Early detection of myocardial dysfunction and heart failure

Wireless and Battery-Free Sensor for Interstitial Fluid Pressure Monitoring

Congestive heart failure (CHF) is a fatal disease with progressive severity and no cure; the heart's inability to adequately pump blood leads to fluid accumulation and frequent hospital readmissions after initial treatments. Therefore, it is imperative to continuously monitor CHF patients during its early stages to slow its progression and enable timely medical interventions for optimal treatment. An increase in interstitial fluid pressure (IFP) is indicative of acute CHF exacerbation, making IFP a viable biomarker for predicting upcoming CHF if continuously monitored. In this paper, we present an inductor-capacitor (LC) sensor for subcutaneous wireless and continuous IFP monitoring. The sensor is composed of inexpensive planar copper coils defined by a simple craft cutter, which serves as both the inductor and capacitor. Because of its sensing mechanism, the sensor does not require batteries and can wirelessly transmit pressure information. The sensor has a low-profile form factor for subcutaneous implantation and can communicate with a readout device through 4 layers of skin (12.7 mm thick in total). With a soft silicone rubber as the dielectric material between the copper coils, the sensor demonstrates an average sensitivity as high as -8.03 MHz/mmHg during in vitro simulations.

Artificial Intelligence and Its Role in Diagnosing Heart Failure: A Narrative Review

Heart failure (HF) is prevalent globally. It is a dynamic disease with varying definitions and classifications due to multiple pathophysiologies and etiologies. The diagnosis, clinical staging, and treatment of HF become complex and subjective, impacting patient prognosis and mortality. Technological advancements, like artificial intelligence (AI), have been significant roleplays in medicine and are increasingly used in cardiovascular medicine to transform drug discovery, clinical care, risk prediction, diagnosis, and treatment. Medical and surgical interventions specific to HF patients rely significantly on early identification of HF. Hospitalization and treatment costs for HF are high, with readmissions increasing the burden. AI can help improve diagnostic accuracy by recognizing patterns and using them in multiple areas of HF management. AI has shown promise in offering early detection and precise diagnoses with the help of ECG analysis, advanced cardiac imaging, leveraging biomarkers, and cardiopulmonary stress testing. However, its challenges include data access, model interpretability, ethical concerns, and generalizability across diverse populations. Despite these ongoing efforts to refine AI models, it suggests a promising future for HF diagnosis. After applying exclusion and inclusion criteria, we searched for data available on PubMed, Google Scholar, and the Cochrane Library and found 150 relevant papers. This review focuses on AI's significant contribution to HF diagnosis in recent years, drastically altering HF treatment and outcomes.

Electrocardiography changes and different stages of heart failure in central Iran: A cross-sectional study from Yazd Health Study

Background and Aims

Electrocardiography (ECG) is a widely accessible, noninvasive, and cost-effective diagnostic instrument used to evaluate patients with suspected heart failure (HF). The aim of this study is to investigate electrocardiographic changes in patients with different stages of HF in a random population of Yazd city.

Methods

This prospective cross-sectional study included 319 individuals, randomly selected, aged 40 years and more, registered in the Yazd Health Study was conducted from March 2022 to May 2023 at Afshar Hospitals. In accordance with the AHA/ACC guidelines, HF was classified into four stages (A, B, C, and D).

Results

The 159 individuals were classified in the stage 0 group, 77 were in Stage A, 65 were in Stage B, and 18 were in Stage C of HF. In the Stage 0, the PR interval (PRi) was 130.5 ± 18.1 ms, while in Stage C, it was 143.3 ± 21.9 ms, with a significant difference (p = 0.047). Similarly, the QRS interval (QRSi) increased with HF staging (p = 0.001). The frequency of diabetes mellitus (DM), hypertension (HTN), hyperlipidemia (HLP), chronic heart disease, alcoholism, and PRi, QRSi, QT interval levels were independent predictors of HF stage in multivariate regression analysis.

Conclusion

The prevalence of HF stages, as classified by the AHA/ACC guidelines, was observed, with significant correlations between ECG parameters and HF progression. abnormal rhythms, left bundle branch block, ischemia, hypertrophy, and left atrial enlargement increased with higher HF stages. Major risk factors like DM and HTN exhibited a heightened prevalence in advanced HF stages, accentuating their pivotal role in the progression of HF.

Identification of novel endogenous control miRNAs in heart failure for normalization of qPCR data

MicroRNAs (miRNAs), a class of non-coding RNAs, are utilized as biomarkers for a wide range of disorders. Circulating miRNAs are proposed as potential markers in the clinical identification of heart failure (HF). However, identifying miRNA biomarkers in HF requires identification of robust endogenous control miRNAs for normalization in differential expression analysis. Hence, this study aimed to identify circulating miRNAs that can be utilized as endogenous controls in HF. We evaluated the expression of eight miRNAs, which were previously reported as endogenous controls in different pathological conditions. Total RNA, including miRNA, was extracted from the serum samples of 30 HF patients (15 HFrEF and 15 HFpEF) and their matched controls (n = 15). We used quantitative PCR to determine the miRNA expression. The stability of the selected endogenous miRNAs was assessed and compared using a standard set of criteria with the RefFinder software. Six of the eight miRNAs analyzed showed consistent expression among all sample groups. Stability analysis ranked hsa-let-7i-5p, hsa-miR-148b-3p, and hsa-miR-484 as the most stable miRNAs, indicating their potential as reliable endogenous controls.

Artificial intelligence in cardiovascular medicine: An updated review of the literature

Screening and early detection of cardiovascular disease (CVD) are crucial for managing progress and preventing related morbidity. In recent years, several studies have reported the important role of Artificial intelligence (AI) technology and its integration into various medical sectors. AI applications are able to deal with the massive amounts of data (medical records, ultrasounds, medications, and experimental results) generated in medicine and identify novel details that would otherwise be forgotten in the mass of healthcare data sets. Nowadays, AI algorithms are currently used to improve diagnosis of some CVDs including heart failure, atrial fibrillation, hypertrophic cardiomyopathy and pulmonary hypertension. This review summarized some AI concepts, critical execution requirements, obstacles, and new applications for CVDs.

The Imageable Genome

Understanding human disease on a molecular level, and translating this understanding into targeted diagnostics and therapies are central tenets of molecular medicine1. Realizing this doctrine requires an efficient adaptation of molecular discoveries into the clinic. We present an approach to facilitate this process by describing the Imageable Genome, the part of the human genome whose expression can be assessed via molecular imaging. Using a deep learning-based hybrid human-AI pipeline, we bridge individual genes and their relevance in human diseases with specific molecular imaging methods. Cross-referencing the Imageable Genome with RNA-seq data from over 60,000 individuals reveals diagnostic, prognostic and predictive imageable genes for a wide variety of major human diseases. Having both the critical size and focus to be altered in its expression during the development and progression of any human disease, the Imageable Genome will generate new imaging tools that improve the understanding, diagnosis and management of human diseases.

Predictive Value of Deep Learning-derived CT Pectoralis Muscle and Adipose Measurements for Incident Heart Failure: Multi-Ethnic Study of Atherosclerosis

Purpose

To develop a deep learning algorithm capable of extracting pectoralis muscle and adipose measurements and to longitudinally investigate associations between these measurements and incident heart failure (HF) in participants from the Multi-Ethnic Study of Atherosclerosis (MESA).

Materials and Methods

MESA is a prospective study of subclinical cardiovascular disease characteristics and risk factors for progression to clinically overt disease approved by institutional review boards of six participating centers (ClinicalTrials.gov identifier: NCT00005487). All participants with adequate imaging and clinical data from the fifth examination of MESA were included in this study. Hence, in this secondary analysis, manual segmentations of 600 chest CT examinations (between the years 2010 and 2012) were used to train and validate a convolutional neural network, which subsequently extracted pectoralis muscle and adipose (intermuscular adipose tissue (IMAT), perimuscular adipose tissue (PAT), extramyocellular lipids and subcutaneous adipose tissue) area measurements from 3031 CT examinations using individualized thresholds for adipose segmentation. Next, 1781 participants without baseline HF were longitudinally investigated for associations between baseline pectoralis muscle and adipose measurements and incident HF using crude and adjusted Cox proportional hazards models. The full models were adjusted for variables in categories of demographic (age, race, sex, income), clinical/laboratory (including physical activity, BMI, and smoking), CT (coronary artery calcium score), and cardiac MRI (left ventricular ejection fraction and mass (% of predicted)) data.

Results

In 1781 participants (median age, 68 (IQR,61, 75) years; 907 [51%] females), 41 incident HF events occurred over a median 6.5-year follow-up. IMAT predicted incident HF in unadjusted (hazard ratio [HR]:1.14; 95% CI: 1.03-1.26) and fully adjusted (HR:1.16, 95% CI: 1.03-1.31) models. PAT also predicted incident HF in crude (HR:1.19; 95% CI: 1.06-1.35) and fully adjusted (HR:1.25; 95% CI: 1.07-1.46) models.

Conclusion

The study demonstrates that fast and reliable deep learning-derived pectoralis muscle and adipose measurements are obtainable from conventional chest CT, which may be predictive of incident HF.©RSNA, 2023.

