Triiodothyronine secretion in early thyroid failure: The adaptive response of central feedforward control

The effects of methimazole combined with propranolol on heart rate, bone metabolism, and thyroid hormone levels in patients with hyperthyroidism: A systematic review and a meta-analysis of case-control studies

BACKGROUND

The combination of methimazole and propranolol is considered an effective treatment regimen for hyperthyroidism in clinical practice; however, detrimental effects on the heart rate, bone metabolism and thyroid hormone levels have been reported. Therefore, the present study aimed to systematically review the efficacy and safety differences in patients with hyperthyroidism and the effects of treatment on heart rate, bone metabolism, cortisol, and adrenocorticotropic hormone levels using case-control studies.

METHODS

Clinical case-control trials of methimazole combined with propranolol for the treatment of hyperthyroidism were selected from Chinese and English databases, and data were collected from the establishment of the database until August 2024. Two independent researchers evaluated the quality of the literature using the Newcastle-Ottawa Scale (NOS). Meta-analysis of each effect index was performed using RevMan software (version 5.3), and the quality of the results was evaluated using the GRADE profiler system letter description method.

RESULTS

Sixteen clinical case-control trials were included in this study. Of these, 2 trials exhibited NOS scores of 7, 6 trials exhibited NOS scores of 6, and 8 trials exhibited NOS scores of 5. These accounted for 12.5% of the high-quality literatures, and included 772 patients treated with methimazole combined with propranolol (observation group) and 771 patients treated with methimazole alone (control group). The results of the meta-analysis demonstrated that methimazole combined with propranolol improved the cure rate, the total effective rate, and heart rate, compared with the control group (P < .05). In addition, calcification, bone glutamate protein, free triiodothyronine, free tetraiodothyronine, thyroid-stimulating hormone, cortisol, and adrenocorticotropic hormone were significantly different between the 2 groups (P < .05). There were no significant differences in leukemia, headache, dizziness, skin pruritus, bone pain, arthralgia, or in improving parathyroid hormone or reducing gastrointestinal reactions between the 2 groups.

CONCLUSION

The present study demonstrated that methimazole combined with propranolol may significantly improve the heart rate, bone metabolism and associated hormone levels in patients with hyperthyroidism, without significantly increasing the risk of adverse reactions. However, due to the impact of primary literature type, quality or research methods high-quality, multicenter, rigorously designed clinical trials are required for further verification.

A minimal human physiologically based kinetic model of thyroid hormones and chemical disruption of plasma thyroid hormone binding proteins

The thyroid hormones (THs), thyroxine (T4) and triiodothyronine (T3), are under homeostatic control by the hypothalamic-pituitary-thyroid axis and plasma TH binding proteins (THBPs), including thyroxine-binding globulin (TBG), transthyretin (TTR), and albumin (ALB). THBPs buffer free THs against transient perturbations and distribute THs to tissues. TH binding to THBPs can be perturbed by structurally similar endocrine-disrupting chemicals (EDCs), yet their impact on circulating THs and health risks are unclear. In the present study, we constructed a human physiologically based kinetic (PBK) model of THs and explored the potential effects of THBP-binding EDCs. The model describes the production, distribution, and metabolism of T4 and T3 in the Body Blood, Thyroid, Liver, and Rest-of-Body (RB) compartments, with explicit consideration of the reversible binding between plasma THs and THBPs. Rigorously parameterized based on literature data, the model recapitulates key quantitative TH kinetic characteristics, including free, THBP-bound, and total T4 and T3 concentrations, TH productions, distributions, metabolisms, clearance, and half-lives. Moreover, the model produces several novel findings. (1) The blood-tissue TH exchanges are fast and nearly at equilibrium especially for T4, providing intrinsic robustness against local metabolic perturbations. (2) Tissue influx is limiting for transient tissue uptake of THs when THBPs are present. (3) Continuous exposure to THBP-binding EDCs does not alter the steady-state levels of THs, while intermittent daily exposure to rapidly metabolized TBG-binding EDCs can cause much greater disruptions to plasma and tissue THs. In summary, the PBK model provides novel insights into TH kinetics and the homeostatic roles of THBPs against thyroid disrupting chemicals.

