Article(id=1241322670709592820, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241322661654098823, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202308236, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1691942400000, receivedDateStr=2023-08-14, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1773883987357, onlineDateStr=2026-03-19, pubDate=1706112000000, pubDateStr=2024-01-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1773883987357, onlineIssueDateStr=2026-03-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1773883987357, creator=13701087609, updateTime=1773883987357, updator=13701087609, issue=Issue{id=1241322661654098823, tenantId=1146029695717560320, journalId=1227665162245664772, year='2024', volume='51', issue='2', pageStart='193', pageEnd='384', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1773883985198, creator=13701087609, updateTime=1773890256678, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1241348966214849502, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241322661654098823, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1241348966214849503, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1241322661654098823, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=335, endPage=342, ext={EN=ArticleExt(id=1241322671221297956, articleId=1241322670709592820, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Influencing factors of H-type hypertension in middle-aged and elderly based on Bayesian network model, columnId=1228016573156360233, journalTitle=Modern Preventive Medicine, columnName=Disease Control and Prevention, runingTitle=null, highlight=null, articleAbstract=
Objective

To construct the Bayesian network model of H-type hypertension in middle-aged and elderly people, and to explore the influencing factors of H-type hypertension and the network relationship between factors, and the strength of each influencing factor on H-type hypertension.

Methods

A total of 1 119 middle-aged and elderly people who underwent physical examination in the hospital health management center from May 2022 to April 2023 were selected as the research objects and relevant data were collected. Univariate logistic regression analysis and multivariate logistic regression analysis models were used for preliminary screening of variables, “bnlearn” Bayesian network software package was used for model construction, and Netica software was used for model inference.

Results

Logistic regression analysis model was used to screen the variables, such as age, gender, education level, smoking, drinking, body mass index(BMI), fasting blood glucose, etc. FBG, total cholesterol(TC), triglyceride(TG), high density lipoprotein cholesterol((HDL-C), low density lipoprotein cholesterol(LDL-C) and uric acid(UA) were included in the Bayesian network model. A Bayesian network model of H-type hypertension related factors in middle-aged and elderly people with 13 nodes and 16 directed edges was constructed by using 12 selected variables as network nodes. Age, FBG, TG, HDL-C and BMI were directly related to H-type hypertension, while gender, smoking, drinking, educational level, TC, LDL-C and UA were indirectly related to H-type hypertension. When the age was≥60years old, FBG≥6.85 mmol/L, BMI≥24.83 kg/m2, HDL-C≥1.02 mmol/L, TG<1.6 mmol/L, the risk of H-type hypertension reached 0.565.

Conclusion

The Bayesian network model reveals the direct and indirect factors and correlation strength of H-type hypertension in middle-aged and elderly people, clarifies the complex network relationship between factors, and provides a scientific basis for early prevention of H-type hypertension in middle-aged and elderly people.

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目的

构建中老年H型高血压的贝叶斯网络模型,探讨H型高血压的影响因素及因素间的网络关系,通过模型推理反映各影响因素对H型高血压的影响程度。

方法

选取2022年5月—2023年4月在医院健康管理中心体检的1 119例中老年人为研究对象并进行相关资料收集。单因素及多因素logistic 回归分析模型进行变量初步筛选,采用“bnlearn”贝叶斯网络软件包进行模型构建,Netica 软件进行模型推理。

结果

logistic 回归分析模型对变量进行筛选后,年龄、性别、文化程度、吸烟、饮酒、体质指数(body mass index,BMI)、空腹血糖(fasting blood glucose,FBG)、总胆固醇(total cholesterol,TC)、甘油三酯(triglyceride,TG)、高密度脂蛋白胆固醇(high density lipoprotein cholesterol,HDL-C)、低密度脂蛋白胆固醇(low density lipoprotein cholesterol,LDL-C)、尿酸(uric acid,UA)被纳入贝叶斯网络模型。以筛选出的12个变量作为网络节点,构建一个含13个节点,16条有向边的中老年H型高血压相关因素的贝叶斯网络模型,年龄、FBG、TG、HDL-C和BMI与H型高血压直接相关,性别、吸烟、饮酒、文化程度、TC、LDL-C和UA与H型高血压间接相关。当年龄≥60岁、FBG≥6.85 mmol/L、BMI≥24.83 kg/m2、HDL-C≥1.02 mmol/L、TG<1.6 mmol/L时,中老年患H型高血压的风险达0.565。

结论

贝叶斯网络模型揭示中老年H型高血压发生的直接和间接因素以及关联强度,阐明了因素间复杂的网络关系,为早期预防中老年H型高血压的发生提供科学依据。

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赵雅宁,E-mail:
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俞美艳(1999—),女,硕士在读,研究方向:慢性病康复

