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Exposure to toxic and non-toxic metals impacts childhood growth and development, but limited data exists on exposure to metal mixtures. Here, we investigated the effects of exposure to individual metals and a mixture of barium, cadmium, cobalt, lead, molybdenum, zinc, and arsenic on growth indicators in children 4-5 years of age.We used urine metal concentrations as biomarkers of exposure in 328 children enrolled in the Spanish INMA-Asturias cohort. Anthropometric measurements (arm, head, and waist circumferences, standing height, and body mass index) and parental sociodemographic variables were collected through face-to-face interviews by trained study staff. Linear regressions were used to estimate the independent effects and were adjusted for each metal in the mixture. We applied Bayesian kernel machine regression to examine non-linear associations and potential interactions.In linear regression, urinary levels of cadmium were associated with reduced arm circumference (βadjusted = -0.44, 95% confidence interval [CI]: -0.73, -0.15), waist circumference (βadjusted = -1.29, 95% CI: -2.10, -0.48), and standing height (βadjusted = -1.09, 95% CI: -1.82, -0.35). Lead and cobalt concentrations were associated with reduced standing height (βadjusted = -0.64, 95% CI: -1.20, -0.07) and smaller head circumference (βadjusted = -0.29, 95% CI: -0.49, -0.09), respectively. However, molybdenum was positively associated with head circumference (βadjusted = 0.22, 95% CI: 0.01, 0.43). BKMR analyses showed strong linear negative associations of cadmium with arm and head circumference and standing height. BKMR analyses also found lead and cobalt in the metal mixture were related to reduce standing height and head circumference, and consistently found molybdenum was related to increased head circumference.Our findings suggest that exposure to metal mixtures impacts growth indicators in children.
Bayes Theorem, http://metadata.un.org/sdg/4, Growth, http://metadata.un.org/sdg/3, Urine, Arsenic, Cohort Studies, Bayesian kernel machine regression, Metal mixture, Metals, Birth Weight, Humans, Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all, Child, Children, Ensure healthy lives and promote well-being for all at all ages
Bayes Theorem, http://metadata.un.org/sdg/4, Growth, http://metadata.un.org/sdg/3, Urine, Arsenic, Cohort Studies, Bayesian kernel machine regression, Metal mixture, Metals, Birth Weight, Humans, Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all, Child, Children, Ensure healthy lives and promote well-being for all at all ages
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