Calculating toxic pressure for mixtures of endocrine disruptors

Abstract

Incidence of autoimmune disorders, birth defects, and neurological diseases rose over the past 50 years due to increasing variety and quantity of pollutants. To date, there appear few methods capable to evaluate and predict mixture effects by endocrine disruptors (EDs). For the first time, we have developed calculus to determine mixture effects by all kinds of EDs. Our method uses the golden ratio ϕ and draws from bifurcation and chaos theory. Using also the concept of molecular mimicry, we developed the equation: $effect=\frac{100\%}{1+e^5{·\sum K_i\left(C_i\right)}^{-n_i{\varphi }^3}}$ . We successfully tested the equation using a range of cohort studies and biomarkers, and for different pollutants like heavy metals, thyroid hormone mimickants, chromate/chlorate, etc. The equation is simple enough to use with only minor prior knowledge and understanding of basic algebra. The method is universal and calculation is data 'light', requiring only pollutant concentrations [C], potencies K and an integer n for endocrinal involvement. This study offers a comprehensive framework to assess the health effects of pollutant exposure across diverse populations, envisioning far-reaching impact, and presenting practical examples and insights.

Fig. 1

Schematic representation of changes in thyroid hormone, iodine and calcium (ng/L, not to scale) with increasing severity of illness. Adapted from [17].

Fig. 2

Depending on levels of endocrine disrupting chemicals (red), the human body needs to allocate hormones and cations/anions (green) to ensure that nutrients remain incorporable via metabolic schemes for growth. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 3

Calcium, iodine and T₃/T₄ versus TSH. Dotted grey lines are the 95 % ranges for normal TSH levels: in this range, negative feedback establishes healthy homeostasis (open symbols). Data from Yagi (T₄) [38], Karaoglan (iodine) [39], Hamza (urinary iodine) [40], Levine (calcium) [15], and Wang (T₃) [13]. Other data corroborates the exponent for T₃, 2.6 ± 0.5 [41].

Fig. 4

Concentrations of metals versus TSH-TSH_ref. Offset between each relationship (solid lines) indicates potency K. TSH_ref = 1.2 mIU/L.

Fig. 5

Fig. 5A. Induction concentration values for the thyroid hormone receptor α versus indicators of electron and proton transfer energies, E_HOMO and pK_a. Outliers are due to missing info on pK_a or bioavailability. Data selection from Ref. [42]. Fig. 5B. Effect on TSH versus concentration dose for dioxins PCBs etc. (TEQ) and T₃. Offsets between the compounds are differences in potency K. TSH_ref = 1.2 mIU/L (dioxin like substances) and TSH_ref = 0.0 mIU/L for T₃.

Fig. 6

Concentrations of iodine mimickants versus TSH-TSH_ref. Offset between each relationship (solid lines) indicates potency K. With TSH_ref = 1.2 mIU/L. Data from Hasan [43] and Ahmed [44] (Cr), Molin [45] (As), Hooth [46] (ClO₃), and Banerjee [47,48].

Fig. 7

Concentration of toxicants versus TSH. Sum of all dioxin TEQ (black), in terms of arsenic (green), and in terms of lead (blue). (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)

Fig. 8

Relationship between logit p and TSH, wherein logit p = ln(p/(1-p)), with p as probability.

Fig. 9

Relationships between TSH and 20 different health effects (A) and 10 different biomarkers (B), related to Ca²⁺ metabolism, brain, bone, heart, kidney, etc. The red dashed lines are guides to the eye denoting no effect (0 %) and full effect (100 %) and the baseline TSH of 1.2 mUI/L. Spread is larger in A because we are dealing with humans instead of cells, and humans differ in terms of nutrition, lifestyle, etc. (For interpretation of the references to colour in this figure legend, the reader is referred to the Web version of this article.)