A metabolite-GWAS (mGWAS) approach to unveil chronic kidney disease progression.
Kidney Int
; 91(6): 1274-1276, 2017 06.
Article
em En
| MEDLINE
| ID: mdl-28501300
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Insuficiência Renal Crônica
/
Estudo de Associação Genômica Ampla
Tipo de estudo:
Prognostic_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2017
Tipo de documento:
Article