I am looking for quantitative data regarding the relationship between free T4 levels and TSH levels in normal thyroid function and treated hypothyroid. The feedback loop means that if all is functioning properly, homeostasis will keep both within normal levels, but how are the two related?

I'd like to be able to understand what might be going on (in a case of treated hypothyroid) if (say) free T4 is normal, but TSH is significantly higher than normal (8-10 miu/dL), or if TRH is out of normal bounds in some fashion. I want to be able to analyze the covariates in the data to answer questions such as: Is this within usual bounds of the feedback loop, or is this not expected? Where in the system is the most likely failure of the control system?

  • I don't want to answer this, because it sounds like a specific case to me, but you can find a lot of easy-to-follow information with nice schemes and tables in this video: youtube.com/watch?v=Zb4ZZG2BPZw . – Jan Dec 24 '18 at 12:32
  • Thank you. This is good general information. I am interested in analyzing a specific case, but my question is more generally to find datasets of empirical findings of the relationship between the controlling and controlled hormones in the cascade. I've edited the question for clarity. – Shlomo Argamon Dec 25 '18 at 14:19
  • So, someone is hypothyroid with low T4, he gets the thyroid hormones and T4 normalizes, and so should TSH, but it doesn't: what are possible explanations? If this is an actual case, you should omit "if say" and mention all the relevant values as they are (T3, T3/T4 ratio, TSH, TRH), eventual other hormonal imbalances, underlying diseases, the person's age, sex and the dynamic of the changes as they happened. If you made the case up, and you don't imagine all exact lab values, then we may be all lost, since thyroid hormones are terribly influenced by many things. – Jan Dec 27 '18 at 9:36

The relation between TSH and free T4 is bidirectional and complex [1–4]. TSH stimulates the secretion of T4 from the thyroid gland. Immediately after being released into the bloodstream the vast majority of T4 is bound to plasma proteins including albumin, thyroxin binding globulin (TBG) and transthyretin (TTR, formerly called thyroxine-binding prealbumin or TBPA). Free and bound hormones are in an equilibrium, which is controlled by fast feedback loops. What is effective, however, is the small free portion of T4. It is converted to T3 via different deiodinases in the tissues, and it has some direct effects at integrin receptors on the cell membrane. T3 and especially 3,5-T2 are the more active thyroid hormones that express mainly genomic effects. This also applies to their central actions, which result in a suppression of TSH (and also TRH [5]) release. Additional control motifs include ultrashort feedback of TSH release [6], dual feedback via T4 and T3 [7], and a TSH-T3-shunt, where TSH directly stimulates T3 formation within the thyroid [reviewed in 2]:

Overview of thyroid homeostasis [Hoermann et al. 2015]

Unfortunately, there is little empirical information available that is suitable for analysis (to refer to your comment from Dec 25). Data from normal subjects representing a closed loop situation are hardly applicable for quantitative analysis. You might be interested in a free data table available from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4899439/bin/table_1.doc, which includes some original values from subjects with different conditions of thyroid homeostasis, where some of them correspond to an open-loop situation:

Distribution of hormone concentrations in certain primary and secondary thyroid conditions compared to normal percentiles of SPINA-GT [from https://doi.org/10.3389/fendo.2016.00057]

Two closely related cybernetic models are described in [6] and [8]. They have some omissions and desiderata, but they at least integrate a good empirical basis with mathematical modelling. A more thorough description of the corresponding equations is available from https://sourceforge.net/projects/simthyr/files/Documentation/Technical%20Reference%20E.pdf and https://doi.org/10.5281/zenodo.1415331, respectively.


  1. Shahid MA, Sharma S. Physiology, Thyroid Hormone. 2018 Oct 27. StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2018 Jan-. Available from http://www.ncbi.nlm.nih.gov/books/NBK500006/ PMID 29763182. https://www.ncbi.nlm.nih.gov/pubmed/29763182

  2. Hoermann R, Midgley JE, Larisch R, Dietrich JW. Homeostatic Control of the Thyroid-Pituitary Axis: Perspectives for Diagnosis and Treatment. Front Endocrinol (Lausanne). 2015 Nov 20;6:177. doi: 10.3389/fendo.2015.00177. PMID 26635726. https://doi.org/10.3389/fendo.2015.00177

  3. Dietrich JW, Landgrafe G, Fotiadou EH. TSH and Thyrotropic Agonists: Key Actors in Thyroid Homeostasis. J Thyroid Res. 2012;2012:351864. doi: 10.1155/2012/351864. PMID 23365787. https://doi.org/10.1155/2012/351864

  4. Abdalla SM, Bianco AC. Defending plasma T3 is a biological priority. Clin Endocrinol (Oxf). 2014 Nov;81(5):633-41. doi: 10.1111/cen.12538. PMID 25040645. https://doi.org/10.1111/cen.12538

  5. Joseph-Bravo P, Jaimes-Hoy L, Charli JL. Advances in TRH signaling. Rev Endocr Metab Disord. 2016 Dec;17(4):545-558. doi: 10.1007/s11154-016-9375-y. PMID 27515033. https://doi.org/10.1007/s11154-016-9375-y

  6. J. W. Dietrich, A. Tesche, C. R. Pickardt & U. Mitzdorf (2004) Thyrotropic Feedback Control: Evidence for an Additional Ultrashort Feedback Loop from Fractal Analysis, Cybernetics and Systems, 35:4, 315-331, DOI: 10.1080/01969720490443354 https://doi.org/10.1080/01969720490443354

  7. Hoermann R, Midgley JEM, Dietrich JW, Larisch R. Dual control of pituitary thyroid stimulating hormone secretion by thyroxine and triiodothyronine in athyreotic patients. Ther Adv Endocrinol Metab. 2017 Jun;8(6):83-95. doi: 10.1177/2042018817716401. PMID 28794850. https://doi.org/10.1177/2042018817716401

  8. Berberich J, Dietrich JW, Hoermann R, Müller MA. Mathematical Modeling of the Pituitary-Thyroid Feedback Loop: Role of a TSH-T(3)-Shunt and Sensitivity Analysis. Front Endocrinol (Lausanne). 2018 Mar 21;9:91. doi: 10.3389/fendo.2018.00091. PMID 29619006. https://doi.org/10.3389/fendo.2018.00091

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