Download PDF
Horm Metab Res 2022; 54(09): 625-632
DOI: 10.1055/a-1882-3967
Original Article: Endocrine Research

Serum Metabolomic Signature Predicts Ovarian Response to Controlled Stimulation

Xin Mu  ‡
1   Center for Translational Medicine, Xi'an Jiaotong University Medical College First Affiliated Hospital, Xi'an, China
2   Assistant Reproductive Center, Northwest Women and Children's Hospital, Xi'an, China
,
Mei-li Pei‡
3   Department of Gynecology and Obstetrics, Xi'an Jiaotong University Medical College First Affiliated Hospital, Xi'an, China
,
Feng Zhu
1   Center for Translational Medicine, Xi'an Jiaotong University Medical College First Affiliated Hospital, Xi'an, China
,
Juan Zi Shi
2   Assistant Reproductive Center, Northwest Women and Children's Hospital, Xi'an, China
,
Peijun Liu
1   Center for Translational Medicine, Xi'an Jiaotong University Medical College First Affiliated Hospital, Xi'an, China
› Author Affiliations
› Further Information
Also available at
    Permissions and Reprints

Abstract

In in vitro fertilization (IVF), it is meaningful to find novel biomarkers predicting ovarian response in advance. The aim of the study was to identify serum metabolomics predicting ovarian response after controlled ovarian stimulation (COS). Blood samples collected at the start of pituitary downregulation and on the fifth day after COS using Liquid chromatography-tandem mass spectrometry (LC-MS/MS) methods were analyzed to quantify metabolites. Demographic data were calculated with SPSS version 22.0 software. Multivariate statistics were used to analyze metabolomics dataset. A receiver operating characteristic (ROC) curve was used to evaluate the diagnostic model. Analyses revealed 50 different metabolomics between the pre- and post-COS groups. Compared with baseline, amino acids increased significantly following COS. At baseline, acetylglycine was more abundant in FOI<1 group, while glycine and lipids increased in FOI≥1 group. After COS, glycine, N-acetyl-L-alanine, D-alanine, and 2-aminomuconic acid were higher in those with FOI≥1, but L-glutamine was abundant in FOI<1. ROC curves indicated that combination of glycine, acetylglycine, and lipids predicts different responses to COS (AUC=0.866). Serum metabolism might reflect the response to ovarian stimulation. Higher glycine and PC may be a good predictor for response to COS.

Key words

serum metabolomics - response - COS


Publication History

Received: 20 March 2022

Accepted after revision: 22 June 2022

Accepted Manuscript online:
22 June 2022

Article published online:
07 September 2022

© 2022. Thieme. All rights reserved.

