Thyroid-disrupting chemicals: interpreting upstream biomarkers of adverse outcomes - PubMed (original) (raw)

Review

. 2009 Jul;117(7):1033-41.

doi: 10.1289/ehp.0800247. Epub 2009 Feb 12.

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Review

Thyroid-disrupting chemicals: interpreting upstream biomarkers of adverse outcomes

Mark D Miller et al. Environ Health Perspect. 2009 Jul.

Abstract

Background: There is increasing evidence in humans and in experimental animals for a relationship between exposure to specific environmental chemicals and perturbations in levels of critically important thyroid hormones (THs). Identification and proper interpretation of these relationships are required for accurate assessment of risk to public health.

Objectives: We review the role of TH in nervous system development and specific outcomes in adults, the impact of xenobiotics on thyroid signaling, the relationship between adverse outcomes of thyroid disruption and upstream causal biomarkers, and the societal implications of perturbations in thyroid signaling by xenobiotic chemicals.

Data sources: We drew on an extensive body of epidemiologic, toxicologic, and mechanistic studies.

Data synthesis: THs are critical for normal nervous system development, and decreased maternal TH levels are associated with adverse neuropsychological development in children. In adult humans, increased thyroid-stimulating hormone is associated with increased blood pressure and poorer blood lipid profiles, both risk factors for cardiovascular disease and death. These effects of thyroid suppression are observed even within the "normal" range for the population. Environmental chemicals may affect thyroid homeostasis by a number of mechanisms, and multiple chemicals have been identified that interfere with thyroid function by each of the identified mechanisms.

Conclusions: Individuals are potentially vulnerable to adverse effects as a consequence of exposure to thyroid-disrupting chemicals. Any degree of thyroid disruption that affects TH levels on a population basis should be considered a biomarker of adverse outcomes, which may have important societal outcomes.

Keywords: children’s health; endocrine disruption; hazard identification; risk assessment; science policy; thyroid hormone; toxicologic assessments.

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Figures

Figure 1

Figure 1

TH control pathways and sites of disruption by xenobiotic chemicals. Abbreviations: Gluc, glucose; HO-PCBs, hydroxyl-PCBs; NIS, sodium/iodide symporter; PBDE, polybrominated diphenyl ether; PTU, propylthiouracil; T4-Gluc, T4-glucuronide; TBG, thyroid-binding globulin; TRH, thyrotropin-releasing hormone; TSH, thyroid-stimulating hormone; TTR, transthyretin; UDPGT, uridine diphosphate glucuronyl-transferase. Sites or processes where xenobiotics are known or hypothesized to act as TDCs are indicated in the boxes and ovals. Xenobiotics that block, inhibit, or up -regulate these processes are shown in bold (modified from Crofton 2008).

Figure 2

Figure 2

Population changes in diastolic blood pressure (A) and cholesterol (B) in relation to serum TSH or free T4, respectively. (A) Diastolic blood pressure in men and women are significantly correlated with serum TSH within the normal reference range for TSH, indicating that as serum T4 declines, diastolic blood pressure increases. (B) Serum cholesterol is negatively associated with serum free T4. An increase in free T4 by 5, 10, or 15 pmol/L would reduce LDL cholesterol by 0.13, 0.53, and 0.93 mmol/L, respectively. The data are redrawn with permission from Asvold (2007b; A) and from Razvi (2007; B) (Copyrights 2007, The Endocrine Society).

Figure 3

Figure 3

A combined mode-of-action model for the effects of TDCs on cancer and developmental outcomes. Abbreviations: TTR, transthyretin; UDPGT, uridine diphosphate glucuronyltransferase. Mixture models are needed to better predict effects of mixtures containing xenobiotics that affect multiple targets with common downstream effects (modified from Crofton and Zoeller 2005; U.S. EPA 2002).

Figure 4

Figure 4

Diagnostic relationships between upstream biomarkers and adverse outcomes.

Figure 5

Figure 5

The predicted and empirical effects of a mixture of dioxins, furans, and PCBs on serum total T4 in rats. Predicted outcomes (additivity model) were generated using a single chemical-required additivity model. Empirical results (empirical model) showed a small but significant departure from dose additivity at the three highest mixture doses, whereas the remaining lower mixture doses were not significantly different than that predicted by additivity (modified from Crofton et al. 2005).

Figure 6

Figure 6

Individual versus population reference range for T4: the distribution of 12 monthly measurements for 15 men compared with one individual. The distribution width for the individual is approximately one-half that of the group [adapted from Andersen et al. (2002); copyright 2002, The Endocrine Society].

Figure 7

Figure 7

Individual risk and mortality associated with MI. ( A ) Individual risk and prevalence for MI associated with increased serum cholesterol levels. The number above each bar represents estimate of attributable deaths per 1,000 per 10 years. Note that individual risk increases linearly (including within the range of values considered normal) but that most deaths attributable to increased cholesterol levels occur in the lower range, because this represents a greater proportion of the population (adapted from Rose 1981; with permission from the BMJ Publishing Group). (B) Death from MI associated with increased diastolic blood pressure in males 45–74 (age-adjusted rate) (adapted from U.S. EPA 1985).

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