Adjusted risks associated with GD in the overall series, in patients without cirrhosis and without diabetes, at multivariate logistic regression analysis.

<p>Each variable adjusted for the others in the table.</p

Percentage of necro inflammatory grading (left), fibrosis staging (middle) and steatosis (right) according to the presence or absence of GD.

<p>For necroinflammatory grade (IV  =  grade IV, V, VI and VII together). Analysis for trend: Necroinflammatory grade p = 0.01, Fibrosis stage p = 0.0001, Steatosis not significant (p = 0.7).</p

Anthropometric, biochemical and clinical variables in patients with NAFLD with or without GD (univariate analysis).

<p>Mean ± SD or number of cases and (%).</p>+<p>P-values (ANOVA or chi-square test).</p>*<p>Variables that maintained significance when the subjects diagnosed with cirrhosis were excluded from analysis.</p

Association of the GCKR rs780094 genotype and liver damage as evaluated by unadjusted and adjusted models in 366 Patients with Non-alcoholic Fatty Liver Disease.

<p>Unadjusted and adjusted OR were presented for PNPLA3 and GCKR SNPs, and only for clinical, metabolic and histological variables significant at univariate analysis.</p><p>PNPLA3 and GCKR SNPs, when not significant, were forced into the models.</p

Baseline Demographic, Laboratory, Metabolic, and Histological Features of 366 Italian Patients with Non-alcoholic Fatty Liver Disease.

<p>Abbreviation: IU, international units; HOMA, homeostasis model assessment; HDL, high density lipoprotein. Data are given as mean ± standard deviation, or as number of cases (%).</p

Prevalence of significant fibrosis according to GCKR rs780094 SNP in the Sicilian cohort, in the Center/Northern Italian cohort, and in the combined cohorts of NAFLD patients.

<p>*p = 0.02 for prevalence of F2–F4 fibrosis according to GCKR genotype; ° p = 0.04 for prevalence of F2–F4 fibrosis according to GCKR genotype; ∧p = 0.001 for prevalence of F2–F4 fibrosis according to GCKR genotype.</p

Association of the rs780094 C→T GCKR SNP with anthropometric, biochemical, metabolic and histological features in 366 Patients with Non-alcoholic Fatty Liver Disease.

<p>Data are given as mean ± SD or as number of cases. HDL: high-density lipoprotein; HOMA: homeostasis model assessment.</p>°<p>by ANOVA;</p>*<p>p = 0.02 versus GCKR CC/CT.</p

Modelling for At-Risk MASH.

AUC for every classifier predicting At-Risk MASH by Feature Set and Model Composition.</p

Clinical variables owing to three feature sets used within this analysis.

Clinical variables owing to three feature sets used within this analysis.</p

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AimsMetabolic 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.MethodsUsing 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.ResultsAnalysing 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.ConclusionsThis 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.</div