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俞美艳(1999—),女,硕士在读,研究方向:慢性病康复

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Chinese Journal of Evidence-Based Medicine, 2022, 22(7): 785-790., articleTitle=Effects of hyperuricemia on the prevalence of dyslipidemia in the elderly: an empirical study based on propensity score matching, refAbstract=null)], funds=[Fund(id=1241322681778360683, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241322670709592820, awardId=18130218A, language=CN, fundingSource=唐山市科技创新团队培养计划(18130218A), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1241322674102784953, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241322670709592820, xref=null, ext=[AuthorCompanyExt(id=1241322674111173562, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241322670709592820, companyId=1241322674102784953, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=School of Nursing and Rehabilitation, North China University of Science and Technology, Tangshan, Hebei 063210, China), AuthorCompanyExt(id=1241322674119562171, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241322670709592820, companyId=1241322674102784953, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=华北理工大学护理与康复学院,河北 唐山 063210)])], figs=[ArticleFig(id=1241322678724907234, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241322670709592820, language=EN, label=Fig.1, caption=Bayesian network of H-type hypertension in middle-aged and elderly constructed by MMHC algorithm, figureFileSmall=hX8A0D3zREdTdp5EopCAXg==, figureFileBig=0vvw1NzX5Dn2QQ68Ul/qzw==, tableContent=null), ArticleFig(id=1241322678859124972, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241322670709592820, language=CN, label=图1, caption=MMHC算法构建中老年发生H型高血压的贝叶斯网络, figureFileSmall=hX8A0D3zREdTdp5EopCAXg==, figureFileBig=0vvw1NzX5Dn2QQ68Ul/qzw==, tableContent=null), ArticleFig(id=1241322679094006017, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241322670709592820, language=EN, label=Fig.2, caption=Bayesian network risk inference for H-type hypertension in middle-aged and elderly, figureFileSmall=cxCJ/rwbZve80Ero/Msm7w==, figureFileBig=RgEJuI/4LyW7Mj0hJMuZjw==, tableContent=null), ArticleFig(id=1241322679194669324, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241322670709592820, language=CN, label=图2, caption=中老年发生H型高血压贝叶斯网络的风险推理, figureFileSmall=cxCJ/rwbZve80Ero/Msm7w==, figureFileBig=RgEJuI/4LyW7Mj0hJMuZjw==, tableContent=null), ArticleFig(id=1241322679282749713, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241322670709592820, language=EN, label=Table 1, caption=

Variable assignment

, figureFileSmall=null, figureFileBig=null, tableContent=
变量赋值情况
是否发生H型高血压0=否,1=是
年龄0=<60,1=≥60
性别0=女,1=男
婚姻状况0=有配偶,1=无配偶
居住地0=城市,1=农村
文化程度0=小学及以下,1=中学及以上
职业0=以脑力劳动为主,1=以体力劳动为主
吸烟0=否,1=是
饮酒0=否,1=是
心理状态0=正常,1=焦虑
睡眠状况0=正常,1=存在障碍
BMI(kg/m20=<24.83,1=≥24.83
FBG(mmol/L)0=<6.85,1=≥6.85
TC(mmol/L)0=<5.30,1=≥5.30
TG(mmol/L)0=<1.60,1=≥1.60
HDL-C(mmol/L)0=≥1.02,1=<1.02
LDL-C(mmol/L)0=<3.33,1=≥3.33
UA(μmol/L)0=<289.50,1=≥289.50
), ArticleFig(id=1241322679425356061, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241322670709592820, language=CN, label=表1, caption=

变量赋值

, figureFileSmall=null, figureFileBig=null, tableContent=
变量赋值情况
是否发生H型高血压0=否,1=是
年龄0=<60,1=≥60
性别0=女,1=男
婚姻状况0=有配偶,1=无配偶
居住地0=城市,1=农村
文化程度0=小学及以下,1=中学及以上
职业0=以脑力劳动为主,1=以体力劳动为主
吸烟0=否,1=是
饮酒0=否,1=是
心理状态0=正常,1=焦虑
睡眠状况0=正常,1=存在障碍
BMI(kg/m20=<24.83,1=≥24.83
FBG(mmol/L)0=<6.85,1=≥6.85
TC(mmol/L)0=<5.30,1=≥5.30
TG(mmol/L)0=<1.60,1=≥1.60
HDL-C(mmol/L)0=≥1.02,1=<1.02
LDL-C(mmol/L)0=<3.33,1=≥3.33
UA(μmol/L)0=<289.50,1=≥289.50
), ArticleFig(id=1241322679538602276, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241322670709592820, language=EN, label=Table 2, caption=

Univariate analysis of H-type hypertension in middle-aged and elderly [n(%)]

