Reproductive Biology and Endocrinology最新文献

Background: Despite the documented increased cardiovascular disease (CVD) risk in women with polycystic ovary syndrome (PCOS), no specific risk prediction tools are recommended for these patients. We aimed to assess the validity of the Framingham Risk Score (FRS) as a predictor of CVD risk in PCOS patients.

Methods: In a community-based prospective study, 4,435 women from the Tehran Lipid and Glucose Study (TLGS) cohort were analyzed. Among them, 215 women aged 30 years or older were diagnosed with PCOS. A Cox proportional hazards model applied to assess the relationship between the FRS and CVD event. Model accuracy was evaluated using the C-statistic, while discrimination and calibration were assessed via the ROC curve, area under the ROC curve (AUC) statistics, and the Hosmer- Lemeshow test.

Results: The Cox proportional hazards (HRs) model revealed that the CVD risk increased by 38% for each one-unit increase in the FRS [HR: 1.38 (95% CI: 1.14, 1.66)] in PCOS patients. The FRS had a C-statistic of 0.765, which indicated a satisfactory fit for CVD prediction in this population. The AUC of the ROC curve was 0.82, which demonstrated a good discrimination of the FRS. The Hosmer-Lemeshow test showed that the predicted probabilities of CVD were consistent with the observed CVD rates (p = 0.217), indicating a good calibration.

Conclusions: This study revealed a significant increase in CVD risk among PCOS patients. The FRS effectively predicts a 38% increment in CVD risk for every one-unit increase in the FRS. Our study further validated the FRS as a predictor of CVD risk in these patients.

Objective: A prospective cohort study was conducted to investigate sleep status during the early and second trimester of pregnancy in pregnant women on adverse birth outcome, such as preterm birth, low birth weight and small for gestational age.

Methods: Multivariable logistic regression models were used to analyze the association of sleep status during the early and second trimester of pregnancy with adverse birth outcomes and generated the odds ratio and 95% confidence interval.

Results: 5,418 pregnant women were included in the analysis. In the multivariable model, compared with 7.1-8 h/night, sleep ≤ 7 h/night during second trimester increases the risk of preterm birth (OR: 1.43, 95% CI: 1.12,1.85), and the risk of preterm birth was decreased in pregnant women who slept > 9 h/night (OR: 0.79, 95% CI: 0.53,0.93). Sleep quality, and sleep changes in the early and second trimesters, and sleep duration in the early pregnancy were not statistically associated with preterm birth, low birth weight and small for gestational age.

Conclusions: Short sleep duration during pregnancy is associated with a higher risk of preterm birth and longer sleep duration at night is associated with a lower risk of preterm birth, but the latter needs further verification. Sleep status during pregnancy was not associated with low birth weight and small for gestational age. In order to reduce risk of adverse birth outcomes, sleep problems in pregnant women should be strengthened during pregnancy care.

Clinical trial number: Not applicable.

Background: A prospective, observational study to identify relationships between body temperature and levels of peripheral P4 blood progesterone, and examine if these differ according to body temperature cycle pattern.

Methods: 62 data points from 18 patients undergoing hormone assisted embryo transfer cycles at IVIRMA IVF clinics in Madrid, Mallorca and Malaga, Spain volunteered to use OvuSense, an intra-vaginal body temperature monitor. Primary outcome measures were OvuSense Raw and Smooth Temperature (ST) (°C), P4 (ng/ml).

Secondary outcome measures: Ongoing Pregnancy, Miscarriage or biochemical pregnancy. Graphical time based comparison analyses and multilevel regression analyses using MLwiN 3.10 [Charlton C, Rasbash J, Browne WJ, Healy M, Cameron B. MlwiN Version 3.10. Centre for Multilevel Modelling. University of Bristol; 2024.] software were conducted.

