Abstract
Background
Air pollution is ubiquitous, yet questions remain regarding its impact on the developing brain. Large changes occur in white matter microstructure across adolescence, with notable differences by sex.
Methods
We investigate sex-stratified effects of annual exposure to fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3) at ages 9–10 years on longitudinal patterns of white matter microstructure over a 2-year period. Diffusion-weighted imaging was collected on 3T MRI scanners for 8182 participants (1–2 scans per subject; 45% with two scans) from the Adolescent Brain Cognitive Development (ABCD) Study®. Restriction spectrum imaging was performed to quantify intracellular isotropic (RNI) and directional (RND) diffusion. Ensemble-based air pollution concentrations were assigned to each child’s primary residential address. Multi-pollutant, sex-stratified linear mixed-effect models assessed associations between pollutants and RNI/RND with age over time, adjusting for sociodemographic factors.
Results
Here we show higher PM2.5 exposure is associated with higher RND at age 9 in both sexes, with no significant effects of PM2.5 on RNI/RND change over time. Higher NO2 exposure is associated with higher RNI at age 9 in both sexes, as well as attenuating RNI over time in females. Higher O3 exposure is associated with differences in RND and RNI at age 9, as well as changes in RND and RNI over time in both sexes.
Conclusions
Criteria air pollutants influence patterns of white matter maturation between 9–13 years old, with some sex-specific differences in the magnitude and anatomical locations of affected tracts. This occurs at concentrations that are below current U.S. standards, suggesting exposure to low-level pollution during adolescence may have long-term consequences.
Plain language summary
Air pollution is known to affect health, but it is unclear whether it affects the growing human brain. We investigated whether there were differences in the development of white matter connections, which allow for faster communication between different brain regions, in children aged 9-13 years living in areas with relatively low or high air pollution in the USA. In a large group of U.S. teens, we find that polluted air is linked to differences in white matter at ages 9-10 years old and over the next two years. In some cases, males and females showed differences in the part of the brain showing changes and the amount of white matter change. Our study suggests that air pollution levels that are deemed acceptable under current regulations in the USA could have long-term effects on how a child’s brain grows. Further studies are needed to better understand the impact of these changes.
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Introduction
Outdoor ambient air pollution is increasingly being recognized for its consequential neurotoxicant effects1. Criteria pollutants include, but are not limited to, particulate matter with diameter <2.5 μm (PM2.5) and nitrogen dioxide (NO2), both of which result from combustion of gasoline, oil, diesel fuel, coal, or wood, as well as ground-level ozone (O3) that results from ultraviolet light-driven photooxidation of volatile organic compounds and other precursors. When inhaled deeply into the lungs, these pollutants cause an innate immune response at the level of the lung alveoli, leading to increased systemic inflammation2. Inflammatory immune components in the bloodstream can then enter the brain via a compromised blood-brain barrier or by traveling along the vagus nerve, bypassing the blood-brain barrier3. Children are thought to be especially vulnerable to the harmful effects of air pollution due to their comparatively higher respiratory rates, rate of neural change, and time spent outside compared to adults4,5. Yet, there is uncertainty regarding the potential long-term effects of exposure on the dynamic neural processes that occur across adolescence and whether these effects vary between the sexes6,7.
The brain undergoes remarkable changes during the second and third decades of life, with robust changes in white matter maturation8. White matter comprises about half of the brain, and these tracts are known as information superhighways, connecting gray matter regions in the service of neural network organization9. This integrated and efficient structural connectivity plays an important role in information processing, working memory, learning, and mental and emotional health outcomes10. Glial cells known as oligodendrocytes produce myelin, insulating the axons to form white matter tracts, and are known to be susceptible to damage resulting from inflammation11. Given the known systemic and neuroinflammatory consequences of air pollution, white matter microstructure development may be particularly vulnerable to environmental neurotoxicant damage1. Supporting this notion are recent ecological and cross-sectional diffusion imaging studies showing that exposure to air pollutants is associated with differences in white matter macro- and microstructure in youth. Calderón-Garcidueñas and colleagues12 found an increased incidence of white matter pathology in the prefrontal cortex in young people from Mexico City exposed to high levels of outdoor air pollution. Multiple studies using the Generation R cohort, a large birth cohort based in the Netherlands, found that prenatal and childhood exposure (0–4 years-old) to PM2.5 and its components, NO2, and nitrogen oxides (NOx) were associated with lower global fractional anisotropy and higher mean diffusivity, suggesting reduced white matter microstructural integrity, when measured at ages 9–12 years13,14. Peterson and colleagues15 assessed the impact of prenatal exposure to PM2.5 on neurodevelopment in 6–14-year-olds across multiple imaging modalities. They found that exposure to higher PM2.5 was associated with a higher average diffusion coefficient in white matter fiber bundles (suggesting less myelin, reduced fiber density, and/or less directional fiber coherence), but not with white matter fractional anisotropy. Taken together, these initial studies suggest that ambient air pollution exposure during development is linked to differences in white matter microstructure. The minor discrepancies might stem from divergent study samples, differences in the timing of exposure, age of brain assessment, and/or differences in MRI techniques. Thus, additional studies are warranted to more fully understand how air pollution exposure influences white matter maturation across childhood and adolescence.
It is plausible that air pollution effects on white matter microstructure, especially during development, may differ based on biological sex. Sex has largely been implicated by both epidemiological and experimental animal studies in how air pollution affects health outcomes16, and there are well-established sex differences in white matter development17,18. Despite this, it remains to be determined if sex-specific effects exist in the susceptibility of white matter maturation to the putative effects of air pollution. For example, while notable sex differences have been observed in various associations between early life air pollution exposure and neurobehavioral and cognitive outcomes19,20, white matter neuroimaging studies to date have either not examined potential sex differences14,21, or failed to find effects13,15. In addition, while diffusion MRI studies to date have examined different developmental windows of pollutant exposure, all have been based on a single MRI assessment. Thus, while providing important information about the potentially harmful effects of air pollution on white matter integrity, these studies have been limited by their cross-sectional design and by overlooking potentially important sex-specific relationships, restricting their ability to comment on how air pollution might impact key neurodevelopmental trajectories in male and female youth. To characterize air pollution exposure more fully as it relates to white matter maturation, longitudinal studies considering sex-specific effects are required.
