Investigation of non-invasive tests for fibrosis diagnosis and prognostication of events in metabolic dysfunction-associated steatotic liver disease: A prospective study cohort

INTRODUCTION AND OBJECTIVES: Risk stratification is important in the management of metabolic dysfunction-associated steatotic liver disease (MASLD) to prioritize monitoring and treatment resources. We evaluated whether non-invasive tests (NITs) can predict major adverse liver outcomes (MALOs) and diagnose advanced fibrosis in patients with MASLD. PATIENTS AND METHODS: A prospective cohort of 270 patients with MASLD (median age 56 years; 44% female; 41% type 2 diabetes) was evaluated using nine NITs: FIB-4, LSM, Agile3+, Agile4, FAST, ADAPT, ELF, PRO-C3, and C1M. Cox proportional hazards models and ROC analyses were used to assess prognostic performance for MALOs and diagnosis of advanced fibrosis, respectively. RESULTS: Over a four-year follow-up period, 25 patients experienced MALOs. All NITs were associated with MALOs, though confidence intervals were wide given the limited number of events. Most NITs demonstrated positive associations with risk (HR range 1.82-5.39; p < 0.05), whereas C1M was inversely associated (HR 0.63, 95% CI 0.49-0.82; p < 0.001), indicating reduced risk. As a secondary objective, we evaluated whether each NIT could diagnose advanced fibrosis. FIB-4 and LSM effectively ruled out advanced fibrosis, while ELF and the Agile scores showed the strongest overall discriminative performance. For confirming advanced fibrosis, ELF (81%/76%) and Agile3+ (85%/79%) achieved the best sensitivity-specificity balance. CONCLUSIONS: Our findings suggest that NITs may hold both diagnostic and prognostic value, helping to identify advanced fibrosis and stratify future risk of MALOs in MASLD which is a clinically heterogenous but clinically relevant outcome. Additional research is needed to evaluate their performance in primary care settings. IMPACT AND IMPLICATIONS: In MASLD, NITs have the potential for identification of at-risk patients. Identifying NITs that predict beneficial as well as detrimental outcomes could also add value to monitoring strategies although further evidence is needed to evaluate this potential.