Results: A graphical analysis showed an apparent relationship between P4 levels and Temperature taken on P4 blood draw day. A multilevel regression analysis using MLwiN 3.10 Centre for Multilevel Modelling. University of Bristol software investigated this relationship, allowing between-patient variation to be accounted for and estimated. This established a strong linear relationship between LnP4 and ST, and cross correlation was carried out which identified the optimum predictor of levels of LnP4 was ST measured on the day prior to blood sampling. Further graphical analyses showed an apparent lower luteal level of P4 for cycles flagged as atypical by OvuSense, and for negative outcomes, except on embryo transfer day.

Conclusions: The results provide extremely strong evidence of a linear relationship between LnP4 and Smooth Temperature (ST) measured the day before blood sampling (Z = 15.6, p < 0.0001, 2 sided). This suggests that ST could provide a less invasive, continuous, and more practical method of assessing P4 response. Secondary outcomes may be related to ST pattern established during an embryo transfer cycle. Further investigation is required to establish the value of the ST pattern for improving outcomes.

Background: Artificial intelligence (AI) models analyzing embryo time-lapse images have been developed to predict the likelihood of pregnancy following in vitro fertilization (IVF). However, limited research exists on methods ensuring AI consistency and reliability in clinical settings during its development and validation process. We present a methodology for developing and validating an AI model across multiple datasets to demonstrate reliable performance in evaluating blastocyst-stage embryos.

Methods: This multicenter analysis utilizes time-lapse images, pregnancy outcomes, and morphologic annotations from embryos collected at 10 IVF clinics across 9 countries between 2018 and 2022. The four-step methodology for developing and evaluating the AI model include: (I) curating annotated datasets that represent the intended clinical use case; (II) developing and optimizing the AI model; (III) evaluating the AI's performance by assessing its discriminative power and associations with pregnancy probability across variable data; and (IV) ensuring interpretability and explainability by correlating AI scores with relevant morphologic features of embryo quality. Three datasets were used: the training and validation dataset (n = 16,935 embryos), the blind test dataset (n = 1,708 embryos; 3 clinics), and the independent dataset (n = 7,445 embryos; 7 clinics) derived from previously unseen clinic cohorts.

Results: The AI was designed as a deep learning classifier ranking embryos by score according to their likelihood of clinical pregnancy. Higher AI score brackets were associated with increased fetal heartbeat (FH) likelihood across all evaluated datasets, showing a trend of increasing odds ratios (OR). The highest OR was observed in the top G4 bracket (test dataset G4 score ≥ 7.5: OR 3.84; independent dataset G4 score ≥ 7.5: OR 4.01), while the lowest was in the G1 bracket (test dataset G1 score < 4.0: OR 0.40; independent dataset G1 score < 4.0: OR 0.45). AI score brackets G2, G3, and G4 displayed OR values above 1.0 (P < 0.05), indicating linear associations with FH likelihood. Average AI scores were consistently higher for FH-positive than for FH-negative embryos within each age subgroup. Positive correlations were also observed between AI scores and key morphologic parameters used to predict embryo quality.

Conclusions: Strong AI performance across multiple datasets demonstrates the value of our four-step methodology in developing and validating the AI as a reliable adjunct to embryo evaluation.

Objective: To study the correlation between anti-Müllerian hormone levels and pregnancy outcomes after in vitro fertilization/intracytoplasmic sperm injection in women with polycystic ovary syndrome, which remains controversial.

Methods: This retrospective cohort study recruited 4,719 women with infertility and polycystic ovary syndrome aged 20-40 years who underwent treatment at the Reproductive Center of Peking University Third Hospital between February 2017 and June 2023. We divided the participants into three groups according to the 25th and 75th percentile cutoffs of serum anti-Müllerian hormone: low (≤ 4.98 ng/mL, n = 1,198), average (4.98 - 10.65 ng/mL, n = 2,346), and high (≥ 10.65 ng/mL, n = 1,175). Pregnancy outcomes included live birth rate, miscarriage rate, clinical pregnancy rate, and cumulative live birth rate.

