A Biological Disease-Staging Model of Polyendocrine Metabolic Ovarian Syndrome: From Plasticity-Dominant States to...

Polyendocrine metabolic ovarian syndrome (PMOS) is a heterogeneous endocrine-metabolic disorder characterized by reproductive dysfunction, metabolic abnormalities, and hormonal dysregulation. Although the Rotterdam criteria are widely used for phenotypic classification, accumulating evidence suggests that disease heterogeneity reflects varying contributions of insulin resistance, hyperandrogenism, chronic inflammation, mitochondrial dysfunction, autophagy dysregulation, and cellular senescence. To review the role of integrated intracellular signaling networks in PMOS and propose a conceptual model that explains disease progression and heterogeneity. A narrative review of studies published through December 2025 was conducted, focusing on insulin resistance, androgen excess, inflammation, autophagy, mitochondrial dysfunction, cellular senescence, and reproductive dysfunction. The present review proposes a three-stage disease spectrum consisting of a plasticity-dominant state, an adaptive-transition state, and a pathological-remodeling state. The plasticity-dominant state is characterized by preserved tissue adaptability despite metabolic and endocrine abnormalities. The adaptive-transition state involves persistent signaling dysregulation accompanied by impaired autophagy and mitochondrial dysfunction. In the pathological-remodeling state, cellular senescence, senescence-associated secretory phenotype (SASP)-driven inflammation, fibrosis, and tissue remodeling become increasingly prominent, resulting in reduced tissue plasticity and sustained disease maintenance. In conclusion, PMOS may be better understood as a dynamic pathophysiological spectrum rather than a static disorder. This model integrates metabolic dysfunction, inflammation, mitochondrial impairment, cellular senescence, and tissue remodeling, providing a complementary framework to the Rotterdam classification and a potential basis for improved disease stratification and personalized therapeutic strategies.