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

作者：Kobayashi Toshihiro^*; Yoshida Tatsunari; Fujisawa Tatsuya; Matsumura Yuriko; Ozawa Toshihiko; Yanai Hiroyuki; Iwasawa Atsuo; Kamachi Toshiaki; Fujiwara Kouichi; Kohno Masahiro; Tanaka Noriaki

来源：Biochemical and Biophysical Research Communications, 2014, 445(2): 412-416.

DOI：10.1016/j.bbrc.2014.02.021

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

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.

• 出版日期2014-3-7