Functional Proteins in Polycystic Ovary Syndrome

Polycystic Ovary Syndrome (PCOS) is currently the most common endocrine disorder in women of reproductive age with an unknown etiology. A predominant metabolic feature of PCOS is insulin resistance (IR), which contributes to the pathogenesis and expression of other symptoms of the syndrome, such as hyperandrogenism. The metabolic disturbances in women with PCOS result in a higher incidence of cardiovascular diseases and endometrial cancer in the premenopausal age. The gold standard for assessing peripheral tissue sensitivity to insulin is the hyperinsulinemic-euglycemic clamp technique; however, this method is not commonly used in clinical practice due to its complexities. Instead, simpler tests, such as the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) and fasting insulin measurements along with glucose tolerance tests, are employed. These methods are utilized for diagnosing insulin resistance rather than identifying predispositions. Therefore, there is an urgent need to identify more precise markers of IR that could be used in routine early-stage assessments. Given the long-term complications associated with PCOS, which can shorten life expectancy, early diagnosis and treatment of IR are crucial. Furthermore, such studies may contribute to exploring the etiopathogenesis of the syndrome. The discovery of "markers" that can predict or diagnose metabolic abnormalities at an early stage, prior to the onset of clinical symptoms, would facilitate early intervention and treatment, thus preventing a decline in quality of life associated with PCOS.

This study will measure and compare the concentrations of locally acting hormones: ghrelin, kisspeptin, and zonulin in three research groups: women with polycystic ovary syndrome (PCOS) and insulin resistance (IR), women with PCOS without IR, and women without PCOS. Measurements will be performed using immunoenzymatic methods (ELISA) and the Erba XL biochemical analyzer. Statistical calculations will be conducted using SPSS Statistics software.