Identification of growth differentiation factor 15 as an early predictive biomarker for metabolic dysfunction-associated steatohepatitis: A nested case-control study of UK Biobank proteomic data.

Abstract

Aims: This study aims to determine the predictive capability for metabolic dysfunction-associated steatohepatitis (MASH) long before its diagnosis by using six previously identified diagnostic biomarkers for metabolic dysfunction-associated steatotic liver disease (MASLD) with proteomic data from the UK Biobank.

Materials and methods: A nested case-control study comprising a MASH group and three age- and sex-matched control groups (metabolic dysfunction-associated steatosis, viral hepatitis and normal liver controls) was conducted. Olink proteomics, anthropometric and biochemical data at baseline levels were obtained from the UK Biobank. The baseline levels of CDCP1, FABP4, FGF21, GDF15, IL-6 and THBS2 were analysed prospectively to determine their predictive accuracy for subsequent diagnosis with a mean lag time of over 10 years.

Results: At baseline, GDF15 demonstrated the best performance for predicting MASH occurrence at 5 and 10 years later, with AUCs of 0.90 at 5 years and 0.86 at 10 years. A predictive model based on four biomarkers (GDF15, FGF21, IL-6 and THBS2) showed AUCs of 0.88 at both 5 and 10 years. Furthermore, a protein-clinical model that included these four circulating protein biomarkers along with three clinical factors (BMI, ALT and TC) yielded AUCs of 0.92 at 5 years and 0.89 at 10 years.

Conclusions: GDF15 at baseline levels outperformed other individual circulating protein biomarkers for the early prediction of MASH. Our data suggest that GDF15 and the GDF15-based model may be used as easy-to-implement tools to identify patients with high risks of developing MASH at a mean lag time of over 10 years.