Aims Diagnosing heart failure with preserved ejection fraction (HFpEF) is challenging. The newly proposed HFA‐PEFF algorithm entails a stepwise approach. Step 1, typically performed in the ambulatory setting, establishes a pre‐test likelihood. The second step calculates a score based on echocardiography and natriuretic peptides. The aim of this study is to validate the diagnostic value and establish the clinical impact of the second step of the HFA‐PEFF score. Methods and results The second step of the HFA‐PEFF score was evaluated in two independent, prospective cohorts, i.e. the Maastricht cohort (228 HFpEF patients and 42 controls) and the Northwestern Chicago cohort (459 HFpEF patients). In Maastricht, the HFA‐PEFF score categorizes 11 (4%) of the total cohort with suspected HFpEF in the low‐likelihood (0–1 points) and 161 (60%) in the high‐likelihood category (5–6 points). A high HFA‐PEFF score can rule in HFpEF with high specificity (93%) and positive predictive value (98%). A low score can rule out HFpEF with a sensitivity of 99% and a negative predictive value of 73%. The diagnostic accuracy of the score is 0.90 (0.84–0.96), by the area under the curve of the receiver operating characteristic curve. However, 98 (36%) are classified in the intermediate‐likelihood category, where additional testing is advised. The distribution of the score shows a similar pattern in the Northwestern (Chicago) and Maastricht HFpEF patients (53% vs. 65% high, 43% vs. 34% intermediate, 4.8% vs. 1.3% low). Conclusion This study validates and characterizes the HFA‐PEFF score in two independent, well phenotyped cohorts. We demonstrate that the HFA‐PEFF score is helpful in clinical practice for the diagnosis of HFpEF.

Pressure overload causes cardiac fibroblast activation and transdifferentiation, leading to increased interstitial fibrosis formation and subsequently myocardial stiffness, diastolic and systolic dysfunction, and eventually heart failure. A better understanding of the molecular mechanisms underlying pressure overload-induced cardiac remodeling and fibrosis will have implications for heart failure treatment strategies. The microRNA (miRNA)-221/222 family, consisting of miR-221-3p and miR-222-3p, is differentially regulated in mouse and human cardiac pathology and inversely associated with kidney and liver fibrosis. We investigated the role of this miRNA family during pressure overload-induced cardiac remodeling. In myocardial biopsies of patients with severe fibrosis and dilated cardiomyopathy or aortic stenosis, we found significantly lower miRNA-221/222 levels as compared to matched patients with nonsevere fibrosis. In addition, miRNA-221/222 levels in aortic stenosis patients correlated negatively with the extent of myocardial fibrosis and with left ventricular stiffness. Inhibition of both miRNAs during AngII (angiotensin II)-mediated pressure overload in mice led to increased fibrosis and aggravated left ventricular dilation and dysfunction. In rat cardiac fibroblasts, inhibition of miRNA-221/222 derepressed TGF-β (transforming growth factor-β)-mediated profibrotic SMAD2 (mothers against decapentaplegic homolog 2) signaling and downstream gene expression, whereas overexpression of both miRNAs blunted TGF-β-induced profibrotic signaling. We found that the miRNA-221/222 family may target several genes involved in TGF-β signaling, including JNK1 (c-Jun N-terminal kinase 1), TGF-β receptor 1 and TGF-β receptor 2, and ETS-1 (ETS proto-oncogene 1). Our findings show that heart failure-associated downregulation of the miRNA-221/222 family enables profibrotic signaling in the pressure-overloaded heart.

Background - Genetic analysis is a first-tier test in dilated cardiomyopathy (DCM). Electrical phenotypes are common in genetic DCM but their exact contribution to the clinical course and outcome is unknown. We determined the prevalence of pathogenic gene variants in a large unselected DCM population, and determined the role of electrical phenotypes in association with outcome. Methods - This study included 689 DCM patients from the Maastricht Cardiomyopathy Registry, undergoing genetic evaluation using a 48 cardiomyopathy-associated gene-panel, echocardiography, endomyocardial biopsies and Holter monitoring. Upon detection of a pathogenic variant in a DCM patient, familial segregation was performed. Outcome was defined as cardiovascular death, heart transplantation, heart failure hospitalization and/or occurrence of life-threatening arrthymias. Results - A (likely) pathogenic gene variant was found in 19% of patients, varying from 36% in familial to 13% in non-familial DCM. Family segregation analysis showed familial disease in 46% of DCM patients who were initially deemed non-familial by history. Overall, 18% of patients with a non-genetic risk factor had a pathogenic gene variant. Almost all pathogenic gene variants occurred in just 12 genes previously shown to have robust disease association with DCM. Genetic DCM was independently associated with electrical phenotypes such as atrial fibrillation (AF), non-sustained ventricular tachycardia (NSVT) and AV-block (AVB), and inversely correlated with the presence of a left bundle branch block(p<0.01). After a median follow-up of 4 years, event-free survival was reduced in genetic versus non-genetic DCM patients(p=0.01). This effect on outcome was mediated by the associated electrical phenotypes of genetic DCM(p<0.001). Conclusions - One in five patients with an established non-genetic risk factor or a non-familial disease still carries a pathogenic gene variant. Genetic DCM is characterized by a profile of electrical phenotypes (AF, NSVT and AVB), which carries increased risk for adverse outcomes. Based on these findings, we envisage a broader role for genetic testing in DCM.

