Non-invasive urinary metabolomics reveals metabolic profiling of polycystic ovary syndrome and its subtypes

•Urinary metabolomics in polycystic ovary syndrome was studied by GC–MS.•A panel of metabolic biomarkers are used to diagnose polycystic ovary syndrome.•The altered pathways included glyoxylate, dicarboxylate metabolism and citrate cycle. Polycystic ovary syndrome (PCOS) is a heterogeneous endocrine...

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Veröffentlicht in:Journal of pharmaceutical and biomedical analysis 2020-06, Vol.185, p.113262-113262, Article 113262
Hauptverfasser: Zhou, Wei, Hong, Yanli, Yin, Ailing, Liu, Shijia, Chen, Minmin, Lv, Xifeng, Nie, Xiaowei, Tan, Ninghua, Zhang, Zhihao
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container_title Journal of pharmaceutical and biomedical analysis
container_volume 185
creator Zhou, Wei
Hong, Yanli
Yin, Ailing
Liu, Shijia
Chen, Minmin
Lv, Xifeng
Nie, Xiaowei
Tan, Ninghua
Zhang, Zhihao
description •Urinary metabolomics in polycystic ovary syndrome was studied by GC–MS.•A panel of metabolic biomarkers are used to diagnose polycystic ovary syndrome.•The altered pathways included glyoxylate, dicarboxylate metabolism and citrate cycle. Polycystic ovary syndrome (PCOS) is a heterogeneous endocrine disorder, which affects 4–10 % women of reproductive age. Though accumulating scientific evidence, its pathogenesis remains unclear. In the current study, metabolic profiling as well as diagnostic biomarkers for different phenotypes of PCOS was investigated using non-invasive urinary GCMS based metabolomics. A total of 371 subjects were recruited for the study. They constituted the following groups: healthy women, those with hyperandrogenism (HA), women with insulin-resistance (IR) in PCOS. Two cross-comparisons with PCOS were performed to characterize metabolic disturbances. A total of 23 differential metabolites were found. The altered metabolic pathways included glyoxylate and dicarboxylate metabolism, pentose and glucuronate interconversions, and citrate cycle and butanoate metabolism. For differential diagnosis, a panel consisting of 9 biomarkers was found from the comparison of PCOS from healthy subjects. The area under the receiver operating characteristic (ROC) curve (AUC) was 0.8461 in the discovery phase. Predictive value of 89.17 % was found in the validation set. Besides, a panel of 8 biomarkers was discovered from PCOS with HA vs IR. The AUC for 8-biomarker panel was 0.8363, and a panel of clinical markers (homeostasis model assessment-insulin resistance and free androgen index) had 0.8327 in AUC. While these metabolites combined with clinical markers reached 0.9065 in AUC from the discovery phase, and 93.18 % in predictive value from the validation set. The result showed that differences of small-molecule metabolites in urine may reflect underlying pathogenesis of PCOS and serve as biomarkers for complementary diagnosis of the different phenotypes of PCOS.
