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Complex environmental contaminant mixtures and their associations with thyroid hormones using supervised and unsupervised machine learning techniques

Bayesian kernel machine regression (BKMR) analysis was used to evaluate the univariate contaminant exposure effect as well as the contaminant mixture effects on levels of thyroid hormones. Significant and positive associations were found between total T3 and PC-2 (high positive nickel and cadmium loadings), total T3 and PC-3 (negative association with negative loading for nickel and positive loading for cadmium) and TSH and PC-1 (high positive loadings for organic contaminants). Environmental Ad

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  1. Environmental Advances 4 (2021) 100054 Contents lists available at ScienceDirect Environmental Advances journal homepage: www.elsevier.com/locate/envadv Complex environmental contaminant mixtures and their associations with thyroid hormones using supervised and unsupervised machine learning techniques Eric N. Liberda a,∗, Aleksandra M. Zuk b, David S. Di a, Robert J. Moriarity c, Ian D. Martin c, Leonard J.S. Tsuji c a School of Occupational and Public Health and Environmental Applied Science and Management, 350 Victoria St, Ryerson University, Toronto M5B2K3, Ontario, Canada b School of Nursing, Queen’s University, Kingston K7L 3N6, Ontario, Canada c Department of Physical and Environmental Sciences, University of Toronto, Toronto M1C 1A4, Ontario, Canada a r t i c l e i n f o a b s t r a c t Keywords: Evaluating complex mixtures and their associated health effects poses a challenge in human populations. Herein, Thyroid we assess the association between 17 organic and metal contaminants in blood with thyroid hormones in a remote Machine learning Indigenous (First Nations) region from Quebec, Canada (n=526). Using principal component analysis (PCA) to Contaminants reduce the number of variables, we generated varimax rotated principal component (PC) loadings of contaminants Indigenous on these uncorrelated synthetic axes. Associations with levels of thyroid hormones (TSH, free T4, and total T3) Exposure BKMR were conducted using multivariable linear regression methods with the participant PC loadings and adjusting for Principal component analysis covariates. Additionally, Bayesian kernel machine regression (BKMR) analysis was used to evaluate the univariate contaminant exposure effect as well as the contaminant mixture effects on levels of thyroid hormones. Significant and positive associations were found between total T3 and PC-2 (high positive nickel and cadmium loadings), total T3 and PC-3 (negative association with negative loading for nickel and positive loading for cadmium) and TSH and PC-1 (high positive loadings for organic contaminants). No significant observations were observed for free T4. BKMR provided additional insight into the PCA results and suggested that nickel, and not cadmium, was responsible for driving the observed effects with this effect remaining when evaluating the entire mixture. BKMR analysis did not support the association of TSH with organic contaminants that were found in the PCA regression. Our findings reinforced other studies which showed that metals such as nickel may alter thyroid hormone levels and highlighted how complex environmental mixtures interact with each other. These observations represent an important step to determining how complex mixtures of contaminants can be assessed in human populations, especially those living a subsistence lifestyle who may have high body burdens of contaminants, and to help understand the resultant net effect of exposures on endogenous thyroid hormones utilizing novel machine learning statistical methods. Introduction and carcinogenic effects (Sutcliffe and Harvey, 2015). Metabolic condi- tions, such as hyperthyroidism, have been associated with environmen- There are a variety of xenobiotic compounds that may affect the tal contaminants − many of these compounds are still present in humans endocrine system, and it is well established that environmental con- even though some (e.g., legacy POPs) have been removed from use for taminants such as persistent organic pollutants (POPs), toxic metals, decades (Li et al., 2017; Nie et al., 2017; Sarkar et al., 2015). Addition- and metalloids play a role in altering the endocrine milieu in humans ally, the effects of environmental contaminants on thyroid