Background
An extensive study on cardiovascular risk factors interaction seems to be of crucial importance in order to prevent cardiovascular (CVD) events. The main focus of this study is understanding direct and indirect relationships between different CVDs risk factors.
Methods
A longitudinal data on adults aged ≥ 35 years, who were free of CVD at baseline, were used to study. The endpoints were CVD events, while their measurements were demographic, socio-economics, life-style components, laboratory findings, anthropometric measures, psychological factors, and quality of life status. A Bayesian structural equation modeling (BSEM) was used to determine the relationships among 21 relevant factors associated with total CVD, stroke, acute coronary syndrome (ACS), and fatal CVDs.
Results
In this study a total of 3161 individuals with complete information were included in the study. Total 407 CVD events were occurred during follow-up. The causal associations between 6 latent variables were identified in the causal network for fatal and non-fatal CVDs. Lipid profile influences the occurrence of CVD events as the most important, but it did so indirectly mediate through the risky behaviors and comorbidities. Lipid profile at baseline was influenced by a wide range of other protective factors, such as quality of life and healthy life style components.
Conclusions
Analyzing a causal network on risk factors reveals the flow of information in direct and indirect paths, as well as determining predictors and demonstrate the utility of integrating multi-factor data in a complex framework to identify novel candidate preventable pathways to lower risk of CVDs.