Socioeconomic factors influence brain development and structure, but most studies have overlooked neurotoxic insults that impair development, such as lead exposure. Childhood lead exposure affects cognitive development at the lowest measurable concentrations, but little is known about its impact on brain development during childhood. We examined cross-sectional associations among brain structure, cognition, geocoded measures of the risk of lead exposure and sociodemographic characteristics in 9,712 9- and 10-year-old children. Here we show stronger negative associations of living in high-lead-risk census tracts in children from lower- versus higher-income families. With increasing risk of exposure, children from lower-income families exhibited lower cognitive test scores, smaller cortical volume and smaller cortical surface area. Reducing environmental insults associated with lead-exposure risk might confer greater benefit to children experiencing more environmental adversity, and further understanding of the factors associated with high lead-exposure risk will be critical for improving such outcomes in children.
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ABCD data are publicly available through the National Institute of Mental Health Data Archive (https://data-archive.nimh.nih.gov/abcd). The blood-lead-level data were not collected as part of the ABCD Study and were made available by the corresponding agencies, entities or individuals identified in the Supplementary information (Supplementary Table 2); these data are, however, available from the authors upon reasonable request and with permission of each of the agencies, entities or individuals.
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We thank the Adolescent Brain Cognitive Development (ABCD) participants and their families for their time and dedication to this project, and G. Dowling and C. Chieh Fan for their comments and feedback during the development of this manuscript. We also thank W. Thompson for statistical support, comments and feedback during the development of this manuscript. ABCD acknowledgement: data used in the preparation of this article were obtained from the ABCD Study (https://abcdstudy.org/), and are held in the NIMH Data Archive. This is a multisite, longitudinal study designed to recruit more than 10,000 children aged 9–10 and follow them over 10 yr into early adulthood. The ABCD Study is supported by the National Institutes of Health (NIH) and additional federal partners under award numbers U01DA041022, U01DA041028, U01DA041048, U01DA041089, U01DA041106, U01DA041117, U01DA041120, U01DA041134, U01DA041148, U01DA041156, U01DA041174, U24DA041123 and U24DA041147. A full list of supporters is available at https://abcdstudy.org/federal-partners/. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/principal-investigators/. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in analysis or writing of this report. This manuscript reflects the views of the authors and may not reflect the opinions or views of the NIH or ABCD consortium investigators. The ABCD data repository grows and changes over time. The ABCD data used in this report came from https://doi.org/10.15154/1503209.
The authors declare no competing interests.
Peer review information Jennifer Sargent was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Each data point represents the random intercept (left) or slope (right) for each state/city (i.e., fixed effect coefficient + random effects deviation). The lines surrounding the data points represent the 95% confidence interval of the coefficient. Aside from Oregon and Colorado (for which the 95% confidence intervals included 0), there were significant increases in elevated-blood-lead-level rates with increasing lead-risk scores for each state/city (right). Analysis employed generalized linear-mixed effects models, which tested the statistical significance of coefficients against a t-distribution.
Overall cognitive test scores declined most steeply with increasing risk of environmental lead exposure in children of low-income parents. The data reflect individual participants. Solid lines represent means of the marginal fitted values of the model. Analysis employed linear mixed-effects models, which tested the statistical significance of coefficients against a t-distribution. Age, sex, parental education, and race/ethnicity were included as covariates in this analysis. The scale of the ordinate differs from that in Fig. 2.
Extended Data Fig. 3 Negative associations of increased risk of lead exposure are greater for children from lower-income families.
Whole-brain cortical surface area declined most steeply with increasing risk of environmental lead exposure in children of low-income parents. The data reflect individual participants. Solid lines represent means of the marginal fitted values of the model. Analysis employed linear mixed-effects models, which tested the statistical significance of coefficients against a t-distribution. Age, sex, parental education, and race/ethnicity were included as covariates in this analysis. The scale of the ordinate differs from that in Fig. 3a.
Extended Data Fig. 4 Negative associations of increased risk of lead exposure are greater for children from lower-income families.
Whole-brain cortical volume declined most steeply with increasing risk of environmental lead exposure in children of low-income parents. The data reflect individual participants. Solid lines represent means of the marginal fitted values of the model. Analysis employed linear mixed-effects models, which tested the statistical significance of coefficients against a t-distribution. Age, sex, parental education, and race/ethnicity were included as covariates in this analysis. The scale of the ordinate differs from that in Fig. 3b.
Extended Data Fig. 5 Associations between family income, lead-exposure risk, and area deprivation index.
Zero-order Spearman’s rank-order correlation matrix of parent-report household income, lead risk and its 2 subcomponents, and the area deprivation index (ADI) and its 17 subcomponents. Along the ordinate, from top to bottom, the variables refer to parent-report total annual household income (“Household Income”), the composite lead-risk score (“Lead Risk: Composite”), estimated percentage of homes at risk for lead exposure given lead-based paint (“Lead Risk: Housing”), percentage of individuals below −125 percent of the poverty level (“Lead Risk: Poverty”), the national ADI percentile (“ADI: Composite”), the percentage of the population at least 25 years old with less than 9 years of education (“ADI: % No HS”; HS = high school), the percentage of the population at least 25 years old with at least a high school diploma (“ADI: % HS Diploma”), the percentage of employed persons at least 16 years old in white-collar jobs (“ADI: % White Collar”), median family income (“ADI: Family Income”), income disparity as defined by Singh67 (“ADI: Income Disparity”), median home value (“ADI: Home Value”), median gross rent (“ADI: Gross Rent”), median monthly mortgage (“ADI: Mortgage”), percentage of owner-occupied housing units (“ADI: % Owned Homes”), percentage of occupied housing units with at least 1 person per room (“ADI: % Crowding”), percentage of civilian labor force at least 16 years old who are unemployed (“ADI: Unemployment”), percentage of families below the poverty level (“ADI: % Poverty”), percentage of the population below 138% of the poverty threshold (“ADI: % −138 Poverty”), percentage of single-parent homes with children who are less than 18 years old (“ADI: % Single-Parent Homes”), percentage of occupied housing units with a motor vehicle (“ADI: % No Vehicle”), percentage of occupied housing units without a telephone (“ADI: % No Phone”), and the percentage of occupied housing units with complete plumbing (“ADI: % Incomplete Plumbing”). With the exception of parent-report household income (which was specific to each family), the lead-risk and ADI data had census-tract-level resolution.
Extended Data Fig. 6 Associations between family income, lead-exposure risk, and area deprivation index.
Zero-order Spearman’s rank-order correlation matrix of parent-report household income, lead risk and its 2 subcomponents, and the area deprivation index (ADI) and its 17 subcomponents, as in Extended Data Fig. 5, except that all correlation coefficients were squared (i.e., pseudo-R2). See Extended Data Fig. 5 caption for variable names.
Extended Data Fig. 7 Lead exposure risk scores predict Maryland’s blood lead levels at the census-tract level.
Left: Distribution of the census-tract-level geometric means of blood lead levels in Maryland, collapsed across the years of 2010 to 2014. Right: Geometric mean blood lead levels as a function of the estimated risk of lead exposure. The smaller gray data points represent individual census tracts. Two measures of central tendency are provided: The larger darker data points represent the means at each risk level, while the larger open data points represent the medians at each risk level. Analysis employed a Spearman’s rank-order correlation.
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Marshall, A.T., Betts, S., Kan, E.C. et al. Association of lead-exposure risk and family income with childhood brain outcomes. Nat Med 26, 91–97 (2020). https://doi.org/10.1038/s41591-019-0713-y
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