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Article(id=1322591177661075955, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322589954702688372, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202409406, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1727020800000, receivedDateStr=2024-09-23, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793259909310, onlineDateStr=2026-10-29, pubDate=1736438400000, pubDateStr=2025-01-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793259909310, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793259909310, creator=13701087609, updateTime=1793259909310, updator=13701087609, issue=Issue{id=1322589954702688372, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='1', pageStart='1', pageEnd='192', issueExtLink='null', onlineDate='null', pubDate='1736438400000', pubDateStr='2025-01-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793259617734, creator='13701087609', updateTime=1793260023378, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322591656147276425, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322589954702688372, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322591656147276426, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322589954702688372, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=21, endPage=26, ext={EN=ArticleExt(id=1322591177837236724, articleId=1322591177661075955, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Analysis of the pathogenesis of non-alcoholic fatty liver disease based on Bayesian network model, columnId=1240413921954295836, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods, runingTitle=null, highlight=null, articleAbstract=
Objective

To identify key influencing factors of non-alcoholic fatty liver disease (NAFLD) and construct a Bayesian network model to explore its pathogenesis.

Methods

The study was based on the Beijing Health Management Cohort. The LASSO regression was employed to screen for relevant influencing factors of NAFLD, and 10-fold cross-validation was used to select the optimal penalty parameter λ. Based on the selected variables, structural learning was conducted using the Bayesian network model, and the model fit was evaluated through the Bayesian Information Criterion (BIC) and cross-validation for path analysis.

Results

During the 5-year follow-up period, 22.83% of the 30 001 study participants developed NAFLD (n=6 850), with an incidence density of 45.7 per 1 000 person-years. Six key influencing factors were identified: AGE, BMI, HDL-C, FPG, ALT, and DBP, and a Bayesian network model was constructed based on these factors. The Bayesian network analysis revealed 23 pathways for the development of NAFLD, with the pathway having BMI as the root node being the most frequent (12 pathways), followed by age groups (5 pathways). The pathway AGE→DBP→FPG→NAFLD showed a significantly higher incidence probability in individuals under 45 years compared to those over 45. In cases of low BMI, the pathway BMI→HDL→ALT→NAFLD indicated a higher probability of NAFLD compared to other BMI groups.

Conclusion

The Bayesian network model elucidated the pathways of NAFLD development and clarified the impact of key factors on its onset, providing a theoretical basis for early screening and intervention of NAFLD.

, authors=Jian-min TANG1, Xuan WANG1, Shuo CHEN2, Yu-mei HAN2, Lin-run KONG2, Xing-hua YANG1, authorsList=Jian-min TANG, Xuan WANG, Shuo CHEN, Yu-mei HAN, Lin-run KONG, Xing-hua YANG, authorCompany=null, correspAuthors=Xing-hua YANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1322591179011641857, articleId=1322591177661075955, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=基于贝叶斯网络模型的非酒精性脂肪肝发病路径分析, columnId=1228016567632462653, journalTitle=现代预防医学, columnName=流行病与统计方法, runingTitle=null, highlight=null, articleAbstract=
目的

识别非酒精性脂肪肝(NAFLD)关键影响因素，并构建贝叶斯网络模型探索其发病路径。

方法

研究基于北京健康管理队列。通过LASSO回归筛选NAFLD的相关影响因素，使用10折交叉验证选择最优惩罚参数λ。基于筛选出的变量，应用贝叶斯网络模型进行结构学习，并通过贝叶斯信息准则(BIC)和交叉验证评估模型拟合优度，进行路径分析。

结果

5年随访期内，30 001名研究对象中有22.83%发生NAFLD (n=6 850)，发病密度为45.7/1 000人年。筛选出6个关键影响因素：AGE、BMI、HDL-C、FPG、ALT、DBP，并基于这些因素构建贝叶斯网络模型。贝叶斯网络分析揭示了23条NAFLD发病路径，以BMI为根节点的路径最多，有12条，其次年龄组有5条。其中AGE→DBP→FPG→NAFLD路径在45岁以下人群中的发病概率显著高于45岁以上人群；BMI为偏低状态下，BMI→HDL→ALT→NAFLD等路径显示NAFLD的发病概率高于其他BMI组。

