Does the statistically-defined “normal” TSH reference range for the healthy population describe the TSH range for a healthy individual? Does having a TSH within the normal laboratory range always mean you, as an individual are biologically euthyroid? Does falling outside statistical TSH normality really mean that you are hypothyroid or thyrotoxic?
This is part 2 of my paraphrase of Hoermann et al’s landmark article on the “relational stability” between TSH and the thyroid hormones T4 and T3.
This section explains why thyroid science and clinical practice has overemphasized TSH testing and has isolated the three hormones TSH, T4 and T3 from each other. This is a distortion of the way thyroid hormone homeostasis works in reality.
In these sections, the article discusses the biological principle of flexible adaptation among the two thyroid hormones and TSH.
First, they explain how the system works under stress to maintain thyroid hormone supply. TSH stimulation of a healthy thyroid protects the body from thyroid hormone insufficiency, while various factors protect the body from excess TSH stimulation.
Then they examine the statistical definition of normality and its many drawbacks. In addition to controversy over the upper boundary of statistically normal TSH, the range that describes what is “normal” in a large population does not describe what is healthy in each individual. Each individual has an unique homeostatic set point or range that is far narrower than the population’s range.
At the end of this section, they put TSH back in its proper biological context as only one “indirect” controlling element in the healthy thyroid hormone economy. TSH is only one hormone in a trio of three that shift in relationship to each other as they constitute the individual’s healthy setpoint in three dimensions — TSH, FT4 and FT3.
Introduction
A primary goal of human health is the maintenance of body composition and its internal environment over long periods of time and during various external challenges.
This is a biological view of normality, which is more narrowly defined as a perceived optimal state for an individual rather than defined by a reference range across a large population.
In contrast, the statistical view of normality is defined mathematically as the “Gaussian” distribution of the variation or measurement error frequently observed in a population.
But biological variation is not simply understood as a mathematical error term in a statistical view of a system — a person’s location in a distribution is not meaningless, arbitrary, or random. Instead, individual biological variation has an important evolutionary role.
The human body is essentially a highly complex, interactive biological system in which normality and variation are tightly controlled by various mechanisms.
Typically, the hypothalamus-pituitary-thyroid axis is closely concerned with energy metabolism in addition to many other specific functions within the organism. The ability to gain control over energy expenditure independent from short-term changes in the supply of energy in the environment offers major systemic advantages.
Therefore, it is unsurprising that the thyroid system embodies the ability to adapt and supply basic biological necessities, as demonstrated by associations between thyroid health and longevity in older populations.
By nature, such systems of adaptive biological normality tend to operate far away from their unstimulated resting point: they are “metastable,” in that they require considerable energy and systemic pressure in order to avoid collapse and to maintain a desired point of equilibrium.
Individuals and populations make subtle variations in thyroid parameters in order to stabilize hormone levels in light of unstable and challenging conditions (such as iodine deficiency or extreme climates) that would otherwise cause wide fluctuations and turbulence within the organism.
In this short review, we examine the normality, variation, and control of thyroid function in humans, and we draw some conclusions for their practical applications in thyroid diagnosis and treatment.
Background
The human thyroid gland produces and then releases into the circulation a large amount of thyroxine (T4) and a lesser proportion of triiodothyronine (T3). Hormone concentrations in the circulation depend on binding to transport proteins, such as thyroxine-binding globulin (TBG), transthyretin, and albumin. Only very small concentrations of T4 and T3 exist in their unbound, free forms. The free molecules are biologically more active, free T3 (FT3) being significantly more active than free T4 (FT4). These two thyroid hormones are interrelated: T4 is converted into T3 by means of deiodinase enzymes that remove a specific iodine atom from the T4 molecule.
Most of the T4 in the human body is found in the circulation, but T3 is predominantly located within cells. T3 is continually transported both in and out of cells by means of transport mechanisms, not by passive diffusion. Within each cell, T3 is eventually transported into the nucleus where it binds to thyroid hormone receptors and performs classical or “genomic” actions (directly regulating gene expression within target bodily tissues). Non-classical (non-genomic) actions of T4 and T3 include their binding to integrin receptors on the cell membrane.
