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Chronically elevated androgen and/or consumption of a Western-style diet impairs oocyte quality and granulosa cell function in the nonhuman primate periovulatory follicle

  • Cecily V. BishopEmail author
  • Taylor E. Reiter
  • David W. Erikson
  • Carol B. Hanna
  • Brittany L. Daughtry
  • Shawn L. Chavez
  • Jon D. Hennebold
  • Richard L. Stouffer
Reproductive Physiology and Disease

Abstract

Purpose

To investigate the impact of chronically elevated androgens in the presence and absence of an obesogenic diet on oocyte quality in the naturally selected primate periovulatory follicle.

Methods

Rhesus macaques were treated using a 2-by-2 factorial design (n = 10/treatment) near the onset of menarche with implants containing either cholesterol (C) or testosterone (T, 4–5-fold increase above C) and a standard or “Western-style” diet alone (WSD) or in combination (T+WSD). Following ~ 3.5 years of treatment, females underwent controlled ovulation (COv, n = 7–10/treatment) cycles, and contents of the naturally selected periovulatory follicle were aspirated. Follicular fluid (FF) was analyzed for cytokines, chemokines, growth factors, and steroids. RNA was extracted from luteinizing granulosa cells (LGCs) and assessed by RNA-seq.

Results

Only healthy, metaphase (M) I/II-stage oocytes (100%) were retrieved in the C group, whereas several degenerated oocytes were recovered in other groups (33–43% of T, WSD, and T+WSD samples). Levels of two chemokines and one growth factor were reduced (p < 0.04) in FF of follicles with a MI/MII oocyte in WSD+T (CCL11) or T and WSD+T groups (CCL2 and FGF2) compared to C and/or WSD. Intrafollicular cortisol was elevated in T compared to C follicles (p < 0.02). Changes in the expression pattern of 640+ gene products were detected in LGC samples from follicles with degenerated versus MI/MII-stage oocytes. Pathway analysis on RNAs altered by T and/or WSD found enrichment of genes mapping to steroidogenic and immune cell pathways.

Conclusions

Female primates experiencing hyperandrogenemia and/or consuming a WSD exhibit an altered intrafollicular microenvironment and reduced oocyte quality/competency, despite displaying menstrual cyclicity.

Keywords

Androgen Follicular cytokines Nonhuman primate Oocyte quality Periovulatory follicle Western-style diet 

Notes

Acknowledgments

The outstanding efforts of the NCTRI NHP Core members Emily Mishler, M.S., Corrine Wilcox, B.S., Kise Bond, P.S.M., and Andrea Calhoun, M.S. under the direction of Ov Slayden, Ph.D. as well as Diana Takahashi, M.S., were critical for the successful execution of all protocols and are greatly appreciated. In addition, the efforts of the staff of the ONPRC Endocrine Technology Support Core contributed to the success of this project. RNA sequencing was performed by the OHSU Massively Parallel Sequencing Shared Resource. Analysis of the RNA-seq data was performed by the ONPRC’s Biostatistics & Bioinformatics Core under the direction of Lucia Carbone, Ph.D. and Suzanne Fei, Ph.D. with statistical analyses performed by Lina Gao, Ph.D. and Byung Park, Ph.D. We greatly appreciate the core’s analysis pipelines that utilize OHSU’s Exacloud compute cluster.

Funding

Research reported in this publication was supported by the Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD) of the National Institutes of Health (NIH) under Award Number P50HD071836 (to RLS). Additional funding was provided by NIH Award Number P51OD011092 (Support for National Primate Research Center and Cores).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures involving rhesus monkeys were reviewed and approved by the ONPRC/Oregon Health and Science University (OHSU) Institutional Animal Care and Use Committee (IACUC) in accordance with the U.S. Public Health Service (PHS) Policy on Humane Care and Use of Laboratory Animals.