Association between the diagnostic classification of newly diagnosed coronary artery disease and future heart failure development

OBJECTIVE

The first clinical manifestation of coronary artery disease (CAD) varies widely from unheralded myocardial infarction (MI) to mild, incidentally detected disease. The primary objective of this study was to quantify the association between different initial CAD diagnostic classifications and future heart failure.

METHODS

This retrospective study incorporated the electronic health record of a single integrated health care system. Newly diagnosed CAD was classified into a mutually exclusive hierarchy as MI, CAD with coronary artery bypass graft (CABG), CAD with percutaneous coronary intervention, CAD only, unstable angina, and stable angina. An acute CAD presentation was defined when the diagnosis was associated with a hospital admission. New heart failure was identified after the CAD diagnosis.

RESULTS

Among 28 693 newly diagnosed CAD patients, initial presentation was acute in 47% and manifested as MI in 26%. Within 30 days of CAD diagnosis, MI [hazard ratio (HR) = 5.1; 95% confidence interval: 4.1-6.5] and unstable angina (3.2; 2.4-4.4) classifications were associated with the highest heart failure risk (compared to stable angina), as was acute presentation (2.9; 2.7-3.2). Among stable, heart failure-free CAD patients followed on average 7.4 years, initial MI (adjusted HR = 1.6; 1.4-1.7) and CAD with CABG (1.5; 1.2-1.8) were associated with higher long-term heart failure risk, but an initial acute presentation was not (1.0; 0.9-1.0).

CONCLUSION

Nearly 50% of initial CAD diagnoses are associated with hospitalization, and these patients are at high risk of early heart failure. Among stable CAD patients, MI remained the diagnostic classification associated with the highest long-term heart failure risk, however, having an initial acute CAD presentation was not associated with long-term heart failure.

Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives

Artificial intelligence (AI), the technique of extracting information from complex database using sophisticated computer algorithms, has incorporated itself in medical field. AI techniques have shown the potential to accelerate the progression of diagnosis and treatment of cardiovascular diseases (CVDs), including heart failure, atrial fibrillation, valvular heart disease, hypertrophic cardiomyopathy, congenital heart disease and so on. In clinical scenario, AI have been proved to apply well in CVD diagnosis, enhance effectiveness of auxiliary tools, disease stratification and typing, and outcome prediction. Deeply developed to capture subtle connections from massive amounts of healthcare data, recent AI algorithms are expected to handle even more complex tasks than traditional methods. The aim of this review is to introduce current applications of AI in CVDs, which may allow clinicians who have limited expertise of computer science to better understand the frontier of the subject and put AI algorithms into clinical practice.

Fat Mass is Associated with Subclinical Left Ventricular Systolic Dysfunction in Patients with Type 2 Diabetes Mellitus Without Established Cardiovascular Diseases

INTRODUCTION

Left ventricular global longitudinal strain (GLS) is considered to be the first marker of diabetes mellitus-related subclinical cardiac dysfunction, but whether it is attributable to fat mass and distribution remains uncertain. In this study, we explored whether fat mass, especially fat mass in the android area, is associated with subclinical systolic dysfunction before the onset of cardiac disease.

METHODS

We conducted a single-center prospective cross-sectional study between November 2021 and August 2022 on inpatients of the Department of Endocrinology, Nanjing Drum Tower Hospital. We included 150 patients aged 18-70 years with no signs, symptoms, or history of clinical cardiac disease. Patients were evaluated with speckle tracking echocardiography and dual energy X-ray absorptiometry. The cutoff values for subclinical systolic dysfunction were set at a global longitudinal strain (GLS) < 18%.

RESULTS

After adjusting for sex and age, patients with GLS < 18% had a higher mean (± standard deviation) fat mass index (8.06 ± 2.39 vs. 7.10 ± 2.09 kg/m², p = 0.02), higher mean trunk fat mass (14.9 ± 4.9 vs. 12.8 ± 4.3 kg, p = 0.01), and higher android fat mass (2.57 ± 1.02 vs. 2.18 ± 0.86 kg, p = 0.02) than those in the GLS ≥ 18%. Partial correlation analysis showed that the fat mass index, truck fat mass, and android fat mass were negatively correlated with GLS after adjusting for sex and age (all p < 0.05). Adjusted for traditional cardiovascular metabolic factors, fat mass index (odds ratio [OR] 1.27, 95% confidence interval [CI] 1.05-1.55, p = 0.02), trunk fat mass (OR 1.13, 95% CI 1.03-1.24, p = 0.01), and android fat mass (OR 1.77, 95% CI 1.16-2.82, p = 0.01) were independent risk factors for GLS < 18%.

CONCLUSION

Among patients with type 2 diabetes mellitus without established clinical cardiac disease, fat mass, especially android fat mass, was associated with subclinical systolic dysfunction independently of age and sex.

Nicotinamide riboside kinase-2 regulates metabolic adaptation in the ischemic heart

Ischemia-induced metabolic remodeling plays a critical role in the pathogenesis of adverse cardiac remodeling and heart failure however, the underlying molecular mechanism is largely unknown. Here, we assess the potential roles of nicotinamide riboside kinase-2 (NRK-2), a muscle-specific protein, in ischemia-induced metabolic switch and heart failure through employing transcriptomic and metabolomic approaches in ischemic NRK-2 knockout mice. The investigations revealed NRK-2 as a novel regulator of several metabolic processes in the ischemic heart. Cardiac metabolism and mitochondrial function and fibrosis were identified as top dysregulated cellular processes in the KO hearts post-MI. Several genes linked to mitochondrial function, metabolism, and cardiomyocyte structural proteins were severely downregulated in the ischemic NRK-2 KO hearts. Analysis revealed significantly upregulated ECM-related pathways which was accompanied by the upregulation of several key cell signaling pathways including SMAD, MAPK, cGMP, integrin, and Akt in the KO heart post-MI. Metabolomic studies identified profound upregulation of metabolites mevalonic acid, 3,4-dihydroxyphenylglycol, 2-penylbutyric acid, and uridine. However, other metabolites stearic acid, 8,11,14-eicosatrienoic acid, and 2-pyrrolidinone were significantly downregulated in the ischemic KO hearts. Taken together, these findings suggest that NRK-2 promotes metabolic adaptation in the ischemic heart. The aberrant metabolism in the ischemic NRK-2 KO heart is largely driven by dysregulated cGMP and Akt and mitochondrial pathways. KEY MESSAGES: Post-myocardial infarction metabolic switch critically regulates the pathogenesis of adverse cardiac remodeling and heart failure. Here, we report NRK-2 as a novel regulator of several cellular processes including metabolism and mitochondrial function post-MI. NRK-2 deficiency leads to downregulation of genes important for mitochondrial pathway, metabolism, and cardiomyocyte structural proteins in the ischemic heart. It was accompanied by upregulation of several key cell signaling pathways including SMAD, MAPK, cGMP, integrin, and Akt and dysregulation of numerous metabolites essential for cardiac bioenergetics. Taken together, these findings suggest that NRK-2 is critical for metabolic adaptation of the ischemic heart.

Plasma Galectin-3 and H-FABP correlate with poor physical performance in patients with congestive heart failure

Heart failure (HF) is often associated with compromised physical capacity in patients. However, it is unclear if established HF markers correlate with the physical performance of patients with congestive HF (CHF). We assessed the left ventricular end-systolic dimension (LVESD) and ejection fraction (LVEF) and, physical performance parameters, including short physical performance battery (SPPB), gait speed (GS), and handgrip strength (HGS) in 80 patients with CHF along with 59 healthy controls. Furthermore, levels of plasma HF markers galectin-3 and heart-specific fatty acid binding protein (H-FABP) were measured in relation to the severity of HF and physical performance. Irrespective of etiology, significantly greater LVESD and lower LVEF were observed in HF patients versus controls. As expected, the levels of HF markers galectin-3 and H-FABP were upregulated in the CHF patients which were accompanied by significantly elevated levels of plasma zonulin and inflammatory marker C-reactive protein (CRP). The SPPB scores, GS, and HGS were significantly lower in the ischemic and non-ischemic HF patients than controls. The level of galectin-3 was inversely correlated with SPPB scores (r2 = 0.089, P = 0.01) and HGS (r2 = 0.078, P = 0.01). Similarly, H-FABP levels were also inversely correlated with SPPB scores (r2 = 0.06, P = 0.03) and HGS (r2 = 0.109, P = 0.004) in the patients with CHF. Taken together, CHF adversely affects physical performance, and galectin-3 and H-FABP may serve as biomarkers of physical disability in patients with CHF. The robust correlations of galectin-3 and H-FABP with the physical performance parameters and CRP in CHF patients suggest that the poor physical performance may partly be caused due to systemic inflammation.

Cost-effectiveness of NT-proBNP-supported screening of chronic heart failure in patients with or without type 2 diabetes in Austria and Switzerland

BACKGROUND

Heart failure (HF) is a clinical syndrome with a global burden. Signs and symptoms of HF are nonspecific and often shared with other conditions. The N-terminal prohormone of brain natriuretic peptide (NT-proBNP) serves as a useful biomarker for the diagnosis of HF not only in patients with acute symptoms but also in outpatients with an ambiguous clinical presentation. The aim of the analysis is to evaluate the cost-effectiveness of implementing NT-proBNP in the diagnostic algorithm in patients with/without type 2 diabetes mellitus (T2DM), compared with a diagnosis based primarily on clinical signs or symptoms from the perspective of the Austrian and Swiss healthcare system.