Moving forward, new advancements in biophysical modeling of diffusion imaging data, such as restriction spectrum imaging (RSI), holds great promise in furthering our understanding of how air pollution impacts white matter development. By quantifying restricted normalized isotropic (RNI) and directional (RND) diffusion, RSI can be used to infer the biological processes contributing to white matter microstructure development, such as diffusion within glial cells as well as axon or fiber bundles, respectively22,23. Our group recently leveraged RSI to examine how one year of annual exposure to ambient PM2.5 was cross-sectionally associated with patterns of white matter RNI and RND in children aged 9–10 years. We found that greater PM2.5 exposure was linked to increased RNI, which may indicate swelling or increased reactivity of glial cells, potentially as a consequence of inflammatory processes near the affected tracts24. Given both the cross-sectional nature of our initial study as well as recently reported developmental changes in RNI and RND with age23, questions remain as to whether this one year of annual air pollution exposure impacts white matter microstructural development over time. Moreover, no study to date has examined how gaseous criterion pollutants may impact these novel RSI metrics of white matter health.
In this longitudinal MRI analysis using data from the Adolescent Brain Cognitive Development (ABCD) Study®, we aim to determine if one year of annual exposure to daily PM2.5, daily NO2, and 8-hour maximum O3 at ages 9–10 years has long-term effects on white matter microstructure development over a 2-year follow-up period from late childhood into early adolescence. For a more detailed evaluation of the potential mechanisms by which air pollution exposure may affect various neural processes underlying pediatric white matter development, we quantify white matter microstructural integrity using RSI to isolate intracellular spaces. Given sex-specific effects in environmental neurotoxicity25 as well as in white matter microstructural development as measured with RSI17, in our primary analysis we examine air pollution effects in each sex separately, reducing the potential for bias26. For completeness, we also conduct three sets of sensitivity analyses, including a formal test of sex differences (i.e., sex-by-pollutant and age-by-sex-by-pollutant interaction terms), exclusion of the second time point for children who moved residences between study visits (N = 565), and accounting for all three age-by-pollutant interactions in our multi-pollutant models. We hypothesize that one year of annual exposure to higher concentrations of outdoor air pollution is associated with altered patterns of white matter microstructural development during the transition to early adolescence and that regions affected may be unique in male versus female youth. Lastly, air pollution exposure concentrations within the ABCD Study fall well below the U.S. EPA’s National Ambient Air Quality Standards27,28, allowing the current study to examine potential adverse neurodevelopmental effects from exposures that adhere to or fall below current environmental regulations. This is especially important given that despite improved air quality, adverse brain health effects continue to be detected at low levels of exposure24,27,28,29. Thus, the current study may provide actionable information to policymakers working to update risk assessment of air pollution exposure on human health.
We demonstrate that one year of outdoor air pollution exposure at ages 9–10 years, even at relatively low levels, is associated with white matter microstructural alterations over time with some sex-specific patterns. Affected tracts connect brain regions that have been implicated in the planning and execution of complex and goal-oriented behaviors30.
Methods
Study population
Longitudinal data were collected as a part of the ongoing longitudinal ABCD Study, which enrolled 11,876 children, ages 9–10 years, across 21 study sites. Study enrollment criteria included age (9–10 years old at initial visit) and English language proficiency. Exclusion criteria included major medical or neurological conditions, history of traumatic brain injury, diagnosis of schizophrenia, moderate/severe autism spectrum disorder, intellectual disability, alcohol/substance use disorder, premature birth (gestational age <28 weeks), low birthweight (<1200 g), and contraindications to MRI scanning31. The ABCD Study® has been reviewed and approved by the University of California San Diego’s (UCSD) Institutional Review Board in accordance with the requirements of the Code of Federal Regulations on the Protection of Human Subjects (45 CFR 46 and 21 CFR 50 and 56), including its relevant Subparts (IRB# 160091). Additionally, the ABCD‐SD study has been reviewed in accordance with the regulations at 28 CFR part 46. The IRB reviewed this protocol in accordance with the guidelines on research involving children as research subjects and found that this project meets the requirements as stated in 45 CFR 46.404 in that this research presents no greater than minimal risk to the children; and adequate provisions exist for soliciting the assent of the children and the permission of the parents or guardians, as set forth in HHS regulations at 45 CFR 46.408. The IRB determined that this project presents no more than minimal risk to human subjects in that the probability and magnitude of harm or discomfort anticipated in the research are not greater in and of themselves than those ordinarily encountered in daily life or during the performance of routine physical or psychological examinations or tests. The UCSD IRB acts as the single IRB for all 21 ABCD Study sites: University of Colorado—Boulder (CUB); Children’s Hospital of Los Angeles (CHLA); Florida International University (FIU); Laureate Institute for Brain Research (LIBR); Medical University of South Carolina (MUSC); Oregon Health and Science University (OHSU); University of Pittsburgh (PITT); SRI International (SRI); University of California, Los Angeles (UCLA); University of California, San Diego (UCSD); University of Florida (UFL); University of Maryland—Baltimore (UMB); University of Rochester (ROC); University of Michigan (UMICH); University of Minnesota (UMN); University of Utah (UTAH); University of Vermont (UVM); University of Wisconsin—Madison/Medical College of Wisconsin (UWM); Virginia Commonwealth University (VCU); Yale University (YALE); Washington University in St. Louis (WUSTL). Participants provided written assent, and legal guardians provided written consent.
We used a subset of data from the ABCD Study, including magnetic resonance imaging (MRI) from the baseline and 2-year-follow-up study visits and measures of participants’ age, sex at birth, and sociodemographic characteristics held constant from the baseline assessment. Only high-quality imaging scans completed before 1 March 2020 were included to remove potential confounding effects of stress inherent to the COVID-19 pandemic. We filtered for valid air pollution estimates (see quality-control details below), and randomly selected one subject per family to reduce the number of hierarchical levels, uneven by study design (i.e., the number of both siblings and twins vary by site). Our final sample included 8182 subjects across 21 study sites. Of these, 3679 (45%) had two-time points of high-quality diffusion-weighted imaging (DWI) data, while 4503 (55%) had one DWI time point, either from the baseline or 2-year follow-up visit (see details below; Table 1, Supplemental Fig. 1). All data used here were obtained from ABCD’s 4.0 data release32.
