Nature Medicine：陈子江院士领衔，建立多囊卵巢综合征分型体系，并揭示各亚型的生殖和代谢结局差异

Core Viewpoint - The article discusses a groundbreaking study on Polycystic Ovary Syndrome (PCOS), identifying four distinct subtypes through AI-driven analysis, which can significantly enhance clinical diagnosis and treatment options for affected women [2][3][6]. Summary by Sections Study Overview - A research team led by Chen Zijiang from Shandong University published a study in Nature Medicine, collaborating with 11 international teams, focusing on the clinical outcomes associated with different PCOS subtypes [2][3]. Identification of Subtypes - The study identified four PCOS subtypes: 1. High Androgen PCOS (HA-PCOS) 2. Obesity PCOS (OB-PCOS) 3. High SHBG PCOS (SHBG-PCOS) 4. High LH/AMH PCOS (LH-PCOS) - This classification was based on nine clinical indicators derived from a large dataset of 11,908 PCOS patients [3][7][9]. Clinical Implications - The research revealed significant differences in reproductive and metabolic outcomes among the identified subtypes, providing evidence for more personalized treatment approaches [3][10]. - The study highlighted that women with PCOS are three times more likely to be obese and have a fourfold increased risk of developing type 2 diabetes before age 40 [5]. Tools for Clinical Application - The research team developed two practical tools, PcosX and a WeChat mini-program for subtype prediction, aimed at facilitating the application of their findings in clinical settings [12]. Longitudinal Findings - A 6.5-year follow-up showed varying reproductive and metabolic trajectories among the subtypes, with HA-PCOS patients experiencing the highest mid-pregnancy loss risk and OB-PCOS patients facing the most severe metabolic complications [10][11]. Validation of Findings - The subtypes demonstrated strong validation across diverse populations, indicating the robustness and applicability of the new classification system [9].