METHODS

A time-discrete Markov model was developed to simulate the effect/improvement (lifetime-costs, quality-adjusted life-years [QALYs], and life-years [LYs]) due to an NT-proBNP screening in undetected HF patients. Undetected HF patients are included in the model according to a distribution of New York Heart Association (NYHA) classes. The model considers disease progression by transition of NYHA classes. Undetected patients may remain undetected or be detected with the help of NT-proBNP or symptoms. Patients with known HF exhibit a slower disease progression. The probability of dying is influenced by the respective NYHA class. Direct costs (2021 € or CHF) were derived from published sources. QALYs, LYs, and costs were discounted (3% p.a.).

RESULTS

In the per-patient analysis (at age 60 over lifetime), the incremental cost-utility ratio (ICUR)/QALY of NT-proBNP vs. no screening was €3,042 for HF patients in Austria. Considering the total cohort of undetected HF patients (n = 9,377) with the corresponding age structure over a lifetime, the ICUR increases to €4,356. In Switzerland, the per-patient results show an ICUR of CHF 897. Considering the total cohort of undetected HF patients (n = 6,826) the ICUR amounts to CHF 4,513. If indirect costs are considered, NT-proBNP screening becomes the dominant strategy in both countries.

CONCLUSION

Overall, the analysis concludes that screening with NT-proBNP is a highly cost-effective or cost-saving diagnostic option for patients with HF, and a sensitivity analysis confirmed these findings.

Galectin 3 enhances platelet aggregation and thrombosis via Dectin-1 activation: a translational study

AIMS

Galectin-3, a β-galactoside-binding lectin, is abnormally increased in cardiovascular disease. Plasma Galectin-3 receives a Class II recommendation for heart failure management and has been extensively studied for multiple cellular functions. The direct effects of Galectin-3 on platelet activation remain unclear. This study explores the direct effects of Galectin-3 on platelet activation and thrombosis.

METHODS AND RESULTS

A strong positive correlation between plasma Galectin-3 concentration and platelet aggregation or whole blood thrombus formation was observed in patients with coronary artery disease (CAD). Multiple platelet function studies demonstrated that Galectin-3 directly potentiated platelet activation and in vivo thrombosis. Mechanistic studies using the Dectin-1 inhibitor, laminarin, and Dectin-1-/- mice revealed that Galectin-3 bound to and activated Dectin-1, a receptor not previously reported in platelets, to phosphorylate spleen tyrosine kinase and thus increased Ca2+ influx, protein kinase C activation, and reactive oxygen species production to regulate platelet hyperreactivity. TD139, a Galectin-3 inhibitor in a Phase II clinical trial, concentration dependently suppressed Galectin-3-potentiated platelet activation and inhibited occlusive thrombosis without exacerbating haemorrhage in ApoE-/- mice, which spontaneously developed increased plasma Galectin-3 levels. TD139 also suppressed microvascular thrombosis to protect the heart from myocardial ischaemia-reperfusion injury in ApoE-/- mice.

CONCLUSION

Galectin-3 is a novel positive regulator of platelet hyperreactivity and thrombus formation in CAD. As TD139 has potent antithrombotic effects without bleeding risk, Galectin-3 inhibitors may have therapeutic advantages as potential antiplatelet drugs for patients with high plasma Galectin-3 levels.

Prediction of heart failure 1 year before diagnosis in general practitioner patients using machine learning algorithms: a retrospective case-control study

OBJECTIVES

Heart failure (HF) is a commonly occurring health problem with high mortality and morbidity. If potential cases could be detected earlier, it may be possible to intervene earlier, which may slow progression in some patients. Preferably, it is desired to reuse already measured data for screening of all persons in an age group, such as general practitioner (GP) data. Furthermore, it is essential to evaluate the number of people needed to screen to find one patient using true incidence rates, as this indicates the generalisability in the true population. Therefore, we aim to create a machine learning model for the prediction of HF using GP data and evaluate the number needed to screen with true incidence rates.

DESIGN, SETTINGS AND PARTICIPANTS

GP data from 8543 patients (-2 to -1 year before diagnosis) and controls aged 70+ years were obtained retrospectively from 01 January 2012 to 31 December 2019 from the Nivel Primary Care Database. Codes about chronic illness, complaints, diagnostics and medication were obtained. Data were split in a train/test set. Datasets describing demographics, the presence of codes (non-sequential) and upon each other following codes (sequential) were created. Logistic regression, random forest and XGBoost models were trained. Predicted outcome was the presence of HF after 1 year. The ratio case:control in the test set matched true incidence rates (1:45).

RESULTS

Sole demographics performed average (area under the curve (AUC) 0.692, CI 0.677 to 0.706). Adding non-sequential information combined with a logistic regression model performed best and significantly improved performance (AUC 0.772, CI 0.759 to 0.785, p<0.001). Further adding sequential information did not alter performance significantly (AUC 0.767, CI 0.754 to 0.780, p=0.07). The number needed to screen dropped from 14.11 to 5.99 false positives per true positive.

CONCLUSION

This study created a model able to identify patients with pending HF a year before diagnosis.

Benefits of SGLT2i for the Treatment of Heart Failure Irrespective of Diabetes Diagnosis: A State-of-the-Art Review

Morbidity and mortality associated with heart failure (HF) has remained high despite advances in therapy. Furthermore, HF-associated risk in patients with type 2 diabetes mellitus (T2D) is even higher than in patients without T2D owing to the strong reciprocal relationship between conditions. However, until recently, no therapy to treat patients with diabetes also reduced cardiovascular risks related to HF. Recent clinical studies (DAPA-HF, EMPEROR-Reduced and EMPEROR-Preserved, SOLOIST-WHF trial) and meta-analysis have demonstrated that sodium-glucose cotransporter-2 inhibitors (SGLT2i) are among the first antidiabetic drugs capable of reducing cardiovascular risks related to HF and improving the prognosis of patients with and without diabetes. Their pleiotropic mechanisms of action place them at the intersection of hemodynamic, metabolic, and neurohumoral pathways, with clear advantages for treating these patients independent of its glucose-lowering effect. Moreover, the benefits of SGLT2i were consistent across the cardiorenal continuum in different populations and clinical settings, which has led to different guidelines introducing SGLT2i as a first-line treatment for HF.

Cardiovascular Biomarkers: Lessons of the Past and Prospects for the Future

Cardiovascular diseases (CVDs) are a major healthcare burden on the population worldwide. Early detection of this disease is important in prevention and treatment to minimise morbidity and mortality. Biomarkers are a critical tool to either diagnose, screen, or provide prognostic information for pathological conditions. This review discusses the historical cardiac biomarkers used to detect these conditions, discussing their application and their limitations. Identification of new biomarkers have since replaced these and are now in use in routine clinical practice, but still do not detect all disease. Future cardiac biomarkers are showing promise in early studies, but further studies are required to show their value in improving detection of CVD above the current biomarkers. Additionally, the analytical platforms that would allow them to be adopted in healthcare are yet to be established. There is also the need to identify whether these biomarkers can be used for diagnostic, prognostic, or screening purposes, which will impact their implementation in routine clinical practice.

A machine learning model to estimate myocardial stiffness from EDPVR

In-vivo estimation of mechanical properties of the myocardium is essential for patient-specific diagnosis and prognosis of cardiac disease involving myocardial remodeling, including myocardial infarction and heart failure with preserved ejection fraction. Current approaches use time-consuming finite-element (FE) inverse methods that involve reconstructing and meshing the heart geometry, imposing measured loading, and conducting computationally expensive iterative FE simulations. In this paper, we propose a machine learning (ML) model that feasibly and accurately predicts passive myocardial properties directly from select geometric, architectural, and hemodynamic measures, thus bypassing exhaustive steps commonly required in cardiac FE inverse problems. Geometric and fiber-orientation features were chosen to be readily obtainable from standard cardiac imaging protocols. The end-diastolic pressure-volume relationship (EDPVR), which can be obtained using a single-point pressure-volume measurement, was used as a hemodynamic (loading) feature. A comprehensive ML training dataset in the geometry-architecture-loading space was generated, including a wide variety of partially synthesized rodent heart geometry and myofiber helicity possibilities, and a broad range of EDPVRs obtained using forward FE simulations. Latin hypercube sampling was used to create 2500 examples for training, validation, and testing. A multi-layer feed-forward neural network (MFNN) was used as a deep learning agent to train the ML model. The model showed excellent performance in predicting stiffness parameters \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$a_f$$\end{document}af and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$b_f$$\end{document}bf associated with fiber direction (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^2_{a_f}=99.471\%$$\end{document}Raf2=99.471% and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^2_{b_f}=92.837\%$$\end{document}Rbf2=92.837%). After conducting permutation feature importance analysis, the ML performance further improved for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$b_f$$\end{document}bf (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R^2_{b_f}=96.240\%$$\end{document}Rbf2=96.240%), and the left ventricular volume and endocardial area were found to be the most critical geometric features for accurate predictions. The ML model predictions were evaluated further in two cases: (i) rat-specific stiffness data measured using ex-vivo mechanical testing, and (ii) patient-specific estimation using FE inverse modeling. Excellent agreements with ML predictions were found for both cases. The trained ML model offers a feasible technology to estimate patient-specific myocardial properties, thus, bridging the gap between EDPVR, as a confounded organ-level metric for tissue stiffness, and intrinsic tissue-level properties. These properties provide incremental information relative to traditional organ-level indices for cardiac function, improving the clinical assessment and prognosis of cardiac diseases.

