Advances in applied homeostatic modelling of the relationship between thyrotropin and free thyroxine

The relationship between pituitary TSH and thyroid hormones is central to our understanding of thyroid physiology and thyroid function testing. Here, we generated distribution patterns by using validated tools of thyroid modelling. We simulated patterns of individual set points under various conditi...

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Veröffentlicht in:PloS one 2017-11, Vol.12 (11), p.e0187232-e0187232
Hauptverfasser: Hoermann, Rudolf, Midgley, John Edward Maurice, Larisch, Rolf, Dietrich, Johannes Wolfgang Christian
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Sprache:eng
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Zusammenfassung:The relationship between pituitary TSH and thyroid hormones is central to our understanding of thyroid physiology and thyroid function testing. Here, we generated distribution patterns by using validated tools of thyroid modelling. We simulated patterns of individual set points under various conditions, based on a homeostatic model of thyroid feedback control. These were compared with observed data points derived from clinical trials. A random mix of individual set points was reconstructed by simulative modelling with defined structural parameters. The pattern displayed by the cluster of hypothetical points resembled that observed in a natural control group. Moderate variation of the TSH-FT4 gradient over the functional range introduced further flexibility, implementing a scenario of adaptive set points. Such a scenario may be a realistic possibility for instance in treatment where relationships and equilibria between thyroid parameters are altered by various influences such as LT4 dose and conversion efficiency. We validated a physiologically based homeostatic model that permits simulative reconstruction of individual set points. This produced a pattern resembling the observed data under various conditions. Applied modelling, although still experimental at this stage, shows a potential to aid our physiological understanding of the interplay between TSH and thyroid hormones. It should eventually benefit personalised clinical decision making.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0187232