, figureFileSmall=null, figureFileBig=null, tableContent=
因素未发生H型高血压(n=736)发生H型高血压(n=383)χ2P
年龄(岁)17.204<0.001
<60258(35.1)88(23.0)
≥60478(64.9)295(77.0)
性别48.989<0.001
306(41.6)79(20.6)
430(58.4)304(79.4)
婚姻状况0.0630.802
有配偶657(89.3)340(88.8)
无配偶79(10.7)43(11.2)
居住地0.4440.505
城市495(67.3)250(65.3)
农村241(32.7)133(34.7)
文化程度13.645<0.001
小学及以下145(19.7)113(29.5)
中学及以上591(80.3)270(70.5)
职业2.2870.130
以脑力劳动为主313(42.5)181(47.3)
以体力劳动为主423(57.5)202(52.7)
吸烟56.826<0.001
534(72.6)191(49.9)
202(27.4)192(50.1)
饮酒60.303<0.001
566(76.9)208(54.3)
170(23.1)175(45.7)
心理状态0.1620.687
正常361(49.0)183(47.8)
焦虑375(51.0)200(52.2)
睡眠状况0.4130.520
正常564(76.6)300(78.3)
存在障碍172(23.4)83(21.7)
BMI(kg/m270.886<0.001
<24.83410(55.7)112(29.2)
≥24.83326(44.3)271(70.8)
FBG(mmol/L)52.420<0.001
<6.85504(68.5)177(46.2)
≥6.85232(31.5)206(53.8)
TC(mmol/L)29.015<0.001
<5.3489(66.4)191(49.9)
≥5.3247(33.6)192(50.1)
TG(mmol/L)55.724<0.001
<1.6477(64.8)159(41.5)
≥1.6259(35.2)224(58.5)
HDL-C(mmol/L)27.112<0.001
<1.02157(21.3)137(35.8)
≥1.02579(78.7)246(64.2)
LDL-C(mmol/L)37.605<0.001
<3.33462(62.8)167(43.6)
≥3.33274(37.2)216(56.4)
UA(μmol/L)28.059<0.001
<289.50366(49.7)127(33.2)
≥289.50370(50.3)256(66.8)
), ArticleFig(id=1241322679697985842, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241322670709592820, language=CN, label=表2, caption=

中老年人发生H型高血压的单因素分析[n(%)]

, figureFileSmall=null, figureFileBig=null, tableContent=
因素未发生H型高血压(n=736)发生H型高血压(n=383)χ2P
年龄(岁)17.204<0.001
<60258(35.1)88(23.0)
≥60478(64.9)295(77.0)
性别48.989<0.001
306(41.6)79(20.6)
430(58.4)304(79.4)
婚姻状况0.0630.802
有配偶657(89.3)340(88.8)
无配偶79(10.7)43(11.2)
居住地0.4440.505
城市495(67.3)250(65.3)
农村241(32.7)133(34.7)
文化程度13.645<0.001
小学及以下145(19.7)113(29.5)
中学及以上591(80.3)270(70.5)
职业2.2870.130
以脑力劳动为主313(42.5)181(47.3)
以体力劳动为主423(57.5)202(52.7)
吸烟56.826<0.001
534(72.6)191(49.9)
202(27.4)192(50.1)
饮酒60.303<0.001
566(76.9)208(54.3)
170(23.1)175(45.7)
心理状态0.1620.687
正常361(49.0)183(47.8)
焦虑375(51.0)200(52.2)
睡眠状况0.4130.520
正常564(76.6)300(78.3)
存在障碍172(23.4)83(21.7)
BMI(kg/m270.886<0.001
<24.83410(55.7)112(29.2)
≥24.83326(44.3)271(70.8)
FBG(mmol/L)52.420<0.001
<6.85504(68.5)177(46.2)
≥6.85232(31.5)206(53.8)
TC(mmol/L)29.015<0.001
<5.3489(66.4)191(49.9)
≥5.3247(33.6)192(50.1)
TG(mmol/L)55.724<0.001
<1.6477(64.8)159(41.5)
≥1.6259(35.2)224(58.5)
HDL-C(mmol/L)27.112<0.001
<1.02157(21.3)137(35.8)
≥1.02579(78.7)246(64.2)
LDL-C(mmol/L)37.605<0.001
<3.33462(62.8)167(43.6)
≥3.33274(37.2)216(56.4)
UA(μmol/L)28.059<0.001
<289.50366(49.7)127(33.2)
≥289.50370(50.3)256(66.8)
), ArticleFig(id=1241322679802843448, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241322670709592820, language=EN, label=Table 3, caption=

Multivariate logistic regression analysis of H-type hypertension in middle-aged and elderly