Ambient air pollution estimates
One year of annual ambient air pollution concentration for daily PM2.5, daily NO2, and 8-hour maximum O3 were assigned to primary residential addresses of each child at ages 9–10 years33. Briefly, daily estimates were derived at a 1-km2 resolution using hybrid spatiotemporal models, utilizing satellite-based aerosol optical depth models, land-use regression, and chemical transport models33, and averaged over the 2016 calendar year, corresponding with enrollment for the ABCD Study. One year of annual average concentrations were then assigned to primary residential address reported at study entry when children were aged 9–10 years. PM2.5 was positively correlated with NO2 (r = 0.21, p = 3.44e-81) and negatively correlated with O3 (r = –0.19, p = 1.78e-64); there was no correlation between NO2 and O3 (r = –0.02, p = 0.12) (Supplemental Fig. 2). Pollutants were then standardized by subtracting the group mean and dividing by 5 for each pollutant.
DWI: acquisition, processing, and quality-control
A harmonized neuroimaging protocol was utilized across sites, given the differences in scanner manufacturer (all 3T magnet strength; 63% of scans on Siemens, 10% of scans on Phillips, 27% of scans on GE). The multi-shell DWI acquisition included a voxel size of 1.7 mm isotropic, implemented multiband EPI34,35 with slice acceleration factor 3, and included a fieldmap scan for B0 distortion correction. Seven b = 0 frames and 96 total diffusion directions at 4 b values (6 with b = 500 s/mm2, 15 with b = 1000 s/mm2, 15 with b = 2000 s/mm2, and 60 with b = 3000 s/mm2) were collected36. All images underwent distortion, bias field, and motion correction, and manual and automated quality-control36. After preprocessing, white matter tracts were identified using the probabilistic atlas AtlasTrack37. Only images without clinically significant incidental findings (mrif_score = 1 or 2) that passed all ABCD quality-control parameters (imgincl_dmri_include = 1) were included in analysis.
Restriction spectrum imaging
RSI utilizes all 96 directions in ABCD’s multi-shell acquisition protocol38. RSI provides detailed information regarding both the extracellular and intracellular compartments of tissue within the brain22. RSI model outputs are normalized measures, unitless on a scale of 0–1. We focused on restricted (intracellular) normalized isotropic signal fraction (RNI) and restricted normalized directional signal fraction (RND) of white matter fiber tract ROIs created with AtlasTrack37. We explored all tracts excluding summary tracts (14 in the left hemisphere, 14 in the right hemisphere, and 3 spanning both hemispheres), including the right and left fornix, cingulate cingulum, parahippocampal cingulum, corticospinal tract, anterior thalamic radiations, uncinate fasciculi, inferior longitudinal fasciculi, inferior fronto-occipital fasciculi, temporal and parietal superior longitudinal fasciculi, frontal and parietal superior corticostriate, striatal to inferior frontal cortex, and inferior frontal to the superior frontal cortex, as well as the forceps major, minor, and entire corpus callosum.
Confounders and covariates
Predictors were chosen as confounders and precision covariates using a directed acyclic graph and included demographic and socioeconomic variables: race/ethnicity (race_ethnicity variable with the following categories: non-Hispanic White, non-Hispanic Black, Hispanic, non-Hispanic Asian, or Other), annual household income (USD; ≥100 K, ≥50-<100 K, <50 K, or Don’t Know/Refuse to Answer), and highest household education (Post-Graduate, Bachelor, Some College, High School Diploma/GED, or <High School Diploma). Pollution levels are higher in minority communities and those from disadvantaged social status backgrounds due to structural racism and class bias increasing the likely proximity of these communities to major sources of pollution in the U.S.39,40. Census Tract Urban Classification (Rural, Urban Clusters, or Urbanized Area) was included as air pollution levels vary by degree of urbanicity. We also included the subject-specific precision variable handedness (right, left, or mixed) and MRI-related precision variables, including scanner manufacturer (Siemens, Philips, GE) to account for differences in both scanner hardware and software, average frame displacement (mm) to account for head motion, and tract volume. Lastly, due to the potential acute differences in seasonality of pollutant concentrations at the time of each visit, we included the meteorological season of the MRI scan date as an additional time-varying variable. Comparison of cohort characteristics and covariates between the full ABCD sample and subset for study analyses can be found in Supplemental Data 1.
Statistics and reproducibility
We used hierarchical linear mixed-effect models, as implemented in lme4::lmer() in R statistical software (Version 4.1.2.)41 to account for the multi-level data structure, including random effects of subjects nested within study sites (i.e., to account for repeated measures as well as study site effects, including scanner). We first tested a developmental model, examining the main effects of age and sex, as well as an age-by-sex interaction term. Next, we tested the longitudinal change of RSI outcomes in the context of specific pollutants (PM2.5, NO2, O3), opting for sex-stratified models over including sex-by-age-by-pollutant or sex-by-pollutant interaction terms; the outcomes and a number of predictors in the model have demonstrated sex-specific effects and the inclusion of an interaction term would likely introduce bias26. Age was centered on the lowest age within our sample (107 months), resulting in a scaled age score of 0 equivalent to 8.9 years. We accounted for non-linearity of age by utilizing a piecewise linear spline model and placing a knot at median age of 127 months (10.6 years). This two-piece linear spline model was parameterized to include an overall effect of age and an age-deviation (ageD) term. To assess how one year of annual air pollution exposures at ages 9–10 years affect age-related maturation in WM microstructure, we have included an interaction of pollutants with the age-specific spline terms. Specifically, each model included standardized values of the pollutant of interest (PM2.5, NO2, or O3), age, ageD, interactions of the specific pollutant with age and ageD, and all predictors discussed above. To account for co-exposure of the three criteria pollutants, we additionally controlled for the other two pollutants not included in the age-by-pollutant interaction term of interest. For example, we included one year of annual exposure to NO2 and O3 as covariates in the model testing the effects of PM2.5, age, and age-by-PM2.5 (plus ageD, ageD-by-PM2.5, and previously mentioned predictors) on RSI outcomes. Parameters of interest included the fixed effects of the pollutant on attainted WM microstructure at age 9 (i.e., scaled age score of 0), age (i.e., time), and the age-by-pollutant interaction term to investigate how WM maturation may be altered by air pollution exposure. To account for multiple comparisons due to modeling RNI and RND for all available white matter tracts, we performed a false discovery rate (FDR) correction for 62 tests (31 total white matter tracts across both sexes). All code for statistical analyses can be found in Figshare42.