Disclaimer

The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Supplementary material

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Supplemental Figure 1:

Examples of estradiol (E2) and progesterone (P4) patterns and oocyte/ fertilization outcomes from several T-treated females. E2 and P4 patterns were not associated with oocyte stage or fertilization success. A premature rise of P4 >0.5ng/ml before hCG administration (orange arrow, bottom graph and image) was associated with follicle rupture (ovulated). (PNG 2249 kb)

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Supplemental Figure 2:

Additional Venn Diagram Analyses comparing all pairwise comparisons from LGC transcriptome analyses. Numbers of RNAs in each contrast and overlap between C vs T (yellow ellipse), C vs WSD (red ellipse) C vs T+WSD (purple ellipse), T vs T+WSD (green ellipse) and WSD vs T+WSD (blue ellipse). Panel A) All RNAs in dataset. Panel B) Only up-regulated RNAs in pairwise comparisons. Panel C) Only down-regulated RNAs in pairwise comparisons. See Methods for details of statistical analyses and Table 7 for total RNAs significantly up-regulated and down-regulated identified by each contrast. (PNG 807 kb)

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Supplemental Figure 3:

The Top Regulator Effect Networks identified by IPA for pairwise comparison C vs T (Supplemental Table 4). Genes in top row are transcription factors by which changes in activity could lead to the observed changes in gene product expression in the center row. Processes identified in the bottom row are the predicted cellular outcomes of the observed changes to gene expression. Red colors indicate activated and green indicate inhibited molecules, while orange/yellow lines indicate direction of predicted activation and blue lines indicate directed of predicted inhibition (dashed lines indicate lower level of confidence for predictions, grey lines indicate those predictions that do not reach statistical significance). (PNG 1862 kb)

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Supplemental Figure 4:

The Top Regulator Effect Networks identified by IPA for pairwise comparison C vs WSD (Supplemental Table 4). Genes in top row are transcription factors by which changes in activity could lead to the observed changes in gene product expression in the center row. Processes identified in the bottom row are the predicted cellular outcomes of the observed changes to gene expression. Red colors indicate activated and green indicate inhibited molecules, while orange/yellow lines indicate direction of predicted activation and blue lines indicate directed of predicted inhibition (dashed lines indicate lower level of confidence for predictions, grey lines indicate those predictions that do not reach statistical significance). (PNG 3593 kb)

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Supplemental Figure 5:

The Top Regulator Effect Networks identified by IPA for pairwise comparisons C vs T+WSD (Panel A) and T vs T+WSD (Panel B; Supplemental Table 4). Genes in top row are transcription factors by which changes in activity could lead to the observed changes in gene product expression in the center row. Processes identified in the bottom row are the predicted cellular outcomes of the observed changes to gene expression. Red colors indicate activated and green indicate inhibited molecules, while orange/yellow lines indicate direction of predicted activation and blue lines indicate directed of predicted inhibition (dashed lines indicate lower level of confidence for predictions, grey lines indicate those predictions that do not reach statistical significance). (PNG 1220 kb)

