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Identifying dietary sources and patterns of exposure to toxic metals and other trace elements in Swedish toddlers and young children.
| Journal of Exposure Science & Environmental Epidemiology | Islam MR, Trask M, Kampouri M, Levi M, Singh T, Gustin K, Bjermo H, Almerud P, Harari F, Vahter M, Lignell S, Kippler M. |
Sweden Tap water Urine-F Human Study
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Abstract

Original full-text study online at
https://www.nature.com/articles/s41370-026-00973-5

EXCERPTS:

Background

Diet is a source of many toxic compounds, but dietary sources of toxic metal exposure in young children are largely unknown.

Objective

To explore dietary sources and patterns of the most commonly occurring toxic metals and key elements of antioxidant defense.

Methods

We studied 1.5-year-olds (n=  305) and 4-year-olds (n = 288) in the Swedish national dietary survey “Riksmaten Young Children”. From a 2-day web-based food diary, habitual consumption (g/day) of specific food groups was estimated using the Multiple Source Method. Dietary patterns were derived using age-specific principal component analysis (PCA) and k-means clustering. Toxic (arsenic, cadmium, cobalt, lead, mercury) and essential elements (copper, manganese, selenium, and zinc) in blood and/or urine were measured with inductively coupled plasma-mass spectrometry, arsenobetaine and inorganic arsenic metabolites [methylarsonic acid (MMA), dimethylarsinic acid (DMA) and remaining inorganic arsenic)] after separation by reversed-phase/ion exchange high-performance liquid chromatography, and fluoride with an ion-specific electrode.

Results

Biomarker concentrations of toxic metals varied, and blood selenium was suboptimal in many children (median: 103 µg/L at both ages). Spearman rank correlation with unsupervised hierarchical cluster analysis revealed that urinary arsenobetaine and blood arsenic, mercury, and selenium correlated positively with seafood and vegetable consumption, particularly among 4-year-olds, where seafood and vegetables clustered together. In children with <80% DMA in urine, inorganic arsenic metabolites correlated positively with vegetables. In multivariable-adjusted regression models, the PCA-derived fruits-vegetables-seafood pattern was positively associated with blood arsenic (mainly arsenobetaine) among 4-year-olds as well as mercury (mainly methylmercury) and selenium at both ages. Dietary clusters dominated by plant-based meat and milk alternatives showed higher cadmium concentrations, and clusters characterized by a western-style diet (meat, cereals, and sweets) showed lower selenium concentrations than clusters with a high-diversity diet.

Significance

Diet contributes to exposure to arsenic compounds, methylmercury, cadmium, and selenium in small Swedish children.

Impact

Concentrations of toxic metals in the blood and urine of young Swedish children were generally low, though they exhibited considerable variability. Simultaneously, around half of the children appeared to have suboptimal selenium status, a key element in antioxidant defense against metal toxicity. Diets rich in plant-based alternatives, nuts, and seeds were associated with increased cadmium exposure, while high-diversity diets rich in fruits, vegetables, and seafood were associated with increased arsenobetaine and methylmercury exposure, in combination with improved selenium status. Furthermore, after excluding the contribution of arsenic compounds from fish, low concentrations of inorganic arsenic appeared to originate from vegetable consumption.

Element analysis

… Whole blood concentrations were measured in the present study as they are preferred over serum or plasma concentrations as exposure biomarkers of most toxic metals [24]. While concentrations of essential elements in plasma or serum are generally the preferred measures in the clinical setting, whole blood concentrations reflect more long-term status, especially for selenium and copper [25,26,27]. Concentrations of arsenic (sum of inorganic arsenic metabolites and arsenobetaine) and cadmium were also measured in urine, along with fluoride. Cadmium concentration in urine reflects long-term exposure since cadmium accumulates in the renal cortex with a half-life of decades [28]. Urinary concentrations of arsenic and fluoride, on the other hand, are reflective of short-term exposure [24, 29].

… Fluoride exposure was assessed by urinary concentrations (U-F) measured using a fluoride-specific electrode (Orion 9609BNWP and Orion Star A214 pH/ISE meter; Thermo Fisher Scientific, Waltham, MA, USA) [34]. Urine was diluted 1:1 with a total ionic strength adjustment buffer (TISAB II, Thermo Fisher Scientific). The external calibration curve was prepared daily through serial dilution of a 100 mg/L fluoride standard solution (Thermo Fisher Scientific) in deionized water and adjustment buffer (50% TISAB II) and ranged between 0.05–2 mg/L. The LOD was 0.011 mg/L. Results of two different reference materials [fluoride in water; Product number: QC3162; Sigma-Aldrich, USA (certified fluoride concentration: 0.42 mg/L) and Seronorm Trace Elements Urine L-2; LOT: 1011645; Sero AS, Norway (recommended fluoride concentrations: 4 mg/L)], analyzed before and after each run were 0.43 ± 0.02 mg/L (n = 24, recovery 102.4%) and 3.9 ± 0.06 mg/L (n = 24, recovery 97.5%), respectively.

