Background: Nocturnal blood pressure (BP) is associated with cardiovascular disease independently of awake BP. However, nocturnal BP measured using an ambulatory monitoring device has limited reproducibility because it is a single-day measurement. We investigated the association between sleep BP measured on multiple days using a timer-equipped home BP monitor and cardiovascular diseases in a general population.
Methods: The study population comprised 5814 community residents. Participants were required to sleep with wrapping cuffs on their upper arm and BP was measured automatically at 0 : 00, 2 : 00, and 4 : 00. Actigraph was used to determine BP measured during sleep. Participants were also measured home morning and evening BP manually using the same device.
Results: During the 7.3-year mean follow-up period, we observed 117 cases of cardiovascular diseases. The association between sleep BP (per 10 mmHg hazard ratio = 1.31, P < 0.001) and cardiovascular events remained significant (hazard ratio = 1.22, P = 0.036) even after adjusting for office BP and confounding factors, such as sleep-disordered breathing. Individuals with sleep-only hypertension ( n = 1047; hazard ratio = 2.23, P = 0.005) had a significant cardiovascular risk. Daytime-only hypertension ( n = 264; hazard ratio = 3.57, P = 0.001) and combined sleep and daytime hypertension ( n = 1216; hazard ratio = 3.69, P < 0.001) was associated with cardiovascular events to the same extent. Sleep BP dipping was not identified as a significant determinant of cardiovascular events.
Conclusion: Sleep BP measured using a home BP monitor was independently associated with the incidence of cardiovascular disease in a general population.
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http://dx.doi.org/10.1097/HJH.0000000000003781 | 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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