The Relationship Between Serum Delta FSH Level and Ovarian Response in IVF/ICSI Cycles

DENND1A desensitizes granulosa cells to FSH by arresting intracellular FSHR transportation

Polycystic ovary syndrome (PCOS) is a complex disorder. Genome-wide association studies (GWAS) have identified several genes associated with this condition, including DENND1A. DENND1A encodes a clathrin-binding protein that functions as a guanine nucleotide exchange factor involved in vesicular transport. However, the specific role of DENND1A in reproductive hormone abnormalities and follicle development disorders in PCOS remain poorly understood. In this study, we investigated DENND1A expression in ovarian granulosa cells (GCs) from PCOS patients and its correlation with hormones. Our results revealed an upregulation of DENND1A expression in GCs from PCOS cases, which was positively correlated with testosterone levels. To further explore the functional implications of DENND1A, we generated a transgenic mouse model overexpressing Dennd1a (TG mice). These TG mice exhibited subfertility, irregular estrous cycles, and increased testosterone production following PMSG stimulation. Additionally, the TG mice displayed diminished responsiveness to FSH, characterized by smaller ovary size, less well-developed follicles, and abnormal expressions of FSH-priming genes. Mechanistically, we found that Dennd1a overexpression disrupted the intracellular trafficking of follicle stimulating hormone receptor (FSHR), promoting its internalization and inhibiting recycling. These findings shed light on the reproductive role of DENND1A and uncover the underlying mechanisms, thereby contributing valuable insights into the pathogenesis of PCOS and providing potential avenues for drug design in PCOS treatment.

Optimizing FSH Concentration Modulation in the Short-Acting GnRH-a Long Protocol for IVF/ICSI: A Retrospective Study

INTRODUCTION

Exogenous gonadotropin (Gn) is given to regulate follicle-stimulating hormone (FSH) levels to achieve optimal ovarian response in in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI). The objective of this study was to analyze the optimal degree of change in FSH blood concentration with ovarian responsiveness in a short-acting gonadotropin-releasing hormone agonist (GnRH-a) long protocol for IVF/ICSI.

METHODS

This retrospective study was conducted at Changzhou Maternity and Child Health Hospital's Reproductive Center from May 2017 to May 2023. A total of 794 ovarian stimulation cycles for IVF/ICSI using the short-acting GnRH-a long protocol was included. Ovarian responsiveness was assessed based on the number of follicles > 14 mm on human chorionic gonadotropin (HCG) trigger day, refine-follicular output rate (Refine-FORT) and good quality embryos. Delta 1 referred to the change in FSH level between days 6-8 of gonadotropin usage and baseline FSH, while Delta 2 referred to the change in FSH level between HCG trigger day and days 6-8 of gonadotropin usage. Simple and multiple linear regression analysis were performed to adjust for confounding factors.

RESULTS

The number of follicles > 14 mm on HCG trigger day was found to be the most suitable indicator for evaluating ovarian responsiveness compared to the number of follicles > 16 mm and the number of retrieved oocytes. When Delta 1 ranged from 1.94 to 3.37, the number of follicles > 14 mm on HCG trigger day was the highest. When Delta 1 ranged from 3.37 to 5.90, the Refine-FORT was the highest. However, when Delta 1 exceeded 5.90, the number of follicles > 14 mm on HCG trigger day, Refine-FORT and good quality embryo all significantly decreased. On the other hand, when Delta 2 was ≤ - 1.58, the number of follicles > 14 mm on HCG trigger day and the Refine-FORT were both the highest.

CONCLUSION

This study identifies optimal Delta 1 and Delta 2 ranges for effective ovarian responsiveness in a short-acting GnRH-a long protocol for IVF/ICSI and introduces the novel measure of the number of follicles > 14 mm on HCG trigger day. The optimal range for Delta 1 was 1.94 to 3.37, and Delta 2 should be < - 1.58 for achieving a higher number and quality of oocytes.

A prediction model for high ovarian response in the GnRH antagonist protocol

Backgrounds

The present study was designed to establish and validate a prediction model for high ovarian response (HOR) in the GnRH antagonist protocol.

Methods

In this retrospective study, the data of 4160 cycles were analyzed following the in vitro fertilization (IVF) at our reproductive medical center from June 2018 to May 2022. The cycles were divided into a training cohort (n=3121) and a validation cohort (n=1039) using a random sampling method. Univariate and multivariate logistic regression analyses were used to screen out the risk factors for HOR, and the nomogram was established based on the regression coefficient of the relevant variables. The area under the receiver operating characteristic curve (AUC), the calibration curve, and the decision curve analysis were used to evaluate the performance of the prediction model.

Results

Multivariate logistic regression analysis revealed that age, body mass index (BMI), follicle-stimulating hormone (FSH), antral follicle count (AFC), and anti-mullerian hormone (AMH) were independent risk factors for HOR (all P< 0.05). The prediction model for HOR was constructed based on these factors. The AUC of the training cohort was 0.884 (95% CI: 0.869-0.899), and the AUC of the validation cohort was 0.884 (95% CI:0.863-0.905).

Conclusion

The prediction model can predict the probability of high ovarian response prior to IVF treatment, enabling clinicians to better predict the risk of HOR and guide treatment strategies.

Construction of the machine learning-based live birth prediction models for the first in vitro fertilization pregnant women

BACKGROUND

This study was to conduct prediction models based on parameters before and after the first cycle, respectively, to predict live births in women who received fresh or frozen in vitro fertilization (IVF) or intracytoplasmic sperm injection (ICSI) for the first time.

