Association of Free Radical Product and Polycystic Ovary Syndrome: A Systematic Review and Meta-analysis

Systematic review and meta-analysis of animal experiments on Bushen formulas for the treatment of polycystic ovary syndrome

The treatment of polycystic ovary syndrome (PCOS) with Bushen traditional Chinese medicine has a long history. Every year, there are animal experiments on Bushen formulas for the treatment of PCOS. The evidence from these preclinical studies is still unclear. This study summarizes and evaluates the animal experimental results of Bushen formulas in the treatment of PCOS. To explore the effects of using Bushen formulas to treat animal models of PCOS on body weight, sexual disorders and insulin resistance. As of 9 October 2023, this review made a detailed search of eight databases to look for random controlled trials (RCTs) focused on the use of Bushen formulas in PCOS animal models. Conduct a systematic review and meta-analysis of the included literature. Screening of the literature was performed independently, and data were extracted and assessed. A meta-analysis was performed using RevMan 5.4 software. Compared with the model group, the animals treated with Bushen formulas showed a decrease in ovarian mass (MD = - 1.01, 95% CI = [- 1.5, - 0.52], P < 0.0001), testosterone (T) level (MD = - 2.57, 95% CI = [- 2.91, - 2.23], P < 0.00001), body weight (MD = - 22.07, 95% CI = [- 31.12, - 13.03], P < 0.00001), luteinizing hormone (LH) level (MD = - 1.43, 95% CI = [- 1.79, - 1.08], P < 0.00001), and homeostasis model assessment of insulin resistance (HOMA-IR) level (MD = - 3.26, 95% CI = [- 4.5]). - 2.02%, P < 0.00001), and an increase in follicle-stimulating hormone (FSH) level (MD = 0.96, 95% CI = [0.61, 1.30], P < 0.00001). The use of Bushen formulas can reduce ovarian mass, testosterone, body weight, LH, HOMA-IR, increase FSH in animal models, and improve symptoms related to polycystic ovary syndrome. However, the heterogeneities between studies are too high, therefore, we need to further investigate their potential mechanisms in order to guide clinical medication better.

Association of TCF7L2 genetic variants rs12255372 and rs7903146 with the polycystic ovary syndrome risk: systemic review and meta-analysis

BACKGROUND

A significant overlap in the pathophysiological features of polycystic ovary syndrome (PCOS) and type 2 diabetes mellitus (T2DM) has been reported; and insulin resistance is considered a central driver in both. The expression and hepatic clearance of insulin and subsequent glucose homeostasis are mediated by TCF7L2 via Wnt signaling. Studies have persistently associated TCF7L2 genetic variations with T2DM, however, its results on PCOS are sparse and inconsistent.

METHODS

We performed a comprehensive literature review of the data published till June 2024, on rs7903146, rs12255372, and PCOS in PubMed, Medline, the Cochrane Library, Google Scholar, Science Direct, Scopus, and Web of Science, followed by a meta-analysis to evaluate the association between these genetic variations and the PCOS risk. Using a random effects model, the pooled odds ratio (OR) and confidence intervals (95%CI) were computed using STATA statistical software.

RESULTS

The genotypic data from 3052 controls and 2291 women with PCOS from ten published studies were analysed. The results indicated no cumulative association between the rs7903146 variant and PCOS risk in either the allelic (C vs. T: OR = 1.21; 95% CI: 0.96-1.47, p > 0.05) or genotypic models (CC vs. CT + TT: OR = 1.06; 95% CI: 0.90-1.23, p > 0.05). Similarly, the genetic variant rs12255372 was not associated with PCOS risk both in the allelic and the dominant inheritance model(p > 0.05). Unlike East Asians (MAF < 0.025), both variants are highly frequent across other global populations including America, South Asia, and Europe (MAF ≥ 0.19).

CONCLUSION

Unlike T2DM, our results showed that rs7903146 and rs12255372 variants of the TCF7L2 gene do not modulate the PCOS risk. However, the role of other TCF7L2 variants remains to be studied in future studies.

