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

Authors

  • Shabnam Heydarzadeh Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Molecular Biology, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran https://orcid.org/0000-0002-9398-9211
  • Raziyeh Abooshahab Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Molecular Biology, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran; Curtin Medical School, Curtin University, Bentley, Australia https://orcid.org/0000-0001-7379-0339
  • Maryam Zarkesh Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Molecular Biology, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran https://orcid.org/0000-0002-8519-4865
  • Mehdi Hedayati Cellular and Molecular Endocrine Research Center, Research Institute for Endocrine Molecular Biology, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. E-mail: hedayati@endocrine.ac.ir, hedayati@sbmu.ac.ir, hedayati47@gmail.com https://orcid.org/0000-0001-5816-775X

DOI:

https://doi.org/10.17179/excli2025-8748

Keywords:

polyautoimmunity, autoimmune thyroid diseases, autoimmune polyendocrine syndrome, autoantibodies, pathology, artificial intelligence

Abstract

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.

Downloads

Published

2025-11-05

How to Cite

Heydarzadeh, S., Abooshahab, R., Zarkesh, M., & Hedayati, M. (2025). Endocrine polyautoimmunity: Mechanistic insights and the future of AI-driven diagnostics. EXCLI Journal, 24, 1500–1519. https://doi.org/10.17179/excli2025-8748

Issue

Vol. 24 (2025)

Section

Review articles

Categories

License

Copyright (c) 2025 Shabnam Heydarzadeh, Raziyeh Abooshahab, Maryam Zarkesh, Mehdi Hedayati

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish in this journal agree to the following terms:

  • The authors keep the copyright and grant the journal the right of first publication under the terms of the Creative Commons Attribution license, CC BY 4.0. This licencse permits unrestricted use, distribution and reproduction in any medium, provided that the original work is properly cited.
  • The use of general descriptive names, trade names, trademarks, and so forth in this publication, even if not specifically identified, does not imply that these names are not protected by the relevant laws and regulations.
  • Because the advice and information in this journal are believed to be true and accurate at the time of publication, neither the authors, the editors, nor the publisher accept any legal responsibility for any errors or omissions presented in the publication. The publisher makes no guarantee, express or implied, with respect to the material contained herein.
  • The authors can enter into additional contracts for the non-exclusive distribution of the journal's published version by citing the initial publication in this journal (e.g. publishing in an institutional repository or in a book).