Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, 030001, Shanxi, China.
Key Laboratory of Public Health Safety of Ministry of Education, School of Public Health, Fudan University, Shanghai, 200032, China.
BMC Public Health. 2021 Jul 12;21(1):1375. doi: 10.1186/s12889-021-11412-5.
This article aims to understand the prevalence of hyperlipidemia and its related factors in Shanxi Province. On the basis of multivariate Logistic regression analysis to find out the influencing factors closely related to hyperlipidemia, the complex network connection between various variables was presented through Bayesian networks(BNs).
Logistic regression was used to screen for hyperlipidemia-related variables, and then the complex network connection between various variables was presented through BNs. Since some drawbacks stand out in the Max-Min Hill-Climbing (MMHC) hybrid algorithm, extra hybrid algorithms are proposed to construct the BN structure: MMPC-Tabu, Fast.iamb-Tabu and Inter.iamb-Tabu. To assess their performance, we made a comparison between these three hybrid algorithms with the widely used MMHC hybrid algorithm on randomly generated datasets. Afterwards, the optimized BN was determined to explore to study related factors for hyperlipidemia. We also make a comparison between the BN model with logistic regression model.
The BN constructed by Inter.iamb-Tabu hybrid algorithm had the best fitting degree to the benchmark networks, and was used to construct the BN model of hyperlipidemia. Multivariate logistic regression analysis suggested that gender, smoking, central obesity, daily average salt intake, daily average oil intake, diabetes mellitus, hypertension and physical activity were associated with hyperlipidemia. BNs model of hyperlipidemia further showed that gender, BMI, and physical activity were directly related to the occurrence of hyperlipidemia, hyperlipidemia was directly related to the occurrence of diabetes mellitus and hypertension; the average daily salt intake, daily average oil consumption, smoking, and central obesity were indirectly related to hyperlipidemia.
The BN of hyperlipidemia constructed by the Inter.iamb-Tabu hybrid algorithm is more reasonable, and allows for the overall linking effect between factors and diseases, revealing the direct and indirect factors associated with hyperlipidemia and correlation between related variables, which can provide a new approach to the study of chronic diseases and their associated factors.
本文旨在了解山西省高脂血症的流行情况及其相关因素。在多变量 Logistic 回归分析的基础上，找出与高脂血症密切相关的影响因素，通过贝叶斯网络（BNs）呈现各变量之间复杂的网络联系。
采用 Logistic 回归筛选高脂血症相关变量，然后通过 BNs 呈现各变量之间复杂的网络联系。由于最大最小爬山（MMHC）混合算法存在一些明显的缺陷，提出了额外的混合算法来构建 BN 结构：MMPC-Tabu、Fast.iamb-Tabu 和 Inter.iamb-Tabu。为了评估它们的性能，我们在随机生成的数据集上比较了这三种混合算法与广泛使用的 MMHC 混合算法。之后，确定优化的 BN 来探讨高脂血症的相关因素。我们还比较了 BN 模型与 Logistic 回归模型。
Inter.iamb-Tabu 混合算法构建的 BN 与基准网络具有最佳的拟合度，用于构建高脂血症的 BN 模型。多变量 logistic 回归分析表明，性别、吸烟、中心性肥胖、每日平均盐摄入量、每日平均油摄入量、糖尿病、高血压和体力活动与高脂血症有关。高脂血症的 BNs 模型进一步表明，性别、BMI 和体力活动与高脂血症的发生直接相关，高脂血症与糖尿病和高血压的发生直接相关；每日平均盐摄入量、每日平均油摄入量、吸烟和中心性肥胖与高脂血症间接相关。
Inter.iamb-Tabu 混合算法构建的高脂血症 BN 更合理，可以整体链接因素与疾病之间的关系，揭示与高脂血症相关的直接和间接因素以及相关变量之间的相关性，为慢性病及其相关因素的研究提供了新的方法。
