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A network analysis to identify pathophysiological pathways distinguishing ischaemic from non-ischaemic heart failure.

ABSTRACT: AIMS:Heart failure (HF) is frequently caused by an ischaemic event (e.g. myocardial infarction) but might also be caused by a primary disease of the myocardium (cardiomyopathy). In order to identify targeted therapies specific for either ischaemic or non-ischaemic HF, it is important to better understand differences in underlying molecular mechanisms. METHODS AND RESULTS:We performed a biological physical protein-protein interaction network analysis to identify pathophysiological pathways distinguishing ischaemic from non-ischaemic HF. First, differentially expressed plasma protein biomarkers were identified in 1160 patients enrolled in the BIOSTAT-CHF study, 715 of whom had ischaemic HF and 445 had non-ischaemic HF. Second, we constructed an enriched physical protein-protein interaction network, followed by a pathway over-representation analysis. Finally, we identified key network proteins. Data were validated in an independent HF cohort comprised of 765 ischaemic and 100 non-ischaemic HF patients. We found 21/92 proteins to be up-regulated and 2/92 down-regulated in ischaemic relative to non-ischaemic HF patients. An enriched network of 18 proteins that were specific for ischaemic heart disease yielded six pathways, which are related to inflammation, endothelial dysfunction superoxide production, coagulation, and atherosclerosis. We identified five key network proteins: acid phosphatase 5, epidermal growth factor receptor, insulin-like growth factor binding protein-1, plasminogen activator urokinase receptor, and secreted phosphoprotein 1. Similar results were observed in the independent validation cohort. CONCLUSIONS:Pathophysiological pathways distinguishing patients with ischaemic HF from those with non-ischaemic HF were related to inflammation, endothelial dysfunction superoxide production, coagulation, and atherosclerosis. The five key pathway proteins identified are potential treatment targets specifically for patients with ischaemic HF.

SUBMITTER: Sama IE

PROVIDER: S-EPMC7319432 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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A network analysis to identify pathophysiological pathways distinguishing ischaemic from non-ischaemic heart failure.

Sama Iziah E IE Woolley Rebecca J RJ Nauta Jan F JF Romaine Simon P R SPR Tromp Jasper J Ter Maaten Jozine M JM van der Meer Peter P Lam Carolyn S P CSP Samani Nilesh J NJ Ng Leong L LL Metra Marco M Dickstein Kenneth K Anker Stefan D SD Zannad Faiez F Lang Chim C CC Cleland John G F JGF van Veldhuisen Dirk J DJ Hillege Hans L HL Voors Adriaan A AA

European journal of heart failure 20200403 5

<h4>Aims</h4>Heart failure (HF) is frequently caused by an ischaemic event (e.g. myocardial infarction) but might also be caused by a primary disease of the myocardium (cardiomyopathy). In order to identify targeted therapies specific for either ischaemic or non-ischaemic HF, it is important to better understand differences in underlying molecular mechanisms.<h4>Methods and results</h4>We performed a biological physical protein-protein interaction network analysis to identify pathophysiological ...[more]

