Metabolomic Profiling of Poor Ovarian Response Identifies Potential Predictive Biomarkers

ObjectiveTo characterize the serum metabolomic profile and its role in the prediction of poor ovarian response (POR).Patient(s)Twenty-five women with normal ovarian reserve (24-33 years, antral follicle count [AFC] ≥5, anti-Müllerian hormone [AMH] ≥1.2 ng/ml) as the control group and another twenty-...

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Autores principales:	Haixia Song, Qin Qin, Caixia Yuan, Hong Li, Fang Zhang, Lingling Fan
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	poor ovarian response (POR) ovarian reserve biomarkers nicotinate and nicotinamide metabolism pathway serum metabolomics Diseases of the endocrine glands. Clinical endocrinology RC648-665
Acceso en línea:	https://doaj.org/article/db84f9d2b9514a32966cafa9f8806032
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spelling	oai:doaj.org-article:db84f9d2b9514a32966cafa9f88060322021-11-30T12:06:56ZMetabolomic Profiling of Poor Ovarian Response Identifies Potential Predictive Biomarkers1664-239210.3389/fendo.2021.774667https://doaj.org/article/db84f9d2b9514a32966cafa9f88060322021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fendo.2021.774667/fullhttps://doaj.org/toc/1664-2392ObjectiveTo characterize the serum metabolomic profile and its role in the prediction of poor ovarian response (POR).Patient(s)Twenty-five women with normal ovarian reserve (24-33 years, antral follicle count [AFC] ≥5, anti-Müllerian hormone [AMH] ≥1.2 ng/ml) as the control group and another twenty-five women with POR (19-35 years, AFC <5, AMH < 1.2 ng/ml) as the study group were collected in our study. The serum levels of the women in both groups were determined from their whole blood by untargeted liquid chromatography–mass spectrometry (LC-MS). Multivariate statistical analysis and cell signal pathways analysis were used to reveal the results.ResultsA total of 538 different metabolites were finally identified in the two groups. Tetracosanoic acid, 2-arachidonoylglycerol, lidocaine, cortexolone, prostaglandin H2,1-naphthylamine, 5-hydroxymethyl-2-furancarboxaldehyde, 2,4-dinitrophenol, and D-erythrulose1-phosphate in POR were significantly different from control as were most important metabolites in support vector machines (p <0.05). Metabolomic profiling, together with support vector machines and pathway analysis found that the nicotinate and nicotinamide metabolism pathway, including L-aspartic acid, 6-hydroxynicotinate, maleic acid, and succinic acid semialdehyde, was identified to have significant differences in POR women compared to control women, which may be associated with ovarian reserve.ConclusionThis study indicated that LC–MS-based untargeted metabolomics analysis of serum provided biological markers for women with POR. The nicotinate and nicotinamide metabolism pathway may offer new insight into the complementary prediction and therapeutic potential of POR. The functional associations of these metabolites need further investigation.Haixia SongQin QinCaixia YuanHong LiFang ZhangLingling FanFrontiers Media S.A.articlepoor ovarian response (POR)ovarian reservebiomarkersnicotinate and nicotinamide metabolism pathwayserum metabolomicsDiseases of the endocrine glands. Clinical endocrinologyRC648-665ENFrontiers in Endocrinology, Vol 12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	poor ovarian response (POR) ovarian reserve biomarkers nicotinate and nicotinamide metabolism pathway serum metabolomics Diseases of the endocrine glands. Clinical endocrinology RC648-665
spellingShingle	poor ovarian response (POR) ovarian reserve biomarkers nicotinate and nicotinamide metabolism pathway serum metabolomics Diseases of the endocrine glands. Clinical endocrinology RC648-665 Haixia Song Qin Qin Caixia Yuan Hong Li Fang Zhang Lingling Fan Metabolomic Profiling of Poor Ovarian Response Identifies Potential Predictive Biomarkers
description	ObjectiveTo characterize the serum metabolomic profile and its role in the prediction of poor ovarian response (POR).Patient(s)Twenty-five women with normal ovarian reserve (24-33 years, antral follicle count [AFC] ≥5, anti-Müllerian hormone [AMH] ≥1.2 ng/ml) as the control group and another twenty-five women with POR (19-35 years, AFC <5, AMH < 1.2 ng/ml) as the study group were collected in our study. The serum levels of the women in both groups were determined from their whole blood by untargeted liquid chromatography–mass spectrometry (LC-MS). Multivariate statistical analysis and cell signal pathways analysis were used to reveal the results.ResultsA total of 538 different metabolites were finally identified in the two groups. Tetracosanoic acid, 2-arachidonoylglycerol, lidocaine, cortexolone, prostaglandin H2,1-naphthylamine, 5-hydroxymethyl-2-furancarboxaldehyde, 2,4-dinitrophenol, and D-erythrulose1-phosphate in POR were significantly different from control as were most important metabolites in support vector machines (p <0.05). Metabolomic profiling, together with support vector machines and pathway analysis found that the nicotinate and nicotinamide metabolism pathway, including L-aspartic acid, 6-hydroxynicotinate, maleic acid, and succinic acid semialdehyde, was identified to have significant differences in POR women compared to control women, which may be associated with ovarian reserve.ConclusionThis study indicated that LC–MS-based untargeted metabolomics analysis of serum provided biological markers for women with POR. The nicotinate and nicotinamide metabolism pathway may offer new insight into the complementary prediction and therapeutic potential of POR. The functional associations of these metabolites need further investigation.
format	article
author	Haixia Song Qin Qin Caixia Yuan Hong Li Fang Zhang Lingling Fan
author_facet	Haixia Song Qin Qin Caixia Yuan Hong Li Fang Zhang Lingling Fan
author_sort	Haixia Song
title	Metabolomic Profiling of Poor Ovarian Response Identifies Potential Predictive Biomarkers
title_short	Metabolomic Profiling of Poor Ovarian Response Identifies Potential Predictive Biomarkers
title_full	Metabolomic Profiling of Poor Ovarian Response Identifies Potential Predictive Biomarkers
title_fullStr	Metabolomic Profiling of Poor Ovarian Response Identifies Potential Predictive Biomarkers
title_full_unstemmed	Metabolomic Profiling of Poor Ovarian Response Identifies Potential Predictive Biomarkers
title_sort	metabolomic profiling of poor ovarian response identifies potential predictive biomarkers
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/db84f9d2b9514a32966cafa9f8806032
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_version_	1718406655897501696

Metabolomic Profiling of Poor Ovarian Response Identifies Potential Predictive Biomarkers

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