A review of antioxidant N-acetylcysteine in addressing polycystic ovary syndrome.

N-acetylcysteine (NAC), a compound known for its cysteine and glutathione precursor properties, has been used in therapeutic applications for many years. Recently, there has been increasing interest in exploring the potential benefits of NAC in addressing polycystic ovary syndrome (PCOS). However, the exact mechanisms underlying NAC's therapeutic and clinical uses remain not fully understood. This review aims to specifically investigate how NAC offers protection against PCOS. This involved an extensive systematic review of the literature, and it made use of PubMed, Embase, and Web of Science databases. By analyzing key findings from over 100 research papers, the potential mechanisms through which NAC produces its effects were explored and summarized. Most studies suggest that NAC, whether used on its own or in combination with other medications, has the potential to counteract oxidative stress, utilize its anti-inflammatory and anti-apoptotic properties, and offer benefits in managing PCOS. Moreover, NAC might have the potential to influence specific signaling pathways in insulin target cells and ß cells. Diverse biological effects of NAC indicate its potential usefulness as a supplementary or therapeutic approach for managing PCOS. As a result, additional research is required to explore its potential in addressing PCOS.

Investigating the causal impact of polycystic ovary syndrome on gestational diabetes mellitus: a two-sample Mendelian randomization study.

Introduction: Determining the causal relationship between polycystic ovary syndrome (PCOS) and gestational diabetes mellitus (GDM) holds significant implications for GDM prevention and treatment. Despite numerous observational studies suggesting an association between PCOS and GDM, it remains unclear whether a definitive causal relationship exists between these two conditions and which specific features of PCOS contribute to increased incidence of GDM. Methods: The causal relationship between polycystic ovary syndrome (PCOS), its characteristic indices, and gestational diabetes mellitus (GDM) was investigated using a two-sample Mendelian randomization study based on publicly available statistics from genome-wide association studies (GWAS). The inverse-variance weighted method was employed as the primary analytical approach to examine the association between PCOS, its characteristic indices, and GDM. MR Egger intercept was used to assess pleiotropy, while Q values and their corresponding P values were utilized to evaluate heterogeneity. It is important to note that this study adopts a two-sample MR design where PCOS and its characteristic indices are considered as exposures, while GDM is treated as an outcome. Results: The study results indicate that there is no causal relationship between PCOS and GDM (all methods P > 0.05, 95% CI of OR values passed 1). The IVW OR value was 1.007 with a 95% CI of 0.906 to 1.119 and a P value of 0.904. Moreover, the MR Egger Q value was 8.141 with a P value of 0.701, while the IVW Q value was also 8.141 with a P value of 0.774, indicating no significant heterogeneity. Additionally, the MR Egger intercept was 0.0004, which was close to zero with a P value of 0.988, suggesting no pleiotropy. However, the study did find a causal relationship between several other factors such as testosterone, high-density lipoprotein, sex hormone-binding globulin, body mass index, waist-hip ratio, apolipoprotein A-I, number of children, diabetes illnesses of mother, father and siblings, hemoglobin A1c, fasting insulin, fasting blood glucose, years of schooling, and GDM based on the IVW method. Conclusion: We observed no association between genetically predicted PCOS and the risk of GDM, implying that PCOS itself does not confer an increased susceptibility to GDM. The presence of other PCOS-related factors such as testosterone, high-density lipoprotein, and sex hormone-binding globulin may elucidate the link between PCOS and GDM. Based on these findings, efforts aimed at preventing GDM in individuals with PCOS should prioritize those exhibiting high-risk features rather than encompassing all women with PCOS.

Progress of the application clinical prediction model in polycystic ovary syndrome.

Clinical prediction models play an important role in the field of medicine. These can help predict the probability of an individual suffering from disease, complications, and treatment outcomes by applying specific methodologies. Polycystic ovary syndrome (PCOS) is a common disease with a high incidence rate, huge heterogeneity, short- and long-term complications, and complex treatments. In this systematic review study, we reviewed the progress of clinical prediction models in PCOS patients, including diagnosis and prediction models for PCOS complications and treatment outcomes. We aimed to provide ideas for medical researchers and clues for the management of PCOS. In the future, models with poor accuracy can be greatly improved by adding well-known parameters and validations, which will further expand our understanding of PCOS in terms of precision medicine. By developing a series of predictive models, we can make the definition of PCOS more accurate, which can improve the diagnosis of PCOS and reduce the likelihood of false positives and false negatives. It will also help discover complications earlier and treatment outcomes being known earlier, which can result in better outcomes for women with PCOS.

Evidence for causal effects of polycystic ovary syndrome on oxidative stress: a two-sample mendelian randomisation study.

BACKGROUND: Polycystic ovary syndrome (PCOS) is often accompanied by increased oxidative stress levels; however, it is still unclear whether PCOS itself is causally related to oxidative stress (OS), whether OS can increase the occurrence of PCOS, and which characteristics of PCOS increase OS levels. Therefore, this study explored the causal relationship between PCOS, its characteristics, and OS. METHODS: Two-sample bidirectional and two-sample Mendelian randomisation studies were performed based on publicly available statistics from genome-wide association studies. PCOS; its characteristics, such as testosterone, low-density lipoprotein, high-density lipoprotein; and 11 major OS markers (superoxide dismutase, glutathione S-transferase, glutathione peroxidase, catalase, uric acid, zinc, tocopherol, ascorbic acid, retinol, albumin, and total bilirubin), were studied. The main analytical method used was inverse variance weighting (IVW). Pleiotropy was evaluated using the Mendelian randomisation-Egger intercept. Q and P values were used to assess heterogeneity. RESULTS: There was no causal relationship between PCOS and the OS indices (all P > 0.05). There was a causal relationship between the OS index, ascorbate level, and PCOS (IVW, odds ratio: 2.112, 95% confidence interval: 1.257-3.549, P = 0.005). In addition, there was a causal relationship between testosterone, low-density lipoprotein, high-density lipoprotein, sex hormone-binding globulin, body mass index, triacylglycerol, age at menarche, and most OS indices according to the IVW method. The F statistics showed that there was no weak instrumental variable. A sensitivity analysis was performed using the leave-one-out method. No pleiotropy was observed. The results were robust, and the conclusions were reliable. CONCLUSIONS: This study showed for the first time that there was no causal relationship between PCOS and OS. However, there was a causal relationship between the OS index, ascorbate level, and PCOS. It revealed that PCOS itself could not increase OS, and the increase in OS in PCOS was related to other potential factors, such as testosterone, low-density lipoprotein, high-density lipoprotein, sex hormone-binding globulin, body mass index, triacylglycerol, and age at menarche.