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Analysis of the pathogenesis of non-alcoholic fatty liver disease based on Bayesian network model
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Jian-min TANG1, Xuan WANG1, Shuo CHEN2, Yu-mei HAN2, Lin-run KONG2, Xing-hua YANG1
Modern Preventive Medicine | 2025, 52(1) : 21 - 26
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Modern Preventive Medicine | 2025, 52(1): 21-26
• Epidemiology and Statistical Methods •
Analysis of the pathogenesis of non-alcoholic fatty liver disease based on Bayesian network model
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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
Published: 2025-01-10 doi: 10.20043/j.cnki.MPM.202409406
Outline
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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
Year 2025 volume 52 Issue 1
56
28
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Article Info
doi: 10.20043/j.cnki.MPM.202409406
  • Receive Date:2024-09-23
  • Online Date:2026-10-29
  • Published:2025-01-10
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  • Received:2024-09-23
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    School of Public Health, Capital Medical University, Beijing 100069, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
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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