Estradiol (E2) Reduction Adversely Affect the Embryo Quality and Clinical Outcomes of In Vitro Fertilization and Embryo transfer (IVF-ET)

Cheng, Jing; Yang, Shuangqing; Ma, Huaqing; Liang, Yingxiu; Zhao, Junzhao

doi:10.1155/2022/2473876

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…Research has con rmed that exposure of human endometrial epithelial cells to high levels of estrogen can lead to abnormal expression of many key implantation molecules [40]. Estradiol reduction adversely affect the embryo quality and clinical outcomes of IVF-ET [41]. However, these studies did not combine estrogen and progesterone with gene expression pro le.…”

Section: Discussionmentioning

confidence: 99%

The clinical efficacy evaluation of endometrial receptivity analysis (ERA) and Study on the influence factors of displaced window of implantation (WOI): A 6-year retrospective study on a large sample

Xu,

Diao,

Xiong

et al. 2024

Preprint

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Background: The endometrium in a receptive status is critical for a successful embryo implantation, but the receptivity of endometrium to embryos is only a short period of time, and the window of implantation (WOI) of each woman has individual variation, which will lead to poor outcomes of assisted reproduction. This so-called window of implantation can be detected by molecular diagnostic method, endometrial receptivity analysis (ERA), which was gradually applied to clinical practice. This study aimed to evaluate the clinical efficacy of personalized embryo transfer (pET) guided by ERA in patients with and without RIF, statistically analyze the clinical factors correlated with displaced WOI. Methods: A total of 3605 patients with previous failed embryo transfer (ET) cycle in the Reproductive Medicine Center, Renmin Hospital, Hubei University of Medicine from January 2016 to October 2022 were retrospectively analyzed. 3605 patients were divided into non-RIF group and RIF group, among them 782 patients who received ERA test underwent personalized embryo transfer (pET). The decision whether to accept ERA test or not was based on the patient's voluntary choice. We divided the 782 patients into normal WOI group and displaced WOI group according to the result of ERA test. The pregnancy outcomes were compared between the different groups. The age, number of previous ET cycle and serum E2/P ratio were mainly analyzed to investigate its relationship with displaced WOI. Results:The clinical pregnancy rate and live birth rate in non-RIF with pET group were higher than that of non-RIF with non-personalized embryo transfer (npET) group (64.5% vs 58.3%, P=0.025; 57.1% vs 48.3%, P=0.003). The clinical pregnancy rate and the live birth rate in RIF with pET group were significantly higher than that of RIF with npET group (62.7% vs 49.3%, P<0.001; 52.5% vs 40.4%, P<0.001) after propensity score matching (PSM). The early abortion rate in the non-RIF with pET group was lower than that in the non-RIF with npET group (8.2% vs 13.0%, P=0.038). There was a significant difference in age and the number of previous failed ET cycle between the normal WOI group and displaced WOI group (age:32.26 vs 33.53 years, P<0.001; the number of previous failed ET cycle:1.68 vs 2.04, P< 0.001). Logistic regression analysis also showed that the age and number of previous failed ET cycles were positively correlated with displaced WOI. The displaced WOI rate increased gradually with the increase of age and number of previous failed ET cycle; the displaced WOI rate in the median group 4.46<E2/P≤10.39 pg/ng was significantly lower than that in the other two groups (54.8% vs 40.6% vs 58.5%, P<0.001). Conclusion:The clinical pregnancy rate and live birth rate of patients with previous failed ET cycle was improved after pET guided by ERA, especially in RIF patients; the early abortion rate of the non-RIF patient was reduced after pET guided by ERA. An appropriate E2/P ratio was beneficial for maintaining endometrial receptivity state, and the age and number of previous failed ET cycle was correlated with increased displaced WOI.

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Section: Discussionmentioning

confidence: 99%

The clinical efficacy evaluation of endometrial receptivity analysis (ERA) and Study on the influence factors of displaced window of implantation (WOI): A 6-year retrospective study on a large sample

Xu,

Diao,

Xiong

et al. 2024

Preprint

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“…The Wilcoxon signed-rank test was applied to compare the concentrations of TEs between serum and follicular fluid. The correlation of the individual TE concentrations in the FF and serum samples was conducted by partial Spearman’s rank correlation analysis, adjusting for participants’ age, body mass index (BMI), dose of gonadotropins, pathogenesis type, treatment protocol, blood estradiol level, and blood luteinizing hormone level on the day of hCG injection, as the above confounders may affect the female reproductive system and the concentration of TEs in the FF. ,− The transfer efficiency of a TE was indicated by the ratio of its concentration in FF to that in serum. To investigate the commonalities of the transportation characteristics of multiple TEs from the comprehensive view, a correlation matrix of TEs’ transfer efficiencies was calculated.…”

Section: Methodsmentioning

confidence: 99%

Using Machine Learning to Construct the Blood–Follicle Distribution Models of Various Trace Elements and Explore the Transport-Related Pathways with Multiomics Data

Zhang,

Lin,

Gao

et al. 2024

Environ. Sci. Technol.

