Relapse prediction in Graves´ disease: Towards mathematical modeling of clinical, immune and genetic markers

Enhanced predictive validity of integrative models for refractory hyperthyroidism considering baseline and early therapy characteristics: a prospective cohort study

BACKGROUND

A subset of Graves' disease (GD) patients develops refractory hyperthyroidism, posing challenges in treatment decisions. The predictive value of baseline characteristics and early therapy indicators in identifying high risk individuals is an area worth exploration.

METHODS

A prospective cohort study (2018-2022) involved 597 newly diagnosed adult GD patients undergoing methimazole (MMI) treatment. Baseline characteristics and 3-month therapy parameters were utilized to develop predictive models for refractory GD, considering antithyroid drug (ATD) dosage regimens.

RESULTS

Among 346 patients analyzed, 49.7% developed ATD-refractory GD, marked by recurrence and sustained Thyrotropin Receptor Antibody (TRAb) positivity. Key baseline factors, including younger age, Graves' ophthalmopathy (GO), larger goiter size, and higher initial free triiodothyronine (fT3), free thyroxine (fT4), and TRAb levels, were all significantly associated with an increased risk of refractory GD, forming the baseline predictive model (Model A). Subsequent analysis based on MMI cumulative dosage at 3 months resulted in two subgroups: a high cumulative dosage group (average ≥ 20 mg/day) and a medium-low cumulative dosage group (average < 20 mg/day). Absolute values, percentage changes, and cumulative values of thyroid function and autoantibodies at 3 months were analyzed. Two combined predictive models, Model B (high cumulative dosage) and Model C (medium-low cumulative dosage), were developed based on stepwise regression and multivariate analysis, incorporating additional 3-month parameters beyond the baseline. In both groups, these combined models outperformed the baseline model in terms of discriminative ability (measured by AUC), concordance with actual outcomes (66.2% comprehensive improvement), and risk classification accuracy (especially for Class I and II patients with baseline predictive risk < 71%). The reliability of the above models was confirmed through additional analysis using random forests. This study also explored ATD dosage regimens, revealing differences in refractory outcomes between predicted risk groups. However, adjusting MMI dosage after early risk assessment did not conclusively improve the prognosis of refractory GD.

CONCLUSION

Integrating baseline and early therapy characteristics enhances the predictive capability for refractory GD outcomes. The study provides valuable insights into refining risk assessment and guiding personalized treatment decisions for GD patients.

Genotype-phenotype correlations in Graves' disease

Graves' disease (GD) is a heterogeneous autoimmune disease affecting with varying degrees of severity the thyroid gland, orbital tissues and skin. The pathogenesis of GD involves a complex interplay between multiple genetic, environmental and endogenous factors. Although the genetic predisposition to GD is well established, the significance of genotype-phenotype correlations and the role of epigenetic modifications in the disease pathogenesis remains largely unknown. In this review, we provide an up-to-date overview of genotype-phenotype correlations and summarize possible clinical implications of genetic and epigenetic markers in GD patients. We will specifically discuss the association of genetic markers and epigenetic modifications with age of GD onset, severity of Graves' hyperthyroidism and the development of clinically evident Graves' orbitopathy.

Clinically practical pharmacometrics computer model to evaluate and personalize pharmacotherapy in pediatric rare diseases: application to Graves' disease

Objectives

Graves' disease (GD) with onset in childhood or adolescence is a rare disease (ORPHA:525731). Current pharmacotherapeutic approaches use antithyroid drugs, such as carbimazole, as monotherapy or in combination with thyroxine hormone substitutes, such as levothyroxine, as block-and-replace therapy to normalize thyroid function and improve patients' quality of life. However, in the context of fluctuating disease activity, especially during puberty, a considerable proportion of pediatric patients with GD is suffering from thyroid hormone concentrations outside the therapeutic reference ranges. Our main goal was to develop a clinically practical pharmacometrics computer model that characterizes and predicts individual disease activity in children with various severity of GD under pharmacotherapy.

Methods

Retrospectively collected clinical data from children and adolescents with GD under up to two years of treatment at four different pediatric hospitals in Switzerland were analyzed. Development of the pharmacometrics computer model is based on the non-linear mixed effects approach accounting for inter-individual variability and incorporating individual patient characteristics. Disease severity groups were defined based on free thyroxine (FT4) measurements at diagnosis.

