Reproductive Biology and Endocrinology最新文献

The ubiquitination is crucial for controlling cellular homeostasis and protein modification, in which ubiquitin-conjugating enzyme (E2) acts as the central player in the ubiquitination system. Ubiquitin-conjugating enzymes, which have special domains that catalyse substrates, have sequence discrepancies and modulate various pathophysiological processes in different cells of multiple organisms. E2s take part in the mitosis of primordial germ cells, meiosis of spermatocytes and the formation of mature haploid spermatids to maintain normal male fertility. In this review, we summarize the various types of E2s and their functions during distinct stages of spermatogenesis.

Objective: Our aim was to explore the relationship between serum uric acid (UA) levels in early pregnancy and the development of gestational diabetes mellitus (GDM), and to further explore whether there is a causal relationship.

Methods: 684 pregnant women with GDM and 1162 pregnant women without GDM participated in this study. 311 pregnant women with GDM and 311 matched controls were enrolled in a 1:1 case-control study. We used conditional logistic regression to explore the relationship between UA levels and the risk of developing GDM. The causal relationship between the two was examined by two-sample Mendelian randomization (MR) analysis.

Results: In the 1:1 matched population, the odds ratio (OR) of developing GDM compared with the extreme tertiles of UA levels was 1.967 (95% confidence interval [CI]: 1.475-2.625; P < 0.001). Restricted cubic spline analyses showed a linear relationship between UA and GDM when UA exceeded 222 µmol/L. GDM and UA levels maintained a statistically significant positive correlation in different stratified regression analyses (P < 0.001). However, no evidence of a causal relationship between uric acid and GDM was found by MR analyses with an OR of 1.06 (95% CI: 0.91-1.25) per unit increase in UA.

Conclusion: There is a positive correlation between UA levels in early pregnancy and the subsequent risk of developing GDM. However, no genetic evidence was found to support a cause-effect relationship between UA and GDM.

Research question: Does luteinizing hormone (LH) levels on human chorionic gonadotropin (HCG) trigger day (LH_HCG) affect the clinical outcomes of patients with diminished ovarian reserve (DOR) undergoing gonadotropin-releasing hormone antagonist (GnRH-ant) protocol?

Methods: Retrospective analysis fresh embryo transfer cycles of DOR patients who underwent GnRH-ant protocol from August 2019 to June 2023. The participants were divided into different groups according to LH_HCG level and age. The clinical data and outcomes were compared between groups.

Results: In patients with DOR, the HCG positive rate (59.3% versus 39.8%, P = 0.005), embryo implantation rate (34.5% versus 19.7%, P = 0.002), clinical pregnancy rate (49.2% versus 28.4%, P = 0.003), live birth rate (41.5% versus 22.7%, P = 0.005) in LH_HCG < 2.58 IU/L group were significantly higher than LH_HCG ≥ 2.58 IU/L group. There was no significant correlation between LH_HCG level and clinical pregnancy in POSEIDON group 3. In POSEIDON group 4, the HCG positive rate (52.8% versus 27.0%, P = 0.015), embryo implantation rate (29.2% versus 13.3%, P = 0.023), clinical pregnancy rate (45.3% versus 18.9%, P = 0.010) in LH_HCG < 3.14 IU/L group were significantly higher than LH_HCG ≥ 3.14 IU/L group. Logistic regression analysis indicated that LH_HCG level was an independent influencing factor for clinical pregnancy in POSEIDON group 4 patients (OR = 3.831, 95% CI: 1.379-10.643, P < 0.05).

Conclusions: LH_HCG level is an independent factor affecting pregnancy outcome of fresh embryo transfer in DOR patients undergoing GnRH-ant protocol, especially for advanced-aged women. LH_HCG had a high predictive value for POSEIDON group 4 patients, and LH_HCG ≥ 3.14 IU/L predicts poor pregnancy outcomes.

