Traffic-Related Air Pollution, APOE ε4 Status, and Neurodevelopmental Outcomes among School Children Enrolled in the BREATHE Project (Catalonia, Spain)
2018; National Institute of Environmental Health Sciences; Volume: 126; Issue: 8 Linguagem: Inglês
10.1289/ehp2246
ISSN1552-9924
AutoresSilvia Alemany, Natàlia Vilor‐Tejedor, Raquel García‐Esteban, Mariona Bustamante, Payam Dadvand, Mikel Esnaola, Marion Mortamais, Joan Forns, Barend L. van Drooge, Mar Álvarez‐Pedrerol, Joan O. Grimalt, Ioar Rivas, Xavier Querol, Jesús Pujol, Jordi Sunyer,
Tópico(s)Energy and Environment Impacts
ResumoVol. 126, No. 8 ResearchOpen AccessTraffic-Related Air Pollution, APOE ε4 Status, and Neurodevelopmental Outcomes among School Children Enrolled in the BREATHE Project (Catalonia, Spain)is companion ofAir Pollution and Risk of Neurobehavioral Problems: Is APOE ε4 Status a Factor? Silvia Alemany, Natàlia Vilor-Tejedor, Raquel García-Esteban, Mariona Bustamante, Payam Dadvand, Mikel Esnaola, Marion Mortamais, Joan Forns, Barend L. van Drooge, Mar Álvarez-Pedrerol, Joan O. Grimalt, Ioar Rivas, Xavier Querol, Jesus Pujol, and Jordi Sunyer Silvia Alemany Address correspondence to S. Alemany, Barcelona Institute for Global Health (ISGlobal), C. Doctor Aiguader 88, 08003 Barcelona, Spain. Telephone: +34 93 214 73 62. Fax: +34 93 214 73 0.2. Email: E-mail Address: [email protected] ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain Universitat Pompeu Fabra (UPF), Barcelona, Spain CIBER Epidemiología y Salud Pública (CIBERESP), Spain Search for more papers by this author , Natàlia Vilor-Tejedor ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain Universitat Pompeu Fabra (UPF), Barcelona, Spain CIBER Epidemiología y Salud Pública (CIBERESP), Spain Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain Search for more papers by this author , Raquel García-Esteban ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain Universitat Pompeu Fabra (UPF), Barcelona, Spain CIBER Epidemiología y Salud Pública (CIBERESP), Spain Search for more papers by this author , Mariona Bustamante ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain Universitat Pompeu Fabra (UPF), Barcelona, Spain CIBER Epidemiología y Salud Pública (CIBERESP), Spain Centre for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona, Spain Search for more papers by this author , Payam Dadvand ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain Universitat Pompeu Fabra (UPF), Barcelona, Spain CIBER Epidemiología y Salud Pública (CIBERESP), Spain Search for more papers by this author , Mikel Esnaola ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain Universitat Pompeu Fabra (UPF), Barcelona, Spain CIBER Epidemiología y Salud Pública (CIBERESP), Spain Search for more papers by this author , Marion Mortamais ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain Universitat Pompeu Fabra (UPF), Barcelona, Spain CIBER Epidemiología y Salud Pública (CIBERESP), Spain Search for more papers by this author , Joan Forns ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain Universitat Pompeu Fabra (UPF), Barcelona, Spain CIBER Epidemiología y Salud Pública (CIBERESP), Spain Search for more papers by this author , Barend L. van Drooge Institute for Environmental Assessment and Water Research (IDÆA-CSIC), Barcelona, Spain Search for more papers by this author , Mar Álvarez-Pedrerol ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain Universitat Pompeu Fabra (UPF), Barcelona, Spain CIBER Epidemiología y Salud Pública (CIBERESP), Spain Search for more papers by this author , Joan O. Grimalt Institute for Environmental Assessment and Water Research (IDÆA-CSIC), Barcelona, Spain Search for more papers by this author , Ioar Rivas ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain Universitat Pompeu Fabra (UPF), Barcelona, Spain CIBER Epidemiología y Salud Pública (CIBERESP), Spain Institute for Environmental Assessment and Water Research (IDÆA-CSIC), Barcelona, Spain Search for more papers by this author , Xavier Querol Institute for Environmental Assessment and Water Research (IDÆA-CSIC), Barcelona, Spain Search for more papers by this author , Jesus Pujol MRI Research Unit, Department of Radiology, Hospital del Mar and Centro Investigación Biomédica en Red de Salud Mental (CIBERSAM G21), Barcelona, Spain