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

Terry Cheuk-Fung Yip, Fei Lyu, Huapeng Lin, Guanlin Li, Pong-Chi Yuen*, Vincent Wai-Sun Wong, Grace Lai-Hung Wong*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

7 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)S171-S183
Number of pages13
JournalClinical and Molecular Hepatology
Volume29
Issue numberSuppl
Early online date12 Dec 2022
DOIs
Publication statusPublished - Feb 2023

Scopus Subject Areas

  • Molecular Biology
  • Hepatology

User-Defined Keywords

  • Cytokeratin-18
  • Deep learning
  • Fatty liver
  • Liver cancer
  • Machine learning

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