Polycystic Ovary Syndrome (PCOS) is a multifaceted endocrine disorder affecting millions of individuals worldwide, characterized by its clinical heterogeneity.^[1] Current diagnostic modality, Rotterdam Criteria, primarily relies upon clinical symptoms, hormonal imbalances (hyperandrogenism), and ultrasound findings.^[2] However, this criterion often falls short in capturing the full spectrum of PCOS manifestations in various phenotypic subgroups. Recognizing the need for a more precise phenotypic classification system, there is a growing consensus that such a system is essential for gaining a deeper understanding of the condition, predicting its clinical trajectory, and tailoring interventions more effectively. While previous research has separately delved into PCOS from hormonal and metabolic perspectives, there has been a conspicuous absence of a comprehensive exploration into the connections between PCOS phenotypes, body composition, hormonal profiles, and vagosympathetic balance. This study seeks to bridge this critical knowledge gap by simultaneously employing advanced body composition analysis techniques ^[3-6], incorporating recent endocrine biomarkers like Anti-Müllerian Hormone (AMH) ^[7], and assessing vagosympathetic balance ^[5] to reclassify individuals with PCOS. The outcomes of this research will illuminate the heterogeneity of PCOS ^[8-10] and contribute to the development of more personalized diagnostic and therapeutic strategies, ultimately leading to improved patient outcomes.