Blood metal levels and early childhood anthropometric measures in a cohort of Canadian children

Environmental Research 179 (2019) 108736

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Blood metal levels and early childhood anthropometric measures in a cohort of Canadian children

T

Jillian Ashley-Martina, Linda Doddsa, Tye E. Arbuckleb, Bruce Lanphearc, Gina Muckled, Maryse F. Boucharde, Mandy Fisherb, Elizabeth Asztalosf, Warren Fosterg, Stefan Kuhlea,∗ a

Perinatal Epidemiology Research Unit, Dalhousie University, Halifax, NS, Canada Environmental Health Science and Research Bureau, Health Canada, Ottawa, ON, Canada c Simon Fraser University, Burnaby, BC, Canada d Laval University and Quebec CHU Research Center, Quebec City, QC, Canada e University of Montreal, Montreal, QC, Canada f University of Toronto, Toronto, ON, Canada g McMaster University, Hamilton, ON, Canada b

A R T I C LE I N FO

A B S T R A C T

Keywords: Metals Body mass index Lead Essential elements Biomonitoring

Fetal exposure to some toxic metals has been associated with reduced fetal growth, but the impact of contemporary, low-level metals on anthropometric measures in childhood is not well understood. Our primary objective was to quantify associations between childhood levels of toxic metals and concurrently measured body mass index (BMI) in a population of Canadian preschool-aged children. We collected biomonitoring data and anthropometric measures on 480 children between the ages of two and five years in the Maternal-Infant Research on Environmental Chemicals (MIREC) Child Development Plus study. Concentrations of four toxic metals (lead, arsenic, cadmium, and mercury) were measured in whole blood collected from pregnant women and their children. Blood levels of key essential elements were also measured in children. Children's weight, height, and BMI z-scores were calculated using the World Health Organization growth standards. We used a series of linear regression models, adjusted for potential parental confounders, concurrently measured metals and elements, and prenatal blood metal levels, to evaluate associations between tertiles of each toxic metal and anthropometric measures. We tested for effect modification by sex. Of the 480 children, 449 (94%) were singleton births and had complete biomonitoring and anthropometric data. The majority of children had detectable concentrations of metals. In the adjusted models, girls with blood lead concentrations in the highest tertile (> 0.82 μg/dL) had, on average, 0.26 (95% Cl: -0.55, 0.03) lower BMI z-scores than those in the referent category. In contrast, boys with lead levels in the highest tertile had, on average, 0.14 higher BMI z-scores (95% Cl: -0.14, 0.41) (p-value heterogeneity = 0.04). In this population of Canadian preschool-aged children with low-level blood lead concentrations, we observed effect modification by sex in the association between Pb and BMI but no statistically significant associations in the sex-specific strata. Child blood levels of As, Cd, and Hg were not associated with childhood BMI, weight, or height in boys or girls.

1. Introduction Fetal exposure to certain toxic elemental metals, such as lead (Pb), arsenic (As), cadmium (Cd), and mercury (Hg), has been associated with reduced fetal growth, but the impact of current low-level exposure to metals on childhood anthropometry is not well understood (Farzan et al., 2013; Karagas et al., 2012; NTP, 2012). Despite declining levels

in recent decades, these metals are a persistent public health concern due to their toxicity, long biological half-lives, and ubiquitous presence (Health Canada, 2010). The potential deleterious effects are of particular concern if exposure occurs during infancy and preschool periods. Due to behavioral patterns (e.g., hand mouth behavior, time spent on floor) and rapid growth, young children are more heavily exposed and more susceptible to the adverse effects of contaminants than older age

Abbreviations: As, Arsenic; Cd, Cadmium; Hg, mercury; Pb, lead; Cu, copper; Mn, manganese; Mo, molybdenum; Ni, nickel; Se, selenium; Zn, zinc ∗ Corresponding author. Perinatal Epidemiology Research Unit, Dalhousie University PO Box 9700, 5850-5980 University Ave, Halifax NS, B3K 6R8, Canada. E-mail address: [email protected] (S. Kuhle). https://doi.org/10.1016/j.envres.2019.108736 Received 14 June 2019; Received in revised form 3 September 2019; Accepted 7 September 2019 Available online 12 September 2019 0013-9351/ Crown Copyright © 2019 Published by Elsevier Inc. All rights reserved.

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Table 1 Descriptive statistics of toxic metals and essential elements measured in the blood of children in the MIREC-CD Plus cohort (n = 449). Child (2–5 years) Toxic Metals Arsenic (μg/L) Cadmium (μg/L) Mercury (μg/L) Lead (μg/dL) Essential Elements Copper (μg/dL) Manganese (μg/L) Molybdenum (μg/L) Nickel (μg/L) Selenium (μg/dL) Zinc (μg/dL)

% < LOD

LOD

Minimum

25th perc.

50th perc.

75th perc.

Maximum

19.0 13.5 30.5 0

0.150 0.0450 0.100 0.104

LOD LOD LOD 0.141

0.219 0.0616 0.0642 0.471

0.464 0.0891 0.223 0.663

0.891 0.119 0.534 0.962

20.7 0.700 5.78 5.49

0 0 0 22.4 0 0

1.90 0.412 0.0960 0.294 0.790 13.1

61.0 2.95 0.334 LOD 8.84 231

93.4 8.77 0.878 0.328 13.8 414

102 10.3 1.13 0.528 14.9 454

113 12.4 1.49 0.772 16.1 498

170 27.1 5.35 8.16 21.7 732

Abbreviations: LOD limit of detection, perc. percentile.