结论

贝叶斯网络模型揭示了NAFLD的发病路径，明确了关键因素对其发病的影响，为NAFLD的早期筛查和干预提供了理论依据。

, authors=汤建敏1, 王璇1, 陈硕2, 韩玉梅2, 孔邻润2, 杨兴华1, authorsList=汤建敏, 王璇, 陈硕, 韩玉梅, 孔邻润, 杨兴华, authorCompany=null, correspAuthors=杨兴华, authorNote=

汤建敏（1999—），女，硕士在读，研究方向：流行病与卫生统计学

, correspAuthorsNote=
杨兴华，E-mail:
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Baseline characteristics of study participants ［n（%）（）］

, figureFileSmall=null, figureFileBig=null, tableContent=
变量总人群(n=30 001)男性(n=11 802)女性(n=18 199)统计量P值
吸烟6 659.84<0.001
是4 295 (14.3)4 108 ( 34.8)187 ( 1.0)
否25 706 (85.7)7 694 ( 65.2)18 012 ( 99.0)
BMI分组2 838.27<0.001
偏低15 487 (51.6)4 133 ( 35.0)11 354 ( 62.4)
超重6 933 (23.1)3 644 ( 30.9)3 289 ( 18.1)
肥胖5 837 (19.5)3 732 ( 31.6)2 105 ( 11.6)
正常1 744 ( 5.8)293 ( 2.5)1 451 ( 8.0)
高血压2 210.29<0.001
是6 062 (20.2)3 982 ( 33.7)2 080 ( 11.4)
否23 939 (79.8)7 820 ( 66.3)16 119 ( 88.6)
血脂异常770.41<0.001
是10 331 (34.4)5 180 ( 43.9)5 151 ( 28.3)
否19 670 (65.6)6 622 ( 56.1)13 048 ( 71.7)
高尿酸血症4 075.28<0.001
是2 922 ( 9.7)2 751 ( 23.3)171 ( 0.9)
否27 079 (90.3)9 051 ( 76.7)18 028 ( 99.1)
腹型肥胖1 637.82<0.001
是2 743 ( 9.1)2 066 ( 17.5)677 ( 3.7)
否27 258 (90.9)9 736 ( 82.5)17 522 ( 96.3)
年龄 (岁)37.53±10.7238.06±11.6637.19±10.05365.55<0.001
BMI (kg/m2)22.65±2.8423.85±2.7521.88±2.6226.73<0.001
ALT (U/L)16.54±12.8121.29±15.6113.46±9.40862.89<0.001
LDL-C (mmol/L)2.74±0.732.86±0.742.67±0.7217.18<0.001
TC (mmol/L)4.66±0.874.67±0.874.65±0.860.090.025
TG(mmol/L)1.02±0.651.24±0.830.88±0.461 093.82<0.001
HDL-C(mmol/L)1.42±0.321.26±0.261.52±0.31238.12<0.001
BUN (mmol/L)4.47±1.124.94±1.134.16±1.00151.96<0.001
UA(μmol/L)306.35±82.39371.01±75.22264.41±55.241 085.19<0.001
SBP(mm Hg)117.06±13.86123.46±13.35112.91±12.5453.59<0.001
DBP(mm Hg)71.95±9.9975.73±10.0469.51±9.15125.03<0.001
AST (U/L)17.76±8.1719.51±9.3216.62±7.11193.73<0.001
FPG((mmol/L)5.05±0.595.17±0.704.97±0.48194.34<0.001
WC (cm)75.79±9.8782.44±7.6971.48±8.66111.93<0.001
), ArticleFig(id=1322591181981209142, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322591177661075955, language=CN, label=表1, caption=

