Background: Recently it was suggested that urine albumin-to-creatinine ratio (uACR), even within the normal range, can be associated with hypertension, but only a few studies have examined. Therefore, this study aimed to determine the association between normal range uACR and the prevalence of hypertension.
Methods: The research used data from the 2009 to 2018 National Health and Nutrition Examination Survey, which included 14,919 participants. We defined the uACR as the amount of albumin (mg/dL) divided by creatinine (g/dL) in randomly voided urine. Hypertension was defined as mean systolic blood pressure ≥130 mmHg, or diastolic ≥80 mmHg, or were taking hypertension medication or were informed of a hypertension diagnosis by a physician/health professional.
Results: In multivariable-adjusted models, per 5 mg/g uACR increment, the hypertension prevalence increased 1.31-fold (OR, 1.31; 95% CI 1.23-1.40), the odds [95% confidence interval (CI)] for hypertension prevalence were 2.25 (1.86-2.72) for those in the highest quartile compared to those in the lowest quartile. The nonlinear relationship between hypertension prevalence and uACR was found by visually assessing images (p for nonlinearity<.001). In addition, in the subgroup analysis stratified by body mass index, the lower the BMI, the stronger the association between uACR and hypertension prevalence.
Conclusions: Even within the normal range, subtly elevated uACR was associated with an increased prevalence of hypertension in the USA general population, and this association may be enhanced in individuals with low BMI. Further research is needed to assess the clinical applicability of these findings.
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http://dx.doi.org/10.1002/clc.24012 | DOI Listing |
Comput Biol Med
January 2025
College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua, 321004, China; Zhejiang Institute of Optoelectronics, Jinhua, 321004, China. Electronic address:
Accurate segmentation of brain tumors from MRI scans is a critical task in medical image analysis, yet it remains challenging due to the complex and variable nature of tumor shapes and sizes. Traditional convolutional neural networks (CNNs), while effective for local feature extraction, struggle to capture long-range dependencies crucial for 3D medical image analysis. To address these limitations, this paper presents VcaNet, a novel architecture that integrates a Vision Transformer (ViT) with a fusion channel and spatial attention module (CBAM), aimed at enhancing 3D brain tumor segmentation.
View Article and Find Full Text PDFACS Nano
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Department of Chemistry, Purdue University, West Lafayette, Indiana 47907, United States.
Controlling charge transport at the interfaces of nanostructures is crucial for their successful use in optoelectronic and solar energy applications. Mixed-dimensional heterostructures based on single-walled carbon nanotubes (SWCNTs) and transition metal dichalcogenides (TMDCs) have demonstrated exceptionally long-lived charge-separated states. However, the factors that control the charge transport at these interfaces remain unclear.
View Article and Find Full Text PDFJ Appl Oral Sci
January 2025
Universidade Federal de Uberlândia, Faculdade de Odontologia, Departamento de Periodontia e Implantodontia, Uberlândia, Brasil.
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View Article and Find Full Text PDFJ Appl Oral Sci
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Universitas Airlangga, Faculty of Dental Medicine, Department of Oral Biology, Surabaya, East Java, Indonesia.
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