Search for more papers by this author , and Jordi Sunyer ISGlobal, Barcelona Institute for Global Health, Barcelona, Spain Universitat Pompeu Fabra (UPF), Barcelona, Spain CIBER Epidemiología y Salud Pública (CIBERESP), Spain IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain Search for more papers by this author Published:2 August 2018CID: 087001https://doi.org/10.1289/EHP2246Cited by:3AboutSectionsPDF Supplemental Materials ToolsDownload CitationsTrack CitationsCopy LTI LinkHTMLAbstractPDF ShareShare onFacebookTwitterLinked InRedditEmail AbstractBackground:Traffic-related air pollution is emerging as a risk factor for Alzheimer's disease (AD) and impaired brain development. Individual differences in vulnerability to air pollution may involve the ε4 allele of Apolipoprotein E (APOE) gene, the primary genetic risk factor for AD.Objective:We analyzed whether the association between traffic air pollution and neurodevelopmental outcomes is modified by APOEε4 status in children.Methods:Data on parent-reported behavior problems (total difficulties scores, Strengths and Difficulties Questionnaire), teacher-reported attention-deficit hyperactivity disorder (ADHD) symptom scores, cognitive performance trajectories (computerized tests of inattentiveness and working memory repeated 2–4 times during January 2012–March 2013), and APOE genotypes were obtained for 1,667 children age 7–11 y attending 39 schools in or near Barcelona. Basal ganglia volume (putamen, caudate, and globus pallidum) was measured in 163 of the children by MRI (October 2012–April 2014.) Average annual outdoor polycyclic aromatic hydrocarbons (PAHs), elemental carbon (EC), and nitrogen dioxide (NO2) concentrations were estimated based on measurements at each school (two 1-wk campaigns conducted 6 months apart in 2012).Results:APOEε4 allele carriers had significantly higher behavior problem scores than noncarriers, and adverse associations with PAHs and NO2 were stronger or limited to ε4 carriers for behavior problem scores (P-interaction 0.03 and 0.04), caudate volume (P-interaction 0.04 and 0.03), and inattentiveness trajectories (P-interaction 0.15 and 0.08, respectively). Patterns of associations with the same outcomes were similar for EC.Conclusion:PAHs, EC, and NO2 were associated with higher behavior problem scores, smaller reductions in inattentiveness over time, and smaller caudate volume in APOEε4 allele carriers in our study population, and corresponding associations were weak or absent among ε4 noncarriers. These findings support a potential role of APOE in biological mechanisms that may contribute to associations between air pollution and neurobehavioral outcomes in children. https://doi.org/10.1289/EHP2246IntroductionThere is growing evidence that exposure to traffic-related air pollution (TRAP) has a detrimental effect on cognitive and behavioral developmental outcomes in children. In the BREATHE project (n=2897 schoolchildren), conducted in the Barcelona metropolitan area, we found that outdoor and indoor estimated concentrations of elemental carbon (EC, equivalent to black carbon) and nitrogen dioxide (NO2), two pollutants highly correlated with road traffic emissions (Amato et al. 2014; Reche et al. 2014; Rivas et al. 2014), were associated with slower improvements in cognitive function over time (Sunyer et al. 2015) and higher scores on parent-reported behavior problems (Forns et al. 2016). Using a weighted average estimate of EC and NO2 levels, brain changes of a functional but not structural nature were associated with air pollution exposure in children drawn from the BREATHE project who underwent MRI scan (n=263) (Pujol et al. 2016). Specifically, higher air pollution levels were associated with lower functional integration and segregation in key brain networks (Pujol et al. 2016). Another study based on BREATHE cohort (n=242) found that exposure to polycyclic aromatic hydrocarbons (PAHs) was associated with smaller caudate volumes (Mortamais et al. 2017).These findings are consistent with other studies reporting associations between TRAP and neurobehavioral outcomes in children. For instance, prenatal exposure to PAHs has been associated with cognitive developmental delays at age 3 (Perera et al. 2006) and decreased intelligence quotient at age 5 (Perera et