2. Materials and methods

groups (Bellinger and Dietrick, 1994; Faustman et al., 2000). In a review of the health effects of low-level exposure to Pb, the US National Toxicology Program (NTP) reported that there is sufficient evidence that blood Pb levels below 10 μg/dL in children are associated with reduced postnatal growth (as measured by indicators such as head circumference, height, weight, or chest circumference), but that there is inadequate evidence of an association between child Pb levels less than 5 μg/dL and postnatal growth (NTP, 2012). One of the primary mechanisms underlying these growth deficits is Pb-related impairment to bone growth and growth plate morphology (ATSDR, 2007). Evidence from epidemiological studies also suggests inverse associations between maternal exposure to Hg and As and fetal growth (Thomas et al., 2015), but there is limited information on potential growth-related effects of childhood exposure (Farzan et al., 2013; Karagas et al., 2012; Wigle et al., 2008), particularly at levels typical of contemporary North American populations. Mercury, a known neurotoxicant, has been shown to reduce birth weight in animal studies possibly via disruptions in calcium homeostasis or oxidative stress (ATSDR, 1999; Bjørklund et al., 2017). Arsenic is also a known carcinogen and may effect growth via oxidative stress (Vahter, 2007). Experimental studies have demonstrated that administered Cd is associated with reduced body weight and growth in animals (ATSDR, 2018). In humans, prenatal Cd has been inversely associated with birth weight, but evidence of growth-related effects in children is limited (Zheng et al., 2016). Some of these metalrelated effects on growth may differ according to child sex (Freire et al., 2019; Lamichhane et al., 2018). One of the challenges in assessing the influence of childhood metal exposure on concurrently assessed anthropometric measurements is the potential confounding due to co-occurring exposures as well as in utero metal exposure. Essential elements such as zinc (Zn) and selenium (Se) have been shown to influence metal toxicity in epidemiological (Cantoral et al., 2015; Wells et al., 2016) and experimental (Bushnell and Leven, 1983; MacDonald et al., 2015) studies. Additionally, in utero metal exposure may influence associations between metal exposure and child anthropometric measures via correlation with child metal levels (Gardner et al., 2013) or fetal growth restriction (Luo et al., 2017). Our primary objective was to quantify associations between levels of four metals (As, Cd, Hg, and Pb) and concurrently measured anthropometric indices in a sample of Canadian preschool children and to determine whether these associations differ according to sex of the child. The secondary objective was to isolate the effects of concurrent child blood metal levels from co-occurring and in utero exposures. We accounted for potential confounding due to concurrent essential elements and metals as well as maternal prenatal blood metal (As, Cd, Hg, Pb) levels.

2.1. Study population The Maternal-Infant Research on Environmental Chemicals (MIREC) study is a national-level pregnancy cohort of 2001 women from 10 cities across Canada including Vancouver, Edmonton, Winnipeg, Sudbury, Ottawa, Kingston, Toronto, Hamilton, Montreal, and Halifax. Participants were recruited in the first trimester of pregnancy between 2008 and 2011 and followed through delivery (Arbuckle et al., 2013). Parents from six of the study sites that had the highest recruitment rates (Vancouver, Toronto, Hamilton, Kingston, Montreal, and Halifax) were invited to participate in a follow-up study of child growth, neurodevelopment, and exposure biomonitoring (MIREC-Child Development Plus (MIREC-CD Plus)). Biomonitoring of metals and nutrients was conducted on 480 children. The study was reviewed by the Health Canada Research Ethics Board (Ottawa, ON) and the Research Ethics committee at St. Justine's Hospital (Montreal, QC), and parents signed an informed consent form for their and their child's participation. 2.2. Metals and essential elements The primary exposures of interest in this analysis were whole blood levels of As, Cd, Hg, and Pb measured at the time of the MIREC-CD Plus visit; children were between two and five years of age at this visit. Concentrations of both the primary metals of interest (As, Cd, Hg, Pb) and essential nutrients (copper (Cu), manganese (Mn), molybdenum (Mo), nickel (Ni), Se, and Zn) were measured in children's whole blood samples using inductively-coupled plasma-mass spectrometry. This method enables simultaneous analysis of several metals in one sample and is highly sensitive, specific, and accurate. Machine reading values were assigned to concentrations below the limit of detection (LOD). Table 1 depicts LODs for all metals and essential elements measured at the time of the MIREC-CD Plus visit. Blood collection was performed during a home visit or, if requested by the parent, a clinic visit. In order to minimize discomfort, the blood draw was performed using a butterfly needle and collected in a 6 mL EDTA vacutainer. After the collection, the EDTA vacutainer was inverted 8 to 10 times to mix the anticoagulant and kept frozen at −80C, before being sent to the Institut National de Santé Publique du Québec (INSPQ) for analysis. For visits taking place at night or during weekends, a maximum window of 72 h after sample collection was allowed before processing (Drammeh et al., 2008). During this period, the samples were kept standing in a fridge to preserve their integrity (Oddoze et al., 2012). Maternal whole blood metal concentrations were evaluated as potential confounders of the association between concurrently measured childhood metal concentrations and growth. Details regarding 2

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For results with effect modification by sex or suggestion of a doseresponse relationship, we used unadjusted generalized additive models (GAM) to visualize the dose-response relation and identify cut-points that may not have been apparent in the use of tertiles. For the GAM models, we trimmed the data to exclude the top and bottom 2% to allow the model to be analyzed over a high density of observations and to prevent variability at the tail ends of the distribution from altering the shape of the curve. To account for missingness in the covariates, we performed multiple imputations with chained equations (m = 30, 10 iterations) (van Buuren and Groothuis-Oudshoorn, 2011). Analyses were conducted in SAS version 9.4 (Cary, NC, USA) and R version 3.4.0 (R, 2018).