Reporting summary
Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.
Results
8182 participants from the ABCD Study, from 21 major urban areas across the U.S., were analyzed to investigate how one year of annual exposure influences attained white matter microstructure at age 9 as well as changes in white matter microstructure development from 9 to 13 years of age in female and male youth separately (Fig. 1). Pollutant concentrations (PM2.5 = 7.69 µg/m3; NO2 = 18.7 ppb; O3 = 41.5 ppb) are significantly lower than the current EPA standards (one-sample t tests against EPA standards: PM2.5: t = −75.65 (µ = 9), df = 8181, p = 0; NO2: t = −550.71 (µ = 53), df = 8181, p = 0; O3 (8-hr): t = −578.01 (µ = 70), df = 8181, p = 0).
Heatmaps demonstrating the direction and magnitude of standardized betas for each association between coefficients of interest and white matter microstructure quantified by restricted directional (RND) and isotropic (RNI) fractions per white matter tract, after adjusting for all socioeconomic, demographic, and MRI precision variables, as well as other pollutants. Significant models that passed FDR correction (>0.05) are denoted with a closed circle [●]. Exact beta coefficients and p values can be found in Supplemental Data 11. NMALE = 4200 biologically independent subjects; 6467 MRI scans; NFEMALE = 3982 biologically independent subjects; 5728 MRI scans.
White matter microstructure development from 9–13 years old
To begin, we first replicated previous white matter development findings from Palmer and colleagues23 showing both RNI and RND increased over time from ages 9–13 years in our specific subsample of the ABCD cohort (Supplemental Data 2). There were almost no significant sex differences in the longitudinal change of RNI or RND over time (i.e., age-by-sex interaction), except for the forceps minor. In this tract alone, RNI in male youth increased faster than in female youth over time; RND increased in male youth but did not change in female youth over time (Supplemental Fig. 3a, b). We did observe significant and widespread relationships between sex and RNI and RND at age 9, in that male youth had lower attained RNI and RND in almost all tracts tested (Supplemental Data 2).
Effects of pollutants on white matter microstructure development in male youth
PM2.5: Higher PM2.5 exposure was associated with higher RND at age 9 in the bilateral superior corticostriate (frontal), left anterior thalamic radiation, and left uncinate fasciculi (Supplemental Data 3). There were no significant age-by-PM2.5 interactions for RND. PM2.5 exposure did not significantly relate to RNI at age 9 in male youth and no significant age-by-PM2.5 interactions were observed (Supplemental Data 3).
NO2: There were no significant effects of NO2 exposure on RND at age 9 or on changes in RND development over time (i.e., age-by-NO2 interactions) (Supplemental Data 4). NO2 exposure was associated with higher RNI in the corpus callosum, including at both the forceps major and minor, at age 9, but no significant age-by-NO2 interactions were found (Supplemental Data 4).
O3: Exposure to higher concentrations of O3 was associated with lower RND at age 9 in the bilateral superior corticostriate (frontal and parietal), bilateral anterior thalamic radiations, left superior longitudinal fasciculus (temporal), bilateral superior longitudinal fasciculi (parietal), left inferior longitudinal fasciculus, left inferior to superior frontal fasciculus, left cingulum (cingulate), left uncinate fasciculus, left corticospinal tract, and left fornix in male youth. Beyond O3 effects at age 9, a significant age-by-O3 interaction was seen for the left superior corticostriate (frontal) in male youth, with higher O3 concentrations related to greater increases in RND with age over time (Fig. 2, Supplemental Data 5, Supplemental Fig. 4). Exposure to higher concentrations of O3 were also associated with higher RNI at age 9 in bilateral fornix in male youth. In males, age-by-O3 interactions were seen for RNI in the right corticospinal tract, bilateral cingulum (parahippocampal), and corpus callosum, including the forceps major (Fig. 3a, Supplemental Data 5, Supplemental Fig. 5), with higher O3 exposure linked to reduced increases in RNI with age from 9–13 years.
Significant effects of age-by-O3 interaction on the left frontal superior corticostriate intracellular directional diffusion (RND) in male youth only (N = 4200 biologically independent subjects; 6467 MRI scans) after adjusting for all covariates, representing the effect of one year of annual O3 exposure on change in RND of this tract over time from the baseline visit (ages 9–10 years) to the follow-up visit (ages 11–13 years) (FDR p < 0.05). On the age-by-pollutant interaction plot, the solid line represents the mean value of pollutants within our sample, and the dashed line represents 5 units higher than the mean; shaded areas represent 95% confidence intervals. The brain represents a visualization of the significantly affected tract. Exact model parameters (beta coefficients, confidence intervals, indication of statistical significance) can be found in Supplemental Data 5, and source data for the interaction plot can be found in Supplemental Data 12. SCS superior corticostriate, ppb parts per billion.
a Significant effects of O3 on changes from the baseline visit (ages 9–10 years) to the follow-up visit (ages 11–13 years) in intracellular isotropic (glial cells) diffusion (RNI) of the corpus callosum, forceps major, bilateral parahippocampal cingulum, and the right corticospinal tract in male youth only (N = 4200 biologically independent subjects; 6467 MRI scans) after adjusting for all covariates (FDR p < 0.05); the plot shows the corpus callosum, but is representative of all significant tracts, as all significant associations were in the same direction and of a similar magnitude. b [left]) Significant effects of O3 on changes from the baseline visit (ages 9–10 years) to the follow-up visit (ages 11–13 years) in the right corticospinal tract RNI in female youth only (N = 3982 biologically independent subjects; 5728 MRI scans) after adjusting for all covariates (FDR p < 0.05). b [right]) Significant effects of NO2 on changes from the baseline visit (ages 9–10 years) to the follow-up visit (ages 11–13 years) in near-global RNI in female youth (N = 3982 biologically independent subjects; 5728 MRI scans) after adjusting for all covariates (FDR p < 0.05); the plot shows the corpus callosum, but is representative of all significant tracts, as all significant associations were in the same direction and of a similar magnitude. On all age-by-pollutant interaction plots, the solid line represents the mean value of pollutants within our sample, and the dashed line represents 5 units higher than the mean; shaded areas represent 95% confidence intervals. All brains represent visualizations of the significantly affected tracts. Exact model parameters (beta coefficients, confidence intervals, indication of statistical significance) can be found in Supplemental Data 4–5, and source data for the interaction plots can be found in Supplemental Data 13–15. ROI region of interest, CC corpus callosum, CING cingulum, CST corticospinal tract, SCS superior corticostriate, IFO inferior fronto-occipital fasciculus, ILF inferior longitudinal fasciculus, SLF superior longitudinal fasciculus, IFSFC inferior frontal to superior frontal cortex, ppb parts per billion.