hormone dis- (Alvarez-Pedrerol et al., 2008; Blanco-Muñoz et al., 2016; Duntas and ruption is particularly important in prenatal exposure scenarios due to Stathatos, 2015; Jacobson et al., 2017; Jain, 2016; Jain and Choi, 2016). the development of the fetal brain (Bergman et al., 2013; Damstra et al., In fact, endocrine disruption has been implicated in adverse cardiovas- 2002; Préau et al., 2015; Zoeller, 2007). Thus, it is no surprise that there cular (Klein and Ojamaa, 2001), nervous system (Mnif et al., 2011), have been several investigations assessing various environmental con- ∗ Corresponding author. E-mail addresses: eric.liberda@ryerson.ca (E.N. Liberda), amz4@queensu.ca (A.M. Zuk), s1di@ryerson.ca (D.S. Di), rob.moriarity@utoronto.ca (R.J. Moriarity), ianmartin@mac.com (I.D. Martin), leonard.tsuji@utoronto.ca (L.J.S. Tsuji). https://doi.org/10.1016/j.envadv.2021.100054 Received 8 December 2020; Received in revised form 21 February 2021; Accepted 10 April 2021 2666-7657/Crown Copyright © 2021 Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
  2. E.N. Liberda, A.M. Zuk, D.S. Di et al. Environmental Advances 4 (2021) 100054 taminants in pregnant women and their infants (Baccarelli et al., 2008; 2014b; Tsuji et al., 2005). Furthermore, although many of these legacy Berg et al., 2017, 2015; Dallaire et al., 2009). Therefore, it is impor- contaminants have been banned, they have been shown to have prefer- tant to assess the association that environmental contaminants may have ential deposition to northern latitudes (Wania and Mackay, 1996), hence with endogenous thyroid hormones, which are tightly regulated by the they present a consistent source of human exposure for years to come. hypothalamic–pituitary–thyroid axis, due to the role these xenobiotics A suite of contaminants known to alter thyroid hormone levels (e.g., may play in a variety of diseases and their effects on cognitive function. organochlorine pesticides) were selected for analysis a priori. Addition- While thyroid hormone concentrations and their associations with ally, we included contaminants such as nickel, which have a paucity of contaminants vary by the compound under examination, studies com- information related to its effects on altering human thyroid hormone monly find that POPs, such as polychlorinated biphenyls (PCBs), poly- concentrations, as we wished to investigate their role in complex mix- brominated diphenyl ethers (PBDEs), and organochlorine pesticides, are ture effects. Our analyses includes an examination of the overall mix- positively associated with thyroid stimulating hormone (TSH) and neg- ture effect with free T4, total T3, and TSH in participants from the atively with thyroxine (T4) and triiodothyronine (T3) (Meeker et al., Eeyou Istchee using principal component analysis (PCA), an unsupervised 2007; Schell et al., 2008; Xu et al., 2014). However there are also al- machine learning technique, and Bayesian kernel machine regression ternative findings to these observations and research has shown that (BKMR), a supervised machine learning technique. the direction of these associations may be dependent upon which con- taminant is under investigation (eg., Blanco-Muñoz et al., 2016; Freire Materials and methods et al., 2012, 2013). Findings for metals and metalloids vary greatly de- pending on which element is being examined, but some toxic metals Data sources such as cadmium have been observed to be positively associated with thyroid hormone levels such as total T3 (Chen et al., 2013), while oth- The Eeyou Istchee traditional territory of the eastern James Bay Cree ers, such as selenium (a metalloid), is negatively associated with total consists of nine communities with varying degrees of isolation ranging T3 (Jain and Choi, 2016). Several studies have examined the effects of from all-season roads access to airplane or boat access (Fig. 1). The Eeyou metal exposure on thyroid hormones levels and have found associations Istchee cross-sectional multi-community environment-and-health study, varying by contaminant (Dundar et al., 2006; Luo and Hendryx, 2014; known as the Nituuchischaayihtitaau Aschii (“Learn about ourselves and Meeker et al., 2009; Yorita Christensen, 2013); however, the role by our earth”), collected both personal data and clinical measurements, which this occurs has yet to be determined. Additionally, compounds such as, socio-demographic status, lifestyle data, and blood samples for such as perfluorooctane