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Permeabilities of various trace elements (TEs) through the blood−follicle barrier (BFB) play an important role in oocyte development. However, it has not been comprehensively described as well as its involved biological pathways. Our study aimed to construct a blood−follicle distribution model of the concerned TEs and explore their related biological pathways. We finally included a total of 168 women from a cohort of in vitro fertilization-embryo transfer conducted in two reproductive centers in Beijing City and Shandong Province, China. The concentrations of 35 TEs in both serum and follicular fluid (FF) samples from the 168 women were measured, as well as the multiomics features of the metabolome, lipidome, and proteome in both plasma and FF samples. Multiomics features associated with the transfer efficiencies of TEs through the BFB were selected by using an elastic net model and further utilized for pathway analysis. Various machine learning (ML) models were built to predict the concentrations of TEs in FF. Overall, there are 21 TEs that exhibited three types of consistent BFB distribution characteristics between Beijing and Shandong centers. Among them, the concentrations of arsenic, manganese, nickel, tin, and bismuth in FF were higher than those in the serum with transfer efficiencies of 1.19−4.38, while a reverse trend was observed for the 15 TEs with transfer efficiencies of 0.076−0.905, e.g., mercury, germanium, selenium, antimony, and titanium. Lastly, cadmium was evenly distributed in the two compartments with transfer efficiencies of 0.998−1.056. Multiomics analysis showed that the enrichment of TEs was associated with the synthesis and action of steroid hormones and the glucose metabolism. Random forest model can provide the most accurate predictions of the concentrations of TEs in FF among the concerned ML models. In conclusion, the selective permeability through the BFB for various TEs may be significantly regulated by the steroid hormones and the glucose metabolism. Also, the concentrations of some TEs in FF can be well predicted by their serum levels with a random forest model.

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The construction of machine learning-based predictive models for high-quality embryo formation in poor ovarian response patients with progestin-primed ovarian stimulation

Xiao,

Hu,

et al. 2024

Reprod Biol Endocrinol

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Objective To explore the optimal models for predicting the formation of high-quality embryos in Poor Ovarian Response (POR) Patients with Progestin-Primed Ovarian Stimulation (PPOS) using machine learning algorithms. Methods A retrospective analysis was conducted on the clinical data of 4,216 POR cycles who underwent in vitro fertilization (IVF) / intracytoplasmic sperm injection (ICSI) at Sichuan Jinxin Xinan Women and Children’s Hospital from January 2015 to December 2021. Based on the presence of high-quality cleavage embryos 72 h post-fertilization, the samples were divided into the high-quality cleavage embryo group (N = 1950) and the non-high-quality cleavage embryo group (N = 2266). Additionally, based on whether high-quality blastocysts were observed following full blastocyst culture, the samples were categorized into the high-quality blastocyst group (N = 124) and the non-high-quality blastocyst group (N = 1800). The factors influencing the formation of high-quality embryos were analyzed using logistic regression. The predictive models based on machine learning methods were constructed and evaluated accordingly. Results Differential analysis revealed that there are statistically significant differences in 14 factors between high-quality and non-high-quality cleavage embryos. Logistic regression analysis identified 14 factors as influential in forming high-quality cleavage embryos. In models excluding three variables (retrieved oocytes, MII oocytes, and 2PN fertilized oocytes), the XGBoost model performed slightly better (AUC = 0.672, 95% CI = 0.636–0.708). Conversely, in models including these three variables, the Random Forest model exhibited the best performance (AUC = 0.788, 95% CI = 0.759–0.818). In the analysis of high-quality blastocysts, significant differences were found in 17 factors. Logistic regression analysis indicated that 13 factors influence the formation of high-quality blastocysts. Including these variables in the predictive model, the XGBoost model showed the highest performance (AUC = 0.813, 95% CI = 0.741–0.884). Conclusion We developed a predictive model for the formation of high-quality embryos using machine learning methods for patients with POR undergoing treatment with the PPOS protocol. This model can help infertility patients better understand the likelihood of forming high-quality embryos following treatment and help clinicians better understand and predict treatment outcomes, thus facilitating more targeted and effective interventions.

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Estradiol (E2) Reduction Adversely Affect the Embryo Quality and Clinical Outcomes of In Vitro Fertilization and Embryo transfer (IVF-ET)

Cited by 3 publications

References 22 publications

The clinical efficacy evaluation of endometrial receptivity analysis (ERA) and Study on the influence factors of displaced window of implantation (WOI): A 6-year retrospective study on a large sample

The clinical efficacy evaluation of endometrial receptivity analysis (ERA) and Study on the influence factors of displaced window of implantation (WOI): A 6-year retrospective study on a large sample

Using Machine Learning to Construct the Blood–Follicle Distribution Models of Various Trace Elements and Explore the Transport-Related Pathways with Multiomics Data

The construction of machine learning-based predictive models for high-quality embryo formation in poor ovarian response patients with progestin-primed ovarian stimulation

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