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Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain
Published on: March 13, 2020
Identification of Potential Biomarkers for Urine Metabolomics of Polycystic Ovary Syndrome Based on Gas
Ying Zou1, Fu-Fan Zhu1, Chao-Ying Fang2
1Department of Obstetrics and Gynecology, The Second Xiangya Hospital of Central South University, Changsha, Hunan 410011, China.
Background:
Polycystic ovary syndrome (PCOS) is a complex endocrine and metabolic disorder, and it's diagnosis is difficult. The aim of this study was to investigate the metabolic profiles of PCOS patients by analyzing urine samples and identify useful biomarkers for diagnosis of PCOS.
Methods:
This study was carried out in the Department of Obstetrics and Gynecology of the Maternal and Child Health Hospital of Hunan Province from December 2014 to July 2016. In this study, the urine samples of 21 women with PCOS and 16 healthy controls were assessed through gas chromatography-mass spectrometry to investigate the urine metabolite characteristics of PCOS and identify useful biomarkers for the diagnosis of this disorder. The Student's t-test and rank sum test were applied to validate the statistical significance of the between the two groups.
Results:
In total, 35 urine metabolites were found to be significantly different between the PCOS patients and the controls. In particular, a significant increase in the levels of lactose (10.01 [0,13.99] mmol/mol creatinine vs. 2.35 [0.16, 3.26] mmol/mol creatinine, P = 0.042), stearic acid (2.35 [1.47, 3.14] mmol/mol creatinine vs. 0.05 [0, 0.14] mmol/mol creatinine, P < 0.001), and palmitic acid (2.13 [1.07, 2.79] mmol/mol creatinine vs. 0 [0, 0] mmol/mol creatinine, P < 0.001) and a decrease in the levels of succinic acid (0 [0, 0] mmol/mol creatinine vs. 38.94 [4.16, 51.30] mmol/mol creatinine, P < 0.001) were found in the PCOS patients compared with the controls. It was possible to cluster the PCOS patients and the healthy controls into two distinct regions based on a principal component analysis model. Of the differentially expressed metabolites, four compounds, including stearic acid, palmitic acid, benzoylglycine, and threonine, were selected as potential biomarkers.
Conclusions:
This study offers new insight into the pathogenesis of PCOS, and the discriminating urine metabolites may provide a prospect for the diagnosis of PCOS.
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