With an increasing global burden of coronary artery disease (CAD), early detection and timely management of risk factors are crucial to reduce morbidity and mortality in such patients. Diabetes mellitus (DM) is considered an independent risk factor for the development of CAD. Metformin, an anti-diabetic drug, has been shown in pre-clinical and clinical studies, to lower the cardiovascular events in the DM patients. Growing evidence suggests that metformin has a protective effect on coronary artery beyond its hypoglycemic effects. Given its global availability, route of administration and cost, metformin provides an alternate/additional therapeutic option for primary and secondary prevention of CAD in DM and non-diabetics alike. Future prospective cohort-based studies and randomized clinical trials are needed to identify ‘at-risk’ population who may potentially benefit from metformin.

Polycystic ovary syndrome (PCOS) is a common endocrine disorder affecting women of reproductive age. It is characterized by symptoms such as hyperandrogenemia, oligo or anovulation and polycystic ovarian, significantly impacting quality of life. However, the practical implementation of machine learning (ML) in PCOS diagnosis is hindered by the limitations related to data size and algorithmic models. To address this research gap, we have increased the sample size in our study and aim to utilize two ML algorithms to analyze and validate diagnostic biomarkers, as well as explore immune cell infiltration patterns in PCOS. We performed RNA-seq analysis on granulosa cell, including 13 samples from normal controls and 25 samples from women with PCOS. The data from our study were combined with publicly available databases. Batch effects were corrected using the ‘sva’ package in R software. Differential expression analysis was performed to identify genes that exhibited significant differences between the two groups. These differentially expressed genes (DEGs) were further analyzed for Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Hub genes were selected by intersecting the results of both methods after using LASSO and SVM-RFE for central gene selection for DEGs. Receiver Operating Characteristic (ROC) curves were employed to verify the accuracy of models by SVM and XGBoost. CIBERSORT analysis was performed to determine the relative abundances of immune cell populations. GSEA was analyzed to illustrate the expression patterns of genes within highly enriched functional pathways. RT-qPCR was used to validate the reliability of hub genes. 824 DEGs were found between the normal control and PCOS groups, including 376 upregulated and 448 downregulated genes. These DEGs were associated with endocytosis, salmonella infection and focal adhesion based on the KEGG enrichment analysis. Through overlapping LASSO and SVM-RFE algorithms, we identified four hub genes (CNTN2, CASR, CACNB3, MFAP2) that are significantly associated with the PCOS group. The diagnostic efficacy validation set using SVM and XGBoost yielded AUC values of 0.795 and 0.875, respectively, indicating their potential as diagnostic biomarkers. Consistent with the data analysis, the upregulation of CNTN2, CASR, CACNB3, and MFAP2 in PCOS was confirmed by RT-qPCR analysis on human granulosa cells. Furthermore, according to CIBERSORT analysis, a significant reduction in CD4 memory resting T cells was revealed in the PCOS group compared to the normal control group (P < 0.05). This study identified CNTN2, CASR, CACNB3, and MFAP2 as potential diagnostic biomarkers for PCOS, which provides strong evidence for existing research on hub genes. Furthermore, the analysis of immune cell infiltration revealed the significant involvement of CD4 memory resting T cells in the onset and progression of PCOS. These findings shed light on potential mechanisms underlying PCOS pathogenesis and provide valuable insights for future research and therapeutic interventions.

Objectives: To explore the relationship between different types of physical activity and female infertility. Methods: This study analyzed data from 2,796 female participants aged 18–44 years in the United States, obtained from the National Health and Nutrition Examination Survey (NHANES) database spanning the years 2013 to 2020. Multiple logistic regression analyses and generalized linear models were used to explore the relationship between different types of physical activity and infertility after adjusting for potential confounding factors. Results: We found a non-linear relationship between recreational activities and infertility with an inflection point of 5.83 h/week (moderate intensity), while work activities and traffic-related activities did not. On the left side of the inflection point, there was no significant association between recreational activity time and infertility (OR = 0.93, 95% CI: 0.86 to 1.02, P = 0.1146), but on the right side of the inflection point, there was a positive association between recreational activity time and the risk of infertility (OR = 1.04, 95% CI: 1.02 to 1.06, P = 0.0008). Conclusions: The relationship between different types of physical activity and female infertility varies. We acknowledge the potential influence of confounding variables on this relationship. However, we have already adjusted for these potential variables in our analysis. Therefore, our findings suggest that appropriate recreational activity programs are essential for promoting reproductive health in women of reproductive age. Nevertheless, it is important to note that the observed association does not imply causality. Given the limitations of cross-sectional studies, further prospective cohort studies are needed to explore the causal relationship while accounting for additional confounding factors.

Angiopoietin Like protein 3 (ANGPTL3) is at present considered as a central molecular target for therapy designed to reduce atherogenic lipids and atherosclerosis. However, concerns about the safety of inactivation of ANGPTL3 in patients with coronary artery disease (CAD) especially myocardial infarction (MI) have been raised. ANGPTL3 is reported to possess proangiogenic property. Angiogenesis is critical to the recovery of MI. Endothelial progenitor cells (EPCs) have multiple differentiation potential and play an important role in the angiogenesis post-MI. Promoting the function of EPCs could facilitate the angiogenesis and recovery of MI. Previous studies have shown that ANGPTL3 can promote angiogenesis in corneal of rats and promote angiogenesis of endothelial cells by binding to integrin ανβ3 receptors and promoting phosphorylation of protein kinase B (AKT). Our institution found that activated AKT can up-regulate the expression of microRNA-126 (miR-126), which can promote the proangiogenic ability of EPCs. The integrin ανβ3 receptors and AKT also express in EPCs and are closely related to proangiogenic function. Therefore, we hypothesized that ANGPTL3 could improve function of EPCs by binding to integrin ανβ3 receptors and up-regulating miR-126 expression via activating AKT, thus promoting the formation of new blood vessels, attenuating myocardial ischemia and improving heart function.

Objectives: To explore the association between tea, coffee, and caffeine consumption and the risk of female infertility. Methods: We analyzed data from 2099 females aged 18 to 44 years, participating in the National Health and Nutrition Examination Survey (NHANES) 2013–2018. We used generalized linear models (GLM) and generalized additive model (GAM) to investigate the dose-response relationship between the tea, coffee, and caffeine consumption and infertility, adjusting for potential confounders. Results: A non-linear relationship was detected between tea consumption and infertility and the inflection point was 2 cups/day. On the right side of the inflection point, we did not detect a significant association. However, on the left side, we found a negative relationship between tea consumption and infertility (OR: 0.73; 95% CI: 0.57 to 0.93; P = 0.0122). Meanwhile, our study found no significant association between coffee (0.96, 0.81 to 1.13, P = 0.6189) or caffeine consumption (1.15, 0.93 to 1.42, P = 0.2148) and female infertility. Conclusions: Tea consumption was non-linearly associated with infertility, whereas no significant associations were found between coffee, caffeine consumption and infertility.