| Literature DB >> 28501300 |
Guanshi Zhang1, Rintaro Saito1, Kumar Sharma2.
Abstract
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. Published by Elsevier Inc.Entities:
Mesh:
Year: 2017 PMID: 28501300 PMCID: PMC5989707 DOI: 10.1016/j.kint.2017.03.022
Source DB: PubMed Journal: Kidney Int ISSN: 0085-2538 Impact factor: 10.612