, figureFileSmall=null, figureFileBig=null, tableContent=
变量βSEWald χ2POR值(95% CI
年龄(岁)0.9410.18625.734<0.0012.563(1.782~3.687)
性别1.0590.20726.222<0.0012.885(1.923~4.327)
文化程度-1.0660.19330.631<0.0010.344(0.236~0.502)
吸烟0.5980.1919.8060.0221.818(1.251~2.643)
饮酒0.7860.19316.663<0.0012.195(1.505~3.203)
BMI(kg/m21.2700.16162.166<0.0013.560(2.597~4.882)
FBG(mmol/L)1.1210.16048.892<0.0013.069(2.241~4.202)
TC(mmol/L)0.6350.2576.0990.0141.887(1.140~3.125)
TG(mmol/L)0.6320.16115.428<0.0011.881(1.372~2.577)
HDL-C(mmol/L)-0.7670.18317.548<0.0010.465(0.325~0.665)
LDL-C(mmol/L)0.5030.2464.1760.0411.653(1.021~2.676)
UA(μmol/L)0.3720.1605.4110.0201.451(1.060~1.986)
), ArticleFig(id=1241322679945449801, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241322670709592820, language=CN, label=表3, caption=

影响中老年人发生H型高血压的多因素logistic 回归分析

, figureFileSmall=null, figureFileBig=null, tableContent=
变量βSEWald χ2POR值(95% CI
年龄(岁)0.9410.18625.734<0.0012.563(1.782~3.687)
性别1.0590.20726.222<0.0012.885(1.923~4.327)
文化程度-1.0660.19330.631<0.0010.344(0.236~0.502)
吸烟0.5980.1919.8060.0221.818(1.251~2.643)
饮酒0.7860.19316.663<0.0012.195(1.505~3.203)
BMI(kg/m21.2700.16162.166<0.0013.560(2.597~4.882)
FBG(mmol/L)1.1210.16048.892<0.0013.069(2.241~4.202)
TC(mmol/L)0.6350.2576.0990.0141.887(1.140~3.125)
TG(mmol/L)0.6320.16115.428<0.0011.881(1.372~2.577)
HDL-C(mmol/L)-0.7670.18317.548<0.0010.465(0.325~0.665)
LDL-C(mmol/L)0.5030.2464.1760.0411.653(1.021~2.676)
UA(μmol/L)0.3720.1605.4110.0201.451(1.060~1.986)
), ArticleFig(id=1241322680067084630, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241322670709592820, language=EN, label=Table 4, caption=

Conditional probability table

, figureFileSmall=null, figureFileBig=null, tableContent=
FBG≥6.85 mmol/L年龄≥60岁HDL-C<1.02 mmol/LTG≥1.6 mmol/LBMI≥24.83 kg/m2H型高血压
0.4350.565
0.5000.500
0.9000.100
0.8750.125
0.2220.778
0.5000.500
0.4000.600
0.5000.500
0.4000.600
0.4090.591
0.7830.217
0.8000.200
0.1670.833
0.2860.714
0.6360.364
0.5710.429
0.5480.452
0.5830.417
0.6480.352
0.8260.174
0.2500.750
0.7500.250
0.5000.500
0.6000.400
0.3540.646
0.4850.515
0.7730.227
0.7500.250
0.1670.833
0.5710.429
0.7500.250
0.6000.400
), ArticleFig(id=1241322680184525154, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1241322670709592820, language=CN, label=表4, caption=

条件概率表

, figureFileSmall=null, figureFileBig=null, tableContent=
FBG≥6.85 mmol/L年龄≥60岁HDL-C<1.02 mmol/LTG≥1.6 mmol/LBMI≥24.83 kg/m2H型高血压
0.4350.565
0.5000.500
0.9000.100
0.8750.125
0.2220.778
0.5000.500
0.4000.600
0.5000.500
0.4000.600
0.4090.591
0.7830.217
0.8000.200
0.1670.833
0.2860.714
0.6360.364
0.5710.429
0.5480.452
0.5830.417
0.6480.352
0.8260.174
0.2500.750
0.7500.250
0.5000.500
0.6000.400
0.3540.646
0.4850.515
0.7730.227
0.7500.250
0.1670.833
0.5710.429
0.7500.250
0.6000.400
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基于贝叶斯网络模型的中老年发生H型高血压的影响因素研究
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俞美艳 , 赵雅宁 , 刘瑶 , 赵大业 , 丁淑平
现代预防医学 | 疾病预防控制 2024,51(2): 335-342
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现代预防医学 | 疾病预防控制 2024, 51(2): 335-342
基于贝叶斯网络模型的中老年发生H型高血压的影响因素研究
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俞美艳, 赵雅宁 , 刘瑶, 赵大业, 丁淑平
作者信息
  • 华北理工大学护理与康复学院,河北 唐山 063210
  • 俞美艳(1999—),女,硕士在读,研究方向:慢性病康复

