Principles

These plots are based on a theoretical model developed by Hoerman et al 2022. The changes in the figures are mine and not discussed with the creators – so I and not the authors are to blame for any error. I would like to show two citations from Rethinking Hypothyroidism, written by A. Bianco.

These two plots illustrate perfect homeostasis

Here I have added L-T4 to the perfect homeostasis illustrating the hormone pattern in a situation where there is no need for L-T4. The gap between TSH/FT4 widens. (~1/3 is getting unnecessary L-T4 treatment. From Rethinking Hypothyroidism, A. Bianco 2022)

• TSH decreases
• FT4 increases
• FT3 increases

Introducing a hypothyroid pattern – TSH increases

L-T4 is added to the hypothyroid pattern and we see that L-T4 treatment is not restoring FT3 although TSH is normalized. ( “Once TSH had normalized, the metabolic rate had not,” from Rethinking Hypothyroidism, A. Bianco 2022)

Is it possible to identify those patients who are not hypothyroid but is treated with L-T4 by the changed hormone pattern? In the perfect homeostasis scenario, the ratio FT4/TSH is close to one whereas in the perfect homeostasis with added LT4 the ratio looks close to 1.5. In the L-T4-treated hypothyroid pattern, the ratio is a little above one.

Will converting the hormone figures to standard values obscure the pattern?

In the article: “Principles of Endocrine Regulation: Reconciling Tensions Between Robustness in Performance and Adaptation to Change.” (Ref.1) the authors describe how their mathematical model can describe perfect homeostasis. The patterns of TRH, TSH, FT3 and FT4 are plotted. The FT3 value is preserved whatever changes in the other hormones.

In the next two plots, the K32* is changed to 0.3 and 3. Changes in the patterns between the three hormones TSH, FT4 and TRH.

Redefinition of FT3 reference ranges?

k32

The FT4 production rate parameter k32 is the proportionality constant,
positive feedforward of TSH onto FT4 (downstream, activating (short red arrow)).
FT4 increases as k32 increases and TRH-TSH decreases (due to the negative feedback k134 and k234 (green and blue bars)

1. Hoermann R, Pekker MJ, Midgley JEM, Larisch R and Dietrich JW (2022) Principles of Endocrine Regulation: Reconciling Tensions Between Robustness in Performance and Adaptation to Change. Front. Endocrinol. 13:825107. doi: 10.3389/fendo.2022.825107 https://www.frontiersin.org/articles/10.3389/fendo.2022.825107/full

All comments here are all theoretical and based on a complicated mathematical model.
Proceeding from page 10 in the supplementary material for:
Hoermann R, Pekker MJ, Midgley JEM, Larisch R and Dietrich JW (2022)
Principles of Endocrine Regulation: Reconciling Tensions Between Robustness in Performance and Adaptation to Change.
Front. Endocrinol. 13:825107. doi: 10.3389/fendo.2022.825107 – I look at the two equations for sys1 and multiple systems.

The two parameters k43, k42, drug4 and drug3 are set to zero in the multiple systems equation. Making the result of the two equations the same. This equals fig. 4a in the article.

In sys. 9 k43 is changed to 1. (k43 increases the FT3 production rate // the FT3 production rate constant of peripheral organs account for non-thyroidal peripheral FT3 production (by adding the term k43*[FT4]) which physiologically depends on FT4, but not on TSH). See fig. 4b in the article.

Changing k42 to one increases FT3 further. (The TSH feedforward on FT3 is switched on (k42 = 1)). The other hormones decreases. The TSH FT4 values follow each other closely. See fig. 4c in the article.

Now a drug is introduced (drug3=0.5) in the equation which gives the following result:

See fig. 4d in the article and the comments.

In the next plot drug3=0.5 (LT3) is replaced by drug4=0.8 (LT4) in the equation, which affects the TSH and FT4 values:

The distance between FT4 and TSH is increased. In the earlier plots, FT4 and TSH were almost in the same line with no spread.

