Having identified the catabolism of silymarin and its impact on the fecal metabolome, our objective was to investigate the interplay between the composition of the gut microbiota and silymarin catabolism in relation to age groups. To this end, we used the DIABLO algorithm on the mixOmics platform (Fig. ). One component was selected for the final model based on the lowest balanced error rate of 0.35 with a centroid distance metric. Subsequent parameter tuning included additional cross-validation with fivefold cross-validation (repeated 20 times), resulting in a final model with a cross-validation error of approximately 0.28 containing 30 OTUs, three silymarin components from LC-MS and five microbial silymarin catabolites from LC-MS. The analysis revealed strong correlations between the gut microbial taxonomic profiles and the microbial silymarin catabolites (r = 0.64), as well as between the silymarin components and microbial catabolites (r = 0.62) (Fig. ). Analysis at the genus level showed that healthy elders had a stronger association with certain bacterial genera, including Coprobacter, Oscillibacter, Ruminoclostridium and Anaerotruncus. On the other hand, Lachnospiraceae, Faecalibacterium and Lactococcus were more significant for the healthy young group. The parent components isosilybin B, silybin A and silydianin were associated with the healthy elder group (Fig. ). These findings are consistent with the results shown in the previous section, where they were hinted at using t-tests (Fig. ). The contribution of the catabolite 399.105@10.4 to the healthy elder group was also confirmed. Conversely, the healthy young group was associated with catabolite 469.113@11.9. The contribution of each selected feature based on its loading weights is depicted in Fig. .