A metabolomics-based approach for predicting stages of chronic kidney disease |
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Authors: | Toshihiro Kobayashi Tatsunari Yoshida Tatsuya Fujisawa Yuriko Matsumura Toshihiko Ozawa Hiroyuki Yanai Atsuo Iwasawa Toshiaki Kamachi Kouichi Fujiwara Masahiro Kohno Noriaki Tanaka |
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Institution: | 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;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. Yokohama College of Pharmacy, 601 Matanocho, Totsuka-ku, Yokohama 245-0066, Japan |
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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. |
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Keywords: | CKD chronic kidney disease GFR glomerular filtration rate eGFR estimated GFR CysC cystatin C LC/MS liquid chromatography/mass spectrometry HMDB human metabolome database IS internal standard OPLS orthogonal partial least squares K/DOQI kidney disease outcomes quality initiative |
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