Hormonal changes in pregnant and lactating women significantly affect bone metabolism and overall stress levels, positioning them as a unique group within the orthodontic population. Fluctuations in estrogen, progesterone, prolactin, and other hormones are closely linked to bone remodeling and the periodontal tissue's response to inflammation caused by dental plaque. Hormones such as thyrotropin, leptin, and melatonin also play crucial roles in pregnancy and bone remodeling, with potential implications for orthodontic tooth movement. Additionally, adverse personal behaviors and changes in dietary habits worsen periodontal conditions and complicate periodontal maintenance during orthodontic treatment. Notably, applying orthodontic force during pregnancy and lactation may trigger stress responses in the endocrine system, altering hormone levels. However, these changes do not appear to adversely affect the mother or fetus. This review comprehensively examines the interaction between hormone levels and orthodontic tooth movement in pregnant and lactating women, offering insights to guide clinical practice.

Background: Obesity is a global health issue with detrimental effects on various human organs, including the reproductive system. Observational human data and several lines of animal experimental data suggest that maternal obesity impairs ovarian function and early embryo development, but the precise pathogenesis remains unclear.

Methods: We established a high-fat diet (HFD)-induced obese female mouse model to assess systemic metabolism, ovarian morphology, and oocyte function in mice. For the first time, this study employed single-cell RNA sequencing to explore the altered transcriptomic landscape of preimplantation embryos at different stages in HFD-induced obese mice. Differential gene expression analysis, enrichment analysis and protein-protein interactions network analysis were performed.

Results: HFD-induced obese female mice exhibited impaired glucolipid metabolism and insulin resistance. The ovaries of HFD mice had a reduced total follicle number, an increased proportion of atretic follicles, and irregular granulosa cell arrangement. Furthermore, the maturation rate of embryonic development by in vitro fertilization of oocytes was significantly decreased in HFD mice. Additionally, the transcriptional landscapes of preimplantation embryos at different stages in mice induced by different diets were significantly distinguished. The maternal-to-zygotic transition was also affected by the failure to remove maternal RNAs and to turn off zygotic genome expression.

Conclusions: HFD-induced obesity impaired ovarian morphology and oocyte function in female mice and further led to alterations in the transcriptional landscape of preimplantation embryos at different stages of HFD mice.

Background: Premature ovarian failure (POF) is a clinical condition characterized by the cessation of ovarian function, leading to infertility. The underlying molecular mechanisms remain unclear, and no predictable biomarkers have been identified. This study aimed to investigate the protein and metabolite contents of serum extracellular vesicles to investigate underlying molecular mechanisms and explore potential biomarkers.

Methods: This study was conducted on a cohort consisting of 14 POF patients and 16 healthy controls. The extracellular vesicles extracted from the serum of each group were subjected to label-free proteomic and unbiased metabolomic analysis. Differentially expressed proteins and metabolites were annotated. Pathway network clustering was conducted with further correlation analysis. The biomarkers were confirmed by ROC analysis and random forest machine learning.

Results: The proteomic and metabolomic profiles of POF patients and healthy controls were compared. Two subgroups of POF patients, Pre-POF and Pro-POF, were identified based on the proteomic profile, while all patients displayed a distinguishable metabolomic profile. Proteomic analysis suggested that inflammation serves as an early factor contributing to the infertility of POF patients. For the metabolomic analysis, despite the dysfunction of metabolism, oxidative stress and hormone imbalance were other key factors appearing in POF patients. Signaling pathway clustering of proteomic and metabolomic profiles revealed the progression of dysfunctional energy metabolism during the development of POF. Moreover, correlation analysis identified that differentially expressed proteins and metabolites were highly associated, with six of them being selected as potential biomarkers. ROC curve analysis, together with random forest machine learning, suggested that AFM combined with 2-oxoarginine was the best diagnostic biomarker for POF.

Conclusions: Omics analysis revealed that inflammation, oxidative stress, and hormone imbalance are factors that damage ovarian tissue, but the progressive dysfunction of energy metabolism might be the critical pathogenic pathway contributing to the development of POF. AFM combined with 2-oxoarginine serves as a precise biomarker for clinical POF diagnosis.