To compensate for variation in urine dilution of the spot urine samples, all obtained concentrations were adjusted to the median specific gravity [adjusted urinary element concentration = unadjusted concentrations × (specific gravitymedian–1)/(specific gravitysample–1)] measured using an ATAGO PAL-10S refractometer (Thermo Fisher Scientific, Waltham, MA, USA). Overall median specific gravity was 1.020 (1.018 in 1.5-year-olds and 1.022 in 4-year-olds).

Statistical analysis

… Correlations between individual element concentrations (toxic elements: B-As, U-AsB, U-iAs, B-Cd, U-Cd, B-Co, U-F, B-Pb, B-Hg; essential elements: B-Cu, B-Mn, B-Se, B-Zn) and habitual consumption of specific food groups (intake in grams/day) were analyzed using Spearman rank correlation in combination with unsupervised hierarchical cluster analysis, which were visualized in heat maps using R package “pheatmap” (version 3.6.2). The unsupervised hierarchical cluster analysis automatically structures the dietary data based on correlations between different food groups, forming clusters. The direction of the correlation between element concentrations and dietary intake data is represented by color: red for positive correlations and blue for negative correlations. Color intensity signifies the strength of correlations, and significance is indicated by stars.

… Linear regression analyses were used to explore associations of the PCA- and clustering-derived dietary patterns with the individual element concentrations [presented as mean concentrations with associated 95% confidence intervals (CI)]. The three different principal component scores were converted into tertiles and used as predictors in the models, resulting in three separate models (one per component) fitted per age group for each element concentration. From k-means clustering, we generated one categorical variable (comprising three diet clusters) for each age group, which was used as the predictor for each element concentration. In total, the contribution to eight toxic element biomarkers (B-As, U-iAs, B-Cd, U-Cd, B-Co, U-F, B-Pb, B-Hg) and four essential biomarkers (B-Cu, B-Mn, B-Se, B-Zn) was explored in each age group. The element concentrations of B-As, UiAs, B-Cd, U-Cd, U-F, and B-Pb were right-skewed, and they were therefore natural log-transformed to obtain normally distributed residuals in the models, and then back-transformed estimates were retrieved directly using the “emmeans” package. Covariates were selected using a directed acyclic graph (Fig. S4). The final models were adjusted for child sex, age (years), parental education <12 years or >12 years of formal education), season of sampling (spring from March–May, summer from June–August, autumn from September–November, and winter from December–February), and the children’s daily energy intake (continuous, kJ/day).

Results

Background characteristics and element concentrations

The study included a similar proportion of boys and girls both at 1.5 and 4 years of age (Table S1). Most of the parents were born in Sweden, had more than 12 years of education, and were non-smokers, and most children consumed public drinking water. The median duration of exclusive breastfeeding was 6 months (10–90th percentile: 3–8 months). Only 54 of the 1.5-year-olds (17.7%) were still breastfed to some extent during the survey. Based on the children’s BMI at 4 years of age, 18% were underweight (<14.51 kg/m2 for boys and <14.30 kg/m2 for girls), 77% were of normal body weight, 5% were overweight (>17.52 kg/m2 for boys and >17.36 kg/m2 for girls), and less than 1% were obese (>19.23 kg/m2 for boys and >19.16 kg/m2 for girls).

There were marked variations in both toxic and essential element concentrations in children’s blood and urine at both ages (Table 1). Compared to 1.5-year-olds, 4-year-old children had significantly higher B-Cd, B-Co, and U-F concentrations. The concentrations of U-iAs metabolites were 4.6 and 4.2 µg/L at 1.5 and 4 years, respectively. Although U-AsB showed a considerably wider concentration range (5th–95th percentile: 0.13-181 µg/L for all children) than that of U-iAs metabolites (1.9–16.9 µg/L), they were significantly correlated in both age groups (Tables S3 and S4, and Figs. S5 and S6). However, the correlation was markedly weakened after restriction to children with less than 80% DMA in urine [rho: 0.02 (n = 31) and 0.20 (n = 188) at 1.5 and 4 years, respectively]. For these children, the median concentrations of U-iAs metabolites were 2.8 versus 3.6 µg/L at 1.5 and 4 years, respectively (Table 1). 4-year-olds had also marginally higher B-Zn, but slightly lower B-Cu and B-Mn concentrations than 1.5-year-olds.

