Mee Kyoung Kim1, Kyungdo Han2, Yong-Moon Park3, Hyuk-Sang Kwon1, Gunseog Kang4, Kun-Ho Yoon5,6, Seung-Hwan Lee5. 1. Department of Internal Medicine, Division of Endocrinology and Metabolism, Yeouido St Mary's Hospital (M.K.K., H.-S.K.), College of Medicine, The Catholic University of Korea, Seoul. 2. Department of Medical Statistics (K.H.), College of Medicine, The Catholic University of Korea, Seoul. 3. Epidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC (Y.-M.P.). 4. Department of Statistics and Actuarial Science, Soongsil University, Seoul, Korea (G.K.). 5. Department of Internal Medicine, Division of Endocrinology and Metabolism, Seoul St Mary's Hospital (K.-H.Y., S.-H.L.), College of Medicine, The Catholic University of Korea, Seoul. 6. Department of Medical Informatics (K.-H.Y.), College of Medicine, The Catholic University of Korea, Seoul.
Abstract
BACKGROUND: Variability in metabolic parameters, such as fasting blood glucose and cholesterol concentrations, blood pressure, and body weight can affect health outcomes. We investigated whether variability in these metabolic parameters has additive effects on the risk of mortality and cardiovascular outcomes in the general population. METHODS: Using nationally representative data from the Korean National Health Insurance System, 6 748 773 people who were free of diabetes mellitus, hypertension, and dyslipidemia and who underwent ≥3 health examinations from 2005 to 2012 were followed to the end of 2015. Variability in fasting blood glucose and total cholesterol concentrations, systolic blood pressure, and body mass index was measured using the coefficient of variation, SD, variability independent of the mean, and average real variability. High variability was defined as the highest quartile of variability. Participants were classified numerically according to the number of high-variability parameters (eg, a score of 4 indicated high variability in all 4 metabolic parameters). Cox proportional hazards models adjusting for age, sex, smoking, alcohol, regular exercise, income, and baseline levels of fasting blood glucose, systolic blood pressure, total cholesterol, and body mass index were used. RESULTS: There were 54 785 deaths (0.8%), 22 498 cases of stroke (0.3%), and 21 452 myocardial infarctions (0.3%) during a median follow-up of 5.5 years. High variability in each metabolic parameter was associated with a higher risk for all-cause mortality, myocardial infarction, and stroke. Furthermore, the risk of outcomes increased significantly with the number of high-variability metabolic parameters. In the multivariable-adjusted model comparing a score of 0 versus 4, the hazard ratios (95% CIs) were 2.27 (2.13-2.42) for all-cause mortality, 1.43 (1.25-1.64) for myocardial infarction, and 1.41 (1.25-1.60) for stroke. Similar results were obtained when modeling the variability using the SD, variability independent of the mean, and average real variability, and in various sensitivity analyses. CONCLUSIONS: High variability of fasting blood glucose and total cholesterol levels, systolic blood pressure, and body mass index was an independent predictor of mortality and cardiovascular events. There was a graded association between the number of high-variability parameters and cardiovascular outcomes.
BACKGROUND: Variability in metabolic parameters, such as fasting blood glucose and cholesterol concentrations, blood pressure, and body weight can affect health outcomes. We investigated whether variability in these metabolic parameters has additive effects on the risk of mortality and cardiovascular outcomes in the general population. METHODS: Using nationally representative data from the Korean National Health Insurance System, 6 748 773 people who were free of diabetes mellitus, hypertension, and dyslipidemia and who underwent ≥3 health examinations from 2005 to 2012 were followed to the end of 2015. Variability in fasting blood glucose and total cholesterol concentrations, systolic blood pressure, and body mass index was measured using the coefficient of variation, SD, variability independent of the mean, and average real variability. High variability was defined as the highest quartile of variability. Participants were classified numerically according to the number of high-variability parameters (eg, a score of 4 indicated high variability in all 4 metabolic parameters). Cox proportional hazards models adjusting for age, sex, smoking, alcohol, regular exercise, income, and baseline levels of fasting blood glucose, systolic blood pressure, total cholesterol, and body mass index were used. RESULTS: There were 54 785 deaths (0.8%), 22 498 cases of stroke (0.3%), and 21 452 myocardial infarctions (0.3%) during a median follow-up of 5.5 years. High variability in each metabolic parameter was associated with a higher risk for all-cause mortality, myocardial infarction, and stroke. Furthermore, the risk of outcomes increased significantly with the number of high-variability metabolic parameters. In the multivariable-adjusted model comparing a score of 0 versus 4, the hazard ratios (95% CIs) were 2.27 (2.13-2.42) for all-cause mortality, 1.43 (1.25-1.64) for myocardial infarction, and 1.41 (1.25-1.60) for stroke. Similar results were obtained when modeling the variability using the SD, variability independent of the mean, and average real variability, and in various sensitivity analyses. CONCLUSIONS: High variability of fasting blood glucose and total cholesterol levels, systolic blood pressure, and body mass index was an independent predictor of mortality and cardiovascular events. There was a graded association between the number of high-variability parameters and cardiovascular outcomes.
Entities:
Keywords:
blood pressure; body mass index; cholesterol; glucose; mortality; myocardial infarction; stroke; variability
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