| Title: | Population stratification using MASLD polygenic risk score improves severe liver disease prediction by clinical fibrosis scores |
| Journal: | Digestive and Liver Disease |
| Published: | 12 Jul 2025 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/40653389/ |
| DOI: | https://doi.org/10.1016/j.dld.2025.06.016 |
Publication 16979
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Abstract
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.</p>
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.</p>
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.</p>
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.</p>
6 Authors
- Zhenqiu Liu
- Huangbo Yuan
- Chen Suo
- Li Jin
- Tiejun Zhang
- Xingdong Chen
1 Application
| Application ID | Title |
|---|---|
| 63726 | Genetic and Biomarker Data Improve Risk Prediction for Gastrointestinal Cancer in large Population Studies |
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