研究对象基线特征［n（%）（）］

, figureFileSmall=null, figureFileBig=null, tableContent=
变量总人群(n=30 001)男性(n=11 802)女性(n=18 199)统计量P值
吸烟6 659.84<0.001
是4 295 (14.3)4 108 ( 34.8)187 ( 1.0)
否25 706 (85.7)7 694 ( 65.2)18 012 ( 99.0)
BMI分组2 838.27<0.001
偏低15 487 (51.6)4 133 ( 35.0)11 354 ( 62.4)
超重6 933 (23.1)3 644 ( 30.9)3 289 ( 18.1)
肥胖5 837 (19.5)3 732 ( 31.6)2 105 ( 11.6)
正常1 744 ( 5.8)293 ( 2.5)1 451 ( 8.0)
高血压2 210.29<0.001
是6 062 (20.2)3 982 ( 33.7)2 080 ( 11.4)
否23 939 (79.8)7 820 ( 66.3)16 119 ( 88.6)
血脂异常770.41<0.001
是10 331 (34.4)5 180 ( 43.9)5 151 ( 28.3)
否19 670 (65.6)6 622 ( 56.1)13 048 ( 71.7)
高尿酸血症4 075.28<0.001
是2 922 ( 9.7)2 751 ( 23.3)171 ( 0.9)
否27 079 (90.3)9 051 ( 76.7)18 028 ( 99.1)
腹型肥胖1 637.82<0.001
是2 743 ( 9.1)2 066 ( 17.5)677 ( 3.7)
否27 258 (90.9)9 736 ( 82.5)17 522 ( 96.3)
年龄 (岁)37.53±10.7238.06±11.6637.19±10.05365.55<0.001
BMI (kg/m2)22.65±2.8423.85±2.7521.88±2.6226.73<0.001
ALT (U/L)16.54±12.8121.29±15.6113.46±9.40862.89<0.001
LDL-C (mmol/L)2.74±0.732.86±0.742.67±0.7217.18<0.001
TC (mmol/L)4.66±0.874.67±0.874.65±0.860.090.025
TG(mmol/L)1.02±0.651.24±0.830.88±0.461 093.82<0.001
HDL-C(mmol/L)1.42±0.321.26±0.261.52±0.31238.12<0.001
BUN (mmol/L)4.47±1.124.94±1.134.16±1.00151.96<0.001
UA(μmol/L)306.35±82.39371.01±75.22264.41±55.241 085.19<0.001
SBP(mm Hg)117.06±13.86123.46±13.35112.91±12.5453.59<0.001
DBP(mm Hg)71.95±9.9975.73±10.0469.51±9.15125.03<0.001
AST (U/L)17.76±8.1719.51±9.3216.62±7.11193.73<0.001
FPG((mmol/L)5.05±0.595.17±0.704.97±0.48194.34<0.001
WC (cm)75.79±9.8782.44±7.6971.48±8.66111.93<0.001
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Causal pathways from root nodes to NAFLD onset

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路径P(%)
<45 岁/正常≥45 岁/异常
AGE→ ALT→ NAFLD25.533.2
AGE→ DBP→ NAFLD23.327.0
AGE→ FPG→ NAFLD27.226.1
AGE→ DBP→ FPG→ NAFLD39.025.0
AGE→ NAFLD22.623.8
HDL-C→ ALT→ NAFLD25.631.7
HDL-C→ NAFLD23.023.1
DBP→ FPG→ NAFLD26.128.2
DBP→ NAFLD22.825.6
ALT→ NAFLD22.426.6
FPG→ NAFLD22.926.5
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各根节点到NAFLD发病的因果路径

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路径P(%)
<45 岁/正常≥45 岁/异常
AGE→ ALT→ NAFLD25.533.2
AGE→ DBP→ NAFLD23.327.0
AGE→ FPG→ NAFLD27.226.1
AGE→ DBP→ FPG→ NAFLD39.025.0
AGE→ NAFLD22.623.8
HDL-C→ ALT→ NAFLD25.631.7
HDL-C→ NAFLD23.023.1
DBP→ FPG→ NAFLD26.128.2
DBP→ NAFLD22.825.6
ALT→ NAFLD22.426.6
FPG→ NAFLD22.926.5
), ArticleFig(id=1322591182195118649, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322591177661075955, language=EN, label=Table 3, caption=

