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논문분류	춘계학술대회 초록집
제목	Changes of Serum Metabolites Depending on CKD Stages: Comparison between Diabetes and Non-diabetes
저자	Ji-Young Choi1, Yong-Kook Kwon2, Hee-Yeon Jung1, Kyung Hoon Kim1, Seung Chan Park1, Jeong-Hoon Lim1, Owen Kwon1, Hye-Myung Ryu1, Jang-Hee Cho1, Sun-Hee Park1, Chan-Duck Kim1, Geum-Sook Hwang2, Yong-Lim Kim1
출판정보	2013; 2013(1):
키워드	만성신질환, 당뇨, 사구체 여과율, 대사체학/ Chronic kidney disease, Diabetes, GFR; metabolomics
초록	Background: Deteriorated renal function in chronic kidney disease (CKD) results in disturbance of serum metabolites. In this study, we aimed to evaluate the differences of serum metabolites according to eGFR and/or presence of diabetes with metabolomic analysis. In addition, we aimed to identify the important metabolites which predict eGFR. Methods: Pre-dialysis CKD Patients (n=291) and healthy controls (n=56) were recruited. First, CKD patients with diabetes were enrolled and classified 3 groups according to eGFR: group 1, ≥60; group 2, 15-59; group 3, <15 ml/min/1.73m2. Then, age-, sex-matched non-diabetic patients in each group and healthy controls were enrolled. Concentrations of serum metabolites were analyzed using 1H-NMR spectroscopy and data were presented by principal components analysis (PCA) and partial least squares (PLS) regression analysis. Results: Serum metabolites directionally distributed depending on CKD stages in PCA score plot. Leucine, acetate and pyruvate were significantly decreased as a decrease of eGFR, whereas phosphocholine, TMAO, urea and creatinine were significantly increased. Creatinine, pyruvate, urea, leucine, TMAO, formate, citrate and glucose showed significantly high variable importance in the projection (VIP) score which reflects the influence of each metabolite on eGFR (VIP score >1.0). R2 of validation for eGFR with all identified metabolites in PLS model was 0.778 (Fig.). Twenty-one metabolites including three metabolites associated with glucose metabolism, such as pyruvate, lactate and sorbitol showed significant difference between diabetic and non-diabetic patients in group 1. However, only 7 metabolites showed significant difference between the two patients groups in group 3, but pyruvate, lactate and sorbitol showed no significant difference in group 3. Conclusions: Serum metabolites of CKD patients directionally distributed as a decrease of eGFR in metabolomic analysis. We identified several important metabolites which predicted eGFR. The differences of metabolites between diabetes and non-diabetes were attenuated as CKD progressed.
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