Modelling of the thyroid hormone synthesis as a part of nonlinear reaction mechanism with feedback

The synthesis of thyroid hormones in the hypothalamic-pituitary-thyroid (HPT) axis was studied. For this purpose, a reaction model for HPT axis with stoichiometric relations between the main reaction species was postulated. Using the law of mass action, this model has been transformed into a set of nonlinear ordinary differential equations. This new model has been examined by stoichiometric network analysis (SNA) with the aim to see if it possesses the ability to reproduce oscillatory ultradian dynamics founded on the internal feedback mechanism. In particular, a feedback regulation of TSH production based on the interplay between TRH, TSH, somatostatin and thyroid hormones was proposed. Besides, the ten times larger amount of produced T₄ with respect to T₃ in the thyroid gland was successfully simulated. The properties of SNA in combination with experimental results, were used to determine the unknown parameters (19 rate constants of particular reaction steps) necessary for numerical investigations. The steady-state concentrations of 15 reactive species were tuned to be consistent with the experimental data. The predictive potential of the proposed model was illustrated on numerical simulations of somatostatin influence on TSH dynamics investigated experimentally by Weeke et al. in 1975. In addition, all programs for SNA analysis were adapted for this kind of a large model. The procedure of calculating rate constants from steady-state reaction rates and very limited available experimental data was developed. For this purpose, a unique numerical method was developed to fine-tune model parameters while preserving the fixed rate ratios and using the magnitude of the experimentally known oscillation period as the only target value. The postulated model was numerically validated by perturbation simulations with somatostatin infusion and the results were compared with experiments available in literature. Finally, as far as we know, this reaction model with 15 variables is the most dimensional one that have been analysed mathematically to obtain instability region and oscillatory dynamic states. Among the existing models of thyroid homeostasis this theory represents a new class that may improve our understanding of basic physiological processes and helps to develop new therapeutic approaches. Additionally, it may pave the way to improved diagnostic methods for pituitary and thyroid disorders.

The role of supporting and disruptive mechanisms of FT3 homeostasis in regulating the hypothalamic-pituitary-thyroid axis

BACKGROUND

Thyroid hormones are controlled by the hypothalamic-pituitary-thyroid (HPT) axis through a complex network of regulatory loops, involving the hormones TRH, TSH, FT4, and FT3. The relationship between TSH and FT4 is widely used for diagnosing thyroid diseases. However, mechanisms of FT3 homeostasis are not well understood.

OBJECTIVE

We used mathematical modelling to further examine mechanisms that exist in the HPT axis regulation for protecting circulating FT3 levels.

METHODS

A mathematical model consisting of a system of four coupled first-order parameterized non-linear ordinary differential equations (ODEs) was developed, accounting for the interdependencies between the hormones in the HPT axis regulation. While TRH and TSH feed forward to the pituitary and thyroid, respectively, FT4 and FT3 feed backward to both the pituitary and hypothalamus. Stable equilibrium solutions of the ODE system express homeostasis for a particular variable, such as FT3, if this variable stays in a narrow range while certain other parameter(s) and system variable(s) may vary substantially.

RESULTS

The model predicts that (1) TSH-feedforward protects FT3 levels if the FT4 production rate declines and (2) combined negative feedback by FT4 and FT3 on both TSH and TRH production rates keeps FT3 levels insensitive to moderate changes in FT4 production rates and FT4 levels. The optimum FT4 and FT3 feedback and TRH and TSH-feedforward ranges that preserve FT3 homeostasis were found by numerical continuation analysis. Model predictions were in close agreement with clinical studies and individual patient examples of hypothyroidism and hyperthyroidism.

CONCLUSIONS

These findings further extend the concept of HPT axis regulation beyond TSH and FT4 to integrate the more active sister hormone FT3 and mechanisms of FT3 homeostasis. Disruption of homeostatic mechanisms leads to disease. This provides a perspective for novel testable concepts in clinical studies to therapeutically target the disruptive mechanisms.

Principles of Endocrine Regulation: Reconciling Tensions Between Robustness in Performance and Adaptation to Change

Endocrine regulation in the hypothalamic-pituitary-thyroid (HPT) axis is orchestrated by physiological circuits which integrate multiple internal and external influences. Essentially, it provides either of the two responses to overt biological challenges: to defend the homeostatic range of a target hormone or adapt it to changing environmental conditions. Under certain conditions, such flexibility may exceed the capability of a simple feedback control loop, rather requiring more intricate networks of communication between the system's components. A new minimal mathematical model, in the form of a parametrized nonlinear dynamical system, is here formulated as a proof-of-concept to elucidate the principles of the HPT axis regulation. In particular, it allows uncovering mechanisms for the homeostasis of the key biologically active hormone free triiodothyronine (FT3). One mechanism supports the preservation of FT3 homeostasis, whilst the other is responsible for the adaptation of the homeostatic state to a new level. Together these allow optimum resilience in stressful situations. Preservation of FT3 homeostasis, despite changes in FT4 and TSH levels, is found to be an achievable system goal by joining elements of top-down and bottom-up regulation in a cascade of targeted feedforward and feedback loops. Simultaneously, the model accounts for the combination of properties regarded as essential to endocrine regulation, namely sensitivity, the anticipation of an adverse event, robustness, and adaptation. The model therefore offers fundamental theoretical insights into the effective system control of the HPT axis.