通讯作者:

赵雅宁,E-mail:
Influencing factors of H-type hypertension in middle-aged and elderly based on Bayesian network model
Mei-yan YU, Ya-ning ZHAO , Yao LIU, Da-ye ZHAO, Shu-ping DING
Affiliations
  • School of Nursing and Rehabilitation, North China University of Science and Technology, Tangshan, Hebei 063210, China
出版时间: 2024-01-25 doi: 10.20043/j.cnki.MPM.202308236
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目的

构建中老年H型高血压的贝叶斯网络模型,探讨H型高血压的影响因素及因素间的网络关系,通过模型推理反映各影响因素对H型高血压的影响程度。

方法

选取2022年5月—2023年4月在医院健康管理中心体检的1 119例中老年人为研究对象并进行相关资料收集。单因素及多因素logistic 回归分析模型进行变量初步筛选,采用“bnlearn”贝叶斯网络软件包进行模型构建,Netica 软件进行模型推理。

结果

logistic 回归分析模型对变量进行筛选后,年龄、性别、文化程度、吸烟、饮酒、体质指数(body mass index,BMI)、空腹血糖(fasting blood glucose,FBG)、总胆固醇(total cholesterol,TC)、甘油三酯(triglyceride,TG)、高密度脂蛋白胆固醇(high density lipoprotein cholesterol,HDL-C)、低密度脂蛋白胆固醇(low density lipoprotein cholesterol,LDL-C)、尿酸(uric acid,UA)被纳入贝叶斯网络模型。以筛选出的12个变量作为网络节点,构建一个含13个节点,16条有向边的中老年H型高血压相关因素的贝叶斯网络模型,年龄、FBG、TG、HDL-C和BMI与H型高血压直接相关,性别、吸烟、饮酒、文化程度、TC、LDL-C和UA与H型高血压间接相关。当年龄≥60岁、FBG≥6.85 mmol/L、BMI≥24.83 kg/m2、HDL-C≥1.02 mmol/L、TG<1.6 mmol/L时,中老年患H型高血压的风险达0.565。

结论

贝叶斯网络模型揭示中老年H型高血压发生的直接和间接因素以及关联强度,阐明了因素间复杂的网络关系,为早期预防中老年H型高血压的发生提供科学依据。

H型高血压  /  中老年  /  贝叶斯网络模型  /  影响因素
Objective

To construct the Bayesian network model of H-type hypertension in middle-aged and elderly people, and to explore the influencing factors of H-type hypertension and the network relationship between factors, and the strength of each influencing factor on H-type hypertension.

Methods

A total of 1 119 middle-aged and elderly people who underwent physical examination in the hospital health management center from May 2022 to April 2023 were selected as the research objects and relevant data were collected. Univariate logistic regression analysis and multivariate logistic regression analysis models were used for preliminary screening of variables, “bnlearn” Bayesian network software package was used for model construction, and Netica software was used for model inference.

Results

Logistic regression analysis model was used to screen the variables, such as age, gender, education level, smoking, drinking, body mass index(BMI), fasting blood glucose, etc. FBG, total cholesterol(TC), triglyceride(TG), high density lipoprotein cholesterol((HDL-C), low density lipoprotein cholesterol(LDL-C) and uric acid(UA) were included in the Bayesian network model. A Bayesian network model of H-type hypertension related factors in middle-aged and elderly people with 13 nodes and 16 directed edges was constructed by using 12 selected variables as network nodes. Age, FBG, TG, HDL-C and BMI were directly related to H-type hypertension, while gender, smoking, drinking, educational level, TC, LDL-C and UA were indirectly related to H-type hypertension. When the age was≥60years old, FBG≥6.85 mmol/L, BMI≥24.83 kg/m2, HDL-C≥1.02 mmol/L, TG<1.6 mmol/L, the risk of H-type hypertension reached 0.565.

Conclusion

The Bayesian network model reveals the direct and indirect factors and correlation strength of H-type hypertension in middle-aged and elderly people, clarifies the complex network relationship between factors, and provides a scientific basis for early prevention of H-type hypertension in middle-aged and elderly people.