al. 2009). Prenatal exposure to PAHs, characterized by air monitoring and specific biomarkers (DNA adducts in maternal and cord blood), has been associated with symptoms of anxiety and depression, and attention problems at age 6–7 (Perera et al. 2012). Furthermore, postnatal PAHs exposure has been shown to contribute to disturbances in prefrontal white matter development in brain at ages 7–9 (Peterson et al. 2015). Other studies have related NO2 exposure at school with neurobehavioral function indicating worse cognitive performance among those exposed to higher air pollution levels (van Kempen et al. 2012; Wang et al. 2009). However, mechanisms through which TRAP might adversely affect neural development remain largely unknown (Calderón-Garcidueñas and Torres-Jardón 2015b).Systemic inflammation and oxidative stress are among the most well-established mechanisms underlying the health effects of air pollution (Block and Calderón-Garcidueñas 2009; Brockmeyer and D’Angiulli 2016). Interestingly, these pathogenic pathways are also involved in neurodegenerative diseases, such as dementia, which is characterized by progressively impaired cognitive function (Reynolds et al. 2007; Rodrigues et al. 2012). In line with this, air pollution has been associated with cognitive impairment in elderly people (Ranft et al. 2009; Wellenius et al. 2012). More recently, a large population-based cohort showed that living near roads with heavy traffic was associated with a higher risk of dementia, supporting a potential link between cognitive impairment, neurodegeneration and exposure to air pollution (Chen et al. 2017). Similarly, Alzheimer’s Disease (AD)-type pathology has been observed in autopsy samples of the frontal cortex from children living in highly polluted areas of Mexico (Calderón-Garcidueñas et al. 2013). Because Apolipoprotein E (APOE) epsilon 4 (ε4) allele is the strongest known genetic risk factor for AD (Liu et al. 2013), Calderón-Garcidueñas et al. (2012) evaluated whether the AD-related pathological processes that are associated with air pollution were more pronounced in children carrying the APOEε4 allele. They found that ε4 carriers had more hyperphosphorylated tau protein and diffuse amyloid-β (Aβ) plaques in comparison with ε3 carriers (Calderón-Garcidueñas et al. 2012). Furthermore, ε4 carriers presented metabolic alterations in frontal white matter and poor cognitive performance affecting their attention and memory functions (Calderón-Garcidueñas et al. 2015a). These results suggest that APOE genotypes could modify responses to air pollution on brain and cognitive function. Cognitive function develops steadily from childhood to early adulthood; later in life, this function can remain stable or decline with age, depending on several factors, including genetics and environmental exposures (Craik and Bialystok 2006). Together, air pollution exposure and APOEε4 allele status may inhibit children’s ability to achieve and consolidate cognitive and behavioral function and may impair these capacities in elderly people. To the best of our knowledge, studies evaluating APOEε4 status as a modifier of the association between air pollution and cognition have been conducted only in elderly women. The first study found that exposure to air pollution, including road traffic emissions, was associated with poorer performance in neuropsychological tasks among ε4 carriers, but not among noncarriers (Schikowski et al. 2015). More recently, ε4/ε4 female carriers were shown to have stronger associations between air pollution exposure and dementia risk and between air pollution exposure and global cognitive decline, than ε3 female carriers (Cacciottolo et al. 2017).We sought to extend these findings by examining whether APOEε4 status modifies associations between TRAP and neurodevelopmental outcomes in children, including measures of behavior, cognitive function, and brain morphology. We hypothesized that associations between the outcomes and TRAP exposure, indexed by outdoor measurements of PAHs, EC and NO2, would be more pronounced among ε4 carriers than among noncarriers.Materials and MethodsStudy Population and SettingParticipants were drawn from the BREATHE project (European Commission: FP7-ERC-2010-AdG, ID 268,479), a population-based cohort of primary schoolchildren