Causal pathways from BMI to NAFLD onset

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路径P(%)
正常偏低超重肥胖
BMI→ HDL-C→ ALT→ NAFLD32.637.632.329.0
BMI→ AGE→ ALT→ NAFLD31.169.029.231.4
BMI→ AGE→ DBP→ FPG→ NAFLD27.150.021.632.0
BMI→ DBP→ FPG→ NAFLD25.950.028.929.3
BMI→ ALT→ NAFLD25.541.327.325.7
BMI→ AGE→ DBP→ NAFLD24.926.029.726.0
BMI→ AGE→ FPG→ NAFLD24.415.927.729.6
BMI→ DBP→ NAFLD24.416.627.525.7
BMI→ AGE→ NAFLD24.321.124.525.2
BMI→ FPG→ NAFLD24.224.128.429.5
BMI→ NAFLD23.022.923.023.3
BMI→ HDL-C→ NAFLD22.432.623.024.6
), ArticleFig(id=1322591182262227514, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322591177661075955, language=CN, label=表3, caption=

BMI为根节点到NAFLD发病的因果路径

, figureFileSmall=null, figureFileBig=null, tableContent=
路径P(%)
正常偏低超重肥胖
BMI→ HDL-C→ ALT→ NAFLD32.637.632.329.0
BMI→ AGE→ ALT→ NAFLD31.169.029.231.4
BMI→ AGE→ DBP→ FPG→ NAFLD27.150.021.632.0
BMI→ DBP→ FPG→ NAFLD25.950.028.929.3
BMI→ ALT→ NAFLD25.541.327.325.7
BMI→ AGE→ DBP→ NAFLD24.926.029.726.0
BMI→ AGE→ FPG→ NAFLD24.415.927.729.6
BMI→ DBP→ NAFLD24.416.627.525.7
BMI→ AGE→ NAFLD24.321.124.525.2
BMI→ FPG→ NAFLD24.224.128.429.5
BMI→ NAFLD23.022.923.023.3
BMI→ HDL-C→ NAFLD22.432.623.024.6
), ArticleFig(id=1322591182329336379, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322591177661075955, language=EN, label=Table 4, caption=

Comparison of conditional probabilities for NAFLD onset across different groupings of fixed nodes

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固定节点NAFLD发病概率(%)概率增加值(%)增加幅度(%)
AGE（岁）
<4522.6
≥4524.31.77.52
BMI
正常23.0
偏低22.9-0.1-0.43
超重23.10.10.43
肥胖23.30.31.30
HDL-C
正常23.0
降低23.70.73.40
FPG
正常22.9
升高26.53.615.72
ALT
正常22.8
异常26.63.816.67
DBP
正常22.8
升高25.83.013.16
), ArticleFig(id=1322591182413222460, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322591177661075955, language=CN, label=表4, caption=

各个固定节点不同分组的NAFLD发病条件概率比较

, figureFileSmall=null, figureFileBig=null, tableContent=
固定节点NAFLD发病概率(%)概率增加值(%)增加幅度(%)
AGE（岁）
<4522.6
≥4524.31.77.52
BMI
正常23.0
偏低22.9-0.1-0.43
超重23.10.10.43
肥胖23.30.31.30
HDL-C
正常23.0
降低23.70.73.40
FPG
正常22.9
升高26.53.615.72
ALT
正常22.8
异常26.63.816.67
DBP
正常22.8
升高25.83.013.16
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基于贝叶斯网络模型的非酒精性脂肪肝发病路径分析
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汤建敏 1 , 王璇 1 , 陈硕 2 , 韩玉梅 2 , 孔邻润 2 , 杨兴华 1
现代预防医学 | 流行病与统计方法 2025,52(1): 21-26
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现代预防医学 |流行病与统计方法 2025 , 52 (1) : 21 -26
基于贝叶斯网络模型的非酒精性脂肪肝发病路径分析
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汤建敏（1999—），女，硕士在读，研究方向：流行病与卫生统计学

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汤建敏1, 王璇1, 陈硕2, 韩玉梅2, 孔邻润2, 杨兴华1
作者信息
  • 1.首都医科大学公共卫生学院，北京 100069
  • 2.北京市体检中心
通讯作者:
杨兴华，E-mail:
作者简介:

