Exploring the diagnostic potential of micro-RNA-320 and anti-Müllerian hormone in women with polycystic ovary syndrome ‒ a case-control study

Abstract

Introduction and aim. Polycystic ovary syndrome (PCOS) is a multifactorial endocrine disorder characterized by hormonal imbalance, insulin resistance, and reproductive dysfunction. Due to its heterogeneous clinical presentation, diagnosis remains challenging. MicroRNA-320a-3p and anti-Müllerian hormone (AMH) have recently emerged as promising biomarkers. This study aimed to assess their diagnostic potential in women with PCOS. Material and methods. A case-control study was performed in 90 women aged 18–40 years, including 45 patients with polycystic ovary syndrome and 45 age- and body mass index-matched healthy controls. Hormonal and metabolic markers were measured using standard immunoassays, and the expression of microRNA-320a-3p was quantified using real-time polymerase chain reaction. Results. Patients with polycystic ovary syndrome demonstrated significantly higher levels of luteinizing hormone (9.45±6.0 vs 5.09±2.2 mIU/mL), increased luteinizing hormone to follicle-stimulating hormone ratio (1.64±0.87 vs 0.76±0.3), and elevated fasting blood glucose (105.5±14.7 vs 94.3±13.5 mg/dL), all with p<0.001. Contrary to expectations, insulin and homeostatic model assessment for insulin resistance values were lower in the polycystic ovary syndrome group, possibly reflecting a predominance of non-obese phenotypes. AMH levels were also reduced (2.27±1.0 vs 3.34±1.1 ng/mL, p<0.001). Expression of microRNA-320a-3p was significantly downregulated (0.61±1.27 vs 2.81±5.03-fold, p=0.0009). MicroRNA-320a-3p expression correlated positively with luteinizing hormone levels and the luteinizing hormone to follicle-stimulating hormone ratio, while AMH was associated with insulin resistance. The combined use of both markers improved diagnostic differentiation between groups. Conclusion. MicroRNA-320a-3p and AMH show promise as diagnostic biomarkers in polycystic ovary syndrome. Their integration with traditional clinical markers may enhance diagnostic accuracy and provide deeper insight into the pathophysiological complexity of the disorder.