sulfonate (PFOS), perfluorooctanoate (PFOA), laboratory analyses. Participants were randomly selected with replace- and other perfluoralkyl acids (PFAAs) have also been found to alter thy- ment from the Beneficiaries List to achieve an acceptable sample size roid hormones concentrations (Jain, 2013; Li et al., 2017), and may that represented various age-sex groups. Based on the varying popula- thus form complex interactions between groups of legacy POP and toxic tion sizes, this resulted in a minimum of 150 persons being invited from metals. For some contaminants, such as nickel, there remains little infor- each community and represented approximately 20-30 persons per age- mation about its potential effects on human thyroid hormones, although sex group. In total, there were 13,714 eligible participants, 3588 in- a recent study found no association between urinary nickel and thyroid vited, and we obtained an all-community participation rate of 48% for hormones (Castiello et al., 2020). Clearly, this poses a problem when as- those invited to the study (range: 35-68% participation per community) sessing human populations because exposures to environmental contam- (Nieboer et al., 2013). Due to community requests for reporting non- inants do not occur in isolation. For example, a person may be exposed disaggregated data, we only reported aggregated statistics. In total, 1730 to a wide variety of xenobiotics from several different pathways and/or participants from the nine Eeyou Istchee communities provided written sources over time rather than a single (or similar group of) contami- informed consent in Cree, English, or French languages. The study ran nants. Therefore, it is possible that the effect of one contaminant may from 2005 to 2009 and included all nine First Nation communities from co-vary with others resulting in synergistic, antagonistic, or potentiative the Eeyou Istchee. Due to the remote location of these communities com- observations which are incorrectly attributed to the single exposure un- bined with the scale of the study approximately two communities were der investigation. The issue of exposures to complex mixtures is not new sampled each year. Ethics approval was granted by McGill University, and this has been a topic of research in the endocrine disruption field Laval University, McMaster University, and the Cree Board of Health with researchers assessing various groups of environmental contaminant and Social Services of James Bay. compounds and their related thyroid hormone outcomes for some time (Ghisari et al., 2015; Grønnestad et al., 2018; Taxvig et al., 2013). Fur- Study Population thermore, the mechanisms of how metals alter thyroid hormones has not been elucidated, although it has been postulated that they may af- Of the initial 1730 recruited participants, persons younger than 19 fect adrenal gland function and morphology, metabolism, synthesis of years of age and older than 80 years of age were excluded from anal- hormones, and even cellular apoptosis (Boas et al., 2006; Brouwer et al., ysis due to concerns of potential abnormal thyroid hormone measures 1998, 1986; Buha et al., 2018; Cheek et al., 1999; Rana, 2014). (Bremner et al., 2012; Gesing et al., 2012). Following case completion As part of the Eeyou Istchee Environment and Health study in north- with all covariate and exposure parameters, and after removing persons ern Quebec, Canada, we investigated the associations between several with medical chart confirmed thyroid diseases (hyper- and hypothy- groups of environmental contaminants including POPs and metals with roidism), the final cohort of participants was 526, of which 307 were thyroid hormone concentrations using machine learning methods. The female and 219 were male. Thyroid diseases such as hyper- and hy- remote communities within Eeyou Istchee are comprised of First Na- pothyroidism were confirmed via medical chart review by a research tions persons who live (in part) a subsistence lifestyle and have higher nurse. All sampling occurred after fasting and at the same time of day. body burdens of contaminants when compared to non-First Nations per- Only contaminants with ≥25% of the data being above the limit of quan- sons from southern Canada [see (Liberda et al., 2014b, 2014a, 2018; titation were selected for analysis. Nieboer et al., 2017)]. Since these contaminants are found ubiquitously in the environment, those who lead a subsistence lifestyle, a classic path- Thyroid Hormone Measurements way of exposure to many contaminants, may be at higher risk to adverse endocrine system effects due to the bioacumulative nature of some of TSH, free T4, and total T3 were measured on the ADVIA Centaur An- these compounds. Long-range transport and point source releases from alyzer automated chemiluminescent immunoassay system (Bayer Health mining and/or hydroelectric development are other potential sources Care Systems, Terrytown, NY). TSH, free T4, and total T3 had functional (Canadian Mining Journal, 2017; Gouin et al., 2004; Liberda et al., sensitivities of 0.01 mIU/L, 1.3 pmol/L, and 0.15 nmol/L, respectively 2
