| Literature DB >> 35069998 |
Navneet Kaur1, Gitanjali Goyal2, Ravinder Garg3, Chaitanya Tapasvi4, Sonia Chawla1, Rajneet Kaur1.
Abstract
Various types of liver disease exist, such as hepatitis and alcoholic liver disease. These liver diseases can result in scarring of liver tissue, cirrhosis, and finally liver failure. During liver fibrosis, there is an excess and disorganized accumulation of extracellular matrix (ECM) components which cause the loss of normal liver cell functions. For patients with chronic liver disease, fibrosis prediction is an essential part of the assessment and management. To diagnose liver fibrosis, several invasive and noninvasive markers have been proposed. However, the adoption of invasive markers remains limited due to their inherent characteristics and poor patient acceptance rate. In contrast, noninvasive markers can expedite the clinical decision through informed judgment about disease stage and prognosis. These noninvasive markers are classified into two types: Imaging techniques and serum biomarkers. However, the diagnostic values of biomarkers associated with liver fibrosis have also been analyzed. For example, the serum levels of ECM proteins can react to either matrix accumulation or degradation. During virus-host interactions, several regulatory steps take place to control gene expression, such as the change in cellular microRNA expression profiles. MicroRNAs are a class of non-coding RNAs (18-20 long nucleotides) that function by post-transcriptional regulation of gene expression. Although various noninvasive markers have been suggested in recent years, certain limitations have restricted their clinical applications. Understanding the potential of non-invasive biomarkers as a therapeutic option to treat liver fibrosis is still in progress. ©The Author(s) 2021. Published by Baishideng Publishing Group Inc. All rights reserved.Entities:
Keywords: Cirrhosis; Fibroscan; Liver fibrosis; MicroRNA; Non-invasive biomarkers; Viral hepatitis
Year: 2021 PMID: 35069998 PMCID: PMC8727215 DOI: 10.4254/wjh.v13.i12.1919
Source DB: PubMed Journal: World J Hepatol
Figure 1Factors promoting liver cell injury leading to fibrosis, cirrhosis, and carcinoma. NAFLD: Non-alcoholic fatty liver disease; HBV: Hepatitis B virus; HCV: Hepatitis C virus; PDGF: Platelet growth factor; IGF: Insulin-like growth factor; TGF: Tissue growth factor; ROS: Reactive oxygen species; ET-1: Endothelin-1; EMT: Epithelial-mesenchymal transition.
Figure 2Various methods for assessment of liver fibrosis. MRI: Magnetic resonance imaging; TE: Transient elastography; SWE: Shear wave elastography; ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; AAR: Aspartate aminotransferase aspartate aminotransferase/alanine aminotransferase ratio; TGF-β: Transforming growth factor β; PDGF: Platelet growth factor; APRI: Aspartate aminotransferase to platelet count ratio; FIB-4: Fibrosis-4; PCICP: Procollagen type 1; PCIIINP: Procollagen type 3; MMP: Matrix metalloproteinase.
Comparison of characteristics of invasive and non-invasive methods
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| 1 | Invasiveness | Yes | No |
| 2 | Sampling error | Yes | No |
| 3 | Cost-effective | No | Yes |
| 4 | Patient-friendly | No | Yes |
| 5 | Hospitalization required | Yes | No |
Scoring systems for liver fibrosis
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| No fibrosis | No fibrosis | Stage 0 | F0 |
| Fibrosis portal expansion | Mild fibrosis | Stage 1 | F1 |
| Few bridges or septa | Moderate fibrosis | Stage 2 | F2 |
| Numerous bridges or septa | Severe fibrosis | Stage 3 | F3 |
| Cirrhosis | Cirrhosis | Stage 4 | F4 |
IASL: International Association for the Study of the Liver.
Correlation of transient elastography cutoffs with METAVIRscore
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| F ≥ 2 (F0-F1 | 7.1 | 0.67 | 0.89 | 0.48 | 0.95 |
| F ≥ 3 (F0-F1-F2 | 9.5 | 0.73 | 0.91 | 0.81 | 0.87 |
| F ≥ 4 (F0-F1-F2-F3 | 12.5 | 0.87 | 0.91 | 0.95 | 0.77 |
TE: Transient elastography; NPV: Negative predictive value; PPV: Positive predictive value.
Figure 3Process of microRNA biogenesis. miRNA: MicroRNA.
Sensitivity and specificity of non-invasive biomarkers in liver fibrosis
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| AST/ALT ratio | AST and ALT | NAFLD; HCV | 0.83; - | 74; 47 | 78; 96 | McPherson |
| BARD score | BMI, AST, ALT, DM | NAFLD | 0.76 | 74 | 66 | Sun |
| APRI | AST, platelet count | NAFLD | 0.67 | 27 | 89 | McPherson |
| ALT | ALT | HCV | 0.716-0.815 | - | - | Pradat |
| Forns index | Age, platelet count, GGT, cholesterol | HCV | 0.81-0.86 | 94 | 51 | Forns |
| PGA and PGAA | Prothrombin time, GGT, apolipoprotein A1, α2 macroglobulin | Acute liver disease | 0.84-0.86 | - | - | Nguyen-Khac |
| FIB-4 | Platelet count, AST, ALT, age | HCV; NAFLD | 0.74-0.77; 0.85 | 67; 84 | 71; 69 | Sebastiani[ |
| Fibro test | Haptoglobin, apolipoprotein A1, α2 macroglobulin, GGT, bilirubin, age, and gender | HBV; HCV; ALD | 0.84; 0.87; 0.83 | 61; 75; - | 80; 85; - | Salkic |
| Hepascore | GGT, bilirubin, HA, α2 macroglobulin, age, and gender | HCV | 0.82 | - | - | Naveau |
| SHASTA index | HA, AST, and albumin | HCV | 0.87 | 50 | 94 | Kelleher |
| Fibrospect II | α2 macroglobulin, HA, and TIMP-1 | HCV | 0.82-0.83 | 77-83 | 66-73 | Patel |
AUROC: Area under receiver operating curve; ALT: Alanine aminotransferase; AST: Aspartate aminotransferase; NAFLD: Non-alcoholic fatty liver disease; HBV: Hepatitis B virus; HCV: Hepatitis C virus; BMI: Body mass index; DM: Diabetes mellitus; APRI: Aspartate aminotransferase to platelet count ratio; FIB-4: Fibrosis-4; GGT: Gamma-glutamyltransferase; HA: Hyaluronic acid; TIMP-1: Tissue inhibitors of metalloproteinases-1.