Results

Data from 44 children with GD (75% female, median age 11 years, 62% receiving monotherapy) were analyzed. FT4 measurements were collected in 13, 15, and 16 pediatric patients with mild, moderate, or severe GD, with a median FT4 at diagnosis of 59.9 pmol/l (IQR 48.4, 76.8), and a total of 494 FT4 measurements during a median follow-up of 1.89 years (IQR 1.69, 1.97). We observed no notable difference between severity groups in terms of patient characteristics, daily carbimazole starting doses, and patient years. The final pharmacometrics computer model was developed based on FT4 measurements and on carbimazole or on carbimazole and levothyroxine doses involving two clinically relevant covariate effects: age at diagnosis and disease severity.

Discussion

We present a tailored pharmacometrics computer model that is able to describe individual FT4 dynamics under both, carbimazole monotherapy and carbimazole/levothyroxine block-and-replace therapy accounting for inter-individual disease progression and treatment response in children and adolescents with GD. Such clinically practical and predictive computer model has the potential to facilitate and enhance personalized pharmacotherapy in pediatric GD, reducing over- and underdosing and avoiding negative short- and long-term consequences. Prospective randomized validation trials are warranted to further validate and fine-tune computer-supported personalized dosing in pediatric GD and other rare pediatric diseases.

Mathematical Modeling of Free Thyroxine Concentrations During Methimazole Treatment for Graves' Disease: Development and Validation of a Computer-Aided Thyroid Treatment Method

BACKGROUND

Methimazole (MMI) is the first-line treatment for patients with Graves' disease (GD). While there are empirical recommendations for initial MMI doses, there is no clear guidance for subsequent MMI dose titrations. We aimed to (a) develop a mathematical model capturing the dynamics of free thyroxine (FT4) during MMI treatment (b), validate this model by use of numerical simulation in comparison with real-life patient data (c), develop the software application Digital Thyroid (DigiThy) serving either as a practice tool for treating virtual patients or as a decision support system with dosing recommendations for MMI, and (d) validate this software framework by comparing the efficacy of its MMI dosing recommendations with that from clinical endocrinologists.

METHODS

Based on concepts of automatic control and by use of optimization techniques, we developed two first order ordinary differential equations for modeling FT4 dynamics during MMI treatment. Clinical data from patients with GD derived from the outpatient clinic of Endocrinology at the Medical University of Graz, Austria, were used to develop and validate this model. It was subsequently used to create the web-based software application DigiThy as a simulation environment for treating virtual patients and an autonomous computer-aided thyroid treatment (CATT) method providing MMI dosing recommendations.

RESULTS

Based on MMI doses, concentrations of FT4, thyroid-stimulating hormone (TSH), and TSH-receptor antibodies (TRAb), a mathematical model with 8 patient-specific constants was developed. Predicted FT4 concentrations were not significantly different compared to the available consecutively measured FT4 concentrations in 9 patients with GD (52 data pairs, p=0.607). Treatment success of MMI dosing recommendations in 41 virtually generated patients defined by achieved target FT4 concentrations preferably with low required MMI doses was similar between CATT and usual care. Statistically, CATT was significantly superior (p<0.001).

CONCLUSIONS

Our mathematical model produced valid FT4 predictions during MMI treatment in GD and provided the basis for the DigiThy application already serving as a training tool for treating virtual patients. Clinical trial data are required to evaluate whether DigiThy can be approved as a decision support system with automatically generated MMI dosing recommendations.

[T-lymphocytes phenotypic composition of peripheral blood in patients with Graves' disease undergoing conservative therapy with thiamazole]

BACKGROUND

Effective control of autoimmune inflammation in Graves' disease determines necessity to study the T helper (Th) and cytotoxic T-lymphocytes dysfunction, as well as the level of regulatory T-cells (Treg) activation in patients with Graves' disease on thyrostatic medication, which will clarify the immunomodulatory effects of long-term thiamazole treatment serve as targets for more specific therapies.

AIM

To study the phenotypic composition of T-lymphocytes in the peripheral blood of patients with Graves' disease to assess the direction of immune response depending on thimazole-induced euthyroidism duration.

MATERIALS AND METHODS

A single-center, cohort, continuous, open-label, controlled trial was conducted to assess the phenotypic composition of T-lymphocytes in peripheral blood in women with Graves' disease on long-term thiamazole treatment. The phenotypic composition of T-lymphocytes was determined by flow cytometry using direct immunofluorescence with conjugated FITC monoclonal antibodies depending on the duration of thimazole-induced euthyroidism of long-term thiamazole treatment.