  3. E.N. Liberda, A.M. Zuk, D.S. Di et al. Environmental Advances 4 (2021) 100054 Fig. 1. Eeyou Istchee Territory, Quebec, Canada. (Bonnier-Viger et al., 2007). The respective reference ranges were 0.25- assessed by a clinical field research nurse. Lipids were determined as 4.5 mIU/L for TSH, 8-27 pmol/L for free T4, and 1.3-3.1 nmol/L for total per Rylander et al. (2012). Iodine was not included as a covariate as T3, which took into account disease diagnostics (Bonnier-Viger et al., we have previously reported these values to be within normal ranges 2007). Although the use of reference ranges as a diagnostic tool is a (Tam et al., 2015), and further confirmed via a sensitivity analysis that debated practice (Chaker et al., 2017), we included them to provide its inclusion in the models did not significantly alter the observed re- context. sults. Age (Bremner et al., 2012), sex (Curtis et al., 2019), smoking status (Jain, 2013), blood lipids (Yorita Christensen, 2013), and BMI Environmental Contaminants Analysis (Yorita Christensen, 2013) were added as covariates in both the PCA and BKMR machine learning analyses as these variables are known a Assessment of metals included total mercury, lead, nickel, and cad- priori risk factors. Age, blood lipids, and BMI were used as continuous mium in whole blood (percent below detection: 1.52%, 0.19%, 2.09%, variables while sex and smoking status were categorical variables. 0.38%). Samples were assessed by inductively coupled plasma mass spectrometry (ICP-MS) on a Perkin Elmer Sciex Elan 6000 instrument. Statistical Analysis The methods and QA/QC have been previously reported (Bonnier- Viger et al., 2007; Nieboer et al., 2017). Blood plasma was assessed Descriptive statistics have been reported as arithmetic and geo- for nine PCBs (118, 138, 153, 156, 163, 170, 180, 183, and 187; metric means (± standard deviations (SD)), frequencies, or percent- percent below detection: 11.22%, 1.71%, 0.38%, 20.34%, 16.73%, ages, where appropriate. Unsupervised (PCA) and supervised (BKMR) 10.27%, 1.33%, 23.95%, 7.79%), and four organochlorine pesticides machine learning techniques were used to evaluate the mixture ef- or metabolites (p,p’-DDE, mirex, oxy-chlordane, and trans-nonachlor; fects of the contaminants on thyroid hormone concentrations, as we percent below detection: 0.38%, 19.77%, 6.65%, and 11.79%, respec- have done in the past (Liberda et al., 2021), and are described be- tively). Briefly, samples were assessed on an Agilent 6890 gas chro- low. Analyses were conducted, and images generated, using v3.5.3 of R matograph equipped with an Agilent G2397A ECD and an Agilent 5973 (R Core Team, 2008) with the RStudio frontend (Rstudio Team, 2019) network mass detector. Full methods and limits of detection for this (v.1.2.1335) and BKMR (Bobb, 2017), tidyverse (Wickham, 2017), fac- analysis have been previously reported by Liberda et al. (2014b). Both toextra (Kassambara and Mundt, 2017), GADMTools (Decorps, 2012), elemental and organic analyses were performed at the Institute Na- and mapdata (Brownrigg, 2018) packages. Values were considered sig- tional De Santé Publique du Québec (INSPQ) which also serves as one nificant when p