汤建敏（1999—），女，硕士在读，研究方向：流行病与卫生统计学

Analysis of the pathogenesis of non-alcoholic fatty liver disease based on Bayesian network model
Jian-min TANG1, Xuan WANG1, Shuo CHEN2, Yu-mei HAN2, Lin-run KONG2, Xing-hua YANG1
Affiliations
  • School of Public Health, Capital Medical University, Beijing 100069, China
出版时间: 2025-01-10 doi: 10.20043/j.cnki.MPM.202409406
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摘要
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目的

识别非酒精性脂肪肝(NAFLD)关键影响因素，并构建贝叶斯网络模型探索其发病路径。

方法

研究基于北京健康管理队列。通过LASSO回归筛选NAFLD的相关影响因素，使用10折交叉验证选择最优惩罚参数λ。基于筛选出的变量，应用贝叶斯网络模型进行结构学习，并通过贝叶斯信息准则(BIC)和交叉验证评估模型拟合优度，进行路径分析。

结果

5年随访期内，30 001名研究对象中有22.83%发生NAFLD (n=6 850)，发病密度为45.7/1 000人年。筛选出6个关键影响因素：AGE、BMI、HDL-C、FPG、ALT、DBP，并基于这些因素构建贝叶斯网络模型。贝叶斯网络分析揭示了23条NAFLD发病路径，以BMI为根节点的路径最多，有12条，其次年龄组有5条。其中AGE→DBP→FPG→NAFLD路径在45岁以下人群中的发病概率显著高于45岁以上人群；BMI为偏低状态下，BMI→HDL→ALT→NAFLD等路径显示NAFLD的发病概率高于其他BMI组。

结论

贝叶斯网络模型揭示了NAFLD的发病路径，明确了关键因素对其发病的影响，为NAFLD的早期筛查和干预提供了理论依据。

非酒精性脂肪性肝病  /  LASSO回归  /  贝叶斯网络  /  发病路径
Objective

To identify key influencing factors of non-alcoholic fatty liver disease (NAFLD) and construct a Bayesian network model to explore its pathogenesis.

Methods

The study was based on the Beijing Health Management Cohort. The LASSO regression was employed to screen for relevant influencing factors of NAFLD, and 10-fold cross-validation was used to select the optimal penalty parameter λ. Based on the selected variables, structural learning was conducted using the Bayesian network model, and the model fit was evaluated through the Bayesian Information Criterion (BIC) and cross-validation for path analysis.

Results

During the 5-year follow-up period, 22.83% of the 30 001 study participants developed NAFLD (n=6 850), with an incidence density of 45.7 per 1 000 person-years. Six key influencing factors were identified: AGE, BMI, HDL-C, FPG, ALT, and DBP, and a Bayesian network model was constructed based on these factors. The Bayesian network analysis revealed 23 pathways for the development of NAFLD, with the pathway having BMI as the root node being the most frequent (12 pathways), followed by age groups (5 pathways). The pathway AGE→DBP→FPG→NAFLD showed a significantly higher incidence probability in individuals under 45 years compared to those over 45. In cases of low BMI, the pathway BMI→HDL→ALT→NAFLD indicated a higher probability of NAFLD compared to other BMI groups.

Conclusion

The Bayesian network model elucidated the pathways of NAFLD development and clarified the impact of key factors on its onset, providing a theoretical basis for early screening and intervention of NAFLD.

