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Pathways Leading to Prevention of Fatal and Non-Fatal Cardiovascular Disease: An Interaction Model on 15 Years Population-Based Cohort Study Publisher Pubmed



Shakibaei N1 ; Hassannejad R2 ; Mohammadifard N3 ; Marateb HR4, 5 ; Mansourian M5, 6 ; Mananas MA5, 7 ; Sarrafzadegan N8, 9
Authors
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Authors Affiliations
  1. 1. School of Public Health, Isfahan University of Medical Sciences, Isfahan, Iran
  2. 2. Interventional Cardiology Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
  3. 3. Hypertension Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
  4. 4. Biomedical Engineering Department, Engineering Faculty, University of Isfahan, Isfahan, Iran
  5. 5. Department of Automatic Control, Biomedical Engineering Research Center, Universitat Politecnica de Catalunya, Barcelona, Spain
  6. 6. Pediatric Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
  7. 7. Biomedical Research Networking Center in Bioengineering, Biomaterialsand Nanomedicine (CIBER-BBN), Barcelona, Spain
  8. 8. Isfahan Cardiovascular Research Center, Cardiovascular Research Institute, Isfahan University of Medical Sciences, Isfahan, Iran
  9. 9. School of Population and Public Health, Faculty of Medicine, University of British Columbia, Vancouver, Canada

Source: Lipids in Health and Disease Published:2020


Abstract

Background: A comprehensive study on the interaction of cardiovascular disease (CVD) risk factors is critical to prevent cardiovascular events. The main focus of this study is thus to understand direct and indirect relationships between different CVD risk factors. Methods: A longitudinal data on adults aged ≥35 years, who were free of CVD at baseline, were used in this study. The endpoints were CVD events, whereas their measurements were demographic, lifestyle components, socio-economics, anthropometric measures, laboratory findings, quality of life status, and psychological factors. A Bayesian structural equation modelling was used to determine the relationships among 21 relevant factors associated with total CVD, stroke, coronary syndrome (ACS), and fatal CVDs. Results: In this study, a total of 3161 individuals with complete information were involved in the study. A total of 407 CVD events, with an average age of 54.77(10.66) years, occurred during follow-up. The causal associations between six latent variables were identified in the causal network for fatal and non-fatal CVDs. Lipid profile, with the coefficient of 0.26 (0.01), influenced the occurrence of CVD events as the most critical factor, while it was indirectly mediated through risky behaviours and comorbidities. Lipid profile at baseline was influenced by a wide range of other protective factors, such as quality of life and healthy lifestyle components. Conclusions: Analysing a causal network of risk factors revealed the flow of information in direct and indirect paths. It also determined predictors and demonstrated the utility of integrating multi-factor data in a complex framework to identify novel preventable pathways to reduce the risk of CVDs. © 2020 The Author(s).
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