  4. E.N. Liberda, A.M. Zuk, D.S. Di et al. Environmental Advances 4 (2021) 100054 Table 1 Descriptive data. Percentile Variable N Mean Geometric mean SD. 25th 75th Demographic Data Age 526 39.78 37.51 13.88 29.20 48.00 BMI 526 33.78 33.15 6.56 29.55 37.85 Blood Lipid (g/L) 526 5.25 5.13 1.23 4.49 5.76 Female (%) 307 58% Current Smoker (%) 256 49% Thyroid Hormones TSH (mIU/L) 526 2.15 1.81 1.38 1.29 2.62 Free T4 (pmol/L) 526 15.21 15.07 2.09 13.73 16.40 Total T3 (nmol/L) 526 2.22 2.18 0.49 1.95 2.41 Contaminants Metals Mercury (nmol/L) 526 34.09 14.01 57.97 6.10 39.00 Lead (μmol/L) 526 0.23 0.15 0.25 0.07 0.31 Nickel (nmol/L) 526 20.80 18.24 10.10 16.00 25.00 Cadmium (nmol/L) 526 13.42 7.82 13.35 3.10 20.00 PCBs PCB-118 (μg/L) 526 0.22 0.06 0.44 0.02 0.21 PCB-138 (μg/L) 526 0.58 0.18 1.04 0.05 0.67 PCB-153 (μg/L) 526 1.35 0.41 2.43 0.12 1.40 PCB-156 (μg/L) 526 0.16 0.05 0.30 0.01 0.16 PCB-163 (μg/L) 526 0.22 0.06 0.44 0.02 0.23 PCB-170 (μg/L) 526 0.31 0.09 0.57 0.03 0.33 PCB-180 (μg/L) 526 1.14 0.32 2.10 0.09 1.18 PCB-183 (μg/L) 526 0.11 0.04 0.19 0.01 0.13 PCB-187 (μg/L) 526 0.43 0.12 0.78 0.03 0.47 Organochlorines Mirex (μg/L) 526 0.26 0.06 0.53 0.01 0.24 Oxychlordane (μg/L) 526 0.07 0.03 0.12 0.01 0.08 Trans-nonachlor (μg/L) 526 0.14 0.05 0.24 0.02 0.15 p’p’-DDE (μg/L) 526 2.41 1.18 3.40 0.46 2.68 SD = Standard deviation learning technique, PCA, was performed on the contaminant concen- terior inclusion probabilities (PIPs), examine the univariate exposure- trations which were previously transformed as log10 to improve the outcome relationships with all other exposures fixed to their 50th quan- normality of the data distribution (Gauch, 1982; Green, 1979). To aid tile, and to summarize the overall contaminant mixture effect on total in interpretation, the PCA was varimax rotated. Any non-detects were T3, free T4, and TSH hormones. imputed as 12 the detection limit. The PCA scores generated from the contaminant concentrations were used to explain variation in the thy- roid hormone measures using multivariable linear regression methods. Results It is important to note that using original concentration data as expo- sure variables in linear regression models is not advisable as the corre- Descriptive results lations between related contaminants are often very high – especially in lipophilic contaminants (r=0.90 or larger) (Wainman et al., 2016). All summary statistics are presented in Table 1. More than half Ultimately, this may lead to misleading interpretation of the effects of (58.4%) of the participants were female and the mean age of the par- individual predictor variables due to the multi-collinearity biases of the ticipants was 39.8 (±13.9) years old. Approximately half of the partici- estimation of the individual regression coefficients; the use of PCA rec- pants were current smokers (48.7%) and the mean blood lipid concen- tifies this issue (Altland et al., 1999; Wainman et al., 2016). tration was 5.3 g/L. TSH, free T4, and total T3 mean concentrations were 2.15 mIU/L, 15.21 pmol/L, and 2.18 nmol/L, respectively. All thyroid Bayesian kernel machine regression (BKMR) hormone concentration means were within their respective reference ranges. As expected, body burdens of contaminants varied greatly with The supervised machine learning technique BKMR was used to ex- the highest concentration being observed for p,p’-DDE. amine the association between single contaminant and total mixture ef- fects with thyroid hormone concentration outcomes. Of particular im- portance, BKMR allows for the assessment of non-linear relationships Contaminant PCA Loadings to be evaluated (Bobb et al., 2018, 2014). BKMR utilizes Bayesian and supervised machine learning techniques to iteratively regress response The principal component (PC) loadings that were generated from variables (thyroid hormones) on the environmental contaminant mix- the participant’s contaminant body burdens are presented in Fig. 2. PC- ture while also adjusting for covariates (Chiu et al., 2018). Addition- 1 (74% of variation explained) was highly and positively loaded for all ally, BKMR was designed for cross-sectional studies (Ashrap et al., 2019) PCBs and organochlorines, likely grouped by lipophilicity. The 2nd PC and allows for hierarchical variable selection to fit the model. For this axis (6% of variation explained) was most highly and positively loaded study, the BKMR model was operated with 100,000 machine iterations for nickel and cadmium. PC-3 (11% of variation explained) was highly by a Markov Chain Monte Carlo method with hierarchical selection to and negatively loaded for nickel and highly and positively loaded for group metals, PCBs, and organochlorine contaminants a priori. We uti- cadmium. In total, the three PC axes explained 92% of the original vari- lized BKMR to estimate the group and conditional (within-group) pos- ation in the contaminant body burdens of the participants. 4