Next, I will reproduce fig. 4 from the article as a test of the R-script:

The FT4 production rate parameter k32, is the proportionality constant positive feedforward of TSH onto FT4 (downstream, activating)

Two parameters may change the value of FT3 in the mathematical model (Ref.1);
k423 – positive feedforward of TSH and FT4 onto FT3 (downstream, activating)
a4 – the FT3 elimination rate constant.

Only examples of the effect of the parameter –

• Hoermann R, Pekker MJ, Midgley JEM, Larisch R and Dietrich JW (2022) Principles of Endocrine Regulation: Reconciling Tensions Between Robustness in Performance and Adaptation to Change. Front. Endocrinol. 13:825107. doi: 10.3389/fendo.2022.825107 https://www.frontiersin.org/articles/10.3389/fendo.2022.825107/full

In the mathematical model (Ref. 1) two parameters may change the outcome of FT4:
k32 – positive feedforward of TSH onto FT4 (downstream, activating) and
a 3 – the FT4 elimination rate constant.

Only examples of the effect of the parameter –

1. Hoermann R, Pekker MJ, Midgley JEM, Larisch R and Dietrich JW (2022) Principles of Endocrine Regulation: Reconciling Tensions Between Robustness in Performance and Adaptation to Change. Front. Endocrinol. 13:825107. doi: 10.3389/fendo.2022.825107 https://www.frontiersin.org/articles/10.3389/fendo.2022.825107/full

Four parameters regulate TSH (in the mathematical model (ref.1)):
k21 – (positive feedforward of TRH onto TSH (downstream, activating)),
c2 – (the secretory capacity constant),
k234 -negative feedback of FT4 and FT3 onto TSH (upstream, repressing) and
a2 – (the TSH elimination rate constant.).

Be aware that the colours for the hormones are different in the rows below.

Positive feedforward of TRH onto TSH (downstream, activating)):

Negative feedback of FT4 and FT3 onto TSH (upstream, repressing)

The TSH elimination rate constant.

The secretory capacity constant

Only examples of the effect of the parameter –

1. Hoermann R, Pekker MJ, Midgley JEM, Larisch R and Dietrich JW (2022) Principles of Endocrine Regulation: Reconciling Tensions Between Robustness in Performance and Adaptation to Change. Front. Endocrinol. 13:825107. doi: 10.3389/fendo.2022.825107 https://www.frontiersin.org/articles/10.3389/fendo.2022.825107/full

Hoerman et al (Ref.1) have developed a mathematical model with 14 different parameters that influence the HPT axis (hypothalamus – pituitary – thyroid axis). I will describe the effects in the following.

There are different parameters which can change the hormones in this mathematical model.

S1 is an input signal with an effect on TRH. As seen in the description above, changes in S1 affect the other hormones as well. The article describes how this knowledge can be used in combination with different parameters.

The first part of the above presentation describes the three different equilibrium solutions for s1=1, 0.1 and 10 and the second part describes the time-dependent solution.

You need not be good at mathematics to see the connection between the used equations and the conceptual drawing of the HPT axis (Ref.1). What is important is the complexity – and flexibility compared to a fixed TSH value as the understanding of the HPT axis.

The first part of the second presentation describes the three different equilibrium solutions for k134 =1, 0.1 and 10 and the second part describes the time-dependent solution.

This is theoretical and only serves as an example of how the hormones are changed by the parameter. K134 is the negative feedback of FT4 and FT3 onto TRH (upstream, repressing).

Finally the a1 parameter ( a1 is the TRH elimination rate constant):

Look at equation 1.1:

We have looked at the effects of s1, k134 and a1 on TRH and the other hormones.

1. Hoermann R, Pekker MJ, Midgley JEM, Larisch R and Dietrich JW (2022) Principles of Endocrine Regulation: Reconciling Tensions Between Robustness in Performance and Adaptation to Change. Front. Endocrinol. 13:825107. doi: 10.3389/fendo.2022.825107 https://www.frontiersin.org/articles/10.3389/fendo.2022.825107/full