measurement and descriptive statistics of the maternal blood samples have been previously described (Arbuckle et al., 2016). Briefly, whole blood samples were collected during the first (6–13 weeks) and third (32–34 weeks) trimesters and were analyzed at the INSPQ using inductively coupled plasma-mass spectrometry. 2.3. Child anthropometric measures Child anthropometry was performed during the home visit and served as a measure of growth at that time. Weight and height were measured using a calibrated scale (Seca model 874 [Seca Corporation, Hanover, MD]) and calibrated stadiometer (Seca model 217). All measurements were completed in duplicate or, if warranted due to predefined differences in duplicate measurements, in triplicate. World Health Organization (WHO) child growth standards (WHO, 2015) for children between two and five years were used to calculate age- and sex-specific z-scores for BMI, height, and weight. Use of z-scores allows for comparison of anthropometric measurements at different ages at the time of follow-up.

3. Results Of the 480 children with available biomonitoring data, 450 were singleton births with sex, weight, height, and age recorded at the time of the MIREC-CD Plus follow-up visit. One participant with a BMI zscore > 5 was identified as an influential observation based on regression diagnostic plots and was excluded from the analysis. As a result, our final sample consisted of 449 mother-children pairs. The majority of children had detectable concentrations of all the toxic metals and essential elements measured at the time of the followup visit. The percent of samples with concentrations below the LOD ranged from 0 (Pb) to 30.5% (Hg). All children had detectable concentrations of all the essential elements except for Ni (Table 1). We observed a weak to moderate degree of correlation among metals and elements; the strongest correlation was between Zn and Se (r = 0.34) (Suppl Table 1). Descriptive statistics for maternal third trimester blood metal levels are presented in Suppl Table 2. Correlations among childhood and prenatal metals were strongest for Pb (r = 0.37 and 0.39 for levels in first and third trimester, respectively) and weakest for As (r = 0.14 and 0.15 for levels in the first and third trimester) (Suppl Table 3). Due to the lack of high correlation among metals and elements measured both in children and mothers, we were able to adjust for multiple exposures without hindering model convergence. Children were, on average, 39 months of age at the time of the follow-up visit (standard deviation (SD) 7.8) with average BMI, height, and weight z-scores of 0.5 (SD 0.9), 0.0 (SD 0.9), and 0.4 (SD 0.8), respectively (Table 2). The characteristics of the study population and the means and SDs of anthropometric measures are shown in Table 2. Children's parents were primarily born in Canada, had moderate to high household incomes and most had received a university undergraduate degree or higher. Most mothers were of normal BMI prior to their pregnancy and at the time of follow-up. Approximately 30% of fathers were of underweight or normal BMI. Based on results of the BMA, models were adjusted as follows; BMI: As, Hg, Zn, Mo; height: Mo, As, Cd, Se; weight: As, Mo, Zn, Hg (Suppl Table 4). Effect estimates for the adjusted associations between blood metal levels and each anthropometric outcome (BMI, weight, and height z-scores) in the total study population were close to the null value with wide confidence intervals (Table 3). We observed effect modification by sex only in the model of Pb and BMI (p-value heterogeneity = 0.04). No interactions between age of child and maternal exposure were observed. In the sex-stratified analyses of Pb, girls in the third tertile of Pb exposure (> 0.82 μg/dL) had lower BMI and weight z-scores than girls in the referent group (Table 4). The magnitude of this association was similar between the covariate adjusted model (M1) (−0.26; 95% CI: -0.55, 0.03; p-value linear test for trend = 0.09) and the model adjusted for prenatal Pb levels (M3) (−0.32; 95% CI: -0.64, 0.0036; p-value linear test for trend = 0.06). When adjusted for concurrently measured metals and essential elements (M2), the effect was slightly attenuated. Among boys, we did not observe any dose-response relationship or association between Pb and BMI z-scores (p-value for linear trend > 0.10 in each model) (Table 5). There were no statistically significant

2.4. Statistical analysis Descriptive statistics were performed on all metals, essential elements, and anthropometric measures. All exposures were log2-transformed to account for skewed distributions. Pearson correlation coefficients were calculated among all transformed metals and elements measured in childhood. We also calculated correlation coefficients among the childhood and prenatal metal levels. We visualized associations between each metal and outcome to evaluate non-linearity. In view of non-linear relationships between certain metals and outcomes, we categorized all toxic metals into tertiles. We present parameter estimates for tertiles of each toxic metal based on three models: 1) adjusted for parental covariates (M1), 2) adjusted for parental covariates and other concurrently measured metals and elements (M2), and 3) adjusted for parental covariates and maternal prenatal metal levels. We selected the set of parental covariates to include in the model by identifying potential determinants of both postnatal metal exposure and childhood growth (Arbuckle et al., 2016; Brisbois et al., 2012; GarcíaEsquinas et al., 2013; Howe et al., 2012; Weng et al., 2012). Based on this criterion, derived from causal diagrams, maternal education, maternal country of birth, age, postnatal BMI (measured at the time of the MIREC-CD Plus visit), maternal prenatal smoking, and paternal BMI were included as categorical variables in these models (M1). We conducted sensitivity analyses among children of mothers who did not smoke during pregnancy. We also evaluated the effect of adjusting for postnatal second-hand smoke vs prenatal smoke exposure. To identify which concurrently measured essential elements and metals to include as potential confounders, we used Bayesian Model Averaging (BMA) (Berger et al., 2018; Raftery et al., 2018). The four metals and elements with the highest probability of inclusion in the BMA model were identified for each outcome (BMI, weight, height) and included as potential confounders in the M2 models (Berger et al., 2018) using restricted cubic splines to account for potential non-linear relationships with the outcomes. In the model adjusted for prenatal exposure (M3), we adjusted for the average of first and third trimester blood metal concentrations. The average of the two prenatal measurements was used to represent exposure throughout gestation. In these models, we examined potential interactions between prenatal metal concentrations and age of the child to account for potential varying effects of prenatal exposure over time. The interaction terms remained in the model if the product term p-value was < 0.1. We explored effect modification by sex by stratifying all analyses by child sex and formally tested for interaction via the p-value of the sex*metal product terms. 3

