Background: Pulse pressure is a derivative of arterial stiffness. We have previously demonstrated ambulatory pulse pressure to be relatively independent from the blood pressure (BP) lowering during sleep, and thus of a neurogenic effect. On the other hand, white coat BP effects are thought to involve neurogenic activation. The aim of this work was to analyze white coat induced variability in pulse pressure.
Methods: Percent clinic-awake differences in systolic BP (SBP) and pulse pressure (white coat effects) were calculated for 688 consecutive subjects (mean age 60 +/- 16 years, 58% female). Of the subjects, 23% had controlled hypertension, 45% uncontrolled hypertension, 8% normotension, and 4% isolated office hypertension; all were referred to our unit for 24 h ambulatory BP monitoring.
Results: Pulse pressure highly correlated with SBP (r = 0.82, P <.00001). We found a larger white coat effect on pulse pressure than on SBP (8.3% and 5.2%, respectively, P < or =.0001). This was true in all subgroups except in normotensive subjects. Specifically, the magnitude of the white coat effect on pulse pressure was greater than on SBP in subjects with treated hypertension, untreated hypertension, and isolated office hypertension, and in young hypertensive subjects, older subjects, and those with diabetes.
Conclusions: Although pulse pressure is related to the mechanical properties of large arteries, it is also influenced by the white coat effect, a neurogenic process. Furthermore, in hypertensive but not in normotensive subjects, the white coat effect on pulse pressure is significantly more pronounced than on SBP.
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http://dx.doi.org/10.1016/j.amjhyper.2004.02.018 | 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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