Non-alcoholic fatty liver disease (NAFLD)  /  LASSO regression  /  Bayesian network  /  Pathogenesis
汤建敏, 王璇, 陈硕, 韩玉梅, 孔邻润, 杨兴华. 基于贝叶斯网络模型的非酒精性脂肪肝发病路径分析. 现代预防医学, 2025 , 52 (1) : 21 -26 . DOI: 10.20043/j.cnki.MPM.202409406
Jian-min TANG, Xuan WANG, Shuo CHEN, Yu-mei HAN, Lin-run KONG, Xing-hua YANG. Analysis of the pathogenesis of non-alcoholic fatty liver disease based on Bayesian network model[J]. Modern Preventive Medicine, 2025 , 52 (1) : 21 -26 . DOI: 10.20043/j.cnki.MPM.202409406
正文
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非酒精性脂肪性肝病(non-alcoholic fatty liver disease, NAFLD)是一种常见的慢性肝病[1]，成年人患病率达25%～30%[2]，在中国部分城市已接近30%[3]。
NAFLD不仅会导致肝病进展，还与2型糖尿病、心血管疾病等代谢综合征相关[4]，并增加肝癌风险，严重影响生活质量[5]。其发病与肥胖、胰岛素抵抗、2型糖尿病、高血压、血脂异常、遗传、饮食和环境等因素有关[6]。改善这些风险因素可显著降低NAFLD风险。
传统研究多采用横断面研究和回归分析，但这些方法难以揭示复杂的因果路径和交互作用。贝叶斯网络因其捕捉变量间复杂依赖关系和因果推断的能力，已广泛应用于流行病学研究中[7]。它不仅能构建风险因素的因果路径，还能量化每个因素的贡献，从而为个体化预防提供依据。本研究基于北京健康管理队列（BHMC）数据，采用LASSO回归筛选NAFLD的影响因素，并通过贝叶斯网络模型探索发病路径。LASSO可有效筛选与NAFLD相关的因素，避免过拟合[8]。贝叶斯网络模型通过构建有向无环图揭示NAFLD的发病机制，助力更有效的预防策略。
1　资料与方法
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1.1　研究对象
本研究基于BHMC，为长期动态队列研究，采用整群抽样，所有受试者签署知情同意书。以2017年为基线，纳入2017和2022年均参与体检的个体。排除标准：（1）年龄<18岁；（2）过量饮酒；（3）未报告疾病史；（4）其他脂肪肝或恶性肿瘤；（5）服用与脂肪肝相关药物；（6）基线患有NAFLD；（7）数据缺失或无随访。最终纳入30 001人，中位随访时间为3.05年（95%CI: 3.04～4.04年）。数据涵盖问卷调查、体检和实验室检查。本研究获得首都医科大学伦理委员会批准（编号:2019SY088）。见图1。
1.2　诊断标准及定义
NAFLD的诊断依据《中国非酒精性脂肪性肝病防治指南（2018年更新版）》[9]。高尿酸血症：空腹血尿酸>420 pmol/L[10]。血脂异常的标准为：总胆固醇（TC）≥5.2 mmol/L，低密度脂蛋白胆固醇（LDL-C）≥3.4 mmol/L，甘油三酯（TG）≥1.7 mmol/L，高密度脂蛋白胆固醇（HDL-C）≤1.0 mmol/L，或正在接受降脂药物治疗[11]。身体质量指数（BMI）分类标准[12]：偏低为BMI<18.5 kg/m2；正常为18.5≤ BMI<24 kg/m2；超重为24 ≤ BMI<28 kg/m2；肥胖为BMI≥28 kg/m2。腹型肥胖[12]定义为男性腰围（WC）≥90 cm，女性腰围≥85 cm。其他指标包括丙氨酸氨基转移酶（ALT）>40 U/L，HDL-C<1.0 mmol/L，空腹血糖（FPG）>6.1 mmol/L，舒张压（DBP）≥90 mm Hg。
1.3　统计分析方法
数据分析使用SPSS 26.0和R 4.4.1软件，双侧检验，α=0.05。计数资料以频数和百分比表示，计量资料以（均数±标准差）表示。
采用LASSO回归筛选NAFLD的关键影响因素，利用10折交叉验证确定最优λ值，避免过拟合。基于筛选因素构建贝叶斯网络模型，结构学习使用最大最小爬山算法（MMHC），并结合黑白名单减少错误，参数学习采用极大似然法。模型拟合通过贝叶斯信息准则（BIC）和交叉验证评估，使用Netica软件进行可视化，并计算条件概率和边缘概率分析NAFLD的关键发病路径。