Results: The live birth rate for fresh embryo transfer was 39.8%, 35.9%, and 30.4% in the low, average, and high anti-Müllerian hormone groups, respectively. The miscarriage rate was 11.3%, 17.1%, and 21.8% in the low, average, and high anti-Müllerian hormone groups, respectively. Significant intergroup differences were observed in the live birth rate (P = 0.017) and miscarriage rate (P = 0.018). No significant intergroup difference was observed in the clinical pregnancy rate (P = 0.204) or cumulative live birth rate (P = 0.423). After adjusting the confounders by multivariable logistic regression analysis, anti-Müllerian hormone was associated with decreased live birth rate in the high anti-Müllerian hormone group compared with that in the low anti-Müllerian hormone group (odds ratio: 0.629, 95% confidence interval: 0.460-0.860). Anti-Müllerian hormone was associated with increased miscarriage rate in the average and high anti-Müllerian hormone groups compared with that in the low anti-Müllerian hormone group (average vs. low: odds ratio: 1.592, 95% confidence interval: 1.017-2.490); high vs. low: odds ratio: 2.045, 95% confidence interval: 1.152-3.633).

Conclusion: High anti-Müllerian hormone is a prognostic factor for reduced live birth rate after fresh embryo transfer in women with polycystic ovary syndrome aged 20-40 years undergoing in vitro fertilization/intracytoplasmic sperm injection, and is associated with increased miscarriage rate in these patients.

Background: Endocrine-disrupting chemicals (EDCs) interfere with the endocrine system and negatively impact reproductive health. Biochanin A (BCA), an isoflavone with anti-inflammatory and estrogen-like properties, has been identified as one such EDC. This study investigates the effects of BCA on transcription, metabolism, and hormone regulation in primary human granulosa cells (GCs), with a specific focus on the activation of bitter taste receptors (TAS2Rs).

Methods: Primary human GCs from 60 participants were treated with 10 µM BCA, and selective antagonists were used to block TAS2R activation. The study assessed the expression of TAS2R14 and TAS2R43, and analyzed the impact on StAR and CYP17A1 gene expression. Intracellular calcium levels, lipid droplet size, and mitochondrial network complexity were measured to evaluate cellular metabolism and energy dynamics.

Results: BCA treatment significantly upregulated TAS2R14 and TAS2R43 expression, leading to a 70% increase in StAR mRNA levels and a twofold increase in CYP17A1 expression (p < 0.05). These effects were reversed by TAS2R antagonists. Additionally, BCA treatment decreased intracellular Ca²⁺ levels (p < 0.01) and reduced lipid droplet size (p < 0.001), both of which were counteracted by antagonists. Enhanced mitochondrial network complexity (p < 0.001) was also observed, suggesting increased mitochondrial fusion and improved cellular energy dynamics.

Conclusion: The findings indicate that BCA modulates transcriptional and metabolic processes in GCs through the activation of TAS2Rs, highlighting their role in endocrine regulation. The statistically significant results emphasize the relevance of further exploring the effects of EDCs like BCA on reproductive health. Collaborative research efforts are essential to address and mitigate the adverse impacts of EDCs on fertility.

Purpose: Prior sperm DNA fragmentation index (DFI) thresholds for diagnosing male infertility and predicting assisted reproduction technology (ART) outcomes fluctuated between 15 and 30%, with no agreed standard. This study aimed to evaluate the impact of the sperm DFI on early embryonic development during ART treatments and establish appropriate DFI cut-off values.

Methods: Retrospectively analyzed 913 couple's ART cycles from 2021 to 2022, encompassing 1,476 IVF and 295 ICSI cycles, following strict criteria. The WHO guidelines directed the semen analysis, while the acridine orange test (AOT) determined the DFI. Male factors (age, BMI, DFI, infertility duration, sperm parameters) and female parameters (age, BMI, AMH, retrieved oocytes) were evaluated. We also assessed embryological parameters like fertilization rate, cleavage rate, and blastocyst quality. Correlations between DFI and embryo quality were examined and DFI cut-off values were established using ROC analysis.

