Abstract
Polycystic ovarian syndrome (PCOS) is the most common endocrine disease that causes reproductive abnormalities in fertile women. It is closely related to the persistent anovulatory, insulin resistance, and high androgen. However, the molecular mechanisms underlying the pathological development of PCOS are still unclear. In this study, we aimed to explore the distinctive metabolic patterns in insulin combined with human chorionic gonadotrophin induced PCOS. The dynamic changes of endogenous metabolites in the development of PCOS were studied using untargeted metabolomic approaches based on nuclear magnetic resonance. The results showed that the degree of PCOS disorder metabolites at different periods was not exactly the same. Twelve significantly differential endogenous metabolites from different time points were selected as potential biomarkers relate to pathological process of PCOS. Among them, six metabolites showed a good diagnostic accuracy with PCOS model. The arginine and proline metabolic pathway was considered as one of the most crucial pathways that affects occurrence and development of PCOS. In addition, IRS-1, Akt, PI3K, IκB, and NF-κB (p65) were significantly changed in the ovary tissue of PCOS rats, which revealed that the IRS-1-PI3K/Akt and NF-κB signal pathway may be involved in the development of PCOS. This study demonstrated that metabolomic analysis is a powerful tool for providing novel insight into understanding the pathogenesis of PCOS and provide a basic reference for the diagnosis of PCOS at the onset stage.
Key words
metabonomics - dynamic analysis - polycystic ovary syndrome - nuclear magnetic resonance