H-type hypertension  /  Middle-aged and elderly  /  Bayesian network model  /  Risk factors
俞美艳, 赵雅宁, 刘瑶, 赵大业, 丁淑平. 基于贝叶斯网络模型的中老年发生H型高血压的影响因素研究. 现代预防医学, 2024 , 51 (2) : 335 -342 . DOI: 10.20043/j.cnki.MPM.202308236
Mei-yan YU, Ya-ning ZHAO, Yao LIU, Da-ye ZHAO, Shu-ping DING. Influencing factors of H-type hypertension in middle-aged and elderly based on Bayesian network model[J]. Modern Preventive Medicine, 2024 , 51 (2) : 335 -342 . DOI: 10.20043/j.cnki.MPM.202308236
高血压是心脑血管疾病的重要危险因素,可导致心、脑、肾等多个脏器发生损害。原发性高血压伴血同型半胱氨酸(homocysteine,Hcy)水平升高(≥10 μmol/L)被称为H型高血压。H型高血压在中国高血压人群中的比例高达73.1%,远高于西方国家[1]。既往研究显示,高血压和高同型半胱氨酸血症(hyperhomocysteinemia,HHcy)具有协同作用,二者共存会增大心脑血管疾病的发生风险,比单纯高血压患者发生心脑血管疾病的风险增加5倍,比正常人高约12倍[2]。随着老龄化进程的不断加快,中老年人群的健康问题备受关注,H型高血压作为常见的慢性疾病,严重影响着中老年人生活质量和健康水平。因此,对中老年H型高血压进行相关因素分析及风险推理,有助于采取针对性的预防措施,降低疾病发生风险。既往关于H型高血压的影响因素研究常以logistic回归分析为主,通过比值比来反映H型高血压与各因素间的相关强度,但H型高血压影响因素关系复杂,因素间可能存在关联,logistic回归分析无法展现因素间的因果相互关系[3-4]。贝叶斯网络是一种基于概率的不确定性推理方法,通过构建有向无环图来直观反映各因素间的潜在关系,使用条件概率表反映各变量间的关联强度,可根据已知某些节点状态,对未知节点进行风险推理[5]。因此,本研究通过构建中老年人发生H型高血压的贝叶斯网络模型,分析相关影响因素,探讨因素间的依赖关系,为有效降低我国中老年H型高血压的发生风险提供新的参考依据。
选取2022年5月—2023年4月在唐山市华北理工大学附属医院健康管理中心体检的中老年人为研究对象。纳入标准:(1)年龄≥45岁;(2)患者知情并同意参加本次研究;(3)听理解能力正常者。排除标准:(1)有严重心、脑、肺、肝、肾、精神疾病以及恶性肿瘤者;(2)近期服用叶酸、B族维生素等药物影响Hcy 水平者;(3)继发性高血压者。最终纳入1 119例符合标准的中老年人,根据是否发生H型高血压分为H型高血压组(n=383)和非H型高血压组(n=736)。本研究经华北理工大学伦理委员会审批准(审批号2021076),获得全部研究对象知情同意。
研究者及相关医护人员经统一培训后对研究对象进行问卷调查、体格检查、实验室指标检测、心理状态和睡眠状况的评定。(1)问卷调查:调查人员采用面对面方式进行,收集的主要内容包括姓名、性别、婚姻状况、居住城市、文化程度、吸烟史、饮酒史。(2)体格检查:主要包括身高、体重和血压的测量。通过身高和体重来计算体质指数(body mass index,BMI),BMI=体重(kg)/身高(m)2。(3)实验室指标测定:主要包括空腹血糖(fasting blood glucose,FBG)、总胆固醇( total cholesterol,TC)、甘油三酯(triglyceride,TG)、高密度脂蛋白胆固醇(high density lipoprotein cholesterol,HDL-C) 、低密度脂蛋白胆固醇(low density lipoprotein cholesterol,LDL-C)、尿酸 (uric acid,UA)及Hcy水平。(4)心理状态评定:采用由Hamilton编制[6],汤毓华等[7]汉化后的汉密尔顿焦虑量表(Hamilton Anxiety Scale,HAMA),包含焦虑心境、紧张、害怕、失眠、认知功能、抑郁心境、肌肉系统、感觉系统、心血管系统症状、呼吸系症状、胃肠道症状、生殖泌尿系症状、植物神经系症状和会谈时行为表现共14个条目,采用5级评分(0~4分),得分越高说明焦虑症状越严重,当总分≥7分时,提示可能存在焦虑。量表Cronbach α系数为0.93。(5)睡眠状况评定:采用由Buysse等[8]编制,刘贤臣等[9]修订的匹兹堡睡眠质量指数(Pittsburgh Sleep Quality Index,PSQI)量表,该量表包括主观睡眠质量、入睡时间、睡眠时间、睡眠效率、睡眠障碍、催眠药物和日间功能障碍共7个维度,18个条目,采用4级评分(0~3分),得分越高表示睡眠质量越差,当总分>7分,提示存在睡眠障碍。量表Cronbach α系数为0.784。
