*4.4. Statistical Analysis*

Differences between the two groups were looked for by means of a paired *t*-test applied to the concentrations of each molecule, transformed by the Box-Cox algorithm for normal distribution [60].

We highlighted any trend characterizing the samples with robust principal component analysis (rPCA) models [61], based on the molecules accepted by the univariate analysis. To this purpose, we employed the PcaHubert algorithm implemented in the rrcov package. The main features of each rPCA model are summarized by a score plot and by a Pearson correlation plot. The former is the projection of the samples in the PC space and highlights the underlying structure of the data. The latter relates the concentration of each variable to the components of the model.

Metabolic pathway analysis was conducted by relying on the Reactome pathway knowledgebase [62], with overrepresentation analysis (ORA) based on a hypergeometric test [63].
