Frontiers in immunology · 2026
Metabolic dysfunction-associated fatty liver disease in chronic hepatitis B: dual effects on hepatocarcinogenesis and evolving strategies for risk prediction.
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Tao Huihui, Duan Yuxin, Yu Guoyang, Yan Wei, Yin Jianhua
Аннотация
AIM: Chronic hepatitis B (CHB) remains a major global cause of hepatocellular carcinoma (HCC), particularly in regions with high hepatitis B virus endemicity. Meanwhile, metabolic dysfunction-associated fatty liver disease (MAFLD) has emerged as a rapidly growing metabolic contributor to hepatocarcinogenesis. Their co-occurrence influences liver cancer development through complex biological interactions. This review aims to summarize recent advances regarding how CHB-MAFLD comorbidity affects HCC risk and the development of related predictive models.
METHODS: This review synthesizes evidence from recent clinical and basic research exploring the relationship between CHB-MAFLD comorbidity and cancer risk. It evaluates the performance of traditional and next-generation risk prediction models and examines the potential of artificial intelligence in integrating multimodal data to improve accuracy.
RESULTS: The underlying pathophysiology exhibits a dual nature. While metabolic disturbances can suppress hepatitis B virus replication and facilitate surface antigen clearance, metabolic dysfunctions such as obesity and diabetes independently accelerate fibrosis and increase cancer risk. This paradox originates from heterogeneous effects: simple steatosis may reduce risk, whereas cumulative metabolic abnormalities increase risk in a dose-dependent manner. Although traditional models retain prognostic value, their sensitivity is suboptimal. Next-generation models incorporating metabolic parameters have demonstrated improved accuracy. Furthermore, machine learning shows considerable potential for modeling complex non-linear interactions and enabling individualized risk prediction.
CONCLUSION: This review summarizes how metabolic dysfunction-associated fatty liver disease influences cancer risk in patients with chronic hepatitis B. It provides a framework for identifying high-risk individuals to guide stratified, precision management strategies.
METHODS: This review synthesizes evidence from recent clinical and basic research exploring the relationship between CHB-MAFLD comorbidity and cancer risk. It evaluates the performance of traditional and next-generation risk prediction models and examines the potential of artificial intelligence in integrating multimodal data to improve accuracy.
RESULTS: The underlying pathophysiology exhibits a dual nature. While metabolic disturbances can suppress hepatitis B virus replication and facilitate surface antigen clearance, metabolic dysfunctions such as obesity and diabetes independently accelerate fibrosis and increase cancer risk. This paradox originates from heterogeneous effects: simple steatosis may reduce risk, whereas cumulative metabolic abnormalities increase risk in a dose-dependent manner. Although traditional models retain prognostic value, their sensitivity is suboptimal. Next-generation models incorporating metabolic parameters have demonstrated improved accuracy. Furthermore, machine learning shows considerable potential for modeling complex non-linear interactions and enabling individualized risk prediction.
CONCLUSION: This review summarizes how metabolic dysfunction-associated fatty liver disease influences cancer risk in patients with chronic hepatitis B. It provides a framework for identifying high-risk individuals to guide stratified, precision management strategies.