designed to analyze the association between air pollution and behavior, cognitive function and brain morphology. We used modeled NO2 levels to select 39 primary schools so as to maximize the contrast in TRAP levels at schools (Sunyer et al. 2015). Thirty-eight schools were located in Barcelona, and one school was in an adjacent municipality, Sant Cugat del Vallés. The socioeconomic vulnerability index and NO2 levels estimated for the participating schools were similar to those for the remaining 380 schools in Barcelona city (Socioeconomic vulnerability index: 0.66 vs. 0.62, p=0.20; NO2 levels: 51.5 vs. 50.9μg/m3, p=0.81). All families of children without special needs who were enrolled in second, third, and fourth grades at the selected schools were invited to participate in the study (2012). A total of 2,897 children ages 7 to 11 y accepted the invitation and participated in the project. Genotype data were available for 1,667 children of European ethnic origin. Among these, MRI data was available for 163 children, who were scanned between October 2012 and April 2014. Further details on recruitment of the MRI subsample is available elsewhere (Pujol et al. 2016). Briefly, with the aim of including children from all participating schools in BREATHE project, a document asking whether they were interested in further information regarding the MRI study was given to all children. From the initial sample (n=2897), the document was returned by 1,564 families, of whom 810 indicated that they were interested in participating in the MRI study. From those, parents of 491 children were successfully contacted. Twenty-one children were excluded because of dental braces, consent to participate was not obtained in 165 cases, and 27 children were lost before the assessment. From these children, 263 completed the imaging protocol. Further exclusions included poor-quality brain scans (n=19), and no genetic data available (n=81), leaving a final sample of 163 children with MRI and genetic data available.All children had been in the school for more than 6 months before the beginning of the study, and 98% for more than 1 y. A full description of the project is available elsewhere (Sunyer et al. 2015). All parents or legal guardians gave written informed consent, and the study was approved by the IMIM-Parc de Salut Mar Research Ethics Committee (No. 2010/41,221/I), Barcelona, Spain; and the FP7-ERC-2010-AdG Ethics Review Committee (268,479-22,022,011).ExposureWe investigated exposure to outdoor PAHs, EC, and NO2 measured at each school as surrogates for TRAP exposure at each school. We analyzed EC and NO2 because exposures to these pollutants at schools have been shown to be (i) highly associated with vehicle exhaust levels in Barcelona (Amato et al. 2014); (ii) associated with parent-reported behavior problem scores and teacher-reported ADHD symptom scores (Forns et al. 2016); and (iii) involving the pollutants most consistently associated with working memory, superior working memory, and inattentiveness in this sample (Basagaña et al. 2016; Forns et al. 2017). Similarly, in a subset of BREATHE project participants, PAHs levels at schools were associated with differences such as smaller basal ganglia volumes (Mortamais et al. 2017). The levels of these pollutants in the schoolyards were measured twice during two 1-wk periods separated by 6 months, in the warm and cold periods of the year 2012. The average of these two 1-wk measurements was used to estimate yearly outdoor air pollution levels at the schools. Not all participating schools were monitored simultaneously, so to eliminate the effects of temporal fluctuation in background air pollution levels, the levels of each pollutant were adjusted for the weekly average level of that pollutant (during the corresponding sampling week for each school), as measured by a background monitoring station in Barcelona (Rivas et al. 2014).Samples of ambient air particulate matter <2.5μm (PM2.5) were collected for 8 h (school time, from 09:00 to 17:00 h) at each school using a high-volume Sampler (MCV SA) with quartz filters (Pall, 15mm). Further details of the measurement campaigns and analysis of PM2.5 filter chemicals to determine the concentration of several pollutants (including EC and PAHs) are described elsewhere (Alier et al. 2013; Amato