Novel endotypes in heart failure: effects on guideline-directed medical therapy

Tromp, Jasper; Ouwerkerk, Wouter; Demissei, Biniyam G.; Anker, Stefan D.; Cleland, John G.F.; Dickstein, Kenneth; Filippatos, Gerasimos; Harst, Pim van der; Hillege, Hans L.; Lang, Chim C.; Metra, Marco; Ng, L.L.; Ponikowski, Piotr; Samani, Nilesh J.; Veldhuisen, Dirk J. van; Zannad, Faı̈ez; Zwinderman, A. H.; Voors, Adriaan A.; Meer, Peter van der

doi:10.1093/eurheartj/ehy712

Cited by 52 publications

(61 citation statements)

References 24 publications

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“…The protein biomarker data used for this study have been described in recent papers . In brief, we used the Cardiovascular III (CVDIII) panel of 92 cardiovascular disease‐related proteins provided by Olink Bioscience analysis service (Uppsala, Sweden).…”

Section: Methodsmentioning

confidence: 99%

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

Sama

Woolley

Nauta

et al. 2020

European J of Heart Fail

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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.

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Section: Methodsmentioning

confidence: 99%

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

Sama

Woolley

Nauta

et al. 2020

European J of Heart Fail

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“…Cox proportional hazard regression analysis was performed for all end-points to evaluate the independent prognostic value of each tumour biomarker and NT-proBNP. All-cause mortality, HF hospitalization and the composite end-point of both were corrected for their respective BIOSTAT risk model (including age, blood urea nitrogen [BUN], NT-proBNP, haemoglobin and beta blocker use at baseline), as previously published [12,14]. The models for CV mortality were corrected for age, BUN, NT-proBNP, troponin T and sodium, while non-CV mortality models included age, haemoglobin, C-reactive protein and history of malignancy.…”

Section: Statistical Analysesmentioning

confidence: 99%

Tumour biomarkers: association with heart failure outcomes

et al. 2020

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Background. There is increasing recognition that heart failure (HF) and cancer are conditions with a number of shared characteristics. Objectives. To explore the association between tumour biomarkers and HF outcomes. Methods. In 2,079 patients of BIOSTAT-CHF cohort, we measured six established tumour biomarkers: CA125, CA15-3, CA19-9, CEA, CYFRA 21-1 and AFP. Results. During a median follow-up of 21 months, 555 (27%) patients reached the primary end-point of all-cause mortality. CA125, CYFRA 21-1, CEA and CA19-9 levels were positively correlated with NT-proBNP quartiles (all P < 0.001, P for trend < 0.001) and were, respectively, associated with a hazard ratio of 1.17 (95% CI 1.12-1.23; P < 0.0001), 1.45 (95% CI 1.30-1.61; P < 0.0001), 1.19 (95% CI 1.09-1.30; P = 0.006) and 1.10 (95% CI 1.05-1.16; P < 0.001) for all-cause mortality after correction for BIOSTAT risk model (age, BUN, NT-proBNP, haemoglobin and beta blocker). All tumour biomarkers (except AFP) had significant associations with secondary end-points (composite of all-cause mortality and HF hospitalization, HF hospitalization, cardiovascular (CV) mortality and non-CV mortality). ROC curves showed the AUC of CYFRA 21-1 (0.64) had a noninferior AUC compared with NT-proBNP (0.68) for all-cause mortality (P = 0.08). A combination of CYFRA 21-1 and NT-proBNP (AUC = 0.71) improved the predictive value of the model for all-cause mortality (P = 0.0002 compared with NT-proBNP). Conclusions. Several established tumour biomarkers showed independent associations with indices of severity of HF and independent prognostic value for HF outcomes. This demonstrates that pathophysiological pathways sensed by these tumour biomarkers are also dysregulated in HF.

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“…Our study presents a completely new approach towards biomarkers -something to be expected in the era of personalized medicine when "one fits all" approach will be replaced by analysis of simultaneous changes of multiple substances reflecting different mechanisms. According to current research, collective biomarkers better reflect the complexity of changes in the organism and possibly will increase likelihood to define particular sub-phenotypes of diseases (in this case heart failure) [11][12][13] . Previous research has already proven that metabolomics may constitute a powerful diagnostic and prognostic tool in chronic HF 14,15 .…”

Section: Discussionmentioning

confidence: 99%

Machine-learning facilitates selection of a novel diagnostic panel of metabolites for the detection of heart failure

Marcinkiewicz-Siemion

Kamiński

Ciborowski

et al. 2020

Sci Rep

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the metabolic derangement is common in heart failure with reduced ejection fraction (Hfref). the aim of the study was to check feasibility of the combined approach of untargeted metabolomics and machine learning to create a simple and potentially clinically useful diagnostic panel for Hfref. the study included 67 chronic HFrEF patients (left ventricular ejection fraction-LVEF 24.3 ± 5.9%) and 39 controls without the disease. Fasting serum samples were fingerprinted by liquid chromatographymass spectrometry. Feature selection based on random-forest models fitted to resampled data and followed by linear modelling, resulted in selection of eight metabolites (uric acid, two isomers of Lpc 18:2, LPC 20:1, deoxycholic acid, docosahexaenoic acid and one unknown metabolite), demonstrating their predictive value in Hfref. the accuracy of a model based on metabolites panel was comparable to BNP (0.85 vs 0.82), as verified on the test set. Selected metabolites correlated with clinical, echocardiographic and functional parameters. the combination of two innovative tools (metabolomics and machine-learning methods), both unrestrained by the gaps in the current knowledge, enables identification of a novel diagnostic panel. Its diagnostic value seems to be comparable to BNP. Large scale, multi-center studies using validated targeted methods are crucial to confirm clinical utility of proposed markers.

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Novel endotypes in heart failure: effects on guideline-directed medical therapy

Cited by 52 publications

References 24 publications

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

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

Tumour biomarkers: association with heart failure outcomes

Machine-learning facilitates selection of a novel diagnostic panel of metabolites for the detection of heart failure

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