Objective: To verify the effects of a six-month non-supervised physical training program followed via the Internet on blood pressure and body composition in normotensive and borderline hypertensive individuals.
Methods: One hundred and thirty five individuals were divided into two groups: 1) normotensive individual (n = 57), 43 +/- 1 years of age, systolic blood pressure (SBP) < 120 and diastolic blood pressure (DBP) < 80 mmHg (GI); and 2) borderline hypertensive individual (n = 78), 46 +/- 1 years of age, SBP 120 to 139 and DBP 80 to 89 mmHg (GII).
Results: After a three and six-month physical training, GII individuals showed a significant reduction in SBP (-3.6 +/- 0.94 and -10 +/- 0.94 mmHg, p < 0.05, respectively) and PAD (-6.5 +/- 1 and -7.1 +/- 0.9 mmHg, p < 0.05, respectively), body weight (-1.12 +/- 0.26 and -1.25 +/- 0.31 kg, p < 0.05, respectively), BMI (-0.79 +/- 0.4 and -0.84 +/- 0.41 kg/m2, p < 0.05, respectively) and waist circumference (-1.12 +/- 0.53 and -1.84 +/- 0.56 cm, p < 0.05, respectively). In the GI group, the physical training led to a decrease in waist circumference at the sixth month (-1.6 +/- 0.63 cm, p < 0.05).
Conclusion: This program decreases blood pressure, body weight, BMI, and waist circumference in borderline hypertensive individuals, and is therefore a safe and low-cost strategy in the prevention of cardiovascular diseases and improvement of health status of the population.
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http://dx.doi.org/10.1590/s0066-782x2006000400009 | 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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