DNA damage is a key factor affecting gametogenesis and embryo development. The integrity and stability of DNA are fundamental to a woman's successful conception, embryonic development, pregnancy and the production of healthy offspring. Aging, reactive oxygen species, radiation therapy, and chemotherapy often induce oocyte DNA damage, diminished ovarian reserve, and infertility in women. With the increase of infertility population, there is an increasing need to study the relationship between infertility related diseases and DNA damage and repair. Researchers have tried various methods to reduce DNA damage in oocytes and enhance their DNA repair capabilities in an attempt to protect oocytes. In this review, we summarize recent advances in the DNA damage response mechanisms in infertility diseases such as PCOS, endometriosis, diminished ovarian reserve and hydrosalpinx, which has important implications for fertility preservation.

Background: Vitamin D deficiency, a common occurrence among pregnant women, is an emerging public health concern worldwide. According to research, prenatal vitamin D deficiency is associated with various complications. This study assessed the vitamin D status of pregnant women in Yanbian, Jilin Province, as well as the correlation and predictive value of their vitamin D levels in relation to gestational length (weeks) and fetal weight, aiming to provide a basis for clinical diagnosis and treatment.

Methods: We conducted a population-based retrospective study involving 510 pregnant women from August 2019 to October 2022. Blood samples were collected at 16-20 weeks of gestation for the detection of serum vitamin D levels. Statistical analyses were performed using SPSS 28.0 and R 4.1.0 software. Multifactorial logistic regression analysis was employed to establish whether each variable was a risk factor for deliveries at ≤ 38 gestational weeks and low fetal weight. These results were used to construct a risk prediction model, and the model's predictive efficacy was evaluated. Results or differences with p < 0.05 were considered statistically significant.

Results: Multifactorial logistic regression analysis revealed that vitamin D ≤ 14.7 ng/mL(OR: 1.611; 95% CI: 1.120-2.318; P = 0.010), Bone Mineral Density (BMD) T-value ≤-1(OR: 1.540; 95%CI: 1.067-2.223; P = 0.021), and gestational hypertension(OR: 7.173; 95% CI: 1.482-34.724; P = 0.014) were the independent risk factors for deliveries at ≤ 38 gestational weeks. Additionally, vitamin D ≤ 14.7 ng/mL(OR: 1.610; 95%CI: 1.123-2.307; P = 0.009), BMD T-value ≤ -1(OR: 1.560; 95%CI: 1.085-2.243; P = 0.016), and gestational hypertension(OR: 4.262; 95% CI: 1.058-17.167; P = 0.041) were the independent risk factors for low fetal weight (< 3400 g).

Conclusion: This study revealed that low vitamin D levels are an independent risk factor for a short gestational length and low fetal weight. Prenatal low BMD T-value and comorbid hypertensive disorders were also found to increase the risk of a short gestational length and low fetal weight.

Purpose: To determine the factors influencing the likelihood of biochemical pregnancy loss (BPL) after transfer of a euploid embryo from preimplantation genetic testing for aneuploidy (PGT-A) cycles.

Methods: The study employed an observational, retrospective cohort design, encompassing 6020 embryos from 2879 PGT-A cycles conducted between February 2013 and September 2021. Trophectoderm biopsies in day 5 (D5) or day 6 (D6) blastocysts were analyzed by next generation sequencing (NGS). Only single embryo transfers (SET) were considered, totaling 1161 transfers. Of these, 49.9% resulted in positive pregnancy tests, with 18.3% experiencing BPL. To establish a predictive model for BPL, both classical statistical methods and five different supervised classification machine learning algorithms were used. A total of forty-seven factors were incorporated as predictor variables in the machine learning models.

Results: Throughout the optimization process for each model, various performance metrics were computed. Random Forest model emerged as the best model, boasting the highest area under the ROC curve (AUC) value of 0.913, alongside an accuracy of 0.830, positive predictive value of 0.857, and negative predictive value of 0.807. For the selected model, SHAP (SHapley Additive exPlanations) values were determined for each of the variables to establish which had the best predictive ability. Notably, variables pertaining to embryo biopsy demonstrated the greatest predictive capacity, followed by factors associated with ovarian stimulation (COS), maternal age, and paternal age.

Conclusions: The Random Forest model had a higher predictive power for identifying BPL occurrences in PGT-A cycles. Specifically, variables associated with the embryo biopsy procedure (biopsy day, number of biopsied embryos, and number of biopsied cells) and ovarian stimulation (number of oocytes retrieved and duration of stimulation), exhibited the strongest predictive power.