Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2022 Nov;118(5):970-979.
doi: 10.1016/j.fertnstert.2022.07.023. Epub 2022 Sep 26.

Characterizing the follicular fluid metabolome: quantifying the correlation across follicles and differences with the serum metabolome

Affiliations

Characterizing the follicular fluid metabolome: quantifying the correlation across follicles and differences with the serum metabolome

Robert B Hood et al. Fertil Steril. 2022 Nov.

Abstract

Objective: To compare the variability in metabolomes between the serum and follicular fluid, as well as across 3 dominant follicles.

Design: Prospective cohort study.

Setting: An academic fertility clinic in the northeastern United States, 2005-2015.

Patients: One hundred thirty-five women undergoing in vitro fertilization treatment who provided a serum sample during ovarian stimulation and up to 3 follicular fluid samples during oocyte retrieval.

Intervention(s): None.

Main outcome measure(s): Samples were analyzed using liquid chromatography with high-resolution mass spectrometry and 2 chromatography columns (C18 hydrophobic negative and hydrophilic interaction chromatography [HILIC] positive). We calculated overall, feature-specific, and subject-specific correlation coefficients to describe how strongly the intensity of overlapping metabolic features were associated between the serum and follicular fluid and between the 1st-2nd, 1st-3rd, and 2nd-3rd follicles. Feature-specific correlations were adjusted for age, body mass index, infertility diagnosis, ovarian stimulation protocol, and year.

Result(s): From the C18-negative column and the high-resolution mass spectrometry, 7,830 serum features and 10,790 follicular fluid features were detected in ≥20% of samples. After screening retention times and checking for 1:1 matching, 1,928 features overlapped between the 2 metabolomes. From the HILIC-positive column and the high-resolution mass spectrometry, after applying the same exclusion criteria, there were 9,074 serum features, 5,542 follicular fluid features, and 1,149 features that overlapped. When comparing the feature intensity of overlapping metabolites in the serum and the follicular fluid, the overall (C18, 0.45; HILIC, 0.63), median feature-specific (C18, 0.35; HILIC, 0.37), and median subject-specific (C18, 0.42; HILIC, 0.59) correlations were low to moderate. In contrast, among the overlapping features across all 3 follicles, the overall (C18, all 0.99; HILIC, all 0.99), median feature-specific (C18, 0.74-0.81; HILIC, 0.79-0.85), and median subject-specific (C18, 0.88-0.89; HILIC, 0.90-0.91) correlations between follicular fluid metabolomics features within a woman were high.

Conclusion(s): We observed minimal overlap and weak-to-moderate correlation between metabolomic features in the serum and follicular fluid but a large overlap and strong correlation between metabolomic features across follicles within a woman. The follicular fluid appears to represent a novel matrix, distinct from serum, which may be a rich source of biologic predictors of female fertility and reproductive outcomes.

Keywords: Correlation; follicles; follicular fluid metabolome; reproductive health; serum metabolome.

PubMed Disclaimer

Conflict of interest statement

CONFLICT OF INTEREST:

None to declare

Figures

Figure 1.
Figure 1.
Flowchart of serum and follicular fluid features among 125 participants enrolled in the metabolomics sub-study of the Environmental and Reproductive Health cohort. A A Metabolites were identified using level-1 evidence

Comment in

Similar articles

Cited by

References

    1. Nicholson JK, Lindon JC, Holmes E. ‘Metabonomics’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data. Xenobiotica. 1999;29:1181–9. Epub 1999/12/22. doi: 10.1080/004982599238047. - DOI - PubMed
    1. Fiordelisi A, Piscitelli P, Trimarco B, Coscioni E, Iaccarino G, Sorriento D. The mechanisms of air pollution and particulate matter in cardiovascular diseases. Heart Fail Rev. 2017;22:337–47. Epub 2017/03/18. doi: 10.1007/s10741-017-9606-7. - DOI - PubMed
    1. Liang D, Moutinho JL, Golan R, Yu T, Ladva CN, Niedzwiecki M, Walker DI, Sarnat SE, Chang HH, Greenwald R, Jones DP, Russell AG, Sarnat JA. Use of high-resolution metabolomics for the identification of metabolic signals associated with traffic-related air pollution. Environ Int. 2018;120:145–54. Epub 2018/08/10. doi: 10.1016/j.envint.2018.07.044. - DOI - PMC - PubMed
    1. Yang X, Zhang M, Lu T, Chen S, Sun X, Guan Y, Zhang Y, Zhang T, Sun R, Hang B, Wang X, Chen M, Chen Y, Xia Y. Metabolomics study and meta-analysis on the association between maternal pesticide exposome and birth outcomes. Environ Res. 2020;182:109087. Epub 2020/02/20. doi: 10.1016/j.envres.2019.109087. - DOI - PubMed
    1. Bonvallot N, Tremblay-Franco M, Chevrier C, Canlet C, Warembourg C, Cravedi JP, Cordier S. Metabolomics tools for describing complex pesticide exposure in pregnant women in Brittany (France). PLoS One. 2013;8:e64433. Epub 2013/05/25. doi: 10.1371/journal.pone.0064433. - DOI - PMC - PubMed

Publication types

Substances