The thyroid gland’s basal output of thyroid hormones is relatively low, but not absent, without any TSH stimulation. The thyroid gland only makes the euthyroid T3 and T4 supply possible as it is stimulated by the pituitary gland’s secretion of thyroid-stimulating hormone (TSH).
Stimulation by TSH puts the entire system of T4 and T3 production under constant high pressure.
Consequently, such a high-pressure system requires defensive mechanisms to protect the cells from the dangers of being flooded and overwhelmed by an over-supply of thyroid hormones. These defensive mechanisms include
- The binding of thyroid hormones to plasma proteins (bound versus free hormones in the bloodstream),
- The activation of the pro-hormone T4 (The variable rate of global and local T4–T3 conversion via deiodinase enzymes),
- Gate control at cell entry (active transport of T4 and T3 across the cell membrane), and
- Production of counter-regulatory thyroid hormone derivatives such as Reverse T3 (RT3) and the thyronamines or thyroid hormone analogues. These hormones exert short-loop inhibitory control where they are locally expressed. (The “thyronamines” are nine known thyroid-hormone-like compounds such as T1AM. The T1AM hormone is known to operate in some ways that can contrast T3 excess, decreasing many metabolic rates including heart rate, cardiac output, and neurological responses [Chiellini et al, 2017]).
- In addition, a systemic negative feedback control at the pituitary and hypothalamic level both controls thyroid hormone production by the thyroid gland and sets an appropriate internal reference point within the pituitary and hypothalamus, establishing the secretion of TSH. (Rising FT4 and/or FT3 will depress hypothalamic TRH, which will stimulate relatively less pituitary TSH release).
Modern laboratory tests provide easy access to measurements of all three thyroid parameters, TSH, Free T4 and Free T3.
However, they do not measure the hypothalamus’ TRH (Thyrotropin-Releasing Hormone, which stimulates the pituitary gland to release TSH) because its concentration in blood is too low for reliable detection. Besides, TRH enters the blood circulation from multiple sources, not only from the hypothalamus, but also from the spinal cord and gastrointestinal tract.
At the time this article was written in 2015-2016, PubMed had 27,184 publications focusing on the TSH hormone. However, most of these studies have either focused exclusively on pituitary TSH alone, or examined TSH, FT4 and FT3 as separate independent variables, downplaying the interrelationships among them and with other thyroid hormones.
TSH has now achieved the status of a dominant and statistically independent marker, partly because of its “sensitivity,” which is a medical term that refers to its ability to identify primary thyroid disease when it is elevated beyond reference range or lowered below reference range prior to therapy.
TSH’s sensitivity when screening for primary thyroid disease has caused the research community to deemphasize the fact that TSH only plays an indirect role in controlling thyroid hormone balance. It is not the only controlling role, as local T3 concentrations in the pituitary and hypothalamus are not the only variable in adjusting TSH secretion. (As Baloch et al, 2003, have acknowledged, “TSH is a labile hormone and subject to nonthyroidal pituitary influences [glucocorticoids, somatostatin, dopamine etc.] that can disrupt the TSH/FT4 relationship.”)
The dominance of TSH in the literature has fostered a widely held but mistaken belief that the local sensitivity of TSH to thyroid hormone negative feedback makes it a superior diagnostic tool that renders measurement of thyroid hormones redundant and unnecessary. We critique the validity of this belief in this article.
Normality of Thyroid Parameters
Statistical normality applies to FT4 and FT3 measurements in a sufficiently large and healthy population, making it mathematically easy to define appropriate 95% confidence limits or “reference intervals” for a population.
However, TSH levels in the population do not express true statistical normality, so a logarithmic statistical transformation has been accepted as the procedure to derive TSH reference intervals. But this logarithmic derivation has had limited success, as distortions exist. The distortions have been explained by the existence of undiagnosed autoimmune thyroid disease present in the populations sampled.
Further studies revealed an unexpected variation in the upper limit of the TSH reference range that characterizes the transition to hypothyroidism.
Controversy surrounds the definition of the “euthyroid” TSH reference range, often focusing of factors such as research methodology, geography, ethnicity, and age to explain discrepancies between one region’s TSH range and another region’s TSH range. The debate has not yet been resolved. The uncertainty over the TSH reference range may indicate an important underlying problem with the HPT axis model.