doi_str_mv 10.1016/j.jpba.2020.113262
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Polycystic ovary syndrome (PCOS) is a heterogeneous endocrine disorder, which affects 4–10 % women of reproductive age. Though accumulating scientific evidence, its pathogenesis remains unclear. In the current study, metabolic profiling as well as diagnostic biomarkers for different phenotypes of PCOS was investigated using non-invasive urinary GCMS based metabolomics. A total of 371 subjects were recruited for the study. They constituted the following groups: healthy women, those with hyperandrogenism (HA), women with insulin-resistance (IR) in PCOS. Two cross-comparisons with PCOS were performed to characterize metabolic disturbances. A total of 23 differential metabolites were found. The altered metabolic pathways included glyoxylate and dicarboxylate metabolism, pentose and glucuronate interconversions, and citrate cycle and butanoate metabolism. For differential diagnosis, a panel consisting of 9 biomarkers was found from the comparison of PCOS from healthy subjects. The area under the receiver operating characteristic (ROC) curve (AUC) was 0.8461 in the discovery phase. Predictive value of 89.17 % was found in the validation set. Besides, a panel of 8 biomarkers was discovered from PCOS with HA vs IR. The AUC for 8-biomarker panel was 0.8363, and a panel of clinical markers (homeostasis model assessment-insulin resistance and free androgen index) had 0.8327 in AUC. While these metabolites combined with clinical markers reached 0.9065 in AUC from the discovery phase, and 93.18 % in predictive value from the validation set. 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Polycystic ovary syndrome (PCOS) is a heterogeneous endocrine disorder, which affects 4–10 % women of reproductive age. Though accumulating scientific evidence, its pathogenesis remains unclear. In the current study, metabolic profiling as well as diagnostic biomarkers for different phenotypes of PCOS was investigated using non-invasive urinary GCMS based metabolomics. A total of 371 subjects were recruited for the study. They constituted the following groups: healthy women, those with hyperandrogenism (HA), women with insulin-resistance (IR) in PCOS. Two cross-comparisons with PCOS were performed to characterize metabolic disturbances. A total of 23 differential metabolites were found. The altered metabolic pathways included glyoxylate and dicarboxylate metabolism, pentose and glucuronate interconversions, and citrate cycle and butanoate metabolism. For differential diagnosis, a panel consisting of 9 biomarkers was found from the comparison of PCOS from healthy subjects. The area under the receiver operating characteristic (ROC) curve (AUC) was 0.8461 in the discovery phase. Predictive value of 89.17 % was found in the validation set. Besides, a panel of 8 biomarkers was discovered from PCOS with HA vs IR. The AUC for 8-biomarker panel was 0.8363, and a panel of clinical markers (homeostasis model assessment-insulin resistance and free androgen index) had 0.8327 in AUC. While these metabolites combined with clinical markers reached 0.9065 in AUC from the discovery phase, and 93.18 % in predictive value from the validation set. The result showed that differences of small-molecule metabolites in urine may reflect underlying pathogenesis of PCOS and serve as biomarkers for complementary diagnosis of the different phenotypes of PCOS.</description><subject>Adolescent</subject><subject>Adult</subject><subject>Androgens - blood</subject><subject>Biomarkers - metabolism</subject><subject>Biomarkers - urine</subject><subject>Citric Acid Cycle</subject><subject>Cross-Sectional Studies</subject><subject>Diagnosis, Differential</subject><subject>Dicarboxylic Acids - isolation &amp; purification</subject><subject>Dicarboxylic Acids - metabolism</subject><subject>Dicarboxylic Acids - urine</subject><subject>Female</subject><subject>Gas Chromatography-Mass Spectrometry - methods</subject><subject>Glyoxylates - isolation &amp; purification</subject><subject>Glyoxylates - metabolism</subject><subject>Glyoxylates - urine</subject><subject>Healthy Volunteers</subject><subject>Humans</subject><subject>Hyperandrogenism</subject><subject>Hyperandrogenism - blood</subject><subject>Hyperandrogenism - diagnosis</subject><subject>Hyperandrogenism - metabolism</subject><subject>Hyperandrogenism - urine</subject><subject>Insulin Resistance</subject><subject>Metabolic Networks and Pathways</subject><subject>Metabolomics</subject><subject>Metabolomics - methods</subject><subject>Non-invasive biomarkers</subject><subject>Polycystic