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.
Department of Medical Statistics (K.H.), College of Medicine, The Catholic University of Korea, Seoul.
Circulation. 2018 Dec 4;138(23):2627-2637. doi: 10.1161/CIRCULATIONAHA.118.034978.
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.
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.
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.
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.
代谢参数的变异性，如空腹血糖和胆固醇浓度、血压和体重，可能会影响健康结果。我们研究了这些代谢参数的变异性是否对一般人群的死亡率和心血管结局有累加效应。
利用韩国国家健康保险系统的全国代表性数据，我们对 2005 年至 2012 年间接受≥3 次健康检查且无糖尿病、高血压和血脂异常的 6748773 人进行了随访，随访至 2015 年底。使用变异系数、标准差、均值独立变异和平均真实变异来衡量空腹血糖和总胆固醇浓度、收缩压和体重指数的变异性。高变异性定义为最高四分位变异。参与者根据高变异性参数的数量进行数值分类（例如，得分为 4 表示 4 种代谢参数的变异性均较高）。使用 Cox 比例风险模型，调整年龄、性别、吸烟、饮酒、规律运动、收入以及空腹血糖、收缩压、总胆固醇和体重指数的基线水平后进行分析。
在中位随访 5.5 年期间，共有 54785 人死亡（0.8%）、22498 人发生中风（0.3%）和 21452 人发生心肌梗死（0.3%）。每个代谢参数的高变异性与全因死亡率、心肌梗死和中风的风险增加有关。此外，随着高变异性代谢参数数量的增加，结局风险显著增加。在多变量调整模型中，与评分 0 相比，评分 4 的全因死亡率的危险比（95%CI）为 2.27（2.13-2.42）、心肌梗死为 1.43（1.25-1.64）、中风为 1.41（1.25-1.60）。当使用标准差、均值独立变异和平均真实变异对变异性进行建模，以及在各种敏感性分析中，都得到了类似的结果。
空腹血糖和总胆固醇水平、收缩压和体重指数的高变异性是死亡率和心血管事件的独立预测因素。高变异性参数数量与心血管结局之间存在分级关联。