Effects of pollutants on white matter microstructure development in female youth
PM2.5: Higher PM2.5 exposure was associated with higher RND at age 9 in the bilateral superior corticostriate (frontal), bilateral anterior thalamic radiations, left uncinate fasciculi, left striatal inferior frontal cortex, left superior longitudinal fasciculi (temporal and parietal), left inferior longitudinal fasciculus, left inferior fronto-occipital fasciculus, bilateral inferior to the superior frontal cortex, left cingulum (cingulate and parahippocampal), and corpus callosum, driven by the forceps minor (Supplemental Data 3). Exposure to higher PM2.5 concentrations were also associated with higher RNI at age 9 in the left superior corticostriate (frontal) as well as in the bilateral anterior thalamic radiations, and bilateral uncinate fasciculi. There were no significant age-by-PM2.5 interactions for RND or RNI, suggesting that one year of annual PM2.5 exposure does not modify developmental changes in white matter microstructure between the ages of 9–13 years (Supplemental Data 3).
NO2: There were no significant effects of NO2 exposure on RND at age 9 or on changes in RND with development over time (i.e., age-by-NO2 interactions) (Supplemental Data 4). NO2 exposure was associated with higher RNI at age 9 in the right superior corticostriate (frontal and parietal), bilateral striatal inferior frontal cortex, left corticospinal tract, bilateral cingulum (parahippocampal), bilateral fornix, and forceps minor. NO2 exposure also significantly influenced RNI changes with development from 9–13 years in female youth in a number of tracts (i.e., age-by-NO2 interactions), including the left superior corticostriate (frontal and parietal), bilateral superior longitudinal fasciculi (temporal and parietal), bilateral inferior longitudinal fasciculi, bilateral inferior fronto-occipital fasciculi, right inferior frontal to superior frontal cortex, right cingulate (cingulum), and corpus callosum driven by the forceps major (Fig. 3b, Supplemental Data 4, Supplemental Fig. 6). Higher NO2 exposure was related to both higher levels of RNI at age 9 as well as reduced age-related increases in RNI over time (i.e., smaller positive slope over time) in most affected tracts. However, in the right cingulate (cingulum) and right inferior frontal to superior frontal cortex, RNI was similar at age 9, but with reduced increases in RNI with age (i.e., smaller positive slope over time).
O3: Exposure to higher concentrations of O3 was associated with lower RND at age 9 in the bilateral superior corticostriate (frontal and parietal), bilateral anterior thalamic radiations, bilateral superior longitudinal fasciculi (left temporal, bilateral parietal), left inferior longitudinal fasciculus, left inferior fronto-occipital fasciculus, left inferior frontal to superior frontal fasciculus, left cingulum (cingulate and parahippocampal), and left uncinate fasciculus in female youth (Supplemental Data 5). No significant age-by-O3 interactions were found in female youth. Exposure to higher concentrations of O3 was also associated with higher RNI at age 9 in the left corticospinal tract, left superior longitudinal fasciculus (parietal), bilateral fornix, and corpus callosum in female youth. A significant age-by-O3 interaction was seen in the right corticospinal tract in female youth, with higher O3 exposure linked to higher RNI levels at age 9, but reduced increases in RNI with age (Fig. 3b, Supplemental Data 5, Supplemental Fig. 5).
Sensitivity analyses
Three sets of sensitivity analyses were completed. First, we formally tested for sex differences, by adding a three-way sex-by-age-by-pollutant interaction as well as associated additional two-way interactions, including between pollutant and sex (Supplemental Data 6–8, Supplemental Figs. 7–10). We found that though very few associations passed FDR correction, the trending results (uncorrected p’s <0.05) largely supported the conclusions of our sex-stratified models. Specifically, it is clear from the three-way interaction plots that female youth are driving the longitudinal relationship between NO2 and RNI (Supplemental Fig. 9) and male youth are driving the longitudinal relationship between O3 and RNI (Supplemental Fig. 10).
Secondly, because the ABCD 4.0 release is limited by pollutant levels from the baseline visit only (i.e., no follow-up estimates were available from the ABCD consortium at the time of analysis), we repeated our original analyses, excluding the follow-up time point from subjects who reported moving in the past 12 months at their second follow-up visit (N = 565) via the Youth Life Events questionnaire. Results for PM2.5 and O3 were nearly identical across the board. For the associations between NO2 and RNI, we found fewer main effects of the pollutant at age 9, but nearly identical results for the age-by-pollutant term (Supplemental Data 9–10).
Lastly, our initial modeling approach included one age-by-pollutant interaction while controlling for other pollutants as fixed covariates. Given the identified age-by-pollutant interactions found with each pollutant across models, for completeness, we also ran an additional sensitivity analysis that included all three age-by-pollutant interactions (as well as their counterpart ageD-by-pollutant interactions, for a total of six interactions) per model. While p-values fluctuated due to changes in power resulting from adding many interactions per model, the magnitude and direction of standardized betas from these sensitivity analyses were consistent with our original results presented above (Supplemental Data 9–10).
Discussion
To our knowledge, this is the first longitudinal, nationwide study in the U.S. to demonstrate that one year of annual outdoor air pollution exposure at ages 9–10 years is linked to altered white matter microstructure development over time, with differential patterns of tracts affected in male and female youth. Depending on the pollutant, we find that one year of annual exposure to even relatively low levels is associated with small, but significant disruptions in white matter at age 9 and/or developmental change spanning ages 9–13 years, with effect sizes and tracts affected varying between male and female youth. Criteria pollutants (PM2.5, NO2, and O3) were significantly associated with changes in both intracellular isotropic diffusion, which may reflect changes in the number and/or morphology of glial cells, such as microglia, astrocytes, or oligodendrocytes23, as well as RND, which is thought to index changes in axonal caliber, density, and/or myelination23. Affected tracts include projection, association, and commissural fibers that connect brain regions important in the planning and execution of complex and goal-oriented behaviors30. Most importantly, the notable effects of ambient air pollution on white matter development were seen at concentrations that fall below current EPA standards. These findings lend further support to a growing body of literature and the recent recommendations from the World Health Organization43 that suggest that air quality standards should be lowered to protect the brain health of developing youth.