2　结果
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2.1　研究对象的基线特征描述
研究纳入30 001人，平均年龄（37.53±10.72）岁，男性占39.34%，女性占60.66%。随访5年内，6 850人（22.83%）发生NAFLD，发病密度为45.7/1 000人年，基线信息见表1。
2.2　基于LASSO回归筛选NAFLD发病影响因素
LASSO回归筛选出6个与NAFLD发病显著相关的因素：年龄(AGE)、BMI、HDL-C、FPG、ALT和DBP，这些变量为贝叶斯网络模型提供了依据，见图2。
2.3　NAFLD发病贝叶斯网络学习
基于LASSO回归筛选出的6个变量，采用MMHC构建了NAFLD发病的贝叶斯网络模型。最终模型由7个节点及23条有向无环图(DAG)路径构成，见图3和表2。通过交叉验证评估模型的稳定性和泛化能力，BIC评分范围为-9 554.90～-8 169.23，均值为-8 590.78，表明模型具有良好的稳定性和泛化能力。
2.4　NAFLD发病贝叶斯参数学习
通过极大似然估计法对贝叶斯网络进行参数学习，计算各节点在不同父节点状态下的条件概率。构建的NAFLD贝叶斯网络包含23条因果路径，图4显示了各条件概率的可视化分布。模型揭示了年龄(AGE)和BMI为关键节点的主要发病路径，具体路径及其相对重要性见表3和4。不同节点在NAFLD发病中的影响复杂且相互作用显著。
表2和3展示了各根节点到NAFLD发病的因果路径。将其他节点设为最高水平，比较根节点不同分组的发病概率。结果显示，AGE→ALT→NAFLD路径在≥45岁组的发病率最高，为33.2%；AGE→DBP→FPG→NAFLD路径在<45岁组发病率最高，为39.0%；HDL-C降低时，HDL-C→ALT→NAFLD的发病概率较高，为31.7%；DBP升高时，DBP→FPG→NAFLD的发病概率校高，为28.2%；ALT异常和FPG升高时，两条路径的概率均为26.5%；BMI→HDL-C→ALT→NAFLD路径在正常组和超重组的发病率均为最高；BMI→AGE→ALT→NAFLD路径在偏低组发病率最高，为69.0%；BMI→ AGE→ DBP→ FPG→ NAFLD路径在肥胖组发病率最高，为32.0%。
2.5　NAFLD发病贝叶斯网络推理
分析结果显示，NAFLD发病概率在不同年龄、BMI、HDL-C、FPG、ALT和DBP组别存在差异。高年龄、肥胖、HDL-C降低、FPG升高、ALT异常和DBP升高的组别患病风险更高，具体数值见表4。
3　讨论
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本研究基于BMHC数据，使用贝叶斯网络模型分析NAFLD的发病路径。通过LASSO回归筛选影响因素，构建了多个关键路径，揭示了各节点对NAFLD发病的影响。随访期间，6 850名研究对象发生NAFLD，总发病率为22.83%，发病密度为45.92/1 000人年。通过LASSO回归筛选出6个与NAFLD密切相关的变量，并基于此构建贝叶斯网络模型。结果显示，BMI、FPG和ALT直接影响NAFLD，而HDL-C和DBP则通过间接路径影响发病。贝叶斯网络模型直观展示了变量间的因果关系，10折交叉验证的BIC评分表明模型在各子集中的拟合效果一致，具有良好的泛化能力和可靠性[13]。
在以年龄为根节点的路径中，我们发现≥45岁且伴有ALT升高、DBP升高、FPG异常的人群NAFLD发病概率显著升高，这表明随着年龄的增长，肝脏修复能力下降，而血压升高和血糖异常会显著增加NAFLD的患病风险。已有研究表明，早期的代谢异常和血糖调节机制与NAFLD发病相关[14]。因此，年轻人应对代谢健康问题保持高度警惕，以有效预防NAFLD的发生。在以BMI为根节点的路径中，BMI通过影响多个代谢指标（如AGE、DBP、FPG和ALT）增加了NAFLD的风险，且多条路径显示<45岁组的NAFLD发病率较高（例如AGE→ DBP→ FPG→NAFLD路径的发病率为39.0%），这说明在年轻肥胖人群中，代谢异常是NAFLD发病的重要原因。研究还发现，在BMI → HDL → ALT → NAFLD等路径中BMI为偏低状态时NAFLD的发病概率高于其他BMI分组。这可能与低体重人群中的营养不良、代谢异常等因素有关[15]。低体重可能导致身体对能量储存和利用异常，影响脂质代谢、增加肝脏的脂肪沉积[16]。此外，BMI → AGE → DBP → FPG → NAFLD等路径的NAFLD发病概率在偏低体重人群中也较高，说明低BMI个体在NAFLD发病中的特殊风险。因此，体重管理不仅要关注超重和肥胖，也应重视偏低体重的管理。
