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Plasma metabolomic profile in nonalcoholic fatty liver disease

脂肪变性 非酒精性脂肪肝 内科学 代谢组学 脂肪肝 医学 谷胱甘肽 脂肪性肝炎 内分泌学 化学 生物 生物化学 疾病 生物信息学
作者
Satish C. Kalhan,Lining Guo,John M. Edmison,Srinivasan Dasarathy,Arthur J. McCullough,Richard W. Hanson,Mike Milburn
出处
期刊:Metabolism-clinical and Experimental [Elsevier BV]
卷期号:60 (3): 404-413 被引量:490
标识
DOI:10.1016/j.metabol.2010.03.006
摘要

The plasma profile of subjects with nonalcoholic fatty liver disease (NAFLD), steatosis, and steatohepatitis (NASH) was examined using an untargeted global metabolomic analysis to identify specific disease-related patterns and to identify potential noninvasive biomarkers. Plasma samples were obtained after an overnight fast from histologically confirmed nondiabetic subjects with hepatic steatosis (n = 11) or NASH (n = 24) and were compared with healthy, age- and sex-matched controls (n = 25). Subjects with NAFLD were obese, were insulin resistant, and had higher plasma concentrations of homocysteine and total cysteine and lower plasma concentrations of total glutathione. Metabolomic analysis showed markedly higher levels of glycocholate, taurocholate, and glycochenodeoxycholate in subjects with NAFLD. Plasma concentrations of long-chain fatty acids were lower and concentrations of free carnitine, butyrylcarnitine, and methylbutyrylcarnitine were higher in NASH. Several glutamyl dipeptides were higher whereas cysteine-glutathione levels were lower in NASH and steatosis. Other changes included higher branched-chain amino acids, phosphocholine, carbohydrates (glucose, mannose), lactate, pyruvate, and several unknown metabolites. Random forest analysis and recursive partitioning of the metabolomic data could separate healthy subjects from NAFLD with an error rate of approximately 8% and separate NASH from healthy controls with an error rate of 4%. Hepatic steatosis and steatohepatitis could not be separated using the metabolomic profile. Plasma metabolomic analysis revealed marked changes in bile salts and in biochemicals related to glutathione in subjects with NAFLD. Statistical analysis identified a panel of biomarkers that could effectively separate healthy controls from NAFLD and healthy controls from NASH. These biomarkers can potentially be used to follow response to therapeutic interventions.
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