Closer look at circulating nitric oxide levels and their association with polycystic ovary syndrome: A meta-analytical exploration

Background

Polycystic ovary syndrome (PCOS) casts a wide shadow over the reproductive health of millions of women worldwide, emerging as one of the most complex and multifaceted endocrine disorders. In addition, nitric oxide (NO) stands out as a pivotal signaling molecule, orchestrating a symphony of physiological processes.

Objective

This meta-analysis aims to elucidate the association between NO levels and PCOS, investigate the potential of NO as a biomarker for PCOS diagnosis, and evaluate its clinical significance.

Materials and Methods

A systematic review was conducted in several electronic databases, including PubMed, Web of Science, Cochrane Library, Scopus, EMBASE, and Google Scholar, to identify relevant studies published up to January 2024. Standardized mean difference and 95% CI were calculated using a random effects model to assess the overall effect size. Meta-regressions and subgroup analysis were performed to investigate sources of heterogeneity.

Results

A meta-analysis of 14 studies with 1171 participants showed that NO levels were significantly lower in the PCOS group than in the control group. The pooled analysis yielded a standardized mean difference of -0.482; 95% CI: -0.908 to -0.056; p = 0.027. Subgroup analyses further demonstrated variations in NO levels between different PCOS phenotypes and in relation to metabolic parameters.

Conclusion

This meta-analysis provides evidence for an association between PCOS and dysregulated NO levels and suggests a potential role of NO as a biomarker in the diagnosis and pathogenesis of PCOS.

Update prognostic potency of surfactant protein D (SP-D) in the COVID-19 landscape: an In-depth meta-analytical exploration

INTRODUCTION

Surfactant Protein D (SP-D), a key component of the innate immune system, has attracted significant interest for its potential role in the pathophysiology and prognosis of COVID-19. This systematic review and meta-analysis aim to clarify the prognostic importance of SP-D levels in COVID-19 patients.

METHODS

A comprehensive literature search was conducted using PubMed, Web of Science, Cochrane Library, Scopus, EMBASE, and Google Scholar, covering studies published from January 2000 to January 2024. The inclusion criteria focused on studies measuring SP-D levels in the serum or plasma of COVID-19 patients, comparing severe and non-severe cases. Standardized mean differences (SMD) with 95% confidence intervals (CI) were calculated using a random-effects model to assess overall effect sizes. Meta-regressions and subgroup analyses were performed to identify potential sources of heterogeneity, including patient age, assay techniques, and gender ratio.

RESULTS

The meta-analysis incorporated data from nine studies involving 5,410 COVID-19 patients. Elevated SP-D levels were significantly correlated with increased disease severity, yielding an SMD of 0.642 (95% CI: 0.314 to 0.870; p = 0.012).

CONCLUSION

This meta-analysis confirms the prognostic significance of SP-D in the context of COVID-19. Elevated SP-D levels are associated with severe disease outcomes, highlighting its potential as a prognostic biomarker.

A closer look at Galectin-3: its association with gestational diabetes mellitus revealed by systematic review and meta-analysis

Purpose

Gestational diabetes mellitus (GDM) represents a significant metabolic disorder that affects pregnant women worldwide and has negative consequences for both the mother and her offspring. This research aims to investigate the relation between circulating levels of Galectin-3 and the incidence of GDM, and to evaluate its potential as a biomarker for monitoring and early detection of the disease.

Methods

A thorough search of the literature has been performed using databases such as Scopus, Web of science, Embase, Cochrane Library and PubMed. The standardized mean difference (SMD) and corresponding confidence intervals (CIs) were used to compute the effect size from individual records and pooled using the Random-effect model.

