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Regulatory aspects of the human hypothalamus-pituitary-thyroid axis

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Thyroid hormones are essential for growth, differentiation and metabolism during prenatal and postnatal life. The hypothalamus-pituitary-thyroid (HPT)-axis is optimized for these actions. Knowledge of this hormonal axis is derived from decades of experiments in animals and man, and more recently from spontaneous mutations in man and constructed mutations in mice. This review examines the HPT-axis in relation to 24 h TSH profiles in men in various physiological and pathophysiological conditions, including obesity, age, longevity, and primary as well as central hypothyroidism. Hormone rhythms can be analyzed by quantitative methods, e.g. operator-independent deconvolution, approximate entropy and fitting the 24-h component by Cosinor analysis or related procedures. These approaches have identified some of the regulatory components in (patho)physiological conditions.

Introduction

The pituitary gland plays a central role in regulating the hypothalamus-pituitary-thyroid (HPT) axis by secreting thyroid stimulating hormone (TSH) acting on the thyroid gland. In addition to hormonal signals peripheral organs, including endocrine glands, are innervated by the autonomic nervous system which is important for fine tuning of metabolic and secretory processes. In the early sixties, it became evident that hormones such as cortisol, LH, GH, TSH and ACTH are secreted episodically and with a diurnal variation, each with their specific secretion pattern under physiological conditions. Further understanding of regulatory aspects became possible by the isolation of hypothalamic hormones (stimulatory or inhibitory) acting via the pituitary portal blood system on specific pituitary cell systems. The first isolated hypothalamic hormone was thyrotropin-releasing hormone (TRH) acting on the thyrotrope and causing release of TSH. The TSH antagonist, somatostatin, was discovered shortly thereafter [1], [2].

Insights of human pituitary hormone regulation has been obtained during decades of investigations analyzing 24-h hormone rhythms under various conditions, including fasting, changes in the wake–sleep cycle, puberty, the menstrual cycle, aging, obesity and pituitary diseases including acromegaly and Cushing's disease. The purpose of this review is to summarize current knowledge on 24-h TSH secretion profiles in 1) healthy individuals and describe the influence of sex, age, body composition, wake–sleep cycle and the effects of certain drugs; 2) major endocrine disorders, including hyperthyroidism, primary hypothyroidism, central hypothyroidism, low triiodothyronine (T3) syndrome; and 3) to highlight current lack of knowledge of TSH profiles in patients with thyroid hormone resistance syndromes, T4-transporter defects, or deiodinase polymorphisms. Before discussing these aspects, a brief summary is provided of the central and peripheral regulation of the HPT-axis.

Precise regulation of circulating and intracellular concentrations of thyroid hormones (thyroxine (T4)) and the biologically active thyroid hormone triiodothyronine (T3) is essential for the control of metabolic processes, heat production, physical development, weight maintenance, cell differentiation and growth [3]. The prime stimulatory hormone for the thyroid gland is thyrotropin (TSH), with numerous secondary modulators, including insulin-like growth factor type I (IGF-I), inflammatory cytokines (Il-1, IL-6, and TNF-α), and iodide availability [4]. TSH release in turn is under the stimulatory control of the hypophysiotropic paraventricular TRH neurons, and under the inhibitory control of the neurotransmitters, dopamine and somatostatin, and negative feedback control by T4 and T3 [5]. The discovery that TRH was upregulated or downregulated by hypothyroidism and hyperthyroidism, respectively, established the central role of TRH in the HPT-axis [6]. The bioactive hypothalamic tripeptide TRH is processed from prepro-TRH in a subset of neurons in the paired paraventricular nuclei (PVN). TRH neurons project on the external median eminence, where nerve terminals secrete TRH into the portal blood-vessel system [7], [8]. TRH is essential for proper functioning of the TSH-thyroid complex. Thus, human TRH-receptor mutations and murine mutations of the TRH gene both lead to (central) hypothyroidism [9], [10]. Hypophysiotropic TRH neurons in the PVN receive monosynaptic input from leptin-responsive neurons in the arcuate nucleus, where the blood–brain barrier is less prominent. These neurons synthesize either proopiomelanocortin and cocaine-and-amphetamine-related transcript (POMC/CART) or agouti-related peptide and neuropeptide Y (AGRP/NPY). These metabotropic neurons, which express leptin receptors are crucial for energy homeostasis and food intake [11]. Several studies in human and rodents have shown downregulation of the HPT-axis at the central and peripheral levels during fasting, and this effect is mediated by a downregulation of the hypophysiotropic TRH neurons due to diminished serum leptin concentration, which induces increased NPY expression in the arcuate nucleus and increased local T3 concentration in the arcuate nucleus [12].

Feedback signaling by thyroid hormones on TRH gene transcription and peptide synthesis is important. Studies in the rat have shown that thyroid hormone feedback on TRH is regulated via local intracellular conversion of T4 into T3 by type 2 deiodinase (D2) in the tanycyte, a specialized glial cell, lining the third ventricle ∗[11], [13]. Thus, D2 knockout mice have inappropriately elevated TSH concentrations in the face of increased T4 concentrations [14].

