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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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. 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><identifier>ISSN: 0731-7085</identifier><identifier>EISSN: 1873-264X</identifier><identifier>DOI: 10.1016/j.jpba.2020.113262</identifier><identifier>PMID: 32222648</identifier><language>eng</language><publisher>England: Elsevier B.V</publisher><subject>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</subject><ispartof>Journal of pharmaceutical and biomedical analysis, 2020-06, Vol.185, p.113262-113262, Article 113262</ispartof><rights>2020 Elsevier B.V.</rights><rights>Copyright © 2020 Elsevier B.V. All rights reserved.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c356t-6fa3a90fe86d70406f8c35d566f59578404335bdc0190469c37ede581ca8e5703</citedby><cites>FETCH-LOGICAL-c356t-6fa3a90fe86d70406f8c35d566f59578404335bdc0190469c37ede581ca8e5703</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.jpba.2020.113262$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32222648$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Zhou, Wei</creatorcontrib><creatorcontrib>Hong, Yanli</creatorcontrib><creatorcontrib>Yin, Ailing</creatorcontrib><creatorcontrib>Liu, Shijia</creatorcontrib><creatorcontrib>Chen, Minmin</creatorcontrib><creatorcontrib>Lv, Xifeng</creatorcontrib><creatorcontrib>Nie, Xiaowei</creatorcontrib><creatorcontrib>Tan, Ninghua</creatorcontrib><creatorcontrib>Zhang, Zhihao</creatorcontrib><title>Non-invasive urinary metabolomics reveals metabolic profiling of polycystic ovary syndrome and its subtypes</title><title>Journal of pharmaceutical and biomedical analysis</title><addtitle>J Pharm Biomed Anal</addtitle><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.</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 & 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 & 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 ; Hong, Yanli ; Yin, Ailing ; Liu, Shijia ; Chen, Minmin ; Lv, Xifeng ; Nie, Xiaowei ; Tan, Ninghua ; Zhang, Zhihao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c356t-6fa3a90fe86d70406f8c35d566f59578404335bdc0190469c37ede581ca8e5703</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Adolescent</topic><topic>Adult</topic><topic>Androgens - blood</topic><topic>Biomarkers - metabolism</topic><topic>Biomarkers - urine</topic><topic>Citric Acid Cycle</topic><topic>Cross-Sectional Studies</topic><topic>Diagnosis, Differential</topic><topic>Dicarboxylic Acids - isolation & purification</topic><topic>Dicarboxylic Acids - metabolism</topic><topic>Dicarboxylic Acids - urine</topic><topic>Female</topic><topic>Gas Chromatography-Mass Spectrometry - methods</topic><topic>Glyoxylates - isolation & purification</topic><topic>Glyoxylates - metabolism</topic><topic>Glyoxylates - urine</topic><topic>Healthy Volunteers</topic><topic>Humans</topic><topic>Hyperandrogenism</topic><topic>Hyperandrogenism - blood</topic><topic>Hyperandrogenism - diagnosis</topic><topic>Hyperandrogenism - metabolism</topic><topic>Hyperandrogenism - urine</topic><topic>Insulin Resistance</topic><topic>Metabolic Networks and Pathways</topic><topic>Metabolomics</topic><topic>Metabolomics - methods</topic><topic>Non-invasive biomarkers</topic><topic>Polycystic ovary syndrome</topic><topic>Polycystic Ovary Syndrome - blood</topic><topic>Polycystic Ovary Syndrome - diagnosis</topic><topic>Polycystic Ovary Syndrome - metabolism</topic><topic>Polycystic Ovary Syndrome - urine</topic><topic>Predictive Value of Tests</topic><topic>ROC Curve</topic><topic>Tandem Mass Spectrometry - methods</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Zhou, Wei</creatorcontrib><creatorcontrib>Hong, Yanli</creatorcontrib><creatorcontrib>Yin, Ailing</creatorcontrib><creatorcontrib>Liu, Shijia</creatorcontrib><creatorcontrib>Chen, Minmin</creatorcontrib><creatorcontrib>Lv, Xifeng</creatorcontrib><creatorcontrib>Nie, Xiaowei</creatorcontrib><creatorcontrib>Tan, Ninghua</creatorcontrib><creatorcontrib>Zhang, Zhihao</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Journal of pharmaceutical and biomedical analysis</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Zhou, Wei</au><au>Hong, Yanli</au><au>Yin, Ailing</au><au>Liu, Shijia</au><au>Chen, Minmin</au><au>Lv, Xifeng</au><au>Nie, Xiaowei</au><au>Tan, Ninghua</au><au>Zhang, Zhihao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Non-invasive urinary metabolomics reveals metabolic profiling of polycystic ovary syndrome and its subtypes</atitle><jtitle>Journal of pharmaceutical and biomedical analysis</jtitle><addtitle>J Pharm Biomed Anal</addtitle><date>2020-06-05</date><risdate>2020</risdate><volume>185</volume><spage>113262</spage><epage>113262</epage><pages>113262-113262</pages><artnum>113262</artnum><issn>0731-7085</issn><eissn>1873-264X</eissn><abstract>•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.</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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