A metabolite-augmented FIB-4 machine learning panel achieves superior liver fibrosis staging in chronic liver disease

Accurate, non-invasive liver fibrosis detection is essential for chronic liver disease management, particularly with rising metabolic dysfunction-associated liver disease (MASLD) and chronic hepatitis B (CHB). While the Fibrosis-4 (FIB-4) index is widely used, its performance for advanced fibrosis is limited. We develop Met-FIB using metabolomics and machine learning, integrating FIB-4 parameters (age, aspartate aminotransferase, alanine aminotransferase, and platelet count) with tyrosine and taurocholic acid identified in a CHB discovery cohort (n = 3,251). Validation includes one CHB cohort (n = 729) and two MASLD cohorts (n = 149, n = 155). Met-FIB outperforms FIB-4, FibroScan, and other serum markers across all fibrosis stages. In CHB, Met-FIB achieves 96.3% rule-out sensitivity and 85.4% rule-in specificity for significant fibrosis, with rule-in specificity reaching 98.6% and 98.8% for advanced fibrosis and cirrhosis. In MASLD, corresponding values are 93.9% and 90.2% for significant fibrosis, with >97.9% specificity for late-stage disease. Met-FIB demonstrates clinical utility for non-invasive fibrosis staging across diverse etiologies.

Authors

Institutions

Publication Details

Journal
Cell Reports Medicine
Published
2026-03-31
DOI
https://doi.org/10.1016/j.xcrm.2026.102726
Primary Topic
Liver Disease Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A metabolite-augmented FIB-4 machine learning panel achieves superior liver fibrosis staging in chronic liver disease

Mengci Li, Keke Ding, Xianzhang Huang, Xinliang Zhang et al.
Cell Reports Medicine
Liver Disease Diagnosis and Treatment
article

A metabolite-augmented FIB-4 machine learning panel achieves superior liver fibrosis staging in chronic liver disease

Mengci Li, Keke Ding, Xianzhang Huang, Xinliang Zhang, Zheng Zhang, Huanming Xiao, Tao Sun, Alice Pik-Shan Kong, V. W.S. Wong, Kejun Zhou, Xiaoning Wang, Tianbiao Yang, Xufei Peng, Ping Liu, Yuanyuan Chen, Xiaoling Chi, Wei Jia, Tianlu Chen, Quan Liu, Xin Deng, Guoxiang Xie
article en

Abstract

Accurate, non-invasive liver fibrosis detection is essential for chronic liver disease management, particularly with rising metabolic dysfunction-associated liver disease (MASLD) and chronic hepatitis B (CHB). While the Fibrosis-4 (FIB-4) index is widely used, its performance for advanced fibrosis is limited. We develop Met-FIB using metabolomics and machine learning, integrating FIB-4 parameters (age, aspartate aminotransferase, alanine aminotransferase, and platelet count) with tyrosine and taurocholic acid identified in a CHB discovery cohort (n = 3,251). Validation includes one CHB cohort (n = 729) and two MASLD cohorts (n = 149, n = 155). Met-FIB outperforms FIB-4, FibroScan, and other serum markers across all fibrosis stages. In CHB, Met-FIB achieves 96.3% rule-out sensitivity and 85.4% rule-in specificity for significant fibrosis, with rule-in specificity reaching 98.6% and 98.8% for advanced fibrosis and cirrhosis. In MASLD, corresponding values are 93.9% and 90.2% for significant fibrosis, with >97.9% specificity for late-stage disease. Met-FIB demonstrates clinical utility for non-invasive fibrosis staging across diverse etiologies.

Cell Reports MedicineVol. 7(4)
Ningbo University (CN), Guangzhou University of Chinese Medicine (CN), Sun Yat-sen University (CN), Shenzhen Institute of Information Technology (CN), Chinese University of Hong Kong (HK), Anhui Medical University (CN), Shanghai University of Traditional Chinese Medicine (CN), Shanghai Sixth People's Hospital (CN), Third Affiliated Hospital of Sun Yat-sen University (CN), Shuguang Hospital (CN), Second Affiliated Hospital of Anhui Medical University (CN), Shenzhen Third People’s Hospital (CN), University of Hong Kong (HK)
Research Grants Council, University Grants Committee, Science and Technology Department of Zhejiang Province
Good health and well-being
Openalex Percentile: Top 33%
Liver Disease Diagnosis and Treatment
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.