Results: The Sperm DFI demonstrated a positive correlation with male age and a negative correlation with sperm motility, concentration, and normal morphology, while showing no relation to BMI. No connection between DFI and embryological parameters in only IVF and ICSI groups was found, but a negative correlation with fertilization rate was seen in all ART cycles. ROC curve analysis revealed a DFI cut-off value of 21.15% having 36.7% sensitivity and 28.9% specificity in predicting high fertilization rate (≥ 80%).

Conclusion: Sperm DFI had a negative correlation with fertilization rate, but limited predictive efficacy and no significant link to other embryological parameters. DFI assessments may improve early embryo development prediction during ART treatments, particularly in older males or those exhibiting poor sperm quality.

As part of a conventional controlled ovarian hyperstimulation (COH) regimen, final follicular maturation is usually triggered by a single bolus dose of human chorionic gonadotropin (hCG). COH, which combines GnRH antagonist co-treatment with GnRH agonist(GnRHa) trigger, is often used in attempts to eliminate severe early ovarian hyperstimulation syndrome and to improve oocyte/embryo yield and quality. Recently, the combination of GnRHa, with hCG trigger has also been implemented into clinical practice. Here, we analyze and discuss published studies on various ways of triggering final follicular maturation, seeking to elucidate the appropriateness of each approach for specific patient subgroups.

Recurrent spontaneous abortion (RSA), characterized by the loss of two or more pregnancies, impacts approximately 1-2% of couples and poses a significant challenge for individuals of childbearing age. The precise mechanisms underlying RSA remain incompletely understood. Concurrently, the global prevalence of obesity is on the rise, with obesity being closely associated with female reproductive disorders and infertility. This study initially examines the pathways through which obesity contributes to RSA, encompassing factors such as embryonic euploid miscarriage, endometrial development, immune function, among others. Furthermore, adipokines and the fat mass and obesity-related (FTO) are identified as potential contributors to RSA. The study also explores the enhancement of pregnancy outcomes through various weight management strategies, with a particular focus on the roles of dietary interventions, physical activity, and weight control during pregnancy. Obesity is closely related to RSA in multiple aspects. Additional clinical prospective and experimental studies are required to explore its precise pathogenesis. Through this review, we aim to provide strategies for improvement and treatment approaches for RSA related to obesity. Through this review, we suggest potential clinical management strategies and research avenues aimed at offering enhancements and therapeutic insights for miscarriages linked to obesity and its associated risk factors.

Objective: This study aimed to develop a predictive model for the risk of no usable blastocyst formation in patients with normal ovarian reserve undergoing IVF.

Methods: The model was derived from 7,901 patients who underwent their first oocyte retrieval and subsequent blastocyst culture, of which 446 cases have no usable blastocysts formed. Univariate regression analyses, least absolute shrinkage and selection operator regression analysis were used to identify the association of patient and cycle characteristics with the presence of no available blastocyst and to create a nomogram. The performance of the nomogram was assessed using the receiver operating characteristic (ROC) curve and calibration curve, the net benefit threshold of prediction was determined using decision curve analysis (DCA).

Results: Multivariate analysis identified three independent predictors: the number of day 3 (D3) embryos, the number of high-quality D3 embryos, and the number of embryos used for blastocyst culture. A nomogram model was developed and internally validated using bootstrapping, demonstrating good discriminative ability with an area under the receiver operating characteristic curve (AUC) of 0.879(95%CI: 0.861-0.890).

Conclusions: The cycle-based nomogram can anticipate the probability of no available blastocyst formation in IVF/ICSI treatment. This can help doctors make appropriate clinical judgments and assist patients in managing their expectations effectively.