ovary syndrome</subject><subject>Polycystic Ovary Syndrome - blood</subject><subject>Polycystic Ovary Syndrome - diagnosis</subject><subject>Polycystic Ovary Syndrome - metabolism</subject><subject>Polycystic Ovary Syndrome - urine</subject><subject>Predictive Value of Tests</subject><subject>ROC Curve</subject><subject>Tandem Mass Spectrometry - methods</subject><subject>Young Adult</subject><issn>0731-7085</issn><issn>1873-264X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kE9L5TAUxYM4jE-dLzALydJNn0nzpym4EXFUEGczA-5CmtxK3rRNTdpCv_3k8dSld3Ph5JxD7g-hn5RsKaHyarfdjY3ZlqTMAmWlLI_QhqqKFaXkL8doQypGi4oocYJOU9oRQgSt-Xd0wso8kqsN-vcchsIPi0l-ATxHP5i44h4m04Qu9N4mHGEB06UP0Vs8xtD6zg-vOLR4DN1q1zRlPSz7cFoHF0MP2AwO-ynhNDfTOkI6R9_aXAQ_3vcZ-vvr7s_tQ_H0-_7x9uapsEzIqZCtYaYmLSjpKsKJbFV-cELKVtSiUpxwxkTjLKE14bK2rAIHQlFrFIiKsDN0eejN_3ybIU2698lC15kBwpx0yRTndS2ZzNbyYLUxpBSh1WP0fb5CU6L3kPVO7yHrPWR9gJxDF-_9c9OD-4x8UM2G64MB8pWLh6iT9TBYcD6CnbQL_qv-_xXLj0s</recordid><startdate>20200605</startdate><enddate>20200605</enddate><creator>Zhou, Wei</creator><creator>Hong, Yanli</creator><creator>Yin, Ailing</creator><creator>Liu, Shijia</creator><creator>Chen, Minmin</creator><creator>Lv, Xifeng</creator><creator>Nie, Xiaowei</creator><creator>Tan, Ninghua</creator><creator>Zhang, Zhihao</creator><general>Elsevier B.V</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>20200605</creationdate><title>Non-invasive urinary metabolomics reveals metabolic profiling of polycystic ovary syndrome and its subtypes</title><author>Zhou, Wei ; 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Polycystic ovary syndrome (PCOS) is a heterogeneous endocrine disorder, which affects 4–10 % women of reproductive age. Though accumulating scientific evidence, its pathogenesis remains unclear. In the current study, metabolic profiling as well as diagnostic biomarkers for different phenotypes of PCOS was investigated using non-invasive urinary GCMS based metabolomics. A total of 371 subjects were recruited for the study. They constituted the following groups: healthy women, those with hyperandrogenism (HA), women with insulin-resistance (IR) in PCOS. Two cross-comparisons with PCOS were performed to characterize metabolic disturbances. A total of 23 differential metabolites were found. The altered metabolic pathways included glyoxylate and dicarboxylate metabolism, pentose and glucuronate interconversions, and citrate cycle and butanoate metabolism. For differential diagnosis, a panel consisting of 9 biomarkers was found from the comparison of PCOS from healthy subjects. The area under the receiver operating characteristic (ROC) curve (AUC) was 0.8461 in the discovery phase. Predictive value of 89.17 % was found in the validation set. Besides, a panel of 8 biomarkers was discovered from PCOS with HA vs IR. The AUC for 8-biomarker panel was 0.8363, and a panel of clinical markers (homeostasis model assessment-insulin resistance and free androgen index) had 0.8327 in AUC. While these metabolites combined with clinical markers reached 0.9065 in AUC from the discovery phase, and 93.18 % in predictive value from the validation set. The result showed that differences of small-molecule metabolites in urine may reflect underlying pathogenesis of PCOS and serve as biomarkers for complementary diagnosis of the different phenotypes of PCOS.</abstract><cop>England</cop><pub>Elsevier B.V</pub><pmid>32222648</pmid><doi>10.1016/j.jpba.2020.113262</doi><tpages>1</tpages></addata></record>
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subjects Adolescent
Adult
Androgens - blood
Biomarkers - metabolism
Biomarkers - urine
Citric Acid Cycle
Cross-Sectional Studies
Diagnosis, Differential
Dicarboxylic Acids - isolation & purification
Dicarboxylic Acids - metabolism
Dicarboxylic Acids - urine
Female
Gas Chromatography-Mass Spectrometry - methods
Glyoxylates - isolation & purification
Glyoxylates - metabolism
Glyoxylates - urine
Healthy Volunteers
Humans
Hyperandrogenism
Hyperandrogenism - blood
Hyperandrogenism - diagnosis
Hyperandrogenism - metabolism
Hyperandrogenism - urine
Insulin Resistance
Metabolic Networks and Pathways
Metabolomics
Metabolomics - methods
Non-invasive biomarkers
Polycystic ovary syndrome
Polycystic Ovary Syndrome - blood
Polycystic Ovary Syndrome - diagnosis
Polycystic Ovary Syndrome - metabolism
Polycystic Ovary Syndrome - urine
Predictive Value of Tests
ROC Curve
Tandem Mass Spectrometry - methods
Young Adult
title Non-invasive urinary metabolomics reveals metabolic profiling of polycystic ovary syndrome and its subtypes
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