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Trajectory of Smoking and Incidence of Atherosclerotic Cardiovascular Disease among Korean Young Adult Men.

Yongho JeeJooeun JeonJoung Hwan BackMikyung RyuSung-Ii Cho
Published in: International journal of environmental research and public health (2019)
Introduction: Smoking among young adults is associated with atherosclerotic cardiovascular disease (ASCVD) in middle age. Our aim was to analyze the trajectory of smoking in young adults and analyze the effects of the trajectory group on incident ASCVD. Methods: This study was conducted among 60,709 young adult men aged 20-29 years who received health screening every two years from 1992-2004. Trajectory analysis was performed through smoking survey data measured 7 times during this period. ASCVD, including ischemic heart disease (IHD) and stroke events were confirmed from 2005-2015. The association between the trajectory group and ASCVD risk was analyzed using Cox proportional hazard models, controlling for covariates and mediators. Results: Trajectory analysis showed that smoking categorized into five groups as follows: Group 1 (28.3%), low steady; Group 2 (14.7%), lowering; Group 3 (17.3%), high steady; Group 4 (15.6%), rise and fall; and Group 5 (24.2%), very high steady. The model performance of the trajectory model (Akaike information criterion; AIC = 51,670.78) with mediators was better than the model (AIC = 51,847.85) without mediators. Group 5 showed a 49% higher risk of ASCVD than Group 1. The risk of IHD was 1.63-times higher for Group 5 and 1.31-times higher for Group 4, compared to Group 1. Compared to Group 1, Group 5 had a 1.36- and 1.58-times higher risk for total stroke and ischemic stroke, respectively. Conclusions: In young adult men, the multiple measured trajectory model with mediators was far more informative than one-time smoking for explaining the association with cardiovascular disease.
Keyphrases
  • young adults
  • cardiovascular disease
  • healthcare
  • atrial fibrillation
  • mental health
  • brain injury
  • machine learning
  • blood brain barrier
  • deep learning
  • cardiovascular risk factors
  • big data