PMID: 32243695

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Project description:AimsA causal link between non-ischaemic heart failure (HF) and humoral autoimmunity against G-protein-coupled receptors (GPCR) remains unclear except for Chagas' cardiomyopathy. Uncertainty arises from ambiguous reports on incidences of GPCR autoantibodies, spurious correlations of autoantibody levels with disease activity, and lack of standardization and validation of measuring procedures for putatively cardio-pathogenic GPCR autoantibodies. Here, we use validated and certified immune assays presenting native receptors as binding targets. We compared candidate GPCR autoantibody species between HF patients and healthy controls and tested associations of serum autoantibody levels with serological, haemodynamic, metabolic, and functional parameters in HF.MethodsNinety-five non-ischaemic HF patients undergoing transcatheter endomyocardial biopsy and 60 healthy controls were included. GPCR autoantibodies were determined in serum by IgG binding to native receptors or a cyclic peptide (for β1AR autoantibodies). In patients, cardiac function, volumes, and myocardial structural properties were assessed by cardiac magnetic resonance imaging; right heart catheterization served for determination of cardiac haemodynamics; endomyocardial biopsies were used for histological assessment of cardiomyopathy and determination of cardiac mitochondrial oxidative function by high-resolution respirometry.ResultsAutoantibodies against β1 adrenergic (β1 AR) , M5-muscarinic (M5AR), and angiotensin II type 2 receptors (AT2R) were increased in HF (all P < 0.001). Autoantibodies against α1 -adrenergic (α1 AR) and angiotensin II type 1 receptors (AT1R) were decreased in HF (all P < 0.001). Correlation of alterations of GPCR autoantibodies with markers of cardiac or systemic inflammation or cardiac damage, haemodynamics, myocardial histology, or left ventricular inflammation (judged by T2 mapping) were weak, even when corrected for total IgG. β1 AR autoantibodies were related inversely to markers of left ventricular fibrosis indicated by T1 mapping (r = -0.362, P < 0.05) and global longitudinal strain (r = -0.323, P < 0.05). AT2R autoantibodies were associated with improved myocardial mitochondrial coupling as measured by high-resolution respirometry in myocardial biopsies (r = -0.352, P < 0.05). In insulin-resistant HF patients, AT2R autoantibodies were decreased (r = -.240, P < 0.05), and AT1R autoantibodies were increased (r = 0.212, P < 0.05).ConclusionsGPCR autoantibodies are markedly altered in HF. However, they are correlated poorly or even inversely to haemodynamic, metabolic, and functional markers of disease severity, myocardial histology, and myocardial mitochondrial efficiency. These observations do not hint towards a specific cardio-pathogenic role of GPCR autoantibodies and suggest that further investigations are required before specific therapies directed at GPCR autoantibodies can be clinically tested in non-ischaemic HF.

Project description:AimsHeart failure with preserved ejection fraction (HFpEF) is a heterogeneous condition, and tissue congestion manifested by oedema is not present in all patients. We compared clinical characteristics, exercise capacity, and outcomes in patients with HFpEF with and without oedema.Methods and resultsThis study was a post hoc analysis of pooled data of patients with left ventricular ejection fraction of ≥50% enrolled in the DOSE, CARRESS-HF, RELAX, ATHENA, ROSE, INDIE, and NEAT trials. Patients were dichotomized by the severity of oedema. Cox proportional hazard regression and generalized linear regression models were used to assess associations between oedema, symptoms, and clinical outcomes. The ambulatory cohort included 393 patients (228 with and 165 without oedema), and the hospitalized cohort included 338 patients (249 with ≥moderate oedema and 89 with mild or none). Among ambulatory patients, patients with oedema had a higher body mass index (35.2 kg/m2 [inter-quartile range, IQR 30.5, 41.6] vs. 31.6 kg/m2 [IQR 27.9, 36.3], P < 0.001), greater burden of co-morbidities, higher intravascular pressures estimated on physical examination (elevated jugular venous pressure: 50% vs. 24.7%, P < 0.001), poorer renal function (creatinine: 1.2 mg/dL [IQR 0.9, 1.5] vs. 1 mg/dL [IQR 0.8, 1.3], P = 0.003), and lower peak VO2 (adjusted mean difference -1.04 mL/kg/min, 95% confidence interval [-1.71, -0.37], P < 0.003). Among hospitalized patients, despite greater in-hospital fluid/weight loss in the ≥moderate oedema group, there was no difference in the improvement in dyspnoea by the visual analogue scale or well-being visual analogue scale from baseline to 3-4 days and no statistically significant difference in the rate of 60 day rehospitalization/death (adjusted hazard ratio 1.44, 95% confidence interval [0.87, 2.39], P = 0.156).ConclusionsPatients with HFpEF and oedema display higher body mass, greater burden of co-morbidities, and more severe exercise intolerance, but clinical responses to treatment appear similar. Further research is required to better understand the nature of volume distribution in different HFpEF phenotypes.

Dataset Information

A network analysis to identify pathophysiological pathways distinguishing ischaemic from non-ischaemic heart failure.

Publications

A network analysis to identify pathophysiological pathways distinguishing ischaemic from non-ischaemic heart failure.

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