(The population’s distribution is skewed very low in reference range. The vast majority of TSH values in the healthy population is below 2.5, as shown in Zöphel et al, 2005)
Physiologically, the TSH is closely interlocked with Free T4, and TSH’s main role is to drive thyroidal production of T4 up to its normal level.
In thyroid health, this TSH-T4 relationship creates a “set point” around which the body maintains a healthy equilibrium.
When represented on a graph, the set point is at the intersecting point between the curve for thyroidal FT4 production and the curve for pituitary TSH secretion.
[Compare with Hoermann et al, 2015, Figure 2]
In addition to the population-wide statistics, one must consider between-individual variation and within-individual variation.
The set point of an individual is variable over time, but the movement of the TSH-T4 set point within a single individual is approximately 50% narrower than the variation of set points among many individuals (a population’s reference range). [see figure 1 from Andersen et al, 2002, Biological variation.]
In contrast to TSH and the thyroid hormones, many other laboratory tests’ reference ranges for biomarkers are almost exactly the same within a single healthy individual as they are among many healthy individuals in a population.
Therefore, TSH and thyroid hormone reference ranges are unique, and one cannot judge euthyroidism in an individual merely by locating one variable, such as TSH, anywhere within the population’s normal range, despite this being the common practice.
This physiological principle questions the current use of isolated and univariate reference ranges for each hormone to determine a very broad view of statistical normality.
(The common practice is to consider TSH separately from FT4, to correlate the statistically normal TSH with statistically normal FT4 [See the inner circle in the TSH-FT4 diagnosis model in Baloch et al, 2003].
(The standard practice is also to consider FT4 and TSH separately from FT3 most of the time; FT3 is only introduced when TSH is low and there may be a statistically abnormal elevation in FT3. There is an utter lack of concern for the contrary, a statistically or biologically abnormal loss of FT3.)
However, the true “set point” for a healthy individual’s thyroid balance is not determined by TSH alone, but by the two-dimensional distribution of TSH and FT4 as well as the three-dimensional distribution of TSH, FT4, and FT3. These levels determine “clusters” of set points appropriate for healthy individuals. A cluster, or a trio of hormone markers, as maintained by a healthy HPT axis, constitutes the more complete and precise view of the set-point of biological normality or euthyroidism in an individual.
A more complete, relational understanding of thyroid hormone balance promotes the use of a composite expression of multivariate normality in the larger population.
In previous publications, we have proposed mathematical formulas that derive reference ranges based on bivariate and trivariate hormone measurements. We showed that these calculations differ considerably from reference intervals derived only from the single reference point of TSH alone. [See Figure from Hoermann et al, 2016]
The alternative calculations made a significant difference between classifying a patient’s case as “thyroid dysfunction” vs. “euthyroidism.” The calculation
- reclassified 26% of cases when using bivariate measurement (TSH + FT4); and it
- reclassified 42% when using trivariate measurement (TSH + FT4 + FT3).
Other studies that apply multivariate measures to clinical data have showed similar differences between reference ranges derived from multivariate versus univariate models.
Therefore, the mathematical calculations involved in establishing “normality” in the TSH reference range play an unreasonably large role in diagnosis. It exaggerates the weight of TSH alone, along with the uncertainties of the TSH reference range boundaries and narrow TSH individual setpoints. It can cause errors in diagnosis whenever we rely primarily on the statistical normality of TSH to classify a person as biologically euthyroid.
Even more fundamentally, the current statistically-derived univariate reference interval for TSH is not well matched with physiological tissue-based definitions of thyroid function.
(Despite frequent claims that TSH is the most specific and sensitive reflection of tissue thyroid hormone status, a true “tissue-based definition” of euthyroid status would need to account for the effects of thyroid hormone levels on various tissues throughout the body, not just the effects of thyroid hormones and other health factors on the hypothalamus and pituitary tissues, which is represented by TSH.)
We urgently need to establish new biomarkers as “clinical endpoints” of euthyroid status. (“Clinical endpoints” are clinical measurements and laboratory tests and interpretations of them that would help us to estimate thyroid hormone health more accurately for each individual.)
We should scale back our overreliance on TSH alone. Too often, our use of TSH as the “gold standard” thyroid test has impeded the advancement of thyroid medicine.
We must once again take into account the full history of symptoms displayed by a patient, which is the primary tool at the basis of good clinical practice.
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