在HDL、FPG、ALT和DBP的状态分析中，我们发现HDL降低、FPG升高、ALT异常和DBP升高均显著增加NAFLD的发病概率。FPG升高时NAFLD的发病概率增加幅度最大，ALT升高也与NAFLD的发病有强关联，提示肝脏功能异常与胰岛素抵抗是NAFLD发病的主要机制之一[17]。ALT作为肝脏损伤的标志物，其升高反映了肝细胞的炎症反应，进一步促进脂肪肝的病程进展[18]。HDL通过与BMI和ALT的交互作用间接影响NAFLD，表明高密度脂蛋白不仅仅是单一的心血管风险标志物，其在脂肪肝的发病中可能具有复杂的代谢调节作用，HDL-C水平下降可能通过引发脂质代谢紊乱和氧化应激，增加肝脏负担，进而加剧NAFLD[19]。DBP的影响主要体现在其对BMI和FPG的间接路径上，提示血压管理在NAFLD的防治中可能具有重要作用，血压升高可能通过改变血流动力学影响肝脏的血液供应和代谢环境，从而间接加重NAFLD的发生[20]。
在所有节点中，BMI对NAFLD发病的贡献率最高，其次是FPG和ALT。BMI的变化直接影响肝脏的脂质代谢环境，过高或过低的BMI均可增加NAFLD风险，可能是由于脂肪组织过多或不足对肝脏代谢功能的调节异常。FPG的升高提示胰岛素抵抗是NAFLD的重要致病因素，长期的胰岛素抵抗会增加肝脏脂肪的沉积，从而导致NAFLD的发生[21]。ALT的升高反映了肝细胞损伤和炎症反应，可能进一步促进脂肪肝病程的恶化[22]。HDL的影响主要表现为对脂质代谢的调节，低水平的HDL可能导致脂肪酸运输和清除受阻，进一步加重肝脏的脂肪沉积[23]。而DBP可能通过血流动力学改变影响肝脏代谢功能，这些因素的综合作用可能解释了其在NAFLD中的间接影响[20]。
本研究利用贝叶斯网络模型探索NAFLD的发病路径，这种方法不仅能够识别影响NAFLD的多个关键因素，还能揭示这些因素之间的复杂相互作用关系。LASSO回归的应用使得我们在众多潜在影响因素中精准筛选出最具临床意义的变量。贝叶斯网络模型的因果推断能力和路径分析功能，使得我们能够提供更直观、更全面的NAFLD发病机制图谱。本研究的创新之处在于首次将贝叶斯网络与NAFLD发病机制的深入分析结合，为揭示疾病的复杂因果关系提供了新的视角。
本研究也存在一定局限性。研究依赖于观察性数据，虽然通过统计方法控制了部分混杂偏倚，但仍不能完全排除未测量混杂因素的影响。由于随访时间较短，可能无法充分评估长期因素的累积效应。模型的预测结果基于现有数据集，其在其他种群中的适用性仍需进一步验证。此外，本研究未能深入分析药物、饮食和运动等生活方式干预对NAFLD路径的调节作用。
综上所述，本研究通过贝叶斯网络模型成功构建了NAFLD的发病路径图谱，阐明了关键代谢指标对NAFLD发病的复杂机制。这些发现为NAFLD的早期筛查和干预提供了重要的理论依据，尤其是在临床实践中针对BMI、FPG和ALT等指标的管理，可能有效降低NAFLD的发病风险。未来的研究应进一步验证这些发现，探索更长随访时间对NAFLD发病的影响，以优化防治策略。
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  • 北京市自然科学基金(7202010)
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2025年第52卷第1期
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doi: 10.20043/j.cnki.MPM.202409406
  • 接收时间：2024-09-23
  • 首发时间：2026-10-29
  • 出版时间：2025-01-10
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  • 收稿日期：2024-09-23
基金
北京市自然科学基金(7202010)
作者信息
    1.首都医科大学公共卫生学院，北京 100069
    2.北京市体检中心

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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
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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
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