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제120회 대한화학회 학술발표회, 총회 및 기기전시회 안내 Potential biomarkers of diabetic kidney disease detected by NMR-based metabolite profiling

등록일
2017년 8월 30일 18시 54분 02초
접수번호
3128
발표코드
ANAL2.O-31 이곳을 클릭하시면 발표코드에 대한 설명을 보실 수 있습니다.
발표시간
금 10시 : 21분
발표형식
구두발표
발표분야
Analytical Chemistry - Oral Presentation of Young Analytical Chemists II
저자 및
공동저자
Jin Seong Hyeon, Geum-Sook Hwang*
Western Seoul Center, Korea Basic Science Institute, Korea
Previous studies described metabolite profiles of diabetic animals; however, they were highly variable because of heterogeneity of the studies. We aimed to characterize the metabolite changes in the early and late stages of diabetic kidney disease to suggest potential biomarkers for early detection and its progression. Metabolite profiling using high-resolution nuclear magnetic resonance spectroscopy and multivariate statistical analysis was performed in db/db mice. We compared concentrations of serum and urinary metabolites between db/m and db/db mice at 8 or 20 weeks of age and investigated whether changes between 8 and 20 weeks in each group were significant. Correlation analysis was used to determine associations between urinary metabolites and urinary albumin excretion. Partial least squares-discriminant analysis score plots showed a significant distinction between db/m and db/db mice. The metabolic profiles demonstrated significantly increased urine levels of glucose and tricarboxylic acid cycle intermediates, such as fumarate, citrate, and 2-oxoglutarate, at both 8 and 20 weeks in db/db mice. These intermediates also exhibited strong positive associations with urinary albumin excretion, suggesting that they may be potential biomarkers for early diagnosis. On the contrary, branched chain amino acid and homocysteine-methionine metabolism were activated early in the disease, whereas ketone and fatty acid metabolism were significantly changed in the late phase of the disease. We demonstrated phase-specific alterations in metabolites during progression of diabetic kidney disease. This study provides insights into perturbed mechanisms during evolution of the disease and identifies potential novel biomarkers for diabetic kidney disease.

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