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连续贝叶斯网络在尿酸与慢性代谢性疾病相关性中的应用

崔宇 宋伟梅 赵瑞青 任浩 王旭春 乔宇超 赵执扬 任家辉 刘静 李一汀 仇丽霞

崔宇, 宋伟梅, 赵瑞青, 任浩, 王旭春, 乔宇超, 赵执扬, 任家辉, 刘静, 李一汀, 仇丽霞. 连续贝叶斯网络在尿酸与慢性代谢性疾病相关性中的应用[J]. 中华疾病控制杂志, 2023, 27(9): 1078-1083. doi: 10.16462/j.cnki.zhjbkz.2023.09.016
引用本文: 崔宇, 宋伟梅, 赵瑞青, 任浩, 王旭春, 乔宇超, 赵执扬, 任家辉, 刘静, 李一汀, 仇丽霞. 连续贝叶斯网络在尿酸与慢性代谢性疾病相关性中的应用[J]. 中华疾病控制杂志, 2023, 27(9): 1078-1083. doi: 10.16462/j.cnki.zhjbkz.2023.09.016
CUI Yu, SONG Weimei, ZHAO Ruiqing, REN Hao, WANG Xuchun, QIAO Yuchao, ZHAO Zhiyang, REN Jiahui, LIU Jing, LI Yiting, QIU Lixia. Application of continuous Bayesian networks in the association study between uric acid and chronic metabolic diseases[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(9): 1078-1083. doi: 10.16462/j.cnki.zhjbkz.2023.09.016
Citation: CUI Yu, SONG Weimei, ZHAO Ruiqing, REN Hao, WANG Xuchun, QIAO Yuchao, ZHAO Zhiyang, REN Jiahui, LIU Jing, LI Yiting, QIU Lixia. Application of continuous Bayesian networks in the association study between uric acid and chronic metabolic diseases[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2023, 27(9): 1078-1083. doi: 10.16462/j.cnki.zhjbkz.2023.09.016

连续贝叶斯网络在尿酸与慢性代谢性疾病相关性中的应用

doi: 10.16462/j.cnki.zhjbkz.2023.09.016
基金项目: 

国家自然科学基金 81973155

详细信息
    通讯作者:

    仇丽霞,E-mail: qlx_1126@163.com

  • 中图分类号: R195.1

Application of continuous Bayesian networks in the association study between uric acid and chronic metabolic diseases

Funds: 

National Natural Science Foundation of China 81973155

More Information
  • 摘要:   目的  基于改进的偏相关(improved partial-correlation-based, IPCB)算法建立连续贝叶斯网络模型,探寻尿酸(uric acid, UA)的代谢性影响因素,并通过与传统的多重线性回归模型分析比较,分析连续贝叶斯网络模型对疾病影响因素的效果和优势。  方法  以2015年山西省慢性病监测的4 846例监测人群数据为研究对象,分别用多重线性回归模型和连续贝叶斯网络模型分析UA与其余9个代谢性疾病的特征指标的相关性,比较两种方法结果的优劣。  结果  经多重线性回归模型逐步分析,三酰甘油(triglyceride, TG)、SBP、DBP、低密度脂蛋白(low density lipoprotein, LDL)、高密度脂蛋白(high density lipoprotein, HDL)共5个变量与UA水平直接相关,解释了UA 9.5%的变异。连续贝叶斯网络模型共包含24条有向边,年龄、TG、LDL、HDL、SBP、DBP与UA水平直接相关,随着年龄、TG、LDL的增加和HDL的降低均会导致UA水平升高,而UA水平升高又导致SBP、DBP升高;总胆固醇(total cholesterol, TC)与UA间接相关。  结论  连续贝叶斯网络模型能发现更多UA的直接影响因素,还可以找到UA的间接影响因素,整体解释度更好。
  • 图  1  标准化残差的直方图