  5. E.N. Liberda, A.M. Zuk, D.S. Di et al. Environmental Advances 4 (2021) 100054 Fig. 2. PCA loadings of environmental contaminants. Table 2 important group in the model, with oxychlordane being the most im- Principal component analysis regression results. portant contaminant within-group. For total T3, metals has the high- Dependent variable: est group PIP with nickel being the sole contaminant being chosen for Thyroid Hormone model inclusion. For TSH, PCBs were most important group, with PCB- Free T4 Total T3 TSH 118 followed by PCB-163 being the most important congeners within- group. The standardized exposure effect on total T3 is shown in Fig. 3. PC-1 𝛽= 0.005 0.003 0.030∗ Increases in nickel concentration result in an increase in total T3 with C.I.= (-0.001, 0.010) (-0.004, 0.010) (0.005, 0.055) p= 0.086 0.366 0.019 other contaminants fixed at their 50th percentile (Fig. 3A). The overall PC-2 𝛽= -0.003 0.042∗ -0.020 mixture effect results in an increase in total T3 as quantile concentra- C.I.= (-0.016, 0.010) (0.026, 0.058) (-0.081, 0.040) tions increase (Fig. 3B). No significant results for free T4 or TSH were p= 0.634 0.00000 0.507 observed (figures not shown). PC-3 𝛽= -0.001 -0.030∗ -0.029 C.I.= (-0.017, 0.014) (-0.048, -0.011) (-0.100, 0.043) p= 0.870 0.003 0.433 Discussion Observations 526 526 526 Note: ∗ p
  6. E.N. Liberda, A.M. Zuk, D.S. Di et al. Environmental Advances 4 (2021) 100054 Table 3 Bayesian Kernel Machine Regression (BKMR) Group and Conditional Posterior Inclusion Criteria (PIPs). Free T4 Total T3 TSH Contaminant Group PIPs Conditional PIPs Group PIPs Conditional PIPs Group PIPs Conditional PIPs Group 1: Metals 0.47 1.0 0.34 Mercury (nmol/L) 0.125 0.0 0.205 Lead (μmol/L) 0.585 0.0 0.260 Nickel (nmol/L) 0.066 1.0 0.179 Cadmium (nmol/L) 0.223 0.0 0.356 Group 2: PCBs 0.627 0.402 0.672 PCB-118 (μg/L) 0.249 0.104 0.229 PCB-138 (μg/L) 0.118 0.107 0.071 PCB-153 (μg/L) 0.074 0.103 0.084 PCB-156 (μg/L) 0.148 0.094 0.126 PCB-163 (μg/L) 0.086 0.135 0.130 PCB-170 (μg/L) 0.093 0.114 0.103 PCB-180 (μg/L) 0.089 0.137 0.095 PCB-183 (μg/L) 0.064 0.121 0.082 PCB-187 (μg/L) 0.077 0.084 0.081 Group 2: Organochlorines 0.849 0.483 0.513 Mirex (μg/L) 0.105 0.205 0.246 Oxychlordane (μg/L) 0.482 0.292 0.247 Transnonachlor (μg/L) 0.257 0.395 0.304 p,p’-DDE (μg/L) 0.156 0.108 0.204 Fig. 3. BKMR results for T3. A. Univariate dose-response model between T3 and scaled fixed contaminant concentrations with credible interval. B. Overall risk model output for T3 in response to varying quantile concentration of entire complex mixture. also shown that nickel may decrease T3 concentrations in rats, not in- airborne particulate matter from mining operations can impact commu- crease it, as we observed (Cheng et al., 1997). Unfortunately, there are nities from the James Bay region (Liberda et al., 2015). Lastly, nickel few human studies assessing the association between, or the effect of, contributions to human body burden may be due to cigarette smoke, nickel and its consequences on thyroid hormone concentrations, which however, our previous analyses found that cadmium, and not nickel, may ultimately play an important role in thyroid disruption. Interest- was associated with this exposure (Charania et al., 2014). ingly, in a murine study, Stangl and Kirchgessner (1998) found that As noted, our PCA analyses also suggested that cadmium was pos- nickel depressed status significantly lowered all circulating thyroid hor- itively associated with total T3, a finding that Chen et al. (2013) also mones (free and total T4, and total T3), and thus it is possible that excess found, except in urinary cadmium. However, our additional BKMR anal- nickel could cause an increase in these hormones – as we observed – but ysis did not support this association. The association between the PC- this has not been confirmed experimentally. 2 axis was most likely being driven by nickel and could be confirmed While both PCA and BKMR analyses highlight the significant associa- by the significant association with PC-3 clearly highlighting the differ- tion of nickel with total T3, it is important to note that this contaminant ences between nickel and cadmium. BKMR had the power to differen- is ubiquitous due to its use in stainless steel products (Sterzl et al., 1999). tiate between these two contaminants while PCA required additional While the collection and analyses of blood occurred using non-stainless examination. While the roles of metals in thyroid hormones is not fully steel products, the needle of the syringe was stainless steel. Additionally, understood, the mechanism for the associations observed with nickel cooking