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4. Discussion

Table 2 Study population characteristics and child anthropometric measures (n = 449). N

%

BMI z-score

Height z-score

Weight z-score

Mean

SD

Mean

SD

Mean

SD

25 42 34

0.4 0.6 0.5

(0.9) (0.8) (0.9)

0.0 −0.1 0.1

(1.0) (0.9) (0.9)

0.3 0.4 0.4

(0.9) (0.8) (0.8)

6.0

0.5

(0.8)

0.2

(0.7)

0.5

(0.6)

27

0.4

(1.0)

−0.1

(0.9)

0.3

(0.9)

67

0.6

(0.8)

0.0

(1.0)

0.4

(0.8)

54 24 22

0.4 0.6 0.8

(0.8) (0.9) (0.9

−0.0 0.1 −0.0

(1.0) (1.0) (0.8)

0.2 0.5 0.5

(0.8) (0.9) (0.8)

82 18

0.5 0.4

(0.8) (1.0)

−0.1 0.2

(0.9) (1.0)

0.4 0.4

(0.8) (1.0)

3.1 28 69

0.5 0.6 0.5

(0.9) (0.8) (0.9)

−0.1 −0.0 0.0

(0.7) (0.9) (1.0)

0.5 0.4 0.3

(1.1) (0.7) (0.9)

31 46 23

0.4 0.6 0.6

(0.8) (0.8) (0.9)

−0.0 0.0 0.1

(0.9) (0.9) (1.0)

0.2 0.4 0.5

(0.8) (0.8) (0.9)

46 40 14

0.5 0.5 0.5

(0.9) (0.9) (0.8)

−0.1 0.1 0.0

(0.9) (1.0) (0.9)

0.3 0.4 0.4

(0.8) (0.9) (0.8)

2.9 53 45

0.1 0.4 0.7

(0.8) (0.8) (0.9)

−0.8 −0.1 0.2

(1.1) (0.9) (0.9)

−0.4 0.2 0.6

(1.1) (0.8) (0.8)

6.1 94

0.2 0.5

(0.8) (0.9)

−0.4 0.0

(0.9) (0.9)

−0.4 0.0

(0.9) (1.0)

53 47

0.6 0.4

(0.9) (0.8)

0.0 −0.1

(0.9) (1.0)

0.5 0.2

(0.8) (0.8)

In this cohort of Canadian mothers and their children, the association between Pb and BMI z-scores differed between boys and girls. In contrast to boys, girls with blood lead > 0.82 μg/dL tended to have, on average, lower BMI z-scores than girls in the referent group, but these results were not statistically significant. We did not observe effect modification by sex for any other metal or any dose-response relationships between the other metals (As, Cd, Hg) and the anthropometric measures. The deleterious effects of Pb on childhood growth, which is supported by numerous cross-sectional and prospective epidemiological studies (NTP, 2012), is corroborated with experimental data (de Figueiredo et al., 2014; Kimmel et al., 1980). Although the NTP reports that there is sufficient evidence that maternal blood Pb levels below 5 μg/dL are associated with reduced fetal growth (NTP, 2012), few studies have evaluated growth-related effects of low-level Pb in children. Further, compared to the well-established associations between child Pb and reduced height (NTP, 2012), the potential Pb-related effects on BMI have received limited attention. In the present study, it was not possible, due to the imprecision in our results, to determine whether the potential Pb-related changes in BMI were driven by reductions in weight or height. We hypothesize that decreases in BMI are driven by a reduction in weight as a reduction in height with no change in weight would result in the counterintuitive finding of an elevated BMI. Previous studies examining child Pb and BMI have reported null (Afeiche et al., 2012; Hauser et al., 2008; Min et al., 2008) and positive (Kim et al., 1995) associations. In contrast to the present study, former studies evaluated exposures in older children (Hauser et al., 2008; Min et al., 2008), had higher mean Pb blood concentrations, and did not report effect modification by child sex (Afeiche et al., 2012; Hauser et al., 2008; Min et al., 2008). The potential sex-specific effects of child Pb exposure on child growth have not been explored extensively; studies showing differential effects by child sex have been conducted in older children. For example, in a cross-sectional Polish study of children between the ages of 7 and 15, blood Pb (mean = 8 μg/dL) was associated with a significant reduction in weight and BMI in girls but not boys (Ignasiak and Awin, 2006). A cross-sectional Italian study of children ages 11–13 reported that blood Pb (mean = 9 μg/dL (boys), 7 μg/dL (girls)) was inversely related with height and weight in boys and with height in girls (Vivoli et al., 1993). Lead absorption and toxicity may be exacerbated in individuals who have inadequate levels of nutrients such as calcium, iron, and Zn. In addition, Pb may disrupt the function of these nutrients that are critical to maintain adequate growth and development (ATSDR, 2007). The attenuated results in the model adjusted for concurrently measured metals and elements insinuates that these co-exposures (As, Hg, Mo, Zn) may mitigate the adverse effects of Pb. Previous evidence of interactions between Zn and Pb (ATSDR, 2007; Bushnell and Leven, 1983) suggest that Zn may play an integral role in the present findings. Being of moderate to high socioeconomic status, MIREC study participants are unlikely to be malnourished. The minimum blood Zn level among MIREC participants was 2313 μg/L, well above the Zn deficiency cut-off of 650 μg/L (Gibson et al., 2018). It is, however, possible that children with high Zn levels may be less susceptible to the potential growthrelated effects of Pb. We found no evidence for an association between childhood exposures to As, Cd, and Hg and any of the anthropometric measures. Previous literature regarding the effects of Hg and Cd on childhood growth has largely been inconsistent, with associations primarily observed at concentrations higher than those observed in MIREC participants (Gardner et al., 2013; Wigle et al., 2008; Zheng et al., 2016). It is worth noting that, in contrast to Pb where all participants had measurable concentrations, a significant proportion (19–31%) of samples for the other metals had undetectable concentrations. The use of tertiles