METHODS

This retrospective cohort study population consisted of 1,857 women undergoing the IVF cycle from 2019 to 2021 at Huizhou Municipal Central Hospital. The data between 2019 and 2020 were completely randomly divided into a training set and a validation set (8:2). The data from 2021 was used as the testing set, and the bootstrap validation was carried out by extracting 30% of the data for 200 times on the total data set. In the training set, variables are divided into those before the first cycle and after the first cycle. Then, predictive factors before the first cycle and after the first cycle were screened. Based on the predictive factors, four supervised machine learning algorithms were respectively considered to build the predictive models: logistic regression (LR), random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LGBM). The performances of the prediction models were evaluated by the area under the receiver operator characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy.

RESULTS

Totally, 851 women (45.83%) had a live birth. The LGBM model showed a robust performance in predicting live birth before the first cycle, with AUC being 0.678 [95% confidence interval (CI): 0.651 to 0.706] in the training set, 0.612 (95% CI: 0.553 to 0.670) in the validation set, 0.634 (95% CI: 0.511 to 0.758) in the testing set, and 0.670 (95% CI: 0.626 to 0.715) in the bootstrap validation. The AUC value in the training set, validation set, testing set, and bootstrap of LGBM to predict live birth after the first cycle was 0.841 (95% CI: 0.821 to 0.861), 0.816 (95% CI: 0.773 to 0.859), 0.835 (95% CI: 0.743 to 0.926), and 0.839 (95% CI: 0.806 to 0.871), respectively.

CONCLUSION

The LGBM model based on the predictive factors before and after the first cycle for live birth in women showed a good predictive performance. Therefore, it may assist fertility specialists and patients to adjust the appropriate treatment strategy.

The serum follicle stimulating hormone-to-luteinizing hormone ratios can predict assisted reproductive technology outcomes in women undergoing gonadotropin releasing hormone antagonist protocol

BACKGROUND

The basal follicle stimulating hormone (FSH)/luteinizing hormone (LH) ratio is a useful predictor of ovarian response. In this study, we investigated whether the FSH/LH ratios during the entire controlled ovarian stimulation (COS) can be used as effective predictors of outcomes in women undergoing in vitro fertilization (IVF) treatment using the gonadotropin releasing hormone antagonist (GnRH-ant) protocol.

METHODS

A total of 1,681 women undergoing their first GnRH-ant protocol were enrolled in this retrospective cohort study. A Poisson regression model was used to analyze the association between the FSH/LH ratios during COS and embryological outcomes. Receiver operating characteristic analysis was performed to determine the optimal cutoff values for poor responders (≤ 5 oocytes) or poor reproductive potential (≤ 3 available embryos). A nomogram model was constructed to provide a tool for predicting the cycle outcomes of individual IVF treatments.

RESULTS

The FSH/LH ratios (at the basal day, stimulation day 6 (SD6) and trigger day) were significantly correlated with the embryological outcomes. The basal FSH/LH ratio was the most reliable predictor of poor responders with a cutoff value of 1.875 (area under the curve (AUC) = 72.3%, P < 0.05), or of poor reproductive potential with a cutoff value of 2.515 (AUC = 66.3%, P < 0.05). The SD6 FSH/LH ratio predicted poor reproductive potential with a cutoff value of 4.14 (AUC = 63.8%, P < 0.05). The trigger day FSH/LH ratio predicted poor responders with a cutoff value of 9.665 (AUC = 63.1%, P < 0.05). The basal FSH/LH ratio, combined with the SD6 and trigger day FSH/LH ratios, slightly increased these AUC values and improved the prediction sensitivity. The nomogram provides a reliable model with which to assess the risk of poor response or poor reproductive potential directly based on the combined indicators.

CONCLUSIONS

FSH/LH ratios are useful predictors of poor ovarian response or reproductive potential throughout the entire COS with the GnRH antagonist protocol. Our findings also provide insights into the potential for LH supplementation and regimen adjustment during COS to achieve improved outcomes.

Development and validation of a live birth prediction model for expected poor ovarian response patients during IVF/ICSI

BACKGROUND

A number of live birth predictive model during assisted reproductive technology treatment have been available in recent years, but few targeted evaluating the chances of live birth in poor ovarian response(POR) patients. The aim of this study was to develop a nomogram based on POSEIDON criteria to predict live birth in patients with expected POR.

METHODS

This retrospective cohort study using clinical data from 657 patients in POSEIDON Groups 3 and 4 (antral follicle count [AFC] ≤5 and AMH <1.2 ng/ml) in the Center for Reproductive Medicine, First Affiliated Hospital of Xinjiang Medical University, and Construction a nomogram model t.

RESULTS

Among 657 expected POR patients, 111 (16.89%) had live births, and 546 (83.11%) did not have live births. These were divided into a training set(n=438) and a validation set (n=219). Multivariate logistic regression analysis showed that the age (OR = 0.91, 95% CI: 0.86-0.97), BMI (OR = 1.98, 95% CI: 1.09-3.67), AMH (OR = 3.48, 95% CI: 1.45-8.51), normal fertilized oocytes (OR = 1.40, 95% CI: 1.21-1.63), and the basal FSH (OR = 0.89, 95% CI: 0.80-0.98) of the female were independent factors predicting live birth in patients with expected POR. Then, an individualized nomogram prediction model was built from these five factors. The area under the ROC curve of the live birth prediction model was 0.820 in the training set and 0.879 in the validation set.

CONCLUSION

We have developed a nomogram combining clinical and laboratory factors to predict the probability of live birth in patients with an expected POR during IVF/ICSI, which can helpful for clinician in decision-making. However, the data comes from the same center, needs a prospective multicenter study for further in-depth evaluation and validation of this prediction model.