The efficacy and safety of losartan and valsartan were evaluated in a multicenter, double-blind, randomized trial in patients with mild to moderate essential hypertension. Blood pressure responses to once-daily treatment with either losartan 50 mg (n = 93) or valsartan 80 mg (n = 94) for 6 weeks were assessed through measurements taken in the clinic and by 24-hour ambulatory blood pressure monitoring (ABPM). Both drugs significantly reduced clinic sitting systolic (SiSBP) and diastolic blood pressure (SiDBP) at 2, 4, and 6 weeks. Maximum reductions from baseline in SiSBP and SiDBP on 24-hour ABPM were also significant with the two treatments. The reduction in blood pressure was more consistent across patients in the losartan group, as indicated by a numerically smaller variability in change from baseline on all ABPM measures, which achieved significance at peak (P = .017) and during the day (P = .002). In addition, the numerically larger smoothness index with losartan suggested a more homogeneous antihypertensive effect throughout the 24-hour dosing interval. The antihypertensive response rate was 54% with losartan and 46% with valsartan. Three days after discontinuation of therapy, SiDBP remained below baseline in 73% of losartan and 63% of valsartan patients. Both agents were generally well tolerated. Losartan, but not valsartan, significantly decreased serum uric acid an average 0.4 mg/dL at week 6. In conclusion, once-daily losartan 50 mg and valsartan 80 mg had similar antihypertensive effects in patients with mild to moderate essential hypertension. Losartan produced a more consistent blood pressure-lowering response and significantly lowered uric acid, suggesting potentially meaningful differences between these two A II receptor antagonists.
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http://dx.doi.org/10.1007/BF02854844 | 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.
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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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