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- 저자 : ( Chengfu Xu ) (검색결과 2 건)
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( Chengfu Xu ),( Xingyong Wan ),( Chaohui Yu ),( Lei Xu ),( Ming Yan ),( Honglei Weng ),( Min Miao ),( Yan Sun ),( Genyun Xu ),( Steven Dooley ),( William Coleman ),( Youming Li ) 대한내과학회 2014 대한내과학회 추계학술대회 Vol.2014 No.1
Background: Hyperuricemia has been commonly found in patients with nonalcoholic fatty liver disease (NAFLD). This study aimed to clarify the causal relationship between NAFLD and hyperuricemia and to explore their underlying mechanisms. Methods: First, we evaluated the impact of NAFLD on development of hyperuricemia in a cohort of 5541 hyperuricemia-free individuals. Second, we analyzed the involvement of xanthine oxidase (XO), a rate-limiting enzyme catalyzes uric acid production, in the relationship between NAFLD and hyperuricemia in cultured HepG2 cells and a murine model of NAFLD. Results: In the first study, 7-year prospective analysis found that NAFLD was strongly associated with subsequent development of hyperuricemia. Cox proportional hazards regression analyses showed that the age, gender, and body mass index adjusted hazard ratio (95% CI) for incident hyperuricemia was 1.609 (1.129 - 2.294) in individuals with NAFLD compared with those without NAFLD. In the second study, we observed that the expression and activity of XO were significantly increased in cellular and mouse models of NAFLD. Knocking down XO expression or inhibiting XO activity significantly inhibited uric acid production and attenuated free fatty acids (FFA)-induced fat accumulation in HepG2 cells. Inhibition of XO activity also significantly decreased serum uric acid levels and ameliorated high fat diet-induced hepatic steatosis in mice. Further experiments indicated that XO regulates the activation of NLRP3 inflammasome, which may be essential for the regulatory effect of XO on NAFLD. Conclusions: XO promotes hyperuricemia and the development of NAFLD, which may serve as a novel therapeutic target for NAFLD.
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( Han Ma ),( Chengfu Xu ),( Zhe Shen ),( Chaohui Yu ),( Youming Li ) 대한내과학회 2014 대한내과학회 추계학술대회 Vol.2014 No.1
Background: Nonalcoholic fatty liver disease (NAFLD) is one of the most common chronic liver diseases worldwide. Recent attention focuses on screening and prediction of NAFLD. Machine learning techniques are powerful and promising tools. Methods: A cross-sectional study was performed among 10,508 subjects who attended their annual health examination in the first affiliated hospital, College of Medicine, Zhejiang University, China in 2010. The questionnaires, Physical examinations, laboratory tests and liver ultrasonography were performed. 20 features (e.g., age, laboratory results) were extracted. Machine learning techniques were implemented on the open source software named Weka. The tasks included feature selection and classification. By removing redundant features, feature selection techniques built a screening model. Classification was used to build a prediction model, which was evaluated by F measure. Nine machine learning techniques were investigated, i.e., logistic regression, K-Nearest Neighbor, Support Vector Machine, naive Bayes, Bayesian network, decision tree, Adaboosting, bagging, and random forest. Results: A total of 2522(24%) subjects were fulfilled the diagnostic criteria of NAFLD. By using feature selection techniques, BMI, serum triglyceride, ALT, GGT and uric acid were the top-5 features contributing most to NAFLD. 10-fold cross-validation was used in classification to evaluate machine learning techniques, i.e., subjects were randomly divided into 10 folds, 9 folds were used to build a prediction model, the remaining fold was used to evaluate. The whole process lasted for 10 times, average performance was recorded. The results showed among the nine state-of-the-art machine learning techniques, Bayesian network demonstrated the best performance. It achieves the accuracy, specificity, sensitivity, and F-measure scores up to 83%, 0.787, 0.678, and 0.665, respectively. Compared with logistic regression, Bayesian network improves F-measure score by 10.83%. Conclusions: Novel machine learning techniques may have screening and predictive value for NAFLD.
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