Visit-to-visit variability in the measurements of metabolic syndrome components and the risk of all-cause mortality,cardiovascular disease,and arterial stiffness |
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| Institution: | 1. Department of Maternal and Child Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No.13 Hangkong Road, Wuhan, 430030, China;2. Ministry of Education Key Laboratory of Environment and Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, No.13 Hangkong Road, Wuhan, 430030, China;3. Department of Laboratory Medicine, Wuhan Children''s Hospital, Tongji Medical College, Huazhong University of Science and Technology, No.100 Hong Kong Road, Wuhan, Hubei, 430016, China;4. Department of Cardiology, Kailuan Hospital, North China University of Science and Technology, No.57 Xinhua East Road, Tangshan City, 063001, China;5. Department of Epidemiology and Biostatistics, School of Public Health, Peking University, No.38 Xueyuan Road, Beijing, 100191, China;6. Department of Otorhinolaryngology, Union hospital, Tongji Medical College, Huazhong University of Science and Technology, No. 1277 Jiefang Avenue, Wuhan, 430022, China;1. Division of Cardiology, Department of Medicine, The University of Hong Kong Shenzhen Hospital, Shenzhen, China;2. Department of Medicine, Affiliated Hospital of North Sichuan Medical College, Nanchong City, Sichuan Province, China;3. Division of Cardiology, Department of Medicine, The University of Hong Kong, Queen Mary Hospital, Hong Kong, China;4. Department of Ultrasound, Shenzhen Hospital, Southern Medical University, Shen Zhen, China;5. Division of Ultrasound, Department of Radiology, The University of Hong Kong Shenzhen Hospital, Shenzhen, China;1. Blossom DMO, Hipólito Irigoyen 31, Córdoba, X5000, Argentina;2. Department of Biochemistry, Federal University Oye-Ekiti, Nigeria;3. INICSA, Enrique Barros Pabellón Biología Celular, Ciudad Universitaria, X5000, Córdoba, Argentina;4. Center for Advanced Imaging Research, Department of Diagnostic Radiology and Nuclear Medicine, Maryland University, Baltimore, MA, USA;1. National Demonstration Center for Experimental Mechanical Engineering Education, Key Laboratory of High-efficiency and Clean Mechanical Manufacture, School of Mechanical Engineering, Shandong University, Jinan, 250061, PR China;2. Guangdong Provincial Key Laboratory of Turbulence Research and Applications, Center for Complex Flows and Soft Matter Research and Department of Mechanics and Aerospace Engineering, Southern University of Science and Technology, Shenzhen 518055, PR China;3. Priority Research Centre for Frontier Energy Technologies & Utilisation, The University of Newcastle, Callaghan, NSW 2308, Australia;4. Guangdong-Hong Kong-Macao Joint Laboratory for Data-Driven Fluid Mechanics and Engineering Applications, Southern University of Science and Technology, Shenzhen 518055, China;5. Department of Mechanical and Aeronautical Engineering, Western Michigan University, Kalamazoo, MI 49008, USA;1. Unidad de Investigación de Enfermedades Metabólicas, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico;2. MD/PhD (PECEM) Program, Facultad de Medicina, Universidad Nacional Autónoma de México, Mexico City, Mexico;3. División de Investigación, Instituto Nacional de Geriatría, Mexico City, Mexico;4. Departamento de Endocrinología y Metabolismo del Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán, Mexico City, Mexico;5. Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Mexico City, Mexico;6. Programa AFINES, Facultad de Medicina, Universidad Nacional Autónoma de México, Mexico City, Mexico;1. State Key Laboratory of Quality Research in Chinese Medicine, Guangdong-Hong Kong-Macao Joint Laboratory of Respiratory Infectious Disease, Macau Institute for Applied Research in Medicine and Health, Macau University of Science and Technology, Macau, People''s Republic of China;2. Biology Institute, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250103, People''s Republic of China |
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| Abstract: | Background and aimsThe risk of adverse health conditions varied according to the number of metabolic syndrome components. We aimed to evaluate the risk of mortality and incident cardiovascular events according to the number of components with high variability.Methods and resultsA total of 43,737 Kailuan Study participants with ≥3 examinations of waist circumference, fasting blood glucose, systolic blood pressure, triglyceride, and high-density lipoprotein during 2006–2013 were included in the present study. Visit-to-visit variability in each parameter was defined by the intraindividual standard deviation across visits. High variability was defined as the highest quartile of variability. Participants were classified numerically according to the number of high-variability components (e.g., a score of 0 indicated no high-variability component). There were 1551 deaths during a median follow-up of 5.9 years, and 950 incident cardiovascular disease (CVD) cases during a median follow-up of 4.9 years. In the multivariable adjusted model, compared with participants with low variability for all components, participants with ≥3 high-variability components had significantly higher risks for all-cause mortality (hazards ratio HR], 1.61; 95 % confidence interval CI], 1.35–1.91) and incident CVD event (HR, 1.45; 95 % CI, 1.16–1.82). Additionally, participants with ≥3 high-variability components had increased odds of arterial stiffness, as measured by brachia-ankle pulse wave velocity (odds ratio OR], 1.39; 95 % CI, 1.19–1.63).ConclusionsOur findings suggest that participants with at least three metabolic parameters with high variability experienced increased risk of CVD and all-cause mortality. |
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| Keywords: | Variability Metabolic syndrome Mortality Cardiovascular events |
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