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The Inflammatory State of Follicular Fluid Combined with Negative Emotion Indicators can Predict Pregnancy Outcomes in Patients with PCOS

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Abstract

Polycystic ovary syndrome (PCOS) is a complex endocrine disorder syndrome with an incidence of 6% to 10% in women of reproductive age. Women with PCOS not only exhibit abnormal follicular development and fertility disorders, but also have a greater tendency to develop anxiety and depression. Our aim was to evaluate the ability of inflammatory factors in follicular fluid to predict embryonic developmental potential and pregnancy outcome and to construct a machine learning model that can predict IVF pregnancy outcomes based on indicators such as basic sex hormones, embryonic morphology, the follicular microenvironment, and negative emotion. In this study, inflammatory factors (CRP, IL-6, and TNF-α) in follicular fluid samples obtained from 225 PCOS and 225 non-PCOS women were detected via ELISA. For patients with PCOS, the levels of CRP and IL-6 in the follicular fluid in the pregnant group were significantly lower than those in the nonpregnant group. For non-patients with PCOS, only the level of IL-6 in the follicular fluid was significantly lower in the pregnant group than in the nonpregnant group. In addition, for both PCOS and non-patients with PCOS, compared with those in the pregnant group, patients in the nonpregnant group showed more pronounced signs of anxiety and depression. Finally, the factors that were significantly different between the two subgroups (pregnancy and nonpregnancy) of patients with or without PCOS were identified by an independent sample t test first and further analysed by multilayer perceptron (MLP) and random forest (RF) models to distinguish the two clinical pregnancy outcomes according to the classification function. The accuracy of the RF model in predicting pregnancy outcomes in patients with or without PCOS was 95.6% and 91.1%, respectively. The RF model is more suitable than the MLP model for predicting pregnancy outcomes in IVF patients. This study not only identified inflammatory factors that can affect embryonic development and assessed the anxiety and depression tendencies of PCOS patients, but also constructed an AI model that predict pregnancy outcomes through machine learning methods, which is a beneficial clinical tool.

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Data Availability

The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding authors.

Abbreviations

AFC:

Antral follicle count

AMH:

Anti-müllerian hormone

AUC:

The average area under the curve

BMI:

Body mass index

CRP:

C reactive protein

E2:

Estradiol

FSH:

Follicle-stimulating hormone

hCG:

Human chorionic gonadotropin

HQ:

Blastocysts rate; high quality blastocysts rate

IL-6:

Interleukin-6

IVF:

In vitro fertilization

LH:

Luteotrophic hormone

ML:

Machine learning

MLP:

Multilayer perceptron

PCOS:

Polycystic ovary syndrome

PCA:

Principal component analysis

P:

Progesterone

RF:

Random forest

ROC:

Receiver operating characteristic

SDS:

SAS, Self-rating anxiety Scale; Self-rating depression scale

T:

Testosterone

TNF-α:

Tumor necrosis factor-α

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Acknowledgements

We thank Dr. Jun Zhang from Basecare Medical Device Co. for his guidance in building machine learning models.

Funding

This work was supported by grants from Natural Science Foundation of Shanghai Municipal Science and Technology Commission (General Project) (23ZR1450200).

Author information

Author notes

  1. **n Huang and Zhe Yin have contributed equally to this work and share the first authorship.

Authors and Affiliations

  1. Reproductive Medical Center, Tongji Hospital, Tongji University School of Medicine, Shanghai, 200065, China

    Zhe Yin & Yanqiu Wang

  2. Reproductive Medical Center, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai, 201204, China

    **n Huang & Junting Xu

  3. Reproductive Medical Center, **nhua Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200092, China

    **n Huang

  4. College of Public Health, Temple University, Philadelphia, PA, 19122, USA

    Huanmei Wu

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Contributions

**n Huang and Zhe Yin drafted the manuscript, completed the experiments and performed statistical analysis. **n Huang and Yanqiu Wang designed the experiment and revised the article. Junting Xu collected follicular fluid samples and completed testing for inflammatory factors. Zhe Yin completed the patients’ anxiety and depression score survey and the construction of machine learning models. **n Huang and Yanqiu Wang confirm the authenticity of all the raw data. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Huanmei Wu or Yanqiu Wang.

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Ethics approval and consent to participate

The studies involving human participants were reviewed and approved by Institutional Ethical Review Board of Shanghai Tongji Hospital (Reference: K-W-2019–007). The patients/participants provided their written informed consent to participate in this study.

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The authors declare no competing interests.

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Huang, X., Yin, Z., Xu, J. et al. The Inflammatory State of Follicular Fluid Combined with Negative Emotion Indicators can Predict Pregnancy Outcomes in Patients with PCOS. Reprod. Sci. (2024). https://doi.org/10.1007/s43032-024-01538-3

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