Pathways Leading to Atherosclerosis

Abstract

Abstract-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. 1 These statistics explain the increasing interest of clinical researchers to determine risk factors for CVD. Atherosclerosis is an important etiologic element of CVD, and although causes of atherosclerosis have been explored previously, it remained difficult to investigate several atherosclerosis risk factors simultaneously. Cohort studies have been used to determine associations between risk factors of atherosclerosis. 2,3 However, these studies did not take into account indirect effects of risk factors because only direct effects between 2 variables were analyzed. Thus, such studies lacked to provide a statistical method that could unravel the pathways simultaneously in one path analysis.Structural Equation Modeling (SEM) is an advanced statistical technique that enables solving these issues. SEM has been applied in several research fields but is still rarely used in clinical research, despite its ability to identify, test, and estimate pathways in a non-hypothesis-driven manner. 4 We hypothesized that SEM is a suitable method to unravel multidirectional associations and potential causal pathways in complex origins of diseases such as CVD. This approach, in which the interdependency of risk factors is unraveled simultaneously, is innovative in this field. Our ...

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