Department of Pediatrics, Subdivision of Endocrinology, Erasmus MC/Sophia Children’s Hospital, Rotterdam, The Netherlands.
Hypertension. 2011 Feb;57(2):255-60. doi: 10.1161/HYPERTENSIONAHA.110.163600. Epub 2010 Dec 28.
Several risk factors of cardiovascular diseases have been studied using direct association measures. Because the incidence of obesity and cardiovascular diseases is rising, it is important to correctly model these risk factors involved in development of cardiovascular diseases. Until now, statistical methods lacked to achieve this goal because of complex interrelationships involved. Structural Equation Modeling (SEM) is an advanced statistical technique that enables solving this issue. The aims of this study were to investigate whether SEM could unravel pathways involved in cardiovascular diseases and to visualize these pathways in a model. In 322 healthy participants of the PROGRAM (PROgramming factors for GRowth And Metabolism) study, 18 to 24 years of age, we explored pathways leading to atherosclerosis measured by carotid intima-media thickness. Using SEM, we were able to model these pathways for males and females using body fat percentage, serum lipid levels, and blood pressure. We are the first to present a model of complex direct and indirect effects of fat mass leading to atherosclerosis using SEM. Both male and female path-model had an excellent fit. Fat mass had a significant effect on carotid intima-media thickness through various pathways, with the largest effect size on carotid intima-media thickness via blood pressure. SEM showed that the pathways differed between males and females, with a larger effect of serum lipids on carotid intima-media thickness in males. In conclusion, SEM is suitable in identifying models to unravel potential causal pathways in complex origins of diseases. We present a model involving several pathways, showing that fat mass has an influence on risk factors for atherosclerosis, already at 21 years of age.
几种心血管疾病的危险因素已通过直接关联测量进行了研究。由于肥胖和心血管疾病的发病率不断上升,因此正确建模这些涉及心血管疾病发展的危险因素非常重要。到目前为止,由于涉及的复杂相互关系,统计方法还无法实现这一目标。结构方程模型(SEM)是一种先进的统计技术,可以解决这个问题。本研究的目的是探讨 SEM 是否可以揭示心血管疾病中涉及的途径,并在模型中可视化这些途径。在年龄在 18 至 24 岁的 PROGRAM(PROgramming factors for GRowth And Metabolism)研究的 322 名健康参与者中,我们探讨了通过颈动脉内膜中层厚度测量的动脉粥样硬化的途径。使用 SEM,我们能够使用体脂肪百分比、血清脂质水平和血压为男性和女性建模这些途径。我们是第一个使用 SEM 展示复杂的直接和间接脂肪质量导致动脉粥样硬化的综合途径的人。男性和女性的路径模型都具有极好的拟合度。脂肪质量通过多种途径对颈动脉内膜中层厚度产生显著影响,对颈动脉内膜中层厚度的影响最大的是血压。SEM 表明,男性和女性之间的途径存在差异,男性血清脂质对颈动脉内膜中层厚度的影响更大。总之,SEM 适用于识别模型,以揭示疾病复杂起源中的潜在因果途径。我们提出了一个涉及多种途径的模型,表明脂肪质量对动脉粥样硬化的危险因素有影响,即使在 21 岁时也是如此。