Previous studies have identified robust developmental changes in white matter8. Underlying microstructural changes include increases in both restricted and isotropic intracellular diffusion from ages 9–14 years23, representing increases in axonal density and/or caliber, myelination, and number or size of glial cells. White matter development during the second and third decade of life is intrinsically linked with the brain’s ability to efficiently support cognitive, behavioral, and emotional functioning in everyday life44,45. Moreover, a wide body of literature suggests the pace of brain development, for one’s chronological age, is important. Steady and prolonged neuroplasticity is considered essential, as both delayed and accelerated brain maturation have been linked with impaired cognitive and emotional development46,47,48,49. Factors that accelerate the pace of brain development may be harmful in that it might not ensure sufficient time to learn from and adapt to everyday experiences during adolescence49,50. The current findings suggest that exposure to pollutants during childhood may lead to a more advanced (i.e., older) phenotype, indicated by higher intracellular directional (associated with one year of annual PM2.5 exposure) and isotropic (associated with all three pollutants, with one year of annual NO2 exposure affecting the most tracts) diffusion in white matter tracts important for executive function and emotional regulation at age 9 as well as in slower increases (associated with one year of annual NO2 and O3 exposure) in intracellular isotropic diffusion in these tracts over time as children transition to adolescence. On the other hand, one year of annual exposure to O3 is associated with a more immature (i.e., younger) white matter microstructure phenotype, indicated by lower RND at age 9, followed by an accelerated pace of maturation seen over time. As the first study to our knowledge to relate air quality to longitudinal changes in white matter microstructure in youth, our findings reiterate the potential harms of even low levels of air pollution on-par with what is considered acceptable by the EPA, on white matter development. As only one year of annual exposure to air pollution was available to the current study, future studies are needed to examine potential cumulative effects and to identify other periods of vulnerability during development, like those found in the Generation R cohort of children from the Netherlands13,14,21. Nonetheless, given the associations between air quality over a one year period and changes in white matter development over time, it is reasonable to expect the influence of longer-term exposure across the second and third decade of life may have escalating implications.
Given there are well-documented sex differences in the rate of white matter development as well as various air pollution exposure-related health outcomes16,17,18, the current study examined potentially harmful effects of air pollution exposure on white matter development in each sex separately as well as conducted a follow-up analysis to formally test sex differences. As noted, a stratified analysis versus fitting a model with a by-sex interaction term have different underlying assumptions of how sex might impact associations, and thus, it is not uncommon to see differences in results from these two approaches26. Given the potential unmeasured sex-specific confounding that might not be captured by our identified confounders, we chose a priori to implement a stratified analysis to reduce potential bias26. Nonetheless, unique patterns of associations in each sex do not guarantee a formal statistical test of sex differences, so we completed a follow-up analysis accordingly. Although previous cross-sectional air pollution and white matter studies did not find sex-specific effects13,15, the current sex-stratified findings highlight that while the directionality of air pollution effects was similar across both sexes, tracts affected were sometimes sex- and pollutant-sensitive. At age 9, we see that one year of annual PM2.5 and NO2 exposure affects more tracts in females compared to male youth. Moreover, our longitudinal findings demonstrate that one year of annual O3 exposure preferentially affected white matter development in male youth, while one year of annual NO2 effects were much more prevalent in female youth. While our formal test of sex differences did not pass FDR correction, trending results (uncorrected p’s<0.05) supported the conclusions drawn from our sex-stratified models. Overall, these findings suggest there may be unique patterns in the sensitivity of white matter development to ambient air pollution exposure during adolescence by sex.
The current findings add to a growing literature of in vivo neuroimaging studies suggesting that air pollution exposure is linked with brain structure and function in childhood and adolescence13,14,15,21,24,27,28,29,51,52,53,54,55,56. While a number of these neural targets of ambient air pollution exposure converge with neural systems of cognition and emotional behavior, additional research is needed to clarify how these neural biomarkers of air pollution exposure, including the findings here, may contribute to risk for various neurodevelopmental disorders. Mixed findings in the literature suggest additional research is needed to assess the importance of both the windows of susceptibility and the timing of the assessment to bring clarity to the link between air pollution, brain changes, and risk for cognitive and mental health problems. For example, air pollution has been linked to various neurodevelopmental problems and mental health disorders, such as autism spectrum disorder (ASD), attention-deficit-hyperactivity disorder (ADHD), and schizophrenia1. Yet, other studies present null results,29,57,58,59,60, including a recent study where ambient exposure levels at ages 9–10 years did not relate to greater emotional problems in the ABCD cohort61. However, recent longitudinal studies suggest that childhood and early adolescent exposure to PM2.5 and/or nitrogen oxides (NOx) predicts increased risk for greater mental health disorders, including major depression at age 18 years-old62,63,64. Thus, timing of exposure and outcome measurement likely play an important role, as there are different vulnerabilities at different stages of life. For example, the children in our sample are undergoing immense neurological change, as structural connectivity increases and underutilized connections are pruned. If exposure to neurotoxicants during this stage disrupts the important balance in network connectivity, there could be an increased risk for future cognitive, behavioral, or emotional consequences. These findings posit that air pollution exposure during the transition from childhood to adolescence may lead to asymptomatic alterations, that, in turn, may predispose the brain to risk later in life. In this regard, alterations in white matter connectivity have been linked with various mental health disorders suggesting it may be a potential transdiagnostic marker of mental health risk65,66,67. Moreover, given sex differences in the onset, progression, and prevalence of various mental health disorders18,68,69, it will be important to clarify the potential role of pollutant-induced changes in neurodevelopment on subsequent cognitive, behavioral, and emotional outcomes in each sex as a focus of future research.