using acidic fruits and vegetables may cause more nickel to be or cadmium may be due to alterations in thyroid and adrenal gland released from the cookware contributing further to human body burden function and morphology, metabolism, synthesis, uptake mechanisms, (Christensen and Möller, 1978). It is probable that the nickel mines in or plasma transport effects, and reduced apoptotic activity (Boas et al., this region (Canadian Mining Journal, 2017) may be a responsible for 2006; Brouwer et al., 1998, 1986; Buha et al., 2018; Cheek et al., 1999; the observed body burdens herein and we have previously reported that Rana, 2014). In some cases, the observations may be due to changes in 6
  7. E.N. Liberda, A.M. Zuk, D.S. Di et al. Environmental Advances 4 (2021) 100054 the iodothyronine deiodinases, transportation, or synthesis (Chen et al., difficult. However, subsequent BKMR analyses bolstered the PCA results 2013; Gupta et al., 1997; Stangl et al., 1999) in addition to the pre- and provided further insight into the combined effects of the contami- viously noted mechanisms. Thus, it is possible that the observed asso- nant mixtures. ciations of nickel and total T3 may be driven by its role in disturbing the hypothalamic–pituitary–thyroid axis, but the exact mechanism re- Conclusions mains to be elucidated. As this is a cross-sectional study, we cannot imply causality and therefore we report associations which should be We have previously reported on the iodine status from the com- verified through experimental methods. munities of the Eeyou Istchee territory of eastern James Bay (Quebec, PCA regression showed that PC-1 was significantly and positively Canada) and have found that iodine status is sufficient at the population associated with TSH. Given that PC-1 is highly loaded for PCBs and level (Tam et al., 2015). However, it is possible that contaminants may organochlorines, this finding is not a surprise as other studies have found still play a role in thyroid disorder morbidity via a variety of different this same correlation. The mechanisms by which various organochlo- mechanisms including receptor interference, thyroid antibody genera- rines and PCBs are causing the alterations in thyroid hormone concen- tion, and competitive binding (Boas et al., 2012; Sarkar et al., 2015). trations may not yet be fully elucidated, however, many of these mech- We have shown that supervised (BKMR) and unsupervised (PCA) ma- anisms previously noted have also been postulated for organic contam- chine learning techniques both identified nickel as being significantly inants (Brouwer et al., 1998), and therefore the complexity of chemical and positively associated with total T3. While PCA analyses identified mixtures further complicate the understanding of experimental effects PCBs and organochlorines as being significantly and positively associ- and associations observed with singular contaminant exposures. More ated with TSH, BKMR analysis did not support this significant associ- recently, an in vivo investigation into the mechanisms of DDE found that ation, but did suggest that PCBs were more important in positively al- may be exerting its thyroid disrupting effects by altering genes related tering TSH levels than organochlorines in participants from this study. the hypothalamic pituitary-thyroid axis, specifically those related to thy- Taken together, nickel, and possibly PCBs, may play a role in modify- roid hormone synthesis and development (Wu et al., 2019). Marsan and ing the thyroid hormone milieu, but competing effects appear to temper Bayse (2020) postulated that PCBs may exert their effects via halogen the manifested outcomes due to contaminant mixture interactions. Im- bonding to the iodothyronine deiodinases resulting in thyroid hormone portantly, we note that while we have identified contributing contam- modulation (competitive inhibition) and that this interaction appears to inants that may alter thyroid concentrations, none of our participants be driven by the location of the chlorine groups on the molecules. Re- had thyroid hormone concentrations outside of the reference range. Fu- lated to this, Bai et al. (2018) reported that some PCB congeners appear ture studies should examine the role of nickel and PCBs using murine to act as thyroid receptor agonists while others as antagonists and this models to elucidate their mechanisms of action. This article represents could explain the varying findings between studies