    Figure  1.  Histogram of standardized residuals

    图  2  标准化残差散点图

    Figure  2.  Standardized residual scatter plot

    图  3  连续贝叶斯网络结构

    TC: 总胆固醇;TG: 三酰甘油;LDL: 低密度脂蛋白;HDL: 高密度脂蛋白;UA: 尿酸;FPG: 空腹血糖;HbA1c: 糖化血红蛋白。

    Figure  3.  The structure of continuous Bayesian networks

    TC: total cholesterol; TG: triglyceride; LDL: low density lipoprotein; HDL: high density lipoprotein; UA: uric acid; FPG: fasting plasma glucose; HbA1c: glycosylated hemoglobin type A1c.

    表  1  Durbin-Watson统计量的计算结果

    Table  1.   Calculation results of Durbin-Watson statistics

    RR R2 调整后R2
    Adjusted R2
    s Durbin-Watson
    0.308 0.095 0.094 73.567 2.034
    下载: 导出CSV

    表  2  代谢指标统计描述和正态性检验

    Table  2.   Statistical description and normality test of metabolic indicators

    变量
    Variable
    赋值
    Assignment
    x±s M 正态性检验P
    Normality test P value
    UA/(μmol·L-1) Y 268.29±77.28 257.35 <0.001
    年龄/岁Age/years X1 54.07±13.40 55.00 <0.001
    TC/(mmol·L-1) X2 4.61±0.92 4.54 <0.001
    TG/(mmol·L-1) X3 1.70±1.16 1.40 <0.001
    LDL/(mmol·L-1) X4 2.91±0.80 2.84 <0.001
    HDL/(mmol·L-1) X5 1.17±0.28 1.13 <0.001
    SBP/mmHg X6 138.69±21.58 135.00 <0.001
    DBP/mmHg X7 80.82 ±11.54 80.00 <0.001
    FPG/(mmol·L-1) X8 5.64±1.52 5.31 <0.001
    HbA1c/% X9 5.03±0.91 4.90 <0.001
    注:UA, 尿酸; TC, 总胆固醇; TG, 三酰甘油; LDL, 低密度脂蛋白; HDL, 高密度脂蛋白; FPG, 空腹血糖; HbA1c, 糖化血红蛋白。
    Note: UA, uric acid; TC, total cholesterol; TG, triglyceride; LDL, low density lipoprotein; HDL, high density lipoprotein; FPG, fasting plasma glucose; HbA1c, glycosylated hemoglobin type A1c.
    下载: 导出CSV

    表  3  偏回归系数估计与检验

    Table  3.   Test of partial regression coefficient

    变量
    Variable
    偏回归系数
    Partial regression coefficient
    s 标准化偏回归系数
    Standardized partial regression coefficient
    t
    value
    P
    value
    常数  Constant 221.767 9.989 22.201 <0.001
    TG/(mmol·L-1) 7.968 1.119 0.119 7.122 <0.001
    HDL/(mmol·L-1) -35.051 4.318 -0.128 -8.118 <0.001
    LDL/(mmol·L-1) 11.235 1.495 0.117 7.517 <0.001
    DBP/mmHg 1.295 0.119 0.193 10.904 <0.001
    SBP/mmHg -0.458 0.064 -0.128 -7.192 <0.001
    注:1. TC, 三酰甘油;HDL, 高密度脂蛋白;LDL, 低密度脂蛋白。
    2. “―”无数据。
    Note: 1. TC, total cholesterol; HDL, high density lipoprotein; LDL, low density lipoprotein.
    2. “―”No Date.
    下载: 导出CSV

    表  4  UA与其直接相关变量的偏相关系数

    Table  4.   Partial correlation coefficient between blood uric acid and its directly related variables

