6 results on '"Heerebeek, Loek Van"'
Search Results
2. Additional file 1 of Sex differences in circulating proteins in heart failure with preserved ejection fraction
- Author
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Stienen, Susan, Ferreira, João Pedro, Kobayashi, Masatake, Gregoire Preud’homme, Dobre, Daniela, Jean-Loup Machu, Duarte, Kevin, Bresso, Emmanuel, Marie-Dominique Devignes, Andrés, Natalia López, Girerd, Nicolas, Aakhus, Svend, Ambrosio, Giuseppe, Hans-Peter Brunner-La Rocca, Fontes-Carvalho, Ricardo, Fraser, Alan G., Heerebeek, Loek Van, Keulenaer, Gilles De, Marino, Paolo, McDonald, Kenneth, Mebazaa, Alexandre, Zoltàn Papp, Raddino, Riccardo, Tschöpe, Carsten, Paulus, Walter J., Faiez Zannad, and Rossignol, Patrick
- Abstract
Additional file 1: Supplemental data. Table S1-S3.
- Published
- 2020
- Full Text
- View/download PDF
3. Empagliflozin improves endothelial and cardiomyocyte function in human heart failure with preserved ejection fraction via reduced pro-inflammatory-oxidative pathways and protein kinase Gα oxidation.
- Author
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Kolijn, Detmar, Pabel, Steffen, Tian, Yanna, Lódi, Mária, Herwig, Melissa, Carrizzo, Albino, Zhazykbayeva, Saltanat, Kovács, Árpád, Fülöp, Gábor Á, Falcão-Pires, Inês, Reusch, Peter H, Linthout, Sophie Van, Papp, Zoltán, Heerebeek, Loek van, Vecchione, Carmine, Maier, Lars S, Ciccarelli, Michele, Tschöpe, Carsten, Mügge, Andreas, and Bagi, Zsolt
- Subjects
EMPAGLIFLOZIN ,PROTEIN kinases ,HEART failure ,OXIDATION ,PHOSPHORYLATION ,OXIDATIVE stress - Abstract
Aims Sodium-glucose-cotransporter-2 inhibitors showed favourable cardiovascular outcomes, but the underlying mechanisms are still elusive. This study investigated the mechanisms of empagliflozin in human and murine heart failure with preserved ejection fraction (HFpEF). Methods and results The acute mechanisms of empagliflozin were investigated in human myocardium from patients with HFpEF and murine ZDF obese rats, which were treated in vivo. As shown with immunoblots and ELISA, empagliflozin significantly suppressed increased levels of ICAM-1, VCAM-1, TNF-α, and IL-6 in human and murine HFpEF myocardium and attenuated pathological oxidative parameters (H
2 O2 , 3-nitrotyrosine, GSH, lipid peroxide) in both cardiomyocyte cytosol and mitochondria in addition to improved endothelial vasorelaxation. In HFpEF, we found higher oxidative stress-dependent activation of eNOS leading to PKGIα oxidation. Interestingly, immunofluorescence imaging and electron microscopy revealed that oxidized PKG1α in HFpEF appeared as dimers/polymers localized to the outer-membrane of the cardiomyocyte. Empagliflozin reduced oxidative stress/eNOS-dependent PKGIα oxidation and polymerization resulting in a higher fraction of PKGIα monomers, which translocated back to the cytosol. Consequently, diminished NO levels, sGC activity, cGMP concentration, and PKGIα activity in HFpEF increased upon empagliflozin leading to improved phosphorylation of myofilament proteins. In skinned HFpEF cardiomyocytes, empagliflozin improved cardiomyocyte stiffness in an anti-oxidative/PKGIα-dependent manner. Monovariate linear regression analysis confirmed the correlation of oxidative stress and PKGIα polymerization with increased cardiomyocyte stiffness and diastolic dysfunction of the HFpEF patients. Conclusion Empagliflozin reduces inflammatory and oxidative stress in HFpEF and thereby improves the NO–sGC–cGMP–cascade and PKGIα activity via reduced PKGIα oxidation and polymerization leading to less pathological cardiomyocyte stiffness. [ABSTRACT FROM AUTHOR]- Published
- 2021
- Full Text
- View/download PDF
4. Connecting heart failure with preserved ejection fraction and renal dysfunction: the role of endothelial dysfunction and inflammation.