根据《中国高血压防治指南》(2018年修订版)中高血压的诊断标准[10],未服降压药物的情况下,收缩压≥140 mmHg 和(或)舒张压≥90 mmHg 即诊断为高血压;既往有高血压病史,目前正服用降压药,血压虽已低于140/90 mmHg,亦诊断为高血压。将在原发性高血压基础上伴血Hcy≥10.0 μmol /L诊断为H型高血压。
本研究从H型高血压一级预防的角度出发,借助受试者工作曲线作为变量的划分依据,通过绘制自变量BMI、FBG、TC、TG、HDL-C、LDL-C、UA与因变量H型高血压之间的ROC曲线图来计算各自的最佳截断值来作为变量的划分依据,其结果依次为24.83 kg/m2、6.85 mmol/L、5.30 mmol/L、1.60 mmol/L、1.02 mmol/L、3.33 mmol/L、289.50 μmol /L。
问卷信息采用EpiData 3.1进行双人录入。采用 SPSS 26.0 软件进行统计分析。其中计数资料用相对数表示,单因素分析采用χ2检验,多因素采用logistic回归分析,具体变量赋值见表1。利用R 4.1.3软件的“bnlearn”包进行贝叶斯网络的模型构建,最大最小爬山 ( max-min hill-climbing,MMHC) 算法进行结构学习,参数学习采用极大似然估计,贝叶斯网络拓扑结构及CPT的构建采用Netica软件绘制。检验水准α=0.05。
本研究共纳入1 119例符合标准的中老年人,其中男性734人,占 65.6%,女性385人、占 34.4%;60岁以下346人,占30.9%,60岁以上773,占69.1%;未发生H型高血压者736例,发生H型高血压者383例,患病率达34.2%。
结果显示,年龄、性别、文化程度、吸烟、饮酒、BMI、FBG、TC、TG、HDL-C、LDL-C和UA在两组间差异均有统计学意义(均P<0.05)。见表2
将是否发生H型高血压为因变量,以单因素分析中有统计学意义的因素为自变量进行多因素logistic回归分析。结果显示,年龄、性别、文化程度、吸烟、饮酒、BMI、FBG、TC、TG、HDL-C、LDL-C、UA是中老年发生H型高血压的独立危险因素(均P<0.05)。见表3
根据多因素logistic回归分析模型筛选出的12个有统计学差异的变量作为网络节点,构建一个含13个节点,16条有向边的中老年H型高血压相关因素的贝叶斯网络模型,并获得个节点的条件概率。结果显示,年龄、FBG、TG、HDL-C和BMI与H型高血压直接相关,性别、吸烟、饮酒、文化程度、TC、LDL-C和UA通过影响年龄、FBG、TG、HDL-C与H型高血压间接相关(见图1)。
表4为H型高血压节点的条件概率表,展现了年龄、FBG、HDL-C、TG、BMI与H型高血压之间的概率依赖关系。例如当某研究对象年龄≥60岁、FBG≥6.85 mmol/L、BMI≥24.83 kg/m2、HDL-C≥1.02 mmol/L、TG<1.6 mmol/L时,其患H型高血压的风险为0.565;当年龄<60岁、FBG<6.85 mmol/L、BMI<24.83 kg/m2、HDL-C<1.02 mmol/L、TG≥1.6 mmol/L时,其患H型高血压风险为0.400。通过Netica软件绘制贝叶斯网络模型,点击相应节点添加证据可对中老年H型高血压的发生进行因果推理,通过已知节点的信息对未知节点进行概率预测。若如已知某研究对象FBG≥6.85 mmol/L的同时BMI≥24.83 kg/m2且无其他疾病表现,则通过贝叶斯推理可知患H型高血压风险增加到65.6%(见图2)。
HHcy易引发脑卒中、冠心病、慢性肾病以及阿尔茨海默病等,同时存在高血压会增加心脑血管疾病的发生风险[11-12]。中国脑卒中一级预防研究(China Stroke Primary Prevention Trial,CSPPT)显示,H型高血压比例高达80.3%[13]。本研究结果显示,中老年H型高血压的发生率为34.2%。因此,早期识别高危人群,及早预防H型高血压,对降低中老年心脑血管疾病的发生发展具有重要意义。
本研究结果显示,年龄与H型高血压的发生直接相关,且由贝叶斯网络模型进行风险推理发现,年龄≥60岁H型高血压患病风险较高。随着年龄的增长,人体的肝、肾功能下降消化吸收能力降低,体内维生素B6、维生素B12、叶酸及相关辅助因子缺乏,导致Hcy代谢能力降低,血Hcy浓度升高[11]。文化程度通过年龄间接影响H型高血压的发生,文化程度低的中老年发生H型高血压概率高,这与Sun等[14]研究结果相一致。原因可能在于文化程度较低的中老年人缺乏相关健康知识且对自身预防保健的重视程度不高[14-15]
BMI是发生H型高血压的重要危险因素,与国内多项研究结果一致[4,16-17]。贝叶斯网络结果显示BMI与H型高血压的发生直接相关。超重/肥胖与Hcy之间存在关联,超重/肥胖患者的Hcy水平明显高于健康人群,原因可能在于超重/肥胖患者体内脂肪组织会分泌炎性细胞因子等,进而影响胰岛素分泌障碍,产生胰岛素抵抗,相关代谢酶活性降低,导致Hcy代谢异常[18]。此外,超重/肥胖者可通过激活交感神经系统和肾素-血管紧张素-醛固酮系统引发血压异常升高[16]。FBG为中老年H型高血压的独立危险因素,与H型高血压直接相关。Wang等[19]也发现H型高血压患者的FBG明显高于非H型高血压患者。相关研究证实,糖尿病与Hcy之间存在相关性,这可能与胰岛素抵抗及氧化应激有关[20-21]。既往研究表明,高TG可导致HDL-C含量过低而增加患糖尿病的风险,从而导致H型高血压的发生[22],与本研究结果相似。因此,提示应对BMI和FBG水平异常增高趋势的中老年加强有氧运动训练,制定合理化饮食方案,科学膳食,严格控制营养素的摄入量,有效降低疾病发生风险。
