Genes govern metabolism—Enzymes define pathways and metabolic relationships

Gene centric pathway mapping tools, widely used to interpret untargeted LCMS metabolomics data, may underperform because a single metabolite can generate multiple spectral features, inflating false positive rates. Classic enzymology, which established metabolite flow long before gene sequencing, offers experimentally validated precursor product relationships that could overcome these ambiguities. We evaluated whether enzymology defined precursor product correlations are consistently detectable in human plasma LCMS data and explored their potential to enhance pathway analysis and metabolite identification. Using a high resolution LCMS platform, we detected amino acids and carnitine related metabolites in one individual sampled eight times over five years and in 50 adults sampled 6 to 8 times each. Spearman correlations were calculated for longitudinal and cross sectional data. In the single participant repeated measures, strong positive correlations were observed for every direct precursor product pair except the branch point metabolite palmitoylcarnitine. The longitudinal analysis reproduced these patterns, and the same relationships were retained when analysis was cross sectional. Despite contributions from multiple organ systems, plasma thus preserved core enzymatic relationships. Precursor product proportionality, a fundamental principle of enzymology, is readily detectable in large scale LCMS datasets and remains robust across longitudinal and cross sectional designs. Applying these correlations to metabolomics workflows can improve pathway analysis, help metabolite identification, and reveal how genetic variations, diets, therapeutic drugs, and environmental exposures jointly impact metabolic pathways.