- Author
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ter Maaten, Jozine M., Damman, Kevin, Verhaar, Marianne C., Paulus, Walter J., Duncker, Dirk J., Cheng, Caroline, Heerebeek, Loek van, Hillege, Hans L., Lam, Carolyn S.P., Navis, Gerjan, and Voors, Adriaan A.
- Abstract
Renal dysfunction in heart failure with preserved ejection fraction (HFpEF) is common and is associated with increased mortality. Impaired renal function is also a risk factor for developing HFpEF. A new paradigm for HFpEF, proposing a sequence of events leading to myocardial remodelling and dysfunction in HFpEF, was recently introduced, involving inflammatory, microvascular, and cardiac components. The kidney might play a key role in this systemic process. Renal impairment causes metabolic and systemic derangements in circulating factors, causing an activated systemic inflammatory state and endothelial dysfunction, which may lead to cardiomyocyte stiffening, hypertrophy, and interstitial fibrosis via cross-talk between the endothelium and cardiomyocyte compartments. Here, we review the role of endothelial dysfunction and inflammation to explain the link between renal dysfunction and HFpEF, which allows for identification of new early risk markers, prognostic factors, and unique targets for intervention. [ABSTRACT FROM AUTHOR]
- Published
- 2016
- Full Text
- View/download PDF
5. Enhanced clinical phenotyping by mechanistic bioprofiling in heart failure with preserved ejection fraction: insights from the MEDIA-DHF study (The Metabolic Road to Diastolic Heart Failure)
- Author
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Stienen, Susan, Ferreira, João Pedro, Kobayashi, Masatake, Gregoire Preud’homme, Dobre, Daniela, Jean-Loup Machu, Duarte, Kevin, Bresso, Emmanuel, Marie-Dominique Devignes, López, Natalia, Girerd, Nicolas, Aakhus, Svend, Ambrosio, Giuseppe, Hans-Peter Brunner-La Rocca, Fontes-Carvalho, Ricardo, Fraser, Alan G., Heerebeek, Loek Van, Stephane Heymans, Keulenaer, Gilles De, Marino, Paolo, McDonald, Kenneth, Mebazaa, Alexandre, Zoltàn Papp, Raddino, Riccardo, Tschöpe, Carsten, Paulus, Walter J., Faiez Zannad, and Rossignol, Patrick
- Subjects
3. Good health - Abstract
Background: Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome for which clear evidence of effective therapies is lacking. Understanding which factors determine this heterogeneity may be helped by better phenotyping. An unsupervised statistical approach applied to a large set of biomarkers may identify distinct HFpEF phenotypes. Methods: Relevant proteomic biomarkers were analyzed in 392 HFpEF patients included in Metabolic Road to Diastolic HF (MEDIA-DHF). We performed an unsupervised cluster analysis to define distinct phenotypes. Cluster characteristics were explored with logistic regression. The association between clusters and 1-year cardiovascular (CV) death and/or CV hospitalization was studied using Cox regression. Results: Based on 415 biomarkers, we identified 2 distinct clusters. Clinical variables associated with cluster 2 were diabetes, impaired renal function, loop diuretics and/or betablockers. In addition, 17 biomarkers were higher expressed in cluster 2 vs. 1. Patients in cluster 2 vs. those in 1 experienced higher rates of CV death/CV hospitalization (adj. HR 1.93, 95% CI 1.12–3.32, p = 0.017). Complex-network analyses linked these biomarkers to immune system activation, signal transduction cascades, cell interactions and metabolism. Conclusion: Unsupervised machine-learning algorithms applied to a wide range of biomarkers identified 2 HFpEF clusters with different CV phenotypes and outcomes. The identified pathways may provide a basis for future research.Clinical significanceMore insight is obtained in the mechanisms related to poor outcome in HFpEF patients since it was demonstrated that biomarkers associated with the high-risk cluster were related to the immune system, signal transduction cascades, cell interactions and metabolismBiomarkers (and pathways) identified in this study may help select high-risk HFpEF patients which could be helpful for the inclusion/exclusion of patients in future trials.Our findings may be the basis of investigating therapies specifically targeting these pathways and the potential use of corresponding markers potentially identifying patients with distinct mechanistic bioprofiles most likely to respond to the selected mechanistically targeted therapies. More insight is obtained in the mechanisms related to poor outcome in HFpEF patients since it was demonstrated that biomarkers associated with the high-risk cluster were related to the immune system, signal transduction cascades, cell interactions and metabolism Biomarkers (and pathways) identified in this study may help select high-risk HFpEF patients which could be helpful for the inclusion/exclusion of patients in future trials. Our findings may be the basis of investigating therapies specifically targeting these pathways and the potential use of corresponding markers potentially identifying patients with distinct mechanistic bioprofiles most likely to respond to the selected mechanistically targeted therapies.