Results

Our meta-analysis synthesized data from 9 studies, encompassing 1,286 participants (533 GDM patients and 753 healthy pregnant controls). The findings demonstrated a considerable increase in Galectin-3 levels among individuals diagnosed with GDM as compared to the healthy control (SMD = 0.929; CI: 0.179-1.679; p = 0.015), with observed heterogeneity (I2 = 87%; p < 0.001). Subgroup analyses revealed the influence of factors such as age, BMI, study design, and sample type on Galectin-3 levels. A meta-regression analysis further identified trends indicating that levels of Galectin-3 are linked to gestational age, specific geographical areas, and sample size.

Conclusion

Increased levels of Galectin-3 exhibit a significant association with GDM, indicating its prospective utility as a biomarker for early detection and risk assessment. Further research is warranted to elucidate its regulation and clinical implications in GDM management.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s40200-024-01461-z.

Autoimmune thyroid disorders and polycystic ovary syndrome: Tracing links through systematic review and meta-analysis

Polycystic Ovary Syndrome (PCOS) and Autoimmune Thyroiditis (AIT) are two prevalent endocrine disorders affecting women, often coexisting within the same patient population. This meta-analysis aims to systematically assess and synthesize the existing body of literature to elucidate the intricate relationship between PCOS and AIT. A systematic literature search for relevant observational studies was conducted in electronic databases such as Web of Science, Google Scholar, PubMed, Cochrane, and Scopus until March 2023. All Statistical analyses were performed using CMA Software v3.7 in a random-effects network meta-analysis. In addition, sensitivity and meta-regression analyses were conducted to identify sources of Heterogeneity based on related risk factors. Our meta-analysis included eighteen studies with 3657 participants, which revealed significant differences between PCOS patients and control groups. In particular, a considerable association was detected between PCOS and the presence of AIT (OR = 2.38; 95% CI: 1.63-3.49; P< 0.001) and elevated levels of TSH (SMD = 0.24; 95% CI: 0.06-0.42; P= 0.01), anti-TPO (SMD = 0.36; 95% CI: 0.19-0.53; P< 0.001), anti-TG (SMD = 1.24; 95% CI: 0.37-2.10; P< 0.001), and other positive serum antibodies compared to the control groups. The findings from this meta-analysis may contribute to enhanced diagnostic strategies like complete thyroid function tests, more targeted interventions, and improved patient care for individuals presenting with both PCOS and AIT. Additionally, identifying commonalities between these conditions may pave the way for future research directions, guiding the development of novel therapeutic approaches that address the interconnected nature of PCOS and AIT.

ChatGPT's Efficacy in Queries Regarding Polycystic Ovary Syndrome and Treatment Strategies for Women Experiencing Infertility

This study assesses the efficacy of ChatGPT-4, an advanced artificial intelligence (AI) language model, in delivering precise and comprehensive answers to inquiries regarding managing polycystic ovary syndrome (PCOS)-related infertility. The research team, comprising experienced gynecologists, formulated 460 structured queries encompassing a wide range of common and intricate PCOS scenarios. The queries were: true/false (170), open-ended (165), and multiple-choice (125) and further classified as 'easy', 'moderate', and 'hard'. For true/false questions, ChatGPT-4 achieved a flawless accuracy rate of 100% initially and upon reassessment after 30 days. In the open-ended category, there was a noteworthy enhancement in accuracy, with scores increasing from 5.53 ± 0.89 initially to 5.88 ± 0.43 at the 30-day mark (p < 0.001). Completeness scores for open-ended queries also experienced a significant improvement, rising from 2.35 ± 0.58 to 2.92 ± 0.29 (p < 0.001). In the multiple-choice category, although the accuracy score exhibited a minor decline from 5.96 ± 0.44 to 5.92 ± 0.63 after 30 days (p > 0.05). Completeness scores for multiple-choice questions remained consistent, with initial and 30-day means of 2.98 ± 0.18 and 2.97 ± 0.25, respectively (p > 0.05). ChatGPT-4 demonstrated exceptional performance in true/false queries and significantly improved handling of open-ended questions during the 30 days. These findings emphasize the potential of AI, particularly ChatGPT-4, in enhancing decision-making support for healthcare professionals managing PCOS-related infertility.