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References

  1. 1.
    Rosenfield RL, Ehrmann DA. The pathogenesis of polycystic ovary syndrome (PCOS): the hypothesis of PCOS as functional ovarian hyperandrogenism revisited. Endocr Rev. 2016;37(5):467–520.  https://doi.org/10.1210/er.2015-1104.CrossRefGoogle Scholar
  2. 2.
    De Leo V, Musacchio MC, Cappelli V, Massaro MG, Morgante G, Petraglia F. Genetic, hormonal and metabolic aspects of PCOS: an update. Reprod Biol Endocrinol. 2016;14(1):38.  https://doi.org/10.1186/s12958-016-0173-x.CrossRefGoogle Scholar
  3. 3.
    Huang CC, Tien YJ, Chen MJ, Chen CH, Ho HN, Yang YS. Symptom patterns and phenotypic subgrouping of women with polycystic ovary syndrome: association between endocrine characteristics and metabolic aberrations. Hum Reprod. 2015;30(4):937–46.  https://doi.org/10.1093/humrep/dev010.CrossRefGoogle Scholar
  4. 4.
    Provost MP, Acharya KS, Acharya CR, Yeh JS, Steward RG, Eaton JL, et al. Pregnancy outcomes decline with increasing body mass index: analysis of 239,127 fresh autologous in vitro fertilization cycles from the 2008-2010 Society for Assisted Reproductive Technology registry. Fertil Steril. 2016;105(3):663–9.  https://doi.org/10.1016/j.fertnstert.2015.11.008.
  5. 5.
    Wood JR, Dumesic DA, Abbott DH, Strauss JF 3rd. Molecular abnormalities in oocytes from women with polycystic ovary syndrome revealed by microarray analysis. J Clin Endocrinol Metab. 2007;92(2):705–13.  https://doi.org/10.1210/jc.2006-2123.CrossRefGoogle Scholar
  6. 6.
    Palomba S, Daolio J, La Sala GB. Oocyte competence in women with polycystic ovary syndrome. Trends Endocrinol Metab. 2017;28(3):186–98.  https://doi.org/10.1016/j.tem.2016.11.008.CrossRefGoogle Scholar
  7. 7.
    Murray AA, Swales AKE, Smith RE, Molinek MD, Hillier SG, Spears N. Follicular growth and oocyte competence in the in vitro cultured mouse follicle: effects of gonadotrophins and steroids. MHR: Basic Science of Reproductive Medicine" with: Mol Hum Reprod. 2008;14(2):75–83.  https://doi.org/10.1093/molehr/gam092.CrossRefGoogle Scholar
  8. 8.
    Franks S, Hardy K. Androgen action in the ovary. Front Endocrinol. 2018;9:452.  https://doi.org/10.3389/fendo.2018.00452.CrossRefGoogle Scholar
  9. 9.
    Rodrigues JK, Navarro PA, Zelinski MB, Stouffer RL, Xu J. Direct actions of androgens on the survival, growth and secretion of steroids and anti-Mullerian hormone by individual macaque follicles during three-dimensional culture. Hum Reprod. 2015;30(3):664–74.  https://doi.org/10.1093/humrep/deu335.CrossRefGoogle Scholar
  10. 10.
    Reynolds KA, Boudoures AL, Chi MM, Wang Q, Moley KH. Adverse effects of obesity and/or high-fat diet on oocyte quality and metabolism are not reversible with resumption of regular diet in mice. Reprod Fertil Dev. 2015;27(4):716–24.  https://doi.org/10.1071/RD14251.CrossRefGoogle Scholar
  11. 11.
    Tamer Erel C, Senturk LM. The impact of body mass index on assisted reproduction. Curr Opin Obstet Gynecol. 2009;21(3):228–35.  https://doi.org/10.1097/GCO.0b013e32832aee96.CrossRefGoogle Scholar
  12. 12.
    Vembu R, Reddy NS. Serum AMH level to predict the hyper response in women with PCOS and non-PCOS undergoing controlled ovarian stimulation in ART. J Hum Reprod Sci. 2017;10(2):91–4.  https://doi.org/10.4103/jhrs.JHRS_15_16.
  13. 13.
    Zaadstra BM, Seidell JC, Van Noord PA, te Velde ER, Habbema JD, Vrieswijk B, et al. Fat and female fecundity: prospective study of effect of body fat distribution on conception rates. BMJ. (Clinical research ed). 1993;306(6876):484–7.CrossRefGoogle Scholar
  14. 14.
    Pantasri T, Norman RJ. The effects of being overweight and obese on female reproduction: a review. Gynecol Endocrinol. 2014;30(2):90–4.  https://doi.org/10.3109/09513590.2013.850660.CrossRefGoogle Scholar
  15. 15.
    True CA, Takahashi DL, Burns SE, Mishler EC, Bond KR, Wilcox MC, et al. Chronic combined hyperandrogenemia and western-style diet in young female rhesus macaques causes greater metabolic impairments compared to either treatment alone. Hum Reprod. 2017;32(9):1880–91.  https://doi.org/10.1093/humrep/dex246.