FTZ protects against cardiac hypertrophy and oxidative injury via microRNA-214 / SIRT3 signaling pathway

BACKGROUND

Despite the fact that the initial hypertrophic response to ventricular pressure overload is thought to be compensatory, prolonged stress often leads to heart failure. Previous studies have shown that the Fufang-Zhenzhu-Tiaozhi (FTZ) formula is beneficial for the treatment of dyslipidemia and hyperglycemia. However, the effects of FTZ on cardiac hypertrophy remain unclear.

OBJECTIVE

The aim of this study is to evaluate the protective effects of FTZ on cardiac hypertrophy and determine the underlying mechanisms.

METHODS

TAC was utilized to establish a cardiac hypertrophy animal model, and FTZ was given via gavage for four weeks. Next, echocardiographic measurements were made. The morphology of mouse cardiomyocytes was examined using H&E and WGA staining. In vitro, the neonatal cardiomyocytes were stimulated with angiotensin Ⅱ (Ang Ⅱ). In addition to measuring the size of cardiomyocytes, qRT-PCR and western blotting were conducted to measure cardiac stress markers and pathway.

RESULTS

According to our findings, FTZ alleviated cardiac hypertrophy in mice and cell models. Furthermore, expression of miR-214 was down-regulated following FTZ, whereas the effect of FTZ therapy was reversed using miR-214 transfection. Furthermore, the expression of Sirtuin 3 (SIRT3) was decreased in Ang Ⅱ-induced oxidative damage, which was associated with a reduction in SOD-1, GPX1, and HO-1 and an increase in MDA, while SIRT3 expression was restored following FTZ treatment.

CONCLUSIONS

Collectively, these findings indicate that FTZ is a protective factor for cardiac hypertrophy due to its regulation of the miR-214-SIRT3 axis, which suggests that FTZ may be a therapeutic target for cardiac hypertrophy.

Point-of-care screening for heart failure with reduced ejection fraction using artificial intelligence during ECG-enabled stethoscope examination in London, UK: a prospective, observational, multicentre study

Background

Most patients who have heart failure with a reduced ejection fraction, when left ventricular ejection fraction (LVEF) is 40% or lower, are diagnosed in hospital. This is despite previous presentations to primary care with symptoms. We aimed to test an artificial intelligence (AI) algorithm applied to a single-lead ECG, recorded during ECG-enabled stethoscope examination, to validate a potential point-of-care screening tool for LVEF of 40% or lower.

Methods

We conducted an observational, prospective, multicentre study of a convolutional neural network (known as AI-ECG) that was previously validated for the detection of reduced LVEF using 12-lead ECG as input. We used AI-ECG retrained to interpret single-lead ECG input alone. Patients (aged ≥18 years) attending for transthoracic echocardiogram in London (UK) were recruited. All participants had 15 s of supine, single-lead ECG recorded at the four standard anatomical positions for cardiac auscultation, plus one handheld position, using an ECG-enabled stethoscope. Transthoracic echocardiogram-derived percentage LVEF was used as ground truth. The primary outcome was performance of AI-ECG at classifying reduced LVEF (LVEF ≤40%), measured using metrics including the area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity, with two-sided 95% CIs. The primary outcome was reported for each position individually and with an optimal combination of AI-ECG outputs (interval range 0–1) from two positions using a rule-based approach and several classification models. This study is registered with ClinicalTrials.gov, NCT04601415.

Findings

Between Feb 6 and May 27, 2021, we recruited 1050 patients (mean age 62 years [SD 17·4], 535 [51%] male, 432 [41%] non-White). 945 (90%) had an ejection fraction of at least 40%, and 105 (10%) had an ejection fraction of 40% or lower. Across all positions, ECGs were most frequently of adequate quality for AI-ECG interpretation at the pulmonary position (979 [93·3%] of 1050). Quality was lowest for the aortic position (846 [80·6%]). AI-ECG performed best at the pulmonary valve position (p=0·02), with an AUROC of 0·85 (95% CI 0·81–0·89), sensitivity of 84·8% (76·2–91·3), and specificity of 69·5% (66·4–72·6). Diagnostic odds ratios did not differ by age, sex, or non-White ethnicity. Taking the optimal combination of two positions (pulmonary and handheld positions), the rule-based approach resulted in an AUROC of 0·85 (0·81–0·89), sensitivity of 82·7% (72·7–90·2), and specificity of 79·9% (77·0–82·6). Using AI-ECG outputs from these two positions, a weighted logistic regression with l2 regularisation resulted in an AUROC of 0·91 (0·88–0·95), sensitivity of 91·9% (78·1–98·3), and specificity of 80·2% (75·5–84·3).

Interpretation

A deep learning system applied to single-lead ECGs acquired during a routine examination with an ECG-enabled stethoscope can detect LVEF of 40% or lower. These findings highlight the potential for inexpensive, non-invasive, workflow-adapted, point-of-care screening, for earlier diagnosis and prognostically beneficial treatment.

Funding

NHS Accelerated Access Collaborative, NHSX, and the National Institute for Health Research.

Graphene Quantum Dots-Based Electrochemical Biosensing Platform for Early Detection of Acute Myocardial Infarction

Heart failure resulting from acute myocardial infarction (AMI) is an important global health problem. Treatments of heart failure and AMI have improved significantly over the past two decades; however, the available diagnostic tests only give limited insights into these heterogeneous conditions at a reversible stage and are not precise enough to evaluate the status of the tissue at high risk. Innovative diagnostic tools for more accurate, more reliable, and early diagnosis of AMI are urgently needed. A promising solution is the timely identification of prognostic biomarkers, which is crucial for patients with AMI, as myocardial dysfunction and infarction lead to more severe and irreversible changes in the cardiovascular system over time. The currently available biomarkers for AMI detection include cardiac troponin I (cTnI), cardiac troponin T (cTnT), myoglobin, lactate dehydrogenase, C-reactive protein, and creatine kinase and myoglobin. Most recently, electrochemical biosensing technologies coupled with graphene quantum dots (GQDs) have emerged as a promising platform for the identification of troponin and myoglobin. The results suggest that GQDs-integrated electrochemical biosensors can provide useful prognostic information about AMI at an early, reversible, and potentially curable stage. GQDs offer several advantages over other nanomaterials that are used for the electrochemical detection of AMI such as strong interactions between cTnI and GQDs, low biomarker consumption, and reusability of the electrode; graphene-modified electrodes demonstrate excellent electrochemical responses due to the conductive nature of graphene and other features of GQDs (e.g., high specific surface area, π-π interactions with the analyte, facile electron-transfer mechanisms, size-dependent optical features, interplay between bandgap and photoluminescence, electrochemical luminescence emission capability, biocompatibility, and ease of functionalization). Other advantages include the presence of functional groups such as hydroxyl, carboxyl, carbonyl, and epoxide groups, which enhance the solubility and dispersibility of GQDs in a wide variety of solvents and biological media. In this perspective article, we consider the emerging knowledge regarding the early detection of AMI using GQDs-based electrochemical sensors and address the potential role of this sensing technology which might lead to more efficient care of patients with AMI.

Galectin-3 as a Novel Multifaceted and Not Only Cardiovascular Biomarker in Patients with Psoriasis with Regard to Systemic Treatment-Preliminary Data

Simple Summary

Galectin-3 (gal-3) regulates many different biological processes and diseases, which are common accompanying diseases of psoriasis. Psoriasis is one of the most common skin diseases. There is little information about potential diagnostic role of gal-3 in psoriasis. Serum gal-3 concentrations were measured before and after twelve weeks of antipsoriatic treatment in patients with psoriasis and compared to 11 persons without psoriasis (control group). Serum gal-3 level in patients with psoriasis was significantly higher compared to the control group. In obese patients and long-lasting psoriasis positive relations of gal-3 and index of psoriasis severity were noted. In psoriatics with low gal-3 levels, it was noted that the higher the gal-3, the higher the BMI and glucose level. In patients with long history of psoriasis it was observed that the higher gal-3, the lower the lipids levels. The Gal-3 level might be a factor affecting the course of psoriasis and useful in prediction of cardiometabolic comorbidities, especially in patients with a long history of the disease or obesity. Patients with low serum gal-3 and a short history of psoriasis may have greater risk of diabetes. In obese patients with long-lasting psoriasis, gal-3 may have a beneficial influence against abnormal lipid profiles or perhaps further cardiovascular disorder development.

Abstract

Galectin-3 (gal-3) is a multifunctional regulator of various biological processes and diseases, which are common comorbidities in psoriasis. Data regarding potential diagnostic role of gal-3 in psoriasis are insufficient. Serum gal-3 levels were evaluated before and after twelve weeks of treatment with acitretin or methotrexate in 31 patients with plaque-type psoriasis and compared to 11 healthy control group. The mean serum galectin-3 level in patients with psoriasis was significantly higher compared to the control group (p < 0.01). In patients with obesity and long-lasting psoriasis (>20 years) positive relations of gal-3 and PASI were noted. In psoriatics with low gal-3 levels, positive correlations between the gal-3 and BMI, glucose level, and with the latter in short-lasting psoriasis (<20 years) were noted. In the long history of psoriasis, gal-3 was negatively correlated with lipids levels. The Gal-3 level might be a multifaceted modulator of the course of psoriasis and predictive factor of cardiometabolic comorbidities’ development, especially in patients with a long history of the disease or obesity. Patients with low serum gal-3 and short history of psoriasis are presumably at greater risk of diabetes. In patients with long-lasting psoriasis and concomitant obesity, gal-3 may exert a protective role against dyslipidemia or perhaps further CMD development.

