Abstract
This study aimed to obtain the prevalence of hyperlipidemia and its related factors in Shanxi Province, China using multivariate logistic regression analysis and tabu search-based Bayesian networks (BNs). A multi-stage stratified random sampling method was adopted to obtain samples among the general population aged 18 years or above. The prevalence of hyperlipidemia in Shanxi Province was 42.6%. Multivariate logistic regression analysis indicated that gender, age, region, occupation, vegetable intake level, physical activity, body mass index, central obesity, hypertension, and diabetes mellitus are associated with hyperlipidemia. BNs were used to find connections between those related factors and hyperlipidemia, which were established by a complex network structure. The results showed that BNs can not only be used to find out the correlative factors of hyperlipidemia but also to analyse how these factors affect hyperlipidemia and their interrelationships, which is consistent with practical theory, is superior to logistic regression and has better application prospects.
Highlights
Cardiovascular disease (CVD) is the leading cause of death worldwide, accounting for 30% of all deaths[1]
Most previous studies on factors related to hyperlipidemia have used logistic regression based on independent variables, and odds ratio values to reflect the degree of association; in reality, these factors are often interdependent and may have a complex network structure, which cannot meet the assumptions of a logistic regression model and lead to the failure of logistic regression to describe this relationship
We found a detection rate of dyslipidemia was 42.6% in Shanxi Province of China, which is considerably higher than the nationally reported prevalence of dyslipidemia[9] (41.9%) as well as those reported in other provinces of China[1,3,11,18]
Summary
Cardiovascular disease (CVD) is the leading cause of death worldwide, accounting for 30% of all deaths[1]. Most previous studies on factors related to hyperlipidemia have used logistic regression based on independent variables, and odds ratio values to reflect the degree of association; in reality, these factors are often interdependent and may have a complex network structure, which cannot meet the assumptions of a logistic regression model and lead to the failure of logistic regression to describe this relationship. The relating factors of logistic regression are parallel, which cannot infer the part they play in the occurrence and development of hyperlipidemia. Mancini et al.[12,13] stated that traditional statistical methods such as logistic regression, are ineffective for describing the relationship between variables in the biomedical domain because of their limitations of independency. We applied BNs optimized with a tabu search algorithm to jointly model dyslipidemia and its related factors and determine how these factors impact dyslipidemia, to offer comprehensive strategies for effectively reducing the incidence of hyperlipidemia
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