Parental Maternal Age (years) < 29 110 30-34 188 ≥35 151 Maternal Education High school or 27 less College courses 121 or diploma University 300 degree 2 Postnatal BMI (kg/m ) < 25 241 25-29 106 ≥30 100 Maternal Country of Birth Canada 369 Elsewhere 80 Maternal Prenatal Smoking Current 14 Former 125 Never 310 Paternal BMI (kg/m2) < 25 135 25-29 200 ≥30 101 Child Age at follow-up (months) 22-36 205 37-48 177 > 48 67 Birth weight (g) < 2500 14 2500-3500 235 > 3500 199 Gestational age (weeks) < 37 28 ≥37 421 Child Sex Boy 236 Girl 213

Abbreviations: BMI body mass index; SD standard deviation. Missing information: maternal age (0), maternal education (0.2%), postnatal BMI (0.4%), maternal country of birth (0), prenatal smoking (0), paternal BMI (2.9%), child age at follow-up (0), birth weight (0.2%), gestational age (0), child sex (0).

associations or dose-response relations observed between any of the other metals and anthropometric outcomes in boys or girls (Tables 4 and 5). These results did not change when the study population was restricted to children of mothers who did not smoke during pregnancy or children with no reported second-hand smoke at the time of the follow-up visit. Similarly, results did not differ when adjusted for postnatal second-hand smoke exposure rather than prenatal smoke exposure. The generalized additive model for the relationship between continuous Pb levels and BMI z-score in girls showed a negative slope between −1.0 and 0.0 log2 Pb μg/dL (equivalent to 0.5 and 1 μg/dL not log-transformed). In boys, the relationship between Pb and BMI exhibited a modest linear increase throughout the range of exposure values (Fig. 1).

4

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Table 3 Parameter estimates and 95% CIs for the association between tertiles of metalsa and anthropometric measures (n = 449). BMI z-score As (μg As/L)

Cd (μg/L)

Hg (μg/L)

Pb (μg/dL)

Height z-score As (μg As/L)

Cd (μg/L)

Hg (μg/L)

Pb (μg/dL)

Weight z-score As (μg As/L)

Cd (μg/L)

Hg (μg/L)

Pb (μg/dL)

M1 T1 T2 T3 T1 T2 T3 T1 T2 T3 T1 T2 T3

Ref −0.065 0.018 Ref −0.13 0.038 Ref −0.096 −0.034 Ref 0.097 −0.041

T1 T2 T3 T1 T2 T3 T1 T2 T3 T1 T2 T3

Ref 0.016 −0.13 Ref −0.10 0.034 Ref −0.09 −0.20 Ref −0.015 0.025

T1 T2 T3 T1 T2 T3 T1 T2 T3 T1 T2 T3

Ref −0.029 −0.066 Ref −0.15 0.051 Ref −0.11 −0.15 Ref 0.064 −0.0040

M2 Ref −0.060 −0.010 Ref −0.13 0.039 Ref −0.099 0.0060 Ref 0.11 −0.0070

(-0.26, 0.13) (-0.18, 0.22) (-0.33, 0.07) (-0.16, 0.23) (-0.29, 0.098) (-0.23, 0.17) (-0.098, 0.29) (-0.24, 0.16)

Ref 0.042 −0.066 Ref −0.027 0.11 Ref −0.038 −0.15 Ref −0.036 0.030

(-0.20, 0.23) (-0.35, 0.094) (-0.32, 0.12) (-0.18, 0.25) (-0.30, 0.12) (-0.42, 0.016) (-0.23, 0.20) (-0.20,0.25)

Ref −0.0090 −0.047 Ref −0.10 0.097 Ref −0.084 −0.086 Ref 0.058 0.021

(-0.22, 0.16) (-0.26, 0.12) (-0.33, 0.037) (-0.13, 0.24) (-0.30, 0.071) (-0.34, 0.041) (-0.12, 0.25) (-0.20 0.19)

M3

(-0.26, 0.14) (-0.22, 0.20) (-0.33, 0.067) (-0.16, 0.24) (-0.30, 0.10) (-0.21, 0.22) (-0.088, 0.31) (-0.21, 0.20)