  16. 16.
    Bishop CV, Mishler EC, Takahashi DL, Reiter TE, Bond KR, True CA, et al. Chronic hyperandrogenemia in the presence and absence of a western-style diet impairs ovarian and uterine structure/function in young adult rhesus monkeys. Hum Reprod. 2018;33(1):128–39.  https://doi.org/10.1093/humrep/dex338.
  17. 17.
    Bishop CV, Hennebold JD, Kahl CA, Stouffer RL. Knockdown of progesterone receptor (PGR) in macaque granulosa cells disrupts ovulation and progesterone production. Biol Reprod. 2016;94(5):109.  https://doi.org/10.1095/biolreprod.115.134981.CrossRefGoogle Scholar
  18. 18.
    Wolf DP, Thormahlen S, Ramsey C, Yeoman RR, Fanton J, Mitalipov S. Use of assisted reproductive technologies in the propagation of rhesus macaque offspring. Biol Reprod. 2004;71(2):486–93.  https://doi.org/10.1095/biolreprod.103.025932.CrossRefGoogle Scholar
  19. 19.
    Hanna CB, Yao S, Ramsey CM, Hennebold JD, Zelinski MB, Jensen JT. Phosphodiesterase 3 (PDE3) inhibition with cilostazol does not block in vivo oocyte maturation in rhesus macaques (Macaca mulatta). Contraception. 2015;91:418–22.CrossRefGoogle Scholar
  20. 20.
    Daughtry BL, Chavez SL. Time-lapse imaging for the detection of chromosomal abnormalities in primate preimplantation embryos. Methods Mol Biol (Clifton, NJ). 2018;1769:293–317.  https://doi.org/10.1007/978-1-4939-7780-2_19.CrossRefGoogle Scholar
  21. 21.
    Andrews S. FastQC: a quality control tool for high throughput sequence data. 2010.Google Scholar
  22. 22.
    Ewels P, Magnusson M, Lundin S, Kaller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016;32(19):3047–8.  https://doi.org/10.1093/bioinformatics/btw354.CrossRefGoogle Scholar
  23. 23.
    Bimber B. DISCVR-Seq: LabKey Server Extensions for Management and Analysis of Sequencing Data., at <https://github.com/bbimber/discvr-seq/wiki>. 2015. at <https://github.com/bbimber/discvr-seq/wiki>.
  24. 24.
    Nelson EK, Piehler B, Eckels J, Rauch A, Bellew M, Hussey P, et al. LabKey server: an open source platform for scientific data integration, analysis and collaboration. BMC Bioinformatics. 2011;12(1):71.  https://doi.org/10.1186/1471-2105-12-71.
  25. 25.
    Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30(15):2114–20.  https://doi.org/10.1093/bioinformatics/btu170.CrossRefGoogle Scholar
  26. 26.
    Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29(1):15–21.Google Scholar
  27. 27.
    Engstrom PG, Steijger T, Sipos B, Grant GR, Kahles A, Ratsch G, et al. Systematic evaluation of spliced alignment programs for RNA-seq data. Nat Methods. 2013;10(12):1185–91.  https://doi.org/10.1038/nmeth.2722.CrossRefGoogle Scholar
  28. 28.
    DeLuca DS, Levin JZ, Sivachenko A, Fennell T, Nazaire MD, Williams C, et al. RNA-SeQC: RNA-seq metrics for quality control and process optimization. Bioinformatics. 2012;28(11):1530–2.  https://doi.org/10.1093/bioinformatics/bts196.CrossRefGoogle Scholar
  29. 29.
    Team RC. R: a language and environment for statistical computing, vol. 2014. Vienna: R Foundation for Statistical Computing; 2014.Google Scholar
  30. 30.
    Robinson MD, Oshlack A. A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol. 2010;11(3):R25.  https://doi.org/10.1186/gb-2010-11-3-r25.CrossRefGoogle Scholar
  31. 31.
    Law CW, Chen Y, Shi W, Smyth GK. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 2014;15(2):R29.  https://doi.org/10.1186/gb-2014-15-2-r29.CrossRefGoogle Scholar
  32. 32.
    Liu R, Holik AZ, Su S, Jansz N, Chen K, Leong HS, et al. Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses. Nucleic Acids Res. 2015;43(15):e97.  https://doi.org/10.1093/nar/gkv412.
  33. 33.
    Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47.  https://doi.org/10.1093/nar/gkv007.
  34. 34.
    Heberle H, Meirelles GV, da Silva FR, Telles GP, Minghim R. InteractiVenn: a web-based tool for the analysis of sets through Venn diagrams. BMC Bioinformatics. 2015;16:169.  https://doi.org/10.1186/s12859-015-0611-3.CrossRefGoogle Scholar
