leading cause of worldwide morbidity. Despite its high prevalence, only half of patients with HF respond to guideline-directed medical management, prompting therapeutic efforts to confront the molecular underpinnings of its heterogeneity. In the current study, we examined epigenetics as a yet unexplored source of heterogeneity among patients with end-stage HF. Specifically, a multicohort-based study was designed to quantify cardiac genome-wide cytosine-p-guanine (CpG) methylation of cardiac biopsies from male patients undergoing left ventricular assist device (LVAD) implantation. In both pilot (n = 11) and testing (n = 31) cohorts, unsupervised multidimensional scaling of genome-wide myocardial DNA methylation exhibited a bimodal distribution of CpG methylation found largely to occur in the promoter regions of metabolic genes. Among the available patient attributes, only categorical self-identified patient race could delineate this methylation signature, with African American (AA) and Caucasian American (CA) samples clustering separately. Because race is a social construct, and thus a poor proxy of human physiology, extensive review of medical records was conducted, but ultimately failed to identify covariates of race at the time of LVAD surgery. By contrast, retrospective analysis exposed a higher all-cause mortality among AA (56.3%) relative to CA (16.7%) patients at 2 yr following LVAD placement (P = 0.03). Geocoding-based approximation of patient demographics uncovered disparities in income levels among AA relative to CA patients. Although additional studies are needed, the current analysis implicates cardiac DNA methylation as a previously unrecognized indicator of socioeconomic disparity in human heart failure outcomes.

NEW & NOTEWORTHY A bimodal signature of cardiac DNA methylation in heart failure corresponds with racial differences in all-cause mortality following mechanical circulatory support. Racial differences in promoter methylation disproportionately affect metabolic signaling pathways. Socioeconomic factors are associated with racial differences in the cardiac methylome among men with end-stage heart failure.

Listen to this article’s corresponding podcast at https://ajpheart.podbean.com/e/racial-socioeconomic-determinants-of-the-cardiac-epigenome/.

Pathophysiology of heart failure

Heart failure is an epidemic disease which affects about 1% to 2% of the population worldwide. Both, the etiology and phenotype of heart failure differ largely. Following a cardiac injury (e.g., myocardial infarction, increased preload or afterload) cellular, structural and neurohumoral modulations occur that affect the phenotype being present. These processes influence the cell function among intra- as well as intercellular behavior. In consequence, activation of the sympathoadrenergic and renin-angiotensin-aldosterone-system takes place leading to adaptive mechanisms, which are accompanied by volume overload, tachycardia, dyspnoea and further deterioration of the cellular function (vicious circle). There exists no heart failure specific clinical sign; the clinical symptomatic shows progressive deterioration acutely or chronically. As a measure of cellular dysfunction, the level of neurohormones (norepinephrine) and natriuretic peptides (e.g., NT-pro BNP) increase. For the diagnosis of heart failure, noninvasive (echocardiography, NMR, NT-proBNP) and invasive (heart catheterization, biopsy) diagnostic procedures are implemented. Modulation of the activated systems by ß-blocker, ACE-inhibitors and ARNI improve outcome and symptoms in heart failure patients with left ventricular dysfunction. Interventional and surgical therapy options may be performed as well. The understanding of the underlying pathophysiology of heart failure is essential to initiate the adequate therapeutic option individually for each patient. Furthermore, prevention of cardiovascular risk factors is essential to lower the risk of heart failure.

Heart failure in the last year: progress and perspective

Research about heart failure (HF) has made major progress in the last years. We give here an update on the most recent findings. Landmark trials have established new treatments for HF with reduced ejection fraction. Sacubitril/valsartan was superior to enalapril in PARADIGM-HF trial, and its initiation during hospitalization for acute HF or early after discharge can now be considered. More recently, new therapeutic pathways have been developed. In the DAPA-HF and EMPEROR-Reduced trials, dapagliflozin and empagliflozin reduced the risk of the primary composite endpoint, compared with placebo [hazard ratio (HR) 0.74; 95% confidence interval (CI) 0.65-0.85; P < 0.001 and HR 0.75; 95% CI 0.65-0.86; P < 0.001, respectively]. Second, vericiguat, an oral soluble guanylate cyclase stimulator, reduced the composite endpoint of cardiovascular death or HF hospitalization vs. placebo (HR 0.90; 95% CI 0.82-0.98; P = 0.02). On the other hand, both the diagnosis and treatment of HF with preserved ejection fraction, as well as management of advanced HF and acute HF, remain challenging. A better phenotyping of patients with HF would be helpful for prognostic stratification and treatment selection. Further aspects, such as the use of devices, treatment of arrhythmias, and percutaneous treatment of valvular heart disease in patients with HF, are also discussed and reviewed in this article.

