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.
Keywords: dynamical network biomarkers; mass spectrometry lipidomics; nonalcoholic fatty liver disease (NAFLD); pre-NASH; systems biology.
© The Author (2016). Published by Oxford University Press on behalf of Journal of Molecular Cell Biology, IBCB, SIBS, CAS. All rights reserved.