Interpretable Detection and Location of Myocardial Infarction Based on Ventricular Fusion Rule Features

Myocardial infarction (MI) is one of the most common cardiovascular diseases threatening human life. In order to accurately distinguish myocardial infarction and have a good interpretability, the classification method that combines rule features and ventricular activity features is proposed in this paper. Specifically, according to the clinical diagnosis rule and the pathological changes of myocardial infarction on the electrocardiogram, the local information extracted from the Q wave, ST segment, and T wave is computed as the rule feature. All samples of the QT segment are extracted as ventricular activity features. Then, in order to reduce the computational complexity of the ventricular activity features, the effects of Discrete Wavelet Transform (DWT), Principal Component Analysis (PCA), and Locality Preserving Projections (LPP) on the extracted ventricular activity features are compared. Combining rule features and ventricular activity features, all the 12 leads features are fused as the ultimate feature vector. Finally, eXtreme Gradient Boosting (XGBoost) is used to identify myocardial infarction, and the overall accuracy rate of 99.86% is obtained on the Physikalisch-Technische Bundesanstalt (PTB) database. This method has a good medical diagnosis basis while improving the accuracy, which is very important for clinical decision-making.

Using Deep-Learning Algorithms to Simultaneously Identify Right and Left Ventricular Dysfunction From the Electrocardiogram

OBJECTIVES

This study sought to develop DL models capable of comprehensively quantifying left and right ventricular dysfunction from ECG data in a large, diverse population.

BACKGROUND

Rapid evaluation of left and right ventricular function using deep learning (DL) on electrocardiograms (ECGs) can assist diagnostic workflow. However, DL tools to estimate right- ventricular (RV) function do not exist, whereas those to estimate left ventricular (LV) function are restricted to quantification of very low LV function only.

METHODS

A multicenter study was conducted with data from 5 New York City hospitals: 4 for internal testing and 1 serving as external validation. We created novel DL models to classify left ventricular ejection fraction (LVEF) into categories derived from the latest universal definition of heart failure, estimate LVEF through regression, and predict a composite outcome of either RV systolic dysfunction or RV dilation.

RESULTS

We obtained echocardiogram LVEF estimates for 147,636 patients paired to 715,890 ECGs. We used natural language processing (NLP) to extract RV size and systolic function information from 404,502 echocardiogram reports paired to 761,510 ECGs for 148,227 patients. For LVEF classification in internal testing, area under curve (AUC) at detection of LVEF ≤40%, 40% < LVEF ≤50%, and LVEF >50% was 0.94 (95% CI: 0.94-0.94), 0.82 (95% CI: 0.81-0.83), and 0.89 (95% CI: 0.89-0.89), respectively. For external validation, these results were 0.94 (95% CI: 0.94-0.95), 0.73 (95% CI: 0.72-0.74), and 0.87 (95% CI: 0.87-0.88). For regression, the mean absolute error was 5.84% (95% CI: 5.82%-5.85%) for internal testing and 6.14% (95% CI: 6.13%-6.16%) in external validation. For prediction of the composite RV outcome, AUC was 0.84 (95% CI: 0.84-0.84) in both internal testing and external validation.

CONCLUSIONS

DL on ECG data can be used to create inexpensive screening, diagnostic, and predictive tools for both LV and RV dysfunction. Such tools may bridge the applicability of ECGs and echocardiography and enable prioritization of patients for further interventions for either sided failure progressing to biventricular disease.

Small Endogeneous Peptide Mitigates Myocardial Remodeling in a Mouse Model of Cardioselective Galectin-3 Overexpression

BACKGROUND

Myocardial Gal3 (galectin-3) expression is associated with cardiac inflammation and fibrosis. Increased Gal3 portends susceptibility to heart failure and death. There are no data reporting the causative role of Gal3 to mediate cardiac fibro-inflammatory response and heart failure.

METHODS

We developed a cardioselective Gal3 gain-of-function mouse (Gal3+/+) using α-myosin heavy chain promotor. We confirmed Gal3-transgene expression with real-time polymerase chain reaction and quantified cardiac/circulating Gal3 with Western blot and immunoassays. We used echocardiogram and cardiac magnetic resonance imaging to measure cardiac volumes, function, and myocardial velocities. Ex vivo, we studied myocardial inflammation/fibrosis and downstream TGF (transforming growth factor) β1-mRNA expression. We examined the effects of acute myocardial ischemia in presence of excess Gal3 by inducing acute myocardial infarction in mice. Two subsets of mice including mice treated with N-acetyl-seryl-aspartyl-lysyl-proline (a Gal3-inhibitor) and mice with genetic Gal3 loss-of-function (Gal3-/-) were studied for comparative analysis of Gal3 function.

RESULTS

Gal3+/+ mice had increased cardiac/circulating Gal3. Gal3+/+ mice showed excess pericardial fat pad, dilated ventricles and cardiac dysfunction, which was partly normalized by N-acetyl-seryl-aspartyl-lysyl-proline. Cardiac magnetic resonance imaging showed reduced myocardial contractile velocities in Gal3+/+. The majority of Gal3+/+ mice did not survive acute myocardial infarction, and the survivors had profound cardiac dysfunction. Myocardial histology of Gal3+/+ mice showed macrophage/mast-cell infiltration, fibrosis and higher TGFβ1-mRNA expression, which were mitigated by both Gal3 gene deletion and N-acetyl-seryl-aspartyl-lysyl-proline administration.

CONCLUSIONS

Our study shows that cardioselective Gal3 overexpression leads to multiple cardiac phenotypic defects including ventricular dilation and cardiac dysfunction. Pharmacological Gal3 inhibition conferred protective effects with reduction of inflammation and fibrosis. Our study highlights the importance of translational studies to counteract Gal3 function and prevent cardiac dysfunction.

Detection of pathological mechano-acoustic signatures using precision accelerometer contact microphones in patients with pulmonary disorders

Monitoring pathological mechano-acoustic signals emanating from the lungs is critical for timely and cost-effective healthcare delivery. Adventitious lung sounds including crackles, wheezes, rhonchi, bronchial breath sounds, stridor or pleural rub and abnormal breathing patterns function as essential clinical biomarkers for the early identification, accurate diagnosis and monitoring of pulmonary disorders. Here, we present a wearable sensor module comprising of a hermetically encapsulated, high precision accelerometer contact microphone (ACM) which enables both episodic and longitudinal assessment of lung sounds, breathing patterns and respiratory rates using a single integrated sensor. This enhanced ACM sensor leverages a nano-gap transduction mechanism to achieve high sensitivity to weak high frequency vibrations occurring on the surface of the skin due to underlying lung pathologies. The performance of the ACM sensor was compared to recordings from a state-of-art digital stethoscope, and the efficacy of the developed system is demonstrated by conducting an exploratory research study aimed at recording pathological mechano-acoustic signals from hospitalized patients with a chronic obstructive pulmonary disease (COPD) exacerbation, pneumonia, and acute decompensated heart failure. This unobtrusive wearable system can enable both episodic and longitudinal evaluation of lung sounds that allow for the early detection and/or ongoing monitoring of pulmonary disease.

Omics phenotyping in heart failure: the next frontier

This state-of-the-art review aims to provide an up-to-date look at breakthrough omic technologies that are helping to unravel heart failure (HF) disease mechanisms and heterogeneity. Genomics, transcriptomics, proteomics, and metabolomics in HF are reviewed in depth. In addition, there is a thorough, expert discussion regarding the value of omics in identifying novel disease pathways, advancing understanding of disease mechanisms, differentiating HF phenotypes, yielding biomarkers for diagnosis or prognosis, or identifying new therapeutic targets in HF. The combination of multiple omics technologies may create a more comprehensive picture of the factors and physiology involved in HF than achieved by either one alone and provides a rich resource for predictive phenotype modelling. However, the successful translation of omics tools as solutions to clinical HF requires that the observations are robust and reproducible and can be validated across multiple independent populations to ensure confidence in clinical decision-making.