Functional variants in the promoter region of macrophage migration inhibitory factor rs755622 gene (MIF G173C) among patients with heart failure: Association with echocardiographic indices and disease severity

BACKGROUND

Heart failure (HF) is a serious public health concern resulting in death. An individual predisposition to HF is determined by relationship between genetic and environmental variables. The macrophage migration inhibitory factor (MIF) is a significant mediator that involved in a variety of inflammatory and cardiovascular diseases. To reveal contribution of MIF rs755622 G173C gene variants in the promoter region towards HF pathogenesis and investigate association between recognized genotype and clinical characteristics.

PATIENTS AND METHODS

We recruited 90 patients with HF, 63 with preserved ejection fraction (HFpEF) and 27 with reduced ejection fraction (HFrEF), and 60 age- and sex- matched controls. MIF rs755622 (G>C) single-nucleotide polymorphism was genotyped by PCR-RFLP method.

RESULTS

The GG genotype of MIF rs755622 gene polymorphism was more frequent in HF patients than in controls which increased the risk of HF by about 4.25 times (p<0.05). The distribution of the GG, GC and CC genotypes of MIF were 42%, 21% and 0.0% among HFrEF, and 33.3%, 55.6% and 11.1% among HFpEF respectively. Higher frequency of MIF rs755622 G allele among HFrEF (100%) compared to HFpEF (88.9%) (p = 0.007). MIF-GG genotype variant had significantly lower LVEF. In multivariate analysis, MIF-GG genotype was independent risk predictor among HF (OR 4.6).

CONCLUSION

MIF rs755622 (GG) could be considered as a probable genotypic risk factor for HF, especially in those with HFrEF which increases the possibility that MIF contribute to HF progression. MIF genotype assay may serve as early predictor and help to recognize those at great risk of developing HF.

The heart failure specialists of tomorrow: a network for young cardiovascular scientists and clinicians

The “Heart failure specialists of Tomorrow” (HoT) group gathers young researchers, physicians, basic scientists, nurses and many other professions under the auspices of the Heart Failure Association of the European Society of Cardiology. After its foundation in 2014, it has quickly grown to a large group of currently 925 members. Membership in this growing community offers many advantages during, before, and after the ‘Heart Failure and World Congress on Acute Heart Failure’. These include: eligibility to receive travel grants, participation in moderated poster sessions and young researcher and clinical case sessions, the HoT walk, the career café, access to the networking opportunities, and interaction with a large and cohesive international community that constantly seeks multinational collaborations.

Modelling sarcomeric cardiomyopathies with human cardiomyocytes derived from induced pluripotent stem cells

Cardiomyocytes derived from human induced pluripotent stem cells (iPSCs) provide a unique opportunity to understand the pathophysiological effects of genetic cardiomyopathy mutations. In particular, these cells hold the potential to unmask the effects of mutations on contractile behaviour in vitro, providing new insights into genotype-phenotype relationships. With this goal in mind, several groups have established iPSC lines that contain sarcomeric gene mutations linked to cardiomyopathy in patient populations. Their studies have employed diverse systems and methods for performing mechanical measurements of contractility, ranging from single cell techniques to multicellular tissue-like constructs. Here, we review published results to date within the growing field of iPSC-based sarcomeric cardiomyopathy disease models. We devote special attention to the methods of mechanical characterization selected in each case, and how these relate to the paradigms of classical muscle mechanics. An appreciation of these somewhat subtle paradigms can inform efforts to compare the results of different studies and possibly reconcile discrepancies. Although more work remains to be done to improve and possibly standardize methods for producing, maturing, and mechanically interrogating iPSC-derived cardiomyocytes, the initial results indicate that this approach to modelling cardiomyopathies will continue to provide critical insights into these devastating diseases.

Genetic determinants of heart failure: facts and numbers

The relevance of gene mutations leading to heart diseases and hence heart failure has become evident. The risk for and the course of heart failure depends on genomic variants and mutations underlying the so‐called genetic predisposition. Genetic contribution to heart failure is highly heterogenous and complex. For any patient with a likely inherited heart failure syndrome, genetic counselling is recommended and important. In the last few years, novel sequencing technologies (named next‐generation sequencing – NGS) have dramatically improved the availability of molecular testing, the efficiency of genetic analyses, and moreover reduced the cost for genetic testing. Due to this development, genetic testing has become increasingly accessible and NGS‐based sequencing is now applied in clinical routine diagnostics. One of the most common reasons of heart failure are cardiomyopathies such as the dilated or the hypertrophic cardiomyopathy. Nearly 100 disease‐associated genes have been identified for cardiomyopathies. The knowledge of a pathogenic mutation can be used for genetic counselling, risk and prognosis determination, therapy guidance and hence for a more effective treatment. Besides, family cascade screening for a known familial, pathogenic mutation can lead to an early diagnosis in affected individuals. At that timepoint, a preventative intervention could be used to avoid or delay disease onset or delay disease progression. Understanding the cellular basis of genetic heart failure syndromes in more detail may provide new insights into the molecular biology of physiological and impaired cardiac (cell) function. As our understanding of the molecular and genetic pathophysiology of heart failure will increase, this might help to identify novel therapeutic targets and may lead to the development of new and specific treatment options in patients with heart failure.