… Bivariate correlations between consumption of different food groups and concentration of toxic and essential elements in blood and/or urine [toxic elements: blood arsenic (B-As), urinary arsenobetaine (U-AsB), urinary sum of inorganic arsenic metabolites (U-iAs), blood cadmium (B-Cd), urinary cadmium (U-Cd), blood cobalt (B-Co), urinary fluoride (U-F), blood lead (B-Pb), blood mercury (B-Hg); essential elements: blood copper (B-Cu), blood manganese (B-Mn), blood selenium (B-Se), blood Zn (B-Zn)] are shown among 1.5- (left) and 4-year-olds (right). The correlations are based on the Spearman rank correlation test, and the direction and magnitude are denoted by color, with red representing positive correlations and blue representing negative correlations, and deeper colors signifying stronger

… Discussion

… The highest U-F concentrations were generally found in children in Uppsala, which agrees with the elevated fluoride concentrations in public drinking water in the area [71]. Moreover, consuming well water was related to higher U-F in comparison with consuming public water, especially among 4-year-olds. Fluoride levels in groundwater are elevated in many parts of Sweden. In the southeast of Sweden, 24% of approximately 4800 private wells contained fluoride concentrations above the WHO’s drinking water guideline of 1.5 mg/L [72]. In 4-year-olds, U-F increased with increasing adherence to a diet rich in fruits and berries, potatoes, as well as juice and plant-based milk alternatives. One can speculate that this may be due to the presence of fluoride in drinking water used to dilute juice concentrates. Moreover, we found that 4-year-olds with both parents born in Sweden had higher U-F than those with both parents born abroad, which may reflect different dietary habits and cultural attitudes regarding the use of fluoridated dental products [73]. Toothpaste is often swallowed by smaller children, leading to increased fluoride exposure, as toothpaste for children generally contains about 1000 µg/g of fluoride [74].

… Concentrations of arsenic and fluoride, on the other hand, are reflective of short-term exposure, although during daily intake via food and/or water, urinary concentrations usually reflect ongoing exposure rather well [24, 84]. While the regression models were controlled for relevant confounders, including energy intake, residual confounding cannot be ruled out entirely. For instance, we lacked detailed information on supplement use as well as potential soil or dust contamination. Finally, even though multiple

Data availability

Data can be made available upon reasonable request.

Supplementary information

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Acknowledgements

The regional Environmental and Occupational Medicine clinics in Lund, Gothenburg, Linköping, Örebro, Stockholm, and Umeå are acknowledged for their help with blood and urine sampling. We are also grateful to the families who participated in and showed dedication to the study.

Funding

The Riksmaten Young Children study was conducted by the Swedish Food Agency with funding from the Swedish Environmental Protection Agency (agreement number 215-21-005). Funding for the present work has also been received from the Swedish Research Council Formas (project no. 2022-00808) and Karolinska Institutet. Open access funding provided by Karolinska Institute.

Author information

Author notes

  1. These authors contributed equally: Mohammad Redwanul Islam, Mercedes Trask.

Authors and Affiliations

  1. Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden

    Mohammad Redwanul Islam, Mercedes Trask, Maria Kampouri, Michael Levi, Taranbir Singh, Klara Gustin, Marie Vahter & Maria Kippler

  2. Department of Risk and Benefit Assessment, Swedish Food Agency, Uppsala, Sweden

    Helena Bjermo & Sanna Lignell

  3. Occupational and Environmental Medicine, School of Public Health and Community Medicine, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, Sweden

    Pernilla Almerud & Florencia Harari

Contributions

MRI statistical analysis, interpretation and drafted the manuscript; MT statistical analysis, interpretation and drafted the manuscript; MKa statistical analysis, visualization, drafted and review manuscript; TS fluoride analysis and drafted related method, review and editing manuscript; KG visualization and review and editing manuscript; HB conceptualization, study design, data cleaning, reviewing and editing manuscript; PA data collection and reviewing and editing manuscript; FH data collection and reviewing and editing of manuscript; MV: interpretation and reviewing and editing manuscript; SL conceptualization, methodology, data cleaning, funding and reviewing and editing manuscript; MKi conceptualization, methodology, funding, project administration, supervision, and writing and editing manuscript.

Corresponding author

Correspondence to Maria Kippler.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethical approval

All methods were performed in accordance with the relevant guidelines and regulations. Ethical approval for the Riksmaten Young Children study, including the present analysis, has been obtained from the Swedish Ethical Review Authority (2020-05293). Informed consent was obtained from all parents of participating children.