Georg Thieme Verlag
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Ozkan ZS. Ovarian stimulation modalities in poor responders. Turk J Med Sci 2019; 49: 959-962
    CrossrefPubMedGoogle Scholar
  • 2 Keay SD. Poor ovarian response to gonadotrophin stimulation the role of adjuvant treatments. Hum Fertil (Camb) 2002; 5: S46-S52
    CrossrefPubMedGoogle Scholar
  • 3 Ubaldi F, Vaiarelli A, D'Anna R. et al. Management of poor responders in IVF: is there anything new?. Biomed Res Int 2014; 352098
    PubMedGoogle Scholar
  • 4 La Marca A, Sunkara SK. Individualization of controlled ovarian stimulation in IVF using ovarian reserve markers: from theory to practice. Hum Reprod Update 2014; 20: 124-140
    CrossrefPubMedGoogle Scholar
  • 5 Genro VK, Grynberg M, Scheffer JB. et al. Serum anti-Mullerian hormone levels are negatively related to Follicular Output RaTe (FORT) in normo-cycling women undergoing controlled ovarian hyperstimulation. Hum Reprod 2011; 26: 671-677
    CrossrefPubMedGoogle Scholar
  • 6 Alviggi C, Conforti A, Esteves SC. et al. Understanding ovarian hypo-response to exogenous gonadotropin in ovarian stimulation and its new proposed marker – the follicle-to-oocyte (FOI) index. Front Endocrinol (Lausanne) 2018; 9: 589
    CrossrefPubMedGoogle Scholar
  • 7 Karaer A, Tuncay G, Mumcu A. et al. Metabolomics analysis of follicular fluid in women with ovarian endometriosis undergoing in vitro fertilization. Syst Biol Reprod Med 2019; 65: 39-47
    CrossrefPubMedGoogle Scholar
  • 8 Zhang Y, Liu L, Yin TL. et al. Follicular metabolic changes and effects on oocyte quality in polycystic ovary syndrome patients. Oncotarget 2017; 8: 80472-80480
    CrossrefPubMedGoogle Scholar
  • 9 Luti S, Fiaschi T, Magherini F. et al. Relationship between the metabolic and lipid profile in follicular fluid of women undergoing in vitro fertilization. Mol Reprod Dev 2020; 87: 986997
    CrossrefPubMedGoogle Scholar
  • 10 Zhao Y, Fu L, Li R. et al. Metabolic profiles characterizing different phenotypes of polycystic ovary syndrome: plasma metabolomics analysis. BMC Med 2012; 10: 153
    CrossrefPubMedGoogle Scholar
  • 11 Chen L, Wang H, Zhou H. et al. Follicular output rate and follicle-to-oocyte index of low prognosis patients according to POSEIDON criteria: a retrospective cohort study of 32,128 treatment cycles. Front Endocrinol (Lausanne) 2020; 11: 181
    CrossrefPubMedGoogle Scholar
  • 12 Gallot V, Berwanger da Silva AL, Genro V. et al. Antral follicle responsiveness to follicle-stimulating hormone administration assessed by the Follicular Output RaTe (FORT) may predict in vitro fertilization-embryo transfer outcome. Hum Reprod 2012; 27: 1066-1072
    CrossrefPubMedGoogle Scholar
  • 13 Borges E, Montani DA, Setti AS. et al. Serum metabolites as predictive molecular markers of ovarian response to controlled stimulation: a pilot study. JBRA Assist Reprod 2019; 23: 323-327
    PubMedGoogle Scholar
  • 14 Razak MA, Begum PS, Viswanath B. et al. Multifarious beneficial effect of nonessential amino acid, glycine: a review. Oxid Med Cell Longev 2017; 1716701
    PubMedGoogle Scholar
  • 15 Lewis RM, Godfrey KM, Jackson AA. et al. Low serine hydroxymethyltransferase activity in the human placenta has important implications for fetal glycine supply. J Clin Endocrinol Metab 2005; 90: 1594-1598
    CrossrefPubMedGoogle Scholar
  • 16 Guasch-Ferre M, Hruby A, Toledo E. et al. Metabolomics in prediabetes and diabetes: a systematic review and meta-analysis. Diabetes Care 2016; 39: 833-846
    CrossrefPubMedGoogle Scholar
  • 17 Adeva-Andany M, Souto-Adeva G, Ameneiros-Rodriguez E. et al. Insulin resistance and glycine metabolism in humans. Amino Acids 2018; 50: 11-27
    CrossrefPubMedGoogle Scholar
  • 18 Li X, Sun L, Zhang W. et al. Association of serum glycine levels with metabolic syndrome in an elderly Chinese population. Nutr Metab (Lond) 2018; 15: 89
    CrossrefPubMedGoogle Scholar
  • 19 Sutton-McDowall ML, Gilchrist RB, Thompson JG. The pivotal role of glucose metabolism in determining oocyte developmental competence. Reproduction 2010; 139: 685-695
    CrossrefPubMedGoogle Scholar
  • 20 Warzych E, Lipinska P. Energy metabolism of follicular environment during oocyte growth and maturation. J Reprod Dev 2020; 66: 1-7
    CrossrefPubMedGoogle Scholar
  • 21 Yaribeygi H, Farrokhi FR, Butler AE. et al. Insulin resistance: review of the underlying molecular mechanisms. J Cell Physiol 2019; 234: 8152-8161
    CrossrefPubMedGoogle Scholar
  • 22 Perry RJ, Samuel VT, Petersen KF. et al. The role of hepatic lipids in hepatic insulin resistance and type 2 diabetes. Nature 2014; 510: 84-91
    CrossrefPubMedGoogle Scholar
  • 23 Baumgarten SC, Armouti M, Ko C. et al. IGF1R Expression in ovarian granulosa cells is essential for steroidogenesis, follicle survival, and fertility in female mice. Endocrinology 2017; 158: 2309-2318
    CrossrefPubMedGoogle Scholar
  • 24 Chahal N, Geethadevi A, Kaur S. et al. Direct impact of gonadotropins on glucose uptake and storage in preovulatory granulosa cells: implications in the pathogenesis of polycystic ovary syndrome. Metabolism 2021; 115: 154458
    CrossrefPubMedGoogle Scholar
  • 25 Cataldi T, Cordeiro FB, Costa Ldo V. et al. Lipid profiling of follicular fluid from women undergoing IVF: young poor ovarian responders versus normal responders. Hum Fertil (Camb) 2013; 16: 269-277
    CrossrefPubMedGoogle Scholar
  • 26 Cordeiro FB, Cataldi TR, do Vale Teixeira da Costa L. et al. Follicular fluid lipid fingerprinting from women with PCOS and hyper response during IVF treatment. J Assist Reprod Genet 2015; 32: 45-54
    CrossrefPubMedGoogle Scholar
  • 27 Montani DA, Cordeiro FB, Regiani T. The follicular microenviroment as a predictor of pregnancy: MALDI-TOF MS lipid profile in cumulus cells. J Assist Reprod Genet 2012; 29: 1289-1297
    CrossrefPubMedGoogle Scholar
  • 28 Cordeiro FB, Cataldi TR, de Souza BZ. et al. Hyper response to ovarian stimulation affects the follicular fluid metabolomic profile of women undergoing IVF similarly to polycystic ovary syndrome. Metabolomics 2018; 14: 51
    CrossrefPubMedGoogle Scholar
  • 29 Kristensen SG, Mamsen LS, Jeppesen JV. et al. Hallmarks of human small antral follicle development: implications for regulation of ovarian steroidogenesis and selection of the dominant follicle. Front Endocrinol (Lausanne) 2017; 8: 376
    CrossrefPubMedGoogle Scholar
  • 30 Marchiani S, Tamburrino L, Benini F. et al. LH supplementation of ovarian stimulation protocols influences follicular fluid steroid composition contributing to the improvement of ovarian response in poor responder women. Sci Rep 2020; 10: 12907
    CrossrefPubMedGoogle Scholar