Aims The dilated cardiomyopathy (DCM) phenotype is the result of combined genetic and acquired triggers. Until now, clinical decision-making in DCM has mainly been based on ejection fraction (EF) and NYHA classification, not considering the DCM heterogenicity. The present study aimed to identify patient subgroups by phenotypic clustering integrating aetiologies, comorbidities, and cardiac function along cardiac transcript levels, to unveil pathophysiological differences between DCM subgroups. Methods and results We included 795 consecutive DCM patients from the Maastricht Cardiomyopathy Registry who underwent in-depth phenotyping, comprising extensive clinical data on aetiology and comorbodities, imaging and endomyocardial biopsies. Four mutually exclusive and clinically distinct phenogroups (PG) were identified based upon unsupervised hierarchical clustering of principal components: [PG1] mild systolic dysfunction, [PG2] auto-immune, [PG3] genetic and arrhythmias, and [PG4] severe systolic dysfunction. RNA-sequencing of cardiac samples (n = 91) revealed a distinct underlying molecular profile per PG: pro-inflammatory (PG2, auto-immune), pro-fibrotic (PG3; arrhythmia), and metabolic (PG4, low EF) gene expression. Furthermore, event-free survival differed among the four phenogroups, also when corrected for well-known clinical predictors. Decision tree modelling identified four clinical parameters (auto-immune disease, EF, atrial fibrillation, and kidney function) by which every DCM patient from two independent DCM cohorts could be placed in one of the four phenogroups with corresponding outcome (n = 789; Spain, n = 352 and Italy, n = 437), showing a feasible applicability of the phenogrouping. Conclusion The present study identified four different DCM phenogroups associated with significant differences in clinical presentation, underlying molecular profiles and outcome, paving the way for a more personalized treatment approach.

Diagnosing heart failure with preserved ejection fraction (HFpEF) in the non-acute setting remains challenging. Natriuretic peptides have limited value for this purpose, and a multitude of studies investigating novel diagnostic circulating biomarkers have not resulted in their implementation. This review aims to provide an overview of studies investigating novel circulating biomarkers for the diagnosis of HFpEF and determine their risk of bias (ROB).

We quantified fibrosis in 209 DCM patients at three levels: (i) non-invasive late gadolinium enhancement (LGE) at cardiac magnetic resonance (CMR); (ii) blood biomarkers [amino-terminal propeptide of procollagen type III (PIIINP) and carboxy-terminal propeptide of procollagen type I (PICP)], (iii) invasive endomyocardial biopsy (EMB) (collagen volume fraction, CVF). Both LGE and elevated blood PICP levels, but neither PIIINP nor CVF predicted a worse outcome defined as death, heart transplantation, heart failure hospitalization, or life-threatening arrhythmias, after adjusting for known clinical predictors [adjusted hazard ratios: LGE 3.54, 95% confidence interval (CI) 1.90-6.60; P < 0.001 and PICP 1.02, 95% CI 1.01-1.03; P = 0.001]. The combination of LGE and PICP provided the highest prognostic benefit in prediction (likelihood ratio test P = 0.007) and reclassification (net reclassification index: 0.28, P = 0.02; and integrated discrimination improvement index: 0.139, P = 0.01) when added to the clinical prediction

Background: Metabolomic profiling may have diagnostic and prognostic value in heart failure. This study investigated whether targeted blood and urine metabolomics reflects disease severity in patients with nonischemic dilated cardiomyopathy (DCM) and compared its incremental value on top of N-terminal prohormone of brain natriuretic peptide (NT-proBNP). Methods and Results: A total of 149 metabolites were measured in plasma and urine samples of 273 patients with DCM and with varying stages of disease (patients with DCM and normal left ventricular reverse remodeling, n = 70; asymptomatic DCM, n = 72; and symptomatic DCM, n = 131). Acylcarnitines, sialic acid and glutamic acid are the most distinctive metabolites associated with disease severity, as repeatedly revealed by unibiomarker linear regression, sparse partial least squares discriminant analysis, random forest, and conditional random forest analyses. However, the absolute difference in the metabolic profile among groups was marginal. A decision-tree model based on the top metabolites did not surpass NT-proBNP in classifying stages. However, a combination of NT-proBNP and the top metabolites improved the decision tree to distinguish patients with DCM and left ventricular reverse remodeling from symptomatic DCM (area under the curve 0.813 § 0.138 vs 0.739 § 0.114; P = 0.02). Conclusion: Functional cardiac recovery is reflected in metabolomics. These alterations reveal potential alternative treatment targets in advanced symptomatic DCM. The metabolic profile can complement NT-proBNP in determining disease severity in nonischemic DCM.

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