    变量
    Variable
    UA
    偏相关系数
    Partial correlation coefficient
    P
    value
    年龄/岁Age/years 0.026 0.079
    TG/(mmol·L-1) 0.083 < 0.001
    LDL/(mmol·L-1) 0.028 0.057
    HDL/(mmol·L-1) -0.048 0.001
    SBP/mmHg 0.084 < 0.001
    DBP/mmHg 0.147 < 0.001
    注:TG, 三酰甘油; LDL, 低密度脂蛋白; HDL, 高密度脂蛋白; UA, 尿酸。
    Note: TG, triglyceride; LDL, low density lipoprotein; HDL, high density lipoprotein; UA, uric acid.
    下载: 导出CSV

    表  5  多重线性回归与连续型贝叶斯网络的比较

    Table  5.   Comparison between multiple linear regression and continuous Bayesian network

    条目  Item 多重线性回归分析  Multiple linear regression analysis 连续贝叶斯网络  Continuous Bayesian network
    建模方法
    Modeling method
    以逐步回归法建立多重线性回归模型
    Establishing multilinear regression model by stepwise law of return
    基于偏相关的结构学习算法,MDL评分确定节点之间的边及边的方向
    Based on partial correlation structural learning algorithm, MDL score determines the edges and their directions between nodes
    模型复杂度
    Model complexity
    发现了与UA水平直接相关的5个变量有统计学意义,相对简单
    Five variables directly related to UA levels were found to be statistically significant and relatively simple
    与UA水平相关的关系网络共24条边,相对复杂
    The relationship network related to UA level has a total of 24 edges, which is relatively complex
    直接相关因素
    Direct related factor
    TG、SBP、DBP、LDL和HDL 5个变量与UA水平直接相关,解释了UA水平变异的9.5%,决定系数较小,从专业理论的角度看,尚显不足
    The five variables of TG, SBP, DBP, LDL and HDL are directly related to the level of UA, which explains 9.5% of the variation of the level of UA. The Coefficient of determination is small, which is still insufficient from the perspective of professional theory
    连续型变量提供更多的信息,发现年龄、TG、LDL、HDL直接影响了UA的水平,而UA的水平直接影响了SBP和DBP的水平, 从专业理论的角度看,合理性更强
    Continuous variables provide more information, and it is found that age, TG, LDL, and HDL directly affect the level of UA, while the level of UA directly affects the levels of SBP and DBP. From the perspective of professional theory, the rationality is stronger
    与直接相关因素的关联强度
    The correlation strength with directly related factor
    自变量的偏回归系数反映对因变量的影响程度,标准化回归系数反映不同自变量在模型中的重要性
    The partial regression coefficient of the independent variable reflects the degree of influence on the dependent variable, while the standardized regression coefficient reflects the importance of different independent variables in the model
    子节点与父节点间的偏相关系数,描述了与UA直接相关的6个因素间相关的程度与方向,区分出4个影响因素和2个结局因素
    The partial correlation coefficient between the child node and the parent node describes the degree and direction of correlation between the six factors directly related to UA, distinguishing four influencing factors and two outcome factors
    间接相关因素
    Indirect related factor
    多重线性回归模型无法筛选与UA水平间接相关的影响因素
    Multiple linear regression models cannot screen for influencing factors indirectly related to UA levels
    TC与UA是间接关系,主要体现在年龄以不同的方式影响着TC、TG、LDL、HDL,血脂各指标有着复杂的关系,从而间接影响了UA的水平
    TC and UA are indirectly related, mainly reflected in the fact that age affects TC, TG, LDL, HDL in different ways, and there is a complex relationship between various indicators of blood lipids, thereby indirectly affecting the level of UA
    注:UA,尿酸;TG,三酰甘油;LDL,低密度脂蛋白;HDL,高密度脂蛋白;MDL,最小描述长度; TC,总胆固醇。
    Note: UA, blood uric acid; TG, triglyceride; LDL, low density lipoprotein; HDL, high density lipoprotein; MDL, minimum description length; TC, total cholesterol.
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-09-16
  • 修回日期:  2022-12-05
  • 网络出版日期:  2023-10-12
  • 刊出日期:  2023-09-10

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