Endocrine - metabolic network architecture reveals key bridge biomarkers in polycystic ovary syndrome

Context: Polycystic ovary syndrome (PCOS) is a heterogeneous endocrine disorder involving complex interactions among endocrine, metabolic, inflammatory, and thyroid pathways. However, the systems-level organization of these interactions remains poorly understood. Objective: To reconstruct the endocrine-metabolic biomarker network in women with PCOS and identify bridge biomarkers integrating distinct physiological domains. Design: Retrospective cross-sectional study. Setting: Single tertiary referral center. Participants: A total of 1,286 women diagnosed with PCOS according to the revised Rotterdam criteria. Methods: Twenty-nine routinely measured laboratory biomarkers representing endocrine, metabolic, hematological/inflammatory, and thyroid domains were analyzed. Sparse Gaussian graphical models were estimated using Graphical LASSO with Extended Bayesian Information Criterion model selection. Network topology, node centrality, bridge centrality, bootstrap resampling, and predefined sensitivity analyses were performed. Results: The reconstructed network comprised 29 biomarkers connected by 73 conditional dependency edges (network density, 0.18), demonstrating a modular but highly integrated endocrine-metabolic architecture. Conventional centrality analysis primarily identified biomarkers organizing local physiological modules, whereas bridge-centrality analysis revealed biomarkers coordinating communication between biological domains. Sex hormone-binding globulin exhibited the highest bridge strength, followed by fasting insulin, triglycerides, and high-density lipoprotein cholesterol. Additional reproducible bridge biomarkers included free thyroxine, white blood cell count, 2-hour plasma glucose, absolute neutrophil count, androstenedione, and anti-thyroglobulin antibodies. The leading bridge biomarkers remained stable across bootstrap resampling, complete-case reconstruction, and alternative network specifications. Conclusions: PCOS is characterized by an integrated endocrine-metabolic network organized around a limited number of reproducible bridge biomarkers linking multiple physiological systems. Network analysis provides complementary systems-level information beyond conventional biomarker evaluation and may facilitate future biological phenotyping and precision medicine approaches in PCOS.