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Biochem Biophys Res Commun. 2014 Mar 7;445(2):412-6. doi: 10.1016/j.bbrc.2014.02.021. Epub 2014 Feb 12.

A metabolomics-based approach for predicting stages of chronic kidney disease.

Author information

1
Department of Bioengineering, Graduate School of Bioscience and Biochemistry, Tokyo Institute of Technology, 4259-G1-25 Nagatsuta-cho, Midori-ku, Yokohama 226-8502, Japan. Electronic address: kobayapi@bio.titech.ac.jp.
2
Global Application Development Center, Shimadzu Corporation, 1 Nishinokyo Kuwabara-cho, Nakagyo-ku, Kyoto 604-8511, Japan.
3
Kiyokai Tanaka-Kitanoda Hospital, 707 Kitanoda, Higashi-ku, Sakai 599-8123, Japan.
4
Department of Bioengineering, Graduate School of Bioscience and Biochemistry, Tokyo Institute of Technology, 4259-G1-25 Nagatsuta-cho, Midori-ku, Yokohama 226-8502, Japan.
5
Department of Bioengineering, Graduate School of Bioscience and Biochemistry, Tokyo Institute of Technology, 4259-G1-25 Nagatsuta-cho, Midori-ku, Yokohama 226-8502, Japan; Yokohama College of Pharmacy, 601 Matanocho, Totsuka-ku, Yokohama 245-0066, Japan.

Abstract

Chronic kidney disease (CKD) is a major epidemiologic problem and a risk factor for cardiovascular events and cerebrovascular accidents. Because CKD shows irreversible progression, early diagnosis is desirable. Renal function can be evaluated by measuring creatinine-based estimated glomerular filtration rate (eGFR). This method, however, has low sensitivity during early phases of CKD. Cystatin C (CysC) may be a more sensitive predictor. Using a metabolomic method, we previously identified metabolites in CKD and hemodialysis patients. To develop a new index of renal hypofunction, plasma samples were collected from volunteers with and without CKD and metabolite concentrations were assayed by quantitative liquid chromatography/mass spectrometry. These results were used to construct a multivariate regression equation for an inverse of CysC-based eGFR, with eGFR and CKD stage calculated from concentrations of blood metabolites. This equation was able to predict CKD stages with 81.3% accuracy (range, 73.9-87.0% during 20 repeats). This procedure may become a novel method of identifying patients with early-stage CKD.

KEYWORDS:

CKD; Cystatin C; GFR; LC/MS; OPLS

PMID:
24530913
DOI:
10.1016/j.bbrc.2014.02.021
[Indexed for MEDLINE]

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