Systems-level organization of non-alcoholic fatty liver disease progression network

2017 ◽  
Vol 13 (9) ◽  
pp. 1898-1911 ◽  
Author(s):  
K. Shubham ◽  
L. Vinay ◽  
P. K. Vinod
Keyword(s):  
Extracellular Matrix ◽  
Adipose Tissue ◽  
Liver Disease ◽  
Network Analysis ◽  
Fatty Liver Disease ◽  
Alcoholic Fatty Liver ◽  
Liver Disease Progression ◽  
Extracellular Matrix Remodelling ◽  
Visceral Adipose ◽  
Alcoholic Fatty Liver Disease

Co-expression network analysis of visceral adipose tissue reveals the nexus between metabolism, inflammation, extracellular matrix remodelling and stress in NAFLD.

scholarly journals Association between Visceral Adipose Tissue and Non-Alcoholic Steatohepatitis Histology in Patients with Known or Suspected Non-Alcoholic Fatty Liver Disease

2021 ◽  
Vol 10 (12) ◽  
pp. 2565
Author(s):  
Ilkay S. Idilman ◽  
Hsien Min Low ◽  
Tolga Gidener ◽  
Kenneth Philbrick ◽  
Taofic Mounajjed ◽  
...  
Keyword(s):  
Adipose Tissue ◽  
Liver Disease ◽  
Fatty Liver ◽  
Visceral Adipose Tissue ◽  
Fatty Liver Disease ◽  
Alcoholic Fatty Liver ◽  
Alcoholic Steatohepatitis ◽  
Fat Fraction ◽  
Visceral Adipose ◽  
Alcoholic Fatty Liver Disease

(1) Purpose: To determine the association between visceral adipose tissue (VAT) and proton density fat fraction (PDFF) with magnetic resonance imaging (MRI), and hepatic steatosis (HS), non-alcoholic steatohepatitis (NASH) and hepatic fibrosis (HF) in patients with known or suspected non-alcoholic fatty liver disease (NAFLD). (2) Methods: 135 subjects that had a liver biopsy performed within 3 months (bariatric cohort) or 1 month (NAFLD cohort) of an MRI exam formed the study group. VAT volume was quantified at L2-L3 level on opposed-phase images with signal intensity-based painting using a semi-quantitative software. Liver PDFF and pancreas PDFF were calculated on fat fraction maps. Liver volume (Lvol) and spleen volume (Svol) were also calculated using a semi-automated 3D volume tool available on PACS. A histological analysis was performed by an expert hepatopathologist blinded to imaging findings. (3) Results: The mean Lvol, Svol, liver PDFF, pancreas PDFF and VAT of the study population were 2492.2 mL, 381.6 mL, 13.2%, 12.7% and 120.6 mL, respectively. VAT showed moderate correlation with liver PDFF (r = 0.41, p < 0.001) and weak correlation with Lvol (r = 0.38, p < 0.001), Svol (r = 0.20, p = 0.025) and pancreas PDFF (rs = 0.29, p = 0.001). VAT, Lvol and liver PDFF were significantly higher in patients with HS (p < 0.001), NASH (p < 0.05) and HF (p < 0.05). VAT was also significantly higher in the presence of lobular inflammation (p = 0.019) and hepatocyte ballooning (p = 0.001). The cut-off VAT volumes for predicting HS, NASH and HF were 101.8 mL (AUC, 0.7), 111.8 mL (AUC, 0.64) and 111.6 mL (AUC, 0.66), respectively. (4) Conclusion: The MRI determined VAT can be used for predicting the presence of HS, NASH and HF in patients with known or suspected NAFLD.

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