Exploring potential associations and biomarkers linked polycystic ovarian syndrome with atherosclerosis via comprehensive bioinformatics analysis, machine learning, and animal experiments

Comprehensive Summary

This study by Zhao et al., examines possible associations and biomarkers that connect polycystic ovarian syndrome (PCOS) with atherosclerosis (AS) through analyzing bioinformatics, machine learning algorithms, and murine models. PCOS and AS gene expression datasets from Gene Expression Omnibus (GEO) were used to compare differentially expressed genes (DEGs) in PCOS tissues (granulosa cells, adipose tissues, and skeletal muscles) and the arterial walls of AS. Immune cell recruitment was also studied through examining differentially infiltrated immune cells and chemokine/receptor-immunocyte networks. The results indicated that the secretory pathogenic genes in PCOS are linked to AS and may influence its development through immune inflammation, abnormal lipid metabolism, and insulin metabolism. The possible role of chemokines from PCOS in immune cell recruitment to the atherosclerotic plaque and eventual progression of AS is also supported.

Outcomes and Implications

The increasing recognition of PCOS as a risk factor for cardiometabolic disorders, including AS, has sparked interest in the research of the pathophysiological link between PCOS and AS. It is supported that the endocrine dysfunction of the tissues involved in PCOS and subsequent inflammation contributes to the development of AS. The identification of strong biomarkers in PCOS-related AS is imperative for diagnosing and early-risk stratification. Prospective clinical cohort trials are necessary for the verification of the experimental results.