Abstract
While associations between metal exposure and adult blood pressure (BP) have been widely explored, those between multimetal coexposure and children’s BP remain unclear. We aimed to assess the associations between multimetal coexposure and children’s BP. In this cross-sectional study, we recruited 1165 children aged 6–12 years from China’s heavy metal-polluted areas. Inductively coupled plasma mass spectrometry (ICP‒MS) was used to measure urinary levels of 17 metals (titanium, vanadium, chromium, iron, strontium, cobalt, nickel, copper, arsenic, selenium, rubidium, cadmium, molybdenum, antimony, thallium, lead, and uranium) in children. Least absolute shrinkage and selection operator (LASSO) penalized regression models, multimetal-adjusted logistic or linear regression models and Bayesian kernel machine regression (BKMR) models were used to evaluate the associations of multimetal coexposure with elevated BP (systolic BP and diastolic BP) in children. A total of 253 (21.7%) participants had abnormal BP. Urinary vanadium (V), selenium (Se) and titanium (Ti) were found to have greater effects on abnormal BP in children by LASSO regression. In the covariate-adjusted multimetal (V, Se and Ti) logistic regression models, the highest quartiles of urinary V, Ti and Se were associated with a 5.21 (95% CI: 2.29, 11.88)-, 2.66 (95% CI: 1.22–5.82)- and 0.20 (95% CI: 0.11–0.37)-fold risk of abnormal BP, compared with the lowest quartile. Covariate-adjusted multimetal linear regression analysis and BKMR regression analysis further found that urinary V was positively correlated and urinary Se was negatively correlated with children’s BP levels. Interaction analysis found that higher levels of urinary Se attenuated the effects of urinary V on children’s diastolic BP levels. Higher urinary V and Ti levels were risk factors for abnormal BP in children, and urinary Se was a protective factor.
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Acknowledgements
We thank all investigators who contributed to the field investigation and all study participants who participated in the present study.
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This work was supported by the National Natural Science Foundation of China (82173905, 82070759) and the Ministry of Science and Technology of China (2015FY111100).
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JQ: Conceptualization, Methodology, Data curation, Writing—original draft, Writing—review & editing, Visualization, Project administration. LC: Conceptualization, Writing—original draft, Writing—review & editing, Visualization, Project administration. HY: Conceptualization, Resources, Writing—review & editing. YL: Conceptualization, Methodology, Resources, Writing—review & editing, Project administration. BY: Conceptualization, Methodology, Resources, Writing—review & editing, Project administration, Funding acquisition. XC: Conceptualization, Methodology, Resources, Writing—review & editing, Project administration, Funding acquisition. ZH: Conceptualization, Methodology, Resources, Writing—review & editing, Project administration, Funding acquisition.
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12403_2023_550_MOESM11_ESM.tif
Supplementary file11 (TIF 13922 KB)—Fig. S3 Spearman correlation coefficients among urinary metal concentrations (N=1165). The values presented are the coefficients of Spearman’s rank correlation analysis among creatinine-adjusted metal concentrations. **P < 0.01. Tl, thallium; Fe, iron; U, uranium; V, vanadium; Ni, nickel; Ti, titanium; Cr, chromium; Mo, molybdenum; Cu, copper; Se, selenium; Sb, antimony; Co, cobalt; Rb, rubidium; Pb, lead; As, arsenic; Cd, cadmium; Sr, strontium
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Supplementary file12 (TIF 9772 KB)—Fig. S4 Distribution of DBP and SBP by age (N=1165). The distribution of BP for children aged 6-7 or 10-11 was displayed together due to the small number of participants aged 6, 11 and 12
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Supplementary file13 (TIF 7993 KB)—Fig. S5 Distribution of DBP z score and SBP z score in all children (N=1165). The z scores for children aged 6 to 12 were calculated separately according to certain ages, although the distribution of z scores for all participants was integrated for display
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Supplementary file14 (TIF 10348 KB)—Fig. S6 Principal component analysis of the 14 metals that were excluded by LASSO regression (N=1165). A Scree plot of PCA. The scree plot represents the variance contribution rate and cumulative variance contribution rate of each principal component. B Loadings of the 14 metals on principal components 1 and 2. All the metal concentration values in PCA were creatinine adjusted and log transformed. Tl, thallium; Fe, iron; U, uranium; V, vanadium; Ni, nickel; Ti, titanium; Cr, chromium; Mo, molybdenum; Cu, copper; Se, selenium; Sb, antimony; Co, cobalt; Rb, rubidium; Pb, lead; As, arsenic; Cd, cadmium; Sr, strontium
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Supplementary file15 (TIF 26761 KB)—Fig. S7 Diagnostic plots for Bayesian kernel machine regression model convergence (for DBP z score). A: trace plots; B: autocorrelation plots; C: density plots. Parameters in the model included log-transformed Se [r1], Ti [r2], V [r3] and age [β1], sex [β2], ethnicity [β3], paternal education level [β4], maternal education level [β5], maternal smoking during pregnancy [β6], maternal alcohol drinking during pregnancy [β7] and body mass index [β8]
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Supplementary file16 (TIF 28395 KB)—Fig. S8 Diagnostic plots for Bayesian kernel machine regression model convergence (for SBP z score). A trace plots; B autocorrelation plots; C density plots. Parameters in the model included log-transformed Se [r1], Ti [r2], V [r3] and age [β1], sex [β2], ethnicity [β3], paternal education level [β4], maternal education level [β5], maternal smoking during pregnancy [β6], maternal alcohol drinking during pregnancy [β7] and body mass index [β8]
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Quan, J., Chen, L., Yuan, H. et al. Coexposure to Multiple Metals and the Risk of Abnormal Blood Pressure in Chinese Children. Expo Health 16, 237–252 (2024). https://doi.org/10.1007/s12403-023-00550-3
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DOI: https://doi.org/10.1007/s12403-023-00550-3