Non-invasive biomarkers for liver inflammation in non-alcoholic fatty liver disease: present and future

Inflammation is the key driver of liver fibrosis progression in non-alcoholic fatty liver disease (NAFLD). Unfortunately, it is often challenging to assess inflammation in NAFLD due to its dynamic nature and poor correlation with liver biochemical markers. Liver histology keeps its role as the stand...

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Bibliographic Details
Published in:Clinical and molecular hepatology 2023-02, Vol.29 (Suppl), p.S171-S183
Main Authors: Yip, Terry Cheuk-Fung, Lyu, Fei, Lin, Huapeng, Li, Guanlin, Yuen, Pong-Chi, Wong, Vincent Wai-Sun, Wong, Grace Lai-Hung
Format: Article
Language:English
Subjects:
Apoptosis
Artificial intelligence
Biomarkers
Biopsy
Body mass index
Clinical medicine
Clinical trials
Cytokeratin
cytokeratin-18
deep learning
fatty liver
Hepatitis
Histology
Humans
Inflammation
Inflammation - metabolism
Inflammation - pathology
Liver - pathology
liver cancer
Liver Cirrhosis - complications
Liver Cirrhosis - diagnosis
Liver diseases
machine learning
MicroRNAs
Non-alcoholic Fatty Liver Disease - complications
Non-alcoholic Fatty Liver Disease - diagnosis
Patients
Proteins
Review
Triglycerides
Ultrasonic imaging
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Summary:Inflammation is the key driver of liver fibrosis progression in non-alcoholic fatty liver disease (NAFLD). Unfortunately, it is often challenging to assess inflammation in NAFLD due to its dynamic nature and poor correlation with liver biochemical markers. Liver histology keeps its role as the standard tool, yet it is well-known for substantial sampling, intraobserver, and interobserver variability. Serum proinflammatory cytokines and apoptotic markers, namely cytokeratin-18, are well-studied with reasonable accuracy, whereas serum metabolomics and lipidomics have been adopted in some commercially available diagnostic models. Ultrasound and computed tomography imaging techniques are attractive due to their wide availability; yet their accuracies may not be comparable with magnetic resonance imaging-based tools. Machine learning and deep learning models, be they supervised or unsupervised learning, are promising tools to identify various subtypes of NAFLD, including those with dominating liver inflammation, contributing to sustainable care pathways for NAFLD.
ISSN:2287-2728
2287-285X
DOI:10.3350/cmh.2022.0426