Real-time in situ monitoring of Lon and Caspase-3 for assessing the state of cardiomyocytes under hypoxic conditions via a novel Au-Se fluorescent nanoprobe

Myocardial dysfunction caused by cardiomyocyte apoptosis under ischemic and hypoxic conditions is the pathological basis of most cardiovascular diseases. Current diagnosis of myocardial dysfunction still focuses on the symptomatic stage, usually after the occurrence of the irreversible remodelling and functional impairment. Thus, early stage identification of the apoptotic cardiomyocytes induced by hypoxia is highly significant for preventing the onset and delaying the progression of myocardial dysfunction. Herein, a novel Au-Se nanoprobe with strong anti-interference capability was developed for simultaneous real-time in situ monitoring the expression of Lon protease (Lon) and Caspase-3 with high-fidelity in living cardiomyocytes. As Lon upregulation plays a major role in the initiation of hypoxia-induced apoptosis and Caspase-3 is a marker protein for apoptosis, the nanoprobe has been successfully applied for imaging the activation of Lon-Caspase-3 apoptotic signalling pathway and assessing the state of cardiomyocytes under hypoxic conditions. Furthermore, combining with mitochondrial H₂O₂ probe-MitoPY1, the nanoprobe was also used to confirm the synergistic effect of Lon and ROS on hypoxia-induced apoptosis of cardiomyocytes and evaluate the function of ROS scavenger on attenuating such apoptosis. This work proposed a promising strategy for early diagnosis, prevention and treatment of hypoxic-ischemic myocardial dysfunction.

Surrogate models based on machine learning methods for parameter estimation of left ventricular myocardium

A long-standing problem at the frontier of biomechanical studies is to develop fast methods capable of estimating material properties from clinical data. In this paper, we have studied three surrogate models based on machine learning (ML) methods for fast parameter estimation of left ventricular (LV) myocardium. We use three ML methods named K-nearest neighbour (KNN), XGBoost and multi-layer perceptron (MLP) to emulate the relationships between pressure and volume strains during the diastolic filling. Firstly, to train the surrogate models, a forward finite-element simulator of LV diastolic filling is used. Then the training data are projected in a low-dimensional parametrized space. Next, three ML models are trained to learn the relationships of pressure-volume and pressure-strain. Finally, an inverse parameter estimation problem is formulated by using those trained surrogate models. Our results show that the three ML models can learn the relationships of pressure-volume and pressure-strain very well, and the parameter inference using the surrogate models can be carried out in minutes. Estimated parameters from both the XGBoost and MLP models have much less uncertainties compared with the KNN model. Our results further suggest that the XGBoost model is better for predicting the LV diastolic dynamics and estimating passive parameters than other two surrogate models. Further studies are warranted to investigate how XGBoost can be used for emulating cardiac pump function in a multi-physics and multi-scale framework.

Matrix metalloproteinase-9 in pediatric rheumatic heart disease with and without heart failure

In cardiovascular disorders, the myocardium may be subjected to the breakdown and remodeling of collagen by metalloproteinase-9 (MMP-9). We hypothesized that the serum MMP-9 concentration may be elevated in pediatric patients with rheumatic heart disease (RHD) and heart failure (HF), and its level can be correlated with the HF severity. Thus, in the present study, we aimed to evaluate the sensitivity and accuracy of MMP-9 to predict HF in children with RHD and to determine its effectiveness as an indicator of the degree of HF. This study included 98 consecutive children admitted to the Department of Pediatrics, Zagazig University Hospital, Al Sharqia Governorate, Egypt with newly diagnosed RHD. Their ages ranged from 8.5 to 16 years. Fifty-eight children had RHD without HF while 40 children were complicated with HF which was diagnosed clinically and by echocardiography. A total of 44 healthy children were enrolled as a control group. MMP-9 serum levels were estimated by enzyme-linked immunosorbent assay. The serum MMP-9 concentration was higher in the RHD without HF and RHD with HF groups than this level noted in the control (P<0.001). MMP-9 was a significant predictor of HF; area under the curve (AUC)=0.85 [95% confidence interval (CI), 0.76-0.94]. At the level of 386.9 ng/ml, MMP-9 detected HF with a sensitivity 95% (95% CI, 83.08-99.39), specificity 74.14% (95% CI, 60.96-84.74), positive predictive value 71.70% (95% CI, 61.96-79.75), negative predictive value 95.56% (95% CI, 84.67-98.82) and accuracy 82.65% (95% CI, 73.69-89.56). In addition, MMP-9 showed a significant negative correlation with ejection fraction and fractional shortening (P=0.01 and P=0.02, respectively). In conclusion; MMP-9 may be an independent sensitive marker with which to detect HF in children with RHD and it can predict the prognoses of these patients as it correlates with the severity of HF. Further studies considering MMP-9 in the detection of 'silent' RHD in school aged children and asymptomatic HF in children with known RHD especially in rural areas, are highly recommended.

Innate Immune Nod1/RIP2 Signaling Is Essential for Cardiac Hypertrophy but Requires Mitochondrial Antiviral Signaling Protein for Signal Transductions and Energy Balance

BACKGROUND

Cardiac hypertrophy is a key biological response to injurious stresses such as pressure overload and, when excessive, can lead to heart failure. Innate immune activation by danger signals, through intracellular pattern recognition receptors such as nucleotide-binding oligomerization domain 1 (Nod1) and its adaptor receptor-interacting protein 2 (RIP2), might play a major role in cardiac remodeling and progression to heart failure. We hypothesize that Nod1/RIP2 are major contributors to cardiac hypertrophy, but may not be sufficient to fully express the phenotype alone.

METHODS

To elucidate the contribution of Nod1/RIP2 signaling to cardiac hypertrophy, we randomized Nod1^-/-, RIP2^-/-, or wild-type mice to transverse aortic constriction or sham operations. Cardiac hypertrophy, fibrosis, and cardiac function were examined in these mice.

RESULTS

Nod1 and RIP2 proteins were upregulated in the heart after transverse aortic constriction, and this was paralleled by increased expression of mitochondrial proteins, including mitochondrial antiviral signaling protein (MAVS). Nod1^-/- and RIP2^-/- mice subjected to transverse aortic constriction exhibited better survival, improved cardiac function, and decreased cardiac hypertrophy. Downstream signal transduction pathways that regulate inflammation and fibrosis, including NF (nuclear factor) κB and MAPK (mitogen-activated protein kinase)-GATA4/p300, were reduced in both Nod1^-/- and RIP2^-/- mice after transverse aortic constriction compared with wild-type mice. Coimmunoprecipitation of extracted cardiac proteins and confocal immunofluorescence microscopy showed that Nod1/RIP2 interaction was robust and that this complex also included MAVS as an essential component. Suppression of MAVS expression attenuated the complex formation, NF κB signaling, and myocyte hypertrophy. Interrogation of mitochondrial function compared in the presence or ablation of MAVS revealed that MAVS serves to suppress mitochondrial energy output and mediate fission/fusion related dynamic changes. The latter is possibly linked to mitophagy during cardiomyocytes stress, which may provide an intriguing link between innate immune activation and mitochondrial energy balance under stress or injury conditions.

CONCLUSIONS

We have identified that innate immune Nod1/RIP2 signaling is a major contributor to cardiac remodeling after stress. This process is critically joined by and regulated through the mitochondrial danger signal adapter MAVS. This novel complex coordinates remodeling, inflammatory response, and mitochondrial energy metabolism in stressed cardiomyocytes. Thus, Nod1/RIP2/MAVS signaling complex may represent an attractive new therapeutic approach toward heart failure.

Galectin-3: A Potential Prognostic and Diagnostic Marker for Heart Disease and Detection of Early Stage Pathology

The use of molecular biomarkers for the early detection of heart disease, before their onset of symptoms, is an attractive novel approach. Ideal molecular biomarkers, those that are both sensitive and specific to heart disease, are likely to provide a much earlier diagnosis, thereby providing better treatment outcomes. Galectin-3 is expressed by various immune cells, including mast cells, histiocytes and macrophages, and plays an important role in diverse physiological functions. Since galectin-3 is readily expressed on the cell surface, and is readily secreted by injured and inflammatory cells, it has been suggested that cardiac galectin-3 could be a marker for cardiac disorders such as cardiac inflammation and fibrosis, depending on the specific pathogenesis. Thus, galectin-3 may be a novel candidate biomarker for the diagnosis, analysis and prognosis of various cardiac diseases, including heart failure. The goals of heart disease treatment are to prevent acute onset and to predict their occurrence by using the ideal molecular biomarkers. In this review, we discuss and summarize recent developments of galectin-3 as a next-generation molecular biomarker of heart disease. Furthermore, we describe how galectin-3 may be useful as a diagnostic marker for detecting the early stages of various heart diseases, which may contribute to improved early therapeutic interventions.

Anti-hypertrophic effect of Na+/H+ exchanger-1 inhibition is mediated by reduced cathepsin B

Previous studies have established the role of Na⁺/H⁺ exchanger isoform-1 (NHE1) and cathepsin B (Cat B) in the development of cardiomyocyte hypertrophy (CH). Both NHE1 and Cat B are activated under acidic conditions suggesting that their activities might be interrelated. The inhibition of NHE1 has been demonstrated to reduce cardiac hypertrophy but the mechanism that contributes to the anti-hypertrophic effect of NHE1 inhibition still remains unclear. H9c2 cardiomyoblasts were stimulated with Angiotensin (Ang) II in the presence and absence of N-[2-methyl-4,5-bis(methylsulphonyl)-benzoyl]-guanidine, hydrochloride (EMD, EMD 87580), an NHE1 inhibitor or CA-074Me, a Cat B inhibitor, and various cardiac hypertrophic parameters, namely cell surface area, protein content and atrial natriuretic peptide (ANP) mRNA were analyzed. EMD significantly suppressed markers of cardiomyocyte hypertrophy and inhibited Ang II stimulated Cat B protein and gene expression. Cat B is located within the acidic environment of lysosomes. Cat B proteases are released into the cytoplasm upon disintegration of the lysosomes. EMD or CA-074Me prevented the dispersal of the lysosomes induced by Ang II and reduced the ratio of LC3-II to LC3-I, a marker of autophagy. Moreover, Cat B protein expression and MMP-9 activity in the extracellular space were significantly attenuated in the presence of EMD or CA-074Me. Our study demonstrates a novel mechanism for attenuation of the hypertrophic phenotype by NHE1 inhibition that is mediated by a regression in Cat B. The inhibition of Cat B via EMD or CA-074Me attenuates the autosomal-lysosomal pathway and MMP-9 activation.

