Cardiovascular diseases (CVD) and cancer are the top deadly diseases in the world. Both CVD and cancer have common risk factors; therefore, with the advances in treatment and life span, both diseases may occur simultaneously in patients. It is becoming evident that CVD and cancer are highly connected, establishing a novel discipline known as cardio-oncology. This includes the cardiomyocyte death following any anti-tumor therapy known as cardiotoxicity as well the intricate interplay between heart failure and cancer. Recent studies, using various mouse models, showed that heart failure promotes tumor growth and metastasis spread. Indeed, patients with heart failure were found to be at higher risk of developing malignant diseases. While the effect of heart failure on cancer is well established, little is known regarding the effect of tumors on heart failure. A recent study from our lab has demonstrated that tumor growth and metastasis ameliorate cardiac remodeling in a pressure-overload mouse model. Nevertheless, this study was inconclusive regarding whether tumor growth solely suppresses cardiac remodeling or is able to reverse existing heart failure outcomes as well. Here, we used a regulable transgenic mouse model for cardiac hypertrophy and fibrosis. Cancer cell implantation suppressed cardiac dysfunction and fibrosis as shown using echocardiography, qRT-PCR and fibrosis staining. In addition, tumor growth resulted in an M1 to M2 macrophage switch, which is correlated with cardiac repair. Macrophage depletion using clodronate liposomes completely abrogated the tumors' beneficial effect. This study highly suggests that harnessing tumor paradigms may lead to the development of novel therapeutic strategies for CVDs and fibrosis.
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http://dx.doi.org/10.3390/cells12182289 | DOI Listing |
Acta Pharm
December 2024
Department of Clinical Pharmacy, University Hospital Dubrava, 10000 Zagreb Croatia.
Cardiovascular diseases (CVDs) are the leading cause of mortality and morbidity globally. It is estimated that 17.9 million people died from CVDs in 2019, which represents 32 % of all deaths worldwide.
View Article and Find Full Text PDFEur Heart J Cardiovasc Imaging
January 2025
Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy.
Aim: Computed tomography (CT)-derived extracellular volume fraction (ECV) is a non-invasive method to quantify myocardial fibrosis. Evaluating CT-ECV during aortic valve replacement (AVR) planning CT in severe aortic stenosis (AS) may aid prognostic stratification. This meta-analysis evaluated the prognostic significance of CT-ECV in severe AS necessitating AVR.
View Article and Find Full Text PDFASAIO J
December 2024
Cleveland Clinic Florida, Heart, Vascular and Thoracic Institute, Advanced Heart Failure Program, Weston, Florida.
We investigated the association of preimplant left ventricular end-diastolic diameter (LVEDD) with outcomes after HeartMate 3 (HM3) left ventricular assist device (LVAD) implantation. Patients from the European Registry for Patients with Mechanical Circulatory Support (EUROMACS) registry who underwent HM3 implantation from August 2014 to February 2023 (n = 834) were analyzed according to preoperative LVEDD: less than or equal to 65 (n = 251), 65-80 (n = 441), and greater than or equal to 80 mm (n = 142). The mean age was 54.
View Article and Find Full Text PDFJ Clin Endocrinol Metab
January 2025
Department of Neurosurgery and State Key Laboratory of Trauma, Burn and Combined Injury, Southwest Hospital; Chongqing Key Laboratory of Precision Neuromedicine and Neuroregenaration, Third Military Medical University (Army Medical University), 400038 Chongqing, China.
Background: Phthalates, widely used as chemical additives, are often found as mixtures in the environment. However, the combined impact of phthalate exposure on sarcopenia remains unclear.
Objective: This study aimed to investigate the relationships between phthalates and sarcopenia in adults.
Eur Heart J Acute Cardiovasc Care
January 2025
Department of Medical Informatics, Korea University College of Medicine, Seoul, Republic of Korea.
Background: Acute heart failure (AHF) poses significant diagnostic challenges in the emergency room (ER) because of its varied clinical presentation and limitations of traditional diagnostic methods. This study aimed to develop and evaluate a deep-learning model using electrocardiogram (ECG) data to enhance AHF identification in the ER.
Methods: In this retrospective cohort study, we analyzed the ECG data of 19,285 patients who visited ERs of three hospitals between 2016 and 2020; 9,119 with available left ventricular ejection fraction and N-terminal prohormone of brain natriuretic peptide level data and who were diagnosed with AHF were included in the study.
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