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Urolithiasis. 2019 Apr 4. doi: 10.1007/s00240-019-01132-2. [Epub ahead of print]

1H NMR-based metabolomic study of metabolic profiling for the urine of kidney stone patients.

Duan X1,2, Zhang T1,2, Ou L1,2, Kong Z1,2, Wu W1,2, Zeng G3,4.

Author information

1
Department of Urology, Minimally Invasive Surgery Center, The First Affiliated Hospital of Guangzhou Medical University, Kangda Road 1#, Haizhu District, Guangzhou, 510230, Guangdong, China.
2
Guangdong Key Laboratory of Urology, Guangzhou Institute of Urology, Guangzhou, China.
3
Department of Urology, Minimally Invasive Surgery Center, The First Affiliated Hospital of Guangzhou Medical University, Kangda Road 1#, Haizhu District, Guangzhou, 510230, Guangdong, China. gzgyzgh@vip.sina.com.
4
Guangdong Key Laboratory of Urology, Guangzhou Institute of Urology, Guangzhou, China. gzgyzgh@vip.sina.com.

Abstract

Kidney stone is a chronic metabolic disease that caused by many factors, especially by the metabolic disturbances of urine compositions, but the metabolic profiling of the urine from kidney stone patients remains poorly explored. In the present study, 1H NMR spectroscopy and multivariate pattern recognition analytical techniques were combined to explore the metabolic profiling of the urine from kidney stone patients. A total of 216 urine samples obtained from kidney stone patients (nā€‰=ā€‰110) and healthy controls (nā€‰=ā€‰106) were investigated. The results indicated that principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) models were capable of distinguishing kidney stone patients from healthy controls. In addition, a total of 15 metabolites was obviously different in concentration between the two groups. Furthermore, four metabolic pathways, including glyoxylate and dicarboxylate metabolism, glycine, serine and threonine metabolism, phenylalanine metabolism and citrate cycle (TCA cycle), were closely associated with kidney stone. Together, our results established a preliminary metabolic profiling of the urine from kidney stone patients via using 1H NMR-based analytical techniques for the first time and provided a novel method for recognizing and observing the kidney stone disease.

KEYWORDS:

Kidney stone; Metabolomics; NMR; Pattern recognition; Urine

PMID:
30949730
DOI:
10.1007/s00240-019-01132-2

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