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论文题目: Discovering a critical transition state from nonalcoholic hepatosteatosis to nonalcoholic steatohepatitis by lipidomics and dynamical network biomarkers
英文论文题目: Discovering a critical transition state from nonalcoholic hepatosteatosis to nonalcoholic steatohepatitis by lipidomics and dynamical network biomarkers
第一作者: Sa, RN; Zhang, WW; Ge, J; Wei, XB; Zhou, YH; Landzberg, DR; Wang, ZZ; Han, XL; Chen, LN; Yin, HY
英文第一作者: Sa, RN; Zhang, WW; Ge, J; Wei, XB; Zhou, YH; Landzberg, DR; Wang, ZZ; Han, XL; Chen, LN; Yin, HY
联系作者: Yin, HY (reprint author), Chinese Acad Sci, Shanghai Inst Biol Sci, Inst Nutr Sci, Key Lab Food Safety Res, Shanghai 200031, Peoples R China.
英文联系作者: Yin, HY (reprint author), Chinese Acad Sci, Shanghai Inst Biol Sci, Inst Nutr Sci, Key Lab Food Safety Res, Shanghai 200031, Peoples R China.
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发表年度: 2016
卷: 8
期: 3
页码: 195-206
摘要: Nonalcoholic fatty liver disease (NAFLD) is a major risk factor for type 2 diabetes and metabolic syndrome. However, accurately differentiating nonalcoholic steatohepatitis (NASH) from hepatosteatosis remains a clinical challenge. We identified a critical transition stage (termed pre-NASH) during the progression from hepatosteatosis to NASH in a mouse model of high fat-induced NAFLD, using lipidomics and a mathematical model termed dynamic network biomarkers (DNB). Different from the conventional biomarker approach based on the abundance of molecular expressions, the DNB model exploits collective fluctuations and correlations of different metabolites at a network level. We found that the correlations between the blood and liver lipid species drastically decreased after the transition from steatosis to NASH, which may account for the current difficulty in differentiating NASH from steatosis based on blood lipids. Furthermore, most DNB members in the blood circulation, especially for triacylglycerol (TAG), are also identified in the liver during the disease progression, suggesting a potential clinical application of DNB to diagnose NASH based on blood lipids. We further identified metabolic pathways responsible for this transition. Our study suggests that the transition from steatosis to NASH is not smooth and the existence of pre-NASH may be partially responsible for the current clinical limitations to diagnose NASH. If validated in humans, our study will open a new avenue to reliably diagnose pre-NASH and achieve early intervention of NAFLD.
英文摘要: Nonalcoholic fatty liver disease (NAFLD) is a major risk factor for type 2 diabetes and metabolic syndrome. However, accurately differentiating nonalcoholic steatohepatitis (NASH) from hepatosteatosis remains a clinical challenge. We identified a critical transition stage (termed pre-NASH) during the progression from hepatosteatosis to NASH in a mouse model of high fat-induced NAFLD, using lipidomics and a mathematical model termed dynamic network biomarkers (DNB). Different from the conventional biomarker approach based on the abundance of molecular expressions, the DNB model exploits collective fluctuations and correlations of different metabolites at a network level. We found that the correlations between the blood and liver lipid species drastically decreased after the transition from steatosis to NASH, which may account for the current difficulty in differentiating NASH from steatosis based on blood lipids. Furthermore, most DNB members in the blood circulation, especially for triacylglycerol (TAG), are also identified in the liver during the disease progression, suggesting a potential clinical application of DNB to diagnose NASH based on blood lipids. We further identified metabolic pathways responsible for this transition. Our study suggests that the transition from steatosis to NASH is not smooth and the existence of pre-NASH may be partially responsible for the current clinical limitations to diagnose NASH. If validated in humans, our study will open a new avenue to reliably diagnose pre-NASH and achieve early intervention of NAFLD.
刊物名称: JOURNAL OF MOLECULAR CELL BIOLOGY
英文刊物名称: JOURNAL OF MOLECULAR CELL BIOLOGY
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学科: Cell Biology
英文学科: Cell Biology
影响因子: 5.988
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论文类别: Article
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