Serum TSH concentrations exhibit a distinct diurnal (circadian) rhythm generated by the suprachiasmatic nuclei (SCN), the central biological clock. In intact animals TRH also shows a clear diurnal pattern [15]. Bilateral lesions of the SCN in the rat abolish diurnal rhythms, including that of vasopressin in the central spinal fluid (CSF) originated from the SCN, and serum TSH, T4 and T3 [16]. How the SCN modulates the daily rhythmicity of the TRH neurons is still not completely understood, although a mechanism comparable to that of the hypophysiotropic CRH neurons is likely [17]. In addition, it is possible that the TRH rhythm is (partly) generated via changing intracellular levels of T3 via D2, since the D2 enzyme exhibits a marked diurnal activity in various brain areas and destruction of the SCN abolishes this enzyme rhythm [18]. Other factors, including leptin, dopamine and somatostatin modulate TRH release and/or synthesis [5].

Early approaches to describing 24-h hormone patterns consisted of calculating the mean 24-h concentration, noting time and magnitude of the maximal and minimal values and visually identifying assumed peaks or pulses. A general approach to identifying the acrophase (time of maximal value) is Cosinor analysis, which essentially is the non-linear fitting of a sinusoidal function to the data series. Disadvantages of this procedure are that the time of minimal concentration (nadir) is definitionally 12 h out of phase with the acrophase, which is rarely observed for hormone rhythms, and that only a small part of the physiological pattern is explained. Other methods have also been applied, including Fourier transformation with a 24 h and a 12 h component and the robust baseline fitting of van Cauter [19]. A variant of the Cosinor analysis is a modification which allows for the skewness of the nocturnal peak [20].

In order to explain a greater proportion of short-term secretion variability over time, objective pulse-detection programs were developed. Early methods of pulse analysis used an empirical threshold approach. A brief increase in hormone concentrations greater than that explicable by the intra-assay variability was defined as a pulse. Examples are the Santen and Bardin method (threshold is a 20% increase in any concentration), Cluster analysis (a critical two-sample t-statistic is required for accepting significant upstrokes and downstrokes in a peak), and the regional coefficient of variation method, which employs local sample variance to test for peaks [21]. Semi-empirical methods use nadir and/or baseline estimates to specify superimposed pulsatility, but lack the combination of primary in vivo experimental validation and direct mathematical proof. Desade, Ultra, Detect and Pulsar discriminate baseline or nadir concentrations by numerical filtering, baseline detrending or line segmentation criteria. The first three techniques incorporate secretion estimates, thus representing a deconvolution approach. The performance characteristics and limitations of the empirical and semi-empirical methods are quite similar and extensively discussed in several reviews on this subject [22].

More recently developed deconvolution methods simultaneously assess endogenous half-lives along with pulse number and the other secretion parameters. A significant improvement is the application of automated maximum likelihood estimation-based deconvolution techniques. All parameters, including shape parameters, elimination parameters, basal secretion and random variability are simultaneously calculated for each pulse-timing set. This method has been extensively validated with in vivo experiments in men and animals, as well with simulation techniques [23]. Currently, the operator-independent Matlab-based deconvolution analysis is further refined (unpublished data). It should be realized that these expensive and time-consuming methods are not necessary for diagnostic or therapeutic purposes, but only for detailed, quantitative (patho)physiological studies.

Aside from quantifying hormone secretion, estimating half-life etc., another powerful tool to investigate hormone secretion is Approximate Entropy (ApEn). ApEn is a scale- and model-independent univariate regularity statistic used to quantify the orderliness (subpattern consistency) of serial stationary (nontrending) measurements [24]. Higher ApEn defines reduced regularity of hormone secretion, which in general typifies puberty, aging, diminished negative feedback due to target-gland failure, fixed exogenous stimulation, and autonomous neuroendocrine tumors [25].

Section snippets

Normal serum TSH patterns

The normal serum TSH profile is characterized by lowest TSH concentrations during the afternoon, followed by a rise during the late evening and a maximum in the first part of the night [26], [27]. After the onset of sleep, TSH levels decrease, which is prevented by keeping the subject awake [28], [29]. In addition to this nychthemeral pattern, repeated variably sized bursts of TSH release occur during the 24-h period as quantitated by multifrequency Fourier analysis, Cluster analysis or Desade

TSH secretion in primary hypothyroidism

The diurnal TSH secretion pattern shows increased levels throughout the 24-h day–night cycle, and often, but not always, an absent diurnal variation by comparing mean day and mean night levels [30], [81], [82]. Mild (subclinical) hypothyroidism shared characteristics with normal controls, i.e. intact diurnal secretion pattern, increased pulse amplitude and increased pulse frequency during the nocturnal surge. Total secretion was increased by ten-fold in subclinical hypothyroidism and by

Thyroid hormone resistance syndromes

Multiple steps are required for secreted thyroid hormone to exerts its effects on target tissues, including active transport across the cell membrane, intracellular metabolism and hormone activation, cytosolic and nuclear processing, association with receptors and interaction with co-regulators [109]. The first recognized defect involved the thyroid hormone-receptor β gene (TRβ), discovered in 1967, and became known as ‘resistance to thyroid hormone’ (RTH) [110], [111]. The phenotype of this

Summary

The diurnal serum TSH rhythm is very robust and largely uninfluenced by age and sex. The common condition of obesity enhances TSH secretion without impact on T4 and T3 secretion, while fasting diminishes TSH secretion. Prolonged and severe illness leads to the syndrome of NTIS characterized first by diminished TSH secretion, associated with decreased T3 and increased rT3, and in later stages by decreased T4, an ominous sign of infaust prognosis. While detailed rhythm studies are available in

Conflict of interest

The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.

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