PCOS stratification for precision diagnostics and treatment.

Area of Science:

• Reproductive endocrinology
• Metabolic disorders
• Genetics

Background:

• Polycystic ovary syndrome (PCOS) affects 10% of women globally, presenting a significant health and economic challenge.
• PCOS is a heterogeneous disorder characterized by symptoms like irregular menstruation, hyperandrogenism, and polycystic ovarian morphology, often co-occurring with obesity.
• Diagnostic challenges arise from phenotypic heterogeneity and overlapping symptoms across a woman's reproductive lifespan.

Purpose of the Study:

• To highlight the urgent need for evidence-based, standardized guidelines for PCOS diagnosis and treatment.
• To emphasize the necessity of diverse genomic and clinical data for developing machine learning models for PCOS stratification.
• To advocate for subtype-specific strategies for early screening, accurate diagnosis, and lifelong management of PCOS.

Main Methods:

• Review of current understanding of PCOS etiology, diagnosis, and management.
• Identification of knowledge gaps in PCOS research, particularly concerning diverse populations.
• Proposal for the integration of machine learning and big data analytics for PCOS stratification.

Main Results:

• PCOS diagnosis is complex, often a diagnosis of exclusion, complicated by its heterogeneous nature and comorbidities like obesity.
• Current understanding of PCOS etiology is incomplete, suggesting a complex interplay of metabolic and reproductive factors.
• Lack of standardized guidelines hinders effective and efficient PCOS management.

Conclusions:

• Standardized diagnostic and treatment guidelines for PCOS are critically needed.
• Integration of diverse genomic and clinical data is essential for advancing PCOS research and personalized medicine.
• Subtype-specific, lifelong management strategies will optimize healthcare and improve patient outcomes for PCOS.