6. Enhanced clinical phenotyping by mechanistic bioprofiling in heart failure with preserved ejection fraction: insights from the MEDIA-DHF study (The Metabolic Road to Diastolic Heart Failure)
- Author
-
Stienen, Susan, Ferreira, João Pedro, Kobayashi, Masatake, Gregoire Preud’homme, Dobre, Daniela, Jean-Loup Machu, Duarte, Kevin, Bresso, Emmanuel, Marie-Dominique Devignes, López, Natalia, Girerd, Nicolas, Aakhus, Svend, Ambrosio, Giuseppe, Hans-Peter Brunner-La Rocca, Fontes-Carvalho, Ricardo, Fraser, Alan G., Heerebeek, Loek Van, Stephane Heymans, Keulenaer, Gilles De, Marino, Paolo, McDonald, Kenneth, Mebazaa, Alexandre, Zoltàn Papp, Raddino, Riccardo, Tschöpe, Carsten, Paulus, Walter J., Faiez Zannad, and Rossignol, Patrick
- Subjects
3. Good health - Abstract
Background: Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome for which clear evidence of effective therapies is lacking. Understanding which factors determine this heterogeneity may be helped by better phenotyping. An unsupervised statistical approach applied to a large set of biomarkers may identify distinct HFpEF phenotypes. Methods: Relevant proteomic biomarkers were analyzed in 392 HFpEF patients included in Metabolic Road to Diastolic HF (MEDIA-DHF). We performed an unsupervised cluster analysis to define distinct phenotypes. Cluster characteristics were explored with logistic regression. The association between clusters and 1-year cardiovascular (CV) death and/or CV hospitalization was studied using Cox regression. Results: Based on 415 biomarkers, we identified 2 distinct clusters. Clinical variables associated with cluster 2 were diabetes, impaired renal function, loop diuretics and/or betablockers. In addition, 17 biomarkers were higher expressed in cluster 2 vs. 1. Patients in cluster 2 vs. those in 1 experienced higher rates of CV death/CV hospitalization (adj. HR 1.93, 95% CI 1.12–3.32, p = 0.017). Complex-network analyses linked these biomarkers to immune system activation, signal transduction cascades, cell interactions and metabolism. Conclusion: Unsupervised machine-learning algorithms applied to a wide range of biomarkers identified 2 HFpEF clusters with different CV phenotypes and outcomes. The identified pathways may provide a basis for future research.Clinical significanceMore insight is obtained in the mechanisms related to poor outcome in HFpEF patients since it was demonstrated that biomarkers associated with the high-risk cluster were related to the immune system, signal transduction cascades, cell interactions and metabolismBiomarkers (and pathways) identified in this study may help select high-risk HFpEF patients which could be helpful for the inclusion/exclusion of patients in future trials.Our findings may be the basis of investigating therapies specifically targeting these pathways and the potential use of corresponding markers potentially identifying patients with distinct mechanistic bioprofiles most likely to respond to the selected mechanistically targeted therapies. More insight is obtained in the mechanisms related to poor outcome in HFpEF patients since it was demonstrated that biomarkers associated with the high-risk cluster were related to the immune system, signal transduction cascades, cell interactions and metabolism Biomarkers (and pathways) identified in this study may help select high-risk HFpEF patients which could be helpful for the inclusion/exclusion of patients in future trials. Our findings may be the basis of investigating therapies specifically targeting these pathways and the potential use of corresponding markers potentially identifying patients with distinct mechanistic bioprofiles most likely to respond to the selected mechanistically targeted therapies.
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