RESULTS

The study included 135 women with Graves' disease, mean age 43.09±12.81 years, 120 (88.91%) with a relapse of the disease and 15 (11.09%) with newly diagnosed hyperthyroidism. An increase of activated CD3+CD4+CD25+ was found in patients with Graves' disease with a duration of thimazole-induced euthyroidism 5-8 months and 9-12 months, respectively, Me=0.94 (0.48-1.45), p=0.020) and Me=0.95 (0.41-1.80), p=0.025), in control group - Me=0.12 (0.03-0.68). Compared to the control an increase of CD4+CD25+CD127Low (Treg) was found in patients with a duration of thimazole-induced euthyroidism 5-8 and 9-12 months. The content of Treg in peripheral blood in Graves' disease patients with a duration of thimazole-induced euthyroidism more than 12 months decreases, but remains elevated relative to the control.

CONCLUSION

In patients with Graves' disease with a duration of thimazole-induced euthyroidism 5-8 months and 9-12 months the level of Treg has been increased. The increase of activated Th (CD3+CD4+CD25+) persists independently of thimazole-induced euthyroidism. In patients with Graves' disease with a duration of thimazole-induced euthyroidism for more than 12 months, there is a compensatory increase in regulatory T-lymphocyte, and the total number of T-helpers is restored to the control.

A model of functional thyroid disease status over the lifetime

Mathematical models of the natural history of disease can predict incidence rates based on prevalence data and support simulations of populations where thyroid function affects other aspects of physiology. We developed a Markov chain model of functional thyroid disease status over the lifetime. Subjects were in one of seven thyroid disease states at any given point in their lives [normal, subclinical hypothyroidism, overt hypothyroidism, treated thyroid disease (ever), subclinical hyperthyroidism, overt hyperthyroidism, and reverted to normal thyroid status]. We used a Bayesian approach to fitting model parameters. A priori probabilities of changing from each disease state to another per unit time were based on published data and summarized using meta-analysis, when possible. The probabilities of changing state were fitted to observed prevalence data based on the National Health and Nutrition Examination Survey 2007–2012. The fitted model provided a satisfactory fit to the observed prevalence data for each disease state, by sex and decade of age. For example, for males 50–59 years old, the observed prevalence of ever having treated thyroid disease was 4.4% and the predicted value was 4.6%. Comparing the incidence rates of treated disease predicted from our model with published values revealed that 82% were within a 4-fold difference. The differences seemed to be systematic and were consistent with expectation based on national iodine intakes. The model provided new and comprehensive estimates of functional thyroid disease incidence rates for the U.S. Because the model provides a reasonable fit to national prevalence data and predicts thyroid disease status over the lifetime, it is suitable for simulating populations, thereby making possible quantitative bias analyses of selected epidemiologic data reporting an association of thyroid disease with serum concentrations of environmental contaminants.

Mathematical models applied to thyroid cancer

Thyroid cancer is the most prevalent endocrine neoplasia in the world. The use of mathematical models on the development of tumors has yielded numerous results in this field and modeling with differential equations is present in many papers on cancer. In order to know the use of mathematical models with differential equations or similar in the study of thyroid cancer, studies since 2006 to date was reviewed. Systems with ordinary or partial differential equations were the means most frequently adopted by the authors. The models deal with tumor growth, effective half-life of radioiodine applied after thyroidectomy, the treatment with iodine-131, thyroid volume before thyroidectomy, and others. The variables usually employed in the models includes tumor volume, thyroid volume, amount of iodine, thyroglobulin and thyroxine hormone, radioiodine activity, and physical characteristics such as pressure, density, and displacement of the thyroid molecules. In conclusion, the mathematical models used so far with differential equations approach several aspects of thyroid cancer, including participation in methods of execution or follow-up of treatments. With the development of new models, an increase in the current understanding of the detection, evolution, and treatment of diseases is a step that should be considered.

A patient-specific treatment model for Graves' hyperthyroidism

Background

Graves’ is disease an autoimmune disorder of the thyroid gland caused

by circulating anti-thyroid receptor antibodies (TRAb) in the serum. TRAb mimics the action of thyroid stimulating hormone (TSH) and stimulates the thyroid hormone receptor (TSHR), which results in hyperthyroidism (overactive thyroid gland) and goiter. Methimazole (MMI) is used for hyperthyroidism treatment for patients with Graves’ disease.