The current study uses parameters from a multi-compartment modeling technique that, unlike conventional DTI metrics, is thought to better quantify spherical or elongated shapes of diffusion in intra- and extracellular tissue22. Thus, the current study allows for a more detailed characterization of air pollution’s potential neurotoxic effects on white matter development, as biophysical models of spherical and directional intracellular diffusion mirror biological microstructure such as glial cells and myelin, respectively22. Animal exposure studies suggest pollution causes both neuroinflammation as well as damage to myelin sheaths70,71,72. PM exposure has been associated with cell death, neuroinflammation, oxidative stress, damage to neurovascular units and endothelial cells, and weakening of tissue barriers across organ systems (e.g., nasal, lung, gastrointestinal, and blood-brain barriers)73,74. This neuroinflammatory cascade plausibly results from the infiltration of either ultrafine particles, PM-adsorbed soluble metals, and/or immune cells to the brain75. Immune cells, once inside the brain after passing through a compromised blood-brain barrier, can attack myelin sheaths resulting in microstructural damage to white matter tracts76. Other potentially damaging pathways include PM-associated increases in toxic misfolded protein aggregates and dysfunction in their lysosomal-mediated degradation, even in children and young adults77. NO2 may impact the brain through similar mechanisms, by inducing oxidative stress and subsequent apoptosis78, as well as mitochondrial dysfunction79. Mitochondrial dysfunction is a potentially important factor in how NO2 affects white matter because it has been associated with the degeneration of oligodendrocytes80,81. We find that one year of annual exposure to higher PM2.5 and NO2 at ages 9–10 years is associated with higher restricted directional and isotropic diffusion in female youth, implying that myelination is increasing sooner than expected. However, over time this NO2 pollutant-associated precocious development leads to reduced increases in restricted diffusion (both isotropic and directional), where it is either slowed or reversed, potentially representing myelin and glial cell damage. In contrast, one year of annual exposure to higher O3 at ages 9–10 years is associated with lower restricted directional diffusion, and this seemingly delayed development leads to exaggerated increases in microstructural integrity over time, potentially indicating hypermyelination.
Unlike PM2.5, O3 is not able to penetrate the lung and enter the brain via systemic circulation. Instead, it can cause an innate immune reaction at the level of the lung alveoli and subsequent upregulation of circulating inflammatory cytokines, implying that its neurotoxic effects are largely caused by increased systemic inflammation82. Generally, smaller particle pollutants are thought to infiltrate brain tissue by traveling along the olfactory nerve to the olfactory bulb, resulting in glial cell activation and a subsequent neuroinflammatory cascade that damages the brain’s white matter83. Moreover, consistent with the idea that air pollution may be a physical stressor that disrupts white matter development, both O3 and PM2.5 exposure can activate the hypothalamo-pituitary-adrenal axis, triggering cortisol release and glucocorticoid upregulation, while altering glucocorticoid-regulated gene expression to increase glucocorticoid activity within the brain itself84. Congruent with these findings, changes in morning cortisol serum levels have been found in adolescents as a result of recent exposure to O3 and PM2.585. However, while O3 exposure was associated with increases in morning cortisol levels, PM2.5 was associated with decreases. Studies have found that glucocorticoids can both inhibit and promote the proliferation of oligodendrocyte progenitors depending on dose, duration, and location86. In light of this and our similarly opposing effects of PM2.5 and O3, air pollutants may act through distinct mechanistic pathways that lead to unique and even opposite patterns of endocrine dysregulation, together with their combined effects on neuroinflammatory pathways. Follow-up studies in humans should aim to validate these hypothesized neuro- and systemic inflammatory mechanisms of air pollution, ideally by investigating the potential mediating role of CSF- or blood-based inflammatory biomarkers, such as inflammatory cytokines, in neuroimaging studies of air pollution across the lifespan. A lifespan approach to this future work is especially important given most of the current literature linking air pollution and potential mechanisms is based on either adult humans or animal studies. It is also important to note that while one year of annual PM2.5 exposure effects were more focal compared to more widespread changes observed with NO2 and O3 in the current study, the PM2.5 effect sizes (standardized β’s), were on an order of magnitude larger than those of the gases. This is consistent with prior literature citing PM2.5 as one of the most harmful pollutants to human health87, even at the relatively low doses we observe in the ABCD cohort.
Systemic inflammation may be the main culprit responsible for observed noxious gas-associated findings (as discussed above), but with differential cellular and epigenetic vulnerabilities in male compared to female youth. For example, there is some evidence that NO2 preferentially affects females—increases in NO2 have been associated with elevated serum protein gene product 9.5 (PGP9.5) in women, but decreased in men. Because PGP9.5 originates in the brain, elevated serum PGP9.5 may indicate a compromised blood-brain barrier, implying that increased exposure to ambient NO2 and subsequent oxidative stress may compromise the permeability of the blood-brain barrier. Females also may be at an increased risk of functional impairment from NO2. A recent study observed greater cognitive decline in women associated with increased exposure to NO2. However, other animal model studies indicate that females are more likely to be protected from air pollution’s negative effects by paraoxonase 2 (PON2), an enzyme with antioxidant and anti-inflammatory properties that is more highly expressed in the brains of females than males because it is modulated by estradiol88,89; or through NO2-mediated increases in prolactin gene expression, known for its anti-inflammatory properties in female but not male mice90. Of course, many of these studies focus on women with adult levels of estradiol, which may not directly translate to other stages of the life course such as childhood and early adolescence, when most children are in the early stages of puberty and exhibiting much lower estradiol levels91. Thus, sex differences in air pollution’s impacts on other organ systems may complicate how sex-specific effects manifest in air pollution neurotoxicity. For example, known coupling of immune and endocrine systems92 indicates further research is necessary to understand differential effects of air pollution on pituitary-gonadal-adrenal function and/or sex steroid levels across various periods of the lifespan, which may contribute to these notable sex differences in white matter maturation17,93,94. Similarly, additional research is warranted to understand the degree to which air pollution may alter epigenetics involved in the upregulation of myelin genes in males compared to females. Taken together, additional animal and human-based gene-by-environment studies are necessary to further identify potential endocrine systems and myelin-related epigenetic pathways that may contribute to differential patterns of susceptibility to white matter maturation in developing male and female brains.
