A unified mathematical model of thyroid hormone regulation and implication for personalized treatment of thyroid disorders

Automatic Levothyroxine Dosing Algorithm for Patients Suffering from Hashimoto's Thyroiditis

Hypothyroidism is a condition where the patient's thyroid gland cannot produce sufficient thyroid hormones (mainly triiodothyronine and thyroxine). The primary cause of hypothyroidism is autoimmune-mediated destruction of the thyroid gland, referred to as Hashimoto's thyroiditis. A patient's desired thyroid hormone concentration is achieved by oral administration of thyroid hormone, usually levothyroxine. Establishing individual levothyroxine doses to achieve desired thyroid hormone concentrations requires several patient visits. Additionally, clear guidance for the dosing regimen is lacking, and significant inter-individual differences exist. This study aims to design a digital automatic dosing algorithm for patients suffering from Hashimoto's thyroiditis. The dynamic behaviour of the relevant thyroid function is mathematically modelled. Methods of automatic control are exploited for the design of the proposed robust model-based levothyroxine dosing algorithm. Numerical simulations are performed to evaluate the mathematical model and the dosing algorithm. With the help of the developed controller thyroid hormone concentrations of patients, emulated using Thyrosim, have been regulated under the euthyroid state. The proposed concept demonstrates reliable responses amidst varying patient parameters. Our developed model provides a useful basis for the design of automatic levothyroxine dosing algorithms. The proposed robust feedback loop contributes to the first results for computer-assisted thyroid dosing algorithms.

Autoimmune thyroid diseases as a cost of physiological autoimmune surveillance

Graves' disease (GD) and Hashimoto's thyroiditis (HT) are common autoimmune diseases of the thyroid gland, causing hyperthyroidism and hypothyroidism, respectively. Despite their opposing clinical manifestation, they have several enigmatic links. Here, we propose that GD and HT have the same fundamental origin: both diseases are the cost of a beneficial physiological process called autoimmune surveillance of hypersecreting mutants. Autoreactive T cells selectively eliminate mutant cells that hypersecrete the hormones and threaten to become toxic nodules. These T cells can trigger a humoral response in susceptible individuals, leading to the production of antibodies against thyroid antigens. This shared origin can explain similarities in incidence and risk factors between HT and GD, despite their opposite clinical phenotypes.

Graves' disease induced by Alemtuzumab in relapsing-remitting multiple sclerosis patients: an observational study in a reference center

OBJECTIVES

Graves' disease induced by Alemtuzumab (GD-IA) is one of the most frequently observed adverse events in patients with multiple sclerosis (MS) treated with this drug. The aim of this study is the sequencing and description of these events, along with the identification of the risk factors leading to their development.

MATERIALS AND METHODS

We conducted a retrospective observational study identifying patients with relapsing-remitting multiple sclerosis (RRMS) and GD-IA, studying their baseline clinical features and variables related to the natural history of the disease.

RESULTS

A total of 121 participants treated with Alemtuzumab were included, of whom 41 developed GD-IA (33.9%). A higher percentage of first-degree relatives with autoimmune thyroid disease was documented in the subgroup who developed the abovementioned event (14.6% vs 1.5%; p < 0.01). A total of 70.7% of patients diagnosed with GD-IA (n = 29/41) had fluctuations in thyroid function during follow-up, and 24.4% (n = 10/41) required total thyroidectomy for resolution of the condition. In 54.8% of participants diagnosed with GD-IA, a pattern of significant TSH decline was identified in the month prior to diagnosis of the event, with high predictive ability and associated with a more favorable clinical course (fewer weeks to normalization of thyroid function, HR = 8.99; 95% CI [2.11-38.44]; p = 0.0003).

CONCLUSION

GD-IA has an atypical course compared to classical forms of the disease. The identification of risk factors for the development of the disease before starting treatment with Alemtuzumab and early monitoring of thyroid function once this treatment is initiated prove to be useful strategies in the diagnosis and clinical management of this condition.

The progression of secondary diabetes: A review of modeling studies

Mathematical modeling has provided quantitative information consistent with experimental data, greatly improving our understanding of the progression of type 1 and type 2 diabetes. However, diabetes is a complex metabolic disease and has been found to be involved in crosstalk interactions with diverse endocrine diseases. Mathematical models have also been developed to investigate the quantitative impact of various hormonal disorders on glucose imbalance, advancing the precision treatment for secondary diabetes. Here we review the models established for the study of dysglycemia induced by hormonal disorders, such as excessive glucocorticoids, epinephrine, and growth hormone. To investigate the influence of hyperthyroidism on the glucose regulatory system, we also propose a hyperthyroid-diabetes progression model. Model simulations indicate that timely thyroid treatment can halt the progression of hyperglycemia and prevent beta-cell failure. This highlights the diagnosis of hormonal disorders, together withblood sugar tests, as significant measures for the early diagnosis and treatment of diabetes. The work recapitulates updated biological research on the interactions between the glucose regulatory system and other endocrine axes. Further mathematical modeling of secondary diabetes is desired to promote the quantitative study of the disease and the development of individualized diabetic therapies.

Optimal Hormone Replacement Therapy in Hypothyroidism - A Model Predictive Control Approach

In this paper, we address the problem of optimal thyroid hormone replacement strategy development for hypothyroid patients. This is challenging for the following reasons. First, it is difficult to determine the correct dosage leading to normalized serum thyroid hormone concentrations of a patient. Second, it remains unclear whether a levothyroxine L-T₄) monotherapy or a liothyronine/levothyroxine (L-T₃/L-T₄) combined therapy is more suitable to treat hypothyroidism. Third, the optimal intake frequency of L-T₃/L-T₄ is unclear. We address these issues by extending a mathematical model of the pituitary-thyroid feedback loop to be able to consider an oral intake of L-T₃/L-T₄. A model predictive controller (MPC) is employed to determine optimal dosages with respect to the thyroid hormone concentrations for each type of therapy. The results indicate that the L-T₃/L-T₄ combined therapy is slightly better (in terms of the achieved hormone concentrations) to treat hypothyroidism than the L-T₄ monotherapy. In case of a specific genetic variant, namely genotype CC in polymorphism rs2235544 of gene DIO1, the simulation results suggest that the L-T₄ monotherapy is better to treat hypothyroidism. In turn, when genotype AA is considered, the L-T₃/L-T₄ combined therapy is better to treat hypothyroidism. Furthermore, when genotype CC of polymorphism rs225014 (also referred to as c.274A>G or p.Thr92Ala) in the DIO2 gene is considered, the outcome of the L-T₃/L-T₄ combined therapy is better in terms of the steady-state hormone concentrations (for a triiodothyronine setpoint at the upper limit of the reference range of healthy individuals). Finally, the results suggest that two daily intakes of L-T₃ could be the best trade-off between stable hormone concentrations and inconveniences for the patient.