(-0.18, 0.26) (-0.30, 0.17) (-0.24, 0.19) (-0.11, 0.32) (-0.26, 0.18) (-0.38, 0.087) (-0.25, 0.18) (-0.19, 0.25)

(-0.20, 0.18) (-0.25, 0.16) (-0.29, 0.082) (-0.09, 0.28) (-0.27, 0.11) (-0.29, 0.12) (-0.13, 0.24) (-0.17, 0.21)

Ref −0.071 0.01 Ref −0.13 0.037 Ref −0.097 −0.062 Ref 0.076 −0.086 Ref 0.021 −0.10 Ref −0.13 0.037 Ref −0.090 −0.21 Ref −0.030 −0.0080 Ref −0.029 −0.069 Ref −0.15 0.042 Ref −0.11 −0.17 Ref 0.041 −0.050

(-0.27, 0.13) (-0.22, 0.19) (-0.33, 0.065) (-0.16, 0.24) (-0.29, 0.098) (-0.27, 0.15) (-0.12, 0.28) (-0.30, 0.14)

(-0.20, 0.24) (-0.32, 0.12) (-0.33, 0.07) (-0.16, 0.24) (-0.30, 0.12) (-0.43, 0.023) (-0.25, 0.19) (-0.25, 0.23)

(-0.22, 0.16) (-0.26, 0.12) (-0.33, 0.038) (-0.14, 0.23) (-0.30, 0.071) (-0.37, 0.027) (-0.15, 0.23) (-0.26, 0.16)

M1: adjusted for base covariates (maternal education, country of birth, maternal age, maternal postnatal BMI, prenatal smoking, paternal BMI); M2: adjusted for base covariates + spline of concurrent metals and elements (BMI: As, Hg, Mo, Zn; Height: As, Cd, Mo, Se; Weight: As, Hg, Mo, Zn); M3: adjusted for base covariates and maternal exposures (e.g., prenatal As in As model).; Abbreviations: As arsenic, BMI body mass index, Cd cadmium, CI confidence interval, Hg mercury, Mo molybdenum, Pb lead, Zn Zinc. a Tertile cut-offs are as follows and are the same for each outcome: As T1: < 0.30, T2: 0.30–0.70, T3 > 0.70; Cd T1: < 0.07, T2:0.07–0.11, T3: > 0.11; Hg T1: < 0.12, T2: 0.12–0.38, T3: > 0.38; Pb T1: < 0.54, T2: 0.54–0.82, T3: > 0.82.

issue by using multiple imputation to account for missing covariate values. The variation in the ages of children at the time of the follow-up visit was another potential challenge in our analysis. While the use of age- and sex-adjusted z-scores enabled comparison of anthropometric measurements between children of different ages at follow-up, we cannot account for any potential age-related heterogeneity in metal levels. In addition, our reliance on the use of concurrent blood levels precluded the ability to establish temporality between exposure and outcome. Although blood lead levels have a relatively long half-life (30 days), blood levels at the time of outcome measurement are unlikely to be completely comparable to levels at the time of any exposure-induced physiological changes. On the other hand, primary sources of lead exposure, such as household dust, paint, and soil, are relatively stable over time (Lanphear et al., 2002) and a study examining serial measurements in children ages 1–4 years of age reported an intraclass correlation coefficient of 0.35 (Braun et al., 2012). Further, we observed a moderate degree of correlation between prenatal and child Pb blood levels (r = 0.39). Lead may be a marker for other unmeasured variables such as genetic, ethnic, and sociocultural factors that are also related to both lead levels and child growth. Certain polymorphisms in detoxification enzymes may influence the degree of Pb-related toxicity. A previous MIREC-based analysis of prenatal metal concentrations and risk of small for gestational age (SGA) identified an interaction

rather than a continuous measure of exposure ensures that our results were not biased due to the uncertainty resulting from non-quantifiable values. Our analysis benefited from the comprehensive nature of the MIREC pregnancy and follow-up studies with the ability to measure a broad suite of metals and elements in children. Evaluating the association between blood metal levels and growth in this age group is particularly valuable because children are growing rapidly and are prone to behavior patterns (e.g. crawling, sitting on floors and hand-to-mouth behaviors) that heighten exposure. Children may absorb more than four times as much Pb orally than adults (ATSDR, 2007). Despite the vulnerability of young children to adverse effects of exogenous exposures, there is limited national biomonitoring data on blood metal levels (Arbuckle, 2010). Children under three years of age are not included in the Canadian Health Measures Survey (Tremblay et al., 2007). Compared with the US National Health and Nutrition Examination Survey (NHANES) 2015–16, children in MIREC-CD Plus had lower median levels of Pb (median 0.74 μg/dL in NHANES vs 0.66 μg/dL in MIREC); comparison with other metals is less straightforward. Median Cd and Hg levels were both below the LOD in NHANES and total blood arsenic was not measured. The primary limitation of our study was the modest sample size, particularly in the sex-stratified analysis. We attempted to mitigate this 5

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Table 4 Parameter estimates and 95% CIs for the association between tertiles of metalsa and anthropometric measures among girls (n = 213). BMI z-score As (μg As/L)

Cd (μg/L)

Hg (μg/L)

Pb (μg/dL)

Height z-score As (μg As/L)

Cd (μg/L)

Hg (μg/L)

Pb (μg/dL)

Weight z-score As (μg As/L)

Cd (μg/L)

Hg (μg/L)

Pb (μg/dL)