A few notable limitations of the current study are warranted. Per available data in the 4.0 release provided by the ABCD Consortium, the current study is limited to the one year of annual air pollution exposure when the child was 9–10 years-old. As such, the current study examined how one year of annual exposure at ages 9–10 years related to changes in white matter. Additional sensitivity analyses showed similar effects after removing individuals who reported moving residences in the year prior to the second brain scan. However, the current study is unable to determine potential cumulative impacts or if changes in exposure over time influence white matter development. Moving forward, additional studies are warranted to examine lifetime exposure and/or if health effects vary across other discrete windows of development beyond the current one year of exposure captured here. Moreover, other factors that impact the true amount of air pollution each child experiences, such as time spent outside, indoor air pollution exposure, and exposure outside the home (i.e., at school), could also affect results. The ABCD Consortium does not include this information and future studies should attempt to collect data on confounding lifestyle variables and capture exposure in other settings outside the primary residence. Likewise, additional studies are warranted examining how chemical composition and dose contribute to the effects of ambient air pollution on brain health throughout the lifespan, as well as investigating the potential inflammatory or immune-related mechanisms using blood- or saliva-based biomarkers. PM2.5 is known for its heavy metal speciation and its ability to carry sulfates, nitrates, ammonium, black and organic carbon, and more, with the exact composition varying by region95. NO2 sources in the ABCD project are complex and vary from site to site due to regional differences across the U.S., but are generally attributed to tailpipe exhaust96. Ground-level O3, a secondary pollutant, is well-known to fluctuate based on time of day, temperature, sunlight, and other meteorological variables97,98. In terms of dose, average air pollution levels are low in the ABCD project, which may contribute to the small effect sizes we observed in the current study. However, air pollution effect sizes were on par with those associated with age (i.e., standardized betas: ±0.005-0.01 in Supplemental Data 2–5), an obvious and well-documented predictor of white matter development over time. More broadly, limitations associated with the ABCD dataset include its sampling bias and lack of generalizability to the entire population of American adolescents. Youth from wealthier and more educated backgrounds are over-represented, while black and Asian youth are under-represented in the ABCD cohort compared to the contemporary U.S. population. Therefore, the findings shown here should be replicated in more diverse samples to assess the generalizability of these results.
In conclusion, our results reveal small, albeit likely important, associations between one year of annual exposure to criteria pollutants and white matter microstructural development during the transition from childhood to adolescence. With additional context from animal and human studies, we speculate that systemic and neuroinflammatory processes may underlie pollutants’ effects on white matter health during this vulnerable neurodevelopmental period. Notably, the sex-stratified white matter changes identified here were observed at relatively low levels of exposure; exposure concentrations of the criteria pollutants examined fell below current EPA standards but still exceeded the latest WHO guidelines released in September 2021. As such, the current findings in this U.S.-based sample should be considered by the EPA when revising air pollution regulatory standards.
Data availability
The ABCD Study anonymized data, including all assessment domains, is released annually to the research community. Information on how to access ABCD data through the NDA is available on the ABCD study data-sharing webpage: https://abcdstudy.org/scientists_data_sharing.html. Instructions on how to create an NDA study are available at https://nda.nih.gov/training/modules/study.html). The ABCD data repository grows and changes over time. Source data for Figs. 1–3 can be found in the Supplemental Data Excel files 11–15.
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Acknowledgements
The research described in this article was supported by the National Institutes of Health [NIEHS R01-ES032295, R01-ES031074, P30ES007048-23S1, 3P30ES000002-55S1] and EPA grants [RD 83587201, RD 83544101]. Data used in the preparation of this article were obtained from the Adolescent Brain Cognitive Development (ABCD) Study (https://abcdstudy.org), held in the NIMH Data Archive (NDA). This is a multisite, longitudinal study designed to recruit more than 10,000 children aged 9–10 and follow them over 10 years into early adulthood. The ABCD Study is supported by the National Institutes of Health Grants [U01DA041022, U01DA041028, U01DA041048, U01DA041089, U01DA041106, U01DA041117, U01DA041120, U01DA041134, U01DA041148, U01DA041156, U01DA041174, U24DA041123, U24DA041147]. A full list of supporters is available at https://abcdstudy.org/nih-collaborators. A listing of participating sites and a complete listing of the study investigators can be found at https://abcdstudy.org/principal-investigators.html. ABCD consortium investigators designed and implemented the study and/or provided data but did not necessarily participate in the 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 10.15154/1523041. Additional support for this work was made possible from NIEHS R01-ES032295 and R01-ES031074. In addition, we would like to gratefully acknowledge Jade Li for her help with creating the brain tract visualizations.
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D.L.C.: formal analysis, writing—original draft, writing—review & editing, visualization. H.A., C.C.I., K.L.B.: Supervision, writing—review & editing. W.J.G., R.M., K.B., D.A.H.: methodology, writing—review & editing. J.S.: methodology, data curation, resources, writing—review & editing. J.C.C.: conceptualization, methodology, writing—review & editing. MMH: funding acquisition, conceptualization, methodology, supervision, project administration, writing—review & editing.
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The authors declare the following competing interests: Dr. Megan Herting, PhD reports financial support was provided by the National Institute of Environmental Health Sciences. Dr. W. James Gauderman, PhD reports financial support was provided by National Institute of Environmental Health Sciences. Dr. Rob McConnell, MD, reports financial support was provided by the National Institute of Environmental Health Sciences. Dr. Joel D. Schwartz, PhD reports financial support was provided by the National Institute of Environmental Health Sciences and the United States Environmental Protection Agency. Dr. Daniel Hackman, PhD reports financial support was provided by the Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institute of Environmental Health Sciences, and the Conrad N. Hilton Foundation. Dr. Jiu-Chiuan Chen, MD, reports financial support was provided by the National Institute of Environmental Health Sciences and the National Institute on Aging. Devyn L. Cotter, MSc, Dr. Hedyeh Ahmadi, PhD, Dr. Carlos Cardenas-Iniguez, PhD, Dr. Katherine L. Bottenhorn, PhD, and Dr. Kiros Berhane, PhD have no competing interests to declare.
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Cotter, D.L., Ahmadi, H., Cardenas-Iniguez, C. et al. Exposure to multiple ambient air pollutants changes white matter microstructure during early adolescence with sex-specific differences. Commun Med 4, 155 (2024). https://doi.org/10.1038/s43856-024-00576-x
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DOI: https://doi.org/10.1038/s43856-024-00576-x