  35. 35.
    Kramer A, Green J, Pollard J Jr, Tugendreich S. Causal analysis approaches in ingenuity pathway analysis. Bioinformatics. 2014;30(4):523–30.  https://doi.org/10.1093/bioinformatics/btt703.CrossRefGoogle Scholar
  36. 36.
    Sanders SL, Stouffer RL. Localization of steroidogenic enzymes in macaque luteal tissue during the menstrual cycle and simulated early pregnancy: immunohistochemical evidence supporting the two-cell model for estrogen production in the primate corpus luteum. Biol Reprod. 1997;56(5):1077–87.CrossRefGoogle Scholar
  37. 37.
    Hariri N, Thibault L. High-fat diet-induced obesity in animal models. Nutr Res Rev. 2010;23(2):270–99.  https://doi.org/10.1017/S0954422410000168.CrossRefGoogle Scholar
  38. 38.
    Odermatt A. The Western-style diet: a major risk factor for impaired kidney function and chronic kidney disease. Am J Physiol Renal Physiol. 2011;301(5):F919–31.  https://doi.org/10.1152/ajprenal.00068.2011.CrossRefGoogle Scholar
  39. 39.
    Xu F, Stouffer RL, Muller J, Hennebold JD, Wright JW, Bahar A, et al. Dynamics of the transcriptome in the primate ovulatory follicle. Mol Hum Reprod. 2011;17(3):152–65.  https://doi.org/10.1093/molehr/gaq089.CrossRefGoogle Scholar
  40. 40.
    Xu F, Stouffer RL. Local delivery of angiopoietin-2 into the preovulatory follicle terminates the menstrual cycle in rhesus monkeys. Biol Reprod. 2005;72(6):1352–8.  https://doi.org/10.1095/biolreprod.104.037143.CrossRefGoogle Scholar
  41. 41.
    Bishop CV, Xu F, Xu J, Ting AY, Galbreath E, McGee WK, et al. Western-style diet, with and without chronic androgen treatment, alters the number, structure, and function of small antral follicles in ovaries of young adult monkeys. Fertil Steril. 2016;105(4):1023–34.  https://doi.org/10.1016/j.fertnstert.2015.11.045.CrossRefGoogle Scholar
  42. 42.
    Ting AY, Xu J, Stouffer RL. Differential effects of estrogen and progesterone on development of primate secondary follicles in a steroid-depleted milieu in vitro. Hum Reprod. 2015;30(8):1907–17.  https://doi.org/10.1093/humrep/dev119.CrossRefGoogle Scholar
  43. 43.
    Young KA, Chaffin CL, Molskness TA, Stouffer RL. Controlled ovulation of the dominant follicle: a critical role for LH in the late follicular phase of the menstrual cycle. Hum Reprod. 2003;18(11):2257–63.CrossRefGoogle Scholar
  44. 44.
    Stouffer RL, Xu F, Duffy DM. Molecular control of ovulation and luteinization in the primate follicle. Front Biosci. 2007;12:297–307.CrossRefGoogle Scholar
  45. 45.
    Daughtry BL, Rosenkrantz JL, Lazar NH, Fei SS, Redmayne N, Torkenczy KA, et al. Single-cell sequencing of primate preimplantation embryos reveals chromosome elimination via cellular fragmentation and blastomere exclusion. Genome Res. 2019;29:367–82.  https://doi.org/10.1101/gr.239830.118.
  46. 46.
    Xiong YL, Liang XY, Yang X, Li Y, Wei LN. Low-grade chronic inflammation in the peripheral blood and ovaries of women with polycystic ovarian syndrome. Eur J Obstet Gynecol Reprod Biol. 2011;159(1):148–50.  https://doi.org/10.1016/j.ejogrb.2011.07.012.CrossRefGoogle Scholar
  47. 47.
    Qin L, Xu W, Li X, Meng W, Hu L, Luo Z, et al. Differential expression profile of immunological cytokines in local ovary in patients with polycystic ovarian syndrome: analysis by flow cytometry. Eur J Obstet Gynecol Reprod Biol. 2016;197:136–41.  https://doi.org/10.1016/j.ejogrb.2015.12.003.
  48. 48.
    Robker RL, Wu LL, Yang X. Inflammatory pathways linking obesity and ovarian dysfunction. J Reprod Immunol. 2011;88(2):142–8.  https://doi.org/10.1016/j.jri.2011.01.008.CrossRefGoogle Scholar
  49. 49.
    Roth LW, McCallie B, Alvero R, Schoolcraft WB, Minjarez D, Katz-Jaffe MG. Altered microRNA and gene expression in the follicular fluid of women with polycystic ovary syndrome. J Assist Reprod Genet. 2014;31(3):355–62.  https://doi.org/10.1007/s10815-013-0161-4.CrossRefGoogle Scholar
  50. 50.
