Key result
Lipid profiles indirectly influenced cardiovascular events through risky behaviors and comorbidities, while risky behaviors had significant direct effects on stroke, acute coronary syndrome, and fatal cardiovascular disease.
Why the study?
Studying the interactions and direct and indirect relationships among different CVD risk factors is crucial to prevent CVD events.
Cohort (n=3,161)
Yes
Using a Bayesian structural equation model, this study demonstrates that lipid profiles are the most important predictor of CVD events, with their effects indirectly mediated through risky behaviors and comorbidities.
Answers are grounded in this paper and cite their sources.
Identifies novel CVD risk factor pathways; leaves open causal inference pending randomized validation.
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.
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Shakibaei et al. (2020) conducted a cohort in Cardiovascular disease (n=3,161). Cardiovascular risk factors (e.g., lipid profile, risky behaviors) vs. Lower risk factor profiles was evaluated on Total CVD, stroke, acute coronary syndrome, and fatal CVD events (p=<0.05). Lipid profiles indirectly influenced cardiovascular events through risky behaviors and comorbidities, while risky behaviors had significant direct effects on stroke, acute coronary syndrome, and fatal cardiovascular disease.
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