Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a major and growing cause of chronic liver disease, encompassing a spectrum from steatosis and inflammation to fibrosis and hepatocellular carcinoma. Despite its prevalence, reliable and scalable tools for early diagnosis, disease staging, and prognosis remain limited.
This project aims to identify and validate multi-omic predictors of liver fibrosis, inflammatory activity, steatosis, and cancer risk in individuals with MASLD. Using data from the UK Biobank, we will integrate biochemical, proteomic, and genetic datasets with linked electronic health records (EHRs) and participant-declared outcomes to explore molecular and clinical signatures associated with disease presence, severity, and progression.
Our objectives are to:
1. Discover novel biomarkers and validate proxies of existing diagnostics for liver disease features (fibrosis, inflammation, and steatosis).
2. Assess prognostic associations between identified biomarkers and incident liver cancer or advanced liver outcomes.
3. Develop and evaluate predictive models combining omic and clinical variables to stratify disease risk and improve early detection.
4. Compare biomarker performance across data modalities and demographic or metabolic subgroups to enhance generalizability.
By leveraging the scale and diversity of UK Biobank data, this study will generate robust evidence on the molecular correlates and predictors of MASLD-related liver disease. The findings may support the refinement of non-invasive diagnostic approaches, risk stratification tools, and population-based strategies to reduce the burden of liver disease.