A randomized controlled trial of the effects of non-surgical periodontal therapy on cardiac function assessed by echocardiography in type 2 diabetic patients

BACKGROUND

Periodontitis significantly increases the risk of diabetic complications. This clinical trial investigated the effects of periodontal therapy on cardiac function in patients with type 2 diabetes mellitus (T2DM) and periodontitis.

MATERIALS AND METHODS

Fifty-eight subjects with T2DM and periodontitis were randomly allocated to Treatment Group (n = 29) receiving non-surgical periodontal therapy, and Control Group (n = 29) having only oral hygiene instructions with delayed periodontal treatment until completion of this 6-month study. The left ventricle (LV) diastolic function was assessed by echocardiography with the tissue Doppler imaging index (E/e' ratio); and LV hypertrophy was evaluated by LV mass index (LVMI). Blood samples were collected for biochemical analysis.

RESULTS

The intention-to-treat analysis showed that periodontal treatment significantly reduced the E/e' ratio by 1.66 (95% CI: -2.64 to -0.68, p < .01), along with marked improvement of periodontal conditions (p < .05). LVMI was not altered at the 6-month follow-up. The serum levels of N-terminal pro-B type natriuretic peptide (NT-proBNP) as a cardiac stress biomarker, C-reactive protein and interleukin-6 decreased numerically without reaching statistical significance.

CONCLUSION

The present study provides the first evidence that non-surgical periodontal therapy may improve cardiac diastolic function in type 2 diabetic patients with periodontitis.

Galectin-3 as a Next-Generation Biomarker for Detecting Early Stage of Various Diseases

Galectin-3 is a β-galactoside-binding lectin which is important in numerous biological activities in various organs, including cell proliferation, apoptotic regulation, inflammation, fibrosis, and host defense. Galectin-3 is predominantly located in the cytoplasm and expressed on the cell surface, and then often secreted into biological fluids, like serum and urine. It is also released from injured cells and inflammatory cells under various pathological conditions. Many studies have revealed that galectin-3 plays an important role as a diagnostic or prognostic biomarker for certain types of heart disease, kidney disease, viral infection, autoimmune disease, neurodegenerative disorders, and tumor formation. In particular, it has been recognized that galectin-3 is extremely useful for detecting many of these diseases in their early stages. The purpose of this article is to review and summarize the recent literature focusing on the biomarker characteristics and long-term outcome predictions of galectin-3, in not only patients with various types of diseases, but associated animal models.

Precision wearable accelerometer contact microphones for longitudinal monitoring of mechano-acoustic cardiopulmonary signals

Mechano-acoustic signals emanating from the heart and lungs contain valuable information about the cardiopulmonary system. Unobtrusive wearable sensors capable of monitoring these signals longitudinally can detect early pathological signatures and titrate care accordingly. Here, we present a wearable, hermetically-sealed high-precision vibration sensor that combines the characteristics of an accelerometer and a contact microphone to acquire wideband mechano-acoustic physiological signals, and enable simultaneous monitoring of multiple health factors associated with the cardiopulmonary system including heart and respiratory rate, heart sounds, lung sounds, and body motion and position of an individual. The encapsulated accelerometer contact microphone (ACM) utilizes nano-gap transducers to achieve extraordinary sensitivity in a wide bandwidth (DC-12 kHz) with high dynamic range. The sensors were used to obtain health factors of six control subjects with varying body mass index, and their feasibility in detection of weak mechano-acoustic signals such as pathological heart sounds and shallow breathing patterns is evaluated on patients with preexisting conditions.

Albumin nanocomposites with MnO2/Gd2O3 motifs for precise MR imaging of acute myocardial infarction in rabbit models

The severe mortality and morbidity of myocardial infarction requests appropriate and accurate detection. Considering pathological profile of the acidic myocardial infarction microenvironments, herein, the low pH-sensitive albumin nanocomposites with MnO₂ motifs (MnO₂@BSA) have been engineered for T₁-weighted MR imaging of myocardial infarction, while using non-pH-responsive Gd₂O₃@BSA nanocomposites as control. The nanocomposites were 20-30 nm in diameter with spheroid morphology. Besides, the MnO₂@BSA have exhibited pH-triggered releasing of Mn²⁺, demonstrating approximately 38-fold and 55-fold increased molecular relaxivity at acute myocardial infarction-mimicking pH 6.5 (13.08 mM^-1s^-1) and macrophage intracellular pH 5.0 (18.76 mM^-1s^-1) compared to the extremely low relaxivity (0.34 mM^-1s^-1) at normal physiological conditions (pH 7.4). However, the Gd₂O₃@BSA with molecular relaxivity approximately 10 mM^-1s^-1 were without pH-sensitive properties. Furthermore, the MnO₂@BSA have demonstrated high accumulation in the acute myocardial infarction regions and fast metabolism from the body after systemic injection, accounting high contrast enhancement for accurate MR imaging of acute myocardial infarction in rabbit models, demonstrating better diagnostic performance over the controls.

Biomarkers in diagnosing and treatment of acute heart failure

Acute heart failure (AHF) is a complex disorder involving different pathophysiological pathways. In recent years, there is an increased focus on biomarkers that help with diagnosis, risk stratification and disease monitoring of AHF. Finding a reliable set of biomarkers not only improves morbidity and mortality but it can also potentially reveal the new targets of therapy. In this paper, we have reviewed the biomarkers found useful for the diagnosis as well as for risk stratification and prognostication in patients with AHF. We have discussed the established biomarkers for AHF including cardiac troponins and natriuretic peptides and emerging biomarkers including adiponectin, mi-RNA, sST2, Gal-3, MR-proADM, OPG, CT-proAVP and H-FABP for the purposes of making diagnosis, their use as a guide of therapy or for determination of prognosis.

Silent Myocardial Infarction and Long-Term Risk of Heart Failure: The ARIC Study

BACKGROUND

Although silent myocardial infarction (SMI) accounts for about one-half of the total number of myocardial infarctions (MIs), the risk of heart failure (HF) among patients with SMI is not well established.

OBJECTIVES

The purpose of this study was to examine the association of SMI and clinically manifested myocardial infarction (CMI) with HF, as compared with patients with no MI.

METHODS

This analysis included 9,243 participants from the ARIC (Atherosclerosis Risk In Communities) study who were free of cardiovascular disease at baseline (ARIC visit 1: 1987 to 1989). SMI was defined as electrocardiographic evidence of MI without CMI after the baseline until ARIC visit 4 (1996 to 1998). HF events were ascertained starting from ARIC visit 4 until 2010 in individuals free of HF before that visit.

RESULTS

Between ARIC visits 1 and 4, 305 SMIs and 331 CMIs occurred. After ARIC visit 4 and during a median follow-up of 13.0 years, 976 HF events occurred. The incidence rate of HF was higher in both CMI and SMI participants than in those without MI (incidence rates per 1,000 person-years were 30.4, 16.2, and 7.8, respectively; p < 0.001). In a model adjusted for demographics and HF risk factors, both SMI (hazard ratio [HR]: 1.35; 95% confidence interval [CI]: 1.02 to 1.78) and CMI (HR: 2.85; 95% CI: 2.31 to 3.51) were associated with increased risk of HF compared with no MI. These associations were consistent in subgroups of participants stratified by several HF risk predictors. However, the risk of HF associated with SMI was stronger in those younger than the median age (53 years) (HR: 1.66; 95% CI: 1.00 to 2.75 vs. HR: 1.19; 95% CI: 0.85 to 1.66, respectively; overall interaction p by MI type <0.001).

CONCLUSIONS

SMI is associated with an increased risk of HF. Future research is needed to examine the cost effectiveness of screening for SMI as part of HF risk assessment, and to identify preventive therapies to improve the risk of HF among patients with SMI.

An Aptamer-Based Near-Infrared Fluorescence Nanoprobe for Detecting and Imaging of Phospholamban Micropeptide in Cardiomyocytes

A growing body of evidence indicates that micropeptides encoded by long noncoding RNAs (lncRNAs) act independently or as regulators of larger proteins in fundamental biological processes, especially in the maintenance of cellular homeostasis. However, due to their small size and low intracellular expression, visual monitoring of micropeptides in living cells is still a challenge. In this work, we have designed and synthesized an aptamer-based near-infrared fluorescence nanoprobe for fluorescence imaging of phospholamban (PLN), which is an intracellular micropeptide that affects calcium homeostasis, and is closely associated with human heart failure in the clinic. The nanoprobe could respond specifically to PLN with excellent selectivity, high sensitivity, good nuclease stability, and biocompatibility, and it was successfully applied for imaging of changes in PLN levels in cardiomyocytes and in frozen sections of heart tissues. Further combined with clinical myocardial biopsy, we believe that the developed nanoprobe should be of great significance in later molecular pathology study of heart failure, which may help with diagnosis of early heart failure in the future. More importantly, for the first time nanoprobes were applied to visually monitor the changes of micropeptides in living cells and in frozen tissue sections, and the design concept of the aptamer-based nanoprobe can be extended to fluorescence detection of other micropeptides.

