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 (n = 110) and healthy controls (n = 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.
journal_name
Urolithiasisjournal_title
Urolithiasisauthors
Duan X,Zhang T,Ou L,Kong Z,Wu W,Zeng Gdoi
10.1007/s00240-019-01132-2subject
Has Abstractpub_date
2020-02-01 00:00:00pages
27-35issue
1eissn
2194-7228issn
2194-7236pii
10.1007/s00240-019-01132-2journal_volume
48pub_type
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