and their effects on a significant step towards assessing complex mixtures of organic and thyroid hormones. BKMR analysis did not support the significant find- metal xenobiotics using novel machine learning techniques by evaluat- ing between PCBs and organochlorines with TSH but did suggest that ing their univariate and complete mixture effects on thyroid hormone PCBs were more important than organochlorines in contributing to the levels as a whole, rather than by single or small groups of similar con- association observed via the PCA. This observation may have occurred taminants. This article also highlights the importance of exposures to due to the flexible and highly iterative and non-linear approach BKMR nickel and PCBs, as these may be more relevant to persons living a sub- uses, albeit at a significant computational cost, or, simply due to the sistence lifestyle, and hence, may be at greater risk than those who are interaction of antagonistic and agonistic PCB congeners. minimally exposed. Body burdens of organic and elemental contaminants were greater than their non-Indigenous Canadian counterparts who were over 20 Credit Authorship Contribution Statement years of age (Health Canada, 2019, 2010). Specifically, this study found organic contaminant concentrations to be higher than Dene/Metis Eric N. Liberda: Conceptualization, Methodology, Validation, For- persons, but generally lower than those reported from both the mal analysis, Data curation, Writing - original draft, Writing - review & east and west coast of James Bay, Canada (Health Canada, 2010; editing, Visualization. Aleksandra M. Zuk: Conceptualization, Method- Liberda et al., 2014b; Philibert et al., 2009; Tsuji et al., 2005; ology, Validation, Formal analysis, Data curation, Writing - review & Walker and Van Oostam, 2001). With respect to elemental con- editing. David S. Di: Data analysis, interpretation, and editing. Robert taminants, this cohort had higher concentrations of blood cadmium J Moriarity: Data analysis, interpretation and editing. Ian D. Martin: (34.09 nmol/L vs. 13.5 nmol/L), nickel (20.8 nmol/L vs. 16.1 nmol/L), Conceptualization and data analysis, Leonard J. Tsuji: Conceptualiza- mercury (34.09 nmol/L vs. 24.0 nmol/L) and lead (0.23 μmol/L vs. tion, Methodology, Writing - review & editing. 0.1456 μmol/L) compared to a previous publication from this region (Nieboer et al., 2017). However, none of these mean values exceeded health guidelines with the exception of Cadmium, which we have pre- Funding viously shown was caused by smoking (Charania et al., 2014). While the findings of this analysis highlight the power of machine This scientific communication is a report from the Nituuchischaay- learning to identify associations with various dependent variables, this ihtitaau Aschii: Multi-Community Environment-and Health Study in article is not without limitations. First, the study is cross-sectional and Eeyou Istchee supported by the Cree People of Northern Québec, the hence causality and temporality cannot be implied. Second, although Cree First Nations and the Cree Board of Health and Social Services we have adjusted for various lifestyle effects (e.g., smoking status), it of James Bay through financial contributions from Niskamoon Corpo- is possible that unmeasured covariates such as time spent on the land ration. Additional funding for analyses was provided by the Institute (traditional activities) could contribute to the observed effects. Third, of Indigenous Peoples’ Health, Canadian Institutes of Health Research while we adjusted for current smoking status, the data collected did (Grant #156396). The authors thank the participants from the Eeyou not record pack-years of cigarettes and hence could contribute to resid- Istchee territory in Quebec, Canada. ual confounding. Fourth, this study measures free (unbound free hor- mone, biologically active) T4, total (bound and unbound forms) T3, and Declaration of Competing Interest TSH, however associations with total T4 or free T3, for example, may have been present, but would be less clinically relevant and/or reliable. The authors declare that they have no known competing financial Lastly, as PCA is a linear combination of original contaminant variables interests or personal relationships that could have appeared to influence with projected orthogonal axes, interpretation of results is sometimes the work reported in this paper. 7
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