Current and future circulating biomarkers for cardiac amyloidosis

Cardiac amyloidosis (CA) comprises a heterogeneous group of medical conditions affecting the myocardium. It presents with proteinaceous infiltration with variable degrees of severity, prevalence and evolution. Despite this heterogeneity, erroneous protein folding is the common pathophysiologic process, yielding the formation of a single misfolded protein (monomer) that progressively evolves and ultimately forms amyloid fibers. Additionally, by seeding out from the organs of origin, intermediates called oligomers metastasize and restart the process. Such self-echoing behavior makes the secondary affected organs as important as the primary ones. Unfortunately, CA can be clinically challenging and only suggestive in a late stage of its natural history, leaving a narrow therapeutic time window available. In light of the evolutionary nature of amyloidosis, here, we propose a new classification of the currently used biomarkers based on time stages with different specificity and applicability across CA subtypes. Early markers (free light chains, serum amyloid A, β2-microglobulin, osteopontin and osteoprotegerin) can be employed for disease detection. Intermediate markers [soluble suppression of tumorigenicity 2 (sST-2), midregional proadrenomedullin (MR-proADM), von Willebrand factor (vWF), hepatocyte growth factor (HGF), matrix metalloproteinases (MMPs) and tissue inhibitor metalloproteinases (TIMPs)] can provide information on the biological mechanisms of myocardial damage. As in heart failure, late-stage biomarkers (troponins and natriuretic peptides) can help clinicians with prognosis and therapeutic response evaluation in CA. Such findings have generated a remarkable foundation for our current knowledge on CA. Nevertheless, we envision a future class of biomarkers targeted at upstream events capable of detecting folding defects, which will ultimately expand the therapeutic window.

Metabolic Biomarkers in Heart Failure

Metabolomics is the study of small, organic molecules within biochemical pathways. With advancement of technology, nuclear magnetic resonance, gas chromatography, and mass spectrometry have allowed for the discovery and analysis of large databases of metabolites implicated in heart failure. Metabolomics also explores the patient and environment interactions and unlocks the link between environmental exposures and the development of cardiovascular disease. Although a relatively new field, metabolomics is poised to become a clinically impactful field that develops novel biomarkers and explores new therapeutic interventions in heart failure.

Galectin-3 in Atrial Fibrillation: Mechanisms and Therapeutic Implications

Maintenance of atrial fibrillation is a complex mechanism, including extensive electrical and structural remodeling of the atria which involves progressive fibrogenesis. Galectin-3 is a biomarker of fibrosis, and, thus, may be involved in atrial remodeling in atrial fibrillation patients. We review the role of galectin-3 in AF mechanisms and its potential therapeutic implications.

In search of an efficient strategy to monitor disease status of chronic heart failure outpatients: added value of blood biomarkers to clinical assessment

Introduction

Blood biomarkers have the potential to monitor the severity of chronic heart failure (CHF). Studies correlating repeated measurements of blood biomarkers with repeatedly assessed New York Heart Association (NYHA) class over a prolonged follow-up period, and concomitantly investigating their associations with clinical endpoints, have not yet been performed.

Methods

Between 2011–2013, 263 CHF patients were included. At inclusion and subsequently every 3 months, we measured N‑terminal pro-B-type natriuretic (NT-proBNP), high-sensitivity troponin T (Hs-TnT) and C‑reactive protein (CRP), and assessed NYHA class. The primary endpoint comprised heart failure hospitalisation, cardiovascular mortality, cardiac transplantation or left ventricular assist device implantation. Time-dependent Cox models were used.

Results

Mean age was 67 ± 13 years, 72% were men and 27% were in NYHA class III–IV. We obtained 886 repeated measures (median 3 [IQR 2–5] per patient). The primary endpoint was reached in 41 patients during a median follow-up of 1.0 [0.6–1.4] year. Repeatedly measured NT-proBNP and Hs-TnT were significantly associated with repeatedly assessed NYHA class, whereas CRP was not (NT-proBNP: β [95% CI]: 1.56 [1.17–2.06]ln(ng/l) increase per point increase in NYHA class, p = 0.002; HsTNT: β [95% CI]: 1.58 [1.21–2.07]). Serially measured NT-proBNP (HR [95% CI]:2.86 [1.73–4.73]), CRP (1.69 [1.21–2.34]) and NYHA class (2.33 [1.51–3.62]) were positively and independently associated with the primary endpoint, whereas Hs-TnT lost statistical significance after multivariable adjustment. A model containing serially measured NYHA class and NT-proBNP displayed a C-index of 0.84, while serially measured NYHA class and CRP showed a C-index of 0.82.

Conclusion

Temporal NT-proBNP, CRP and NYHA class patterns are independently associated with adverse clinical outcome. Serially measured NT-proBNP and NYHA class are best suited for monitoring CHF outpatients.

Electronic supplementary material

The online version of this article (10.1007/s12471-017-1040-x) contains supplementary material, which is available to authorized users.

Proteomic footprint of myocardial ischemia/reperfusion injury: Longitudinal study of the at-risk and remote regions in the pig model

Reperfusion alters post-myocardial infarction (MI) healing; however, very few systematic studies report the early molecular changes following ischemia/reperfusion (I/R). Alterations in the remote myocardium have also been neglected, disregarding its contribution to post-MI heart failure (HF) development. This study characterizes protein dynamics and contractile abnormalities in the ischemic and remote myocardium during one week after MI. Closed-chest 40 min I/R was performed in 20 pigs sacrificed at 120 min, 24 hours, 4days, and 7days after reperfusion (n = 5 per group). Myocardial contractility was followed up by cardiac magnetic resonance (CMR) and tissue samples were analyzed by multiplexed quantitative proteomics. At early reperfusion (120 min), the ischemic area showed a coordinated upregulation of inflammatory processes, whereas interstitial proteins, angiogenesis and cardio-renal signaling processes increased at later reperfusion (day 4 and 7). Remote myocardium showed decreased contractility at 120 min- and 24 h-CMR accompanied by transient alterations in contractile and mitochondrial proteins. Subsequent recovery of regional contractility was associated with edema formation on CMR and increases in inflammation and wound healing proteins on post-MI day 7. Our results establish for the first time the altered protein signatures in the ischemic and remote myocardium early after I/R and might have implications for new therapeutic targets to improve early post-MI remodeling.

Novel approaches to the prediction, diagnosis and treatment of cardiac late effects in survivors of childhood cancer: a multi-centre observational study

Background

Anthracycline-induced cardiac toxicity is a cause of significant morbidity and early mortality in survivors of childhood cancer. Current strategies for predicting which children are at greatest risk for toxicity are imperfect and diagnosis of cardiac injury is usually made relatively late in the natural history of the disease. This study aims to identify genomic, biomarker and imaging parameters that can be used as predictors of risk or aid in the early diagnosis of cardiotoxicity.

Methods

This is a prospective longitudinal cohort study that recruited two cohorts of pediatric cancer patients at six participating centres: (1) an Acute Cohort of children newly diagnosed with cancer prior to starting anthracycline therapy (n = 307); and (2) a Survivor Cohort of long-term survivors of childhood cancer with past exposure to anthracycline (n = 818). The study team consists of three collaborative cores. The Genomics Core is identifying genomic variations in anthracycline metabolism and in myocardial response to injury that predispose children to treatment-related cardiac toxicity. The Biomarker Core is identifying existing and novel biomarkers that allow for early diagnosis and prognosis of anthracycline-induced cardiac toxicity. The Imaging Core is identifying echocardiographic and cardiac magnetic resonance (CMR) imaging parameters that correspond to early signs of cardiac dysfunction and remodeling and precede global dysfunction and clinical symptoms. The data generated by the cores will be combined to create an integrated risk-prediction model aimed at more accurate identification of children who are most susceptible to anthracycline toxicity.

Discussion

We aim to identify genomic risk factors that predict risk for anthracycline cardiotoxicity pre-exposure and imaging and biomarkers that facilitate early diagnosis of cardiac injury. This will facilitate a personalized approach to identifying at-risk children with cancer who may benefit from cardio- protective strategies during therapy, and closer surveillance and earlier initiation of medications to preserve heart function after cancer therapy.

Trial registration

NCT01805778. Registered 28 February 2013; retrospectively registered.

Electronic supplementary material

The online version of this article (doi:10.1186/s12885-017-3505-0) contains supplementary material, which is available to authorized users.

Myocardial proteases and cardiac remodeling

Cardiac hypertrophy (CH), characterized by the enlargement of cardiomyocytes, fibrosis and apoptosis, is one of the leading causes of death worldwide. Despite the advances in cardiovascular research, there remains a need to further investigate the signaling pathways that mediate CH in order to identify novel therapeutic targets. One of the hallmarks of CH is the remodeling of the extracellular matrix (ECM). Multiple studies have shown an important role of cysteine proteases and matrix metalloproteinases (MMPs) in the remodeled heart. This review focuses on the role of cysteine cathepins and MMPs in cardiac remodeling.