Profiling retrospective thyroid function data in complete thyroidectomy patients to investigate the HPT axis set point (PREDICT-IT)

BACKGROUND

The homeostatic euthyroid set point of the hypothalamus-pituitary-thyroid axis of any given individual is unique and oscillates narrowly within substantially broader normal population ranges of circulating free thyroxine (FT4) and thyroid-stimulating hormone (TSH), otherwise termed 'thyroid function test (TFT)'. We developed a mathematical algorithm codenamed Thyroid-SPOT that effectively reconstructs the personalized set point in open-loop situations and evaluated its performance in a retrospective patient sample.

METHODS

We computed the set points of 101 patients who underwent total thyroidectomy for non-functioning thyroid disease using Thyroid-SPOT on each patient's own serial post-thyroidectomy TFT. Every predicted set point was compared against its respective healthy pre-operative euthyroid TFT per individual and their separation (i.e. predicted-observed TFT) quantified.

RESULTS

Bland-Altman analysis to measure the agreement between each pair of an individual's predicted and actual set points revealed a mean difference in FT4 and TSH of + 3.03 pmol/L (95% CI 2.64, 3.43) and - 0.03 mIU/L (95% CI - 0.25, 0.19), respectively. These differences are small compared to the width of the reference intervals. Thyroid-SPOT can predict the euthyroid set point remarkably well, especially for TSH with a 10-16-fold spread in magnitude between population normal limits.

CONCLUSION

Every individual's equilibrium euthyroid set point is unique. Thyroid-SPOT serves as an accurate, precise and reliable targeting system for optimal personalized restoration of euthyroidism. This algorithm can guide clinicians in L-thyroxine dose titrations to resolve persistent dysthyroid symptoms among challenging cases harbouring "normal TFT" within the laboratory ranges but differing significantly from their actual euthyroid set points.

Within-Person Variation in Serum Thyrotropin Concentrations: Main Sources, Potential Underlying Biological Mechanisms, and Clinical Implications

BACKGROUND

Individuals exhibit fluctuations in the concentration of serum thyroid-stimulating hormone (TSH) over time. The scale of these variations ranges from minutes to hours, and from months to years. The main factors contributing to the observed within-person fluctuations in serum TSH comprise pulsatile secretion, circadian rhythm, seasonality, and ageing. In clinical practice and clinical research however, such within-person biological variation in serum TSH concentrations is often not considered. The aim of this review is to present an overview of the main sources of within-person variation in TSH levels, as well as the potential underlying biological mechanisms, and the clinical implications.

SUMMARY

In euthyroid individuals, the circadian rhythm, with a nocturnal surge around 02:00-04:00 h and a nadir during daytime has the greatest impact on variations in serum TSH concentrations. Another source of within-person variation in TSH levels is seasonality, with generally higher levels during the cold winter months. Since TSH is secreted in a pulsatile manner, TSH levels also fluctuate over minutes. Furthermore, elevated TSH levels have been observed with ageing. Other factors that affect TSH levels include thyroid peroxidase (TPO)-antibody positivity, BMI, obesity, smoking, critical illness, and many xenobiotics, including environmental pollutants and drugs. Potential underlying biological mechanisms of within-person variation in TSH levels can be safely concluded from the ability of TSH to respond quickly to changes in cues from the internal or external environment in order to maintain homeostasis. Such cues include the biological clock, environmental temperature, and length of day. The observed increase in TSH level with ageing can be explained at a population level and at an organism level. In clinical practice, the season for thyroid testing can influence a patient's test result and it occurs frequently that subclinical hypothyroid patients normalize to euthyroid levels over time without intervention.

CONCLUSIONS

Serum TSH concentrations vary over time within an individual, which is caused by multiple different internal and external factors. It is important to take the within-person variations in serum TSH concentrations into account when testing a patient in clinical practice, but also in performing clinical research.