Wang等[19]、Chen等[23]研究表明血脂异常与H型高血压和心血管疾病的发生发展密切相关。本研究结果显示,TC、TG、HDL-C和LDL-C是中老年发生H型高血压的危险因素。血脂异常使血液粘稠度发生改变,影响机体内Hcy的水平,同时引起动脉粥样硬化,使小动脉外周血阻力增加,进而导致血压升高。而升高的Hcy会通过氧化应激反应和内质网应激影响脂质代谢,造成血管壁脂肪堆积,进一步加大心血管疾病的发生风险[23-24]。贝叶斯网络结果显示,TG、HDL-C与H型高血压的发生直接相关,TG升高、HDL-C降低时,H型高血压的发生风险越大。Li等[25]也研究证实了Hcy水平与高TG和低HDL-C之间独立相关。此外,贝叶斯网络显示,TC和HDL-C相关而间接影响H型高血压的发生,LDL-C则通过TG间接影响H型高血压的发生,这可能与TC为血中所有脂蛋白所含胆固醇之和且机体内LDL-C主要是由极低密度脂蛋白和中间密度脂蛋白中的 TG 经脂酶水解后产生有关[26]
本研究贝叶斯网络显示,吸烟和饮酒与性别直接相关,性别与H型血压间接相关并通过影响TC、HDL-C、FBG水平间接导致H型高血压的发生,这可能与与大部分男性雌激素水平较低以及吸烟、饮酒等不良生活方式较多有关[27]。此外,UA通过TG和HDL-C与H型高血压的发生间接相关。袁空军等[28]研究表明血脂和UA之间存关联,且与TG和HDL-C之间关联性较强,二者会增大动脉粥样硬化的发生风险。因此也提示应对中老年男性人群的健康状态重点关注,实施有效的预防和干预措施,以降低疾病的患病率。
本研究通过logistic回归分析筛选出有统计学差异的变量构建中老年H型高血压的贝叶斯网络模型,与传统的logistic相比,将概率和可视化图相结合,通过分析各影响因素间复杂的网络关系,直观展现了年龄、FBG、TG、HDL-C和BMI与H型高血压直接相关,性别、吸烟、饮酒、文化程度、TC、LDL-C和UA与H型高血压间接相关,深入挖掘各因素间的内部调控关系及潜在危险因素,揭示了因素间的相互关系,并利用风险推理有效预测不同条件下中老年H型高血压的发生概率,这有助于医护人员对高风险人群尽早采取有针对性的干预措施,降低中老年H型高血压及心血管疾病的发生发展,同时也表明了贝叶斯网络模型在医学领域具备较好的临床应用价值。因此,提示医护人员应积极开展健康宣教和定期体检,引导中老年人群建立健康的生活习惯,合理膳食,加强自身体育锻炼,预防H型高血压的发生。
本研究存在局限性,研究人群仅为在医院进行体检的中老年人的单中心研究,可能存在选择偏倚,且缺乏叶酸及膳食等因素的研究,未来可扩大样本含量,增加叶酸及膳食等因素与H型高血压的影响因素分析,进行多中心、大样本的研究,进一步提高模型的应用价值。
  • 唐山市科技创新团队培养计划(18130218A)
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2024年第51卷第2期
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doi: 10.20043/j.cnki.MPM.202308236
  • 接收时间:2023-08-14
  • 首发时间:2026-03-19
  • 出版时间:2024-01-25
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  • 收稿日期:2023-08-14
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唐山市科技创新团队培养计划(18130218A)
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    华北理工大学护理与康复学院,河北 唐山 063210

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2种不同金属材料的力学参数

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genus
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species
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total species (%)

Genus
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species
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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