Diabetes/metabolism research and reviews · 2026
Assessment of Diabetes Risk in Patients With Hepatitis B: A Machine Learning Approach Integrating 11 Inflammatory and Immune Indicators.
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Cai Zhisheng, Liu Jingtao, Zhang Wenjie, Lu Yutong, Su Yumeng, Zhang Tengyue, Zhang Yangyi, Wang Xiaochi, Ye Linyi, Tao Ying, Liu Junjie
Аннотация
BACKGROUND: Chronic hepatitis B virus (HBV) infection is associated with an increased risk of diabetes; however, early detection of diabetes remains challenging due to silent progression in early stages. Given that 30%-50% of diabetic patients develop severe complications such as cardiovascular disease, renal failure, and neuropathy, timely risk assessment is critical for prevention.
METHODS: We developed a machine learning model using data from 14,287 HBV-positive adults across eight NHANES cycles (2003-2018) to identify diabetes. Eleven inflammation-immune composite indicators were integrated with demographic and biochemical parameters. Following LASSO variable selection and a 7:3 train-test split, seven algorithms were compared. Model performance was evaluated using the area under the ROC curve (AUC), calibration curve, and decision curve analysis (DCA).
RESULTS: The Artificial Neural Network (ANN) emerged as the optimal model for diabetes risk assessment. It achieved an AUC of 0.83 (95% CI: 0.80-0.85) and an accuracy of 82% (95% CI: 0.79-0.85). SHAP analysis identified age and UHR as the most influential predictors, while HRR showed significant protective effects.
CONCLUSION: This study presents a robust, interpretable model for diabetes risk assessment in HBV patients that integrates novel inflammation-immune composite indicators. These findings highlight the model's utility as a potential clinical tool for screening individuals with undiagnosed diabetes.
METHODS: We developed a machine learning model using data from 14,287 HBV-positive adults across eight NHANES cycles (2003-2018) to identify diabetes. Eleven inflammation-immune composite indicators were integrated with demographic and biochemical parameters. Following LASSO variable selection and a 7:3 train-test split, seven algorithms were compared. Model performance was evaluated using the area under the ROC curve (AUC), calibration curve, and decision curve analysis (DCA).
RESULTS: The Artificial Neural Network (ANN) emerged as the optimal model for diabetes risk assessment. It achieved an AUC of 0.83 (95% CI: 0.80-0.85) and an accuracy of 82% (95% CI: 0.79-0.85). SHAP analysis identified age and UHR as the most influential predictors, while HRR showed significant protective effects.
CONCLUSION: This study presents a robust, interpretable model for diabetes risk assessment in HBV patients that integrates novel inflammation-immune composite indicators. These findings highlight the model's utility as a potential clinical tool for screening individuals with undiagnosed diabetes.