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

Langenstein, Christoph; Schork, Diana; Badenhoop, Klaus; Herrmann, Eva

doi:10.1007/s11154-016-9386-8

Cited by 18 publications

(14 citation statements)

References 75 publications

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“…Recent studies have used mathematical models for diagnosis and prognosis of thyroid disorders, such as Hashimoto thyroiditis and Graves’ disease (408, 409). Multiple studies showed total step-up deiodinase activity (SPINA-GD) to be significantly reduced in subjects affected by NTIS (61, 299–301).…”

Section: Methods Of Assessment and Differential Diagnosismentioning

confidence: 99%

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

Hoermann²,

et al. 2017

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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.

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Section: Methods Of Assessment and Differential Diagnosismentioning

confidence: 99%

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

Hoermann²,

et al. 2017

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“…Basically, the model describes the clinical progression from hyperthyroidism to euthyroidism in accordance to treatment procedure. An earlier model constructed by Langenstein et al [ 20 ] for Graves’ hyperthyroidism did not keep track of MMI treatment over time. However, their article was an inspiration and provided a base for this new work.…”

Section: Methodsmentioning

confidence: 99%

A patient-specific treatment model for Graves’ hyperthyroidism

Pandiyan

Merrill

Bari

et al. 2018

Theor Biol Med Model

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BackgroundGraves’ is disease an autoimmune disorder of the thyroid gland causedby 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.MethodsWe 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.ResultsWhen 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.ConclusionWe 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 materialThe online version of this article (doi:10.1186/s12976-017-0073-6) contains supplementary material, which is available to authorized users.

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“…Graves' disease (GD), an organ-specific autoimmune disease, is the most common form of spontaneous hyperthyroidism, which is and caused by thyrotropin stimulating hormone (TSH) receptor antibodies (TRAb) [1]. About 1-2 % of females are affected by GD and half of the patients will result in chronic thyroid dysfunction [2].…”

Section: Introductionmentioning

confidence: 99%

MiR-346 and TRAb as Predicative Factors for Relapse in Graves’ Disease Within One Year

Cai

Sun

et al. 2017

Horm Metab Res

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Despite the efficacy and safety, antithyroid drug (ATD) therapy for Graves' disease (GD) is associated with a high risk of relapse, especially within the first year. The inability to predict whether and when relapse may occur is a major problem for ATD therapy. This study was aimed to investigate potential predicative factors for GD patients after ATD withdrawal. Consecutive patients newly diagnosed with GD and treated with ATD [methimazole (MMI)] were enrolled in this study. Univariate and multivariate Cox proportional hazard analyses were used for the analysis of predicative parameters for GD relapse after MMI withdrawal. Kaplan-Meier survival analysis and log-rank test were utilized for presenting the risk of relapse. Of the 103 patients included, 67 (65.0%) remained in remission and 36 (35.0%) had a relapse within 1 year after the MMI withdrawal. The multivariate analysis suggested significant predictive factors for GD relapse: patients with higher miR-346 expressions (≥median value) at diagnosis and at cessation, and lower TRAb levels at cessation. MiR-346 at diagnosis and cessation, and TRAb at cessation could serve as predictive factors for GD relapse within 1 year after drug withdrawal.

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

Cited by 18 publications

References 75 publications

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

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

A patient-specific treatment model for Graves’ hyperthyroidism

MiR-346 and TRAb as Predicative Factors for Relapse in Graves’ Disease Within One Year

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