M1 T1 T2 T3 T1 T2 T3 T1 T2 T3 T1 T2 T3

Ref −0.17 0.12 Ref −0.13 0.013 Ref −0.26 0.035 Ref 0.039 −0.26

T1 T2 T3 T1 T2 T3 T1 T2 T3 T1 T2 T3

Ref 0.037 −0.21 Ref −0.013 −0.020 Ref 0.029 −0.13 Ref 0.022 0.095

T1 T2 T3 T1 T2 T3 T1 T2 T3 T1 T2 T3

Ref −0.081 −0.051 Ref −0.095 −0.0040 Ref −0.015 −0.14 Ref 0.050 −0.11

M2 Ref −0.21 0.045 Ref −0.13 0.018 Ref −0.30 0.028 Ref 0.057 −0.20

(-0.44, 0.099) (-0.17, 0.41) (-0.41, 0.15) (-0.27, 0.29) (-0.54, 0.017) (-0.27, 0.34) (-0.24, 0.32) (-0.55, 0.033)

Ref 0.093 −0.13 Ref 0.09 0.13 Ref 0.19 0.039 Ref 0.046 0.092

(-0.29, 0.36) (-0.55, 0.14) (-0.35, 0.32) (-0.36, 0.32) (-0.31, 0.36) (-0.49, 0.24) (-0.31, 0.35) (-0.26, 0.45)

Ref −0.074 −0.050 Ref −0.035 0.088 Ref 0.0050 −0.081 Ref 0.074 −0.072

(-0.35, 0.19) (-0.34, 0.24) (-0.37, 0.18) (-0.28, 0.27) (-0.27, 0.24) (-0.38, 0.10) (-0.22, 0.32) (-0.40, 0.18)

M3

(-0.49, 0.063) (-0.26, 0.35) (-0.42, 0.16) (-0.27, 0.31) (-0.60, −0.012) (-0.29, 0.35) (-0.22, 0.34) (-0.50, 0.10)

(-0.23, 0.42) (-0.49, 0.23) (-0.25, 0.43) (-0.21, 0.47) (-0.15, 0.53) (-0.34, 0.41) (-0.28, 0.38) (-0.26, 0.44)

(-0.35, 0.20) (-0.35, 0.25) (-0.32, 0.24) (-0.20, 0.37) (-0.25, 0.26) (-0.35, 0.18) (-0.20, 0.35) (-0.37, 0.22)

Ref −0.18 0.10 Ref −0.13 0.015 Ref −0.28 −0.048 Ref 0.0060 −0.32 Ref 0.032 −0.22 Ref −0.13 −0.015 Ref 0.028 −0.13 Ref 0.013 0.081 Ref −0.087 −0.070 Ref −0.100 −0.058 Ref −0.16 −0.12 Ref 0.024 −0.15

(-0.44, 0.10) (-0.19, 0.40) (-0.41, 0.15) (-0.30, 0.27) (-0.56, 0.000050) (-0.35, 0.27) (-0.28, 0.29) (-0.64, 0.0036)

(-0.29, 0.36) (-0.57, 0.13) (-0.41, 0.15) (-0.30, 0.27) (-0.31, 0.37) (-0.51, 0.26) (-0.33, 0.36) (-0.30, 0.46)

(-0.35, 0.18) (-0.36, 0.22) (-0.37, 0.17) (-0.34, 0.22) (-0.43, 0.12) (-0.43, 0.19) (-0.26, 0.30) (-0.46, 0.17)

M1: adjusted for base covariates (maternal education, country of birth, maternal age, maternal postnatal BMI, prenatal smoking, paternal BMI); M2: adjusted for base covariates + spline of concurrent metals and elements (BMI: As, Hg, Mo, Zn; Height: As, Cd, Mo, Se; Weight: As, Hg, Mo, Zn); M3: adjusted for base covariates and maternal exposures (e.g., prenatal As in As model). Abbreviations: As arsenic, BMI body mass index, Cd cadmium, CI confidence interval, Hg mercury, Mo molybdenum, Pb lead, Zn Zinc. a Tertile cut-offs are as follows and are the same for each outcome: As T1: < 0.30, T2: 0.30–0.70, T3 > 0.70; Cd T1: < 0.07, T2:0.07–0.11, T3: > 0.11; Hg T1: < 0.12, T2: 0.12–0.38, T3: > 0.38; Pb T1: < 0.54, T2: 0.54–0.82, T3: > 0.82.

scores. Future follow-up in the MIREC cohort will enable analyses of temporal relations between child blood Pb levels and potential Pb-related growth deficiencies.

(p = 0.06) between Pb and the single nucleotide polymorphism GSTP1 A114V (Thomas et al., 2015). Maternal health consciousness behaviors and infant feeding patterns (breastfeeding vs formula) may contribute to child Pb concentrations and growth patterns. Even though we did not have the capacity to control for these factors, potential confounding is minimized due to adjustment for maternal education and BMI. Since the MIREC cohort consists mainly of healthy mothers and children of moderate to high socioeconomic status and Caucasian ethnicity (Arbuckle et al., 2013), the exposure and health risk profiles among MIREC participants may differ from the general population. As an example, the lack of influence of cigarette smoking on Pb estimates may be driven, in part, by the low prevalence of maternal smoking and second-hand smoke exposure (4%) in children's homes.

Funding sources The MIREC CD Plus study was funded by Health Canada's Chemicals Management Plan. Ethics review The study was reviewed by the Research Ethics Board at Health Canada (Ottawa, ON) and the Research Ethics committee at St Justine's Hospital (Montreal, QC, Canada). Parents signed an informed consent form for their and their child's participation.

