The findings from the DSMN for all three datasets are in line with the pathways described in the original publications, showing that the DSMN can be a useful tool to conduct pathway analysis by joining knowledge from three databases in a concise manner. The DSMN analysis of the second dataset (MTBLS404) revealed that several biomarkers are a maximum of three steps removed from one another while sharing similar log2-ratio values. This finding suggests that these metabolites cannot be regarded as independent variables when creating a model to study the variation of the urine metabolome. Cluster analysis in the original publication revealed a similar finding, which can now be backed up by specific pathway knowledge through the DSMN. Two lower abundant biomarkers (L-Tryptophan and 5-hydroxy indole-3acetic acid (5-HIAA)) are only two metabolic reaction steps removed from each other and connected through two reactions, with either serotonin or oxitriptan as intermediate metabolites (Fig. .4 left-hand side). Oxitriptan is an immediate precursor of the neurotransmitter serotonin, and although serotonin has not been detected directly in the original study, the presence of neurotransmitters is known to decline with age . Similar behavior for two metabolites in reaction proximity to serotonin is expected. The direct link between L-tryptophan and serotonin, as well as the conversion from oxitriptan to 5-HIAA, have only been described in two individual pathways (WP4210, 'Tryptophan catabolism', and WP4156, 'phenylalanine and tetrahydrobiopterin (BH4) metabolism'), which shows the importance of tracing provenance for metabolic reactions in network and graph approaches during interpretation. Two biomarkers are linked through a two-step path (Fig. .4 top center) while having contradicting values for the log2 ratio: L-aspartic acid (-0.84) and fumaric acid (+1.8) (intermediate metabolite N-(L-Arginino)succinate). The original study did not find a correlation between these two metabolites; therefore the finding presented in this paper could be an excellent candidate for future research and investigation of e.g. transcriptomics, proteomics, fluxomics, and/or microRNA involvement to understand the underlying biological mechanisms related to these contradicting metabolic abundances.