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Updated: Sep 15, 2025
Author Spotlight: Advancing Hepatic Fibrosis Diagnosis Using Magnetic Resonance Elastography and AI
Published on: July 21, 2023
Population stratification using MASLD polygenic risk score improves severe liver disease prediction by clinical
Zhenqiu Liu1, Huangbo Yuan1, Chen Suo2
1Human Phenome Institute, Research and Innovation Center, Shanghai Pudong Hospital, Fudan University, Shanghai, 201203, China; Fudan University Taizhou Institute of Health Sciences, Taizhou, 225316, China.
Background And Aim:
Existing polygenic risk scores (PRS) for severe liver disease (SLD) have limited predictive ability, highlighting a possible reorientation for PRS application in clinical practice.
Methods:
Using non-overlapping subsets of the UK Biobank cohort, we first conducted a genome-wide association study of magnetic resonance imaging-derived hepatic fat content (HFC; n = 12,838), and then constructed a polygenic risk score to capture genetically predicted HFC (gHFC), which was applied in an independent sample (n = 426,529) to stratify individuals and evaluate the performance of clinical fibrosis scores.
Results:
Among 426,529 participants, 4417 developed SLD during follow-up. gHFC alone showed limited predictive power for SLD, and adding it to fibrosis scores did not improve AUROC. However, population stratification by gHFC substantially improved the performance of Fibrosis-4 (FIB-4), Forns, and Aspartate aminotransferase-to-Platelet Ratio Index (APRI), particularly for hepatocellular carcinoma (HCC). In the highest gHFC quintile, the areas under the receiver operating characteristic curve for HCC were 0.819 (FIB-4), 0.877 (Forns), and 0.851 (APRI), significantly higher than in the lowest quintile. Similar trends were observed using two alternative HFC PRSs.
Conclusion:
Stratifying the population by PRS before using clinical fibrosis scores to predict SLD is a more effective approach than considering PRS as an alternative or an addition to clinical risk models.
