A New Mathematical Approach for Hashimoto’s Thyroiditis in Children

Marcello Pompa, Andrea De Gaetano, Alessandro Borri, Antonella Farsetti, Simona Nanni, Laura D’Orsi and Simona Panunzi ()
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Marcello Pompa: Institute of Systems Analysis and Informatics “A. Ruberti” (IASI), National Research Council of Italy, Via dei Taurini, 19, 00185 Roma, Italy
Andrea De Gaetano: Institute of Systems Analysis and Informatics “A. Ruberti” (IASI), National Research Council of Italy, Via dei Taurini, 19, 00185 Roma, Italy
Alessandro Borri: Institute of Systems Analysis and Informatics “A. Ruberti” (IASI), National Research Council of Italy, Via dei Taurini, 19, 00185 Roma, Italy
Antonella Farsetti: Institute of Systems Analysis and Informatics “A. Ruberti” (IASI), National Research Council of Italy, Via dei Taurini, 19, 00185 Roma, Italy
Simona Nanni: Institute of Medical Pathology, Università Cattolica del Sacro Cuore, Largo Francesco Vito, 1, 00168 Roma, Italy
Laura D’Orsi: Institute of Systems Analysis and Informatics “A. Ruberti” (IASI), National Research Council of Italy, Via dei Taurini, 19, 00185 Roma, Italy
Simona Panunzi: Institute of Systems Analysis and Informatics “A. Ruberti” (IASI), National Research Council of Italy, Via dei Taurini, 19, 00185 Roma, Italy

Mathematics, 2024, vol. 12, issue 22, 1-16

Abstract: Hashimoto’s thyroiditis (HT) is a prevalent autoimmune disorder marked by chronic inflammation of the thyroid gland, predominantly affecting children and adolescents. In a previous study, we developed a “maximal” mathematical model of thyroid physiology to simulate the complex interactions within the thyroid gland. The present research introduces an enhanced version of the “maximal” model, integrating the pathophysiological impacts of HT. It specifically models the adverse effects of thyroid peroxidase (TPO) and thyroglobulin (Tg) antibodies (TPOAb and TgAb) on TPO, Tg, sodium iodide symporter (NIS), albeit indirectly, and thyroid volume. Additionally, we present a new “minimal” model offering a streamlined interpretation of thyroid physiology and pathophysiology, designed for faster computational analysis while maintaining essential physiological interactions. Both models were fitted against longitudinal clinical data from patients with HT, assessing the concentrations of Thyroid Stimulating Hormone (TSH), Thyroxine (T4), and thyroid volume over 36 months, in both untreated patients and those receiving levothyroxine (LT4) treatment. The adaptation of the models to data shows that both of them accurately reproduce the available observed clinical outcomes, with the “maximal” model providing more detailed physiological insights but requiring extensive data and longer computation times. In contrast, the “minimal” model, despite exhibiting less realistic TSH oscillations, offers rapid parameter estimation and may be more feasible in clinical settings. These models hold significant potential as tools for detailed study and management of HT, enabling simulations of disease progression and therapeutic responses, thus paving the way for personalized treatment strategies.

Keywords: Hashimoto; thyroid; mathematical model; hormones (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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