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Serum metabolomics study of polycystic ovary syndrome based on UPLC-QTOF-MS coupled with a pattern recognition approach

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Abstract

Metabolomics has become an important tool in distinguishing changes in metabolic pathways and the diagnosis of human disease. Polycystic ovary syndrome (PCOS) is a relatively complicated, heterogeneous endocrine disorder. The etiology and pathogenesis of PCOS remain uncertain. In this study, based on the platform of ultra performance liquid chromatography tandem quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) and the method of pattern recognition, a comprehensive metabolomics approach has been applied to explore the changes in metabolic profiling between PCOS patients (n = 20) and controls (n = 15) as well as insulin-resistance (IR) PCOS patients (n = 11) and non-IR PCOS subjects (n = 9) in serum. In total, 36 metabolites were found significantly different between PCOS and controls, and 9 metabolites were discovered significantly different between IR and non-IR PCOS patients. Significant increases in the levels of saturated and unsaturated fatty acids (myristic acid, linoleic acid, 9-/13-HODE, etc.), fatty amides (palmitic amide, oleamide), dehydroepiandrosterone sulfate, l-glutamic acid, azelaic acid, l-glyceric acid, pyroglutamic acid, and decreases in the levels of lysophosphatidylethanolamine, lysophosphatidylcholine, uridine, and l-carnitine were found in PCOS patients compared with controls. In IR PCOS patients, linoleic acid, myristic acid, palmitoleic acid, and vaccenic acid also increased significantly compared with non-IR PCOS patients. All these changed metabolites showed abnormalities of steroid hormone biosynthesis, amino acids and nucleosides metabolism, glutathione metabolism, and lipids and carbohydrates metabolism in PCOS patients. The subgroup IR PCOS patients exhibited greater metabolic deviations than non-IR PCOS patients. These findings may help yield promising insights into the pathogenesis and advance the diagnosis and prevention of PCOS.

Serum metabolomics signature of polycystic ovary syndrome

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Acknowledgments

This work was supported by the National Key Clinical Specialties Construction Program of China (Grant No. 2011 (170)), the Natural Science Foundation Project of CQ (Grant No. CSTC2013jjB10019), and the National Natural Science Foundation of China (Grant No. 81371904). The present study was completed successfully with the help of Medical Examination Centre & Department of Endocrinology of The First Affiliated Hospital of Chongqing Medical University, and Department of Life Science of Chongqing Medical University.

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The authors declare no conflict of interest.

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    Authors and Affiliations

    1. Medical Examination Centre, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China

      Fang Dong, Heng Chen, Rong Luo & Shijia Ding

    2. Department of Medical Statistics, School of Public Health, Chongqing Medical University, Chongqing, 400016, China

      Dan Deng

    3. Molecular Oncology and Epigenetics Laboratory, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China

      Wei Cheng

    4. Department of Endocrinology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China

      Fang Dong, Heng Chen, Qifu Li & Rong Luo

    5. Key Laboratory of Clinical Laboratory Diagnostics (Ministry of Education), College of Laboratory Medicine, Chongqing Medical University, Chongqing, 400016, China

      Shijia Ding

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    1. Fang Dong
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    Correspondence to Rong Luo or Shijia Ding.

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    Fang Dong and Dan Deng contributed equally to this work.

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    Dong, F., Deng, D., Chen, H. et al. Serum metabolomics study of polycystic ovary syndrome based on UPLC-QTOF-MS coupled with a pattern recognition approach. Anal Bioanal Chem 407, 4683–4695 (2015). https://doi.org/10.1007/s00216-015-8670-x

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