Biomarker-driven diagnostics and personalized treatment in polycystic ovary syndrome
Abstract
Polycystic Ovary Syndrome (PCOS) is a common and clinically heterogeneous endocrine disorder affecting approximately 4-20% of women worldwide. Its complex presentation frequently leads to delayed diagnosis, increasing the risk of long-term reproductive and metabolic complications. This review critically examines emerging biomarkers spanning hormonal, metabolic, inflammatory, and molecular pathways to support earlier diagnosis and enable personalized management strategies. The findings indicate that PCOS pathophysiology is multifactorial, underscoring the limitations of relying on individual biomarkers and highlighting the need for a comprehensive multi-marker diagnostic framework. Among hormonal indicators, Anti-Müllerian Hormone (AMH) remains the most reliable marker; however, diagnostic accuracy is enhanced when AMH is integrated with biomarkers of metabolic dysfunction (such as adiponectin and C-peptide), inflammatory status (hs-CRP), and molecular alterations, including microRNAs. Notably, artificial intelligence-assisted diagnostic models incorporating these parameters have demonstrated high predictive performance. Early identification of PCOS is essential for implementing individualized treatment approaches that emphasize lifestyle interventions alongside tailored pharmacological therapies, including combined oral contraceptives, metformin, and letrozole. Despite advances, challenges such as assay variability and the lack of standardized diagnostic criteria persist.…
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