Methods

We have developed a model using a system of ordinary differential equations for hyperthyroidism treatment with MMI. The model has four state variables, namely concentration of MMI (in mg/L), concentration of free thyroxine - FT4 (in pg/mL), and concentration of TRAb (in U/mL) and the functional size of the thyroid gland (in mL) with thirteen parameters. With a treatment parameter, we simulate the time-course of patients’ progression from hyperthyroidism to euthyroidism (normal condition). We validated the model predictions with data from four patients.

Results

When there is no MMI treatment, there is a unique asymptotically stable hyperthyroid state. After the initiation of MMI treatment, the hyperthyroid state moves towards subclinical hyperthyroidism and then euthyroidism.

Conclusion

We can use the model to describe or test and predict patient treatment schedules. More specifically, we can fit the model to individual patients’ data including loading and maintenance doses and describe the mechanism, hyperthyroidism→euthyroidism. The model can be used to predict when to discontinue the treatment based on FT4 levels within the physiological range, which in turn help maintain the remittance of euthyroidism and avoid relapses of hyperthyroidism. Basically, the model can guide with decision-making on oral intake of MMI based on FT4 levels.

Electronic supplementary material

The online version of this article (doi:10.1186/s12976-017-0073-6) contains supplementary material, which is available to authorized users.

Elevated Serum IL-17 Expression at Cessation Associated with Graves' Disease Relapse

BACKGROUND

Antithyroid drug (ATD) treatment occupies the cornerstone therapeutic modality of Graves' disease (GD) with a high relapse rate after discontinuation. This study aimed to assess potential risk factors for GD relapse especially serum interleukin-17 (IL-17) expression.

METHODS

Consecutive newly diagnosed GD patients who were scheduled to undergo ATD therapy from May 2011 to May 2014 were prospectively enrolled. Risk factors for GD relapse were analyzed by univariate and multivariate Cox proportional hazard analyses. The association between serum IL-17 expression at cessation and GD relapse was analyzed with relapse-free survival (RFS) by the Kaplan-Meier survival analysis and log-rank test.

RESULTS

Of the 117 patients, 72 (61.5%) maintained a remission for 12 months after ATD withdrawal and 45 (38.5%) demonstrated GD relapse. The final multivariate Cox analysis indicated elevated IL-17 expression at cessation to be an independent risk factor for GD relapse within 12 months after ATD withdrawal (HR: 3.04, 95% CI: 1.14-7.67, p = 0.021). Patients with higher expressions of IL-17 (≥median value) at cessation demonstrated a significantly higher RFS than those with lower levels by the Kaplan-Meier analysis and log-rank test (p = 0.028).

CONCLUSIONS

This present study indicated elevated serum IL-17 expression at cessation to be a predictor for GD relapse within 12 months.

Thyroid Allostasis-Adaptive Responses of Thyrotropic Feedback Control to Conditions of Strain, Stress, and Developmental Programming

The hypothalamus-pituitary-thyroid feedback control is a dynamic, adaptive system. In situations of illness and deprivation of energy representing type 1 allostasis, the stress response operates to alter both its set point and peripheral transfer parameters. In contrast, type 2 allostatic load, typically effective in psychosocial stress, pregnancy, metabolic syndrome, and adaptation to cold, produces a nearly opposite phenotype of predictive plasticity. The non-thyroidal illness syndrome (NTIS) or thyroid allostasis in critical illness, tumors, uremia, and starvation (TACITUS), commonly observed in hospitalized patients, displays a historically well-studied pattern of allostatic thyroid response. This is characterized by decreased total and free thyroid hormone concentrations and varying levels of thyroid-stimulating hormone (TSH) ranging from decreased (in severe cases) to normal or even elevated (mainly in the recovery phase) TSH concentrations. An acute versus chronic stage (wasting syndrome) of TACITUS can be discerned. The two types differ in molecular mechanisms and prognosis. The acute adaptation of thyroid hormone metabolism to critical illness may prove beneficial to the organism, whereas the far more complex molecular alterations associated with chronic illness frequently lead to allostatic overload. The latter is associated with poor outcome, independently of the underlying disease. Adaptive responses of thyroid homeostasis extend to alterations in thyroid hormone concentrations during fetal life, periods of weight gain or loss, thermoregulation, physical exercise, and psychiatric diseases. The various forms of thyroid allostasis pose serious problems in differential diagnosis of thyroid disease. This review article provides an overview of physiological mechanisms as well as major diagnostic and therapeutic implications of thyroid allostasis under a variety of developmental and straining conditions.