Endocrine polyautoimmunity: Mechanistic insights and the future of AI-driven diagnostics

The most prevalent form of polyautoimmunity is autoimmune thyroid diseases (AITD), which frequently coexist with other autoimmune disorders and often act as a central conductor in the symphony of autoimmunity. Due to overlapping clinical manifestations, diagnosing polyautoimmunity presents significant clinical challenges. Patients with AITD exhibit increased susceptibility to additional autoimmune disorders, in which the exact etiology and underlying mechanisms of these associations remain incompletely understood. In this review, we aim to discuss how mechanistic insights contribute to our understanding of the associations between endocrine autoimmune diseases to recognize shared immunological, genetical, and pathological patterns for these diseases. Recent findings, including epitope spreading, cytokine imbalance, shared thyroidal and non-thyroidal autoantibodies, and common genetic susceptibilities, are highlighted. Additionally, the integration of artificial intelligence (AI) into autoimmune diagnostics is addressed, underscoring AI's potential to enhance early detection, improve diagnostic accuracy, and support personalized treatment approaches. By recognizing distinct immunological, genetical and pathological patterns within polyautoimmunity, clinicians and researchers can more effectively target the root causes of immune dysregulation, enabling improved management through personalized strategies and advanced AI-driven tools. See also the graphical abstract(Fig. 1).