5. Conclusions In this population of Canadian preschool aged children, the majority of children had detectable concentrations of metals. We did not observe any associations between concurrently measured blood levels of Cd, As or Hg and child BMI, height, or weight, nor did we observe any effect modification between sex and these metals. We observed that the association between Pb and BMI differed between boys and girls. Girls, but not boys, with higher blood lead levels tended to have lower BMI z-

Acknowledgements We would like to acknowledge the MIREC Study Group as well as the MIREC study participants and staff for their dedication. We would also like to acknowledge Health Canada's Chemicals Management Plan for funding the MIREC-CD Plus study. 6

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Table 5 Parameter estimates and 95% CIs for the association between tertiles of metalsa and anthropometric measures among boys (n = 236). BMI z-score As (μg As/L)

Cd (μg/L)

Hg (μg/L)

Pb (μg/dL)

Height z-score As (μg As/L)

Cd (μg/L)

Hg (μg/L)

Pb (μg/dL)

Weight z-score As (μg As/L)

Cd (μg/L)

Hg (μg/L)

Pb (μg/dL)

M1 T1 T2 T3 T1 T2 T3 T1 T2 T3 T1 T2 T3

Ref 0.039 −0.041 Ref −0.12 0.054 Ref 0.16 0.0060 Ref 0.15 0.14

T1 T2 T3 T1 T2 T3 T1 T2 T3 T1 T2 T3

Ref −0.025 −0.053 Ref −0.20 0.043 Ref −0.20 −0.24 Ref 0.0030 −0.039

T1 T2 T3 T1 T2 T3 T1 T2 T3 T1 T2 T3

Ref 0.011 −0.058 Ref −0.20 0.072 Ref −0.015 −0.14 Ref 0.11 0.074

M2 Ref 0.055 −0.071 Ref −0.093 0.086 Ref 0.18 0.090 Ref 0.16 0.19

(-0.24, 0.32) (-0.32, 0.24) (-0.40, 0.16) (-0.22, 0.33) (-0.12, 0.43) (-0.26, 0.27) (-0.13, 0.42) (-0.14, 0.41)

Ref −0.010 0.015 Ref −0.21 0.0090 Ref −0.038 −0.15 Ref −0.063 −0.020

(-0.32, 0.27) (-0.34, 0.23) (-0.48, 0.086) (-0.24, 0.32) (-0.48, 0.088) (-0.51, 0.034) (-0.28, 0.29) (-0.32, 0.24)

Ref 0.028 −0.037 Ref −0.19 0.072 Ref 0.0050 −0.081 Ref 0.072 0.12

(-0.25, 0.27) (-0.31, 0.20) (-0.45, 0.050) (-0.18, 0.32) (-0.27, 0.24) (-0.38, 0.10) (-0.15, 0.36) (-0.18, 0.33)

M3

(-0.24, 0.35) (-0.38, 0.23) (-0.38, 0.19) (-0.20, 0.37) (-0.11, 0.46) (-0.20, 0.38) (-0.13, 0.44) (-0.092, 0.47)

(-0.31, 0.29) (-0.29, 0.32) (-0.50, 0.080) (-0.28, 0.30) (-0.26, 0.18) (-0.38, 0.087) (-0.35, 0.22) (-0.30, 0.27)

(-0.24, 0.30) (-0.31, 0.24) (-0.45, 0.069) (-0.19, 0.33) (-0.25, 0.26) (-0.35, 0.18) (-0.19, 0.33) (-0.14, 0.38)

Ref 0.031 −0.087 Ref −0.12 0.067 Ref 0.16 −0.014 Ref 0.14 0.11 Ref −0.003 0.048 Ref −0.12 0.067 Ref −0.20 −0.23 Ref −0.0070 −0.067 Ref 0.020 −0.022 Ref −0.20 0.083 Ref −0.016 −0.13 Ref 0.09 0.040

(-0.25, 0.31) (-0.37, 0.20) (-0.40, 0.15) (-0.21, 0.34) (-0.12, 0.43) (-0.26, 0.29) (-0.14, 0.41) (-0.19, 0.41)

(-0.29, 0.28) (-0.24, 0.34) (-0.40, 0.15) (-0.21, 0.34) (-0.48, 0.088) (-0.51, 0.046) (-0.30, 0.28) (-0.38, 0.24)

(-0.24, 0.28) (-0.28, 0.24) (-0.46, 0.049) (-0.17, 0.33) (-0.27, 0.24) (-0.38, 0.12) (-0.16, 0.35) (-0.24, 0.32)

M1: adjusted for base covariates (maternal education, country of birth, maternal age, maternal postnatal BMI, prenatal smoking, paternal BMI); M2: adjusted for base covariates + spline of concurrent metals and elements (BMI: As, Hg, Mo, Zn; Height: As, Cd, Mo, Se; Weight: As, Hg, Mo, Zn); M3: adjusted for base covariates and maternal exposures (e.g., prenatal As in As model). Abbreviations: As arsenic, BMI body mass index, Cd cadmium, CI confidence interval, Hg mercury, Mo molybdenum, Pb lead, Zn Zinc. a Tertile cut-offs are as follows and are the same for each outcome: As T1: < 0.30, T2: 0.30–0.70, T3 > 0.70; Cd T1: < 0.07, T2:0.07–0.11, T3: > 0.11; Hg T1: < 0.12, T2: 0.12–0.38, T3: > 0.38; Pb T1: < 0.54, T2: 0.54–0.82, T3: > 0.82.

Fig. 1. Generalized additive model curves for the relationship between lead and BMI z-score in girls and boys. 7

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Appendix A. Supplementary data

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