et al. 2014; Rivas et al. 2014). Weekly average NO2 concentrations were estimated using passive samplers (NO2 diffusion tube, Gradko International Ltd.). PAHs included the total sum of benz[a]anthracene (BAAN), chrysene (CHR), benzo[b+j+k]fluoranthene (BFL), benzo[e]pyrene (BEP), benzo[a]pyrene (BAP), indeno(1,2,3-c,d)pyrene (IP) and benzo[g,h,i]perylene (BGP), which were the compounds that showed detectable levels in all samples. To reduce temporal fluctuations when comparing PAHs levels between schools, data were seasonalized after adjusting for the mean daily BAP level measured at three urban monitoring stations in Barcelona. BAP is the only PAH assessed in this study that is also monitored in Barcelona. The urban monitoring stations were exposed to traffic, and BAP was continuously measured during one day at one or more of these sites during the study period. To obtain seasonalized levels, daily concentrations at each school were multiplied by the ratio of the yearly average to the same day concentration at the three fixed air quality background monitoring stations (Rivas et al. 2014).Neurodevelopmental OutcomesBehavioral Outcomes.Behavioral outcomes included scores on behavior problems and attention deficit-hyperactivity disorder (ADHD) symptoms. These measures were obtained at the beginning of cognitive data collection (visit 1) during the first trimester of 2012. Behavioral problems were characterized using the Strengths and Difficulties Questionnaire (SDQ; Goodman 1997), which was rated by parents. The SDQ is a brief behavioral screening questionnaire of 25 items, for which raters are asked to indicate on a 3-point response scale (ranging from not true to certainly true) how well each item described the behavior of the child. The questionnaire consists of four difficulty subscales (emotional problems, peer problems, conduct problems, and hyperactivity), and one strength subscale (prosocial behavior), each including five items. A SDQ total difficulties score, ranging from 0 to 40, is calculated by summing the four difficulties subscales. Higher SDQ total difficulties scores indicate more behavioral problems. ADHD symptoms were assessed using a questionnaire based on the ADHD diagnostic criteria described in Diagnostic and Statistical Manual of Mental Disorders Fourth Edition (DSM-IV; American Psychiatric Association, 2002), which was completed by teachers. The ADHD-DSM-IV questionnaire consists of a list of 18 symptoms, assessing two separate symptom groups: inattention (nine symptoms) and hyperactivity/impulsivity (nine symptoms). Each ADHD symptom is rated on a 4-point frequency scale from never or rarely (0) to very often (3). The questionnaire can be found in Methods S1. The ADHD symptom score ranges from 0 to 54, with higher scores indicating more numerous symptoms.Cognitive Function.Cognitive function included inattentiveness and working memory trajectories. These data were collected between January 2012 and March 2013. During this period, BREATHE participants completed computerized tests assessing inattentiveness and working memory; participants completed this test four times (every 3 months) over one year. These four repeated measurements allowed us to model the 1-y trajectories of inattentiveness and working memory. Because only children assessed at least twice were included in the analyses, the modeled 1-y trajectory may include 2 to 4 repeated measures of inattentiveness or working memory based on available data. Inattentiveness was assessed using the computerized Attentional Network Test (ANT; Rueda et al. 2004). Reaction times (i.e., time between introducing a stimulus and the participant’s reaction to that stimulus) were used to calculate the different outcomes that can be obtained using the ANT. The inattentiveness outcome analyzed in this study is the standard error of reaction time for correct responses [standard error of hit reaction time (HRT-SE)], a measure of intra-individual variability reflecting response speed and consistency throughout the test. We chose HRT-SE because it has previously been associated with exposure to air pollution (Sunyer et al. 2015) and, as a measure of intra-individual variability, it can be considered a good indicator of central nervous system integrity (Hedden and Gabrieli 2004; MacDonald et al. 