2.3. Statistical Analysis
fibrosis
REGRESSION, FIBROSIS
To describe the studied population, the mean and standard deviations (SD) were estimated for continuous variables, and proportions and frequencies were calculated for categorical variables. To evaluate the differences between groups (non-MASLD, MASLD, and MASLD + fibrosis), ANOVA and exact Fisher tests were performed, and Bonferroni post hoc tests were conducted to identify the differences between groups.Metabolite concentrations and biochemical data were median-normalized, square- or cube-root-transformed, and range-scaled. A partial least squares discriminant analysis (PLS-DA) was performed to identify the discrimination between study groups (non-MASLD vs. MASLD, non-MASLD vs. MASLD + fibrosis, and MASLD vs. MASLD + fibrosis). Variable importance projection (VIP) plots were graphed to visualize the metabolites that contributed the most to the discrimination of samples, and heatmaps based on the VIP scores were made to visualize the differences between the groups’ metabolite concentrations and biochemical data.Finally, logistic regression models were constructed considering the metabolites and biochemical data with VIP scores >1.5 as independent variables (different combinations of these variables were tested), and the MASLD groups (Non-MASLD vs. MASLD and Non-MASLD vs. MASLD + fibrosis) as dependent variables. To evaluate whether different sets of metabolites and biochemical data can identify MASLD and MASLD + fibrosis groups, receiver operating characteristic (ROC) curves were estimated. To define the metabolomic phenotype, we selected models with a reduced number of metabolites and an adequate ROC area.A 