TSH and Thyrotropic Agonists: Key Actors in Thyroid Homeostasis

Abstract

This paper provides the reader with an overview of our current knowledge of hypothalamic-pituitary-thyroid feedback from a cybernetic standpoint. Over the past decades we have gained a plethora of information from biochemical, clinical, and epidemiological investigation, especially on the role of TSH and other thyrotropic agonists as critical components of this complex relationship. Integrating these data into a systems perspective delivers new insights into static and dynamic behaviour of thyroid homeostasis. Explicit usage of this information with mathematical methods promises to deliver a better understanding of thyrotropic feedback control and new options for personalised diagnosis of thyroid dysfunction and targeted therapy, also by permitting a new perspective on the conundrum of the TSH reference range.

Figures

Figure 1

Information processing structure of the logarithmic standard model of thyroid homeostasis [105, 121].

Figure 2

Information processing structure of a nonlinear parametrically isomorphic model based on Michaelis-Menten kinetics, noncompetitive divisive inhibition, and pharmacokinetic data [11]. Modified with permission from [49].

Figure 3

SimThyr, a continuous simulation program for thyrotropic feedback control [11].

Figure 4

Characteristic curves of pituitary and thyroid. The area shaded in green denotes univariate reference ranges for TSH and FT4. The dashed red line denotes the pituitary's response in form of TSH incretion to varying FT4 levels; the continuous blue line represents the thyroid's response to TSH. Note that for the response curve of the thyroid—contrary to convention—the ordinate (TSH) is the independent axis, while the dependent axis is the abscissa (FT4). This uncommon notation facilitates superposition of both characteristic curves. Marked is a normal equilibrium point (also referred to as setpoint) defined by the intersection of both 50% percentiles. Response curves were calculated from percentiles for secretory capacities of pituitary (GH) and thyroid (GT) using the mathematical model displayed in Figure 2. Structure parameters were derived from a subgroup of subjects included in the NOMOTHETICOS trial [153].

Figure 5

Successive development of hypothyroidism as a consequence of decreasing GT. Beginning with a hypothetical “sublatent” form defined by reduced GT and still normal levels of TSH and FT4 (panel b), further steps are subclinical hypothyroidism with increased TSH levels and FT4 still in the lowest fraction of the reference region (panel c) and overt hypothyroidism where both parameters have left their reference region (panel d). See text for additional information.

Figure 6

Partial and complete thyrotropic insufficiency as results of nonlinear interaction of pituitary and thyroid. TSH axis is logarithmically scaled in order to zoom small values. GH values are given in percent from normal values. See text for additional information.

Figure 7

Computer simulation of thyrotropic adaptation in critical illness. A gradual increase of central type 2 deiodinase activity over several days with subsequent restoration to normal values has been simulated with SimThyr using the mathematical model shown in Figure 2. Note the temporarily increased TSH values after day 17 that are occasionally observed also in vivo in patients recovering from nonthyroidal illness syndrome.

Figure 8

Interaction of TSH, thyrotropic agonists, and thyrotropic antagonists with TSH receptor. SMLs bind to a pocket within the heptahelical transmembrane domain, while TSH, HCG, and TRAbs bind primarily to the TSHr amino-terminal ectodomain.

Figure 9

Comparison of conventional univariate reference ranges for TSH and FT4 (grey box) and a bihormonal reference region (green kite-like area) from nonlinear modelling of thyroid homeostasis. For more information see text and legend of Figure 4.