2006). We refer to HRT-SE as inattentiveness because higher HRT-SE scores are related to reduced executive and attentional resources, and are characteristic of the performance of patients with ADHD (Bellgrove et al. 2004, MacDonald et al. 2006). Inattentiveness was analyzed as a continuous variable representing the 1-y trajectory, taking into account the four repeated measures. Further details are available elsewhere (Forns et al. 2014).Working memory was assessed using the computerized n-back task (Anderson 2002; Nelson et al. 2000; Vuontela et al. 2003). In this task, the subject is required to monitor a series of stimuli presented in the centre of the screen and to respond whenever a stimulus is presented that matches the one presented in n trials previously (n=1, 2, or 3), which are known as loads (one-back, two-back, and three-back). Higher loads imply higher demands on working memory. Participants complete three blocks (1-, 2-, and 3-back) for each stimulus. Stimuli included colors, letters, numbers, and words. At the two-back level, the target was any stimulus that matched the one presented two trials previously. Here, we used numbers and words as stimuli in the two-back level. We choose this load because it predicts general mental abilities (Shelton et al. 2010) and previous research in this sample showed limited improvement in the trajectories of three-back compared to two-back tasks, which may be due to immaturity of the brain areas supporting processes involved in this higher-demand task, such as storage, processing, and executive-control functions (López-Vicente et al. 2016). We obtained various measures for each trail, including accuracy measures (hits, correct rejections, false alarms, and misses) and hit reaction time (HRT), recorded when the participant correctly identified a target. Usually, the outcome analyzed is a combination of these measures. In this regard, a widely used outcome for assessing working memory is the d prime (d′), which is derived from signal detection theory and allows us to distinguish between signal and noise (Haatveit et al. 2010; Wickens 2002); d′ is computed as z (hit rate) − z (false alarm rate), with higher d′ indicating better signal detection and more accurate performance. Because d′ incorporates more information about working memory capacity, it has been suggested as a better measure of interindividual variability than HRT (Forns et al., 2014). Therefore, we used two-back numbers d′ and two-back words d′ in our analyses. These measurements were analyzed as continuous variables representing the 1-y trajectory, taking into account the four repeated measures.Brain Structure.Among the brain-structure measurements, we focused on basal ganglia volumes (including caudate, putamen, and globus pallidum), based on previous findings in the BREATHE sample (Mortamais et al. 2017) and the key role of basal ganglia on attention function (McKenna et al. 2013; Riccio et al. 2002), which has also been associated with TRAP in this sample (Sunyer et al. 2015). We performed MRI of brain anatomy using a 1.5 Tesla Signa Excite system (General Electric, Inc.) equipped with an eight-channel phased-array head coil and single-shot echo planar imaging (EPI) software. High-resolution 3D anatomical images were obtained using an axial T1-weighted, three dimensional fast spoiled gradient inversion recovery-prepared sequence. A total of 134 contiguous slices were acquired with repetition time of 11.9 ms, echo time of 4.2 ms, flip angle 15°, field of view of 30cm, 256×256 pixel matrix, and slice thickness 1.2mm. To avoid including poor-quality images, all images were visually inspected by a trained researcher before and after the preprocessing steps. Cases were excluded based on expert subjective criteria if the raw images showed obvious motion artifacts (ghost and blurring of the image), ringing or truncation artifacts, and susceptibility phenomena. After preprocessing, cases were excluded if the images showed deformation of the three-dimensional brain anatomy and large truncated brain areas, nonoptimal removal of nonbrain tissue, and obvious tissue (gray and white matter) misclassification. Cortical reconstruction and volumetric segmentation were carried out using the FreeSurfer tool ( http://surfer.nmr.mgh.harvard.edu/). In total, 71 brain