Pharmacological treatment of hypertension is effective in preventing cardiovascular and renal complications. Calcium antagonists and blockers of the renin-angiotensin system are widely used today to initiate antihypertensive therapy but, when given as monotherapy, do not suffice in most patients to normalize blood pressure. Combining the two types of agents considerably increases the antihypertensive efficacy, but not at the expense of a deterioration of tolerability. This is exemplified by the experience accumulated with the recently developed fixed dose combination containing the AT(1)-receptor blocker valsartan (160 mg) and the dihydropyridine amlodipine (5 or 10 mg). In a randomized trial, an 8-week treatment normalized blood pressure (<140/90 mmHg) within 8 weeks in a large fraction of hypertensive patients (78.4% and 85.2% using the 5/160 [n = 371] and 10/160 mg [n = 377] dosage, respectively). Like all AT(1)-receptor blockers valsartan has a placebo-like tolerability. Valsartan prevents to a large extent the occurrence amlodipine-induced peripheral edema. Both amlodipine and valsartan have beneficial effects on cardiovascular morbidity and mortality, as well as protective effects on renal function. The co-administration of these two agents is therefore very attractive, as it enables a rapid and sustained blood pressure control in hypertensive patients. The availability of a fixed-dose combination based on amlodipine and valsartan is expected therefore to facilitate the management of hypertension, to improve long-term adherence with antihypertensive therapy and, ultimately, to have a positive impact on cardiovascular and renal outcomes.
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http://dx.doi.org/10.2147/vhrm.s3134 | DOI Listing |
Circ Genom Precis Med
January 2025
Mary and Steve Wen Cardiovascular Division, Department of Medicine, University of California, Los Angeles. (W.F., N.D.W.).
Background: Lp(a; Lipoprotein[a]) is a predictor of atherosclerotic cardiovascular disease (ASCVD); however, there are few algorithms incorporating Lp(a), especially from real-world settings. We developed an electronic health record (EHR)-based risk prediction algorithm including Lp(a).
Methods: Utilizing a large EHR database, we categorized Lp(a) cut points at 25, 50, and 75 mg/dL and constructed 10-year ASCVD risk prediction models incorporating Lp(a), with external validation in a pooled cohort of 4 US prospective studies.
Hypertension
January 2025
Department of Nephrology, Medical Faculty, University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Germany (S.A.P., I.Q., D. Arifaj, M.K., D. Argov, L.C.R., J.S.).
Background: Ciliary neurotrophic factor (CNTF), mainly known for its neuroprotective properties, belongs to the IL-6 (interleukin-6) cytokine family. In contrast to IL-6, the effects of CNTF on the vasculature have not been explored. Here, we examined the role of CNTF in AngII (angiotensin II)-induced hypertension.
View Article and Find Full Text PDFWounds from gunshots and other explosive devices are a source of loss of substances directly or secondary to a well- conducted debridement. In addition, these types of wounds are by definition contaminated. The major challenge in this context for any surgeon remains coverage.
View Article and Find Full Text PDFJHEP Rep
February 2025
Department of Gastroenterology and Hepatology, Hospital Universitario Ramón y Cajal, Instituto Ramon y Cajal de Investigación Sanitaria (IRYCIS), Universidad de Alcalá, Madrid, Spain.
Background & Aims: Systemic inflammation is a driver of decompensation in cirrhosis with unclear relevance in the compensated stage. We evaluated inflammation and bacterial translocation markers in compensated cirrhosis and their dynamics in relation to the first decompensation.
Methods: This study is nested within the PREDESCI trial, which investigated non-selective beta-blockers for preventing decompensation in compensated cirrhosis and clinically significant portal hypertension (CSPH: hepatic venous pressure gradient ≥10 mmHg).
Front Public Health
January 2025
Department of Computer Science, College of Engineering and Computer Science, Jazan University, Jazan, Saudi Arabia.
Introduction: The growing demand for real-time, affordable, and accessible healthcare has underscored the need for advanced technologies that can provide timely health monitoring. One such area is predicting arterial blood pressure (BP) using non-invasive methods, which is crucial for managing cardiovascular diseases. This research aims to address the limitations of current healthcare systems, particularly in remote areas, by leveraging deep learning techniques in Smart Health Monitoring (SHM).
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