    Malizia BA, Wook YS, Penzias AS, Usheva A. The human ovarian follicular fluid level of interleukin-8 is associated with follicular size and patient age. Fertil Steril. 2010;93(2):537–43.  https://doi.org/10.1016/j.fertnstert.2008.11.033.CrossRefGoogle Scholar
  51. 51.
    Ebejer K, Calleja-Agius J. The role of cytokines in polycystic ovarian syndrome. Gynecol Endocrinol. 2013;29(6):536–40.  https://doi.org/10.3109/09513590.2012.760195.CrossRefGoogle Scholar
  52. 52.
    Dumesic DA, Schramm RD, Bird IM, Peterson E, Paprocki AM, Zhou R, et al. Reduced intrafollicular androstenedione and estradiol levels in early-treated prenatally androgenized female rhesus monkeys receiving follicle-stimulating hormone therapy for in vitro fertilization. Biol Reprod. 2003;69(4):1213–9.  https://doi.org/10.1095/biolreprod.102.015164.CrossRefGoogle Scholar
  53. 53.
    Naessen T, Kushnir MM, Chaika A, Nosenko J, Mogilevkina I, Rockwood AL, et al. Steroid profiles in ovarian follicular fluid in women with and without polycystic ovary syndrome, analyzed by liquid chromatography-tandem mass spectrometry. Fertil Steril. 2010;94(6):2228–33.  https://doi.org/10.1016/j.fertnstert.2009.12.081.
  54. 54.
    Michael AE, Glenn C, Wood PJ, Webb RJ, Pellatt L, Mason HD. Ovarian 11beta-hydroxysteroid dehydrogenase (11betaHSD) activity is suppressed in women with anovulatory polycystic ovary syndrome (PCOS): apparent role for ovarian androgens. J Clin Endocrinol Metab. 2013;98(8):3375–83.  https://doi.org/10.1210/jc.2013-1385.CrossRefGoogle Scholar
  55. 55.
    Bishop CV, Stouffer RL, Takahashi DL, Mishler EC, Wilcox MC, Slayden OD, et al. Chronic hyperandrogenemia and western-style diet beginning at puberty reduces fertility and increases metabolic dysfunction during pregnancy in young adult, female macaques. Hum Reprod. 2018;33(4):694–705.  https://doi.org/10.1093/humrep/dey013.
  56. 56.
    Kuo K, Roberts VHJ, Gaffney J, Takahashi DL, Morgan T, Lo JO, et al. Maternal high fat diet and chronic hyperandrogenemia are associated with placental dysfunction in female rhesus macaques. Endocrinology. 2019; In Press.Google Scholar
  57. 57.
    Palomba S, Falbo A, Chiossi G, Tolino A, Tucci L, La Sala GB, et al. Early trophoblast invasion and placentation in women with different PCOS phenotypes. Reprod BioMed Online. 2014;29(3):370–81.  https://doi.org/10.1016/j.rbmo.2014.04.010.CrossRefGoogle Scholar
  58. 58.
    Cesta CE, Oberg AS, Ibrahimson A, Yusuf I, Larsson H, Almqvist C, et al. Maternal polycystic ovary syndrome and risk of neuropsychiatric disorders in offspring: prenatal androgen exposure or genetic confounding? Psychol Med. 2019:1–9.  https://doi.org/10.1017/s0033291719000424.
  59. 59.
    Mehrabian F, Ghasemi-Tehrani H, Mohamadkhani M, Moeinoddini M, Karimzadeh P. Comparison of the effects of metformin, flutamide plus oral contraceptives, and simvastatin on the metabolic consequences of polycystic ovary syndrome. J Res Med Sci. 2016;21:7.  https://doi.org/10.4103/1735-1995.177354.CrossRefGoogle Scholar
  60. 60.
    Morgante G, Massaro MG, Di Sabatino A, Cappelli V, De Leo V. Therapeutic approach for metabolic disorders and infertility in women with PCOS. Gynecol Endocrinol. 2018;34(1):4–9.  https://doi.org/10.1080/09513590.2017.1370644.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Cecily V. Bishop
    • 1
    • 2
    Email author
  • Taylor E. Reiter
    • 1
  • David W. Erikson
    • 3
  • Carol B. Hanna
    • 1
  • Brittany L. Daughtry
    • 1
  • Shawn L. Chavez
    • 1
    • 4
    • 5
  • Jon D. Hennebold
    • 1
    • 4
    • 5
  • Richard L. Stouffer
    • 1
    • 4
  1. 1.Division of Reproductive & Developmental SciencesOregon Health & Science UniversityBeavertonUSA
  2. 2.Department of Animal and Rangeland SciencesOregon State UniversityCorvallisUSA
  3. 3.Endocrine Technologies Core, Oregon National Primate Research CenterOregon Health & Science UniversityBeavertonUSA
  4. 4.Department of Obstetrics & GynecologyOregon Health & Science UniversityPortlandUSA
  5. 5.Department of Physiology & PharmacologyOregon Health & Science UniversityPortlandUSA

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