measurements were generated using FreeSurfer (version 5.3; FreeSurfer analysis suite). Processing steps included removal of nonbrain tissue, automated Talairach transformation, and segmentation of the subcortical white matter and deep gray matter volumetric structures. Additional details are available in (Pujol et al. 2016; Vilor-Tejedor et al. 2016).APOE Genotypes.The major APOE allelic variants ε2, ε3, and ε4 can be obtained from allelic combinations of the rs429358 and rs7412 polymorphisms. Briefly, the ε4 allele is the combination of the C allele at both sites (Radmanesh et al. 2014). According to the genotypes of these polymorphisms, children were classified as ε4 carriers (with at least one ε4 allele) and noncarriers. Genotype frequencies for the rs429358 and rs7412 polymorphisms were obtained from genome-wide genotyping data for 1,667 participants in the BREATHE project.A full description of the genotyping and quality-control procedures is available elsewhere (Alemany et al. 2016). Briefly, from the 2,897 children participating in the original BREATHE cohort, 2,492 (86%) accepted to provide saliva for DNA genotyping. Saliva samples were collected using the Oragene DNA OG-500kit (DNA Genotek). From these children with available saliva samples, a final subset of 1,778 (61%) children was selected for genome-wide genotyping after applying a filtering criterion. Filtering criteria included low quality DNA (n=64 exclusions), adopted children (n=34 exclusions), siblings or twins (n=92 exclusions), being born outside Europe or having parents born outside Europe (n=482 exclusions), and no data available on residential address (n=42 exclusions).Genome-wide genotyping was performed using the HumanCore BeadChipWG-330-1,101 (Illumina). Genotypes were called using the GeneTrain2.0 algorithm (with a default threshold of 0.15) based on HapMap clusters implemented in the GenomeStudio software. PLINK was used to perform genotyping quality control (Purcell et al. 2007); we included samples with a minimum of 97% call rate (N=3 exclusions) and a maximum of 4 SD heterozygosity (N=5 exclusions), gender discordance excluding mismatch information (N=18 exclusions) and relatedness (N=80 exclusions). Five subjects were excluded due to mental disability. Thus, a total of 111 subjects were excluded, leaving 1,667 individuals in the analysis.The polymorphisms analyzed for this study were imputed using IMPUTE2 (version 2), taking the 1,000 Genomes Project Phase I integrated variant set ( http:/www.1000genomes.org/) as a reference haplotype panel. For rs429358, the minor allele frequency (MAF) and quality of imputation were 0.95 and 0.122, respectively, and for rs7412, were 0.97 and 0.062, respectively. Hardy–Weinberg equilibrium was verified for both polymorphisms (rs429358: χ2(1)=0.30 ; p=0.585; rs7412 χ2(1)=0.61; p=0.436).Covariates.Sociodemographic data were collected by the BREATHE baseline questionnaire, completed by the parents (Sunyer et al. 2015), including child age and sex, maternal educational level (no or primary school/secondary school/university), maternal smoking during pregnancy (yes/no), and exposure to environmental tobacco smoke at home (no smoking at home/smoking outside home (e.g., terrace)/smoking inside). We also obtained data on residential neighborhood socioeconomic status (SES) vulnerability index (based on level of education, unemployment, and occupation in each census tract, the finest spatial census unit, with median area of 0.08km2) (Ministerio de Fomento 2012). Air pollution at home was characterized by NO2 and PM2.5 levels at time of the study, estimated at the geocoded postal address of each participant using land use regression (LUR) models developed in the context of ESCAPE project as described in Supplementary Material (see Methods S2).Statistical AnalysisThe final analysis included 1,667 children with data available for genetic polymorphisms, behavior problem scores (n=1596), ADHD symptom scores (n=1604), inattentiveness (5,999 observations for 1,488 participants), working memory (6,058 observations for 1,591 participants), and basal ganglia volume (n=163) (Figure S1). Associations between predictors and behavior problem scores and ADHD symptom scores (modeled as continuous variables) we
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