A metabolite-GWAS (mGWAS) approach to unveil chronic kidney disease progression.

In this issue, McMahon et al. report that, by combining phenotypic, metabolomic, and genetic data, they could better detect chronic kidney disease at the early stages and provide insight into its pathobiology. The most significant findings of the study are that several urinary metabolites (e.g., glycine and histidine) were identified as early risk factors for chronic kidney disease, and metabolites with genomewide association study analysis identified associations of urinary metabolites (i.e., lysine and NG -monomethyl-l-arginine) with single-nucleotide polymorphisms of SLC7A9.