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Aims: Metabolic dysfunction Associated Steatotic Liver Disease (MASLD) outcomes such as MASH (metabolic dysfunction associated steatohepatitis), fibrosis and cirrhosis are ordinarily determined by resource-intensive and invasive biopsies. We aim to show that routine clinical tests offer sufficient information to predict these endpoints. Methods: Using the LITMUS Metacohort derived from the European NAFLD Registry, the largest MASLD dataset in Europe, we create three combinations of features which vary in degree of procurement including a 19-variable feature set that are attained through a routine clinical appointment or blood test. This data was used to train predictive models using supervised machine learning (ML) algorithm XGBoost, alongside missing imputation technique MICE and class balancing algorithm SMOTE. Shapley Additive exPlanations (SHAP) were added to determine relative importance for each clinical variable. Results: Analysing nine biopsy-derived MASLD outcomes of cohort size ranging between 5385 and 6673 subjects, we were able to predict individuals at training set AUCs ranging from 0.719-0.994, including classifying individuals who are At-Risk MASH at an AUC = 0.899. Using two further feature combinations of 26-variables and 35-variables, which included composite scores known to be good indicators for MASLD endpoints and advanced specialist tests, we found predictive performance did not sufficiently improve. We are also able to present local and global explanations for each ML model, offering clinicians interpretability without the expense of worsening predictive performance. Conclusions: This study developed a series of ML models of accuracy ranging from 71.9—99.4% using only easily extractable and readily available information in predicting MASLD outcomes which are usually determined through highly invasive means. [ABSTRACT FROM AUTHOR]

Metabolic associated fatty liver disease (MAFLD) is the most prevalent liver disease in Western nations, with high heritability. A recent study of Japanese patients with the disease suggested that TLL1 rs17047200 is associated with fibrosis; whether a similar association is observed in Caucasian patients with MAFLD is unknown. We investigated the association of the TLL1 rs17047200 polymorphism with liver fibrosis in a cohort of Caucasian patients with MAFLD (n = 728). We also investigated whether TLL1 expression is altered during liver injury in humans, in murine models of fibrosis, and in in-vitro. While TLL1 expression is upregulated in the liver of humans with MAFLD and in mice, the rs17047200 variant was not associated with fibrosis or any other histological features, or with hepatic TLL1 expression. In conclusion, the TLL1 rs17047200 variant is not a risk variant for fibrosis in Caucasian patients with MAFLD. However, TLL1 could be involved in the pathogenesis of liver fibrosis. [ABSTRACT FROM AUTHOR]